Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy

BIOMARKERS IN DRUG DEVELOPMENT A Handbook of Practice, Application, and Strategy Edited by MICHAEL R. BLEAVINS, Ph.D., ...

Author: Michael R. Bleavins | Claudio Carini | Mallé Jurima-Romet | Ramin Rahbari

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BIOMARKERS IN DRUG DEVELOPMENT A Handbook of Practice, Application, and Strategy Edited by MICHAEL R. BLEAVINS, Ph.D., DABT Michigan Technology and Research Institute Ann Arbor, Michigan

CLAUDIO CARINI, M.D., Ph.D., FRCPath Fresenius Biotech of North America Waltham, Massachusetts

MALLÉ JURIMA-ROMET, Ph.D. MDS Pharma Services Montreal, Quebec, Canada

RAMIN RAHBARI, M.S. Innovative Scientiﬁc Management New York, New York

A JOHN WILEY & SONS, INC., PUBLICATION

BIOMARKERS IN DRUG DEVELOPMENT

BIOMARKERS IN DRUG DEVELOPMENT A Handbook of Practice, Application, and Strategy Edited by MICHAEL R. BLEAVINS, Ph.D., DABT Michigan Technology and Research Institute Ann Arbor, Michigan

CLAUDIO CARINI, M.D., Ph.D., FRCPath Fresenius Biotech of North America Waltham, Massachusetts

MALLÉ JURIMA-ROMET, Ph.D. MDS Pharma Services Montreal, Quebec, Canada

RAMIN RAHBARI, M.S. Innovative Scientiﬁc Management New York, New York

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright 2010 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/ permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and speciﬁcally disclaim any implied warranties of merchantability or ﬁtness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of proﬁt or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Biomarkers in drug development : a handbook of practice, application, and strategy / [edited by] Michael R. Bleavins . . . [et al.]. p. ; cm. Includes index. ISBN 978-0-470-16927-8 (cloth) 1. Biochemical markers 2. Drug development. I. Bleavins, Michael R. [DNLM: 1. Biomarkers, Pharmacological. 2. Drug Design. 3. Drug Discovery. QV 744 B6154 2009] R853.B54B5645 2009 615'.10724—dc22 2009021627 Printed in the United States of America 10

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CONTENTS

CONTRIBUTORS

ix

PREFACE

xv

PART I

1

BIOMARKERS AND THEIR ROLE IN DRUG DEVELOPMENT

Biomarkers Are Not New

1 3

Ian Dews

2

Biomarkers: Facing the Challenges at the Crossroads of Research and Health Care

15

Gregory J. Downing

3

Enabling Go/No Go Decisions

31

J. Fred Pritchard and Mallé Jurima-Romet

PART II

4

IDENTIFYING NEW BIOMARKERS: TECHNOLOGY APPROACHES

Imaging as a Localized Biomarker: Opportunities and Challenges

41 43

Jonathan B. Moody, Philip S. Murphy, and Edward P. Ficaro

5

Protein Biomarker Discovery Using Mass Spectrometry– Based Proteomics

101

Joanna M. Hunter and Daniel Chelsky

6

Quantitative Multiplexed Patterning of ImmuneRelated Biomarkers

121

Dominic Eisinger, Ralph McDade, and Thomas Joos v

vi CONTENTS

7

Gene Expression Proﬁles as Preclinical and Clinical Cancer Biomarkers of Prognosis, Drug Response, and Drug Toxicity

135

Jason A. Sprowl and Amadeo M. Parissenti

8

Use of High-Throughput Proteomic Arrays for the Discovery of Disease-Associated Molecules

155

Douglas M. Molina, W. John W. Morrow, and Xiaowu Liang

PART III 9

CHARACTERIZATION AND VALIDATION

Characterization and Validation Biomarkers in Drug Development: Regulatory Perspective

177

179

Federico Goodsaid

10

Fit-for-Purpose Method Validation and Assays for Biomarker Characterization to Support Drug Development

187

Jean W. Lee, Yuling Wu, and Jin Wang

11

Molecular Biomarkers from a Diagnostic Perspective

215

Klaus Lindpaintner

12

Strategies for the Co-Development of Drugs and Diagnostics: FDA Perspective on Diagnostics Regulation

231

Francis Kalush and Steven Gutman

13

Importance of Statistics in the Qualiﬁcation and Application of Biomarkers

247

Mary Zacour

PART IV

14

BIOMARKERS IN DISCOVERY AND PRECLINICAL SAFETY

Qualiﬁcation of Safety Biomarkers for Application to Early Drug Development

287

289

William B. Mattes and Frank D. Sistare

15

Development of Serum Calcium and Phosphorus as Clinical Biomarkers for Drug-Induced Systemic Mineralization: Case Study with a MEK Inhibitor

301

Alan P. Brown

16 Biomarkers for the Immunogenicity of Therapeutic Proteins and Its Clinical Consequences

323

Claire Cornips and Huub Schellekens

17

New Markers of Kidney Injury Sven A. Beushausen

335

CONTENTS

vii

PART V TRANSLATING FROM PRECLINICAL RESULTS TO CLINICAL AND BACK

359

18 Translational Medicine—A Paradigm Shift in Modern Drug Discovery and Development: The Role of Biomarkers

361

Giora Z. Feuerstein, Salvatore Alesci, Frank L. Walsh, J. Lynn Rutkowski, and Robert R. Ruffolo, Jr.

19

Clinical Validation and Biomarker Translation

375

David Lin, Andreas Scherer, Raymond Ng, Robert Balshaw, Shawna Flynn, Paul Keown, Robert McMaster, and Bruce McManus

20

Predicting and Assessing an Inﬂammatory Disease and Its Complications: Example from Rheumatoid Arthritis

399

Christina Trollmo and Lars Klareskog

21

Pharmacokinetic and Pharmacodynamic Biomarker Correlations

413

J.F. Marier and Keith Gallicano

22 Validating In Vitro Toxicity Biomarkers Against Clinical Endpoints

433

Calvert Louden and Ruth A. Roberts

PART VI 23

BIOMARKERS IN CLINICAL TRIALS

Opportunities and Pitfalls Associated with Early Utilization of Biomarkers: Case Study in Anticoagulant Development

443

445

Kay A. Criswell

24

Integrating Molecular Testing Into Clinical Applications

463

Anthony A. Killeen

25 Biomarkers for Lysosomal Storage Disorders

475

Ari Zimran, Candida Fratazzi, and Deborah Elstein

26 Value Chain in the Development of Biomarkers for Disease Targets

485

Charles W. Richard, III, Arthur O. Tzianabos, and Whaijen Soo

PART VII

27

LESSONS LEARNED: PRACTICAL ASPECTS OF BIOMARKER IMPLEMENTATION

493

Biomarkers in Pharmaceutical Development: The Essential Role of Project Management and Teamwork

495

Lena King, Mallé Jurima-Romet, and Nita Ichhpurani

viii

CONTENTS

28

Integrating Academic Laboratories Into Pharmaceutical Development

515

Peter A. Ward and Kent J. Johnson

29

Funding Biomarker Research and Development Through the Small Business Innovative Research Program

527

James Varani

30

Novel and Traditional Nonclinical Biomarker Utilization in the Estimation of Pharmaceutical Therapeutic Indices

541

Bruce D. Car, Brian Gemzik, and William R. Foster

31 Anti-Unicorn Principle: Appropriate Biomarkers Don’t Need to Be Rare or Hard to Find

551

Michael R. Bleavins and Ramin Rahbari

32

Biomarker Patent Strategies: Opportunities and Risks

565

Cynthia M. Bott and Eric J. Baude

PART VIII WHERE ARE WE HEADING AND WHAT DO WE REALLY NEED?

575

33

577

IT Supporting Biomarker-Enabled Drug Development Michael Hehenberger

34

Redeﬁning Disease and Pharmaceutical Targets Through Molecular Deﬁnitions and Personalized Medicine

593

Craig P. Webb, John F. Thompson, and Bruce H. Littman

35 Ethics of Biomarkers: The Borders of Investigative Research, Informed Consent, and Patient Protection

625

Heather Walmsley, Michael Burgess, Jacquelyn Brinkman, Richard Hegele, Janet Wilson-McManus, and Bruce McManus

36

Pathodynamics: Improving Biomarker Selection by Getting More Information from Changes Over Time

643

Donald C. Trost

37 Optimizing the Use of Biomarkers for Drug Development: A Clinician’s Perspective

693

Alberto Gimona

38

Nanotechnology-Based Biomarker Detection

709

Joshua Reineke

INDEX

731

CONTRIBUTORS

Salvatore Alesci, M.D., Ph.D., Wyeth Research, Collegeville, Pennsylvania Robert Balshaw, Ph.D., Syreon Corporation, Vancouver, British Columbia, Canada Eric J. Baude, Ph.D., Brinks Hofer Gilson & Lione, P.C., Ann Arbor, Michigan Sven A. Beushausen, Ph.D., Pﬁzer Global Research and Development, Chesterﬁeld, Missouri Michael R. Bleavins, Ph.D., DABT, Michigan Technology and Research Institute, Ann Arbor, Michigan Cynthia M. Bott, Ph.D., Honigman Miller Schwartz and Cohn LLP, Ann Arbor, Michigan Jacquelyn Brinkman, M.Sc., University of British Columbia, Vancouver, British Columbia, Canada Alan P. Brown, Ph.D., DABT, Pﬁzer Global Research and Development, Ann Arbor, Michigan Michael Burgess, Ph.D., University of British Columbia, Vancouver, British Columbia, Canada Bruce D. Car, B.V.Sc., Ph.D., Bristol-Myers Squibb Co., Princeton, New Jersey Daniel Chelsky, Ph.D., Caprion Proteomics, Inc., Montreal, Quebec, Canada Claire Cornips, B.Sc., Utrecht University, Utrecht, The Netherlands Kay A. Criswell, Ph.D., Pﬁzer Global Research and Development, Groton, Connecticut Ian Dews, MRCP, FFPM, Envestia Ltd., Thame, Oxfordshire, UK ix

x

CONTRIBUTORS

Gregory J. Downing, D.O., Ph.D., U.S. Department of Health and Human Services, Washington, DC Dominic Eisinger, Ph.D., Rules Based Medicine, Inc., Austin, Texas Deborah Elstein, Ph.D., Gaucher Clinic, Shaare Zedek Medical Center, Jerusalem, Israel Giora Z. Feuerstein, M.D., Wyeth Research, Collegeville, Pennsylvania Edward P. Ficaro, Ph.D., INVIA Medical Imaging Solutions, Ann Arbor, Michigan Shawna Flynn, B.Sc., Syreon Corporation, Vancouver, British Columbia, Canada William R. Foster, Ph.D., Bristol-Myers Squibb Co., Princeton, New Jersey Candida Fratazzi, Massachusetts

M.D., Altus

Pharmaceuticals,

Inc.,

Waltham,

Keith Gallicano, Ph.D., Watson Laboratories, Corona, California Brian Gemzik, Ph.D., Bristol-Myers Squibb Co., Princeton, New Jersey Alberto Gimona, M.D., Merck Serono International, Geneva, Switzerland Federico Goodsaid, Ph.D., Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland Steven Gutman, M.D., M.B.A., University of Central Florida, Orlando, Florida; formerly with the U.S. Food and Drug Administration, Rockville, Maryland Richard Hegele, M.D., Ph.D., University of British Columbia, Vancouver, British Columbia, Canada Michael Hehenberger, Ph.D., IBM Healthcare & Life Sciences, Somers, New York Joanna M. Hunter, Ph.D., Caprion Proteomics, Inc., Montreal, Quebec, Canada Nita Ichhpurani, B.A., PMP, MDS Pharma Services, Mississauga, Ontario, Canada Kent J. Johnson, M.D., The University of Michigan Medical School, Ann Arbor, Michigan Thomas Joos, Ph.D., Rules Based Medicine, Inc., Austin, Texas Mallé Jurima-Romet, Ph.D., MDS Pharma Services, Montreal, Quebec, Canada

CONTRIBUTORS

xi

Francis Kalush, Ph.D., U.S. Food and Drug Administration, Silver Spring, Maryland; formerly with USFDA, Rockville, Maryland Paul Keown, M.D., D.Sc., MBA, University of British Columbia, Vancouver, British Columbia, Canada Anthony A. Killeen, M.D., Ph.D., University of Minnesota, Minneapolis, Minnesota Lena King, Ph.D., DABT, CanBioPharma Consulting, Inc., Guelph, Ontario, Canada Lars Klareskog, M.D., Ph.D., Karolinska Institute, Stockholm, Sweden Jean W. Lee, Ph.D., Amgen, Inc., Thousand Oaks, California Xiaowu Liang, Ph.D., Antigen Discovery, Inc., Irvine, California David Lin, B.MLSc., University of British Columbia, Vancouver, British Columbia, Canada Klaus Lindpaintner, M.D., M.P.H., F. Hoffmann–La Roche AG, Basel, Switzerland Bruce H. Littman, M.D., Translational Medicine Associates, Stonington, Connecticut Calvert Louden, Ph.D., Johnson & Johnson Pharmaceuticals, Raritan, New Jersey J.F. Marier, Ph.D., FCP, Pharsight, A Certara Company, Montreal, Quebec, Canada William B. Mattes, Ph.D., DABT, The Critical Path Institute, Rockville, Maryland Ralph McDade, Ph.D., Rules Based Medicine, Inc., Austin, Texas Bruce McManus, M.D., Ph.D., University of British Columbia, Vancouver, British Columbia, Canada Robert McMaster, D.Phil., University of British Columbia, Vancouver, British Columbia, Canada Douglas M. Molina, Ph.D., Antigen Discovery, Inc., Irvine, California Jonathan B. Moody, Ph.D., INVIA Medical Imaging Solutions, Ann Arbor, Michigan W. John W. Morrow, Ph.D., Antigen Discovery, Inc., Irvine, California Philip S. Murphy, Ph.D., GlaxoSmithKline Research and Development, Uxbridge, Middlesex, UK

xii

CONTRIBUTORS

Raymond Ng, Ph.D., University of British Columbia, Vancouver, British Columbia, Canada Amadeo M. Parissenti, Ph.D., Laurentian University, Sudbury, Ontario, Canada J. Fred Pritchard, Ph.D., MDS Pharma Services, Raleigh, North Carolina Ramin Rahbari, M.S., Innovative Scientiﬁc Management, New York, New York Joshua Reineke, Ph.D., Wayne State University, Detroit, Michigan Charles W. Richard, III, M.D., Ph.D., Shire Human Genetic Therapies, Cambridge, Massachusetts Ruth A. Roberts, Macclesﬁeld, UK

Ph.D., AstraZeneca

Research

and

Development,

Robert R. Ruffolo, Jr., Ph.D., Wyeth Research, Collegeville, Pennsylvania J. Lynn Rutkowski, Ph.D., Wyeth Research, Collegeville, Pennsylvania Huub Schellekens, M.D., Utrecht University, Utrecht, The Netherlands Andreas Scherer, Ph.D., Spheromics, Kontiolahti, Finland Frank D. Sistare, Ph.D., Merck Research Laboratories, West Point, Pennsylvania Whaijen Soo, M.D., Ph.D., Shire Human Genetic Therapies, Cambridge, Massachusetts Jason A. Sprowl, Ph.D., Laurentian University, Sudbury, Ontario, Canada John F. Thompson, M.D., Helicos BioSciences, Cambridge, Massachusetts Christina Trollmo, Ph.D., Karolinska Institute and Roche AB Sweden, Stockholm, Sweden Donald C. Trost, M.D., Ph.D., Analytic Dynamics, Niantic, Connecticut Arthur O. Tzianabos, Ph.D., Shire Human Genetic Therapies, Cambridge, Massachusetts James Varani, Ph.D., The University of Michigan Medical School, Ann Arbor, Michigan Heather Walmsley, M.A., Lancaster University, Bailrigg, Lancaster, UK Frank L. Walsh, Ph.D., Wyeth Research, Collegeville, Pennsylvania Jin Wang, M.S., Amgen, Inc., Thousand Oaks, California Peter A. Ward, M.D., The University of Michigan Medical School, Ann Arbor, Michigan

CONTRIBUTORS

xiii

Craig P. Webb, Ph.D., Van Andel Research Institute, Grand Rapids, Michigan Janet Wilson-McManus, M.T., B.Sc., University of British Columbia, Vancouver, British Columbia, Canada Yuling Wu, Ph.D., Amgen, Inc., Thousand Oaks, California Mary Zacour, Ph.D., BioZac Consulting, Montreal, Quebec, Canada Ari Zimran, M.D., Gaucher Clinic, Shaare Zedek Medical Center, Jerusalem, Israel

PREFACE

The impact of biomarker technologies and strategies in pharmaceutical development is still emerging but is already proving to be signiﬁcant. Biomarker strategy forms the basis for translational medicine and for the current industry and regulatory focus to improve success rates in drug development. The pharmaceutical industry faces greater challenges today than at any time in its history: an ever-increasing expectation of safer, more efﬁcacious, and better understood drugs in the face of escalating costs of drug development and increasing duration of clinical development times; high rates of compound failure in phase II and III clinical trials; remaining blockbuster drugs coming off patent; and many novel but unproven targets emerging from discovery. These factors have pressured pharmaceutical research divisions to look for ways to reduce development costs, make better decisions earlier, reassess traditional testing strategies, and implement new technologies to improve the drug discovery and development processes. There is consensus that biomarkers are valuable drug development tools that enhance target validation, thereby helping us better understand the mechanisms of action and enabling earlier identiﬁcation of compounds with the highest potential for efﬁcacy in humans. These important methods are also essential for eliminating compounds with unacceptable safety risks, enabling the concept of “fail fast, fail early,” and providing more accurate or complete information regarding drug performance and disease progression. At the same time that pharmaceutical scientists are focusing on biomarkers in drug discovery and development, clinical investigators and health care practitioners are using biomarkers increasingly in medical decision making and diagnosis. Similarly, regulatory agencies have recognized the value of biomarkers to guide regulatory decision making about drug safety and efﬁcacy. The magnitude and seriousness of the U.S. Food and Drug Administration (FDA) commitment to biomarkers is reﬂected in its Critical Path initiative. In recent years, several pharmacogenomic tests have been incorporated into product labels and implemented in clinical practice to improve the risk–beneﬁt ratio xv

xvi PREFACE

for patients receiving certain drug therapies (e.g., 6-mercaptopurine, irinotecan, warfarin). Agencies such as the FDA and European Medicines Association have taken a leadership role in encouraging biomarker innovation in the industry and collaboration to identify, evaluate, and qualify novel biomarkers. Moreover, a biomarker strategy facilitates the choice of a critical path to differentiate products in a competitive marketplace. In recent years, the topic of biomarkers has been featured at many specialized scientiﬁc meetings and has received extensive media coverage. We, the coeditors, felt that a book that approached the topic with an emphasis on the practical aspects of biomarker identiﬁcation and use, as well as their strategic implementation, was missing and essential to improve the application of these approaches. We each have experience working with biomarkers in drug development, but we recognized that the specialized knowledge of a diverse group of experts was necessary to create the type of comprehensive book that is needed. Therefore, contributions were invited from authors who are renowned experts in their respective ﬁelds. The contributors include scientists from academia, research hospitals, biotechnology and pharmaceutical companies, contract research organizations and consulting ﬁrms, and the FDA. The result is a book that we believe will appeal broadly to pharmaceutical research scientists, clinical and academic investigators, regulatory scientists, managers, students, and all other professionals engaged in drug development who are interested in furthering their knowledge of biomarkers. As discussed in Part I, biomarkers are not new: They have been used for hundreds of years to help physicians diagnose and treat disease. What is new is a shift from outcome biomarkers to target and mechanistic biomarkers, the availability of “omics,” imaging, and other technologies that allow collection of large amounts of data at the molecular, tissue, and whole-organism levels, and the use of data-rich biomarker information for “translational research,” from the laboratory bench to the clinic and back. Part II chapter is dedicated to highlighting several important technologies that affect drug discovery and development, the conduct of clinical trials, and the treatment of patients. In Part III we’ve invited leaders from industry and regulatory agencies to discuss the qualiﬁcation of biomarker assays in the ﬁt-for-purpose process, including perspectives on the development of diagnostics. The importance of statistics cannot be overlooked, and this topic is also proﬁled in this part with a practical overview of concepts, common mistakes, and helpful tips to ensure credible biomarkers that can address their intended uses. Parts IV to VI present information on concepts and examples of utilizing biomarkers in discovery, preclinical safety assessment, clinical trials, and translational medicine. Examples are drawn from a wide range of target-organ toxicities, therapeutic areas, and product types. We hope that by presenting a wide range of biomarker applications, discussed by knowledgeable and experienced scientists, readers will develop an appreciation of the scope and breadth of biomarker knowledge and ﬁnd examples that will help them in their own work.

PREFACE

xvii

Part VII focuses on “lessons learned” and the practical aspects of implementing biomarkers in drug development programs. Many pharmaceutical companies have created translational research divisions, and increasingly, external partners, including academic and government institutions, contract research organizations, and specialty laboratories, are providing technologies and services to support biomarker programs. This is changing the traditional organizational models within industry and paving the way toward greater collaboration across sectors and even among companies within a competitive industry. Perspectives from contributing authors representing several of these different sectors are presented in this part, as well as a legal perspective on potential intellectual property issues in biomarker development. The book concludes with Part VIII on future trends and developments, including developments in data integration, the reality of personalized medicine, and the addressing of ethical concerns. The ﬁeld of biomarkers in drug development is evolving rapidly and this book presents a snapshot of some exciting new approaches. By utilizing the book as a source of new knowledge, or to reinforce or integrate existing knowledge, we hope that readers will beneﬁt from a greater understanding and appreciation of the strategy and application of biomarkers in drug development and become more effective decision makers and contributors in their own organizations. Michael R. Bleavins Claudio Carini Mallé Jurima-Romet Ramin Rahbari

PART I BIOMARKERS AND THEIR ROLE IN DRUG DEVELOPMENT

1

1 BIOMARKERS ARE NOT NEW Ian Dews, MRCP, FFPM Envestia Ltd., Thame, Oxfordshire, UK

INTRODUCTION The word biomarker in its medical context is a little over 30 years old, having ﬁrst been used by Karpetsky, Humphrey, and Levy in the April 1977 edition of the Journal of the National Cancer Institute, where they reported that the “serum RNase level … was not a biomarker either for the presence or extent of the plasma cell tumor.” Few new words can have proved so popular—a recent PubMed search lists more than 370,000 publications that use it! Part of this success can no doubt be attributed to the fact that the word gave a longoverdue name to a phenomenon that has been around at least since the seventh century b.c., when Sushustra, the “father of Ayurvedic surgery,” recorded that the urine of patients with diabetes attracted ants because of its sweetness. However, although the origins of biomarkers are indeed ancient, it is fair to point out that the pace of progress over the ﬁrst 2500 years was somewhat less than frenetic.

UROSCOPY Because of its easy availability for inspection, urine was for many centuries the focus of attention. The foundation of the “science” of uroscopy is generally attributed to Hippocrates (460–355 b.c.), who hypothesized that urine was a

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

3

4

BIOMARKERS ARE NOT NEW

ﬁltrate of the “humors,” taken from the blood and ﬁltered through the kidneys, a reasonably accurate description. One of his more astute observations was that bubbles on the surface of the urine (now known to be due to proteinuria) were a sign of long-term kidney disease. Galen (a.d. 129–200), the most inﬂuential of the ancient Greco-Roman physicians, sought to make uroscopy more speciﬁc but in reality added little to the subject beyond the weight of his reputation, which served to hinder further progress in this as in many other areas of medicine. Five hundred years later, Theophilus Protospatharius, another Greek writer, moved things one step nearer to the modern world when he investigated the effects of heating urine and hence established the world’s ﬁrst medical laboratory test. He discovered that heating urine from patients with symptoms of kidney disease caused cloudiness (in fact, the precipitation of proteins). In the sixteenth century, Paracelsus (1493–1541) in Switzerland used vinegar to bring out the same cloudiness (acid, like heat, will precipitate proteins). Events continued to move both farther north and closer to modernity when in 1695 Frederick Deckers of Leiden in the Netherlands identiﬁed this cloudiness as resulting from the presence of albumin. The loop was ﬁnally closed when Richard Bright (1789–1858), a physician at Guy’s Hospital in London, made the connection between proteinuria and autopsy ﬁndings of abnormal kidneys. The progress from Hippocrates’ bubbles to Bright disease represents the successful side of uroscopy, but other aspects of the subject now strike us as a mixture of common sense and bizarre superstition. The technique of collecting urine was thought to be of paramount importance for accurate interpretation. In the eleventh century, Ismail of Jurjani insisted on a full 24-hour collection in a vessel that was large and clean (very sensible) and shaped like a bladder, so that the urine would not lose its “form” (not at all sensible). His advice to keep the sample out of the sun and away from heat continues, however, to be wise counsel. Gilles de Corbeil (1165–1213), physician to King Philip Augustus of France, recorded differences in sediment and color of urine which he related to 20 different bodily conditions. He also invented the matula, or jorden, a glass vessel through which the color, consistency, and clarity of the sample could be assessed. Shaped like a bladder rounded at the bottom and made of thin clear glass, the matula was to be held up in the right (not the left) hand for careful inspection against the light. De Corbeil taught that different areas of the body were represented by the urine in different parts of the matula. These connections, which became ever more complex, were recorded on uroscopy charts that were published only in Latin, thus ensuring that the knowledge, and its well-rewarded use in treating wealthy patients, was conﬁned to appropriately educated men. To further this education, de Corbeil, in his role as a professor at the Medical School of Salerno, set out his own ideas and those of the ancient Greek and Persian writers in a work called Poem on the Judgment

BLOOD PRESSURE

5

of Urines, which was set to music in order that medical students could memorize it more easily. It remained popular for several centuries.

BLOOD PRESSURE One of the ﬁrst excursions away from urine in the search for markers of function and disease came in 1555 with the publication of a book called Sphygmicae artis iam mille ducentos annos perditae & desideratae Libri V by a physician from Poznán in Poland named Józef Struś (better known by his Latinized name, Iosephus Struthius). In this 366-page work, Struthius described placing increasing weights on the skin over an artery until the pulse was no longer able to lift the load. The weight needed to achieve this gave a crude measure of what he called “the strength of the pulse” or, as we would call it today, blood pressure. Early attempts at quantitative measurement of blood pressure had to be conducted in animals rather than human subjects because of the invasiveness of the technique. The ﬁrst recorded success with these techniques dates from 1733, when the Reverend Stephen Hales, a British veterinary surgeon, inserted a brass pipe into a horse’s artery and connected the pipe to a glass tube. Hales observed the blood rising in the tube and concluded not only that the rise was due to the pressure of the blood in the artery but also that the height of the rise was a measure of that pressure. By 1847, experimental technique had progressed to the point where it was feasible to measure blood pressure in humans, albeit still invasively. Carl Ludwig inserted brass cannulas directly into an artery and connected them via further brass pipework to a U-shaped manometer. An ivory ﬂoat on the water in the manometer was arranged to move a quill against a rotating drum, and the instrument was known as a kymograph (“wave-writer” in Greek). Meanwhile, in 1834, Jules Hérisson had described his sphygmomètre, which consisted of a steel cup containing mercury, covered by a thin membrane, with a calibrated glass tube projecting from it. The membrane was placed over the skin covering an artery and the pressure in the artery could be gauged from the movements of the mercury into the glass tube. Although minor improvements were suggested by a number of authors over the next few years, credit for the invention of the true sphygmomanometer goes to Samuel Siegfried Karl Ritter von Basch, whose original 1881 model used water in both the cuff and the manometer tube. Five years later, Scipione Riva-Rocci introduced an improved version in which an inﬂatable bag in the cuff was connected to a mercury manometer, but neither of these early machines attracted widespread interest. Only in 1901, when the famous American surgeon Harvey Cushing brought back one of Riva-Rocci’s machines on his return from a trip to Italy, did noninvasive blood pressure measurement really take off.

6

BIOMARKERS ARE NOT NEW

Sphygmomanometers of the late nineteenth century relied on palpation of the pulse and so could only be used to determine systolic blood pressure. Measurement of diastolic pressure only became possible when Nikolai Korotkoff observed in 1905 that characteristic sounds were made by the constriction of the artery at certain points in the inﬂation and deﬂation of the cuff. The greater accuracy allowed by auscultation of these Korotkoff sounds opened the way for the massive expansion in blood pressure research that characterized the twentieth century.

IMAGING To physicians keen to understand the hidden secrets of the human body, few ideas can have been more appealing than the dream of looking through the skin to examine the tissues beneath. The means for achieving this did not appear until a little over a century ago, and then very much by accident. On the evening of November 8, 1895, Wilhem Roentgen, a German physicist working at the University of Würzburg, noticed that light was coming from ﬂuorescent material in his laboratory and worked out that this was the result of radiation escaping from a shielded gas discharge tube with which he was working. He was fascinated by the ability of this radiation to pass through apparently opaque materials and promptly set about investigating its properties in more detail. While conducting experiments with different thicknesses of tinfoil, he noticed that that if the rays passed though his hand, they cast a shadow of the bones. Quick to see the potential medical uses for his new discovery, Roentgen immediately wrote a paper entitled “On a new kind of ray: a preliminary communication” for the Würzburg Physical Medical Society, reprints of which he sent to a number of eminent scientists with whom he was friendly. One of these, Franz Exner of Vienna, was the son of the editor of the Vienna Presse, and hence the news was published quickly, ﬁrst in that paper and then across Europe. Whereas we are inclined to believe that rapid publication is a feature of the Internet age, the Victorians were no slouches in this matter, and by January 24, 1896 a reprint of the Würzburg paper had appeared in the London Electrician, a major journal able to bring details of the new invention to a much wider technical audience. The speed of the response was remarkable. Many physics laboratories already had gas discharge tubes, and within a month physicists in a dozen countries were reproducing Roentgen’s ﬁndings. Edwin Frost produced an x-ray image of a patient’s fractured wrist for his physician brother, Gilmon Frost, at Dartmouth College in the United States, while at McGill University in Montreal, John Cox used the new rays to locate a bullet in a gunshot victim’s leg. Similar results were obtained in cities as far apart as Copenhagen, Prague, and Rijeka in Croatia. Inevitably, not everyone was initially quite so impressed; The Lancet of February 1, 1896 expressed considerable surprise that the

IMAGING

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Belgians had decided to bring x-rays into practical use in hospitals throughout the country! Nevertheless, it was soon clear that a major new diagnostic tool had been presented to the medical world, and there was little surprise when Roentgen received a Nobel Prize in Physics in 1901. Meanwhile, in March 1896, Henri Becquerel, professor of physics at the Muséum National d’Histoire Naturelle in Paris, while investigating Roentgen’s work, wrapped a ﬂuorescent mineral, potassium uranyl sulfate, in photographic plates and black material in preparation for an experiment requiring bright sunlight. However, a period of dull weather intervened, and prior to actually performing the experiment, Becquerel found that the photographic plates were fully exposed. This led him to write: “One must conclude from these experiments that the phosphorescent substance in question emits rays which pass through the opaque paper and reduce silver salts.” Becquerel received a Nobel prize, which he shared with Marie and Pierre Curie, in 1903, but it was to be many years before the use of spontaneous radioactivity reached maturity in medical investigation in such applications as isotope scanning and radioimmunoassay. The use of a ﬂuoroscopic screen on which to view x-ray pictures was implicit in Roentgen’s original discovery and soon became part of the routine equipment not only of hospitals but even of shoe shops, where large numbers of children’s shoe ﬁttings were carried out in the days before the true dangers of radiation were appreciated. However, the greatest value of the real-time viewing approach only emerged following the introduction of electronic image intensiﬁers by the Philips company in 1955. Within months of the introduction of planar x-rays, physicians were asking for a technique that would demonstrate the body in three dimensions. This challenge was taken up by a number of scientists in different countries, but because of the deeply ingrained habit of reviewing only the national, not the international, literature, these workers remained ignorant of each other’s progress for many years. Carl Mayer, a Polish physician, ﬁrst suggested the idea of tomography in 1914. André-Edmund-Marie Bocage in France, Gustav Grossmann in Germany, and Allesandro Vallebona in Italy all developed the idea further and built their own equipment. George Ziedses des Plantes in the Netherlands pulled all these strands together in the 1930s and is generally considered the founder of conventional tomography. Further progress had to wait for the development of powerful computers, and it was not until 1972 that Godfrey Hounsﬁeld, an engineer at EMI, designed the ﬁrst computer-assisted tomographic device, the EMI scanner, installed at Atkinson Morley Hospital, London, an achievement for which he received both a Nobel prize and a knighthood. Parallel with these advances in x-ray imaging were ongoing attempts to make similar use of the spontaneous radioactivity discovered by Becquerel. In 1925, Herrman Blumgart and Otto Yens made the ﬁrst use of radioactivity as a biomarker when they used bismuth-214 to determine the arm-to-arm

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BIOMARKERS ARE NOT NEW

circulation time in patients. Sodium-24, the ﬁrst artiﬁcially created biomarker radioisotope, was used by Joseph Hamilton to investigate electrolyte metabolism in 1937. Unlike x-rays, however, radiation from isotopes weak enough to be safe was not powerful enough to create an image merely by letting it fall on a photographic plate. This problem was solved when Hal Anger of the University of California, building on the efﬁcient gamma-ray capture system using large ﬂat crystals of sodium iodide doped with thallium developed by Robert Hofstadter in 1948, constructed the ﬁrst gamma camera in 1957. The desire for three-dimensional images that led to tomography with x-rays also inﬂuenced radioisotope imaging and drove the development of singlephoton-emission computed tomography (SPECT) by David Kuhl and Roy Edwards in 1968. Positron-emission tomography (PET) also builds images by detecting energy given off by decaying radioactive isotopes in the form of positrons that collide with electrons and produce gamma rays that shoot off in nearly opposite directions. The collisions can be located in space by interpreting the paths of the gamma rays, and this information is then converted into a three-dimensional image slice. The ﬁrst PET camera for human studies was built by Edward Hoffman, Michael Ter-Pogossian, and Michael Phelps in 1973 at Washington University. The ﬁrst whole-body PET scanner appeared in 1977. Radiation, whether from x-ray tubes or from radioisotopes, came to be recognized as having dangers both for the patient and for personnel operating the equipment, and efforts were made to discover media that would produce images without these dangers. In the late 1940s, George Ludwig, a junior lieutenant at the Naval Medical Research Institute in Bethseda, Maryland, undertook experiments using industrial ultrasonic ﬂaw-detection equipment in an attempt to determine the acoustic impedance of various tissues, including human gallstones surgically implanted into the gallbladders of dogs. His observations were detailed in a 30-page project report to the Naval Medical Research Institute dated June 16, 1949, now considered the ﬁrst report of its kind on the diagnostic use of ultrasound. However, a substantial portion of Ludwig’s work was considered classiﬁed information by the Navy and was not published in medical journals. Civilian research into what became the two biggest areas of early ultrasonic diagnosis—cardiology and obstetrics—began in Sweden and Scotland, respectively, both making use of gadgetry initially designed for shipbuilding. In 1953, Inge Edler, a cardiologist at Lund University collaborated with Carl Hellmuth Hertz, a graduate student in the department of nuclear physics who was familiar with using ultrasonic reﬂectoscopes for nondestructive materials testing, and together they developed the idea of using this method in medicine. They made the ﬁrst successful measurement of heart activity on October 29, 1953 using a device borrowed from Kockums, a Malmö shipyard. On December 16 of the same year, the method was used to generate an echo encephalogram. Edler and Hertz published their ﬁndings in 1954.

ELECTROCARDIOGRAPHY

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At around the same time, Ian Donald of the Glasgow Royal Maternity Hospital struck up a relationship with boilermakers Babcock & Wilcox in Renfrew, where he used their industrial ultrasound equipment to conduct experiments assessing the ultrasonic characteristics of various in vitro preparations. With fellow obstetrician John MacVicar and medical physicist Tom Brown, Donald reﬁned the equipment to the point where it could be used successfully on live volunteer patients. These ﬁndings were reported in The Lancet on June 7, 1958 as “Investigation of abdominal masses by pulsed ultrasound.” Nuclear magnetic resonance (NMR) in molecules was ﬁrst described by Isidor Rabi in 1938. His work was followed up eight years later by Felix Bloch and Edward Mills Purcell, who, working independently, noticed that magnetic nuclei such as hydrogen and phosphorus, when placed in a magnetic ﬁeld of a speciﬁc strength, absorb radio-frequency energy, a situation described as being “in resonance.” For the next 20 years NMR found purely physical applications in chemistry and physics, and it was not until 1971 that Raymond Damadian showed that the nuclear magnetic relaxation times of different tissues, especially tumors, differed, thus raising the possibility of using the technique to detect disease. Magnetic resonance imaging (MRI) was ﬁrst demonstrated on small test tube samples in 1973 by Paul Lauterbur, and in 1975 Richard Ernst proposed using phase and frequency encoding and the Fourier transform, the technique that still forms the basis of MRI. The ﬁrst commercial nuclear magnetic imaging scanner allowing imaging of the body appeared in 1980 using Ernst’s technique, which allowed a single image to be acquired in approximately 5 minutes. By 1986, the imaging time was reduced to about 5 seconds without sacriﬁcing too much image quality. In the same year, the NMR microscope was developed, which allowed approximately 10-mm resolution on approximately 1-cm samples. In 1993, functional MRI (fMRI) was developed, thus permitting the mapping of function in various regions of the brain.

ELECTROCARDIOGRAPHY Roentgen’s discovery of x-rays grew out of the detailed investigation of electricity that was a core scientiﬁc concern of the nineteenth century, and it is little surprise that investigators also took a keen interests in the electricity generated by the human body itself. Foremost among these was Willem Einthoven. Before his day, although it was known that the body produced electrical currents, the technology was inadequate to measure or record them with any sort of accuracy. Starting in 1901, Einthoven, a professor at the University of Leiden, conducted a series of experiments using a string galvanometer. In his device, electric currents picked up from electrodes on the patient’s skin passed through a thin ﬁlament running between very

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BIOMARKERS ARE NOT NEW

strong electromagnets. The interaction of the electric and magnetic ﬁelds caused the ﬁlament or “string” to move, and this was detected by using a light to cast a shadow of the moving string onto a moving roll of photographic paper. It was not, at ﬁrst, an easy technique. The apparatus weighed 600 lb, including the water circulation system essential for cooling the electromagnets, and was operated by a team of ﬁve technicians. Over the next two decades Einthoven gradually reﬁned his machine and used it to establish the electrocardiographic (ECG) features of many different heart conditions, work that was eventually recognized with a Nobel prize in 1924. As the ECG became a routine part of medical investigations it was realized that a system that gave only a “snapshot” of a few seconds of the heart’s activity could be unhelpful or even misleading in the investigation of intermittent conditions such as arrhythmias. This problem was addressed by Norman Holter, an American biophysicist, who created his ﬁrst suitcase-sized “ambulatory” monitor as early as 1949, but whose technique is dated in many sources to the major paper that he published on the subject in 1957, and other authors cite an even later, 1961 publication.

HEMATOLOGY The scientiﬁc examination of blood in order to learn more about the health of the patient from whom it was taken can be dated to 1642, when Anthony van Leeuwenhoek ﬁrst observed blood cells through his newly invented microscope. Progress was at ﬁrst slow, and it was not until 1770 that leucocytes were discovered by William Hewson, an English surgeon, who also observed that red cells were ﬂat rather than spherical, as had earlier been supposed. Association of blood cell counts with clinical illness depended on the development of a technical method by which blood cells could be counted. In 1852, Karl Vierordt at the University of Tübingen developed such a technique, which, although too tedious for routine use, was used by one of his students, H. Welcher, to count red blood cells in a patient with “chlorosis” (an old word for what is probably our modern iron-deﬁciency anemia). He found, in 1854, that an anemic patient had signiﬁcantly fewer red blood cells than did a normal person. Platelets, the third major cellular constituent of blood, were identiﬁed in 1862 by a German anatomist, Max Schultze. Remarkably, all these discoveries were made without the beneﬁt of cell staining, an aid to microscopic visualization that was not introduced until 1877 in Paul Ehrlich’s doctoral dissertation at the University of Leipzig. The movement of blood cell studies from the research laboratory to routine support of patient care needed a fast automatic technique for separating and counting cells, which was eventually provided by the Coulter brothers, Wallace and Joseph. In 1953 they patented a machine that detected the change in electrical conductance of a small aperture as ﬂuid containing cells was drawn through.

BLOOD AND URINE CHEMISTRY

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Cells, being nonconducting particles, alter the effective cross section of the conductive channel and so signal both their presence and their size. An alternative technique, ﬂow cytometry, was also developed in stages between the late 1940s and the early 1970s. Frank Gucker at Northwestern University developed a machine for counting bacteria in a laminar stream of air during World War II and used it to test gas masks, the work subsequently being declassiﬁed and published in 1947. Louis Kamentsky at IBM Laboratories and Mack Fulwyler at the Los Alamos National Laboratory experimented with ﬂuidic switching and electrostatic cell detectors, respectively, and both described cell sorters in 1965. The modern approach of detecting cells stained with ﬂuorescent antibodies was developed in 1972 by Leonard Herzenberg and his team at Stanford University, who coined the term ﬂuorescence-activated cell sorter (FACS).

BLOOD AND URINE CHEMISTRY As with hematology, real progress in measuring the chemical constituents of plasma depended largely on the development of the necessary technology. Until such techniques became available, however, ingenious use was made of bioassays, developed in living organisms or preparations made from them, to detect and in some cases quantify complex molecules. A good example of this is the detection of human chorionic gonadotrophin (hCG) in urine as a test for pregnancy. Selmar Aschheim and Bernhard Zondek in Berlin, who ﬁrst isolated this hormone in 1928, went on to devise the Aschheim–Zondek pregnancy test, which involved ﬁve days of injecting urine from the patient repeatedly into an infantile female mouse which was subsequently killed and dissected. The ﬁnding of ovulation in the mouse indicated that the injected urine contained hCG and meant that the patient was pregnant. In the early 1940s, the mouse test gave way to the frog test, introduced by Lancelot Hogben in England. This was a considerable improvement, in that injection of urine or serum from a pregnant woman into the dorsal lymph sac of the female African clawed frog (Xenopus laevis) resulted in ovulation within 4 to 12 hours. Although this test was known to give a relatively high proportion of false negatives, it was regarded as an outstanding step forward in diagnosis. One story from the 1950s recounts that with regard to the possible pregnancy of a particular patient, “opinions were sought from an experienced general practitioner, an eminent gynecologist, and a frog; only the frog proved to be correct.” Pregnancy testing, and many other “biomarker” activities, subsequently moved from out-and-out bioassays to the “halfway house” of immunological tests based on antibodies to the test compound generated in a convenient species but then used in an ex vivo laboratory setting, and in 1960 a hemagglutination inhibition test for pregnancy was developed by Leif Wide and Carl Gemzell in Uppsala.

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BIOMARKERS ARE NOT NEW

Not all immune reactions can be made to modulate hemagglutination, and a problem with the development of immunoassays was ﬁnding a simple way to detect whether the relevant antibody or antigen was present. One answer lay in the use of radiolabeled reagents. Radioimmunoassay was ﬁrst described in a paper by Rosalyn Sussman Yalow and Solomon Berson published in 1960. Radioactivity is difﬁcult to work with because of its safety concerns, so an alternative was sought. This came with the recognition that certain enzymes (such as ABTS or 3,3′,5,5′-tetramethylbenzidine) which react with appropriate substrates to give a color change could be linked to an appropriate antibody. This linking process was developed independently by Stratis Avrameas and G. B. Pierce. Since it is necessary to remove any unbound antibody or antigen by washing, the antibody or antigen must be ﬁxed to the surface of the container, a technique ﬁrst published by Wide and Porath in 1966. In 1971, Peter Perlmann and Eva Engvall at Stockholm University, as well as Anton Schuurs and Bauke van Weemen in the Netherlands, independently published papers that synthesized this knowledge into methods to perform enzyme-linked immunosorbent assay (ELISA). A further step toward physical methods was the development of chromatography. The word was coined in 1903 by the Russian botanist Mikhail Tswett to describe his use of a liquid–solid form of a technique to isolate various plant pigments. His work was not widely accepted at ﬁrst, partly because it was published in Russian and partly because Arthur Stoll and Richard Willstätter, a much better known Swiss–German research team, were unable to repeat the ﬁndings. However, in the late 1930s and early 1940s, Archer Martin and Richard Synge at the Wool Industries Research Association in Leeds devised a form of liquid–liquid chromatography by supporting the stationary phase, in this case water, on silica gel in the form of a packed bed and used it to separate some acetyl amino acids derived from wool. Their 1941 paper included a recommendation that the liquid mobile phase be replaced with a suitable gas that would accelerate the transfer between the two phases and provide more efﬁcient separation: the ﬁrst mention of the concept of gas chromatography. In fact, their insight went even further, in that they also suggested the use of small particles and high pressures to improve the separation, the starting point for high-performance liquid chromatography (HPLC). Gas chromatography was the ﬁrst of these concepts to be taken forward. Erika Cremer working with Fritz Prior in Germany developed gas–solid chromatography, while in the UK, Martin himself cooperated with Anthony James in the early work on gas–liquid chromatography published in 1952. Real progress in HPLC began in 1966 with the work of Csaba Horváth at Yale. The popularity of the technique grew rapidly through the 1970s, so that by 1980, this had become the standard laboratory approach to a wide range of analytes. The continuing problem with liquid or gas chromatography was the identiﬁcation of the molecule eluting from the system, a facet of the techniques that was to be revolutionized by mass spectrometry.

FASHIONABLE “OMICS”

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The foundations of mass spectrometry were laid in the Cavendish Laboratories of Cambridge University in the early years of the twentieth century. Francis Aston built the ﬁrst fully functional mass spectrometer in 1919 using electrostatic and magnetic ﬁelds to separate isotope ions by their masses and focus them onto a photographic plate. By the end of the 1930s, mass spectrometry had become an established technique for the separation of atomic ions by mass. The early 1950s saw attempts to apply the technique to small organic molecules, but the mass spectrometers of that era were extremely limited by mass and resolution. Positive theoretical steps were taken, however, with the description of time-of-ﬂight (TOF) analysis by W. C. Wiley and I. H. Maclaren. and quadruple analysis by Wolfgang Pauli. The next major development was the coupling of gas chromatography to mass spectrometry in 1959 by Roland Gohlke and Fred McLafferty at the Dow Chemical Research Laboratory in Midland, Michigan. This allowed, for the ﬁrst time, an analysis of mixtures of analytes without laborious separation by hand. This, in turn, was the trigger for the development of modern mass spectrometry of biological molecules. The introduction of liquid chromatography–mass spectrometry (LC-MS) in the early 1970s, together with new ionization techniques developed over the last 25 years (i.e., fast particle desorption, electrospray ionization, and matrix-assisted laser desorption/ionzation), have made it possible to analyze almost every class of biological compound class right up into the megadalton range.

FASHIONABLE “OMICS” In Benet Street, Cambridge, stands a rather ordinary pub which on Saturday, February 28, 1953, enjoyed 15 minutes of fame far beyond Andy Warhol’s wildest dreams. Two young men arrived for lunch and, as James Watson watched, Francis Crick announced to the regulars in the bar that “we have found the secret of life.” The more formal announcement of the structure of DNA appeared in Nature on April 2 in a commendably brief paper of two pages with six references. Watson and Crick shared a Nobel prize with Maurice Wilkins, whose work with Rosalind Franklin at King’s College, London had laid the groundwork. Sadly, Franklin’s early death robbed her of a share of the prize, which is never awarded posthumously. Over the next two decades a large number of researchers teased out the details of the genetic control of cells, and by 1972 a team at the Laboratory of Molecular Biology of the University of Ghent, led by Walter Fiers, were the ﬁrst to determine the sequence of a gene (a coat protein from a bacteriophage). The same team followed up in 1976 by publishing the complete RNA nucleotide sequence of the bacteriophage. The ﬁrst DNA-based genome to be sequenced in its entirety was the 5368-base-pair sequence of bacteriophage

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Φ-X174 elucidated by Frederick Sanger in 1977. The science of genomics had been born. Although the rush to sequence the genomes of ever more complex species (including humans in 2001) initially held out considerable hope of yielding new biomarkers, focus gradually shifted to the protein products of the genes. This process is dated by many to the introduction in 1977 by Patrick O’Farrell at the University of Colorado in Boulder of two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). The subject really took off in the 1990s, however, with technical improvements in mass spectrometers combined with computing hardware and software to support the extremely complex analyses involved. The next “omics” to become fashionable was metabolomics, based on the realization that the quantitative and qualitative pattern of metabolites in body ﬂuids reﬂects the functional status of an organism. The concept is by no means new, the ﬁrst paper addressing the idea (but not using the word) having been “Quantitative Analysis of Urine Vapor and Breath by Gas–Liquid Partition Chromatography” by Robinson and Pauling in 1971. The word metabolomics, however, was not coined until the 1990s.

THE FUTURE Two generalizations may perhaps be drawn from the accelerating history of biomarkers over the last 2700 years. The ﬁrst is that each new step depends on an interaction between increasing understanding of the biology and technical improvement of the tools leading to a continuous spiral of innovation. The second is the need for an open but cautious mind. Sushustra’s recognition of the implications of sweet urine has stood the test of time; de Corbeil’s Poem on the Judgment of Urines has not. The ultimate fate of more recent biomarkers will only be revealed by time.

2 BIOMARKERS: FACING THE CHALLENGES AT THE CROSSROADS OF RESEARCH AND HEALTH CARE Gregory J. Downing, D.O., Ph.D. U.S. Department of Health and Human Services, Washington, DC

INTRODUCTION Across many segments of the biomedical research enterprise and the health care delivery sectors, the impact of biomarkers has been transforming in many ways: from business and economics to policy and planning of disease management. The pace of basic discovery research progress has been profound worldwide, with the intertwining of innovative technologies and knowledge providing extensive and comprehensive lists of biological factors now known to play integral roles in disease pathways. These discoveries have had a vast impact on pharmaceutical and biotechnology industries, with tremendous growth in investment in biomarker research reaching into the laboratory technology and services sector. These investments have spawned new biomedical industry sectors, boosted the roles of contract research organizations, supported vast new biomarker discovery programs in large corporate organizations, and prompted the emergence of information management in research. Similarly, growth in the academic research programs supporting biomarker research has greatly expanded training capacity, bench and clinical research capacity, and infrastructure, while fueling the growth of intellectual property. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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THE CROSSROADS OF RESEARCH AND HEALTH CARE

By many reports, private-sector applications of biomarkers in toxicity and early efﬁcacy trials have been fruitful in developing decision-making priorities that are introducing greater efﬁciency in early to midstage medical product development. Despite the heavy emphasis in private and publicly funded research, the reach of the impact of biomarkers into clinical practice interventions at present is challenging to quantify. The costs of development remain high for many drugs, and the numbers of new chemical entities reaching the marketplace have continued to remain relatively low compared to prior years and expectations following robust research expansion of the 1980s and 1990s. Industry concerns about the sustainability of research and development programs have grown in the backdrop of the clinical challenges that are attendant on biomarker applications in clinical trials. Understanding of the clinical implications of disease markers as a consequence of relatively slow evidence development has taken much longer to discern than many had predicted. There have been many challenges in establishing a translational research infrastructure that serves to verify and validate the clinical value of biomarkers as disease endpoints and their value as independent measures of health conditions. The lack of the equivalent of the clinical trial infrastructure for biomarker validation and diagnostics has slowed progress compared to therapeutic and device development. Evidence development processes and evaluations have only now begun to emerge for biomarkers, and wide adoption of them in clinical practice measures has not yet matured. For some, the enthusiasm and economic balance sheets have not been squared, as the clinical measure indices that had been hoped for have been viewed as moderately successful by some and by others as bottlenecks in the pipelines of therapeutic and diagnostic development.

BRIEF HISTORY OF BIOMARKER RESEARCH, 1998–2008: THE FIRST DECADE During the last decade of the twentieth century, biomedical research underwent one of the most dramatic periods of change in history. Inﬂuenced by a multitude of factors—some scientiﬁc, others economic, and still others of policy—new frontiers of science emerged as technology and knowledge converged, and diverged—bringing new discoveries and hope to the forefront of medicine and health. These capabilities came about as a generation’s worth of science that brought to the mainstream of biomedical research the foundation for a molecular basis of disease: recombinant DNA technology. Innovative applications of lasers, novel medical imaging platforms, and other advanced technologies began to yield a remarkable body of knowledge that provided unheralded opportunities for discovery of new approaches to the management of human health and disease. Here we brieﬂy revisit a part of the medical research history that led to the shaping of new directions that is, for now, captured simply by the term

BRIEF HISTORY OF BIOMARKER RESEARCH, 1998–2008: THE FIRST DECADE

17

biomarker, a biological indicator of health or disease. In looking backward to the 1980s and 1990s and the larger scheme of health care, many new challenges were being faced. The international challenges and global economic threats posed by human immunodeﬁciency virus (HIV) and AIDS provided the impetus for one of the ﬁrst steps in target-designed therapies and the use of viral and immune indicators of disease. For the ﬁrst time, strategically directed efforts in discovery and clinical research paradigms were coordinated at the international level using clinical measures of disease at the molecular level. The ﬁrst impact of biomarkers on discovery and translational research, both privately and publicly funded, as related to biological measures of viral load, CD4+ T-lymphocyte counts, and other parameters of immune function and viral resistance came to be a mainstay in research and development. Regulatory authority was put in place to allow “accelerated approval” of medical products using surrogate endpoints for health conditions with grave mortality and morbidity. Simultaneously, clinical cancer therapeutics programs had some initial advances with the use of clinical laboratory tests that aided in the distinction between responders and nonresponders to targeted therapies. The relation of Her2/neu tyrosine kinase receptor in aggressive breast cancer and response to (Herceptin) [1], and similarly, the association of imatinib (Gleevac) responsiveness with the association of the presence of Philadelphia chromosome translocation involving BCR/Abl genes in chronic myelogenous leukemia [2], represented some of the cases where targeted molecular therapies were based on a biomarker test as a surrogate endpoint for patient clinical response. These represented the entry point of pharmaceutical science moving toward co-development, using diagnostic tests to guide selection of therapy around a biomarker. Diverse changes were occurring throughout the health care innovation pipeline in the 1990s. The rise of the biotechnology industry became an economic success story underpinned by successful products in recombinant DNA technology, monoclonal antibody production, and vaccines. The device manufacturing and commercial laboratory industries became major forces. In the United States, the health care delivery system underwent changes with the widespread adoption of managed care programs, and an effort at health care reform failed. For U.S.-based academic research institutions, it was a time of particular tumult for clinical research programs, often supported through clinical care ﬁnances, downsized in response to ﬁnancial shortfalls. At a time when scientiﬁc opportunity in biomedicine was, arguably, reaching its zenith, there were cracks in the enterprise that was responsible for advancing basic biomedical discovery research to the clinic and marketplace. In late 1997, the director of the National Institutes of Health, Harold Varmus, met with biomedical research leaders from academic, industrial, governmental, and clinical research organizations, technology developers, and public advocacy groups to discuss mutual challenges, opportunities, and responsibilities in clinical research. In this setting, some of the ﬁrst strategic considerations regarding “clinical markers” began to emerge among stake-

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THE CROSSROADS OF RESEARCH AND HEALTH CARE

holders in clinical research. From a science policy perspective, steps were taken to explore and organize information that brought to light the need for new paradigms in clinical development. Some of these efforts led to the framing of deﬁnitions of terms to be used in clinical development, such as biomarkers (a characteristic that is measured and evaluated objectively as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention) and surrogate endpoints (a biomarker that is intended to substitute for a clinical endpoint and is expected to predict clinical beneﬁt or harm, or lack of beneﬁt or harm, based on epidemiologic, therapeutic, pathophysiologic, or other scientiﬁc evidence) and descriptions of the information needs and strategic and tactical approaches needed to apply them in clinical development [3]. A workshop was held to address statistical analysis, methodology, and research design issues in bridging empirical and mechanism-based knowledge in evaluating potential surrogate endpoints [4]. In-depth analyses were held to examine information needs, clinical training skills, database issues, regulatory policies, technology applications, and candidate disease conditions and clinical trials that were suitable for exploring biomarker research programs. As a conﬂuence of these organizational activities, in April 1999 an international conference was hosted by the National Institutes of Health (NIH) and U.S. Food and Drug Administration (FDA) [5]. The leadership focused on innovations in technology applications, such as multiplexed gene analysis using polymerase chain reaction technologies, large-scale gel analysis of proteins, and positron-emission tomography (PET) and magnetic resonance imaging (MRI). A summary analysis was crafted for all candidate markers for a wide variety of disease states, and a framework was formed for multiple disease-based public–private partnerships in biomarker development. A series of research initiatives supported by industry, NIH, and FDA were planned and executed in ensuring months. New infrastructure for discovery and validation of cancer biomarkers was put in place. Public–private partnerships for biomarker discovery and characterization were initiated in osteoarthritis, Alzheimer disease, and multiple sclerosis. Research activities in toxicology markers for cardiovascular disease and metabolism by renal and hepatic transformation systems were initiated by FDA. These events did not yield a cross-sector strategic action plan, but did serve as a framework for further engagement across governmental, academic, industrial, and nongovernmental organizations. Among the breakthroughs was the recognition that new statistical analysis methods and clinical research designs would be needed to address multiple variables measured simultaneously and to conduct metaanalyses from various clinical studies to comprehend the effects of a biomarker over time and its role as a reliable surrogate endpoint. Further, it was recognized that there would be needs for data management, informatics, clinical registries, and repositories of biological specimens, imaging ﬁles, and common reagents. Over the next several years, swift movement across the research and development enterprise was under way. It is obvious that future biomarker research

SCIENCE AND TECHNOLOGY ADVANCES IN BIOMARKER RESEARCH

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TABLE 1 Major Scientiﬁc Contributions and Research Infrastructure Supporting Biomarker Discovery Human Genome Project Mouse models of disease (recombinant DNA technology) Information management (informatics tools, open-source databases, open-source publishing, biomarker reference services) Population-based studies and gene–environment interaction studies Computational biology and biophysics Medical imaging: structural and functional High-throughput technologies: in vitro cell-based screening, nanotechnology platforms, molecular separation techniques, robotics, automated microassays, high-resolution optics Proteomics, metabolomics, epigenomics Pharmacogenomics Molecular toxicology Genome-wide association studies Molecular pathways, systems biology, and systems engineering

was driven in the 1990s and early years of the twenty-ﬁrst century by the rapid pace of genome mapping and the fall in cost of large-scale genomic sequencing technology, driven by the Human Genome Project. A decade later, it is now apparent that biomarker research in the realm of clinical application has acquired a momentum of its own and is self-sustaining. The major schemes for applications of biomarkers can be described in a generalized fashion in four areas: (1) molecular target discovery, (2) earlyphase drug development, (3) clinical trials and late-stage therapeutic development, and (4) clinical applications for health status and disease monitoring. The building blocks for biomarker discovery and early-stage validation over the last decade are reﬂected in Table 1. Notable to completion of the international Human Genome Project was the vast investment in technology, database development, training, and infrastructure that have been applied throughout industry toward clinical research applications.

SCIENCE AND TECHNOLOGY ADVANCES IN BIOMARKER RESEARCH In the past decade of biomarker research, far and away the most inﬂuential driving force was completion of the Human Genome Project in 2003. The impact of this project on biomarker research has many facets beyond establishment of the reference data for human DNA sequences. This mammoth undertaking initiated in 1990 led to the sequence for the nearly 25,000 human genes and to making them accessible for further biological study. Beyond this and the other species genomes that have been characterized,

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THE CROSSROADS OF RESEARCH AND HEALTH CARE

human initiatives to deﬁne individual differences in the genome provided some of the earliest large-scale biomarker discovery efforts. The human haplotype map (HapMap) project deﬁned differences in single-nucleotide polymorphisms (SNPs) in various populations around the world to provide insights into the genetic basis of disease and into genes that have relevance for individual differences in health outcomes. A collaboration among 10 pharmaceutical industry companies and the Wellcome Trust Foundation, known as the SNP consortium, was formed in 1999 to produce a public resource of SNPs in the human genome [6]. The SNP consortium used DNA resources from a pool of samples obtained from 24 people representing several racial groups. The initial goal was to discover 300,000 SNPs in two years, but the ﬁnal results exceeded this, as 1.8 million SNPs had been released into the public domain at the end of 2002 when the discovery phase was completed. The SNP consortium was notable, as it would serve as a foundation for further cross-industry public–private partnerships that would be spawned as a wide variety of community-based efforts to hasten the discovery of genomic biomarkers (see below). The next phase of establishing the basic infrastructure to support biomarker discovery, particularly for common chronic diseases, came in 2002 through the International HapMap Project, a collaboration among scientists and funding agencies from Japan, the United Kingdom, Canada, China, Nigeria, and the United States [7]. A haplotype is a set of SNPs on a single chromatid that are associated statistically. This rich resource not only mapped over 3.1 million SNPs, but established additional capacity for identifying speciﬁc gene markers in chronic diseases and represented a critical reference set for enabling population-based genomic studies to be done that could establish a gene– environmental basis for many diseases [8]. Within a short time of completing the description of the human genome, a substantial information base was in place to enable disease–gene discoveries on a larger scale. This approach to referencing populations to the welldescribed SNP maps is now the major undertaking for deﬁning gene-based biomarkers. In recent years, research groups around the world have rapidly been establishing genome-wide association studies to identify speciﬁc gene sets associated with diseases for a wide range of chronic diseases. This new era in population-based genetics began with a small-scale study that led to the ﬁnding that age-related macular degeneration is associated with a variation in the gene for complement factor H, which produces a protein that regulates inﬂammation [9]. The ﬁrst major implication in a common disease was revealed in 2007 through a study of type II diabetes variants [10]. To demonstrate the rapid pace of discovery of disease gene variants: At the time of this writing, within 18 months following the study, there are now 18 disease gene variants associated with defects in insulin secretion [11]. The rapid growth in genome-wide association studies (GWASs) is identifying a large number of multigene variants that are leading to subclassiﬁcation

SCIENCE AND TECHNOLOGY ADVANCES IN BIOMARKER RESEARCH

21

of diseases with common phenotype presentations. Among the databases being established for enabling researchers public access to these association studies is dbGaP, the database of genotype and phenotype. The database, which was developed and is operated by the National Library of Medicine’s National Center for Biotechnology Information, archives and distributes data from studies that have investigated the relationship between phenotype and genotype, such as GWASs. At present, dbGAP contains 36 population-based studies that include genotype and phenotype information. Worldwide, dozens if not hundreds of GWASs are under way for a plethora of health and disease conditions associated with genetic features. Many of these projects are collaborative, involve many countries, and are supported through public–private partnerships. An example is the Genomics Association Information Network (GAIN), which is making genotype–phenotype information publicly available for a variety of studies in mental health disorders, psoriasis, and diabetic nephropathy [12]. For the foreseeable future, substantial large-scale efforts will continue to characterize disease states and catalog genes associated with clinically manifested diseases. As technology and information structures advance, other parameters of genetic modiﬁcations represent new biomarker discovery opportunities. The use of metabolomics, proteomics, and epigenomics in clinical and translational research is now being actively engaged. A new large-scale project to sequence human cancers, the Cancer Genome Atlas, is focused on applying large-scale biology in the hunt for new tumor genes, drug targets, and regulatory pathways. This project is focused not only on polymorphisms but also on DNA methylation patterns and copy numbers as biomarker parameters [13]. Again, technological advances are providing scientists with novel approaches to inferring sites of DNA methylation at nucleotide-level resolution using a technique known as high-throughput bisulﬁte sequencing (HTBS). Large-scale initiatives are also under way to bring a structured approach to relating protein biomarkers into focus for disease conditions. Advances in mass spectrometry, protein structure resolution, bioinformatics for archiving protein-based information, and worldwide teams devoted to disease proteomes have solidiﬁed in recent years. Although at a more nascent stage of progress in disease characterization, each of these emerging new ﬁelds is playing a key complementary role in biomarker discovery in genetics and genomics. Supporting this growth in biomarker discovery is massive investment over the last 10 years worldwide by public and private ﬁnancers that has spawned hundreds of new commercial entities worldwide. Private-sector ﬁnancing for biomarker discovery and ﬁnancing has become a major component of biomedical research and development (R&D) costs in pharmaceutical development. Although detailed budget summaries have not been established for U.S. federal funding of biomarker research, in a recent survey by McKinsey and Co., biomarker R&D expenditures in 2009 were estimated at $5.3 billion, up from $2.2 billion in 2003 [14].

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THE CROSSROADS OF RESEARCH AND HEALTH CARE

POLICIES AND PARTNERSHIPS Although progress in biomarker R&D has accelerated, the clinical translation of disease biomarkers as endpoints in disease management and as the foundation for diagnostic products has had more extensive challenges. A broad array of international policy matters over the past decade have moved to facilitate biomarker discovery and validation (Table 2). In the United States, the FDA has taken a series of actions to facilitate applications of biomarkers in drug development and use in clinical practices as diagnostic and therapeutic monitoring. A voluntary submission process of genomic data from therapeutic development was initiated by the pharmaceutical industry and the FDA in 2002 [15]. This program has yielded many insights into the role of drug-metabolizing enzymes in the clinical pharmacodynamic parameters of biomarkers in drug development. In July 2007, guidelines for the use of multiplexed genetic tests in clinical practice to monitor drug therapy were issued by the FDA [16] More recently, the FDA has begun providing label requirements indicating those therapeutic agents for which biomarker assessment can be recommended to avoid toxicity and enhance the achievement of therapeutic responses [17]. In 2007, Congress authorized the establishment of a private–public resource to support collaborative research with the FDA. One of the major obstacles to clinical genomic research expressed over the years has been a concern that research participants may be discriminated against in employment and provision of health insurance beneﬁts as a result of the association of genetic disease markers. After many years of deliberation, the U.S. Congress passed legislation known as the Genetic Information Non-discrimination Act of 2008, preventing the use of genetic information to deny employment and health insurance. The past decade has seen many new cross-organizational collaborations and organizations developed to support biomarker development. For example,

TABLE 2

Major International Policy Issues Related to Biomarker Research

Partnerships and collaborations: industry, team science Expanded clinical research capacity through increases in public and private ﬁnancing Open-source publishing, data-release policies Standards development and harmonization FDA Critical Path Initiative Regulatory guidances for medical product development Biomarkers Consortium Evidence-based medicine and quality measures of disease International regulatory harmonization efforts Public advocacy in medical research Genetic Information Non-discrimination Act of 2008 (U.S.)

POLICIES AND PARTNERSHIPS

23

the American Society for Clinical Oncology, the American Association for Cancer Research, and the FDA established collaborations in workshops and research discussions regarding the use of biomarkers for ovarian cancer as surrogate endpoints in clinical trials [18]. The FDA Critical Path Initiative was developed in 2006, with many opportunities described for advancing biomarkers and surrogate endpoints in a broad range of areas for therapeutic development [19,20]. Progress in these areas have augmented industry knowledge of application of biomarkers in clinical development programs and fostered harmony with international regulatory organizations in the ever-expanding global research environment. This program has been making progress on expanding the toolbox for clinical development—many of the components foster development and application of biomarkers. As an example of international coordination among regulatory bodies, recently the FDA and the European Medicines Agency (EMEA) for the ﬁrst time worked together to develop a framework allowing submission, in a single application to the two agencies, of the results of seven new biomarker tests that evaluate kidney damage during animal testing of new drugs. The new biomarkers are KIM-1, albumin, total protein, β2-microglobulin, cystatin C, clusterin, and trefoil factor-3, replacing blood urea nitrogen (BUN) and creatinine in assessing acute toxicity [21]. The development of this framework is discussed in more detail by Goodsaid in Chapter 9. In 2007, Congress established legislation that formed the Reagan–Udall Foundation, a not-for-proﬁt corporation to advance the FDA’s mission to modernize medical, veterinary, food, food ingredient, and cosmetic product development, accelerate innovation, and enhance product safety. Another important policy in biomarker development occurred with the abovementioned establishment of the Critical Path Institute in 2006 to facilitate precompetitive collaborative research among pharmaceutical developers. In working closely with the FDA, these collaborations have focused on toxicology and therapeutic biomarker validation [22]. In 2007, a new collaboration building on public–private partnerships with industry was formed to develop clinical biomarkers. In 2006, the Biomarkers Consortium was established as a public–private initiative with industry and government to spur biomarker development and validation projects in cancer, central nervous system, and metabolic disorders in its initial phase [23]. These programs all support information exchange and optimize the potential to apply well-characterized biomarkers to facilitate pharmaceutical and diagnostic development programs. Other policies that are broadening the dissemination of research ﬁndings relate to the growing directions toward open-source publishing. In 2003, the Public Library of Science began an open-source publication process that provides instant access to publications [24]. Many scientiﬁc journals have moved to make their archives available six to 12 months after publication. In 2008, the National Institutes of Health implemented policy that requires publications of scientiﬁc research with U.S. federal funding to be placed in the public domain within 12 months of publication [25]. All of these policy actions are

24

THE CROSSROADS OF RESEARCH AND HEALTH CARE

favoring biomarker research by accelerating the transfer of knowledge from discovery to development. New commercial management tools have been developed to provide extensive descriptions of biomarkers and their state of development. Such resources can help enhance industry application of wellcharacterized descriptive information and increase efﬁciency of research by avoiding duplication and establishing centralized credentialing of biomarker information [26]. New business models are emerging among industry and patient advocacy organizations to increase the diversity of ﬁnancing options for early-stage clinical development [27]. Private philanthropy conducted with key roles of patient groups are supporting research in proof-of-concept research and target validation with the expectation that these targeted approaches will lead to commercial interests in therapeutic development. Patient advocacy foundations are supporting translational science in muscular dystrophy, amyotrophic lateral sclerosis, juvenile diabetes, multiple myeloma, and Pompe disease, often with partnerships from private companies [28,29].

CHALLENGES AND SETBACKS While progress in biomarker R&D has accelerated, the clinical translation of disease biomarkers as endpoints in disease management and as the foundation for diagnostic products has had more extensive challenges [30]. For example, we have not observed a large number of surrogate endpoints emerging as clinical trial decision points. Notable exceptions to this include imaging endpoints that have grown in substantial numbers. In most cases for drug development, biomarkers are being applied in therapeutic development to stratify patients into subgroups of responders, to aid in pharmacodynamic assessment, and to identify early toxicity indicators to avoid late-stage failures in therapeutic development. There are difﬁculties in aligning the biomarker science to clinical outcome parameters to establish the clinical value in medical practice decision making. As applied in clinical practice, biomarkers have their most anticipated applications in pharmacotherapeutic decisions in treatment selection and dosing, risk assessment, and stratiﬁcation of populations for disease preemption and prevention. In the United States, challenges to the marketplace are presented by the lack of extensive experience with pathways for medical product review and reimbursement systems that establish ﬁnancial incentives for biomarker development as diagnostic assays. Clinical practice guidelines for biomarker application in many diseases are lacking, leaving clinicians uncertain about what roles biomarker assays play in disease management. In addition, few studies have been done to evaluate the cost-effectiveness of inclusion of biomarkers and molecular diagnostics in disease management [31]. The lack of these key pieces in a system of modern health care can cripple plans for integration of valuable technologies into clinical practice.

LOOKING FORWARD

25

Scientiﬁc setbacks have also occurred across the frontier of discovery and development. Among notable instances was the use of pattern recognition of tandem mass spectrometric measurements of blood specimens in ovarian cancer patients. After enthusiastic support of the application in clinical settings, early successes were erased when technical errors and study design issues led to faulty assumptions about the ﬁndings. Across clinical development areas, deﬁciencies in clinical study design have left initial study ﬁndings unconﬁrmed, often due to overﬁtting of sample size to populations, and improper control for selection and design bias [32]. Commercial development of large-scale biology companies has also struggled in some ways to identify workable commercial models. Initial enthusiasm early in the decade about private marketing of genomic studies in disease models faltered as public data resources emerged. Corporate models for developing large proteomic databases faltered based on a lack of distinguished market value, little documented clinical beneﬁt, and wide variability in quality of clinical biospecimens. Evidence development to support clinical utility of many biomarkers to be used as clinical diagnostics is difﬁcult to establish, as clinical trial infrastructure has not yet been established to validate candidate biomarkers for clinical practice. An obstacle to this has been access to well-characterized biospecimens coupled with clinical phenotype information. This has led to calls for centralized approaches to biospecimen collection and archiving to support molecular analysis and biomarker research [33]. Furthermore, a wide variety of tissue collection methods and DNA and protein preparation for molecular analysis has been at the root of many problems of irreproducibility. Standards development and best practices have been represented as cornerstones in facilitating biomarker validation [34,35]. Similarly, reacting to the lack of reproducibility of ﬁndings in some studies, proposals have been made for standards in study design for biomarker validation for risk classiﬁcation and prediction [36].

LOOKING FORWARD The next decade of biomarker research is promising, with a push toward more clinical applications to be anticipated. Key factors on the horizon that will be integral to clinical adoption are summarized in Table 3. The conﬂuence of basic and translational research has set the stage for personalized medicine, a term of art used widely now, which indicates that health care practices can be customized to meet speciﬁc biological and patient differences. The term was not part of the lexicon in 1997 but speaks to the consumer-directed aspects of biomedical research. Genomic services offered to consumers have emerged with the use of GWASs, although clinical value and impact are not known. It is clear that the emergence of biomarkers in an ever-changing health care delivery system will in some fashion incorporate the consumer marketplace.

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THE CROSSROADS OF RESEARCH AND HEALTH CARE

TABLE 3

Looking Ahead: Implementing Biomarkers in Clinical Care

Intellectual property policy Phenotypic disease characterization Clinical translation: biomarker validation and veriﬁcation Clinical trials stratiﬁcation based on biological diversity Surrogate endpoints: managing uncertainty and deﬁning boundaries in medical practice Data-sharing models Dynamic forces in industry ﬁnancing Co-development of diagnostics and therapeutics Clinical infrastructure for evidence development and clinical utility of diagnostics Health information exchange and network services Consumer genomic information services

Prospects for biomarkers to continue to play a major role in the transformation of pharmaceutical industry research remain high as new technology platforms, bioinformatics infrastructure, and credentialed biomarkers evolve. The emergence of a clearer role for federal regulators and increased attention to appraisal of value from genomic-based diagnostics will help provide guideposts for investment and a landscape for clinical application. One can anticipate that the impact of genomics in particular will probably provide a clinical beneﬁt in chronic diseases and disorders, where multiple biomarker analyses reﬂect models and pathways of diseases. An emerging clinical marketplace is evolving for the development and application of biomarker assays as clinical diagnostics. The pathway for laboratory-developed tests will probably evolve to include FDA oversight of certain tests with added complexity of multiple variables integrated into index scoring approaches to assist in therapeutic selection. Clinical practice guidelines are beginning to emerge for the inclusion of biomarkers to guide stratiﬁcation of patients and therapeutic decision making. The early impact of these approaches is now evident in oncology, cardiovascular, and infectious disease and immune disorders. Advancing clinical biomarker to improve safety and quality of health care as a mainstay remains many years away. The clinical evaluation processes for diagnostics and targeted molecular therapy as a systematic approach have not yet been ﬁrmly established. The use of electronic health information, and integration of information from health plans and longitudinal data collection and randomized clinical trials will need integration and coordination for effective implementation in medical decision making. In 2008, the ﬁrst impact was felt for over-the-counter or electronically available consumer services based on genetic tests. Utilizing public and private genome-wide association databases, private-sector resources using powerful search engines coupled with family history information and SNP analysis developed a consumer service that identiﬁes possible health risks. Although

REFERENCES

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the medical beneﬁt of such services remains undocumented, the successful entry of several services and the growth of online commercial genomic services indicates interest among health-conscious citizens for understanding inherited disease risk. Another noteworthy factor that will probably play an important role in the next decade of clinical biomarker adoption is the development of standards and interoperability speciﬁcations for the health care delivery system and consumers. Interoperable health record environments will probably provide more ﬂexibility and mobility of laboratory information and provide advantages to consumer empowerment in prevention and disease management. One of the most important and sweeping challenges with biomarkers is the development of intellectual property policies that will bring opportunity and entrepreneurship in balance with meeting unmet market needs and clinical value. Because of the likelihood that single gene mutations or simple protein assays are not by themselves a discovery equivalent to diagnostic tests or new clinical markers, the arraying of the convergence of circles of technologies and knowledge will need new approaches to management for the combined aspects of technology to be brokered as real value in health care. Indeed, the challenges ahead in noting the importance overall for this notion is underscored more globally by Alan Greenspan in noting that “arguably, the single most important economic decision our lawmakers and courts will face in the next twenty-ﬁve years is to clarify the rules of intellectual property” [37]. Overall, a decade’s worth of work has charted a robust and vibrant course for biomarkers across the biomedical research and development landscape. Clinical applications of biomarkers in medical practice are coming more into focus through diagnostics and molecularly targeted therapies, but a long period of time may pass before biomarker-based medicine becomes a standard in all areas of health care practice.

REFERENCES 1. Ross JS, Fletcher JA, Linette GP, et al. (2003). The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy. Oncologist, 8:307–325. 2. Deininger M, Druker BJ (2003). Speciﬁc targeted therapy of chronic myelogenous leukemia with Imatinib. Pharmacol Rev, 55:401–423. 3. Biomarkers Deﬁnitions Working Group (2001). Biomarkers and surrogate endpoints: preferred deﬁnitions and conceptual framework. Clin Pharmacol Ther, 69:89–95. 4. De Grottola VG, et al. (2001). Considerations in the evaluation of surrogate endpoints in clinical trials: summary of a National Institutes of Health Workshop. Control Clin Trials, 22:485–502. 5. Downing GJ (ed.) (2000). Biomarkers and Surrogate Endpoints: Clinical Research and Applications. Elsevier Science, Amsterdam.

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6. Sachidanandam R, Weissman D, Schmidt S, et al. (The International SNP Map Working Group) (2001). A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 409:928–933. 7. The International HapMap Consortium (2003). The International HapMap Project. Nature, 426:789–796. 8. The International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449:851–861. 9. Klein RJ, Zeiss C, Chew EY, et al. (2005). Complement factor H polymorphism in age-related macular degeneration. Science, 308:385–389. 10. Sladek R, Rocheleau G, Rung J, et al. (2007). A genome-wide association study identiﬁes novel risk loci for type 2 diabetes. Nature, 445:881–885. 11. Perry JR, Frayling TN (2008). New gene variants alter type 2 diabetes risk predominantly through reduced beta-cell function. Curr Opin Clin Nutr Metab Care, 11:371–378. 12. GAIN Collaborative Research Group (2007). New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat Genet, 39(9):1045–1051. 13. Collis FS, Barker AD (2007). Mapping the cancer genome: pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies. Sci Am, 296;50–57. 14. Conway M, McKinsey and Co. (2007). Personalized medicine: deep impact on the health care landscape. http://sequencing.hpcgg.org/PM/presentations/Tue_08_ Conway_061120%20Michael%20Conway%20Harvard%20Pers%20Med%20 Presentation.pdf (accessed Sept. 5, 2008). 15. Orr MS, Goodsaid F, Amur S, Rudman A, Frueh FW (2007). The experience with voluntary genomic data submissions at the FDA and a vision for the future of the voluntary data submission program. Clin Pharmacol, 81:294–297. 16. FDA (2007). Guidance for Industry and FDA Staff: Pharmacogenetic tests and genetic tests for heritable markers. http://www.fda.gov/cdrh/oivd/guidance/1549. html. 17. Frueh FW, et al. (2008).Pharmacogenomic biomarker information in drug labels approved by the United States Food and Drug Administration: prevalence of related drug use. Pharmacotherapy, 28:992–998. 18. Bast RC, Thigpen JT, Arbuck SG, et al. (2007). Clinical trial endpoints in ovarian cancer: report of an FDA/ASCO/AACR Public Workshop. Gynecol Oncol, 107(2):173–176. 19. The critical path to new medical products. http://www.fda.gov/oc/initiatives/ criticalpath/report2007.html. 20. FDA (2004). Challenge and opportunity on the critical path to new medical products. http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html (accessed Aug. 23, 2008). 21. FDA (2008). European Medicines Agency to consider additional test results when assessing new drug safety. http://www.fda.gov/bbs/topics/NEWS/2008/NEW01850. html. 22. Woolsey RL, Cossman J (2007). Drug development and the FDA’s Critical Path Initiative. Clin Pharmacol Ther, 81:129–133.

REFERENCES

29

23. The Biomarkers Consortium (2008). On the critical path of drug discovery. Clin Pharmacol Ther, 83:361–364. 24. Public Library of Science. http://www.plos.org. 25. National Institutes of Health (2008). Revised policy on enhancing public access to archived publications resulting from NIH-funded research. NOT 08–033. http:// grants.nih.gov/grants/guide/notice-ﬁles/not-od-08-033.html. 26. Thomson Reuters. BIOMARKERcenter. http://scientiﬁc.thomsonreuters.com/ products/biomarkercenter/ (accessed Sept. 23, 2008). 27. Kessel M, Frank F (2007). A better prescription for drug-development ﬁnancing. Nat Biotechnol, 25:859–866. 28. PriceWaterhouseCoopers (2007). Personalized medicine: the emerging pharmacogenomics revolution. Blogal Technology Centre, Health Research Institute, San Jose, CA. 29. Trusheim MR, Berndt ER, Douglas FL (2007). Stratiﬁed medicine: strategic and economic implications of combiing drugs and clinical biomarkers. Nat Rev Drug Discov, 6(4):287–293. 30. Phillips KA, Van Bebber S, Issa A (2006). Priming the pipeline: a review of the clinical research and policy agenda for diagnostics and biomarker development. Nat Revi Drug Discov, 5(6):463–469. 31. Phillips KA, Van Bebber SL (2004). A systematic review of cost-effectiveness analyses of pharmacogenomic interventions. Pharmacogenomics, 5(8):1139–1149. 32. Ransohoff DW (2005). Lessons from controversy: ovarian cancer screening and serum proteomics. J Natl Cancer Inst, 97:315–319. 33. Ginsburg GS, Burke TW, Febbo T (2008). Centralized biospecimen repositories for genetic and genomic research. JAMA, 299:1359–1361. 34. National Cancer Institute (2007). National Cancer Institute best practices for biospecimen resources. http://biospecimens.cancer.gov/practices/. 35. Thomson Reuters (2008). Establishing the standards for biomarkers research. http://scientific.thomsonreuters.com/pm/biomarkers_white_paper_0308.pdf (accessed Sept. 4, 2008). 36. Pepe MS, Feng Z, Janes H, Bossoyt PM, Potter JD (2008). Pivotal evaluation of the accuracy of a biomarker used for classiﬁcation or prediction: standards for study design. J Natl Cancer Inst, eprint, Oct. 8. 37. Greenspan A (ed.) (2007). The Age of Turbulence: Adventures in a New World. Penguin Press, New York.

3 ENABLING GO/NO GO DECISIONS J. Fred Pritchard, Ph.D. MDS Pharma Services, Raleigh, North Carolina

Mallé Jurima-Romet, Ph.D. MDS Pharma Services, Montreal, Quebec, Canada

UNDERSTANDING RISK There is no question that most people who know something about our industry consider developing a drug product as a risky business. Usually, they mean that the investment of time and money is high while the chance of a successful outcome is low compared to other industries that create new products. Yet the rewards can be great, not only in terms of monetary return on investment (ROI) but also in the social value of contributing an important product to the treatment of human disease. Risk is deﬁned as “the possibility of loss or injury” [1]. Therefore, inherent in the concept is the mathematical sense of probability of occurrence of something unwanted. Everyday decisions and actions that people take are guided by conscious and unconscious assessments of risk, and we are comfortable with compartmentalized schemes where we sense that a situation is very high, high, medium, low, or very low risk. We often deal with the concept of a relative risk (e.g., in comparison to other options). Some risks can be deﬁned in more absolute terms, such as some type of population measure based on trend analysis of prior incidence statistics (e.g., current risk of postmenopausal Caucasian women in the United States being diagnosed with breast cancer).

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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ENABLING GO/NO GO DECISIONS

These types of population-based risk data, while often much debated in the scientiﬁc and popular press, do affect decision making at the individual level. As opposed to an individual’s skill at assessing risks, decisions and actions taken during drug development require objective and systematic risk assessment by groups of people. There are many stakeholders involved in the development of a drug product. These include the specialists that do the science required in drug development. Also included are the investors and managers who make decisions about how limited resources will be used. Clinical investigators who administer the drug and the patients who agree to participate in clinical trials are stakeholders, as are the regulators and institutional review boards (IRBs) or ethics committees that approve use of an experimental drug in humans. Each stakeholder has his or her unique perspective of risk. The prime focus of some is the business risk involved, including how much work and money is invested to progress the drug at each phase of development. On the other hand, IRBs, regulators, investigators, and patients are concerned primarily with the safety risk to the patient. Figure 1 depicts the major factors that contribute to three different perspectives of risk, broadly classiﬁed as risk to patient, business risk, and risk of therapeutic failure. Investigators and patients are concerned primarily with safety and efﬁcacy factors associated with a particular therapy. Although regulators are concerned primarily with patient safety, regulations do affect how much development will be required for a particular drug candidate. The business risk can be greatly affected by the investment and development partners involved and the expectations set with owners and managers. Potential competitors in the marketplace affect risk, as does the available pool of money for development. The common factor for all three risk perspectives is the novelty

Figure 1 Major factors affecting three main perspectives of risk in drug development: business risk, risk to patient, and risk of therapeutic failure.

DECISION GATES

33

of the product. Drug candidates that represent new targets for therapy while representing a hope for improved efﬁcacy do require more interactions with regulators and investigators, adding to development expense. In addition, because the target is unproven, there is a greater relative risk for therapeutic failure compared to proven pharmacological targets of disease intervention. Therefore, when attempting to express the risks involved in developing a drug, it is important to understand the varying perspectives of each major group of stakeholders. Before risk assessment can be fully appreciated in the context of decision making in drug development, it must be balanced with the perceived beneﬁts of the product. For example, the patient and physician would tolerate a high level of risk to the patient if the potential beneﬁts offered by a novel therapeutic are for a life-threatening disease for which there is no effective treatment. In a similar way, a high degree of business risk may be tolerated by investors in novel drug candidates if the return on investment, if successful, were very high. Like risk, beneﬁts are also in the eye of the beholder. Ideally, on many occasions during drug development, each stakeholder is asked to assess his or her view of risk versus beneﬁt based on current data. This assessment will become part of the decision-making process that drives drug development in a logical and, hopefully, collaborative way. Effective decision making requires integrating these varying risk–beneﬁt assessments in a balanced way. The decision gate approach is a useful way to integrate these needs into the drug development process.

DECISION GATES Drug development is a process that proceeds through several high-level decision gates from identiﬁcation of a potential therapeutic agent through to marketing a new drug product [2]. A decision gate is a good analogy. For a drug candidate to progress further in drug development it must meet a set of criteria that have been agreed to by the decision makers before they will open the gate. It is “go/no go” because the future product life of the drug hangs in the balance. Once a new drug candidate has progressed through a decision gate, the organization should be committed to expend even greater resources (money and scientists’ time) to do the studies to address criteria for the next decision gate along the development path. Disciplined planning and decision making is required to leverage the value of the decision gate approach. An example of a decision grid applicable to the early phase of drug development is depicted in Table 1. Initially, a clear set of questions at each decision gate needs to be agreed upon and understood by the decision makers. In later stages of drug development these questions and criteria are often presented in a target product proﬁle (TPP), which is, in essence, a summary of a drug development plan described in terms of labeling concepts. The FDA has formalized the agency’s expectations for a TPP in a

34

Rodent: NOAEL >10 mg/kg; dog: NOAEL >10 mg/kg; no genotoxicity no CV; effects <100 mg/kg

Rodent: NOAEL >5 mg/kg; dog: NOAEL >5 mg/kg; equivocal genotoxicity at highest exposure; no CV effects <30 mg/kg

Rodent model: ED90<1 mg/kg; human receptor: IC50<10 μM

Rodent model: ED90<2 mg/kg; human receptor: IC50<50 μM

Base case (invest in next phase)

Minimum required (proceed with caution)

Rodent: NOAEL >50 mg/kg; dog: NOAEL >50 mg/kg; no genotoxicity; no CV effects

Animal Safety

Rodent model: ED90<0.3 mg/kg; human receptor: IC50<1 μM

Efﬁcacy API stable for at least 6 mo Cost of goods for API <$10 K/kg GMP tablet API stable for at least 3 mo Cost of goods for API <$20 K/kg GMP tablet

Dog half-life >8 h; dog BA >90%; no active metabolites; no CYP2D6 inhibition Dog half-life >4 h; dog BA >50%; <10% active metabolites; CYP2D6 inhibition: Ki>10 μM Dog half-life >2 h; dog BA >30%; <30% active metabolites; CYP2D6 inhibition: Ki>1 μM

API stable for at least 3 mo Cost of goods for API <$30 K/kg GMP tablet

CMC

PK/ADME

Patients: multisite dose escalation; intensive CV monitoring; FDA review after each dose escalation

Cohorts of healthy normal subjects: staggered (SD to MD) dose escalation; normal safety monitoring Cohorts of healthy normal subjects: sequential dose escalation; intensive CV monitoring

Phase I Study Design

Decision Gate Grid Deﬁning Criteria Required to Move Through the Decision Gate “Safe to Give to Humans?”

Best achievable (enhance investment)

TABLE 1

ROLE OF BIOMARKERS IN DISCOVERY AND PRECLINICAL DEVELOPMENT

35

guidance document [3]. Many companies start evolving a TPP even at the earliest go/no go decision gates of drug development. In this way, a common way of thinking is preserved throughout the life of the product. The development program plan is assembled by determining what studies need to be done and their design that would provide information critical to answering the key questions at each go/no go decision gate. The value of a solid drug development plan based on a decision gate approach can be leveraged only if there is discipline in the decision-making process. What method will be used to make a decision: a majority vote of a committee or a single decider who is advised by others? Each stakeholder needs to understand clearly his or her role in making the decision at each gate. Is he or she a decider, a consultant, or just someone who needs to know what the decision is in order to do the job effectively? Go/no go decisions need to be made when all information required to answer the key questions is available. Go/no go decisions should not be revisited. If this occurs, there is either a lack of decision discipline or key information was missing when the original decision was made, representing a failure in planning. Some go/no go decision gates require agreement to proceed by different groups of stakeholders. This is particularly true when the question “Is the drug candidate safe to give to humans?” is addressed. The sponsor must decide whether to ﬁle an investigational new drug (IND) application based on the data and information collected so far from animal tests and in vitro assays. The same data are also evaluated by the regulators (e.g., the FDA), who must have time (30 days) to object if they feel the safety of the subjects in the ﬁrst few clinical trials will be compromised unduly. Finally, the data are reviewed again by the IRB, who look speciﬁcally at how safety will be evaluated and managed in the clinical trials and who represent the interests of the volunteers. It is as if the gate has three locks, each with a key owned by a separate entity which will decide independently whether they open their lock. One cannot pass through the gate until all three locks are open.

ROLE OF BIOMARKERS IN DISCOVERY AND PRECLINICAL DEVELOPMENT Less than 10% of drug candidates that enter clinical testing result in a marketed product [4]. This means that at some point during clinical development over 90% of drug candidates fail. If failure occurs late in clinical development, up to hundreds of millions of dollars and a great deal of time and effort will have been invested, with little or no return. Proper incorporation of biomarkers in drug development strategy enables the concept “fail fast, fail early.” Early failure actually lowers overall risk because it enables one to move resources and utilize available patients on other promising therapies. Biomarker data can be critical to a go/no go decision. By deﬁnition, biomarkers may reﬂect the biology or the progression of disease and/or the effect

36

ENABLING GO/NO GO DECISIONS

of drug treatment. Therefore, information provided by properly selected biomarkers can greatly inﬂuence the decision as to whether or not to progress through a go/no go decision gate. The challenge is to identify relevant biomarkers early enough to implement them for go/no go decisions at the critical early stages of the discovery–development process. Biomarkers in discovery are valuable tools for understanding the pathobiology of a disease and the pharmacology of a target and/or compounds (hits or leads) under investigation. Moreover, even at this very early stage, a biomarker may already be identiﬁed as a potential clinical safety or efﬁcacy biomarker. Decision gates in discovery are frequently directed toward selection of a lead or a small number of leads to take forward to preclinical development, and prioritization of the leads. For this purpose, a number of commonly employed screening assays can generate useful biomarker data. For example, membrane permeability coefﬁcients and metabolic stability data from Caco-2 and hepatocyte screening assays, respectively, are predictive biomarkers of in vivo intestinal absorption and liver metabolism, and can be used to weed out compounds unlikely to have acceptable pharmacokinetic (PK) properties for the clinical use intended. During preclinical development, the focus of studies is on revealing potential toxicity of the drug candidate. Accordingly, there is interest in identifying and monitoring safety biomarkers that can be used for go/no go decisions. Particularly for classes of compounds whose development has been plagued by unusual ﬁndings in animals that have not translated into human risk (e.g., ﬁbrates and peroxisome proliferation in rodents), safety biomarkers that translate well across species are highly desirable but not always easy to ﬁnd. In both discovery and preclinical development, the abundance of new technologies, high content screening, imaging, genomics, proteomics, metabonomics, and systems biology, to name a few, has opened the door to the identiﬁcation of theoretically innumerable novel biomarkers along with an expectation that this will result in enhanced efﬁciency and better decision making. However, the technologies, and the biomarkers that they produce, have to be evaluated as to how they provide insight into understanding the biology and pharmacology being investigated, and how they can be used effectively by scientists and managers in decision making. As discussed in Chapter 4, increased volume and complexity of data require sophisticated data analysis and informatics infrastructure to be in place before the new knowledge generated can be applied meaningfully to decision making.

ROLE OF BIOMARKERS IN EARLY CLINICAL RESEARCH Biomarkers may be the only way to address a decision gate that asks the question: Is there evidence that the drug is working in humans using the same

ROLE OF BIOMARKERS IN PHASE III AND POSTMARKETING DECISIONS

37

mechanism of action deﬁned by animal studies? This question is answered by a clinical proof-of-concept study: often, a phase IIa study in a small number of patients. However, occasionally, clinical proof of concept can be addressed during phase I development if the mechanism of action can be demonstrated in healthy volunteers (e.g., effects on blood pressure, body weight, lipids). The changes in the data collected during this study do not achieve statistical signiﬁcance; rather, there needs to be enough indication of potential efﬁcacy to convince decision makers to open the gate and spend the resources to progress the drug further. Often, the minimal result for progression can be deﬁned ahead of time, making the actual decision-making process much easier, more objective, and transparent. Early in clinical development, multiple biomarker assays or technologies may be utilized in order to understand more fully the safety and actions of the drug in humans. One may use a collection of biomarker methods, most of which are not likely to be fully validated surrogate markers of effect. For early decision making, it is not necessary to have results that meet the same standards of accuracy and quality as those used for drug approval. Indeed, it is during these early trials that promising tests for use in later clinical development or marketing are ﬁrst identiﬁed from among the pool of available experimental biomarkers. Resources can then be put to further developing the selected biomarker methodology to meet the quality standards expected for continued future use. Knowledge of the biology of the disease may be evolving at the same time as the drug is being developed. Therefore, new biomarkers may need to be added during the development process as the science evolves. It should not be assumed that the “tried and true” biomarkers are the only ones worthy of consideration or that a biomarker that was used to obtain marketing approval in the past is the one that should be used today. Good science supported by convincing data can convince stakeholders that a novel biomarker is better than one already established.

ROLE OF BIOMARKERS IN PHASE III AND POSTMARKETING DECISIONS Traditional clinical trial endpoints, such as morbidity and mortality, often require extended time frames and may be difﬁcult to evaluate. Fully validated surrogate markers can be used as primary endpoints for pivotal phase IIb and III safety and efﬁcacy trials. Lowering blood pressure and lowering LDL cholesterol are both examples of surrogate biomarker endpoints upon which several drugs have been approved, because it has been demonstrated that effects on these markers directly affect morbidity and mortality several years later.

38

ENABLING GO/NO GO DECISIONS

New technologies are now rapidly emerging that show similar promise in providing efﬁcacy signals earlier in the treatment of certain slowly progressing diseases. Imaging-based biomarkers are providing objective endpoints that may be conﬁdently evaluated in a reasonable time frame. Imaging techniques tend to be expensive, but can be cost-effective when used in well-deﬁned situations where subjective assessment has been the only approach available. As discussed in greater detail elsewhere in this book, examples of therapeutic areas where imaging is reshaping clinical trial design include Alzheimer disease, pain, and osteoarthritis. In affected joints, as well as in tissues expressing speciﬁc receptors, magnetic resonance imaging, x-ray computed tomography, position-emission tomography, and single-photon-emission computed tomography imaging are delivering new information to clinicians and researchers. Patient selection is another area where decisions are made using biomarker information. For example, in developing Herceptin, patients who tested negative for the Her2Neu receptor responded poorly to drug treatment, whereas good effects were observed in patients who expressed the receptor. This is consistent with the mechanism of action of the drug and resulted in regulatory approval for patients positive for Her2Neu. The outcome was that a diagnostic test was required before this drug could be marketed effectively. So this is now a biomarker that drives the treatment decisions of breast cancer oncologists around the world. Identifying potential responders or nonresponders prior to phase III clinical trials can have a profound effect on the size of a clinical trial. Eliminating nonresponders can dramatically reduce the intersubject variability in response, thereby reducing the number of subjects in each group of a pivotal clinical trial required to demonstrate effect. The result is a saving in time and large savings in costs. However, if the biomarker is novel, a diagnostic will need to be developed along with the drug before one can market the product effectively.

SUMMARY Biomarkers have the potential to provide valuable information that can be translated into knowledge to aid decision making at all stages of the drug discovery–development process. Different stakeholders have different perspectives of risk associated with development and clinical use of a drug. Although biomarkers cannot eliminate the inherent challenges to integrating risk assessment in this complex environment, the associated data can be evaluated objectively. As informatic tools evolve to analyze more efﬁciently and effectively the large data sets associated with the new biomarker technologies, decision making, in turn, will become better. Regardless of the technologies employed and the complexity of data, a disciplined planning and decisionmaking process is essential to successful drug development.

REFERENCES

39

REFERENCES 1. Merriam-Webster (1988). Webster’s Ninth New Collegiate Dictionary. MerriamWebster, Inc., Springﬁeld, MA. 2. Pritchard JF, Jurima-Romet M, Reimer MLJ, Mortimer E, Rolfe B, Cayen MN (2003). Making better drugs: decision gates in non-clinical drug development. Nat Rev Drug Discov, 2:542–553. 3. FDA (2007). Guidance for Industry and Review Staff: Target product proﬁle—a strategic development process tool. http://www.fda.gov/cder/guidance/6910dft.htm. 4. Kola I, Landis J (2004). Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov, 3:711–715.

PART II IDENTIFYING NEW BIOMARKERS: TECHNOLOGY APPROACHES

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4 IMAGING AS A LOCALIZED BIOMARKER: OPPORTUNITIES AND CHALLENGES Jonathan B. Moody, Ph.D. INVIA Medical Imaging Solutions, Ann Arbor, Michigan

Philip S. Murphy, Ph.D. GlaxoSmithKline Research and Development, Uxbridge, UK

Edward P. Ficaro, Ph.D. INVIA Medical Imaging Solutions, Ann Arbor, Michigan

INTRODUCTION Medical imaging is well known for its ability to produce pictures of patient anatomy for the diagnosis of disease, but perhaps more signiﬁcant for drug development is its ability to produce images of physiological and physicochemical processes in a living subject. It is the latter that gives rise to the concept of imaging biomarkers, which we may deﬁne as the subset of all biomarkers that utilize medical imaging technologies to acquire data. Medical imaging technologies include a range of modalities used in a routine clinical setting: x-ray computed tomography (CT), positron-emission tomography (PET), single-photon-emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound, and optical imaging. In its 2006 Critical Path Opportunities Report, the U.S. Food and Drug Administration (FDA) deﬁned biomarkers as “measurable characteristics that Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

43

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OPPORTUNITIES AND CHALLENGES

reﬂect physiological, pharmacological, or disease processes in animals or humans. Changes in biomarkers following treatments reﬂect the clinical response to the product. Biomarkers can reduce uncertainty by providing quantitative predictions about performance” [1]. In this chapter we will discuss imaging as a localized biomarker. Although imaging biomarkers hold great potential for accelerating drug discovery and development, in practice they are often difﬁcult and costly to implement for speciﬁc applications, may be used inappropriately in a development setting, or may fail to provide a robust answer to the development questions being asked. We focus our discussion on understanding the opportunities for using imaging biomarkers as tools in drug development while recognizing the current challenges and how efforts to overcome these challenges are evolving. We begin by presenting a brief overview of biological imaging. Then we discuss the general characteristics and context of an imaging biomarker along with key advantages and limitations of imaging biomarkers. We place the deﬁnition of a biomarker given above into the context of biological imaging in general and diagnostic medical imaging in particular. We follow this with an overview of the scope of imaging in drug development, highlight speciﬁc examples of established and emerging imaging biomarkers from oncology that have found utility in drug development and illustrate considerations for imaging biomarker selection. A key issue in the implementation and use of imaging biomarkers is the challenge of standardization which we then examine in some detail. We conclude by discussing current efforts to address the unique needs of imaging biomarker development.

OVERVIEW OF IMAGING IN BIOLOGICAL RESEARCH From their inception, the various noninvasive imaging modalities have been used widely in basic biological research. The broad utility of imaging in biological research reﬂects the breadth of fundamental physical processes that underlie all imaging technologies. The same types of probes and methods of detection that are used in biochemistry and in molecular and cellular biology [e.g., optical, nuclear (gamma emission), nuclear magnetic resonance (NMR) spectroscopy, x-ray] also form the basis of imaging technologies. When the electron microscope was introduced in the 1930s, the morphology of submicroscopic cellular structures that had only been inferred by indirect methods such as x-ray diffraction or structural chemistry could be visualized directly for the ﬁrst time in electron micrographs [2]. By analogy, the development of medical imaging technologies (e.g., SPECT [3], PET [4,5], CT [6], and MRI [7,8]) has provided the ability to visualize in vivo biological processes previously accessible only by indirect or invasive means. As the science of biological imaging has grown, the development of new imaging applications has been driven by the unique ability of noninvasive imaging to reveal information

OVERVIEW OF IMAGING IN BIOLOGICAL RESEARCH

45

about anatomical structure and physiological function as well as various disease states in living subjects. It will be helpful to review brieﬂy the use of imaging in biological research in order to understand the context and source of imaging biomarkers. We describe some common clinical imaging technologies and then highlight some of the image-based methods for measuring biological function that have been proposed in the literature, providing a background for detailed examples. Several examples of biological imaging applications in drug discovery are also provided in a recent monograph [9]. Clinical Imaging Technologies Although we limit our discussion here to nuclear PET and SPECT, CT, and MRI modalities, the following applies equally well to other imaging technologies, including ultrasound, which is widely used clinically, and various forms of optical imaging which are becoming more prevalent [10]. An accessible general introduction to imaging technologies is available [11], as is a comprehensive treatment of the theoretical basis of imaging [12]. CT, the most commonly used clinical imaging modality, provides threedimensional images of anatomical structure. The main source of contrast in CT images arises from the differential attenuation of x-rays by tissues in the body. In CT images, mineralized tissues appear brighter due to higher attenuation of the x-rays, and soft tissues have intermediate intensity due to lower attenuation. Because of limited contrast differences between soft tissues, an injectable iodine-based contrast agent is often necessary to better highlight anatomical lesions. Despite this limitation, CT provides the highest spatial resolution (less than 0.5 mm), and acquisition of CT data is straightforward and rapid. MRI may be characterized as a large family of methods to detect molecular- and cellular-dependent image contrasts through the manipulation of nuclear spin magnetic resonance during data acquisition. The proton nuclei of hydrogen in water and lipids are the most commonly imaged, although in principle the NMR basis of detection permits imaging or spatially localized spectroscopy of other nuclei, such as 13C, 19F, 23Na, and 31P. The sensitivity of MRI is relatively poor, generally requiring tens of micromolar to millimolar volumes of nuclei for detection. However, excellent soft tissue contrast with high spatial resolution (ca. 1 mm) may be obtained. Acquisition of MRI data is controlled by a pulse sequence, which is a computer program that orchestrates the transmission of radio-frequency pulses and magnetic ﬁeld gradients, as well as the reception of radio-frequency signals from the subject [13]. Vendor-supplied pulse sequences provide a broad range of available acquisition methods; however, additional pulse sequences and improved methods are constantly being introduced in MRI research. While MRI applications tend to be focused on the seemingly limitless variations in MR acquisition methods, data acquisition in nuclear imaging (PET

46

OPPORTUNITIES AND CHALLENGES

and SPECT) is relatively straightforward, being based on either detection of single gamma photons (SPECT) or coincident collinear pairs of gamma photons (PET). Image contrast in nuclear imaging relies on the use of targeted biomolecular probes or tracers labeled with radionuclides to detect in vivo molecular processes and biological function. Thus, the scope of radionuclide imaging is limited primarily by the ability to efﬁciently radiolabel biologically relevant molecules, and applications are centered primarily on the skill and creativity of the radiochemist. In general, the sensitivity of nuclear imaging methods is the highest of all clinical modalities, with the picomolar sensitivity of PET being about two to three orders of magnitude higher than that of SPECT [14]. The low intrinsic resolution of PET (ca. 5 mm) and SPECT (ca. 15 mm) has been mitigated somewhat through the development of multimodality image acquisition combining PET/CT or SPECT/CT [15], and more recently PET/MRI [16], which provide high-resolution anatomical images registered with functional nuclear images. In all imaging modalities, the resulting images must be reconstructed from the data acquired using computational methods of varying complexity [17,18], and additionally, in some cases, a series of images may be used to calculate parametric images representing image-based measures corresponding to particular biological functions. Image-Based Measures of Biological Function Many imaged-based measures of biological and physiological function have arisen in imaging and medical research. For example, the Center for Biomarkers in Imaging (http://www.biomarkers.org) makes available an online catalog of approximately 350 image-based biological measures that have been investigated primarily at Massachusetts General Hospital in Boston. Apart from differences in modality, such image-based measures may be classiﬁed into three general types according to the way in which image contrast is generated: by endogenous biophysical mechanisms, exogenous contrast agents, or molecular probes [19]. For example, endogenous contrast can be produced by differences in MRI relaxation times such as T1, T2, T2*, macromolecular cross-relaxation, or transport mechanisms such as blood ﬂow, perfusion, and diffusion. These contrast mechanisms are often enhanced by the use of exogenous contrast agents in CT and MRI as well as ultrasound. Table 1 lists some examples of endogenous and contrast agent–enhanced biological measures using MRI. Numerous other MRI approaches providing imagebased measures of disease processes are available [20], as well as MRI-based measures speciﬁc to diseases of the brain [21]. Imaging with the use of molecular probes, known as molecular imaging [22], also involves the injection of an exogenous agent to provide image contrast. In this case the imaging agent is designed for cellular uptake or binding with a high afﬁnity for a speciﬁc in vivo molecular target or process [23]. Molecular imaging is able to provide new information on disease processes not available

OVERVIEW OF IMAGING IN BIOLOGICAL RESEARCH

47

TABLE 1 Examples of Image-Based Measures of Biological Function in MRI Using Endogenous Mechanisms or Exogenous Contrast Agents MRI Imaging Methoda

Image-Based Measure or Parameter

DSC bolus tracking

T2- and T2*-weighted intensity changes

BOLD

T2- and T2*-weighted intensity correlation with stimulus Difference between spin-inverted and control images Phase-shifted images from water–fat chemical shift

ASL

Chemical shift encoding T2-weighted MRI

MRTI

CEST/ PARACEST CEST

Integral increase in T2 values Weighted sum of biexponential T2 values Temperature-dependent proton resonance frequency shift Amide proton transfer Amide proton transfer

Biological Function or Parameter Relative cerebral blood volume, relative cerebral blood ﬂow, mean transit time Task-induced functional activation in the brain

Reference [149]

[195]

Blood ﬂow

[149]

In vivo water distribution, in vivo fat distribution Peripheral edema

[196]

[70]

Liver iron concentration

[197]

In vivo temperature distribution

[198]

Spatial distribution of pH Glycogen concentration

[199,200] [201]

a

DCS, dynamic susceptibility contrast; BOLD, blood-oxygenation-level-dependent; ASL, arterial spin labeling; MRTI, magnetic resonance temperature imaging; CEST, chemical exchange saturation transfer.

from other methods. For example, molecular imaging was used recently to establish the relationship between inﬂammation and microcalciﬁcation in early-stage atherosclerotic plaques [24]; it has been used to visualize the in vivo spatial distribution and time course of vascular endothelial growth factor receptor expression in a model of peripheral arterial disease [25]; and it has been combined with conditional disease models using transgenic mice and genetic proﬁling to yield a powerful system for in vivo preclinical study of disease processes and potential therapeutic targets [26–29]. The most common molecular imaging applications utilize radiolabeled tracers for detection by the nuclear modalities. However, many nonnuclear approaches have also been developed with optical [10], MRI [20,22], and ultrasound modalities [30,31]. The majority of such image-based measures are distinguished from direct imaging of anatomy by being connected indirectly to the biology. A biological,

48

OPPORTUNITIES AND CHALLENGES

biophysical, or molecular model is introduced to provide a bridge from the speciﬁc image contrast to the physiology, and a correlation is typically established using invasive (nonimaging) or histological measures. In the case of molecular imaging, the model is represented by the underlying signal generation and targeting mechanisms of the probe, which may be veriﬁed independent of the imaging experiment. In other cases, the model is implicit and an empirical correlation provides only a phenomenological link between the image-based measurement and a physiological parameter or process. The invasive or histological correlate often provides an initial rationale for the imaging approach, and may guide development of a model and subsequent interpretation of the measurements. It is helpful to think of such methods as image-based noninvasive assays, in the sense that the measurements extract or infer some biological, physiological, or molecular characteristic from the subject as a whole. Although not always clinically diagnostic for disease, such image-based assays can often provide the ability to monitor disease progression [19]. The associated biological or biophysical models provide widely varying degrees of linkage between measurement and biology. Molecular imaging provides perhaps the most direct linkage since the characteristics of target, signal generation, and the probe’s afﬁnity for the target can be determined empirically, leading to relative conﬁdence in the functional or molecular basis of the image-based signal. The use of [18F]ﬂuorodeoxyglucose, a glucose analog, as a radiotracer in PET imaging relies on the empirical facts of glucose uptake and phosphorylation by metabolically active cells. In other cases, a single image-derived parameter corresponds to a complex of multiple functional parameters. For example, kinetic modeling of dynamic contrast-enhanced (DCE) MRI data produces a model parameter, Ktrans, that can be inﬂuenced by several factors, such as blood ﬂow, vascular permeability, and vascular surface area, potentially complicating interpretation of this image-based measure. Even when an explicit biophysical model is available, as is the case for magnetization transfer MRI [32], it may be impractical to acquire the imagebased measurements necessary to apply that model. In this particular case, a related phenomenological model has proven useful to relate a simpler measurement (the magnetization transfer ratio) to the diffuse macromolecular changes and demyelination associated with multiple sclerosis [33]. The lack of speciﬁcity of some image-based measures warrants caution in their use prior to adequate assay validation. Early ex vivo NMR measurements of bulk T1 and T2 relaxation of tumors [34] were initially believed to distinguish malignancy from nonmalignancy, and even inspired development of the ﬁrst wholebody MRI system [35]. However, these results have not subsequently be validated in vivo, although several other MRI methods for characterizing tumors and evaluating therapy have since been developed [36]. Anatomical and structural imaging can also provide functional measures if appropriate image quantiﬁcation is applied, such as measuring tumor size or volume, which leads to an image-based measure of tumor growth.

ANATOMY OF AN IMAGING BIOMARKER

49

Despite the exploratory nature of many imaging research applications, the diversity of image-based functional measures often furnishes much of the interest and enthusiasm for potential imaging biomarkers. However, numerous additional factors must be addressed to establish the reliability, validity, and suitability of a particular image-based measurement as a tool for evaluating novel therapeutics.

ANATOMY OF AN IMAGING BIOMARKER Like the drug development process itself, developing, implementing, and using imaging biomarkers is a highly collaborative multidisciplinary effort. As depicted in Figure 1, an imaging biomarker may be deﬁned conceptually by two aspects: image acquisition and image analysis. Image acquisition refers to all activities and technologies related and leading to the acquisition of image data, such as imaging hardware operation, maintenance and quality control, subject positioning, contrast agent or radiotracer preparation, injection protocols, and physiological monitoring. Image analysis includes all postacquisition activities, such as image reconstruction, processing, quantiﬁcation, and interpretation. These two aspects of an imaging biomarker require signiﬁcant

Biomedical Engineering

Physiology

Mathematics

Molecular Biology Pharmacology

Statistics

Imaging Physics

Radiochemistry

Nuclear Engineering

Medicine

Computer Science

Image Analysis Analysis Image (Reconstruction, (Reconstruction, Processing, Processing, Interpretation) Interpretation)

Image Image Acquisition Acquisition

Imaging Imaging Biomarker Biomarker

Figure 1 An imaging biomarker may be deﬁned by its methods for image acquisition and analysis. Both aspects require input from numerous scientiﬁc and engineering disciplines.

50

OPPORTUNITIES AND CHALLENGES

input from a diverse array of scientiﬁc and engineering disciplines, including biology, chemistry, physics, biomedical and nuclear engineering, computer science, and mathematics as well as medicine. Each of these specialties has several critical roles in the implementation and use of imaging biomarkers: • Biologist/physiologist/molecular biologist/pathologist: help establish the biological relationship between the image-based measurement and the physiological function or disease process; help establish the rationale for studying particular image-based measures; and help interpret the quantitative results. • Pharmacologist/physician/pathologist: help establish the clinical relationship between the imaged-based measurement and the relevant therapeutic outcomes during treatment; and help understand all possible actions of the treatment (beneﬁcial and deleterious) [37] to aid in the interpretation of imaging results. • Biochemist/radiochemist: provide understanding of physiological processes and molecular targets at the biochemical level; and design, synthesize, characterize, and test optical, magnetic, and molecular probes and radiotracers. • Mathematician/statistician/computer scientist: provide the fundamental mathematical, statistical, and quantitative basis for image acquisition, reconstruction, and analysis; and provide software tools for performing many tasks associated with image acquisition, reconstruction, quantiﬁcation, analysis, visualization, and interpretation. • Imaging physicist/biomedical and nuclear engineer/technologist: provide the physical basis for imaging technologies, signal generation and detection, hardware design, and optimization; provide the technological expertise for isotope production to be used for generating radiotracers; and operate the imaging equipment, performing image acquisition, processing, and analysis tasks. Essential Characteristics of Imaging Biomarkers Earlier we highlighted the use of imaging in biological research. In such studies, when a correlation is demonstrated between an image-based measurement and a particular physiological characteristic or disease process, it is tempting to identify that measurement as an imaging biomarker. However, this is not always useful. Here we identify several additional characteristics of imaging biomarkers that will generally become essential as an image-based functional measure progresses through the process of validation and clinical qualiﬁcation (Figure 2). Scientiﬁc Characteristics For an image-based measurement to be an effective tool in drug development, the “model” linking the measurement to a

ANATOMY OF AN IMAGING BIOMARKER

Assay validation

Clinical qualification

Biological model

Image-based Measure

51

Clinical trial

Biological Function or Disease Process

Clinical Endpoint

Figure 2 Two-step process for imaging biomarker validation/qualiﬁcation. Assay validation establishes the link between the image-based measure and a speciﬁc biological function or disease process by way of a biological, biophysical, or molecular “model.” This includes characterizing the measurement’s intra- and interobserver and test–retest variability (measurement reliability), as well as correlating the measurement to an established (nonimaging or invasive) standard. The goal of the second step, clinical qualiﬁcation, is to establish empirically, by clinical trials, the relationship between the image-based functional measure and a relevant clinical endpoint or outcome. (See insert for color reproduction of the ﬁgure.)

biological or disease process must be characterized, which is termed assay validation. This process includes: • Characterizing the validity of the model by correlation with an accepted standard • Characterizing the reliability of the measurement: • Intraobserver variability • Interobserver variability • Test–retest variability • Deﬁning the conditions under which the image-based measurement will result in the expected model validity and measurement reliability: • Biological conditions • State and stage of disease • Class of therapy • Optimizing the image acquisition and analysis systems for the task of parameter estimation as opposed to classiﬁcation Basic research studies of image-based biological measures may provide only partial validation by attempting to establish the validity of the model and occasionally reporting some measure of reliability in a limited sample size,

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OPPORTUNITIES AND CHALLENGES

often in preclinical studies. Additional development may be necessary to fully characterize and optimize the measure in both animal models and in humans. Like the underlying image-based methodology, the imaging biomarker is necessarily quantitative, which requires parameter estimation from the images, a process that should aim to be as efﬁcient and objective as possible. Clinical Characteristics In addition to the scientiﬁc concerns of assay validation, several clinical issues must be addressed. The relationship of an imagebased measure of biological function to a speciﬁed clinical endpoint must be established empirically through clinical trials, which may be termed clinical qualiﬁcation (Figure 2). Clinical qualiﬁcation seeks to: • Deﬁne a relevant clinical endpoint or outcome • Establish empirically the relationship between the image-based measure and the clinical endpoint • Characterize the propagation of measurement uncertainty to uncertainty in clinical outcome • Establish the safety of the imaging procedure These aims are often hindered by the need for large amounts of data to support clinical qualiﬁcation. The imaged-based measure may be more sensitive to biological changes than the corresponding clinical endpoint, reﬂecting ﬂuctuations that are not clinically relevant; or the clinical endpoint might be somewhat subjective, subject to large variability, or require lengthy trials. Additional Criteria Apart from the scientiﬁc and clinical issues of biomarker qualiﬁcation, further logistical and economic concerns must be addressed as an imaging biomarker progresses through clinical qualiﬁcation. • The necessary acquisition hardware, methods, and image analysis tools must be readily available. • The image acquisition and analysis methods must be standardized across hardware platforms and imaging centers. • The imaging biomarker must be cost-effective and efﬁcient compared to other available imaging and nonimaging biomarkers with similar purpose. Development Stages It is clear that most imaging biomarkers today fall short of achieving all of these characteristics. To describe the development stage of imaging biomarkers, the FDA has identiﬁed four distinct classes of imaging biomarkers which are focused on drug development applications: prebiomarkers, biomarkers, surrogate biomarkers, and clinical diagnostic surrogate biomarkers [38]. According to this classiﬁcation, pre-biomarkers are those at an incomplete stage of assay validation; biomarkers are those that have completed assay validation along with an appropriate safety proﬁle; surrogate

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53

biomarkers are those for which sufﬁcient clinical evidence exists to qualify the biomarker for use in drug development; and clinical diagnostic surrogate biomarkers are those that have been validated and qualiﬁed to such an extent as to be “integrated into an approved/licensed therapeutic regimen” and are able to “assure/improve safety and/or efﬁcacy” [38]. Imaging biomarkers are useful tools for a variety of tasks in addition to drug development. Therefore, another perspective, which perhaps includes a wider spectrum of methods, is to stage imaging biomarkers according to their intended use [39]. There are at least four distinct categories of use for imaging biomarkers in the wider sense: 1. Elucidate biological function and disease processes in vivo (basic biological research). 2. Evaluate novel therapies in vivo for industry decision making (drug development—industry decisions). 3. Evaluate novel therapeutics in vivo for government licensure (drug development—government registration). 4. Enhance routine diagnosis of disease with better disease management, prognosis, and therapeutic decision making (clinical practice). The four stages correspond roughly to the FDA imaging biomarker classes described above in terms of the level of validation and qualiﬁcation within each stage (Table 2). They also parallel the conceptual biomarker hierarchy

TABLE 2 Classiﬁcation of Imaging Biomarkers According to Their Intended Usea and the Corresponding Stage of Validation and Qualiﬁcation Stage 1 2

3

4 a

Imaging Biomarker Assay Intended Use Validation Basic research Drug development: industry decisions Drug development: government registration Clinical practice

Clinical Qualiﬁcation

Additional Criteriab

Potential Impactc

Partial Yes

No Partial

No No

* **

Yes

Yes

Partial

***

Yes

Yes

Yes

****

Intended uses are “backward compatible,” in that an imaging biomarker that achieves a given stage of validation and qualiﬁcation will fulﬁll the requirements for all preceding uses. Note that regulatory scrutiny would be limited to imaging biomarkers at stages 3 and 4. b Nonscientiﬁc and nonclinical criteria, including logistical and economic issues such as availability of technology, standardization of methodology, and cost-effectiveness. c At each development stage, the potential impact of an imaging biomarker on drug discovery and development and on human health.

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suggested by Frank et al. [40] using the body of evidence supporting each class: plausibility, correlation/association, predictive power, and cause. The categories of “probable valid biomarker” and “known valid biomarker” suggested by the FDA [41] would be contained in stage 2, while stage 3 corresponds to “surrogate endpoints” [39]. These four categories of use emphasize that there may be an appropriate context and use of imaging biomarkers at each stage of validation or qualiﬁcation. Basic biological research provides fundamental knowledge of disease processes as well as a ready source for new biomarker discovery; the use of imaging biomarkers in clinical practice clearly overlaps with the routine use of imaging in clinical diagnosis. The signiﬁcance of these potential imaging biomarker uses is that they can be leveraged to facilitate further development and adoption of imaging biomarkers as drug development tools, particularly as surrogate endpoints for regulatory decision making. Imaging Biomarkers and Diagnostic Imaging The foremost application of medical imaging is the routine clinical diagnosis of disease. Historically, the development of imaging systems capable of scanning humans followed shortly after the initial technological development of the various imaging modalities [3,5–7,35,42–44]. Moreover, medical imaging applications emphasizing medical diagnosis have driven much of the technological improvements to the present day. Although the primary diagnostic purpose of medical imaging overlaps considerably with imaging biomarker applications, there are important distinctions between the context and goals of their use [45]. Diagnostic imaging is generally focused on detection of lesions or abnormality, and image assessments are usually made in a subjective, qualitative manner relying on the training and experience of a medical professional. In this case, the primary goal is the medical management of individual patients. By contrast, imaging biomarkers are focused on the quantitative characterization of disease which is generally already known to be present, with the aim of objectively measuring effects related to a medical treatment or intervention. Typically, serial measurements from one or more follow-up imaging sessions are compared to a baseline measurement, and measurements are collected from patient groups drawn from well-deﬁned patient populations. Why is it important to draw a distinction between imaging biomarkers and diagnostic imaging? It is widely recognized that the most reliable and objective approach to assess image quality, and thus optimize the imaging process, is, ﬁrst, to specify the purpose or task for which the image is produced, and second, to determine quantitatively how well that task is performed [46]. Two general categories of tasks that relate to medical imaging are classiﬁcation tasks, which are the typical diagnostic tasks of deciding whether a patient has or does not have disease, and estimation tasks, which involve estimating quantities of interest from medical images [46]. An example of a classiﬁcation task

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would be an oncologist using images to detect or locate abnormal lesions, and attempting to answer the questions: Are the lesions malignant or nonmalignant, and if malignant, what is the grade or stage of the cancer? Examples of estimation tasks would be a cardiologist estimating the extent and severity of a myocardial perfusion defect or an oncologist estimating the metabolic uptake of [18F] ﬂuorodeoxyglucose in a tumor. Diagnostic imaging platforms and analysis tools are by deﬁnition concerned with classiﬁcation (detection) tasks and have been uniformly optimized for those purposes. It should be clear that imaging biomarkers, being inherently quantitative, are concerned with either estimation tasks or combined classiﬁcation/estimation tasks [47,48]. Thus, the requirements and standards for diagnostic imaging systems are somewhat different from that of imaging biomarkers. For example, the demands of image quantiﬁcation and the extraction of objective image features for imaging biomarkers place strict limits on image quality that may differ from that required for diagnostic purposes, where the experience and skill of the clinician may compensate for typical nonidealities in the images. Evidently, qualitative visual image evaluation sufﬁces for a majority of diagnostic imaging purposes. On the other hand, objective quantitative image assessment is essential for imaging biomarkers, and image acquisition and analysis systems must be optimized for this purpose. Such optimization may involve differences in hardware design, evaluation of image quality control, image reconstruction, processing and quantiﬁcation methods, and software. Nuclear Cardiology as an Imaging Biomarker SPECT myocardial perfusion imaging (MPI) is a nuclear cardiology procedure developed for the clinical diagnosis of coronary artery disease [49]. The imaging procedure is based on cellular uptake of a gamma-emitting radiopharmaceutical (such as thallium-201 or various technetium-99m-labeled agents) in proportion to the regional blood ﬂow in the myocardium [50]. The imaging procedure is typically performed twice: ﬁrst, with the subject at rest, and second, after inducing hyperemia through exercise or pharmacological stress. The resulting images provide a relative measure of myocardial perfusion and allow the identiﬁcation of perfusion defects. In general, perfusion defects may represent regions of possible ischemia (reversible defects) or necrotic tissue resulting from infarction (scar or ﬁxed defects). Quantitative analysis of rest and stress images provides estimates of the perfusion defect extent and severity as well as discrimination between ischemic and scar regions [51]. In addition, numerous other quantitative measures of cardiac function can be derived when the images are acquired with electrocardiogram (ECG) gating. Such image-based measures play a critical role in guiding subsequent medical intervention and clinical management of coronary artery disease. As a mature imaging technology, SPECT MPI has been developed and improved continuously for nearly 25 years, and apparently has many characteristics of a qualiﬁed stage 4 imaging biomarker:

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• Extensive validation of image-based quantitative measures of myocardial perfusion (perfusion defect type, extent, and severity) • Extensive clinical qualiﬁcation for the diagnosis of coronary artery disease • Standardized protocols for image acquisition and reporting • Standardized automated image processing and quantiﬁcation methods [52] • Validated imaging hardware and software tools readily available [53] • Cost-effective imaging procedures used widely in clinical practice In addition to the availability of standardized image quantiﬁcation, an important aspect of SPECT MPI that sets it apart from many other diagnostic imaging applications is the routine use of normal subject databases. Imagebased assessment of abnormal perfusion is done by computer-automated comparison of perfusion parameters for a given patient with normal limits deﬁned by databases of subjects with a low likelihood of coronary artery disease [51]. Moreover, the accuracy of automated assessment is comparable to human visual interpretation of the images, but with potentially lower variability [54]. Despite these characteristics and its broad acceptance in diagnostic imaging, SPECT MPI appears to have only limited utility currently as a tool for drug development. As a diagnostic tool, SPECT MPI technology and standards have been optimized for the clinical diagnostic setting. In particular, recent use of SPECT MPI for serial imaging of patients has highlighted limitations and the need for further research to optimize SPECT MPI for serial image assessment [55,56]. Furthermore, despite the availability of more objective quantitative measures of myocardial perfusion deﬁcit, recent clinical trials that have used SPECT MPI evaluation as a primary endpoint have been compelled to use visual categorical interpretations and semiquantitative measures for drug registration [57,58]. We may speculate that these limitations are due in part to imaging systems that have been optimized for the classiﬁcation task rather than the estimation task required of imaging biomarkers in drug development. For example, the quantitative thresholds that have been established for the use of normal subject databases in SPECT MPI software have generally been optimized by reference to expert visual reads in the single study setting, which may not be optimal for quantitative evaluation of serial studies. Thus, SPECT MPI technology illustrates the subtle, yet important differences between conventional diagnostic imaging and imaging biomarkers. Key Advantages of Imaging Biomarkers As a tool in drug development, imaging biomarkers share several general advantages among all imaging modalities. Imaging biomarkers provide the unique ability to visualize anatomy as well as to spatially localize physiological and molecular function in vivo. Additionally, most imaging modalities have the capability to acquire repeated or temporally resolved images which allow a study of various dynamic biological processes. In vivo image-based evalua-

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tion of disease processes or drug targets occurs in their respective biological microenvironments, accounting for both systemic and local modulators. As in vivo assays, imaging biomarkers also share with in vitro biomarkers the ability to determine drug–target interactions, to help optimize drug dose, and to evaluate longitudinally the response to therapy in vivo [59]. A broad range of sensitivity and spatial resolution is also available, providing great ﬂexibility in the choice of applications. High-resolution CT and MRI anatomical imaging biomarkers can evaluate gross therapeutic effects at the level of organ systems; nuclear, CT, and MRI perfusion imaging biomarkers can probe functional effects at a regional tissue and vascular level; nuclear, MRI, and optical molecular imaging biomarkers can provide readouts down to the level of cellular and molecular effects. This range can sometimes be an advantage when considering the process of clinical qualiﬁcation. For example, although molecular imaging applications may attain the level of detail necessary to test for proof of mechanism, clinical qualiﬁcation of imaging biomarkers will probably be facilitated by coarser biological “resolution” at the macroscopic level of organ systems which may more readily be related to a clinical endpoint. In addition to the clinical relevance of the various imaging modalities, there is also the possibility for translation of preclinical imaging applications to the clinic, which provides a potential for continuity of readout parameters between preclinical safety and efﬁcacy studies in animal models and eventual clinical evaluations in humans. Furthermore, a majority of hospitals and medical centers (often, the sites participating in later-phase clinical drug trials) typically have direct access to clinical imaging centers or imaging equipment. Longitudinal assessment of individual subjects provides a potential for reduced statistical variability, and consequently a reduction in the number of study subjects. Clearly, this can beneﬁt drug development through faster studies and reduced costs; however, in practice, many additional factors can inﬂuence the net cost savings and efﬁciency achieved in using an imaging biomarker. These factors are related to the implementation of imaging biomarkers and must be evaluated carefully for each imaging biomarker application. Limitations of Imaging Biomarkers There are generally three categories of imaging biomarker limitations. First, there are limitations that are likely to be addressed in the near future by the accelerating rate of imaging biomarker development. The complex and multidisciplinary nature of image acquisition hardware and analysis methods has hindered the translation of promising image-based functional measures into validated and qualiﬁed imaging biomarkers. Similarly, a lack of standardization has also impeded imaging biomarker development, since variations in methodologies, as well as hardware platforms with differing capabilities, have made it difﬁcult to compare and evaluate potential imaging biomarkers across sites. Compared to in vitro biomarkers, it may be costly and time consuming

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to acquire the clinical evidence necessary to establish a biological relationship between an imaging biomarker and a particular disease state. All of these factors have limited the introduction of imaging biomarkers into clinical practice for patient management, diminishing the incentive to commercialize such technologies. Additionally, despite the impressive preclinical accomplishments of molecular imaging to date, expansion of such applications to the clinic has been limited by the fact that relatively few molecular imaging agents have been approved for clinical use in humans [22,60]. However, these limitations are starting to be addressed by novel approaches to imaging biomarker development, broad collaborations to standardize technology, and federal initiatives to foster the development of imaging agents and increase the availability of component tools and methods. In addition, radioisotope production for some important radionuclides, such as 18F, 11C, 13 N, and 15O, although complex and expensive, is beginning to be addressed by recent innovations in cyclotron technology [61]. Second, there are limitations in the underlying imaging technologies and methodologies that we might expect to be addressed by ongoing basic research and technology development already occurring independent of imaging biomarker development. The widespread diagnostic use of imaging motivates continual research and vendor improvements in hardware, software, and acquisition methods. Such improvements have recently included faster imaging and hardware improvements for MRI, innovations that reduce radiation dose in CT, new hardware designs that improve sensitivity and resolution of nuclear imaging modalities, and new capabilities afforded by combining modalities, as in SPECT/CT, PET/CT, and most recently, PET/MRI. Furthermore, many image analysis approaches and tools have been borrowed from ongoing computer vision research. All such advances have the potential to beneﬁt imaging biomarkers’ performance; however, careful attention must be paid to the differing criteria for system optimization between diagnostic and biomarker applications. Finally, there are inherent limitations in imaging biomarkers that are not likely to be fully mitigated by future development. Within the spectrum of potential imaging biomarkers, those that rely on endogenous contrast mechanisms have the advantage of avoiding the added complexity and safety concerns introduced by the use of exogenous imaging agents. However, such image-based functional measures are often inherently nonspeciﬁc, relying on biological models in which the connection between measurement and function may not be interpreted easily or directly. In addition, there is a growing perception that the increased use of nuclear and CT modalities, which utilize ionizing radiation, presents a public health concern [62,63]. Diagnostic imaging has been guided by the principle that the risks of ionizing radiation should always be weighed against the beneﬁts of the imaging procedure to the patient [64]; however, in some cases, radiation doses from medical tests may exceed limits known to increase the risk of cancer, particularly in the case of serial imaging [64]. Although progress has recently been made in reducing radiation

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doses in CT procedures [65], the image quality of CT depends inherently on a nonnegligible radiation exposure. Additional advantages and limitations speciﬁc to different imaging modalities and methods are discussed further in a later section. Importance of Context: Disease, Therapeutic Strategy, Development Stage, and Alternative Approaches The development or implementation of any particular imaging biomarker must be preceded by consideration of the current research environment, the local availability of technology and resources, and an attempt to evaluate the relative utility, efﬁciency, and cost-effectiveness of alternative approaches. Local implementation of an imaging biomarker is a time-consuming process, even for methods validated and published previously. Commercially available imaging systems are generally tailored toward diagnostic rather than quantitative applications, and as a consequence, the implementation of an imaging biomarker often involves applying customized acquisition, radiotracer synthesis, or analysis methods that must be locally optimized and compared to published results. In a preclinical setting, the implementation process places great demands on the local availability of resources such as MRI pulse sequences and acquisition methods, radiotracer production facilities, appropriate animal models, and image analysis tools, among many others. In a clinical research setting, an imaging biomarker may have to be implemented across multiple centers. An overriding concern in the usability of any particular imaging biomarker once implemented is the overall throughput that can be achieved [66]. Alongside these practical concerns are various scientiﬁc questions about which technique among several will be optimal. Within any one imaging biomarker application, there may be multiple methods for acquiring similar data, and multiple approaches for image reconstruction, processing, and quantiﬁcation. Similarly, there may be multiple imaging or in vitro biomarkers that address the same biological function or clinical endpoint. Such choices typically have to account for incomplete data comparing the methods and limited available resources. When considering the use of imaging biomarkers, the dominant focus is often on the technology, implementation issues, or necessary tools. Although implementation choices must be weighed carefully, it is important to emphasize that imaging biomarkers, like any other biomarker, should be speciﬁed in terms of the disease context and therapeutic strategy, as well as the development stage and questions to be answered. Deﬁning the clinical context of a given imaging biomarker is part of the process of biomarker validation, which answers the question: What are the biological conditions, disease states, and therapies under which the use of the imaging biomarker will result in the empirically determined reliability and validity? For example, the location of a tumor may limit our ability to obtain a robust quantitative assessment of treatment effects due to excessive respiratory or cardiac motion. An imaging

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biomarker that is validated and qualiﬁed for a given tumor type and therapeutic class cannot be assumed to be validated and qualiﬁed for tumors of other types or anatomical locations or other therapies without additional validation data. Conversely, in drug development, time efﬁciency and the value of investing in imaging technology resources can be maximized by selecting and implementing cross-mechanism imaging biomarkers [67]. For example, multiple imaging biomarkers may share a common MRI pulse sequence or acquisition method, such as T2*-weighted dynamic imaging for dynamic susceptibility contrast (DSC) bolus tracking and blood-oxygenation-level-dependent (BOLD) functional MRI, or T2 mapping for measuring peripheral edema or liver iron concentration. Similarly, a custom PET radiotracer may be developed for a speciﬁc development program but may have utility in other therapeutic areas [45]. Such coordination requires signiﬁcant planning and broad alignment across discovery and development groups and therapeutic areas, which may be difﬁcult to achieve in large organizations.

SCOPE OF IMAGING BIOMARKERS IN DRUG DEVELOPMENT Parallel developments in our knowledge and understanding of biology and disease, and advances in biomedical technologies, have dramatically increased our ability to discover and develop new therapies as well as to evaluate the biological effects of disease interventions. Imaging technologies have played an important role across the spectrum of drug discovery and development [9,20]. Apart from basic biological research, the roles of imaging biomarkers in drug development generally fall into two categories: supporting internal decision making, and to support the demonstration of efﬁcacy in pivotal clinical trials for registration. Within the category of imaging biomarkers used for decision making in drug development, there are three fundamental applications that imaging biomarkers have in common with in vitro biomarker technologies: conﬁrming that a drug candidate hits the intended biological target, testing whether hitting the target, alters the disease process (proof of mechanism), and testing whether altering the disease process affects the clinical status of the patient (proof-of-therapeutic concept) [40,45]. Imaging biomarkers have provided early critical information at each of these development stage gates through in vivo molecular imaging studies of competitive binding or receptor occupancy [45], imaging of target distribution and radiolabeled drug distribution [68,69], lead optimization, and preclinical and early clinical efﬁcacy testing [19,70]. Other tasks in early clinical development that imaging biomarkers have addressed include appropriate dose and regimen selection and in vivo pharmacokinetic/pharmacodynamic studies [71,72], patient stratiﬁcation, and study population enrichment [40]. Importantly, imaging biomarkers for internal industry decision making have demonstrated value for drug development without comprehensive vali-

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dation or clinical qualiﬁcation [67]. Indeed, many such biomarkers may be proprietary and speciﬁc to a particular development program, therapeutic target, or mechanism [45]. However, achieving a stage of assay validation appropriate to a given task and the associated risk is a crucial but often timeconsuming process. The use of novel imaging biomarkers is most effective if development and characterization activities, such as PET radiotracer development or MRI pulse sequence and method development, are begun as early as possible and ideally in tandem with the corresponding drug development program [45]. Although imaging biomarkers have proven useful in many aspects of drug development without being fully clinically qualiﬁed, there is renewed interest from multiple stakeholders to develop qualiﬁed surrogate endpoints and expedite the lengthy and costly drug approval process [39]. In addition to the welldocumented difﬁculties of clinical qualiﬁcation [45], the potential impact of such tools for regulatory decision making will be limited unless they achieve some measure of standardization and cost-effectiveness, and become widely accepted and widely available. In the next section we examine in some detail a few of the imaging biomarkers that are currently, or are poised to become, such high-impact tools for drug development in oncology. PROFILES OF IMAGING BIOMARKERS FROM ONCOLOGY Morphological Evaluation of Tumor Response There is a broad range of imaging biomarkers applicable to oncology drug development [59]. This spans exploratory imaging methodology that may contribute to early development decision making in small studies (e.g., DCEMRI assessment of anti-angiogenics [73]) to robust and broadly applicable assessments of disease burden (e.g., RECIST (response evaluation criteria in solid tumors) [74]). RECIST provides a framework to use imaging (predominantly CT) to document baseline disease burden and deﬁne change over time. It could be argued that RECIST should not be considered a biomarker since the aim of RECIST is to provide a broad categorization of response. However, the quantitative components of RECIST, the lesion size measurements, are measurable characteristics reﬂecting disease and response to treatment, and it therefore seems reasonable to consider it alongside other imaging biomarkers. Furthermore, understanding the attributes of RECIST will help inform the development of other imaging-derived biomarkers: (1) how it was developed; (2) how it became an international standard of assessment; (3) how it addresses the challenges of multisite application, including multiple modalities, acquisition parameters, interpreting radiologists; and (4) how it is implemented into large-scale clinical trials. Background In the early 1980s the World Health Organization (WHO) published recommendations aimed at standardizing tumor response assess-

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ment [75] based on criteria formulated in 1979. The recommendations described minimum requirements for reporting response assessments using clinical, radiological, biochemical, and pathological factors. In terms of radiological assessment, this was to be performed on single or multiple lesions using a bidimensional approach to estimate tumor size. Further, clinical or radiological assessment could be used to review new lesions which would lead to disease progression irrespective of other changes. Four groups were described: complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). This thresholding into discrete response categories was subsequently applied widely and continues to this day. Despite the longevity of this simple classiﬁcation scheme it was noted early that there are signiﬁcant advantages of reporting continuous tumor size estimates, “especially in the phase II setting” [76]. Although adoption of the broad principles was seen, subsequent modiﬁcation based on different expert group preferences and tumor-speciﬁc requirements resulted in a heterogeneous use of the WHO criteria as described in the commentary by Michaelis and Ratain [77]. It was apparent that a new standard was required for measuring response of solid tumors in clinical trials. This was with a backdrop of dramatic change in standard imaging methodology and application (including improvements in CT, ultrasound, MRI, and PET) that offered potentially many new or improved ways in which response could be evaluated. With such a diversity of potential assessments the new criteria had to speciﬁcally address how lesion sizes needed to be estimated to avoid continual diversiﬁcation of applied methodology. A collaboration between the European Organization for Research and Treatment of Cancer, the National Cancer Institute, and the National Cancer Institute of Canada culminated in a publication of the new guidelines in 2000 [78]. The new guidelines maintained the classiﬁcation scheme of CR, PR, SD, and PD but made a number of important changes: adoption of a simpliﬁed tumor size estimation (i.e., longest diameter), a deﬁnition of how many lesions (target lesions) should contribute to the size estimation, a description of how nontarget lesions should be assessed, and a description of which imaging methodologies should be considered for response assessment. RECIST has been adopted widely and continues to contribute to many endpoints in cancer clinical trials, including progression-free survival. Limitations Tumor-Speciﬁc Limitations As with all such generalized criteria, there are instances when the approach becomes problematic. Since the publication of the original RECIST guidelines, many subsequent publications have detailed the limitations of applying RECIST for certain tumor types and certain therapies, such as cytostatic agents and general comparisons of RECIST with WHO [79]. The review by Therasse et al., in 2006 [80] documents the tumor-speciﬁc issues pertinent to RECIST.

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It is clear that with challenges associated with different tumor types, modiﬁcations to the RECIST framework may provide beneﬁts. Examples include the evaluation of gastrointestinal stromal tumors (GISTs) treated with imatinib. It was found that response by RECIST did not sufﬁciently translate into survival. However, when response was deﬁned as a 10% reduction in tumor size or a 15% reduction in contrast-enhanced CT density, better linkage with outcome was observed [81,82]. Another example is that of mesothelioma, where Byrne and Nowak describe modiﬁcations to the RECIST criteria [83] that improve correlation with both survival and lung function. Limitations of Pure Radiological Assessment RECIST offers a generalized framework for deﬁning response in clinical trials which has been integral to many successful drug development programs. However, there are many instances where radiological evaluation alone is limited and does not typify decision making in clinical practice. Often, the clinical decision making will be based on multiple factors (such as CA-125 for ovarian cancer), and clinical trial assessments may need to incorporate nonradiological parameters alongside radiological assessment. Centralized Radiological Review One of the recommendations in the RECIST guidelines is that response assessments should be made based on independent assessment, and this is often considered a requirement for regulatory submission. Central and independent image review aims to reduce the variability of assessment and remove or reduce the potential for investigatorintroduced bias. Central review of high volumes of imaging data from hundreds of sites across many countries does come with high complexity and high cost to the study sponsor. Furthermore, how do we deal with disagreements between the central reviewer and the site-based assessment? The sitebased radiologist is likely to be performing RECIST differently from the central reviewer: selecting a different number of target lesions, deﬁning the longest diameter of the lesion differently, a different judgment on nontarget lesions, and equivocal new lesions may be interpreted differently. When there is subjectivity associated with the application of criteria such as RECIST it is important to identify sources of variability associated with image interpretation such that different results from different readers can be understood. Unidimensional Evaluations A frequently raised issue with RECIST and cause of discordance with WHO is the reliance on a single-dimensional size estimation. Schwartz et al. [84,85] discussed this issue in relation to esophageal cancer. With multidetector CT technology and MRI a three-dimensional lesion assessment can be performed and offers a theoretical advantage in terms of likely sensitivity to change and avoidance of lesion eccentricity problems. However, one of the most signiﬁcant advantages of RECIST is the simplicity of the imaging required. If three-dimensional imaging was required

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in a 100-site clinical trial, there would be many challenges to ensuring that this is done to a level that would allow robust volumetric assessments with acceptable variability. Further, the additional radiology review time (central and site based) required to outline multiple lesions on multiple slices would be substantial. It is likely that three-dimensional assessments will have a clear beneﬁt in a number of applications, especially when deployment of a standardized three-dimensional acquisition can be achieved readily. A signiﬁcant amount of data is required, however, to demonstrate that the theoretical advantages translate broadly into a measure more predictive of response and that the practical issues can be overcome for multisite clinical trials. Imaging Technology The ability to deﬁne meaningful response is strongly inﬂuenced by the methodology available and the mass of data that links a method with clinical outcome. RECIST focuses on morphological assessment with CT since the technology is available across all clinical trial centers, provides a relatively consistent acquisition, and is used most widely across the majority of cancers. However, it is recognized that technologies such as FDGPET as indicators of metabolic response could provide improved response assessments in the future. Owing to the tumor-speciﬁc utility of metabolic imaging, it is likely that approaches such as FDG-PET will become very important for some indications, but not broadly applicable. In lymphoma, for example, FDG-PET is an integral component of the current recommendations for response evaluation [86]. Continual Development of Response Criteria Response criteria will need to evolve continually with imaging technologies, whether new molecular imaging approaches become available or there are broad improvements in standard morphological assessment with CT or MRI. After extensive metaanalyses and in response to changes in imaging practice since RECIST was ﬁrst published, modiﬁcations were proposed in 2009 in criteria termed RECIST 1.1 [202]. One challenge in applying the new criteria will be to ensure consistency within any a given clinical trial. Since oncology trials may often take years to complete, there will be an overlap between use of the original RECIST and the new guidelines. Learning from RECIST Development There are many important lessons to learn from the way in which RECIST has evolved to become an integral component of many clinical trials involving solid tumors: • Developing a consensus around response criteria takes a considerable effort requiring multidisciplinary input and broad representation from stakeholders. • Criteria should be sufﬁciently broad that revision from year to year is not required if, for example, imaging technologies change.

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• To demonstrate a robust relationship to clinical outcome, very signiﬁcant data sets are required. The new RECIST modiﬁcations use data from over 6500 subjects. • Response criteria applicable to large multicenter trials need to be pragmatic to ensure utility across many different imaging platforms and alignment with country-speciﬁc practices and radiologist preferences. Although RECIST may seem far removed from the concept of an imagingbiomarker it represents the most widely used imaging-based clinical trial methodology in oncology. For this reason, an understanding of how it has evolved and is deployed practically is important to consider when other techniques are being developed. [18F]FDG-PET The increased glucose metabolism associated with many cancers has been established for some time and the origin and consequences remain areas of continued discussion [87]. [18F]ﬂuorodeoxyglucose (FDG) PET ([18F]FDGPET) exploits this general phenomenon, facilitating the widespread application of a molecular imaging technique for oncology. FDG is taken in cells via glucose transporters (Glut-1), phosphorylated by hexokinase, and becomes trapped. The rate of uptake is related to glucose utilization and therefore an [18F]FDG-PET examination interrogates the glucose uptake and subsequent metabolism. The [18F]FDG-PET study therefore broadly deﬁnes tumor metabolic rate and is also considered to reﬂect viable cell fraction, although the relationship is not necessarily straightforward [88]. The extensive review by Gambhir et al. outlines the broad use of [18F]FDGPET for diagnosis, staging, assessing recurrence, and monitoring response [89]. It is interesting to note the varied sensitivity to detect primary cancers relative to the generally high avidity to metastases. A more recent review by Kelloff et al. [90] provides an extensive review of [18F]FDG-PET applied to clinical practice and oncology drug development. Although the role in all tumor types is yet to be established, such reviews emphasize the high availability of [18F]FDG-PET and broad applicability, both of which are important deﬁning points in the role of [18F]FDG-PET in drug development. Application of FDG-PET to Drug Development As explained by Kelloff et al. [90], current data suggest that [18F]FDG-PET response is likely to be sufﬁciently predictive of clinical beneﬁt. Such surrogacy status would broaden the impact of [18F]FDG-PET in its ability to be used as a provider of key trial endpoints, including increased support of proof of concept through phase II studies to even the potential to provide a true surrogate endpoint for phase III studies. Although full validation studies are yet to be completed, data from some indications (NSCLC [91] and lymphoma, for example) suggest that [18F] FDG-PET can be used successfully to deﬁne response to approved therapies

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and predict endpoints such as overall survival and progression-free survival [92–94]. Sarcoma is also an indication where [18F]FDG-PET response provides a better prediction than tumor size change of histopathological response to neoadjuvant therapy [95]. Published and ongoing studies of response with approved therapies are likely to provide a signiﬁcant body of data to understand the scope of [18F]FDG-PET-based endpoints in clinical trials of many tumor types. With an increased understanding of [18F]FDG-PET responses to standard chemotherapies will come an accelerated application to support the clinical development of novel therapeutics. The demonstration by Stroobants et al. [96] that PET provides an early indicator of metabolic response to imatinib mesylate for subjects with soft tissues sarcoma was a convincing example of how metabolic response can signiﬁcantly precede any morphological change. Even 48 hours after beginning treatment, it was demonstrated that response could be identiﬁed in the subgroup contributing this early time point. Additional data supported the sensitivity of [18F]FDG-PET to deﬁne treatment response in gastrointestinal stromal tumors (GISTs) [97,98]. A review of the rapidly evolving role of [18F]FDG-PET for GISTs is given by Van den Abbeele [99], who emphasizes that the experiences with GISTs are being seen to provide more personalized assessment of response of targeted therapy. Method Standardization Consistent acquisition and analysis of [18F]FDGPET data within clinical trials is paramount to successful interpretation of the clinical trial data. [18F]FDG-PET analysis can simply be undertaken by visual inspection of the PET images. Whilst commonly done in clinical practice it is often limited for clinical trials. However, there are some instances where visual inspection is considered sufﬁcient for clinical trials such as for response criteria for lymphoma [86]. Generally, a more quantitative assessment is considered necessary for applying [18F]FDG-PET to clinical trials. The most comprehensive quantiﬁcation of FDG uptake is achieved by acquiring a dynamic scan with simultaneous assessment of tracer concentration in arterial blood. Subsequent Patlak analysis (and other methods) can yield metabolic rate constants. The dynamic scanning, arterial line, and complex analysis are not suitable for all clinical trials. The most widely used approach for [18F]FDGPET analysis in clinical trials is a semiquantitative approach providing the standardized uptake value (SUV). This approach simply normalizes tumor uptake to injected dose and body weight, and the acquisition procedures are favorable to a busy PET center. In 1999 the EORTC (European Organization for Research and Treatment of Cancer) PET study group provided recommendations on how PET should be performed for studying clinical response [100]. These recommendations emphasized the need for consistent measurement, analysis, and interpretation of response. In 2006, Shankar et al. [101] provided recommendations on how [18F]FDG-PET should be applied to study therapeutic response in National Cancer Institute trials. Other groups from both industry and academia have

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deﬁned protocol standards for clinical trials [102,103]. Other publications provide important insight into the practical issues associated with [18F]FDGPET in clinical trials. For example, Weber [104] describes many pitfalls of seemingly straightforward SUV analyses, including errors associated with incorrect scanner calibration and varied period between injection and imaging. Furthermore, Westerterp et al. [105] describe the impact of image processing and ROI deﬁnition procedures on the SUV analysis. It demonstrates that despite the conceptual simplicity of SUV, every attention needs to be paid to all components in the imaging chain to ensure successful measurement in a multicenter setting. Also important may be tumor-type speciﬁc nuances of the PET approach as described by Lind et al. [106], where speciﬁc technical considerations are important to optimize sensitivity to detect disease during follow-up of breast cancer. The published recommendations and further practical insights can be used to form an [18F]FDG-PET protocol to deﬁne unambiguously how the patient is to be prepared, how the images are to be acquired, and how the images are to be processed and reported for a given clinical trial. Although there are still multiple standards that could be adopted for [18F]FDG-PET, the attention focused on standardization encourages wide adoption of quantitative [18F]FDG-PET. Many investigating sites now have experience of clinical trial imaging using [18F]FDG-PET and consistently demonstrate a willingness to follow a standard protocol. Given the varied approaches by which analysis could be performed, centralized review of PET data for SUV analysis is preferable. This is achieved either using a contract research organization with expertise in quantitative imaging or coordinated via an academic network of centers where one center takes the lead role in analysis. In addition to SUV analyses in the study of treatment-induced change, it is important to note the use of [18F]FDG-PET with visual interpretation for screening bone lesions, where it may have sensitivity advantages over bone scintigraphy [107]. Such bone lesion screening remains important for assessments of disease burden in many clinical studies. In some instances it may replace the need for scintigraphy where whole-body [18F]FDG-PET is undertaken as a matter of routine. However, given the high cost of PET relative to bone scintigraphy, it remains uncertain what the role of [18F]FDG-PET will ultimately be in this setting. New Tracers Whereas [18F]FDG-PET remains a developing tool in terms of its role in oncology drug development, PET has the potential to do much more [108]. Speciﬁcally, FDG has signiﬁcant limitations in a number of settings. Kelloff et al. [109] review the extensive array of molecular imaging probes able to deﬁne molecular targets and understand processes to support oncology drug development. The tracers in development (including probes for SPECT) facilitate the study of proliferation, apoptosis, hypoxia, and angiogenesis and can study androgen and estrogen receptors. These molecular imaging platforms will provide an array of tools applicable for different stages of drug development, including:

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1. Biodistribution and in vivo concentration. Studies of radiolabeled drugs to interrogate in vivo concentration and distribution [110], in particular the concept of microdosing where subtherapeutic doses of labeled drugs can be given, hold great promise [111]. 2. Receptor analyses. Understanding receptors pertinent to cancer growth continues to be of interest, particularly receptors highly relevant to therapeutic intervention (e.g., estrogen; see Katzenellenbogen et al. [112], and for HER2 expression, [113]). In a review by Mankoff et al. [114] the potential of tumor receptor imaging is reviewed with an emphasis on the complementary role that imaging can play vis-à-vis biopsy by studying receptor expression heterogeneity. 3. Proliferation. Labeled thymidine analogs offer a route to noninvasive assessment of tumor proliferation [115]. Of the potential agents, [18F]FLT (3′-deoxy-3′-ﬂuorothymidine) has been established as a suitable agent for wide use [116]. Reports have demonstrated the key advantages of [18F]FLT-PET over [18F]FDG-PET in a number of tumor types [117–119]. The role of [18F]FLT-PET continues to grow, and increased availability of the tracer is likely to further establish the technique within clinical drug development. 4. Apoptosis. A noninvasive probe of apoptosis would undoubtedly provide general insight into early therapeutic success both for clinical assessments and for drug development decision making. Annexin-V has received considerable attention as an apoptosis probe labeled with either 18F [120] or 124I [121]. Different approaches to measure apoptosis are also in development, as described in the review of apoptosis tracer labeling strategies by Lahorte et al. [122]. It is a clear that a robust apoptosis imaging agent will play an important role in clinical drug development. 5. Angiogenesis. The speciﬁcity and sensitivity of currently available surrogates of angiogenic activity (e.g., dynamic contrast-enhanced MRI) are limited. PET tracers targeting receptors up-regulated with angiogenesis, such as the integrin αvβ3, make attractive approaches to a study of angiogenesis [123] with close linkage to the tumor biology. Such agents are currently in clinical development [124,125] for cancer imaging and can be used for quantitative assessment of integrin levels in tumors [126]. Given the preponderance of antiangiogenic targets in different stages of clinical development, PET tracers able to study change in this process remain highly attractive. The diversity of labeling strategies means that many drugs and additional tracers will become available to complement the already developing array of PET tracers. Some of these agents are clearly broadly applicable (such as proliferation and apoptosis) and could follow the route that [18F]FDG-PET has followed: to wide clinical and drug development use. The cost–beneﬁt

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relationship of such agents is clear. Tracer development for speciﬁc drug programs (e.g., labeling a drug for biodistribution studies) may be costly in terms of ﬁnance, time, and resource, but if the biodistribution analysis identiﬁes an important safety signal, for example, the investment is likely to be rewarding. Molecular imaging development with required multidisciplinary teams and substantial infrastructure remains a costly endeavor. However, relative to potentially late-failing drug development programs the investment can be justiﬁed in many instances.

Dynamic Contrast-Enhanced MRI Dynamic contrast-enhanced MRI (DCE-MRI) is a functional imaging technique performed by rapidly acquiring multiple T1-weighted MR images from a given tissue during injection of a paramagnetic contrast agent. The consequential tissue signal changes are quantiﬁed, and parameters with biological linkage are extracted. A review of the experiment with a focus on the analysis required is given by Parker and Padhani [127]. Further, standardized terminology and deﬁnitions of DCE-MRI-derived parameters have been deﬁned by Tofts et al. [128]. Parameters typically extracted and used in the study of pharmacological response include: • Ktrans (min−1): the volume transfer constant between plasma and the extravascular extracellular space. The parameter is related to both ﬂow and vascular permeability and, as such, change cannot be interpreted precisely. However, successful pharmacological intervention is expected to reduce this parameter by either ﬂow and/or permeability change. • ve (unitless): the volume fraction of the extravascular extracellular space. This is assumed to relate to interstitial space, although the relationship remains complex and posttreatment changes are not predictable. • kep (min−1): the rate constant between the extravascular extracellular space and the plasma. • IAUC: the initial area under the curve, typically over the ﬁrst 60s. This is a semiquantitative expression of the initial contrast enhancement, which requires no data modeling. The relationship to physiology is complex [129], although both ﬂow and permeability are likely to affect the parameter, and successful antiangiogenic treatment is expected to reduce the value. In the recommendations by Leach et al. [130] both Ktrans and IAUC should be used as primary DCE-MRI endpoints, with ve and kep as secondary endpoints. As emphasized in the review by O’Connor et al. [131], DCE-MRI has been used widely for the study of angiogenesis inhibitors. Since this class of agents has received considerable attention across industry, interest in DCE-MRI has

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become widespread. The review by Jayson and Waterton [132] outlines the progress in use of the technique for drug development, focusing on fundamental aspects such as design of the protocol, measurement validity, and reproducibility. Work focusing on DCE-MRI as a pharmacodynamic endpoint has been complemented by developing our understanding of the technique for more routine clinical use: for prognostication, for example. The use of DCE-MRI offers signiﬁcant advantages over other methods able to interrogate aspects of tissue vascularity. With careful implementation, multisite standardization can be achieved and sufﬁcient reproducibility relative to typical pharmacodynamic effects can be realized. However, it could be argued that angiogenesis inhibitors either currently in development or marketed may have proceeded without DCE-MRI within the development program. So what does DCE-MRI add to the development paradigm, and why, if such a localized indicator of antiangiogenic activity exists does it not form a fundamental step in the development of such agents? First, there are technical challenges to successful implementation that form impediments to broader use. Second, there is still insufﬁcient understanding of the relationship between given DCEMRI parameters and both histopathology and clinical outcome. These uncertainties lead to difﬁculties in interpreting DCE-MRI studies. For example, how much of a change in a DCE-MRI parameter is enough to predict beneﬁt?

Method Standardization and Deployment Challenges Site Limitations DCE-MRI remains a specialized technique with limited routine application, and therefore many radiology departments will have limited experience with its use. It is important to consider the practicalities of consistent DCE-MRI implementation across multiple sites required for a given clinical trial. The ﬁrst step requiring consideration is the selection of sites capable of performing DCE-MRI by adhering to a common acquisition for the study. One factor to consider is what experience site personnel have in performing DCE-MRI. However, this is not to say that sites with few experienced personnel are not capable of undertaking DCE-MRI, but the users must be committed to adhering to a prescribed protocol. Statistically, it may be better to have fewer subjects scanned at a small number of sites where the imaging can be tightly controlled versus many sites with greater diversity of equipment and experience. A pharmaceutical company considering multiple studies could consider developing a network of specialized imaging sites able to perform consistent imaging and where good communication between sites can help optimize imaging. Implementing DCE-MRI Within the Clinical Protocol A great variety of options exist regarding incorporating DCE-MRI within the clinical protocol. Some of the most important factors to consider are:

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• Is a repeat baseline examination necessary to establish methodological reproducibility? • When should posttherapeutic administration imaging be performed? • What are the primary DCE-MRI endpoints? These questions should be answered by considering what is known about the expected pharmacology of the drug and what has been done previously with similar agents. Important practical parameters include how many examinations will be acceptable to the subject (with a careful review of other required procedures) and whether close time points present scheduling challenges to the radiology department. It has been recommended that reproducibility examinations be built into the study where possible in the form of a repeat baseline [130]. This would typically be done by performing two examinations within a week prior to beginning of therapy. An example of analysis of a repeat baseline study includes that by Galbraith et al. [133], where repeat examinations performed one week apart provided conﬁdence intervals for Ktrans and IAUC90 (64% and 61%, respectively). Other examples include that by Roberts et al. [134], where investigators assessed repeat examinations from data across two centers in multiple tumor types, comparing compartmental modeling versus model-free analysis. The 95% conﬁdence intervals for Ktrans and IAUC60 were calculated to be 35% and 55%, respectively. Clear differences in the reproducibility are apparent from the published examples and are expected to arise from differences in acquisition, modeling techniques, and assumptions in the model. This gives credence to the idea that a reproducibility assessment is often important, and that if performed it must be representative of subsequent study conditions. Although changes following administration of drug are often large relative to the methodological variability, in many instances it may be important to minimize the variability as much as possible. In a publication by Parker et al. [135] it is demonstrated that signiﬁcant improvement in reproducibility of Ktrans and other parameters can be attained by using a population-based arterial input function. Such improvements in reproducibility are likely to be important. In a study where dose is varied, using DCE-MRI to understanding the lower limit of biological effectiveness could be important, and the measurement reproducibility will have a direct impact on the ability to deﬁne subtle change at low doses. Published DCE-MRI studies show a range of selected posttherapy measurement time points [131]. Between different drugs there will be signiﬁcant dosing differences, pharmacokinetic properties, and differences in mechanism likely to lead to varied time scales of DCE-MRI response. Therefore, there is no consistent formula to select posttreatment time points. However, where studies do include an early time point (such as one or two days after therapy has begun), a large response is generally seen and is similar in magnitude to subsequent time points. The data by Morgan et al. [136], Mross et al. [137], and Thomas et al. [138] emphasize the early response (day 2) that can be

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measured consistently between studies, which then persists (out to day 28). It would thus seem that in most instances an early time point provides signiﬁcant data to evaluate vascular response. Choosing a Standardized Protocol A number of acquisition and analysis options exist to perform DCE-MRI. For any one study, consistent application for patients being investigated is an obvious requirement to keep variability minimized. However, for a given company there are clear beneﬁts in maintaining a consistent acquisition and analysis protocol across multiple studies: reduced operational burden to develop acquisition guidelines and the ability to compare one study to the next potentially to compare the magnitude of response between different drugs, doses, or dosing schedules. There are also beneﬁts to developing consistent practices for both academic- and industrysponsored DCE-MRI-based studies: • Increased awareness of a standard protocol to minimize the complexity encountered by radiology departments in dealing with many different DCE-MRI protocols for different studies. • Potential to perform metaanalyses on data from multiple studies to query relationships between DCE-MRI response and clinical outcome. Leach et al., on behalf of Cancer Research UK, proposed recommendations for performing DCE-MRI for studying antiangiogenic and antivascular therapies [130]. Their technique provides an academic and industry perspective on the key attributes of a standardized DCE-MRI acquisition. Owing to the differences in the way the technique is implemented by different scanners, different acquisition protocols will often be required from one center to the next. However, provided that cross-site implementation is undertaken carefully, a DCE-MRI measurement at one center should be comparable with that taken at another. MRI technology changes at a signiﬁcant pace, evidenced by multichannel technology and availability of scanners operating at static magnetic ﬁeld strengths of 3 T and above. Standardizing measurements from one study to the next is a challenge with a background of technological evolution. When a new technology (e.g., 3 T MRI) is introduced, a careful comparison with existing technology is required to ensure equivalence of measurement. Responsibilities for Deployment and Centralized Analysis Ideally, multiple sites with quantitative imaging expertise will be selectable to ensure that signiﬁcant recruitment goals can be achieved in a reasonable period of time. Often, this may not be the case, and sites with limited imaging experience are required. For this reason, deployment of the DCE-MRI acquisition is complex, costly, and time consuming, requiring the following activities: review of the adequacy of scanner speciﬁcations, implementation of the MRI sequences required, scanner performance evaluation, training of site technologists,

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instructions to upload the data in a timely manner on scan completion, robust quality control steps, and centralized analysis. Often, a third-party company, typically a contract research organization with imaging expertise, can be given responsibility for these activities. Alternatively, one clinical site in a network can be responsible for deﬁning the acquisition protocol and communicating imaging details with partnering sites. The latter is particularly applicable where all sites have expertise in quantitative imaging. Whatever the approach selected, it is important that data acquired be transported readily (ideally via electronic transfer) to a central analysis lab in order that any important deviations from the acquisition protocol can be identiﬁed to ensure future consistency. Owing to the diversity of software platforms able to analyze DCE-MRI and the options available for analysis, it is imperative that data sets be reviewed centrally using a consistent process. This will ensure that variability in the analysis is kept to an absolute minimum. Typically, each analyzable lesion has an associated DCE-MRI parameter created (Ktrans, IAUC, and others) following pixel-by-pixel analysis. A mean value associated with each lesion is typically assumed to provide the simplest output with more advanced analysis (such as heterogeneity analyses) potentially able to provide further insight into the data. Toxicity of Gadolinium-Based Contrast Agents Data show that administration of gadolinium-based contrast media is associated with a risk of developing nephrogenic systemic ﬁbrosis (NSF) [139–141]. Although understanding is still limited, it appears that risk factors for NSF include reduced kidney function; multiple exposures to gadolinium-based chelates; the use of linear chelates, indicating a potential relationship to thermodynamic stability [142]; and the presence of major tissue injury, termed pro-inﬂammatory conditions [143]. It is clear that much is still to be understood about NSF and risk factors involved. However, it is prudent to minimize the risk of NSF for all subjects being exposed to gadolinium as part of a DCE-MRI examination or other MRI study based on existing data. DCE-MRI studies used for drug development differ from clinical practice and should approach the issue with particular caution, owing to the following factors: • The need for multiple injections of gadolinium-based contrast agents within a short period of time, particularly if a repeat baseline and early posttreatment follow-up is required. • For clinical diagnostic purposes the justiﬁcation of gadolinium in highrisk subjects can be clearly deﬁned, but for a patient in a clinical drug study the beneﬁts of receiving multiple examinations are less clear. The safety of the patient is paramount at the same time as acquisition of data to assist the development of novel therapies remains important. A conservative approach is recommended to minimize risk. This could include:

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• Minimizing the number of DCE-MRI examinations while still being able to provide an understanding of the pharmacology • Use of a single-dose gadolinium agent • Careful monitoring of renal function and setting boundaries below which subjects should not be studied • Use of contrast agents that, based on current data, appear to have the lowest association with NSF • Continual review of emerging data with necessary regular revision of guidelines DCE-MRI for Drug Development Decision Making As reviewed by O’Connor et al. [131] DCE-MRI has been used extensively to study multiple antiangiogenic and antivascular therapeutics. Many of the DCE-MRI experiments performed to date have been undertaken on drugs still in development. DCE-MRI certainly appears to be a methodology applied consistently within antiangiogenic development programs. Examples include the evaluation of AG-013736 by Liu et al. [144], where a relationship was observed between drug exposure and change in both Ktrans and IAUC from baseline to day 2. DCE-MRI was also incorporated into phase I investigations of the kinase inhibitor (VEGFR, PDGFR, FGFR) BIBF 1120 [145,146]. Both studies reported DCE-MRI ﬁndings after baseline, early (2 days [145] and 3 days [146]), and later time points (28 days [145] and 30 days [146]). Although DCE-MRI was obtained at multiple drug doses, it is not clear whether there was any dose–response relationship to support a dose decision for future studies. A methodology able to deﬁne dose versus vascular response for antiangiogenic therapies should in theory play a signiﬁcant role in many drug development programs. There do remain, however, a number of signiﬁcant challenges. First, most published studies where DCE-MRI has been applied to novel therapies have been undertaken on subjects with a range of tumor types, typical of the phase I setting. This creates methodological challenges likely to add to measurement variability (and measurement failure in a percentage of patients), and biological response will be less uniform than a study of a given tumor type. Another challenge is interpretation of data. How much of a change in Ktrans is sufﬁcient to predict subsequent treatment success? Without data linking DCE-MRI parameter change with metrics of clinical outcome in large studies, with different tumor types, and different therapies, it is not possible to establish a broad threshold of, say, the Ktrans change required to predict therapeutic success. If this could be realized, a ﬁrm understanding of radiological effectiveness from DCE-MRI could be used to inform dose decisions for a drug development program. Until such data are available, DCEMRI is likely to be used as a supportive function contributing at least to dose decisions, evaluating early signals of efﬁcacy, and understanding scheduling. The value that DCE-MRI brings, however, would increase signiﬁcantly, and

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implementation cost would clearly be justiﬁed if there were greater understanding of different levels of DCE-MRI response. Collins [147] has critiqued how imaging (both PET and MRI) contributed to decision making in the development of combretastatin A4 phosphate. Such analyses are important in order to understand how imaging has actually contributed to decision making, the many instances when it has not, and what can be done to enhance the role of imaging in clinical drug development. Future Improvements in the Use of DCE-MRI As described previously, published recommendations will help to encourage consistent DCE-MRI measurements. These should enable pharmaceutical companies and academic groups to focus on the challenges of drug development over methodological optimization. However, the current recommendations that exist remain appropriately broad since they require applicability to scanners from different manufacturers. Greater harmonization of methods between scanner manufacturers would considerably beneﬁt the DCE-MRI experiment, lowering the barrier to implementation and enabling broader use in drug development. Increased standards of measurement would deliver more studies that could be subjected to metaanalysis: for example, to study DCE-MRI response versus outcome. With such relationships understood, the role of DCE-MRI in early drug development can potentially move from an interesting secondary objective to delivering primary study goals. Clinical trials are also likely to beneﬁt increasingly from the multiparameter assessments possible within a single MR scan session. In a study of the panVEGF receptor tyrosine kinase inhibitor AZD2171 in glioblastoma patients, Batchelor et al. [148] demonstrated that an extensive MRI evaluation provides signiﬁcant beneﬁt over DCE-MRI alone. The study incorporated lesion volume and apparent diffusion coefﬁcient measurements, together with DCEMRI-derived indices, including Ktrans and relative vessel size estimations. Such approaches are likely to be necessary to fully interpret the complex changes occurring following antiangiogenic therapy and to promote understanding of drug scheduling and how optimally to combine different therapies. Improved standardization will both enhance the ability to incorporate DCE-MRI into drug development and promote understanding of how different DCE-MRI response levels are interpreted.

Apparent Diffusion Coefﬁcient MRI A number of other image-based functional measures are being investigated as potential imaging biomarkers in ongoing exploratory research. Here we brieﬂy highlight one additional cancer application. Diffusion MRI is an imagebased method used to measure the molecular diffusion of water molecules in biological tissues, and derives from the well-established NMR method of

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molecular diffusion measurement [149]. The parameter measured, the apparent diffusion coefﬁcient (ADC), reﬂects the diffusivity or mobility of water molecules and is termed apparent because of the undetermined effects of perfusion, multiple tissue water compartments, restricted diffusion due to cellular and extracellular structures, and tissue anisotropy [149]. Measurement of tissue water diffusion has proven to be remarkably versatile for characterizing tissue structure, as well as tissue changes due to pathology or the effects of therapy. Diffusion MRI has been well studied in the clinical evaluation of ischemic stroke [150]. In oncology, the application of diffusion MRI has focused on the image-based measurement of tumor ADC to detect early changes associated with treatment response [151]. ADC-MRI has been applied with some preliminary success in preclinical and clinical research as an imaging biomarker of treatment response in cancers of the brain [152,153], head and neck [154], cervix [155], breast [156], and prostate [157]. A related MRI technique, diffusion tensor imaging (DTI), has been used for noninvasive characterization of tissue anisotropy, structural integrity, and connectivity in myocardium [158] and white matter tracts in the brain [159]. In particular, the latter application has been useful in preoperative planning for brain tumor patients [160]. The concept of using ADC-MRI to evaluate the effects of therapy in solid tumors is based on the general observation of an increase in tumor ADC after cytotoxic treatments in several tumor types [161–163]. Lyng et al. [163] demonstrated in four different melanoma xenograft models that tumor ADC is inversely proportional to viable tissue cell density. It is thought that an increase in tumor ADC upon treatment reﬂects the development of necrosis through increased cell membrane permeability and extracellular fraction, and decreased cellularity which ultimately results in greater water mobility [164]. In some tumor types such as intracranial gliomas, an early transient decrease in ADC has also been observed prior to subsequent ADC elevation, which may be due to treatment-induced cellular swelling [152,165]. These changes generally precede gross tumor regression, so that changes in tumor ADC appear to predict such regression. ADC-MRI offers some key advantages as a potential imaging biomarker of tumor treatment response. ADC-MRI does not require the injection of exogenous contrast agents, since it relies on the endogenous contrast mechanism of water diffusion. In addition, as an MRI-based method it does not involve exposure to ionizing radiation. Moreover, the ADC is an absolute biophysical quantity which, in principle, should be comparable across MRI hardware platforms. However, varying acquisition methodologies currently lead to some variability across hardware platforms and between different imaging centers, which could be addressed through better standardization of acquisition and processing protocols. A primary limitation currently with ADC-MRI is the enhanced sensitivity of diffusion MRI to patient motion, such that involuntary or unrelated physiological motion can produce quantitative artifacts. A variety of acquisition

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and image-processing techniques have been devised to correct or minimize such image artifacts (e.g., [165–167]), which must be addressed and optimized in the process of standardizing the imaging protocol. An additional concern is the fact that tumor ADC response to therapy can also reﬂect formation of edema secondary to treatment [151]. Recently developed voxel-based statistical classiﬁcation approaches have improved the robustness of ADC-MRI against such potentially confounding factors [152,153]. Although ADC-MRI appears to be a promising imaging biomarker for oncology treatment monitoring, further clinical studies are needed to fully characterize the validity and reliability of ADC-MRI for different tumor types and therapeutic classes, as well as to compare the relative value of ADC-MRI with similar measures of tumor viability, such as FDG-PET. Selecting the Right Technique Very often, similar measurements of biological and disease processes are provided by multiple imaging modalities or even distinct approaches within a single modality. In selecting the right technique for a given problem, it is important to consider the range of possible biomarkers (both imaging and nonimaging), as well as the varying technical requirements and available resources, any signiﬁcant differences between hardware platforms, and the limitations of speciﬁc modalities. Here we illustrate the considerations for selecting a particular imaging biomarker for the problem of assessing tumor vascularity. Example: Studying Aspects of Tissue Vascularity Let us assume that we wish to evaluate the tumor response to a new antiangiogenic agent for the purposes of deﬁning the lower limit of biological effectiveness across a dose range. What parameters need consideration when deciding on the appropriate methodology? The advantages and disadvantages of techniques are seldom reviewed comprehensively, and more speciﬁcally, there are few studies actually comparing methodologies for their potential to study therapy. De Langen et al. provide an insightful comparison of [15O]H2O-PET versus DCE-MRI for tumor blood ﬂow measurements [168]. The review focuses on both the practical aspects of the two techniques and the ability of the different techniques to probe ﬂow. The conclusion from the comparison is that both approaches are viable methods for the study of ﬂow, yet important differences need to be considered for each study. Goh and Padhani compare the merits of DCE-CT versus DCEMRI in the study of tumor angiogenesis [169]. Again, it is concluded that both techniques should be considered, but a number of factors should determine selection, including drug mechanism of action, tumor location, patient characteristics, and available infrastructure and expertise [169]. The potential techniques able to deﬁne tumor vascular characteristics include the following:

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Dynamic Contrast-Enhanced CT • Endpoints. DCE-CT can measure blood ﬂow, blood volume, permeability, and mean transit time. • Tumor localization. Multidetector CT technology enables a signiﬁcant coverage of the tumor during the dynamic assessment, overcoming previous limitations of the approach. Furthermore, CT offers some key advantages when imaging certain anatomical regions, as summarized by Goh and Padhani [169]. • Clinical trial practicalities. The technique is widely available, although it is preferable that sites have previous expertise in dynamic CT. However, training imaging centers with a standard protocol is achievable. Also, since CT is generally required for RECIST assessment of disease burden, the use of CT for a pharmacodynamic investigation may result in fewer scanning sessions. • Risks. Dynamic CT may add a signiﬁcant radiation dose burden that may become problematic in many patient populations. This is particularly true if these subjects are already receiving regular CT scans for standard radiological assessment, perhaps also with nuclear medicine investigations accumulating a signiﬁcant dose. This is likely to limit the number of time points that are measurable within a short clinical trial, particularly compared to DCE-MRI and microbubble ultrasound. • Quantiﬁcation. The straightforward relationship between CT contrast agent concentration and tissue enhancement is often cited as a signiﬁcant advantage that DCE-CT has over DCE-MRI, making absolute quantiﬁcation of perfusion achievable. For analysis, the availability of commercial software on the scanning platform holds a signiﬁcant advantage relative to other methods, although centralized analysis is often required anyway, so this advantage may be limited. In summary, DCE-CT is a widely available, robust methodology for providing perfusion quantiﬁcation [170]. However, the associated radiation dose is likely to limit the use of CT in certain patient populations and certainly keep the number of time points low. Dynamic Contrast-Enhanced MRI • Endpoints. DCE-MRI typically provides indices relating to permeability and ﬂow (Ktrans and IAUC) in addition to indices relating to the tissue microenvironment (ve). • Tumor localization. The localization can be similar to CT in that a single level is typically scanned using multiple slices. However, coronal scanning, for example, may facilitate greater coverage if multiple lesions

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require measurement. Compared to DCE-CT, DCE-MRI is not optimal for all lesions and may often suffer from artifacts rendering measurement impossible (e.g., in the mediastinum). • Clinical trial practicalities. The equipment required to perform DCEMRI is widely available, yet the expertise in quantitative MRI measurement is limited. This can often be overcome by working closely with the imaging center and providing rapid feedback on data generated. In many instances, willingness of a site to adhere to a consistent imaging protocol is preferable to a site with extensive experience, insisting that the measurement be performed a particular way. The number of time points available from DCE-MRI is one advantage, with many publications demonstrating ﬁve measurements or more are feasible. However, recent concerns regarding NSF are likely to limit the number of time points, especially in patient populations with compromised renal function. • Risks. The only signiﬁcant risk to the subject is from the exposure to gadolinium-containing contrast media. These risks can be minimized, but in patient populations with compromised renal function an alternative imaging technique may be deemed appropriate. • Quantiﬁcation. With the lack of consistent and widely available image analysis platforms for DCE-MRI, central analysis of multicenter data is paramount. A signiﬁcant limitation is the inability to deﬁne ﬂow separately from permeability. Although Ktrans and IAUC may provide useful general indicators of vascular pharmacology, interpretation can be challenging. Without the radiation dose burden and the ability to perform multiple posttherapy time points, DCE-MRI remains attractive for many studies. However, the challenge to implement across multiple centers with the associated cost and time should not be overlooked. Furthermore, ambiguity of the imaging parameters needs to be considered carefully to ensure that DCE-MRI will contribute sufﬁcient insight into the pharmacology. Microbubble Ultrasound • Endpoints. The reported endpoints are varied and include parameters such as area under the enhancement curve, arrival time, time to peak enhancement, and counting of identiﬁed vessels. • Tumor localization. Bone and air attenuation sets signiﬁcant limits on accessibility of this technique to some lesions. This is a fundamental limitation that should be considered when establishing the appropriate patient population to study. • Clinical trial practicalities. Ultrasound remains widely available, although centers experienced in quantitative microbubble techniques remain limited. Furthermore, differences in techniques at those centers need

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harmonizing before a clinical trial can begin. Several repeat measurements are possible [171]. This could allow studies in multiple lesions or repeat measurement in the event of failure or repeated measurements averaged to minimize variability. The technique is likely to be favorable to the patient, requiring relatively quick examination times, and comfort during the examination is likely to be high. The low cost may also be an important factor, and if implemented appropriately would constitute only a small fraction of a total study budget. A frequently quoted criticism of quantitative ultrasound is the operator dependence of the measurement. However, it has been shown that with careful implementation, such dependence can be controlled [172]. How this would translate into large multicenter studies, however, is yet to be established. • Risks. No signiﬁcant risks are expected with repeated microbubble ultrasound investigations. Microbubble contrast media are considered generally safe, with serious adverse events rarely observed [173]. • Quantiﬁcation. Analysis is currently limited to empirical analyses of the microbubble dynamics. As such, there is likely to be signiﬁcant variability of measurement from one scanner to the next where different performance characteristics and software implementations will affect measurement. A cross-site calibration exercise combined with central analysis could overcome many of the limitations. Understanding the relationship of the ultrasound parameters to the biology may be challenging. Microbubble ultrasound remains a quick, low-cost, highly accessible methodology for gauging vascular change [174]. Further understanding is needed of the multisite performance together with more insight into the biological linkage. For single or small multisite studies it could provide a useful method to enable many repeat measurements over a short period of time to understand relationships between pharmacokinetics and pharmacodynamic response. [15O]H2O-PET • Endpoints. Flow and distribution volume are typically derived. Since the tracer is rapidly diffusible, permeability is not extracted. • Tumor localization. The poor spatial resolution of PET will limit the study of small lesions. Even if such lesions are identiﬁable, partial volume effects are likely to make robust quantiﬁcation challenging [175]. Careful screening of subjects is required to ensure that there exist, for example, at least 2-cm lesions prior to entering into the study. • Clinical trial practicalities. Owing to the short half-life of 15O (2 minutes), an onsite cyclotron is required to generate the tracer and facilitate rapid administration. Even sites with a cyclotron may not produce [15O]H2O

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routinely. If this technique is required at multiple sites, the infrastructure and expertise of potential sites must be assessed carefully. One beneﬁt of the short half-life is that repeat examinations can be performed within 10 minutes of each other. This allows either repeat perfusion assessments early after therapy or use of correlative imaging such as [18F]FDG-PET without interference. Since an arterial input function needs to be deﬁned for quantiﬁcation, rapid arterial blood sampling is required during the study. This adds signiﬁcant complexity onto an imaging examination, and the practicalities need consideration when selecting this technique. • Risks. Although the effective radiation dose is lower than an extensive CT scan or an [18F]FDG-PET scan, the additional radiation dose this brings to a subject who has been scanned extensively should be considered carefully. This is particularly true where there may be no direct beneﬁt to the subject of such a pharmacodynamic endpoint versus a diagnostic CT or PET scan. • Quantiﬁcation. A signiﬁcant body of work exists on quantiﬁcation of dynamic PET data, particularly from the brain and heart. Adaptation of such analyses can facilitate robust measurement of tumor pathophysiology [176]. Owing to different implementation options, central analysis is required if data are obtained from multiple centers. PET provides a robust technique to quantify absolute tumor perfusion, and the quantiﬁcation has many advantages over other methodologies. However, the limited availability across sites, the relatively complex patient setup, and limited performance in small lesions need careful consideration. As outlined above, there exist many practical and basic differences between measurement techniques able to study different aspects of the same phenomenon. All decisions will be dependent on the study question being asked. For example, if analysis of lung tumor perfusion across multiple centers is required, DCE-CT is likely to be an appropriate technology. If multiple time points are required to assess relative vascular change of liver lesions, microbubble ultrasound may be an appropriate methodology. The following parameters will deﬁne the appropriateness of the various techniques: the ability to perform centralized analysis; the patient characteristics (radiation dose considerations and sensitivity to both MR and CT contrast media), the number of subjects and study centers required for delivery of the primary study endpoint, the number of time points required to fully assess the pharmacodynamics, the drug mechanism, and the probable importance of perfusion analysis versus a measure also sensitive to permeability. There is no single technique ideally suited to address all questions in all types of clinical trials. DCE-MRI has tended to receive considerable attention for studies of anti-angiogenics. This is probably because although it has

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limitations in terms of the quantiﬁcation, it comes with high-resolution robust measurement in a variety of tumor types, and multiple repeat measurements are possible in most patient populations. Despite these advantages, the limitations of DCE-MRI should be evaluated and all measurement options considered.

THE ONGOING CHALLENGE OF IMAGING STANDARDIZATION Without standardization of imaging methods, the variety of imaging practices, tracers, equipment, and interpretation would lead to variability far greater than any drug-induced change. Since the extent of the drug-induced change is generally unknown before the experiment, study setup should aim to minimize measurement variability as far as practically possible. Standardization of imaging is required at a number of levels: 1. Clinical protocol. The clinical protocol deﬁnes how the imaging endpoint is incorporated into the clinical protocol. For example, when are imaging time points relative to drug dosing? What size lesions are expected to be measurable by the technique chosen? Consistency of how imaging is incorporated is certainly required within any drug development program to ensure that cross-comparison of studies can be made. 2. Technique selection. What technique is chosen to deliver a given endpoint? Deciding what techniques would generally be used for a given drug development program or throughout a development organization would aid consistency of decision making and, through familiarization, lower implementation costs and resource requirements. 3. Patient preparation. Standardization of patient preparation for an imaging examination includes management within the scanner: fasting requirements, patient position, and cannulation. Patient preparation is particularly important for metabolic imaging (e.g., [18F]FDG-PET) and functional imaging (e.g., DCE-CT), where multiple factors can signiﬁcantly impact the imaging result (e.g., via modulation of blood glucose level or liver blood ﬂow). 4. Scanner setup. How the imaging technique is implemented on a given scanner and how this is done between different scanners at different institutions deﬁnes the most signiﬁcant complexity of multisite imaging. The variety of equipment is growing as manufacturers offer more hardware and software options. This diversiﬁcation increases the demands to set up imaging between two different scanners with equivalence. 5. Image analysis and reporting. Generally, the software platforms used by centers (on the scanner, third-party, or site proprietary) are so diverse that data must be analyzed centrally. When this is done, standardization within a study and between studies can readily be controlled.

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Unique Challenges to Standardized Imaging The diversity of imaging hardware creates one of the most signiﬁcant sources of measurement variability. Seemingly equivalent imaging methods deployed on two different scanners may provide equivalent diagnostic information and images of similar appearance. However, for quantitative imaging where imaging-derived biomarkers are required, very signiﬁcant differences in derived values can occur. Also, there may exist proprietary algorithms for image reconstruction or subsequent analysis, the details of which are not known and therefore cannot be compensated for. When implementing complex imaging across multiple centers, expert resources are required to (1) compile a site-speciﬁc imaging protocol (e.g., detail acquisition requirements for a given manufacturer), (2) provide training to the relevant site staff likely to undertake the imaging (technologist and radiologist), (3) send standard phantoms to sites and review data to ensure expected performance and equivalence between sites, (4) ensure that data are received rapidly, preferably via electronic image transfer, so that the data can be reviewed rapidly and feedback given on issues, and (5) analyze the data and provide the imaging endpoints to the sponsor for statistical analysis. Achieving Protocol Consensus by Relevant Stakeholders An effective method to provide consistent imaging biomarker application is to develop an agreed protocol between experts internationally. Examples of this include DCE-MRI and [18F]FDG-PET [101,130]. Development of such recommendations is challenging to achieve given the complexity and likely disagreement on certain components during rapid evolution of imaging technology. Such efforts tend to result in general principles that are sufﬁciently ﬂexible to work across many centers and ensure that similar endpoints are reported out from different studies. How Much Standardization Is Enough? The degree of standardization needs to be considered for each technique requiring deployment. Standardization also comes at a cost in terms of resource and time in particular. There should be realistic expectations that imaging cannot, for example, be comprehensively standardized at 50 centers to the extent that it can be controlled at three centers. Some imaging techniques (e.g., [18F]FDG-PET) can be standardized by a well-considered imaging protocol likely to be adhered to by the site imaging experts. However, complex MRI methodologies may require phantoms to be shipped to all participating sites and phantom data submitted for central review prior to any patients being scanned. If budget is limited, it may be preferable to perform consistent imaging on fewer subjects at a small number of centers rather than acquiring data from more subjects without investing in standardization.

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It is important to remember that standardization is never likely to be achieved completely. With the rapid developments in imaging hardware, radiology best practice and contrast media/tracers standardization need to adapt. For example, with the increasing availability of 3T MRI scanners relative to the standard 1.5T scanner, when should 3T be recommended? A reasonable approach is that where data support the use of new hardware for a given application, equivalence should be deﬁned prior to wider adoption. Mechanisms for Implementing Standardized Imaging A number of bodies that represent imaging experts or users of imaging endpoints are optimally placed to develop consensus on imaging techniques and to disseminate recommendations. This includes government bodies such as the National Cancer Institute [101,177] and Cancer Research UK [130], accreditation bodies such as the American College of Radiology (ACR; http:// www.acr.org) and the Intersocietal Accreditation Commission (IAC; http:// www.intersocietal.org/intersocietal.htm), as well as the many professional societies related to medical imaging. ACR and IAC have traditionally provided voluntary accreditation for diagnostic imaging providers, although a recent U.S. health insurance quality improvement initiative and changes in Medicare law have mandated accreditation as a condition for insurance reimbursement. Standards have been established by IAC and ACR that specify the qualiﬁcations of imaging personnel, hardware quality control, conduct of imaging studies, image interpretation, and reporting. The scope of these standards is diagnostic imaging in clinical practice, and they are not directly applicable to imaging biomarkers. However, with the development of appropriate standards for quantitative imaging biomarkers, ACR and IAC may be ideally suited to facilitate the accreditation and education of imaging centers for imaging biomarkers. In response to the need for quantitative imaging standards, the Radiological Society of North America (RSNA) has recently initiated the Quantitative Imaging Biomarkers Alliance (QIBA; http://www.rsna.org/Research/qiba_ intro.cfm), represented by pharmaceutical companies, imaging hardware and software vendors, government agencies, imaging societies, RSNA leadership, and clinical trialists [178]. Modeled on the collaboration known as Integrating the Healthcare Enterprise (IHE; http://www.ihe.net/), QIBA aims “to advance quantitative imaging by focusing on standardizing the use of imaging biomarkers in clinical trials” with the long-term goal of “enhancing the use of quantitative imaging methods in clinical practice.” Initially, planned efforts will address standardizing quantitative anatomical CT, FDG-PET, and DCE-MRI. Although QIBA has been formed under the auspices of RSNA, it seems probable that a dedicated standard setting organization may eventually be needed for ongoing development, maintenance, and dissemination of new imaging biomarker standards.

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MECHANISMS TO FACILITATE IMAGING BIOMARKER DEVELOPMENT Despite the potential of imaging biomarkers as unique tools in drug development, the multidisciplinary nature and complexity of these technologies have somewhat hindered biomarker development. In addition, implementing and using imaging biomarkers has tended to be isolated to research institutions and centers with the necessary range of resources. The difﬁculties of imaging standardization and the expense of imaging biomarker qualiﬁcation has also limited the incentive for commercialization of these technologies. Many stakeholders in government and industry have recognized the inadequacy of traditional research and development approaches as applied to biomarker development. Other biomarkers in use today, such as blood pressure or plasma cholesterol, have taken decades to achieve their current utility [40]. In its Critical Path Initiative the FDA acknowledges the urgent need for new approaches and broad collaboration to address the unique challenges of biomarker development [179]. Two new approaches that are beginning to have a positive impact are public–private partnerships and the open-source development paradigm. Public–Private Partnerships The Foundation for the National Institutes of Health (FNIH), in collaboration with the National Institutes of Health (NIH), the FDA, and the Pharmaceutical Research and Manufacturers of America (PhRMA), established in 2006 the Biomarkers Consortium [180], a public–private partnership focused on the discovery, development, and qualiﬁcation of biomarkers, including imaging biomarkers, through collaborative biomedical research [181]. The Biomarkers Consortium is unprecedented in its size and coordination of government, academic, and industry aims for biomedical research focused on new biomarker technologies. Government partners, including the NIH, FDA, and Centers for Medicare and Medicaid Services, contribute scientiﬁc and technical expertise as well as ﬁnancial resources and project management to the consortium. Membership in the consortium is open to nonproﬁt and for-proﬁt organizations and patient advocacy groups with an interest in helping the consortium reach its goals. Consortium members may nominate subject-area specialists as representatives on steering committees devoted to various therapeutic areas that are charged with evaluating biomarker project proposals. According to its Web site, “the Biomarkers Consortium wants all members to be actively engaged in this collaborative multi-sector approach to speed the discovery and validation of disease biomarkers and surrogates. Given the expense and logistical challenges of moving biomarker science forward, we’ve adopted this collaborative multi-sector approach that encompasses all relevant stakeholders.”

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Following biomarker project approval by the appropriate steering committee, private-sector fundraising for biomarker projects is facilitated by the FNIH. Several large-scale imaging biomarker development and qualiﬁcation studies have been initiated with assistance from the Biomarker Consortium. Two studies, to be conducted by the National Cancer Institute, are evaluating the utility of FDG-PET to monitor cytotoxic treatment of non-Hodgkin lymphoma and non-small cell lung carcinoma, providing data to support clinical qualiﬁcation of FDG-PET as a possible surrogate endpoint [182]. A multicenter MRI study of carotid atherosclerotic plaque is determining the test– retest and intraobserver variability of MRI-based measures of plaque size and composition to facilitate imaging assay validation [183]. The Osteoarthritis Initiative (OAI) is a four-year, multicenter, longitudinal study of the clinical onset and progression of knee osteoarthritis which seeks to “characterize imaging, biochemical and genetic biomarkers that predict and track the course of [osteoarthritic] disease” [184]. Clinical data and images from almost 5000 patients will be made publicly available for further biomarker research worldwide [185]. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is the largest project to date, with $25 million of its $60 million funding being raised through the FNIH. ADNI is a ﬁve-year study tracking 823 normal, mildly cognitively impaired, and Alzheimer disease patients using MRI and PET as well as laboratory and cognitive tests [186]. As with the OAI study, ADNI clinical data and images have been made available to the general scientiﬁc community for continued biomarker research and development [187]. These and other imaging-related projects supported by the Biomarker Consortium are addressing the need for clinical trials to establish the validity and clinical qualiﬁcation of imaging biomarkers. Such trials would be too costly and would make little sense for any single organization to pursue but are made possible through collaborative sharing of the costs and associated risks among consortium members. Open-Source Collaboration The success of the open-source paradigm for software development [188] has led to the open-source approach being applied to the development of biomedical informatics tools [e.g., Open Bioinformatics Foundation (http://www.openbio.org) and Bioinformatics Organization (http://www.bioinformatics.org/)] as well as a gradual adoption of “open-source” collaborative models of biomedical research [189]. Key features of the open-source model are: (1) participation is open to anyone with the interest and skills to contribute; (2) development and communication is centralized through an Internet-based organization; and (3) the results are freely shared. Among drug development public–private partnerships, a hybrid approach is emerging in which the knowledge-based aspects of the research follow an open-source model, while “rule-based” aspects (such as toxicology, chemistry manufacturing and controls, clinical trials) are outsourced [190,191].

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Large publicly funded research initiatives are similarly adopting an opensource approach. The cancer Biomedical Informatics Grid (caBIG; https:// cabig.nci.nih.gov/) is an ambitious collaboration launched by the National Cancer Institute in 2003 to develop and share computing tools, infrastructure, and data across the spectrum of cancer research [192]. Data sharing follows a federated model [193] and integrates diverse cancer data from basic and clinical sources, as well as cancer imaging and tissue pathology data. A broad set of tools have been developed based on widely available open-source components and standards, including bioinformatics software, image analysis and database tools [194], and a complete clinical trials management system. Sharing of tools and data is facilitated by an open-source grid-based computing infrastructure. Although the focus of the caBIG research enterprise is cancer, the infrastructure and many of the tools are generic and may be useful in other therapeutic areas. One recent example has been the launch of the CardioVascular Research Grid (cvrGRID; http://cvrgrid.org) by the National Heart Lung and Blood Institute. Given the complexity and difﬁculties of imaging biomarker development, it may be advantageous to apply the open-source paradigm to an integrated development effort for imaging biomarkers. As the foundational imagerelated tools from caBIG reach maturity, they could form the basis of an open-source development model that integrates image acquisition, analysis, protocol speciﬁcation, cross-platform compatibility, data standards, validation, and qualiﬁcation. Such an approach could address development and resource hurdles through the open participation of any interested party, standardization of protocols through centralized development and dissemination, and wide availability of common image analysis tools. Although some development issues would still need to be resolved, such as intellectual property protection and regulatory concerns, an open-source model may ultimately be the most efﬁcient approach to developing, maintaining, and delivering the imaging biomarker tools that can affect drug development.

CONCLUSIONS: CHALLENGES AND FUTURE OPPORTUNITIES One of the most salient features of imaging science, which is relevant to both diagnostic imaging and imaging biomarkers, is that the state of the technology does not stand still. Continuous research and development for more than 30 years has produced a steady stream of new imaging technologies as well as incremental improvements that address the limitations of existing technologies. Development and implementation strategies for imaging biomarkers must account for this continuous change and attempt to balance the needs of standardization with the adoption of new innovative technologies. Like software, imaging biomarkers will probably need to be “versioned” to account for changes to the underlying technologies. However, this will also require a continuous process of development and maintenance, for which an open-source

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model may be the most appropriate. Moreover, with the continued introduction of new imaging technologies, in the future we might expect the translation of additional image-based biological measures to promising new imaging biomarkers. This will become increasingly feasible as innovative systems make imaging biomarker development more routine. It seems likely that no one biomarker will be able to adequately capture all of the important aspects of a given disease process or biological response to therapy. A combination of imaging and other biomarkers may be the most effective surrogate endpoint tool for drug development. In addition to the requirements of biomarker validation and qualiﬁcation, this will require that the component biomarker technologies are cost-effective as a combination. Current collaborative efforts being led by public–private partnerships such as the FNIH and by the NIH and QIBA are starting to address many of the hurdles of imaging biomarker development and standardization. New approaches that allow contributions from all stakeholders will accelerate the work of imaging biomarker validation and qualiﬁcation, and we might expect that these initiatives will soon lead to broader availability of new imaging biomarker tools for drug development. REFERENCES 1. FDA (2006). Critical path opportunities report. http://www.fda.gov/oc/initiatives/ criticalpath/reports/opp_report.pdf. 2. Frey-Wyssling A (1953). Submicroscopic Morphology of Protoplasm. Elsevier, Amsterdam. 3. Kuhl DE, Edwards RQ (1963). Image separation radioisotope scanning. Radiology, 80:653–662. 4. Wrenn FR, Good ML, Handler P (1951). The use of positron-emitting radioisotopes for the localization of brain tumors. Science, 113:525–527. 5. Brownell GL, Sweet WH (1953). Localization of brain tumors with positron emitters. Nucleonics, 11:40–45. 6. Hounsﬁeld GN (1973). Computerized transverse axial scanning (tomography): 1. Description of system. Br J Radiol, 46:1016–1022. 7. Lauterbur PC (1973). Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature, 242:190–191. 8. Mansﬁeld P, Grannell PK (1973). NMR diffraction in solids. J Phys C, 6:L422–L426. 9. Rudin M (ed.) (2005). Imaging in Drug Discovery and Early Clinical Trials. Birkhauser, Basel, Switzerland. 10. Weissleder R, Pittet MJ (2008). Imaging in the era of molecular oncology. Nature, 452:580–589. 11. Schaeffter T (2005). Imaging modalities: principles and information content. Imaging in Drug Discovery and Early Clinical Trials. In Rudin M (ed.), Birkhauser, Basel, Switzerland, pp. 15–81.

REFERENCES

89

12. Barrett HH, Myers K (2003). Foundations of Image Science. Wiley–Interscience, Hoboken, NJ. 13. Bernstein MA, King KF, Zhou XJ (2004). Handbook of MRI Pulse Sequences. Academic Press, San Diego, CA. 14. Rahmim A, Zaidi H (2008). PET versus SPECT: strengths, limitations and challenges. Nucl Med Commun, 29:193–207. 15. Zaidi H (ed.) (2005). Quantitative Analysis in Nuclear Medicine Imaging. Springer Verlag, New York. 16. Judenhofer MS, Wehrl HF, Newport DF, et al. (2008). Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat Med, 14:459–465. 17. Defrise M (2001). A short reader’s guide to 3D tomographic reconstruction. Comput Med Imaging Graphics, 25:113–116. 18. Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999). Magnetic Resonance Imaging: Physical Principles and Sequence Design, Wiley–Liss, New York. 19. Rudin M, Beckmann N, Rausch M (2005). Evaluation of drug candidates: efﬁ cacy readouts during lead optimization. In Rudin M (ed.), Imaging in Drug Discovery and Early Clinical Trials. Birkhauser, Basel, Switzerland, pp. 185–255. 20. Beckmann N (ed.) (2006). In Vivo MR Techniques in Drug Discovery and Development. Informa HealthCare, London. 21. Tofts P (ed.) (2003). Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Wiley, Hoboken, NJ. 22. Jaffer FA, Weissleder R (2005). Molecular imaging in the clinical arena. JAMA, 293:855–862. 23. Massoud TF, Gambhir SS (2003). Molecular imaging in living subjects: seeing fundamental biological processes in a new light. Genes Dev, 17:545–580. 24. Aikawa E, Nahrendorf M, Figueiredo J, et al. (2007). Osteogenesis associates with inﬂammation in early-stage atherosclerosis evaluated by molecular imaging in vivo. Circulation, 116:2841–2850. 25. Willmann JK, Chen K, Wang H, et al. (2008). Monitoring of the biological response to murine hindlimb ischemia with 64Cu-labeled vascular endothelial growth factor-121 positron emission tomography. Circulation, 117:915–922. 26. Grimm J, Kirsch DG, Windsor SD, et al. (2005). Use of gene expression proﬁling to direct in vivo molecular imaging of lung cancer. Proc Natl Acad Sci USA, 102:14404–14409. 27. Koo V, Hamilton PW, Williamson K (2006). Non-invasive in vivo imaging in small animal research. Cell Oncol, 28:127–139. 28. Pomper MG, Lee JS (2005). Small animal imaging in drug development. Curr Pharm Des, 11:3247–3272. 29. Singh M, Johnson L (2006). Using genetically engineered mouse models of cancer to aid drug development: an industry perspective. Clin Cancer Res, 12: 5312–5328. 30. Lanza GM, Wickline SA (2003). Targeted ultrasonic contrast agents for molecular imaging and therapy. Curr Probl Cardiol, 28:625–653.

90

OPPORTUNITIES AND CHALLENGES

31. Kaufmann BA, Lindner JR (2007). Molecular imaging with targeted contrast ultrasound. Curr Opin Biotechnol, 18:11–16. 32. Henkelman RM, Stanisz GJ, Graham SJ (2001). Magnetization transfer in MRI: a review. NMR Biomed, 14:57–64. 33. Khaleeli Z, Sastre-Garriga J, Ciccarelli O, Miller DH, Thompson AJ (2007). Magnetisation transfer ratio in the normal appearing white matter predicts progression of disability over one year in early primary progressive multiple sclerosis. J Neurol Neurosurg Psychiatry, 78:1076–1082. 34. Damadian R (1971). Tumor detection by nuclear magnetic resonance. Science, 171:1151–1153. 35. Damadian R, Goldsmith M, Minkoff L (1977). NMR in cancer: XVI. FONAR image of the live human body. Physiol Chem Phys, 9:97–100, 108. 36. Sorensen AG (2006). Magnetic resonance as a cancer imaging biomarker. J Clin Oncol, 24:3274–3281. 37. Katz R (2004). Biomarkers and surrogate markers: an FDA perspective. NeuroRX, 1:189–195. 38. Mills G (2005). Biomarker imaging in drug development and licensed products. http://www.fda.gov/CDER/REGULATORY/medImaging/ImagingWorkshop. ppt. 39. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther, 81:104–107. 40. Frank R, Hargreaves R (2003). Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov, 2:566–580. 41. FDA (2005). Guidance for Industry: Pharmacogenomic data submissions. http:// www.fda.gov/CDER/GUIDANCE/6400fnl.pdf. 42. Kuhl D, Edwards R (1970). The Mark III scanner: a compact device for multipleview and section scanning of the brain. Radiology, 96:563–570. 43. Phelps ME, Hoffman EJ, Mullani NA, Higgins CS, Pogossian MMT (1976). Design considerations for a positron emission transaxial tomograph (PETT III). IEEE Trans Nucl Sci, 23:516–522. 44. Mansﬁeld P, Pykett IL, Morris PG (1978). Human whole body line-scan imaging by NMR. Br J Radiol, 51:921–922. 45. Hargreaves R (2008). The role of molecular imaging in drug discovery and development. Clin Pharmacol Ther, 83:349–353. 46. Barrett HH (1990). Objective assessment of image quality: effects of quantum noise and object variability. J Opt Soc Am A, 7:1266–1278. 47. Kupinski MA, Clarkson E, Gross K, Hoppin JW (2003). Optimizing imaging hardware for estimation tasks. In Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA. SPIE, Bellingham, WA, pp. 309–313. 48. Clarkson E (2007). Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks. J Opt Soc Am A, 24: B91–B98. 49. DePuey EG, Garcia EV, Berman DS (eds.) (2001). Cardiac SPECT Imaging. Lippincott Williams & Wilkins, Philadelphia.

REFERENCES

91

50. Taillefer EG (2001). Radiopharmaceuticals. In DePuey EG, Garcia EV, Berman DS (eds.), Cardiac SPECT Imaging. Lippincott Williams & Wilkins, Philadelphia, pp. 117–152. 51. Van Train CF, Garcia EV, Cooke CD, Areeda JS (2001). Quantitative analysis of SPECT myocardial perfusion. In DePeuy EG, Garcia EV, Berman DS (eds.), Cardiac SPECT Imaging. Lippincott Williams & Wilkins, Philadelphia, pp. 41–64. 52. Lin GS, Hines HH, Grant G, Taylor K, Ryals C (2006). Automated quantiﬁcation of myocardial ischemia and wall motion defects by use of cardiac spect polar mapping and 4-dimensional surface rendering. J Nucl Med Technol, 34: 3–17. 53. Ficaro EP, Lee BC, Kritzman JN, Corbett JR (2007). Corridor4DM: the Michigan method for quantitative nuclear cardiology. J Nucl Cardiol, 14:455–465. 54. Leslie WD, Tully SA, Yogendran MS, Ward LM, Nour KA, Metge CJ (2004). Automated quantiﬁcation of 99mTc sestamibi myocardial perfusion compared with visual analysis. Nucl Med Commun, 25:833–838. 55. Iskandrian AE, Garcia EV, Faber T (2008). Analysis of serial images: a challenge and an opportunity. J Nuclear Cardiol, 15:23–26. 56. Slomka PJ, Berman DS, Germano G (2004). Quantiﬁcation of serial changes in myocardial perfusion. J Nucl Med, 45:1978–1980. 57. Iskandrian AE, Bateman TM, Belardinelli L, et al. (2007). Adenosine versus regadenoson comparative evaluation in myocardial perfusion imaging: results of the ADVANCE phase 3 multicenter international trial. J Nucl Cardiol, 14: 645–658. 58. Udelson JE (2008). Lessons from the development of new adenosine A2A receptor agonists. J Am Coll Cardiol Imaging, 1:317–320. 59. Shields AF, Price P (2007). In Vivo Imaging of Cancer Therapy. Humana Press, Totowa, NJ. 60. Weissleder R (2006). Molecular imaging in cancer. Science, 312:1168–1171. 61. Nutt R, Vento LJ, Ridinger MHT (2007). In vivo molecular imaging biomarkers: clinical pharmacology’s new PET? Clin Pharmacol Ther, 81:792–795. 62. Thompson RC, Cullom SJ (2006). Issues regarding radiation dosage of cardiac nuclear and radiography procedures. J Nucl Cardiol, 13:19–23. 63. Einstein AJ, Henzlova MJ, Rajagopalan S (2007). Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography. JAMA, 298:317–323. 64. Amis ES, Butler PF, Applegate KE, et al. (2007). American College of Radiology White Paper on Radiation Dose in Medicine, 4:272–284. 65. McCollough CH, Bruesewitz MR, Koﬂer JM (2006). CT dose reduction and dose management tools: overview of available options. Radiographics, 26:503–512. 66. McConville PJ, Moody JB, Moffat BA (2005). High-throughput magnetic resonance imaging in mice for phenotyping and therapeutic evaluation. Curr Opin Chem Biol, 9:413–420. 67. Hargreaves R, Wagner JA (2006). Imaging as biomarker for decision-making in drug development. In Beckmann N (ed.), In Vivo MR Techniques in Drug Discovery and Development. Informa HealthCare, London, pp. 31–44.

92

OPPORTUNITIES AND CHALLENGES

68. Bergström M, Långström B (2005). Pharmacokinetic studies with PET. In Rudin M (ed.), Imaging in Drug Discovery and Early Clinical Trials. Birkhauser, Basel Switzerland, pp. 279–317. 69. Willmann JK, van Bruggen N, Dinkelborg LM, Gambhir SS (2008). Molecular imaging in drug development. Nat Rev Drug Discov, 7:591–607. 70. Major TC, Dhamija S, Black N, et al. (2008). The T- and L-type calcium channel blocker (CCB) mibefradil attenuates leg edema induced by the L-type CCB nifedipine in the spontaneously hypertensive rat: a novel differentiating assay. J Pharmacol Exp Ther, 325:723–731. 71. Seddon BM, Workman P (2003). The role of functional and molecular imaging in cancer drug discovery and development. Br J Radiol, 76:S128–S138. 72. Uppoor RS, Mummaneni P, Cooper E, et al. (2008). The use of imaging in the early development of neuropharmacological drugs: a survey of approved NDAs. Clin Pharmacol Ther, 84:69–74. 73. Evelhoch JL (2007). Magnetic resonance measurement of tumor perfusion and vascularity. In Shields AF, Price P (eds.), In Vivo Imaging of Cancer Therapy. Humana Press, Totowa, NJ, pp. 73–84. 74. Gwyther SJ (2007). Anatomical measure of tumor growth with computed tomography and magnetic resonance imaging. In Shields AF, Price P (eds.), In Vivo Imaging of Cancer Therapy. Humana Press, Totowa, NJ, pp. 33–46. 75. Miller AB, Hoogstraten B, Staquet M, Winkler A (1981). Reporting results of cancer treatment. Cancer, 47:207–214. 76. Lavin PT (1981). An alternative model for the evaluation of antitumor activity. Cancer Clin Trials, 4:451–457. 77. Michaelis LC, Ratain MJ (2006). Measuring response in a post-RECIST world: from black and white to shades of grey. Nat Rev Cancer, 6:409–414. 78. Therasse P, Arbuck SG, Eisenhauer EA, et al. (2000). New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst, 92:205–216. 79. Tuma RS (2006). Sometimes size doesn’t matter: reevaluating RECIST and tumor response rate endpoints. J Natl Cancer Inst, 98:1272–1274. 80. Therasse P, Eisenhauer E, Verweij J (2006). RECIST revisited: a review of validation studies on tumour assessment. Eur J Cancer, 42:1031–1039. 81. Benjamin RS, Choi H, Macapinlac HA, et al. (2007). We should desist using RECIST, at least in GIST. J Clin Oncol, 25:1760–1764. 82. Choi H (2008). Response evaluation of gastrointestinal stromal tumors. Oncologist, 13:4–7. 83. Byrne MJ, Nowak AK (2004). Modiﬁed RECIST criteria for assessment of response in malignant pleural mesothelioma. Ann Oncol, 15:257–260. 84. Schwartz LH, Curran S, Trocola R, et al. (2007). Volumetric 3D CT analysis: an early predictor of response to therapy. J Clin Oncol (Meet Abstr), 25:4576. 85. Schwartz LH, Colville JAC, Ginsberg MS, et al. (2006). Measuring tumor response and shape change on CT: esophageal cancer as a paradigm. Ann Oncol, 17:1018–1023. 86. Cheson BD, Pﬁstner B, Juweid ME, et al. (2007). Revised response criteria for malignant lymphoma. J Clin Oncol, 25:579–586.

REFERENCES

93

87. Gillies RJ, Robey I, Gatenby RA (2008). Causes and consequences of increased glucose metabolism of cancers. J Nucl Med, 49:24S–42S. 88. Spaepen K, Stroobants S, Dupont P, et al. (2003). [18F]FDG PET monitoring of tumour response to chemotherapy: Does [18F]FDG uptake correlate with the viable tumour cell fraction? Eur J Nucl Med Mol Imaging, 30:682–688. 89. Gambhir SS, Czernin J, Schwimmer J, Silverman DH, Coleman RE, Phelps ME (2001). A tabulated summary of the FDG PET literature. J Nucl Med, 42:1S–93S. 90. Kelloff GJ, Hoffman JM, Johnson B, et al. (2005). Progress and promise of FDGPET imaging for cancer patient management and oncologic drug development. Clin Cancer Res, 11:2785–2808. 91. Weber WA, Petersen V, Schmidt B, et al. (2003). Positron emission tomography in non-small-cell lung cancer: prediction of response to chemotherapy by quantitative assessment of glucose use. J Clin Oncol, 21:2651–2657. 92. Mikhaeel NG, Hutchings M, Fields PA, O’Doherty MJ, Timothy AR (2005). FDG-PET after two to three cycles of chemotherapy predicts progression-free and overall survival in high-grade non-Hodgkin lymphoma. Ann Oncol, 16:1514–1523. 93. Lin C, Itti E, Haioun C, et al. (2007). Early 18F-FDG PET for prediction of prognosis in patients with diffuse large B-cell lymphoma: SUV-based assessment versus visual analysis. J Nucl Med, 48:1626–1632. 94. Hutchings M, Loft A, Hansen M, et al. (2006). FDG-PET after two cycles of chemotherapy predicts treatment failure and progression-free survival in Hodgkin lymphoma. Blood, 107:52–59. 95. Evilevitch V, Weber WA, Tap WD, et al. (2008). Reduction of glucose metabolic activity is more accurate than change in size at predicting histopathologic response to neoadjuvant therapy in high-grade soft-tissue sarcomas. Clin Cancer Res, 14:715–720. 96. Stroobants S, Goeminne J, Seegers M, et al. (2003). 18FDG-positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec). Eur J Cancer, 39:2012–2020. 97. Jager PL, Gietema JA, van der Graaf WTA (2004). Imatinib mesylate for the treatment of gastrointestinal stromal tumours: best monitored with FDG PET. Nucl Med Commun, 25:433–438. 98. Antoch G, Kanja J, Bauer S, et al. (2004). Comparison of PET, CT, and dualmodality PET/CT imaging for monitoring of Imatinib (STI571) therapy in patients with gastrointestinal stromal tumors. J Nucl Med, 45:357–365. 99. Van den Abbeele AD (2008). The lessons of GIST–PET and PET/CT: a new paradigm for imaging. Oncologist, 13:8–13. 100. Young H, Baum R, Cremerius U, et al. (1999). Measurement of clinical and subclinical tumour response using [18F]ﬂuorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. Eur J Cancer, 35:1773–1782. 101. Shankar LK, Hoffman JM, Bacharach S, et al. (2006). Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute trials. J Nucl Med, 47:1059–1066.

94

OPPORTUNITIES AND CHALLENGES

102. Boellaard R, Oyen W, Hoekstra C, et al. (2008). The Netherlands protocol for standardisation and quantiﬁcation of FDG whole body PET studies in multicentre trials. Eur J Nucl Med Mol Imaging, http://dx.doi.org/10.1007/ s00259-008-0874-2. 103. Hallett WA, Maguire RP, McCarthy TJ, Schmidt ME, Young H (2007). Considerations for generic oncology FDG-PET/CT protocol preparation in drug development. IDrugs, 10:791–796. 104. Weber WA (2005). Use of PET for monitoring cancer therapy and for predicting outcome. J Nucl Med, 46:983–995. 105. Westerterp M, Pruim J, Oyen W, et al. (2007). Quantiﬁcation of FDG PET studies using standardised uptake values in multi-centre trials: effects of image reconstruction, resolution and ROI deﬁnition parameters. Eur J Nucl Med Mol Imaging, 34:392–404. 106. Lind P, Igerc I, Beyer T, Reinprecht P, Hausegger K (2004). Advantages and limitations of FDG PET in the follow-up of breast cancer. Eur J Nucl Med Mol Imaging, 31:S125–S134. 107. Hamaoka T, Madewell JE, Podoloff DA, Hortobagyi GN, Ueno NT (2004). Bone imaging in metastatic breast cancer. J Clin Oncol, 22:2942–2953. 108. Mankoff DA, Eary JF, Link JM, et al. (2007). Tumor-speciﬁc positron emission tomography imaging in patients: [18F]ﬂuorodeoxyglucose and beyond. Clin Cancer Res, 13:3460–3469. 109. Kelloff GJ, Krohn KA, Larson SM, et al. (2005). The progress and promise of molecular imaging probes in oncologic drug development. Clin Cancer Res, 11:7967–7985. 110. Gupta N, Price PM, Aboagye EO (2002). PET for in vivo pharmacokinetic and pharmacodynamic measurements. Eur J Cancer, 38:2094–2107. 111. Bergström M, Grahnén A, Långström B (2003). Positron emission tomography microdosing: a new concept with application in tracer and early clinical drug development. Eur J Clin Pharmacol, 59:357–366. 112. Katzenellenbogen JA, Welch MJ, Dehdashti F (1997). The development of estrogen and progestin radiopharmaceuticals for imaging breast cancer. Anticancer Res, 17:1573–1576. 113. Smith-Jones PM, Solit D, Afroze F, Rosen N, Larson SM (2006). Early tumor response to Hsp90 therapy using HER2 PET: comparison with 18F-FDG PET. J Nucl Med, 47:793–796. 114. Mankoff DA, Link JM, Linden HM, Sundararajan L, Krohn KA (2008). Tumor receptor imaging. J Nucl Med, 49:149S–163S. 115. Shields AF, Grierson JR, Kozawa SM, Zheng M (1996). Development of labeled thymidine analogs for imaging tumor proliferation. Nucl Med Biol, 23:17–22. 116. Shields AF, Grierson JR, Dohmen BM, et al. (1998). Imaging proliferation in vivo with [F-18]FLT and positron emission tomography. Nat Med, 4:1334– 1336. 117. Buck AK, Halter G, Schirrmeister H, et al. (2003). Imaging proliferation in lung tumors with PET: 18F-FLT versus 18F-FDG. J Nucl Med, 44:1426–1431. 118. Chen W, Cloughesy T, Kamdar N, et al. (2005). Imaging proliferation in brain tumors with 18F-FLT PET: comparison with 18F-FDG. J Nucl Med, 46:945–952.

REFERENCES

95

119. Smyczek-Gargya B, Fersis N, Dittmann H, et al. (2004). PET with [18F]ﬂuorothymidine for imaging of primary breast cancer: a pilot study. Eur J Nucl Med Mol Imaging, 31:720–724. 120. Grierson J, Yagle K, Eary J, et al. (2004). Production of [F-18]ﬂuoroannexin for imaging apoptosis with PET. Bioconjug Chem, 15:373–379. 121. Glaser M, Collingridge DR, Aboagye EO, et al. (2003). Iodine-124 labelled Annexin-V as a potential radiotracer to study apoptosis using positron emission tomography. Appl Radiat Isotopes, 58:55–62. 122. Lahorte C, Vanderheyden J, Steinmetz N, Wiele C, Dierckx R, Slegers G (2004). Apoptosis-detecting radioligands: current state of the art and future perspectives. Eur J Nucl Med Mol Imaging, 31:887–919. 123. Haubner R, Wester H, Burkhart F, et al. (2001). Glycosylated RGD-containing peptides: tracer for tumor targeting and angiogenesis imaging with improved biokinetics. J Nucl Med, 42:326–336. 124. Kenny LM, Coombes RC, Oulie I, et al. (2008). Phase I trial of the positronemitting Arg-Gly-Asp (RGD) peptide radioligand 18F-AH111585 in breast cancer patients. J Nucl Med, 49:879–886. 125. Beer AJ, Haubner R, Goebel M, et al. (2005). Biodistribution and pharmacokinetics of the ανβ3-selective tracer 18F-Galacto-RGD in cancer patients. J Nucl Med, 46:1333–1341. 126. Zhang X, Xiong Z, Wu Y, et al. (2006). Quantitative PET imaging of tumor integrin ανβ3 expression with 18F-FRGD2. J Nucl Med, 47:113–121. 127. Parker GJM, Padhani AR (2003). T1-W DCE-MRI: T1-weighted dynamic contrast-enhanced MRI. In Tofts P (ed.), Quantitative MRI of the Brain. Wiley, Hoboken, NJ, pp. 341–364. http://dx.doi.org/10.1002/0470869526.ch10. 128. Tofts PS, Brix G, Buckley DL, et al. (1999). Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging, 10:223–232. 129. He ZQ, Evelhoch JL (1998). Analysis of dynamic contrast-enhanced MRI in tumors: relationship of derived parameters with physiologic factors. In Proceedings of the International Society for Magnetic Resonance in Medicine, Sydney, Australia. Wiley, New York, p. 1652. http://cds.ismrm.org/ismrm-1998/ PDF6/P1652.PDF. 130. Leach M, Brindle K, Evelhoch J, et al. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer, 92:1599–1610. 131. O’Connor JPB, Jackson A, Parker GJM, Jayson GC (2007). DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer, 96:189–195. 132. Jayson G, Waterton J (2005). Applications of dynamic contrast-enhanced MRI in oncology drug development. In Baert A, Jackson A, Buckley D, Parker G (eds.), Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology. Springer, New York, 281–298. 133. Galbraith SM, Lodge MA, Taylor NJ, et al. (2002). Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed, 15:132–142.

96

OPPORTUNITIES AND CHALLENGES

134. Roberts C, Issa B, Stone A, Jackson A, Waterton JC, Parker GJ (2006). Comparative study into the robustness of compartmental modeling and modelfree analysis in DCE-MRI studies. J Magn Reson Imaging, 23:554–563. 135. Parker GJM, Roberts C, Macdonald A, et al. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med, 56: 993–1000. 136. Morgan B, Thomas AL, Drevs J, et al. (2003). Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies. J Clin Oncol, 21:3955–3964. 137. Mross K, Drevs J, Müller M, et al. (2005). Phase I clinical and pharmacokinetic study of PTK/ZK, a multiple VEGF receptor inhibitor, in patients with liver metastases from solid tumours. Eur J Cancer, 41:1291–1299. 138. Thomas AL, Morgan B, Horsﬁeld MA, et al. (2005). Phase I study of the safety, tolerability, pharmacokinetics, and pharmacodynamics of PTK787/ZK222584 administered twice daily in patients with advanced cancer. J Clin Oncol, 23: 4162–4171. 139. Prince MR, Zhang H, Morris M, et al. (2008). Incidence of nephrogenic systemic ﬁbrosis at two large medical centers. Radiology, 248:807–816. 140. Deo A, Fogel M, Cowper SE (2007). Nephrogenic systemic ﬁbrosis: a population study examining the relationship of disease development to gadolinium exposure. Clin J Am Soc Nephrol, 2:264–267. 141. Wertman R, Altun E, Martin DR, et al. (2008). Risk of nephrogenic systemic ﬁbrosis: evaluation of Gadolinium chelate contrast agents at four American universities. Radiology, 248:799–806. 142. Penﬁeld JG, Reilly RF (2008). Nephrogenic systemic ﬁbrosis risk: Is there a difference between Gadolinium-based contrast agents? Semin Dial, 21:129–134. 143. Sadowski EA, Bennett LK, Chan MR, et al. (2007). Nephrogenic systemic ﬁbrosis: risk factors and incidence estimation. Radiology, 243:148–157. 144. Liu G, Rugo HS, Wilding G, et al. (2005). Dynamic contrast-enhanced magnetic resonance imaging as a pharmacodynamic measure of response after acute dosing of AG-013736, an oral angiogenesis inhibitor, in patients with advanced solid tumors: results from a phase I study. J Clin Oncol, 23:5464–5473. 145. Lee CP, Taylor NJ, Attard G, et al. (2006). A phase I study of BIBF 1120, an orally active triple angiokinase inhibitor (VEGFR, PDGFR, FGFR) given continuously to patients with advanced solid tumours, incorporating dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). J Clin Oncol (Meet Abstr), p. 3015. http://meeting.ascopubs.org/cgi/content/abstract/24/18_ suppl/3015. 146. Mross KB, Gmehling D, Frost A, et al. (2005). A clinical phase I, pharmacokinetic (PK), and pharmacodynamic study of twice daily BIBF 1120 in advanced cancer patients. J Clin Oncol (Meet Abstr), p. 3031. http://meeting.ascopubs.org/ cgi/content/abstract/23/16_suppl/3031. 147. Collins JM (2003). Functional imaging in phase I studies: decorations or decision making? J Clin Oncol, 21:2807–2809.

REFERENCES

97

148. Batchelor TT, Sorensen AG, di Tomaso E, et al. (2007). AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell, 11:83–95. 149. Thomas DL, Lythgoe MF, Pell GS, Calamante F, Ordidge RJ (2000). The measurement of diffusion and perfusion in biological systems using magnetic resonance imaging. Phys Med Biol, 45:R97–R138. 150. Warach S, Gaa J, Siewert B, Wielopolski P, Edelman RR (1995). Acute human stroke studied by whole brain echo planar diffusion-weighted magnetic resonance imaging. Ann Neurol, 37:231–241. 151. Chenevert T, McKeever P, Ross B (1997). Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res, 3:1457–1466. 152. Moffat BA, Chenevert TL, Lawrence TS, et al. (2005). Functional diffusion map: a noninvasive MRI biomarker for early stratiﬁcation of clinical brain tumor response. Proc Natl Acad Sci USA, 102:5524–5529. 153. Hamstra DA, Galbán CJ, Meyer CR, et al. (2008). Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol, 26: 3387–3394. 154. Razek AAKA, Megahed AS, Denewer A, Motamed A, Tawﬁk A, Nada N (2008). Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors. Acta Radiol, 49:364–370. 155. McVeigh P, Syed A, Milosevic M, Fyles A, Haider M (2008). Diffusion-weighted MRI in cervical cancer. Eur Radiol, 18:1058–1064. 156. Lee KC, Moffat BA, Schott AF, et al. (2007). Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res, 13:443–450. 157. Lee KC, Sud S, Meyer CR, et al. (2007). An imaging biomarker of early treatment response in prostate cancer that has metastasized to the bone. Cancer Res, 67: 3524–3528. 158. Helm P, Beg MF, Miller MI, Winslow RL (2005). Measuring and mapping cardiac ﬁber and laminar architecture using diffusion tensor MR imaging. Ann NY Acad Sci, 1047:296–307. 159. Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL (2004). Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, ﬁber tract anatomy, and tumor imaging patterns. Am J Neuroradiol, 25: 356–369. 160. Mori S, Frederiksen K, van Zijl PCM, et al. (2002). Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol, 51: 377–380. 161. Ross BD, Chenevert TL, Kim B, Ben-Yoseph O (1994). Magnetic resonance imaging and spectroscopy: application to experimental neuro-oncology. Q Magn Reson Biol Med, 1:89–106. 162. Zhao M, Pipe JG, Bonnett J, Evelhoch JL (1996). Early detection of treatment response by diffusion-weighted 1H-NMR spectroscopy in a murine tumour in vivo. Br J Cancer, 73:61–64.

98

OPPORTUNITIES AND CHALLENGES

163. Lyng H, Haraldseth O, Rofstad EK (2000). Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med, 43:828–836. 164. Ross BD, Moffat BA, Lawrence TS, et al. (2003). Evaluation of cancer therapy using diffusion magnetic resonance imaging. Mol Cancer Ther, 2:581–587. 165. Koh D, Collins DJ (2007). Diffusion-weighted MRI in the body: applications and challenges in oncology. Am J Roentgenol, 188:1622–1635. 166. Rohde G, Barnett A, Basser P, Marenco S, Pierpaoli C (2004). Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn Reson Med, 51:103–114. 167. Pipe JG, Zwart N (2006). Turboprop: improved PROPELLER imaging. Magn Reson Med, 55:380–385. 168. de Langen AJ, van den Boogaart VEM, Marcus JT, Lubberink M (2008). Use of H215O-PET and DCE-MRI to measure tumor blood ﬂow. Oncologist, 13:631–644. 169. Goh V, Padhani A (2006). Imaging tumor angiogenesis: functional assessment using MDCT or MRI? Abdominal Imaging, 31:194–199. 170. Miles KA (2002). Functional computed tomography in oncology. Eur J Cancer, 38:2079–2084. 171. Lamuraglia M, Escudier B, Chami L, et al. (2006). To predict progression-free survival and overall survival in metastatic renal cancer treated with sorafenib: pilot study using dynamic contrast-enhanced Doppler ultrasound. Eur J Cancer, 42:2472–2479. 172. Roufﬁac V, Bouquet C, Lassau N, et al. (2004). Validation of a new method for quantifying in vivo murine tumor necrosis by sonography. Invest Radiol, 39:350–356. 173. Jakobsen JÅ, Oyen R, Thomsen HS, Morcos SK (2005). Safety of ultrasound contrast agents. Eur Radiol, 15:941–945. 174. Lassau N, Chami L, Benatsou B, Peronneau P, Roche A (2007). Dynamic contrast-enhanced ultrasonography (DCE-US) with quantiﬁcation of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. Eur Radiol Suppl, 17:89–98. 175. Soret M, Bacharach SL, Buvat I (2007). Partial-volume effect in PET tumor imaging. J Nucl Med, 48:932–945. 176. Bacharach SL, Libutti SK, Carrasquillo JA (2000). Measuring tumor blood ﬂow with H215O: practical considerations. Nucl Med Biol, 27:671–676. 177. Evelhoch J, Garwood M, Vigneron D, et al. (2005). Expanding the use of magnetic resonance in the assessment of tumor response to therapy: workshop report. Cancer Res, 65:7041–7044. 178. Frank R (2008). Quantitative Imaging Biomarkers Alliance FDG-PET/CT Working Group Report. Mol Imaging Biol, 10:305. 179. Woodcock J (2005). The Critical Path Initiative: one year later. http://www.fda. gov/CDER/REGULATORY/medImaging/woodcock.ppt. 180. The Biomarkers Consortium (2008). http://www.biomarkersconsortium.org. 181. Altar C (2008). The Biomarkers Consortium: on the critical path of drug discovery. Clin Pharmacol Ther, 83:361–364.

REFERENCES

99

182. Foundation for the National Institutes of Health (2008). Biomarkers: FDG-PET lung and lymphoma. http://www.fnih.org/index.php?option=com_content&task= view&id=503&Itemid=638. 183. Foundation for the National Institutes of Health (2008). Biomarkers: carotid MRI reproducibility study. http://www.fnih.org/index.php?option=com_content &task=view&id=489&Itemid=600. 184. Nevitt MC (2007). Osteoarthritis Initiative (OAI): Design, subject characteristics, data and images. http://www.oai.ucsf.edu/datarelease/docs/presentations/ oarsi12062007/Nevitt_OARSI2007.pdf. 185. OAI:Home (2008). http://www.oai.ucsf.edu/datarelease/. 186. Foundation for the National Institutes of Health–Alzheimer’s Disease Neuroimaging Initiative (2008). http://fnih.org/index.php?option=com_content& task=view&id=103&Itemid=227. 187. Alzheimer’s Disease Neuroimaging Initiative LONI–ADNI (2008). http://www. loni.ucla.edu/ADNI/. 188. Maurer SM, Scotchmer S (2006). Open Source Software: The New Intellectual Property Paradigm. National Bureau of Economic Research Working Paper Series, no. 12148. http://www.nber.org/papers/w12148. 189. Rai AK (2004). Open and collaborative research: a new model for biomedicine. http://ssrn.com/paper=574863. 190. Maurer SM (2007). Open source drug discovery: ﬁnding a niche (or maybe several). http://ssrn.com/paper=1114371. 191. Munos B (2006). Can open-source R&D reinvigorate drug research? Nat Rev Drug Discov, 5:723–729. 192. Eschenbach ACV, Buetow K (2006). Cancer Informatics Vision: caBIG. Cancer Inf, 2:22–24. 193. Piwowar HA, Becich MJ, Bilofsky H, Crowley RS (2008). Towards a data sharing culture: recommendations for leadership from academic health centers. PLoS Med, 5:e183 EP. 194. Prior FW, Erickson BJ, Tarbox L (2007). Open Source Software Projects of the caBIG In Vivo Imaging Workspace Software Special Interest Group. J Digital Imaging, 20:94–100. 195. Jezzard P, Ramsey NF (2003). Functional MRI. In Tofts P (ed.), Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Wiley, Hoboken, NJ, pp. 415–453. 196. Börnert P, Keupp J, Eggers H, Aldefeld B (2007). Whole-body 3D water/fat resolved continuously moving table imaging. J Magn Reson Imaging, 25:660–665. 197. St Pierre TG, Clark PR, Chua-anusorn W, et al. (2005). Noninvasive measurement and imaging of liver iron concentrations using proton magnetic resonance. Blood, 105:855–861. 198. Stafford RJ, Hazle JD (2006). Magnetic resonance temperature imaging for focused ultrasound surgery: a review. Top Magn Reson Imaging, 17:153–163. 199. Zhou J, Payen J, Wilson DA, Traystman RJ, van Zijl PCM (2003). Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med, 9:1085–1090.

100

OPPORTUNITIES AND CHALLENGES

200. Woods M, Woessner DE, Sherry AD (2006). Paramagnetic lanthanide complexes as PARACEST agents for medical imaging. Chem Soc Rev, 35:500–511. 201. van Zijl PCM, Jones CK, Ren J, Malloy CR, Sherry AD (2007). MRI detection of glycogen in vivo by using chemical exchange saturation transfer imaging (glycoCEST). Proc Natl Acad Sci USA, 104:4359–4364. 202. Eisenhauer EA, Therasse P, Bogaerts J et al. (2009) New response evaluation criteria in solid tumors: revised RECIST guideline (version 1.1). Eur J Cancer, 45:228–247.

5 PROTEIN BIOMARKER DISCOVERY USING MASS SPECTROMETRY– BASED PROTEOMICS Joanna M. Hunter, Ph.D., and Daniel Chelsky, Ph.D. Caprion Proteomics, Inc., Montreal, Quebec, Canada

INTRODUCTION Recent advances in biological sample preparation, automated sample handling, and sensitive mass spectrometry with a variety of sample ionization sources have catalyzed the emergence of these methods as tools for clinical proteomic analyses [1,2]. Concurrently, proteomic data analysis has been aided by the expansion and improvement of publicly available protein and gene databases. Among the methods reported for quantitative comparison of proteins of biological origin, several techniques based on liquid-phase separation of proteins and peptides rather than gel electrophoresis have been optimized and implemented within the last few years. These include stable isotope labeling as well as label-free methods [3–9]. Direct measurement and comparison of unlabeled peptide ion peak intensities has become the standard, particularly for high-throughput applications. Quantitative, label-free mass spectrometry–based methods for protein expression proﬁling include those based on pattern recognition [10,11], peptide counting [12–14], and peptide ion intensity or area [4,5,15–20]. Of these, the latter seems to be the most robust and applicable to clinical studies. The correlation between observed peptide intensity differences and protein abundance differences is the fundamental basis of label-free protein expression proﬁling. In particular, the relaBiomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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tionship between liquid chromatography–mass spectrometry (LC-MS) signals and peptide abundance has been shown to vary linearly with concentration, even in complex samples [5,21–23]. Mass spectrometry–based protein expression proﬁling, without the use of labels or internal standards, is applicable to a wide range of sample types, including tissues, cells, organelles, and organisms, as well as body ﬂuids such as plasma, cerebrospinal ﬂuid, and urine. It has the additional advantage that comparisons can be made post hoc on a per patient basis, thereby dramatically increasing the granularity of information that can be obtained from the data. Multiple approaches can be taken for the label-free identiﬁcation of differentially expressed proteins in large sample sets. At Caprion we have developed a speciﬁc approach, called CellCarta, which has proven to be very effective and reliable. Features of this platform will be used as an example in describing issues and solutions for biomarker discovery. The platform has low overall variability [<15% coefﬁcient of variation (CV) in the absence of biological variability] and provides an accurate and quantitative measure of the biological modulation of protein abundance. Thus, statistically signiﬁcant differences in plasma protein abundance can be detected, making this technology highly applicable to biomarker discovery in clinical samples. Plasma markers of disease, drug reversion of disease, predictive markers of drug response, pharmacodynamic markers of drug action, and early markers of both efﬁcacy and toxicity can be detected using this technology. Studies may be conducted in animal models (preclinical) as well as with human clinical samples [24–26]. One requirement for a useful biomarker is that it can be measured in an easily accessible body ﬂuid. In this regard, blood (serum or plasma) is preferred, and it is used routinely for many clinical assays [27]. However, the discovery of biomarkers in blood brings some technical challenges. Plasma has been estimated to contain anywhere from several hundred to several thousand proteins [28,29], whose concentrations are known to vary by up to 10 orders of magnitude. Many of the proteins with desirable biomarker characteristics are expected to be at the low end of the plasma protein dynamic range of concentration, typically at or below 100 ng/mL. Two approaches to this issue are described here. One approach depends on the immunoafﬁnity-based depletion of some of the most abundant plasma proteins, thus making it possible to see more of the lower-abundance proteins. The second approach is based on identifying and comparing plasma proteins just prior to release from their cells or tissue of origin. While residing in the secretory apparatus, proteins destined for the blood are highly concentrated, thus allowing for a comprehensive picture of tissue-speciﬁc markers.

PROTEIN PROFILING PLATFORM FOR BIOMARKER DISCOVERY One of the major challenges of biomarker discovery is the execution of unbiased large-scale studies to avoid the confusion of process artifacts with actual

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biomarkers. Unbiased experimental design and study implementation are arguably among the most critical components of a differential protein expression platform [30]. Because subtle errors in a work plan can result in large biases, careful study planning and implementation are necessary in order to discover biomarkers that are robust to population variation, sample collection variability, processing artifacts, and other confounding factors. The ﬁrst step in setting up a study is to clearly deﬁne the questions that must be addressed. From these goals, the minimum number of patients in each condition to be compared can be ﬁxed. Although the group sizes can be small from a purely analytical perspective, the number of samples required is dictated by the possibility of confounding factors within the target group and/or by the need to sample a diverse target population. Multiple comparisons across large sample sets are achievable and fully exploit the analytical platform. In addition, the order in which samples are processed and analyzed must be considered. Depending on the comparisons to be made, samples must be interleaved and/ or randomized between processing steps. Alternatively, the samples to be compared are block randomized, with comparisons made within each block, thus ensuring the greatest processing consistency within a comparison. After samples have been processed, data analysis techniques are employed to ensure that biases have not occurred. If biases appear, appropriate sample pairing and statistical tests are utilized to ensure that the biases do not inﬂuence the conclusions drawn from the study. To generate highly reproducible results, stringent control of sample preparation, analysis, and data normalization procedures are required. Low peptide signal intensity variation across study samples is required to detect statistically signiﬁcant and subtle protein changes against background variation. Protocols followed rigorously, including quality control checks, are necessary throughout the study, from sample collection to data analysis. For example, strictly following a standard operating procedure (SOP) for blood collection and generation of plasma enables blood samples to be collected from multiple sites for the same study. Furthermore, a well-documented sample “chain of custody” is obligatory when handling clinical samples. As part of this documentation process, samples are barcoded, logged into a laboratory information management system (LIMS), and stored in locked freezers. All subsequent steps in sample handling and analysis are monitored and logged, and all data are secured. Thus, SOPs and quality control procedures ensure sample integrity throughout the process. Speciﬁc features of the CellCarta work ﬂow are detailed below. Focusing on the Relevant Proteome Without enrichment of a targeted proteome, samples contain far too many proteins to be visualized in a reasonable number of analytical runs, and only the most abundant proteins are detected. Enrichment of subproteomes is necessary to reduce the sample complexity and to decrease the limit of detection. More comprehensive protein identiﬁcation as well as information on

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subcellular localization is obtained by isolating organelles such as plasma membranes, endosomes, phagosomes, mitochondria, endoplasmic reticulum, nuclei, or Golgi apparatus from cells or tissue homogenates. Similarly, blood (plasma or serum) is enriched for lower abundance proteins by the removal of high-abundance proteins. For example, depletion of the 14 most abundant proteins using the Multiple Afﬁnity Removal System (MARS, Agilent Technologies) removes over 96% of total protein and consequently enhances the detection of the remaining lower-abundance components [31,32]. Various depletion approaches are being used to get deeper into the plasma proteome [33]. The key criteria for implementing enrichment or depletion strategies are that they provide minimal contamination of the nontargeted proteins and are highly reproducible. Sample Fractionation Regardless of the origin of the puriﬁed protein samples, the complexity is typically too great—both in terms of the number of proteins and the dynamic range of concentration—for direct analysis by mass spectrometry. Reversedphase liquid chromatography coupled to mass spectrometry (LC-MS) can reproducibly distinguish and quantify over 5000 peptides in an hour-long analytical run. A single LC-MS injection is therefore suitable for samples comprised of some 500 to 1000 proteins. Subproteome enrichment typically reduces the total number of proteins to fewer than 10,000. To characterize these complex samples more comprehensively, a fractionation step must be applied before the LC-MS. At the protein level, separations such as strong anion exchange provide effective fractionation prior to trypsin digestion and LC-MS. Alternatively, the proteins are ﬁrst proteolyzed to tryptic peptides, with subsequent separation of the peptides by strong cation exchange (SCX) liquid chromatography [34]. The CellCarta work ﬂow employs this method. Differential Protein Expression by LC-MS For optimal matching of peptide signals across many samples, the LC-MS platform must be as reproducible as possible (Figure 1). Technically, the requirements of stable chromatography, high resolution, and mass accuracy may be addressed by a variety of instruments. Peptide proﬁling takes place on mass spectrometers that are capable of providing sensitive, high-mass-accuracy measurements, such as time-of-ﬂight or high-resolution ion-trapping instruments. Peptide sequencing is performed on quadrupole-time-of-ﬂight instruments such as the QSTAR (Applied Biosystems) or the QTOF (Waters), or on an ion trap such as the LTQ-Orbitrap (ThermoFisher), which are sensitive and can provide high-quality MS/MS spectra. Regardless of the mass spectrometer type, capillary reversed-phase LC columns are coupled to these instruments via an electrospray interface. The CellCarta mass spectrometry platform utilizes capillary liquid chromatography (CapLC) and QTOF LC-MS

PROTEIN PROFILING PLATFORM FOR BIOMARKER DISCOVERY

m/z

m/z

RT (min) RT (min) Increasing intensity of selected ion

Human Plasma 3

m/z

Human Plasma 2

Human Plasma 1

105

RT (min)

Figure 1 Consistent peptide intensity across all samples allows the detection of differentially expressed peptide ions. Shown is a partial view of peptide ion maps (as measured by LC-MS) from the plasma of three individuals. The horizontal axis is chromatographic retention time, the vertical axis is mass-to-charge ratio (m/z), and the peptide ion intensity is denoted by the size and color of the spots. The peptide ion circled shows differential expression across patients and increases in abundance from sample 1 to sample 3. (See insert for color reproduction of the ﬁgure.)

systems (Waters). The LC-MS measurements are reliable and reproducible, providing a median CV of peak intensity of 8 to 9% for peptides matched across six replicate injections of a standard sample. To quantify the differentially expressed peptides, a suite of proprietary bioinformatics tools was developed at Caprion and implemented into CellCarta. Matching peptides across large sample sets (peak alignment) is the ﬁrst step in the differential expression analysis (Figure 2). Algorithms to perform mass-to-charge ratio (m/z) and chromatographic retention-time alignment result in the conﬁdent detection of signiﬁcant peptide intensity differences between samples. Peptide ions are detected and matched across all the samples in the study. Each study peptide is characterized by m/z, charge, retention time, and intensity. Bioinformatics software tools map peptides across all samples, comparing ion intensity for all reproducible peptide ions across all fractions. Those peptides that show a statistically signiﬁcant differential abundance are targeted for protein identiﬁcation. Protein Identiﬁcation Identiﬁcation of the candidate protein biomarkers is accomplished using two different and complementary tools. First, database searching of LC-MS/MS spectra for peptide identiﬁcation is accomplished using Mascot (MatrixScience), which searches for the best ﬁt between the recorded spectra and theoretical spectra calculated from tryptic peptides from proteins in the reference

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855 850 845

m/z

840 835 830 825 820 815 27

27.5

28

28.5 29 Retention time

29.5

30

Figure 2 Related peptide ions are clustered across the entire study. High conﬁdence matching is achieved, independent of small variations in mass and retention time detected. Each symbol represents a detected ion in one sample, plotted at the observed mass-to-charge ratio and chromatographic retention time.

database. For human plasma studies, the human International Protein Index (IPI; European Bioinformatics Institute) is searched. For samples not of human origin, other public databases may be used, depending on the species. The search results are parsed based on peptide and protein score thresholds that are determined by setting an acceptable false-positive rate of identiﬁcation calculated by searching a randomized database [35]. In addition, we have developed novel software that adds a differential abundance correlation ﬁlter to mass and retention-time ﬁngerprinting [virtual mass spectrometry (VMS)]. Protein identiﬁcation using mass and retentiontime ﬁngerprinting alone suffers from the high level of mass redundancy for peptides from complex species. VMS incorporates the protein identiﬁcation and peptide expression resolver (PIPER) ﬁlter, which requires all peptides pointing to the same protein to share the same relative intensity across all samples, a logical requirement for association. Application of the PIPER ﬁlter to the identiﬁed peptides results in false-positive protein identiﬁcation rates below 5% [36]. This method provides for the identiﬁcation of proteins where the peptide ions can be detected but are not sufﬁciently intense to provide an interpretable MS/MS fragmentation pattern [37]. In addition to adding new protein identiﬁcations, VMS can also be used to supplement LC-MS/MS by

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increasing conﬁdence in low-scoring sequence matches or single-peptide identiﬁcations. Thus, the combined use of LC-MS/MS sequencing with VMS greatly expands the ability to identify and quantify novel proteins by expanding the dynamic range (number and concentration) of identiﬁable proteins as well as the conﬁdence (number of identiﬁed peptides per protein) of protein identiﬁcation. Candidate Biomarker Results Visualization Proteomics analyses can often generate large data sets, including all of the associated peptide sequence and expression data. To enable rapid evaluation and interpretation of results, it is very helpful to organize the output into a searchable database with a specialized but simple user interface. We have created such a tool that is Java-based and accessible on a secure Web portal. The Data Report provides query, ﬁltering, and data analysis capabilities as well as visualization of peptide differential expression and protein identiﬁcation across all of the samples in the study. Included in the Data Report are listings of all the peptides, protein accession numbers, and gene names identiﬁed, along with differential expression information for each. Graphical displays of peptide intensities, differential expression, and MS/MS spectra are also available (Figure 3). In addition, selected data can be exported into standard formats. Candidate Biomarker Panels Historically, circulating biomarkers have been deﬁned by a single analyte that can be used to distinguish given groups of individuals with high speciﬁcity and sensitivity (positive and negative predictive values). In the last two decades, the development of high-throughput multivariate analytical platforms, such as protein and gene chips, has enabled the identiﬁcation and validation of multivariate biomarkers. Due to population variation and the multitude of effects that treatment can have within a person and across the population, it is acknowledged that panels of proteins are likely to produce a biomarker that is signiﬁcantly more sensitive and speciﬁc than any single protein alone [38]. In a typical biomarker discovery study performed on the CellCarta platform, hundreds of proteins are found to be signiﬁcantly differentially modulated. These proteins are then prioritized into panels of 3 to 10 proteins. This process involves ﬁltering based on several criteria, including diagnostic strength [i.e., the area under the receiver operator characteristic (ROC) curve or AUC] and linear regression to clinical factors. In particular, the panel of proteins must have a high composite AUC in addition to strong individual protein AUC scores. Biomarker panels with high discriminating power (positive and negative predictive values) can thus be composed from the individual biomarker candidates. The number of protein candidates included in the panel

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Figure 3 Caprion Data Report user interface. An example Data Report screenshot is shown. The upper left panel contains a summary of the proteomic data for the proteins selected by the user. The upper right panel contains several tabs that permit additional details to be displayed, including a hierarchical clustering of the peptides. The lower left panel contains search options, while the lower right panel contains several tabs that provide additional proteomic data, such as peptide intensities, differential expression, and MS/MS spectra.

represents a trade-off between cost and risk. Although larger protein panels are more costly to verify, they are more resilient to population variability. Prioritized candidate biomarkers are then qualiﬁed and veriﬁed further using immunoassays such as enzyme-linked immunosorbent assay (ELISA) or mass spectrometry–based assays such as multiple reaction monitoring (MRM) [39,40].

PROTEIN BIOMARKER DISCOVERY IN BLOOD PLASMA The CellCarta platform enables the measurement of biological changes of interest in human plasma proﬁling studies for protein biomarker discovery. The following examples illustrate that protein biomarkers can be found in plasma using a highly controlled proteomics technology platform and employing an appropriate study design.

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109

Global Proteomics Applied to Alzheimer Disease A collaboration with Hyman Schipper, a neuropsychologist at the McGill University Memory Clinic, part of the Jewish General Hospital (JGH; Montreal, Canada), was undertaken to investigate the effect of drug treatment on Alzheimer disease (AD) patients. Plasma was collected from patients after obtaining written informed consent and with approval of the research and ethics committee of the JGH. Included in the study were 33 age-matched healthy control, 19 untreated AD, and 25 donepezil-treated AD patients. Each patient was administered the Folstein Mini-Mental State Examination (MMSE), a qualitative test of disease severity [41]. In addition, clinical histories were obtained. In the course of the study, a “global proteomics” approach to data visualization and analysis was developed and implemented. Following unsupervised clustering by multidimensional scaling (MDS), the untreated disease group was found to cluster apart from the healthy group (Figure 4). Subsequently, a centroid (median value in three dimensions) was calculated for each group. The line intersecting the normal and disease group centroids is deﬁned as the

DonepezilTreated Patients

Healthy Controls

Figure 4 Global proteomics analysis on Alzheimer disease and normal patients. Multidimensional scaling of proteomics data demonstrates the separation of healthy individuals (green spheres) from Alzheimer patients (red spheres). Caprion has deﬁned a disease axis that is used to quantify relative disease state. The axis is a line that passes through the disease and healthy centroids (yellow spheres). Each patient is then positioned on the axis according to its orthogonal intercept. Donepezil-treated Alzheimer patients (purple spheres), as a group, are shifted on the disease axis from the disease group toward the healthy group. (See insert for color reproduction of the ﬁgure.)

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PROTEIN BIOMARKER DISCOVERY USING MS–BASED PROTEOMICS

disease axis (Figure 4). The disease severity for each patient is indicated by the distance between its orthogonal intercept with the disease axis and the normal centroid. The disease severity of each patient, as determined by this approach, correlated well with the patient MMSE score, with a Pearson correlation coefﬁcient of 0.75. When the relative position on the disease axis was matched to relative peptide intensity, 282 peptides were found to correlate with the disease severity proﬁle. The proteins corresponding to these peptides were subsequently identiﬁed. Although there is currently no blood-based diagnostic test for AD, these results suggest that a test monitoring blood protein levels could measure AD severity. The global proteomics approach was applied to assess the effect on AD patients of treatment with donepezil. A total of 75 peptides were found to be highly correlated with the disease severity proﬁle of treated versus untreated AD patients. These peptides are a subset of the 282 disease-related peptides. The CellCarta platform can thus be employed as an assay that enables the analysis of drug response for patient segregation. As an extension, pharmacodynamic analyses such as dose optimization and drug efﬁcacy could be performed using this method. This study demonstrates that global proteomics can be an effective approach to pharmacodynamic biomarker identiﬁcation. Oncology Biomarkers: Blinded Case Study The goal of this second study was to identify circulating markers of ovarian and breast cancer. Two sets of patient samples were examined. The ﬁrst set was composed of eight samples each from patients with breast cancer or ovarian cancer as well as eight matched healthy subjects. The second set of samples had the same composition, but the identities were blinded to Caprion. All samples were processed concomitantly though the CellCarta platform. Of the 53,628 peptide ions detected across the study, 4089 were differentially modulated reproducibly and signiﬁcantly (p < 0.005) in one of the three cohorts. These peptides were further analyzed by the bioinformatics tool, multidimensional scaling, to determine the relationship between each of the patients at the global proteomics level. Results, shown in Figure 5, indicate that each of the three groups of samples cluster separately. The samples in each group are therefore more similar to other members of the same group than to members of other groups. This similarity within a group translates to peptides (and proteins) that distinguish and separate each group. The behavior of the peptides that were found to distinguish each of the three groups was analyzed for each of the 24 blinded samples. Each sample was therefore categorized according to which of the three groups it was most related. When the samples were unblinded, it was determined that 22 of the 24 assignments were correct. Only one pair, consisting of a normal and a breast cancer plasma sample, was assigned incorrectly. In this example, all differentially expressed peptides were used to discriminate the three groups in order to maximize the ability of the small learning

PROTEIN BIOMARKER DISCOVERY IN BLOOD PLASMA

111

Multidimensional Scaling (Log Transform OFF) Ovarian Breast Normal

MDS 3

MDS 1

MDS 2

Figure 5 Sample groups distinguished by differentially expressed peptides. Multidimensional scaling analysis was performed using the intensity values for 4089 differentially expressed peptide ions from 24 samples. Separation along three axes of variance (MDS1 to MDS3) is shown, where each sphere represents a patient sample. The groups are identiﬁed by the colors indicated. (See insert for color reproduction of the ﬁgure.)

set (ﬁrst 24 samples) to determine the identity of the blinded sample test set. None of the individual peptide ions could discriminate the three groups very well. However, sets of ﬁve or 10 peptides were much more effective, as shown in the receiver operating characteristic (ROC) plots in Figure 6. In the case of multiple peptide analysis, peptides were chosen randomly from the population of differentially expressed peptides in order to provide multiple examples of the range of discrimination possible. The AUC required to fully discriminate groups is a value of 1. This was most frequently achieved as the panel size increased from one to 10 proteins (Figure 6, right panels). To better understand the biology underlying the differentially expressed peptides, their parent proteins were identiﬁed. This was accomplished by LC-MS/MS and VMS to identify groups of peptides representing a single protein. This result was further ﬁltered by requiring that the relative intensity of a minimum of three peptide ions be well correlated across all samples. Approximately 200 proteins met these and other strict conﬁdence criteria. Half of these proteins distinguished either breast cancer or ovarian cancer from the other two conditions. The remaining half of the proteins distinguished cancer (breast or ovarian) from normal healthy subjects. These results suggest that circulating blood proteins provide a rich source of high-quality

112 True Positive 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1 0 0 0 0 0 0 0 0 0

0.2 0.4 0.6

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100

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300

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450 400 350 300 250 200 150 100 50 0 –0.2 400

Figure 6 Multiple peptide panels are effective at discriminating groups. ROC plots show improved performance when going from one to ﬁve to 10 peptides. Displayed are curves (left) representing true-positive (x-axis) and false-positive (y-axis) ratios and the area under the curve (AUC; right column) for single-peptide (top), ﬁve-peptide (middle) and 10-peptide panels (bottom). In the case of multiple-peptide analysis, peptides were chosen randomly from the population of differentially expressed peptides. The optimal AUC for discriminating groups is a value of 1. This was best achieved as the panel size increased. (See insert for color reproduction of the ﬁgure.)

the median AUC panels of size 5 is under 0.4.

* Starting with random sets of 890 peptides,

(1000 random combinations)

10-peptide panel

(1000 random combinations)

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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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disease biomarkers that can be discovered using mass spectrometry–based protein expression proﬁling.

PROTEIN BIOMARKER DISCOVERY IN THE SECRETORY APPARATUS Secreted Proteins as Biomarkers In the quest for circulating biomarkers of disease or drug response, much effort has been made to avoid the overwhelming effect of high-abundance proteins in the blood that obscure lower abundance and potentially more interesting proteins. Removal of the high-abundance proteins with multiple afﬁnity antibody columns has been very effective in this regard. An alternative approach that has been successful as well is to isolate plasma proteins as they are being released from the source tissues or cells. One common such method involves harvesting proteins released by tissue culture cells into conditioned media [42,43]. Although this approach has been productive, proteins may be lost due to dilution or adherence to the culture container, and it is generally limited to cultured cells rather than whole tissue. Analysis of tissues can provide a more physiological context, as they include primary cells as well as the natural composition of multiple cell types that may modulate secretion. Tissues can also be taken from the host, typically animal models of disease, following drug treatment, in order to assess the impact of treatment on the secretome. This can lead to a better understanding of the mechanism of action of the drug treatment as well as the identiﬁcation of possible markers of therapeutic response. Proteins released from tissue into culture medium have also been studied [44], but contamination from lysed cells and culture medium can obscure the actual proteins secreted. Reliance on those proteins with a signal sequence as well as isotopic labeling of de novo synthesized proteins from the tissue are usually necessary. An alternative approach to identifying and quantifying secreted proteins relies on the isolation of such proteins directly from the secretory pathway of the cells or tissue. The contents of the Golgi and related secretory vesicles provide a highly concentrated collection of proteins, just prior to their release from the cell [45,46]. This method avoids dilution into the plasma, which would obscure all but the more abundant secreted proteins. Dilution as well as contamination by cell lysis proteins in culture medium is also eliminated as a concern. Tissues can be as easily investigated as cultured cells, with the added advantage that the samples do not need to be viable at the time of analysis. Thus, frozen surgical samples from patients or animal models are amenable to study and comparison, even when samples are collected at different time points. To isolate secretory proteins directly from cell culture or tissue, the homogenized samples are separated on a density gradient to isolate secretory vesicles.

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Secreted Proteins Differentially Expressed in Prostate Cancer The prostate secretes a range of proteins related to its function, including proteolytic enzymes, acid phosphatase (ACPP), and prostate-speciﬁc antigen (PSA). In prostate tumors, some of these proteins are known to increase or decrease in the amount secreted. To test the application of secretory vesicle isolation to frozen human surgical specimens, samples were obtained from patients with prostate cancer, with institutional review body (IRB) approval and patient consent. Tumor and normal tissue were separated as described [47] and the content of the secretory apparatus was isolated. Proteins were digested with trypsin and analyzed by LC-MS in order to compare expression between each normal and tumor pair from all six patients. Signiﬁcantly differentially expressed peptides were identiﬁed. Four of the best known prostate cancer–associated secreted proteins were found to be up-regulated, as expected, in the tumors. PSA, ACPP, kallekrin 2 (KLK2), and macrophage migration inhibitory factor (MIF) are all low-abundance proteins in the blood, at levels ranging from 0.2 to 3.5 ng/mL in normal individuals. Detection of these proteins by direct LC-MS analysis of plasma would not be possible, yet was readily accomplished by examination of the secretory pathway. Two of these proteins, PSA and MIF, were also evaluated by commercial ELISA in the plasma of the same patients. The levels observed in the secretory vesicles by unlabeled mass spectrometry were directly comparable to levels in the plasma from the same patients, with correlation coefﬁcients of 0.69 and 0.74, respectively [46]. Thus, analysis of differentially expressed proteins in the secretory apparatus is predictive of relative expression in the plasma. This correlation means that discovery can be done in the highly concentrated milieu of the vesicles, while veriﬁcation and validation studies can be conducted with more sensitive antibody-based assays directly in blood. In addition to the well-known prostate cancer markers, other known cancer-related proteins were found, for a total of 40 proteins with known cancer association. A further 20 proteins were identiﬁed as differentially expressed in the tumors that were not previously known to have a cancer association. The known function of the proteins typically ﬁt well with their observed expression patterns. For example, of 11 proteins involved with sugar metabolism, all were found to go up in the tumors. On the other hand, of nine proteins involved with contraction and adhesion, six were found to be expressed at lower levels in the tumor samples than in the matching normal tissue.

Proteins Secreted by Visceral Adipose Tissue Visceral adipose tissue is much more than a storage medium for excess energy. This realization came with the discovery in 1994 of leptin and its wide-ranging activities, which include reduction of appetite, angiogenesis, hematopoiesis, and bone formation [48–50]. Visceral adipose tissue is now recognized as an endocrine organ that releases a large number of biologically active molecules

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called adipokines, which have pleiotropic effects on a variety of metabolic pathways [48,51]. Proteins released from adipose tissue include regulators of inﬂammation, inducers of angiogenesis, and modulators of hypertension, among others. Visceral adipose tissue, in particular, has been implicated in the release of proteins responsible for metabolic syndrome and type 2 diabetes [52]. For this reason, evaluation of visceral adipose tissue, either alone or in comparison to subcutaneous adipose tissue, is of great interest [52,53]. As a pilot study of the eventual comparison of proteins secreted from visceral and subcutaneous adipose tissue, visceral adipose tissue was obtained from three obese patients undergoing gastric bypass surgery. The contents of the secretory apparatus were isolated from each subject and analyzed by LC-MS/MS to determine feasibility and yield, as well as to catalog the proteins and verify that known adipocyte-secreted proteins could be found. In a limited analysis, 155 proteins were identiﬁed, including many of the well-known adipokines. Among the proteins identiﬁed were cytokines (TGFb1; IL 25), acutephase proteins (SPARC; lipocalin 2/NGAL), proteins that regulate blood pressure (angiotensinogen; ARTS-1), regulators of lipid metabolism (retinolbinding protein 4; lipoprotein lipase), and signaling molecules (galectin-1; prohibitin). Only 65 of the proteins were also found in one of three secretedprotein databases [54–56]. Although this could suggest that some of the proteins are contaminants, it could also point to the possibility that many secreted proteins are missed by conventional discovery methods that typically screen for proteins with an N-terminal signal sequence [44,57]. Some well-known

TABLE 1 Five Proteins Found in the Secretory Apparatus of Human Visceral Adipose Tissue That Are Targets for Therapies in Clinical Trials Target Leucocyte elastase Transforming growth factor-b-1 Clusterin

Transthyretin (TTR)

Condition

Drug

MoA

Phase

Acute respiratory distress syndrome Pulmonary ﬁbrosis

Depelestat

Inhibitor of elastase activity

II

GC1008

Neutralizing antibody

I

Lung and prostate cancers Familial amyloidosis

OGX-011

Inhibition of gene expression

II

Diﬂunisal

Stabilization of TTR to prevent formation of amyloid ﬁbrils Gene therapy

III

Pigment epithe- Macular lium-derived degeneraprotein tion

AdGVPEDF.11D

I

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secreted proteins, including FGF-1, FGF-2, IL-1, and galectins, do not have a classical N-terminal signal sequence [58]. Direct detection of these nonclassical secreted proteins will be an important approach to expanding on the secretome as well as validating the predictive tools currently under development for these proteins, such as SecretomeP [59]. An important aspect of studying the contents of the secretory apparatus is the ability to identify low-abundance secreted proteins that would be difﬁcult to detect without speciﬁc antibodies, such as IL-25, TGFb1, and lipocalin 2. Also important is that the proteins identiﬁed have important biological functions that could eventually aid in the discovery of novel approaches to therapeutics. Of interest, therefore, is the observation that ﬁve of the proteins identiﬁed in the pilot adipose tissue study are current targets of therapies in clinical trials (Table 1). These therapies are not necessarily involved in adipose-related diseases, but still serve to demonstrate that proteins identiﬁed by this approach include those that are important to drug discovery.

SUMMARY The global proteomics approach for candidate biomarker discovery using label-free mass spectrometry-based methods is able to detect signiﬁcantly differentially expressed proteins in a variety of biological systems. An advantage of this technology is that it enables varied study designs involving comparisons between large cohorts or multiple experimental conditions. For example, pharmacodynamic markers of drug treatment, markers of disease severity, or predictive markers of efﬁcacy may be discovered, depending on the study design. Candidate biomarkers are combined into panels having sufﬁcient discriminating power (positive and negative predictive values) to provide meaningful diagnostic or prognostic information and perform better than single-protein biomarkers against broad populations. Consequently, these high-conﬁdence panels provide biomarkers that appear suitable for development into high-throughput screening assays. Multiple approaches to identifying lower-abundance proteins in plasma have demonstrated value, including antibody depletion of high-abundance proteins as well as the analysis of the contents of the secretory apparatus. These two techniques are seen as complementary in terms of the plasma protein concentration range interrogated. Typically, the limit of detection of direct analysis of plasma or serum proteins is approximately 10 to 100 ng/mL, which has proven to be a very productive range for candidate biomarker identiﬁcation. The blood concentrations of proteins secreted from Golgi lumen are approximately two orders of magnitude below this, allowing for an even more comprehensive view of the low-abundance tissue-speciﬁc blood proteome. Further improvements are expected in the future, but clearly the tools now exist to identify novel and useful biomarkers of disease and drug response.

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REFERENCES 1. Marko-Varga G, Lindberg H, Löfdahl CG, et al. (2005). Discovery of biomarker candidates within disease by protein proﬁling: principles and concepts. J Proteome Res, 4:1200–1212. 2. Engwegen JY, Gast MC, Schellens JH, Beijnen JH (2006). Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry. Trends Pharmacol Sci, 27:251–259. 3. Julka S, Regnier FJ (2005). Recent advancements in differential proteomics based on stable isotope coding. Brief Funct Genom Proteom, 4:158–177. 4. Silva JC, Denny R, Dorschel CA, et al. (2005). Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem, 77:2187–2200. 5. Wang W, Zhou H, Lin H, et al. (2003). Quantiﬁcation of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem, 75:4818–4826. 6. Wiener MC, Sachs JR, Deyanova EG, Yates NA (2004). Differential mass spectrometry: a label-free LC-MS method for ﬁnding signiﬁcant differences in complex peptide and protein mixtures. Anal Chem, 76:6085–6096. 7. Wang G, Wu WW, Zeng W, Chou CL, Shen RF (2006). Label-free protein quantiﬁcation using LC-coupled ion trap or FT mass spectrometry: reproducibility, linearity, and application with complex proteomes. J Proteome Res, 5:1214–1223. 8. DeSouza LV, Grigull J, Ghanny S, et al. (2007). Endometrial carcinoma biomarker discovery and veriﬁcation using differentially tagged clinical samples with multidimensional liquid chromatography and tandem mass spectrometry. Mol Cell Proteom, 6:1170–1182. 9. Wu WW, Wang G, Baek SJ, Shen RF (2006). Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel or LCMALDI TOF/TOF. J Proteome Res, 5:651–658. 10. Kislinger K, Gramolini AO, MacLennan DH, Emili A (2005). Multidimensional protein identiﬁcation technology (MudPIT): technical overview of a proﬁling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue. J Am Soc Mass Spectrom, 16:1207–1220. 11. Calvo KR, Liotta LA, Petricoin EF (2005). Clinical proteomics: from biomarker discovery and cell signaling proﬁles to individualized personal therapy. Biosci Rep, 25:107–125. 12. Gao J, Friedrichs MS, Dongre AR, Opiteck GJ (2005). Guidelines for the routine application of the peptide hits technique. J Am Soc Mass Spectrom, 16:1231–1238. 13. Old WM, Meyer-Arendt K, Aveline-Wolf L, et al. (2005). Comparison of labelfree methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteom, 4:1487–1502. 14. Lu P, Vogel C, Wang R, Yao X, Marcotte EM (2007). Absolute protein expression proﬁling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol, 25:117–124. 15. Patil ST, Higgs RE, Brandt JE, et al. (2007). Identifying pharmacodynamic protein markers of centrally active drugs in humans: a pilot study in a novel clinical model. J Proteome Res, 6:955–966.

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16. Roy SM, Becker C (2007). Quantiﬁcation of proteins and metabolites by mass spectrometry without isotopic labeling. Methods Mol Biol, 359:87–105. 17. Finney GL, Blackler AR, Hoopmann MR, Canterbury AD, Wu CC, MacCoss MJ (2008). Label-free comparative analysis of proteomics mixtures using chromatographic alignment of high-resolution LC-MS data. Anal Chem, 80(4):961–971. 18. Prakash A, Piening B, Whiteaker J, et al. (2007). Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics. Mol Cell Proteom, 6:1741–1748. 19. Fang R, Elias DA, Monroe ME, et al. (2006). Mol Cell Proteom, 5:714–725. 20. Gaspari M, Verhoeckx KCM, Verheij ER, van der Greef J (2006). Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples. Anal Chem, 78:2286–2296. 21. Follettie MT, Pinard M, Keith JC Jr, et al. (2006). Organ messenger ribonucleic acid and plasma proteome changes in the adjuvant-induced arthritis model: responses to disease induction and therapy with the estrogen receptor-b selective agonist ERB-041. Endocrinology, 147:714–723. 22. Chelius D, Bondarenko P (2002). Quantitative proﬁling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J Proteome Res, 1:317–323. 23. Lamontagne J, Butler H, Chaves-Olarte E, et al. (2007). Extensive cell envelope modulation is associated with virulence in Brucella abortus. J Proteome Res, 6:1519–1529. 24. Sonnen JA, Keene CD, Montine KS, et al. (2007). Biomarkers for Alzheimer’s disease. Expert Rev Neurother, 7:1021. 25. Maurya P, Meleady P, Dowling P, Clynes M (2007). Proteomic approaches for serum biomarker discovery in cancer. Anticancer Res, 27:1247. 26. Fu Q, Van Eyk JE (2006). Proteomics and heart disease: identifying biomarkers of clinical utility. Expert Rev Proteom, 3:237. 27. Burtis CA, Aswood EA, Burns DE (2005). Tietz Textbook of Clinical Chemistry. Elsevier Saunders, Philadelphia. 28. Anderson NL, Anderson NG (2002). The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteom, 1:845. 29. Thadikkaran L, Siegenthaler MA, Crettaz D, Queloz PA, Schneider P, Tissot JD (2005). Recent advances in blood-related proteomics. Proteomics, 5:3019–3034. 30. Hendriks MM, Smit S, Akkermans WL, et al. (2007). How to distinguish healthy from diseased? Classiﬁcation strategy for mass spectrometry-based clinical proteomics. Proteomics, 7:3672–3680. 31. Sitnikov D, Chan D, Thibaudeau E, Pinard M, Hunter JM (2006). Protein depletion from blood plasma using a volatile buffer. J Chromatogn B, 832:41–46. 32. Bjorhall K, Miliotis T, Davidsson P (2005). Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples. Proteomics, 5:307–317. 33. Whiteaker J, Zhang H, Eng JK, et al. (2007). Head-to-head comparison of serum fractionation techniques. J Proteome Res, 6:828–836. 34. Sitnikov D, Hunter JM, Hayward C, et al. (2007). Peptide shifter: enhancing separation reproducibility using correlated expression proﬁles. J Am Soc Mass Spectrom, 18:1638–1645.

REFERENCES

119

35. Peng J, Elias JE, Thoreen CC, Licklider LJ, Gygi SP (2003). Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LCMS/ MS) for large-scale protein analysis: The yeast proteome. J Proteome Res, 2:43–50. 36. Kearney P, Butler H, Eng K, Hugo P (2008). Harmonizing protein identiﬁcation with protein expression data. J Proteome Res, 7:234–244. 37. Lekpor K, Benoit MJ, Butler H, et al. (2007). An evaluation of multidimensional ﬁngerprinting in the context of clinical proteomics. Proteom Clin Appl, 1:457–466. 38. Rifai N, Gillette MA, Carr SA (2006). Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol, 24:971. 39. Barnidge DR, Goodmanson MK, Klee GC, Muddiman DC (2004). Absolute quantiﬁcation of the model biomarker prostate-speciﬁc antigen in serum by LC-MS/MS using protein cleavage and isotope dilution mass spectrometry. J Proteome Res, 3:644–652. 40. Bondar OP, Barnidge DR, Klee EW, Davis BJ, Klee GG (2007). LC-MS/MS quantiﬁcation of Zn-α2 glycoprotein: a potential serum biomarker for prostate cancer. Clin Chem, 53:673–678. 41. Folstein MF, Folstein SE, McHugh PR (1975). Mini-Mental State: A practical method for grading the state of patients for the clinician. J Psych Res, 12:189–198. 42. Kratchmarova I, Kalume DE, Blagoev B, et al. (2002). A proteomic approach for identiﬁcation of secreted proteins during the differentiation of 3T3-L1 preadipocytes to adipocytes. Mol Cell Proteom, 1:213–222. 43. Chen X, Cushman SW, Pannell LK, Hess S (2004). Quantitative proteomic analysis of the secretory proteins from rat adipose cells using 2D liquid chromatographyMS/MS approach. J Proteome Res, 4:570–577. 44. Alvarez-Llamas G, Szalowska E, de Vries MP, et al. (2007). Characterization of the human visceral adipose tissue secretome. Mol Cell Proteom, 6:589–600. 45. Lee MC, Miller EA, Goldberg J, Orci L, Schekman R (2004). Bi-directional protein transport between the ER and Golgi. Annu Rev Cell Dev Biol, 20: 87–123. 46. Lanoix J, Paramithiotis E (2008). Secretory vesicle analysis for discovery of low abundance plasma biomarkers. Expert Opin Med Diagn, 2(5):475–485. 47. Sircar K, Gaboury L, Ouadi L, et al. (2006). Isolation of human prostatic epithelial plasma membranes for proteomics using mirror image tissue banking of radical prostatectomy specimens. Clin Cancer Res, 12:4178–4184. 48. Yang R, Barouch LA (2007). Leptin signaling and obesity: cardiovascular consequences. Circ Res, 101:545–559. 49. Malendowicz LK, Rucinski M, Belloni AS, Ziolkowska A, Nussdorfer GG (2007). Leptin and the regulation of the hypothalamic–pituitary–adrenal axis. Int Rev Cytol, 263:63–102. 50. Louis GW, Myers MG (2007). The role of leptin in the regulation of neuroendocrine function and CNS development. Rev Endocr Metab Disord, 8:85–94. 51. Lago F, Dieguez C, Gomez-Reino J, Gualillo O (2007). Adipokines as emerging mediators of immune response and inﬂammation. Nat Clin Pract Rheumatol, 3:716–724.

120

PROTEIN BIOMARKER DISCOVERY USING MS–BASED PROTEOMICS

52. Iannucci CV, Capoccia D, Calabria M, Leonetti F (2007). Metabolic syndrome and adipose tissue: new clinical aspects and therapeutic targets. Curr Pharm Des, 13:2148. 53. Vohl MC, Sladek R, Robitaille J, et al. (2004). A survey of genes differentially expressed in subcutaneous and visceral adipose tissue in men. Obes Res, 12:1217–1222. 54. Secreted Protein Database. http://spd.cbi.pku.edu.cn/. 55. Polanski M, Anderson NL (2006). A list of candidate cancer biomarkers for targeted proteomics. Biomarker Insights, 2:1–48. 56. States DJ, Omenn GS, Blackwell TW, et al. (2006). Challenges in deriving highconﬁdence protein identiﬁcations from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol, 24:333–338. 57. Arnoys EJ, Wang JL (2007). Dual localization: proteins in extracellular and intracellular compartments. Acta Histochem, 109:89–110. 58. Walter N (2005). Unconventional secretory routes: direct protein export across the plasma membrane of mammalian cells. Trafﬁc, 6:607–614. 59. Bendtsen JD, Jensen LJ, Blom N, von Heijne G, Brunak S (2004). Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel, 17(4):349–356.

6 QUANTITATIVE MULTIPLEXED PATTERNING OF IMMUNERELATED BIOMARKERS Dominic Eisinger, Ph.D., Ralph McDade, Ph.D., and Thomas Joos, Ph.D. Rules Based Medicine, Inc., Austin, Texas

INTRODUCTION The human immune system is a complex cellular and molecular network unlike any other biological system. The complex interplay of multiple immune cells that rid the body of foreign invaders and diseased tissues is governed by a vast number of cell-to-cell communications. In this chapter we focus on the expanding utility of measuring these secreted protein messengers as biomarkers for drug development and diagnostic efforts that are relevant to a variety of diseases. Herein we use a broad acronym, IB (immune-related biomarker), to represent protein biomarkers for a variety of immune-related disorders. The IBs most prominently monitored in drug development efforts are the cytokines and chemokines, but also include acute-phase reactants, tissue remodeling factors, vascular markers, and growth factors. This complexity has led to the rapid adoption of newer multiplexed measurement tools. Key features of these include the quantitation, sensitivity, and perhaps most important, precision of each measurement. Ecosanoids, prostaglandins, prostacyclins, the thromboxanes, and the leukotrienes are also key elements of immune processes but do not ﬁt under the paradigm of protein biomarkers and are not addressed in this chapter. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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This ability to measure multiple IBs simultaneously from a small biological sample has ushered in an era of exploration known as biomarker patterning. It would be convenient if the single biomarker paradigm of the past were to continue unabated. However, the rate of introduction of new U.S. Food and Drug Administration (FDA)–approved diagnostic protein assays has fallen dramatically to an average of one or less per year [1]. This unfortunate trend does not suggest relying on a single biomarker but, rather, a group or set of biomarkers for drug development and diagnostic efforts. The theory of biomarker stacking is that by combining the predictive power of each marker, the optimal sensitivity and speciﬁcity of the multiplexed test will be achieved [2]. Rather than a single biomarker to ﬁt with a speciﬁc disease or drug effect, a pattern of multiple biomarkers provides stronger predictive value, due to their “stacking” effects. In this chapter we focus on the multiplexed immunoassay measurement technologies as well as some recent and relevant examples of successful biomarker patterning. These approaches are rapidly bringing multiplexed biomarker assays into everyday use in drug development and diagnostics for personalized medicine.

MEASUREMENT PROTEOMICS VS. TRADITIONAL PROTEOMICS Multiplexed immunoassays used to identify novel biomarker patterns is a form of proteomics. However, it is often confused with traditional proteomics, which typically use complex separation technologies such as two-dimensional gel electrophoresis and/or mass spectrometry to identify patterns of peaks that are peptides or proteins of unknown origin. This approach provides new targets for future research but lacks the quantitation and certainly the precision necessary for clinical use. In contrast, the approach discussed here measures only those analytes where a speciﬁc assay has been developed. It requires speciﬁc ligand-binding reagents, generally antibodies, and a physical address to localize that binding reaction. This technique has been described as measurement proteomics, where a restricted set of analytes is measured and data reported in absolute terms (e.g., pg/mL) [3]. One caveat of measurement proteomics that must be addressed is the inherent variability among individuals for expression of different IBs. This variation results from a blend of genetic and environmental factors. Before one can accurately predict the biological response to a disease or its therapy, the natural variance within a population as well as within a person must be determined for the protein analytes in question. This is typically performed by assessing the biomarker patterning data for a control group of people while simultaneously measuring the identical markers in the diseased or drug-treated group. Statistical tools and means are widely available to use either cutoff values for individual analytes or multivariate analysis for multiple biomarkers in order to achieve a valuable test. Experience has shown that the level of a given protein biomarker or biomarker pattern often correlates with a particu-

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lar disease state. In addition, these biomarker levels in serum or plasma have been shown to be stable in a person over time [1,2].

MEASUREMENT PROTEOMICS PLATFORM TECHNOLOGIES ELISA-Based Immunometric (Sandwich) Immunoassays Many of the key IBs (cytokines and chemokines in particular) exist at relatively low concentrations in serum or plasma, requiring the femtomolar sensitivity provided by the sandwich immunoassay. Sandwich immunoassays get their name from the fact that the antigen is “sandwiched” in between two antibodies. This is typically a more sensitive technique than single-antibody competitive assays. Traditional single-plex methods such as the 96-well microtiter plate–based enzyme-linked immunoassay (ELISA) use a capture antibody anchored either hydrophobically or covalently to the polystyrene wall of each well. After binding of antigen from the liquid sample, the assay is completed by adding a second antibody solution speciﬁc for the same target, thus completing the sandwich. A reporter signal attached to the second antibody provides the quantitative signal. ELISAs have been the standard in the research lab for decades, while the same sandwich immunoassay technique has been applied in a more industrial fashion in fully automated random access immunoanalyzers such as the Immulite from Siemens, built for FDA-approved assays in the clinical lab.

Multiplexed Immunometric (Sandwich) Immunoassays The random access immunoanalyzers found in the clinical lab do provide a form of multiplexing by physically splitting the loaded serum or plasma sample into multiple single-plex assays. Although this is an efﬁcient approach for the work ﬂow in a lab, it still requires a minimum of sample volume for each assay and does not reduce the cost per assay signiﬁcantly. True multiplexing, as delivered by today’s newer platforms, does provide signiﬁcant savings in both the sample volume requirements and the cost per assay. Although a number of interesting multiplexed immunoassay platforms have been launched in the last decade, two primary types of platforms have survived as viable commercial entities: planar arrays and microsphere-based arrays.

Planar Arrays Planar microarrays are highly miniaturized and parallelized solid-phase assay systems that use a large number of different capture molecules immobilized in physically addressable locations called microspots. These spots, which typically have diameters of less than 250 μm and are arrayed in rows and columns,

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are where the capture antibody for each assay is deposited. This deposition by physical touching with pins or spraying with piezoelectric tips is a technologically demanding step that was ﬁrst pioneered in the gene expression industry for detection of nucleic acids by sequence-speciﬁc hybridization [4]. This approach has been used to generate antibody arrays consisting of several hundred capture antibodies by depositing them on nitrocellulose-coated microscope slides. Typically, these large arrays have then been probed either by using a direct labeling approach, where the mixture of antigens is ﬂuorescently labeled; or conversely, a cocktail of second antibodies labeled with a ﬂuorescent tag have been used to complete the sandwich assays. In either case, quantitation was achieved by imaging the spots with a change-coupled device (CCD) camera. The two limiting features of this approach have been (1) the irreproducibility of the spotting approach, where each new array is considered a new lot of material from a quality control point of view; and (2) the high probability of antibody–antibody interactions when greater than 25 antigen– antibody reactions are performed in a shared physical space. The second is a limiting factor for all multiplexed immunoassays and has been considered in the three most popular platforms. Microtiter-Based Planar Arrays Glass slide–based immunoassay microarrays have been adapted to the microtiter plate format to increase throughput and to use existing automated solutions. Although this limits the number of microspots available per well, it helps to avoid the problem of antibody–antibody interactions. Such an approach was adapted by Thermo Scientiﬁc with their SearchLight Protein Array Technology. On the bottom of each microtiter well, an array consisting of up to 16 microspot features are generated, which allows the measurement of 16 analytes from 50 μL of sample. After incubation with sample, the wells are washed as with an ELISA and the captured antigen visualized using a secondary antibody labeled with a chemiluminescent probe. Quantitation of captured analytes is provided by a CCD imaging system using a calibration curve set up in parallel wells (www.piercenet.com). Meso Scale Discovery (http://www.meso-scale.com) also uses a microtiter well microarray strategy called Multi-Array. Each well is arranged with 1 to 25 carbon microspots that are in essence miniature electrodes integrated into the bottom of the plate. Onto each carbon electrode the capture antibodies are immobilized, which after multiplexed immunoassay development results in a bound electrochemiluminescent (ECL) label. Quantitation of each assay is performed by passing electrical current through each electrode, producing a luminescent signal which is detected by a CCD camera imaging system. The microtiter plate format allows the use of standard liquid-handling automation with a multiplexed control and calibration strategy. As each Multi-Array plate can be imaged across all 96 wells within a few seconds, the sample throughput of this approach is large and quite scalable. The ECL detection system is also

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thought to provide signiﬁcantly better sensitivity for immunoassays than can be provided by chemiluminescence or ﬂuorescence. The two planar array systems described above are typically operated in a manual mode, although both are capable of interfacing with automated liquidhandling systems. One fully automated planar array system in the marketplace is the Evidence, an automated biochip system developed by Randox Laboratories (http://www.randox.com). It provides multiplexed analysis of miniaturized and parallelized immunoassays in a macroarray format. This macroarray contains 25 features and uses a chemiluminescence-based readout. Several immunoassays panels are available, including fertility, cardiac disease, tumors, cytokines and growth factors, and drug residue panels. Microsphere Arrays Robust and ﬂexible microsphere-based array systems have been developed over the last decade [5]. For the purposes of this review, there is only one microsphere-based array developed and commercialized in a manner parallel to the planar array discussed previously that has succeeded in garnering the most signiﬁcant portion of the multiplexed immunoassay market: xMAP from Luminex Corporation (http://www.luminexcorp.com). Luminex technology can perform up to 100 multiplexed, microsphere-based assays in a single reaction vessel by combining optical classiﬁcation schemes, biochemical assays, ﬂow cytometry, and advanced digital signal-processing hardware and software. Multiplexing is accomplished by assigning each analyte-speciﬁc assay a microsphere set labeled with a unique ﬂuorescence signature. To attain 100 distinct microsphere signatures, two ﬂuorescent dyes, red and infrared, are mixed in various combinations using 10 intensity levels of each dye (i.e., 10 × 10). Each batch or set of microspheres is encoded with a ﬂuorescent signature by impregnating the microspheres with one of these dye combinations. After the encoding process, assay-speciﬁc capture antibodies are conjugated covalently to each unique set of microspheres. Coupling is performed on large numbers of individual microspheres (107 to 109 microspheres) simultaneously within each unique set, resulting in low microsphere-to-microsphere variability. After optimizing the parameters of each assay separately, multianalyte proﬁles (MAP) are performed by mixing up to 100 different sets of the microspheres in a single well of a 96-well microtiter plate. A few microliters of sample is added to the well and allowed to react with the microspheres. The assay-speciﬁc capture reagent on each microsphere binds the analyte of interest. A cocktail of assay-speciﬁc, biotinylated antibodies is reacted with the microsphere mixture, followed by a streptavidin-labeled ﬂuorescent “reporter” molecule (typically, phycoerythrin). Finally, the multiplex is washed to remove unbound detecting reagents. After washing, the mixture of microspheres is analyzed using a Luminex instrument which uses hydrodynamic focusing to pass the microspheres in

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single ﬁle through two lasers. As each individual microsphere passes through the excitation beams, it is analyzed for size, encoded ﬂuorescence signature, and the amount of ﬂuorescence generated in proportion to the analyte. Microsphere size, determined by measuring the 90 ° light scatter as the microspheres pass through a red diode laser (633 nm), is used to eliminate microsphere aggregates from the analysis. While in the red excitation beam, the encoded red and far-red dyes are excited and the resulting ﬂuorescence signature (ratio 660 nm/720 nm) is ﬁltered, measured using avalanche photodiodes, and classiﬁed to a microsphere set. Since each microsphere is encoded with a unique signature, the classiﬁcation identiﬁes the analyte being measured on that individual microsphere. As the microsphere passes through a green diode-pumped solid-state laser (532 nm), the ﬂuorescence “reporter” signal (580 nm) is generated in proportion to the analyte concentration, ﬁltered, and measured using a photomultiplier tube. Data acquisition, analysis, and reporting are performed in real time on all microsphere sets included in the MAP. A minimum of 50 individual microspheres from each unique set are analyzed and the median value of the analyte-speciﬁc, or reporter, ﬂuorescence is logged. Using calibrators and controls of known analyte quantity, sensitive and quantitative results are achieved with precision enhanced by the redundant oversampling at each data point. xMAP provided several key advantages over the two-dimensional planar arrays. First, consistent coating of the polystyrene microsphere surface with antibodies has been performed in the diagnostic industry for decades, so it is a reproducible technique. Microsphere coating does not suffer from the irreproducibility that plagues planar arrays. Second, the lot size for manufacturing of a microsphere array is generally in the thousands to millions of tests per lot. Lower lot-to-lot variability coupled with extremely large manufacturing lots means that the problem of lot variation is greatly diminished. The primary disadvantage of microsphere arrays is throughput, as each sample must be read in turn in the ﬂow system. So far, no group has solved this problem be developing multiple-channel ﬂow systems, but this is the next logical step of the technology.

APPLICATIONS AND EXAMPLES IN DRUG DEVELOPMENT AND DIAGNOSTICS Whether one uses planar arrays or microsphere arrays, the resulting quantitative, multiplexed data are then mined for distinctive patterns of biomarkers that indicate disease, drug toxicity or efﬁcacy, or some other useful biochemical phenotype. We will not discuss the data mining techniques available but simply describe several examples where data mining of these complex data sets has revealed meaningful biomarker patterns. Typically, data mining is ﬁrst done using traditional statistics to identify those analytes whose values are signiﬁcantly different between two experimental groups. This is often done by

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determining mean analyte values and applying Student’s t-tests to determine p-values of signiﬁcance. The next step is often to use more complex multivariate analysis tools, such as principal component analysis, for pattern recognition. The reports of IB monitoring cited most often are related to inﬂammatory disease. Asthma, rheumatoid arthritis (RA), multiple sclerosis (MS), systemic lupus erythematosus (SLE), chronic obstructive pulmonary disease (COPD), and psoriasis (PS) are thought of as classic inﬂammatory diseases. In recent years, a host of other diseases have been found to have a signiﬁcant inﬂammatory component. These include cardiovascular disease (CVD), central nervous system diseases such as Alzheimer disease, most infectious diseases, and various cancers [6]. IBs are thus very useful for quantifying, characterizing, and monitoring the inﬂammatory and anti-inﬂammatory components of a disease in addition to therapeutic beneﬁts during drug trials. Measurement of inﬂammatory markers is a popular application of multiplexed proﬁling in drug development and diagnostic efforts. The duration and pattern of cytokine and chemokine expression in serum or plasma have been the most common uses of multiplexed immunoassays. Common inﬂammatory mediators are often cited (e.g., TNFα, IL-1, IL-6, IL-8, MIP-1) due to the large set of publications describing the association of these classical inﬂammatory markers with numerous diseases, conditions, and drug treatments. Although important, other IBs that are related directly or indirectly to the disease and/ or therapy in question will often add signiﬁcant prognostic value to biomarker patterning efforts. Inﬂammatory Bowel Disease in a Mouse Model Crohn disease and ulcerative colitis are chronic inﬂammatory diseases of the gut that cause tremendous human suffering and loss of productivity. There is a clear need for better diagnostic tools that are less invasive or costly than endoscopy and histology. Torrence et al. published a study [7] of an inﬂammatory bowel disease model in mouse where they identiﬁed a serum-based IB pattern using data generated from a microsphere array. These data were generated using serum samples of mice infected with Helicobacter bilis, which causes an inﬂammation in the gut. These IB patterns were then compared with those of control animals. Samples were sent to Rules-Based Medicine (RBM), Inc., Austin, Texas for biomarker analysis using their RodentMAP (http:// www.rbmmaps.com). RBM has an automated testing laboratory that utilizes xMAP technology under regulatory compliance to both good laboratory practices (GLPs) and the Clinical Laboratory Improvement Act (CLIA). RBM performed a multianalyte proﬁle consisting of 58 different quantitative immunoassays and reported the data for each analyte in mass per milliliter of sample. Results indicated that serum levels of IL-11, IL-17, interferon gammainducible protein (IP-10), lymphotactin, monocyte chemoattractant protein

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(MCP-1), and vascular cell adhesion molecule (VCAM-1) were elevated in early disease. In later, more severe disease, IL-11, IP-10, haptoglobin, matrix metalloproteinase-9 (MMP-9), macrophage inhibitory protein 1 alpha (MIP 1α), ﬁbrinogen, immunoglobulin A, apolipoprotein A1, and IL-18 were elevated. All of these biomarkers are considered IBs with the exception of apolipoprotein A1, which is considered a metabolic biomarker. Interestingly, all of the IBs correlated with histopathological scores. Antibiotic treatment of the infected mice both improved the histopathology scores and decreased the mean serum values for most of the IBs. These data suggest that the serum IB patterns could be useful for both diagnosis and prognosis of similar human diseases. Scleroderma Scleroderma is an autoimmune disease that manifests itself in the skin, resulting in ﬁbrosis and damage to the vasculature. It is thought that IBs play a major role in the course of the disease and therefore should be good targets for both therapeutic intervention and diagnostic/prognostic development. Recently, Duan et al. [8] have compared the serum IB patterns of scleroderma patients with controls using the RBM testing approach, measuring 188 different biomarkers. In addition, this group compared the serum protein expression patterns with the messenger RNA expression from the monocytes and lymphocytes isolated from these patients’ peripheral blood. As in the previous mouse study, the majority of biomarkers that were differentially expressed were IBs. Acute-phase reactants (ﬁbrinogen, haptoglobin, and von Willebrand factor), cytokines (IL-1α, IL-16, soluble tumor necrosis factor receptor 2, and MIP-1α), tissue remodeling proteins (intercellular adhesion molecule type 1 and tissue inhibitor of metalloproteinase type 1), and one metabolic biomarker, apolipoprotein CIII, were all elevated in the scleroderma patient sera compared with normal age-matched controls. Comparison of the gene expression patterns from the various immune cell populations correlated poorly with the serum IB proﬁling data. This was not surprising, as these two proﬁles often do not correlate well. The reason(s) for the gene expression and IB proﬁling differences are unknown. Results indicated that the pattern of IB elevation in scleroderma could be used as a more speciﬁc diagnostic to help the rheumatologist differentiate scleroderma from other autoimmune conditions or skin maladies. More important, the intensity of IB elevation could be used to monitor therapeutic intervention in clinical trials as well as after drugs developed for this indication are approved. Rheumatoid Arthritis Rheumatoid arthritis (RA) is an autoimmune disease where the current focus of drug development efforts is devoted to speciﬁc blockade of certain cytokine

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activities. It is therefore highly likely that IB monitoring will play an important role in RA disease prognosis and drug development programs. A brief review of the cytokine blockade strategies for RA starts with the inﬂammatory cytokine TNFα, a major therapeutic advancement in the treatment of RA [9]. There are now several TNFα biological drugs on the market (Humira, Abbott; Enbrel, Amgen; Remicade, Centicor) that in addition to treatment of RA, are also being used off-label for psoriasis and for Crohn disease. Blockade of IL-1 is another option available for RA therapy. Anakinra (Kineret, Amgen), a recombinant interleukin-1 receptor antagonist, is used for treatment of rheumatoid arthritis. The next major advancement in anticytokine therapy for RA appears to be the humanized anti-IL-6 receptor blocking antibody tocilizumab (Actemra, Roche). The acute-phase reactant serum amyloid A was identiﬁed in a serum proﬁle that differentiated response from no response to Actemra therapy [10]. Intervention of IL-15 also looks promising for treatment of RA and psoriatic arthritis in addition to pulmonary inﬂammatory diseases [11]. Targeted cytokine neutralization as a therapeutic option in autoimmune diseases such as RA will probably lead to novel IB patterns with both prognostic and diagnostic value during drug efﬁcacy trials. For example, in a recent study of Sjögren syndrome, a panel of 25 IBs provided diagnosis and disease management in this debilitating condition [12]. Cardiovascular Disease Over the past 25 years, the number of risk factors for coronary artery disease has increased dramatically. Systemic inﬂammation and abnormal lipoprotein metabolism are important contributors to the progression of atherosclerotic disease leading to plaque instability [13]. Even so, the vast majority of patients who experience an acute coronary event have no prior symptoms. Further complicating the diagnosis of acute coronary syndrome (ACS) is the frequent occurrence of patients who present symptoms of chest pain that can be attributable to other completely nonrelated events, such as acute gastroesophageal reﬂux disease. Given the serious life-threatening nature of ACS, an improvement in the early diagnosis of heart attacks would be a major medical advancement. Matrix metalloproteinases (MMPs) are promising IBs for early identiﬁcation of high-risk patients. Pro-inﬂammatory cytokines stimulate the secretion of MMPs, and an increase in systemic MMPs has been documented in ACS and correlated to unstable plaque metabolism [13]. In a recent study by Gurbel and co-workers, MMP-2, MMP-3, and MMP-9 were sensitive and speciﬁc markers differentiating asymptomatic coronary artery disease (A-CAD) from symptomatic CAD (S-CAD). The stable quiescent disease group of A-CAD patients were documented by angiography, a history of coronary artery bypass grafting, or revascularization (stenting or balloon angioplasty). The progressive S-CAD group of patients were enrolled immediately before coronary intervention and presented either myocardial infarction or stable angina.

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Patients with S-CAD had markedly elevated levels of speciﬁc MMP-2 and MMP-9, whereas patients with A-CAD had signiﬁcantly greater levels of MMP-3. In addition to the MMPs, other IBs (TIMP-1, C-reactive protein IL-8, IL-10, RANTES, endothelin, plasminogen activator inhibitor type 1, apolipoprotein C-III, and IL-1a) reinforced the MMP results for distinguishing the two groups [13]. Hepatitis C Wright and co-workers documented cytokine proﬁles of patients grouped based on their viral titers before and after standard therapy [14]. They found that they were able to demonstrate the prognostic power of serum cytokine proﬁling in chronic hepatitis C virus (HCV) infection. The study demonstrated that overall serum cytokine levels were signiﬁcantly higher in patients than in controls and that the levels dropped signiﬁcantly after therapy in concert with their viral titer. Distinct cohort subgroups based on changes in viral titers correlated to speciﬁc sets of cytokines that decreased in each group. This could allow for new efforts to stratify patients and increase efforts to ﬁnd improved therapies.

Ovarian Cancer Ovarian cancer has been called the silent killer, as it is often not diagnosed until late in the disease progression, when therapeutic intervention is rarely successful. The unmet medical need for a sensitive and speciﬁc diagnostic test that will detect early-stage ovarian cancer and differentiate it from benign ovarian disease is strong. Bertenshaw et al. recently reported the identiﬁcation of a biomarker pattern of eight serum analytes that using a multivariate algorithm could differentiate stage 2 ovarian cancer patients from benign ovarian disease [15]. In addition to Cancer Antigen (CA)-125, seven IBs were identiﬁed (C-reactive protein, epidermal growth factor, IL-10, IL-8, connective tissue growth factor, haptoglobin, and MMP-1) as being of importance to a potential diagnostic. These IBs, as well as CA-125, were identiﬁed by comparing the plasma biomarker patterns of samples from ovarian cancers in various stages, common benign gynecological conditions, as well as age-matched normal controls.

EMERGING PARADIGM: EX VIVO IMMUNE BIOMARKER MONITORING WITH WHOLE-BLOOD CELL CULTURES A fortuitous aspect of applying measurement proteomics to the immune system is the relative ease with which many properties of the immune system can be quantiﬁed outside the human body, ex vivo. (Herein, ex vivo refers to

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the in vitro culture and ex vivo analysis of the immune system outside the body.) Both innate and adaptive immune system responses in blood leukocytes can be measured with multiplex IB measurements. Indeed, much of our understanding of the immune system has been generated by experimentation on human leukocytes cultures ex vivo. Given the complexity of all the different types of immune cells present in blood, a reductionist approach of isolating subpopulations of leukocytes was invaluable. For example, density gradient centrifugation of blood to obtain peripheral blood mononuclear cells (PBMCs) is a common procedure to obtain cell populations enriched for lymphocytes and monocytes but depleted of granulocytes, platelets, and red blood cells [16]. The implementation of ex vivo analysis of the immune system has only recently been deployed in the clinical drug development setting with the primary applications of safety (immunotoxicity) and pharmacodynamics. An example of an immunotoxicity application would be the Tegenero incident of 2006, in which the CD28 superagonistic antibody TGN1412 was administered to healthy phase I volunteers. Within a few hours, a single intravenous dose induced severe lymphopenia and a systemic cytokine storm, leading to multiorgan failure [17]. Clearly, it is preferable to identify the potential for such a catastrophic drug effect during an ex vivo study. An example of a pharmacocynamic application would be quantifying IBs in the culture media that correlate with the drug mechanism of action. The utility of ex vivo analysis of the immune system for drug development programs can be divided into two broad categories. Initially, a candidate drug can be tested ex vivo for its effects on the immune system in a resting inactivated state in addition to an activated state created by the addition of an immune stimulant to mimic inﬂammation. Later in the drug development process, a candidate drug can be tested in vivo in a trial subject, and the subject’s immune system tested ex vivo with and without immune stimulation. Unfortunately, the historic research methodology of utilizing PBMCs for ex vivo studies is not very adaptable to the clinical setting, nor does it provide a physiologically relevant situation for drug development studies. Compared to isolated PBMC cultures, the whole-blood culture approach has a major advantage, due to the fact that all components of the blood, including cellular (e.g., granulocytes: neutrophils, basophils, eosinophils; lymphocytes, monocytes, NK-cells), pseudocellular (e.g., red blood cells, platelets), or subcellular components (e.g., enzyme inhibitors, complement system, kininogens), are present. Therefore, functional assays performed with a whole-blood culture reﬂect the situation of a person’s whole immune repertoire much better than do PBMC-based cultures [18–20]. In a whole-blood stimulation assay, the leukocytes are within the center of a well-composed concert of regulatory components that relies on an entire series of feedback loops, which modulate in a positive or negative manner the function of the immune system. T-cells, for example, can amplify the activities of monocytes and macrophages, and platelets produce and release precursors of leukotrienes, which are taken up

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and processed further to become full-ﬂedged mediators by polymorphonuclear granulocytes [21,22]. Red blood cells, which are not present in PBMC preparations, can produce leukotrienes and modulate the action of chemokines by expressing receptors for chemokines on their surface [23]. Plasma proteins like the complement system and the kininogens form readily available precursors of peptides exhibiting strong immunoregulating activities [24]. Some of these precursors are cleaved to become active only in the presence of enzymes, released for example by activated polymorphonuclear granulocytes [25]. Using whole-blood cultures, the functionality of the human immune system can be analyzed as close as possible to the in vivo situation. The difﬁculty with implementing whole-blood cultures in the clinical setting is the need for a specialized cell culture laboratory and highly trained technicians, a very expensive prospect. Shipping samples to a centralized cell culture facility dramatically reduces the quality and reproducibility of results. Recently, a standardized, fully closed, whole-blood culture system has been developed for the clinical setting that does not require cell culture facilities or specialized personnel [26,27]. The TruCulture system incorporates a 3-mL blood collection tube with cell culture media to which test substances can be added. Blood is drawn from a subject directly into a collection tube that contains a medium for an immediate and standardized initiation of the whole-blood culture. After incubation in a 37 °C heating block, the cells are physically separated from the culture medium by insertion of a valve separator, and the unit is frozen until IBs in the culture medium can be analyzed. This system will greatly aid the routine implementation of ex vivo immune monitoring in clinical trials.

CONCLUSIONS History has shown repeatedly that the application of a new breakthrough technology is the driving force into the next phase of meaningful discovery. The development and commercialization of quantitative multiplexed immunoassay platforms is a prime example. The two primary array platforms, planar and microsphere, provide investigators with more comprehensive biomarker data for multiple analytes with the same sensitivity and precision of single-plex testing. The technology has been invaluable to basic research and drug discovery efforts and is now moving rapidly into the clinical arena of drug development, with the goal that useful biomarker patterns will allow more informed drug development decisions to be made. Immune-related biomarker patterning has great potential to improve the success rate of drug approval in a highly cost-effective manner. The efﬁciency of simultaneous proﬁling of multiple immune-related biomarkers with very small sample volumes has revolutionized our ability to discover and validate biomarker patterns in order to stratify patients and ﬁnd markers of efﬁcacy and safety. Central to many diseases are the immune-related biomarkers dis-

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cussed in this chapter. Their modulation as drug targets themselves has also become a core theme for many of the autoimmune diseases. Finally, innovative products designed for the clinical trial setting, such as the TruCulture whole-blood culture system, will help bring immune-related biomarker patterning into the everyday realm of clinical drug development and companion diagnostics.

REFERENCES 1. Anderson NL, Anderson NG (2002). The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteom, 1(11):845–867. 2. Bodovitz S, Patterson S (2003). Protein biomarker strategies. Drug Discov World, Fall, pp. 67–78. 3. Heuer J, Cummins D, Edmonds B (2005). Multiplex proteomic approaches to sepsis research: case studies employing new technologies. Expert Rev Proteom, 2(5):669–680. 4. Kricka LJ, Master SR, Joos TO, Fortina P (2006). Current perspectives in protein array technology. Ann Clin Biochem, 43(6):457–467. 5. Templin M, Stoll D, Bachmann J, Joos T (2004). Protein microarrays and multiplexed sandwich immunoassays: What beats the beads? Combin Chem High Throughput Screening, 7(3):223–229. 6. Lucas S, Rothwell NJ, Gibson RM (2006). The role of inﬂammation in CNS injury and disease. Br J Pharmacol, 147:S232–S240. 7. Torrence A, Brabb T, Bielefeldt-Ohmann H, et al. (2008). Serum biomarkers in a mouse model of bacterial-induced inﬂammatory bowel disease. Inﬂamm Bowel Dis, 14(4):480–490. 8. Duan H, Fleming J, Pritchard D, et al. (2008). Combined analysis of monocyte and lymphocyte messenger RNA expression with serum protein proﬁles in patients with scleroderma. Arthritis Rheum, 58(5):1465–1474. 9. Fogler WE (2008). Treating rheumatoid arthritis with DMARDs and biologics. Drug Discov World, Fall, pp. 15–18. 10. Miyamae T, Malehorn D, Lemster B, et al. (2005). Serum protein proﬁle in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy. Arthritis Res Ther, 7(4):R746–R755. 11. McInnes IB, Gracie JA (2004). Interleukin-15: a new cytokine target for the treatment of inﬂammatory diseases. Curr Opin Pharmacol, 4(4):392–397. 12. Szodoray P, Alex P, Brun JG, Centola M, Jonsson R (2004). Circulating cytokines in primary Sjogren’s syndrome determined by a multiplex cytokine array system. Scand J Immunol, 59(6):592–599. 13. Gurbel P, Kreutz R, Bliden K, DiChiara J, Tantry U (2008). Biomarker analysis by ﬂuorokine multianalyte proﬁling distinguishes patients requiring intervention from patients with long-term quiescent coronary artery disease: a potential approach to identify atherosclerotic disease progression. Am Heart J, 155(1): 56–61.

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14. Wright H, Alex P, Nguyen T, et al. (2005). Multiplex cytokine proﬁling of initial therapeutic response in patients with chronic hepatitis C virus infection. Dig Dis Sci, 50(10):1793–1803. 15. Bertenshaw GP, Yip P, Seshaiah P, et al. (2008). Multianalyte proﬁling of serum antigens and autoimmune and infectious disease molecules to identify biomarkers dysregulated in epithelial ovarian cancer. Cancer Epidemiol Biomarkers Prev, 17(10):2872–2881. 16. Jackson A (1990). Basic phenotyping of lymphocytes: selection and testing of reagents and interpretation of data. Clin Immunol Newsl, 10:43–55. 17. Suntharalingam G, Perry MR, Ward S, et al. (2006). Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med, 355(10):1018–1028. 18. Esteve E, Ricart W, Fernandez-Real JM (2004). Dyslipidemia and inﬂammation: an evolutionary conserved mechanism. Clin Nutr, 24(1):16–31. 19. Jensen LE, Whitehead AS (1998). Regulation of serum amyloid A protein expression during the acute-phase response. Biochem J, 334(3):489–503. 20. Kemper C, Atkinson JP (2007). T-cell regulation: with complements from innate immunity. Nat Rev Immunol, 7(1):9–18. 21. Danese S, de la Motte C, Reyes BMR, Sans M, Levine AD, Fiocchi C (2004). Cutting Edge: T cells trigger CD40-dependent platelet activation and granular RANTES release: a novel pathway for immune response ampliﬁcation. J Immunol, 172(4):2011–2015. 22. Murdoch C, Finn A (2000). Chemokine receptors and their role in inﬂammation and infectious diseases. Blood, 95(10):3032–3043. 23. Pruenster M, Rot A (2006). Throwing light on DARC. Biochem Soc Trans, 34:1005–1008. 24. Bank U, Ansorge S (2001). More than destructive: neutrophil-derived serine proteases in cytokine bioactivity control. J Leukoc Biol, 69:97–206. 25. Ellis TN, Beaman BL (2004). Interferon-gamma activation of polymorphonuclear neutrophil function. Immunology, 112(1):2–12. 26. Schmolz M, Hurst TL, Bailey DM, et al. (2004). Validation of a new highly standardised, lab-independent whole-blood leukocyte function assay for clinical trials (ILCS). Exp Gerontol, 39(4):667–671. 27. http://www.rulesbasedmedicine.com/products-services/TruCulture.asp.

7 GENE EXPRESSION PROFILES AS PRECLINICAL AND CLINICAL CANCER BIOMARKERS OF PROGNOSIS, DRUG RESPONSE, AND DRUG TOXICITY Jason A. Sprowl, Ph.D., and Amadeo M. Parissenti, Ph.D. Laurentian University, Sudbury, Ontario, Canada

INTRODUCTION Measurement of the expression level of speciﬁc genetic or protein biomarkers in patient serum or biopsies can be extremely valuable in the diagnosis and treatment of a variety of human neoplasms. Often, the careful and timeconsuming evaluation of several biomarkers by highly trained pathologists is required to construct a deﬁnitive diagnosis and treatment regimen. The expression level of single or several biomarkers can also be useful in the prediction of patient prognosis or response to chemotherapy, but these approaches generally have only limited accuracy [1–4]. Current studies suggest that no single biomarker will sufﬁce for the accurate prediction of patient prognosis or tumor response to chemotherapy [5–7]. It is therefore thought that the tumor or serum level of large groups of proteins or transcripts is necessary to predict patient prognosis or outcome reliably after chemotherapy. In this chapter we review the current progress that has been achieved in the use of gene proﬁling to predict patient prognosis and response or toxicity to speciﬁc Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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chemotherapy regimens. Moreover, the impact of experimental design and the approach to data analysis on the ability of gene proﬁling experiments to reliably identify large groups of genes that can serve as prognostic or predictive biomarkers is discussed. A critical assessment of the prospect for gene proﬁling experiments to affect the clinical management of cancer patients and drug development is also provided.

PLATFORMS AND TOOLS FOR GENOME PROFILING EXPERIMENTS The task of gene expression proﬁling is complex, and knowledge of the platforms and tools used to accomplish such a task is essential. The most popular approaches for proﬁling of gene expression in biological samples are DNA microarray analysis and quantitative reverse-transcription polymerase chain reaction (Q-PCR). In a typical microarray experiment (reviewed by Villeneuve and Parissenti [8]), RNA is isolated from cells, and mRNAs present within the sample are reverse-transcribed with oligo-dT primers in the presence of ﬂuorescent or radioactive-labeled nucleotides to yield a series of labeled cDNA probes. After denaturation, the labeled cDNA probes are hybridized to known denatured PCR products or single-stranded oligonucleotides immobilized in a grid pattern on either glass slides or nylon membranes. After washing to remove unbound probe, the intensity of label associated with the various PCR products or oligonucleotides on the array can be quantiﬁed by autoradiography or by using an array scanner. The amount of labeled cDNA hybridizing to a speciﬁc gene on the microarray is a measure of its expression (level of transcription). Often, two samples can be compared for differences in gene expression by labeling the cDNA preparations from the two samples with nucleotides conjugated to dyes that ﬂuoresce at different wavelengths (e.g., Cy3 and Cy5 dyes). In addition, cDNAs can be transcribed using three wild-type nucleotides and one aminoallyl-labeled ribonucleotide to generate labeled aminoallyl RNAs as probes [9]. While many investigators use microarrays that are prepared within their laboratories [10–13], a variety of companies now produce microarrays with widely varying numbers of oligonucleotides or PCR products immobilized to them. This creates signiﬁcant variability across experiments. The oligonucleotides can also be immobilized on microarrays via different methods, including photolithography (Affymetrix) or by printing directly on glass slides. This introduces additional variability in microarray data across laboratories, resulting in signiﬁcant difﬁculty in identifying common genetic proﬁles across experiments. Moreover, while standards for the performance of microarray experiments have been established (minimal information about microarray experiments (MIAME) standards [14]), not all microarray studies follow these standards. Some genome proﬁling studies are also conducted using high-throughput Q-PCR. This approach is highly quantitative, providing a more reliable

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measure of gene expression, but the number of genes that can be quantiﬁed is limited, since two gene-speciﬁc primers are required for each gene whose expression is measured. In addition, gene expression is measured relative to speciﬁc reference genes whose expression must also be measured. The choice of reference genes also varies signiﬁcantly between studies. Multiplexing of reactions, and approaches in which each PCR reaction product has a distinct wavelength for ﬂuorescence emission (e.g., the Luminex system), have helped increase the number of genes whose expression can be proﬁled by PCR-based approaches. PCR-based methods of genome proﬁling are quite distinct from those used in microarray analyses, making comparisons between Q-PCR- and microarray-based ﬁndings more difﬁcult. Nevertheless, various PCR-based gene proﬁling methods have been used to identify sets of genes associated with response to chemotherapy or recurrence of disease post-chemotherapy [15–17].

DATA ANALYSIS PITFALLS ASSOCIATED WITH GENE PROFILING EXPERIMENTS Other difﬁculties in comparing data across genome proﬁling experiments relate to the large genetic variation that exists in humans, the design of microarray or Q-PCR experiments, and the widely varying approaches used to analyze data from such experiments. This is particularly the case for DNA microarray studies. While microarray and, in particular, PCR-based analyses are capable of measuring the expression of a large number of genes with acceptable accuracy, gene expression varies widely across patients as well as in the same person over time. In addition, the probability of identifying genes correlating with a speciﬁc phenomenon such as chemotherapy drug response is very high when tens of thousands of genes are surveyed. Unfortunately, such correlations are often not replicated in additional independent data sets, suggesting that the false discovery rate is quite high using these approaches. The use of multiple replicate arrays per sample in microarray experiments helps considerably to reduce the false discovery rate, as does the use of multiple independent data sets from highly controlled experiments. In addition to variable gene expression among individuals, microarray procedures often yield noisy data sets with differing efﬁciencies of labeling and background labeling across arrays. Therefore, a variety of algorithms and statistical approaches have been developed to normalize and analyze microarray data to improve the accuracy of identifying signiﬁcant changes in gene expression between groups. Statistical tests include the computation of t-tests for each gene and/or the use of SAM, a signiﬁcance analysis for microarrays algorithm that permits one to identify signiﬁcant changes in gene expression after a statistical computation of a false discovery rate based on the data set. Common additional algorithms used for microarray data analysis include weighted voting (WV) [16,18,19], supporting vector machine (SVM) [16,20,21],

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and k-nearest-neighbor (K-NN) [16,22]. The resulting list of differentially expressed genes is commonly quite large, and each gene in this training set should then be validated by several methods, including a “leave-one-out” cross-validation. This involves the removal of one patient sample from the training set and determining if the remaining gene set is capable of classifying correctly the left-out sample. Once the training set has demonstrated accurate prediction within the samples from which it was derived, the genetic proﬁle should be further tested within a large patient population, ideally outside of that used to create the training set. Should the genetic proﬁle result continuously in high predictability in the classiﬁcation of interest, useful sets of biomarkers have been established. However, the use of varying data analysis methods can often result in completely different sets of differentially expressed genes. Consequently, multiple data analysis techniques should be used to obtain a list of genes commonly identiﬁed across methods [23–25]. These differences in gene expression can be further validated using other independent approaches, such as Q-PCR and immunoblotting experiments. In addition to the data analysis tools above, clustering algorithms are being used routinely to identify sets of genes whose expression is signiﬁcantly and consistently different between various groups of patients (e.g., between responders and nonresponders of chemotherapy). However, it is important to note that recent ﬁndings suggest that unsupervised methods of data analysis, such as data clustering, are often not effective in identifying predictive or prognostic biomarkers. The reason for this may be that cluster analysis does not utilize information related to the original sample grouping, and it is therefore a subjective strategy for data analysis. Furthermore, the number of genes that distinguish between classiﬁcations with the use of clustering algorithms is often small, and the distances calculated in cluster analysis may not properly portray the importance of certain genes, while excluding others of great importance [26].

GENE PROFILES AS BIOMARKERS FOR THE DIAGNOSIS AND CLASSIFICATION OF HUMAN CANCERS Gene proﬁling has proven quite valuable in identifying genes whose expression can be used to diagnose speciﬁc cancers, including many that are difﬁcult to distinguish by other methods. For example, Kohlman et al. in 2003 used gene proﬁling to differentiate between acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) [18]. Bone marrow samples were isolated from 90 patients, 25 having been diagnosed with ALL, and 65 with AML. RNA was extracted from the samples and labeled cDNAs hybridized to Affymetrix U95Av2 and U133 microarrays. The WV algorithm and the leaveone-out cross-validation approach were then used to identify a subset of genes that were expressed differentially between the two leukemias. The expression of 24 genes was then used to accurately identify patients with AML, while 19

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genes were sufﬁcient to diagnose ALL. However, upon comparing the genes identiﬁed by the two types of microarrays, it was observed that the 24 genes identiﬁed by the U133 arrays were different from those identiﬁed by U95Av2 chips. Furthermore, only ﬁve genes were observed to be in common within the 19-gene set when the two types of array tools were used. These results show the importance of using the same array platforms when comparing across experiments. The utility of the U133 arrays was studied further in the diagnosis of leukemia using 937 patients (892 with clinically relevant leukemia subtypes and 45 nonleukemic patients) [27]. Patients were divided into 13 subgroups of leukemia type, and each subgroup was split equally into training and validation sets. Class-speciﬁc gene expression was determined using an SVM approach, with an overall classiﬁcation accuracy of 95.1% when the top 100 genes for leukemia class discrimination were used. The studies cited above clearly demonstrate the utility of gene proﬁling for the diagnosis of cancers and suggest that this approach can be used further in the classiﬁcation of cancers into various subgroups. Sørlie et al.’s study in 2001 further illustrates the latter utility by demonstrating that breast cancers could be classiﬁed into a basal epithelial-like group, an ERBB2-overexpressing group, a normal breastlike group, and a luminal epithelial/estrogen receptor–positive group, which could be further divided into at least two additional subgroups, each with a distinct gene expression proﬁle. Interestingly, survival analyses using a subset of uniformly treated patients with locally advanced breast cancer revealed clear differences in outcomes among the various groups. For example, the basal-like group had a poor prognosis, and the two estrogen receptor–positive groups had clear differences in treatment outcome [28]. These ﬁndings have been well corroborated in additional experiments performed across array platforms and using RNA extracted from parafﬁn-embedded tissues [29–31]. Similar approaches and ﬁndings have been observed for endometrial cancer, hepatocellular carcinoma, sarcomas, neuroblastoma, and other cancers [32–35].

GENE PROFILES AS BIOMARKERS OF PROGNOSIS IN CANCER PATIENTS Despite the problems associated with gene proﬁling approaches that were noted above, there have recently been signiﬁcant advances in using gene proﬁling to predict prognosis in cancer patients. For example, van’t Veer and colleagues isolated at diagnosis 78 core samples from breast cancer patients under the age of 55 [36]. Of these patients, all were lymph node–free at the time of diagnosis. By measuring patient outcome over ﬁve years (regardless of treatment regimen), it was observed that 34 patients developed distant metastases, whereas 44 patients did not. Microarrays containing 25,000 human genes were prepared by the investigators, to which labeled cDNA samples from the patients and a pooled reference RNA sample were hybridized.

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Following supervised data analysis and correct classiﬁcation by a leave-oneout cross-validation approach, a classiﬁer of 70 genes was developed in which disease outcome could be predicted. When used within the 78-patient sample training set, the genetic proﬁle predicted patient outcome with an accuracy of 83%. In support of the classiﬁer, the 70-gene set predicted with 89% accuracy the prognosis of an additional 19 patients who were not included within the training set. The utility of the van’t Veer genes as prognostic biomarkers has been tested further using a larger study, which included 295 patients [13]. Sixty-one patient samples (from the study in which the 70-gene classiﬁer was developed) were included as a control. Of the patients used, 151 were lymph node–negative and 144 were lymph node–positive. Kaplan–Meier analysis was performed to determine the probability of patients remaining metastatis-free. The 70-gene classiﬁer showed excellent accuracy at predicting prognosis in all patients ( p < 0.001), including lymph node–negative patients (which generally have better outcomes). These ﬁndings further suggested that the acquisition of metastases in patients is already deﬁned through gene expression in their tumors rather than being acquired late during tumorgenesis. The ﬁndings from the two studies clearly indicate that the 70-gene classiﬁer can reliably predict prognosis in breast cancer patients and therefore may be useful in preventing overtreatment in some patients while identifying those that would probably beneﬁt from adjuvant therapy [13]. The 70-gene set (also known as MammaPrint) has recently been approved for use in the United States by the Food and Drug Administration (FDA). The utility of the van’t Veer classiﬁer in predicting prognosis for cancers of tissue origins other than breast is currently unknown. A recent investigation attempted to predict outcome in breast cancer patients using RNA from tumors of 162 patients [11] and from several cell lines to serve as the reference RNA (SW872, WM115, NTERA2, MCF-7, HEPG2, MOLT4, Hs578t, HL60, OVCAR3, COLO205, and RPMI 8226 cells). Both labeled reference and tumor RNAs were hybridized to arrays consisting of 10,368 unique genes. Analysis of the hybridized arrays by SAM, prediction analysis for microarrays (PAM), and the approaches described in the van’t Veer et al. study [36] resulted in the identiﬁcation of 49 genes that correlated with patient outcome. The gene list, when further reduced to 21 genes by including only those genes observed in all three analyses, displayed 69% accuracy by the leave-one-out cross-validation strategy. Using the gene set, the classiﬁer appeared to predict patient outcome with 65% accuracy using the van’t Veer data and 62% accuracy using data from another study [37]. Considering chance alone can yield as much as 60% accuracy; the gene set above was not validated. Furthermore, the expression of the van’t Veer prognostic genes was unable to classify the patients successfully in terms of disease outcome, though over half of the genes identiﬁed in the van’t Veer study were not present on the microarrays used. This underscores the difﬁculties in comparing across microarray experiments when different array platforms are used.

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In addition, successful prediction of prognosis using this subset of the 70-gene classiﬁer may have been increased by dividing patients according to lymph node status as was done in the van’t Veer follow-up study [13]. While the van’t Veer classiﬁer shows signiﬁcant promise, it has yet to be used widely in a clinical setting. An alternative gene classiﬁer has undergone considerable validation in a clinical setting. This classiﬁer was developed by selecting 250 candidate genes that showed promise as prognostic biomarkers, based on literature searches and previous microarray experiments [13,19,28,38]. The expression of these genes was then measured in tumor biopsies of 447 patients associated with three independent clinical trials. Analysis of the relationship between the tumor expression of each gene and patient outcome resulted in the identiﬁcation of 21 prognostic genes, including 16 cancerrelated genes and ﬁve reference genes. This 21-gene prognostic classiﬁer is now known as the Oncotype DX proﬁle [15]. Interestingly, ﬁve of the 16 genes in the Oncotype DX proﬁle were identiﬁed previously in the study by van’t Veer et al. [36]. The Oncotype DX genetic proﬁle measures the probability of disease recurrence in patients diagnosed with estrogen receptor–positive, lymph node–negative breast cancer who were treated with tamoxifen. The likelihood of recurrence is measured using a recurrence score, which is calculated by measuring the expression of the 21 genes and converts these values into a score ranging from 1 to 100. Low risk of recurrence is determined by a score of <18, while ranges from 18 to 30 and ≥31 indicates intermediate and high risk of recurrence, respectively. The Oncotype DX gene set has been validated in three additional clinical trials [15,39,40] and is currently undergoing further validation in a phase III study known as TAILORx. Furthermore, the Oncotype DX gene set has been studied further for its ability to predict factors other than disease recurrence. The recurrence score was recently used to measure recurrence risk in 651 patients who were randomly assigned to treatment with tamoxifen or tamoxifen and chemotherapy [41]. The 10-year distant recurrence rates were determined for the two treatment groups and it was revealed that low- and intermediate-risk patients (as predicted by RS) did not beneﬁt from chemotherapy. In contrast, high-risk patients derived great beneﬁt from chemotherapy. Therefore, the Oncotype DX recurrence score appears capable of identifying in a group of patients who typically have a relatively low risk of disease recurrence those who will probably beneﬁt from chemotherapy. This, in turn, would enable us to spend health care resources more efﬁciently by providing chemotherapy only to those who will beneﬁt from treatment.

CLASSIFICATION OF TOXINS AND PREDICTION OF DRUG TOXICITY USING GENE EXPRESSION PROFILING Proﬁling of gene expression in cells is not limited to the diagnosis, classiﬁcation, or prognosis of disease. One can also proﬁle changes in gene expression

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elicited by external agents such as chemotherapy drugs or toxicants. This can provide signiﬁcant insight into their mechanisms of action. It is theorized that certain families of drugs or toxicants induce similar changes in gene expression in various cell lines, such that gene expression proﬁles or signatures can be used to classify known and unknown drugs or toxicants. Nuwaysir and colleagues reviewed the potential utility of microarrays in the identiﬁcation, classiﬁcation, and characterization of toxicants in toxicology studies [42]. The authors suggested a procedure involving the treatment of speciﬁc cell lines or animals with or without a known toxicant, isolation of RNA from the cell lines or animal organs, reverse transcription of the RNAs to create differentially labeled cDNAs, and the proﬁling of gene expression by microarray analysis. The use of toxicants of a speciﬁc class with a known mechanism of action in gene proﬁling experiments would then allow researchers reliably to identify changes in gene expression associated with a known class of toxicant (a toxicant signature). After a wide variety of toxicant signatures have been established for known toxicants, the toxicant signature for an unknown toxicant could be established and compared to standard toxicant signatures in order to identify and/or classify the unknown toxicant. Moreover, the toxicant signature could provide signiﬁcant insight into the biochemical mechanisms responsible for its toxicity. Nuwaysir et al. further discussed how microarray technology could be used in toxicology studies involving animal models [42]. Current methods of toxicology measure toxin levels or the expression of speciﬁc hepatic enzymes levels within blood [42,43]. The use of animal models often requires that studies take place over an extensive period of time, particularly for chronic exposure to low levels of toxins. In contrast, gene expression changes typically occur quickly (in the manner of hours) and may require only low levels of toxicants. Therefore, speciﬁc posttreatment changes in gene expression may be effective measures of exposure to certain toxicants and may provide insight both into the mechanism of toxicity and into effective measures to combat this toxicity. As raised by Nuwaysir and colleagues [42], speciﬁc standards would have to be established, including the animal model to be used, the organs to be assessed for changes in gene expression, the concentration of compounds to be administered, and the time following exposure when gene expression will be measured. Such factors will probably be dependent on the toxicant, its properties, and its target organ. Other inﬂuences on the degree of toxicity and on gene expression would also have to be minimized, including the age of subjects or animals, variations in diet, and gender. Recently, there have been several attempts to use the approach described above to identify changes in gene expression associated with exposure to known hepatotoxic or nephrotoxic compounds in either human or animal models. Fielden et al. [44] used gene proﬁling approaches in rats to identify changes in gene expression induced by nephrotoxic agents, after conﬁrmation of renal tubular degeneration by histopathology. By identifying gene proﬁles associated with the onset of nephrotoxicity in rats, these authors theorized

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that drug development could be strongly facilitated by focusing on lead compounds that, when proﬁled, did not exhibit nephrotoxicity-associated changes in gene expression. In the study, rats were subjected to short, repeated exposures to well-classiﬁed toxicants or the vehicle in which they were dissolved. Changes in gene expression induced in the rats by 15 nephrotoxic and 49 nonnephrotoxic compounds were then studied by microarray analysis. Blood samples were assessed for changes in cholesterol or albumin levels, which would be consistent with the onset of nephrotoxicity. Rats were only sacriﬁced upon detection of nephrotoxicity-associated changes in blood levels of cholesterol or albumin. RNA was isolated from kidneys, and toxicant-induced changes in gene expression were determined for these compounds by cDNA microarray analysis using Amersham Codelink Uniset Rat1 Bioarrays and the sparse linear programming (SPLP) algorithm. Thirty-ﬁve genes were identiﬁed as changing expression only upon administration of nephrotoxic compounds to rats. Interestingly, within the training set, the 35-gene classiﬁer was found to have 83% accuracy for identifying nephrotoxic compounds with renal tubular degeneration and 94% accuracy for identiﬁcation of compounds that are not nephrotoxic. Studies were then conducted for cross-validation of the classiﬁer with an additional 21 independent rodent samples. In the validation experiments, nephrotoxic agents were identiﬁed with 79% accuracy and nonnephrotoxic samples with 75% accuracy. Although complete accuracy at identifying nephrotoxic agents was not obtained, this approach would nevertheless help in drug discovery programs to eliminate lead compounds that have a signiﬁcantly higher likelihood of being nephrotoxic. However, the identity of the 35 genes did not provide signiﬁcant insight into likely mechanisms for nephrotoxicity. It is also unknown whether the gene expression changes identiﬁed in rats were speciﬁc to kidney tissue. In addition, it should be noted that there are signiﬁcant differences between rats and humans in terms of their metabolism of and sensitivity to drugs and toxicants. Thus, while the study did appear able to show a signiﬁcant link between the expression of speciﬁc genes and nephrotoxicity, it remains unclear whether rats serve as a good model for nephrotoxicity in humans. An attempt to address the differences in toxicant metabolism between rats and humans was addressed in a study by Martin et al. [45]. This study utilized the potential of two toxicogenomic knowledge bases to estimate the hepatotoxicity of various compounds in humans based on changes in gene expression induced by the toxic compounds within rat in vitro and in vivo models. The in vitro analysis consisted of observing through microarray analysis changes in gene expression induced by 86 compounds, 36 of which were nonhepatotoxins. In contrast, the in vivo study analyzed gene expression changes induced by 111 compounds, 32 of which were nonhepatotoxic. Affymetrix RGU34 microarrays were used containing probes for 8799 genes. Toxicant-induced changes in gene expression were identiﬁed through comparison to control cell lines or rats exposed to the solvent in which the toxicant was dissolved. Using a tree-based machine learning technique known as gradient boosting, 173

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genes were identiﬁed whose expression would be expected to be modiﬁed by hepatotoxins in humans. In the in vivo experiments, 168 genes would be expected to change expression in humans by hepatotoxins. Unfortunately, while the in vitro and in vivo data identiﬁed real gene classiﬁers associated with hepatotoxicity, only ﬁve genes were identiﬁed in both the in vitro and in vivo experiments. Furthermore, none of the common genes identiﬁed were within the top 30 changes in gene expression, and validation experiments strongly suggested that both the in vitro and in vivo gene sets were found to be poor predictors of toxicity. To increase the reliability of predictive gene sets and decrease the degree of variability within the data, it is recommended that higher numbers of nontoxic compounds also be monitored for changes in gene expression (as controls) and that associated toxicology databases have better coverage of human studies. Given the ﬁndings described above, it appears that signiﬁcant additional work is required before reliable gene classiﬁers for the identiﬁcation of nephrotoxic or hepatotoxic compounds are available. Most toxicology studies are performed in animal model systems due to ethical concerns and it appears unlikely that such studies can be extrapolated easily to humans (particularly if rodents are used). Human cell lines could be used, but such in vitro experiments are unlikely to be replicated in living human subjects. Nevertheless, the development of gene classiﬁers for toxicants in nonhuman systems could assist the drug discovery process by avoiding lead compounds that induce changes in gene expression in both rodents and human cell lines which resemble strongly those of known nephrotoxic and hepatotoxic compounds. In addition, gene classiﬁers for agents capable of inducing toxicity to other organs (e.g., cardiotoxicity) can possibly be developed using this approach.

USE OF GENE PROFILING TO PREDICT DRUG SENSITIVITY OR RESISTANCE Approaches similar to those described above for toxicants can also be used to classify drugs and their modes of action. Moreover, it may be possible to use genome proﬁling of patients to identify those that would probably beneﬁt from administration of a particular drug. Considerable emphasis has been placed recently on the identiﬁcation of genes whose expression in the tumors or host tissues of cancer patients can predict or measure the response to speciﬁc chemotherapy agents or regimens. Various families of chemotherapy drugs act by different mechanisms within tumors, such as the inhibition of topoisomerase II or DNA replication [46–49] or the stabilization of microtubules [50]. Destruction of such essential functions would be expected to trigger cell cycle checkpoints and promote cell death mechanisms, resulting in speciﬁc changes in gene expression that are characteristic to the administered drug. In addition, since tumors can often be heterogeneous, there may exist within the tumor population variants that before or after drug treatment are better able to

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survive in the presence of chemotherapy agents. These or additional variants may also be capable of inactivating speciﬁc chemotherapy agents or blocking drug-induced cell death pathways. This would result in drug resistance, which is often observed in cancer patients before or after treatment with chemotherapy agents. By genome proﬁling of tumor or host tissues from cancer patients, it may be possible to identify which tumors or patients are likely to exhibit resistance to a given chemotherapy agent. Moreover, the genome proﬁle may enable the oncologist to select a chemotherapy agent that has the highest probability of killing a given tumor. The proﬁling and classiﬁcation of tumors into speciﬁc subtypes (as described above) may aid further in this goal. Chang et al. were the ﬁrst researchers to use gene proﬁling successfully to identify possible genes whose expression in tumors pretreatment could differentiate between responders and nonresponders of docetaxel chemotherapy [10]. Patient tumor size was measured throughout treatment, allowing classiﬁcation of 24 patients into 11 responders and 13 nonresponders (who had progressive disease or no response to treatment). RNA was isolated from tumors before chemotherapy, ampliﬁed, reverse-transcribed to cDNA, labeled with Cy3 or Cy5 ﬂuorescent dyes, and hybridized to HgU95-Av2 Affymetrix microarrays. Student’s t-tests were used to identify 92 genes whose expression was signiﬁcantly different between responders and nonresponders. Using these genes in a leave-one-out cross-validation approach, the gene set appeared to correctly classify responders and nonresponders with an accuracy of 88% within its training set. The genes identiﬁed were found to be involved in various cellular functions, such as cell signaling, immunologic response, DNA damage detection or repair, cell cycle regulation, and tumor suppressors. To conﬁrm this gene set, the expression level of 15 differentially expressed genes was determined by reverse-transcription polymerase chain reaction. Thirteen positive correlations were obtained, of which six were found to be highly signiﬁcant between responders and nonresponders. To test this genetic proﬁle further , RNA was isolated from six additional patients who were not involved in the training set. Quantitation of expression of the 92 genes using the same approach correctly identiﬁed all six as drug responders. The accuracy of the 92-gene proﬁle to predict response to docetaxel chemotherapy was then further evaluated in a follow-up study by the same investigators [51]. The authors observed gene expression of resistant and sensitive tumors after 3 weeks of treatment in a subset of 13 patients. Surprisingly, very few differences in gene expression were observed. Therefore, it would appear that changes in tumor gene expression shortly after administration of docetaxel cannot be used to differentiate between responding and nonresponding tumors. A study by Iwao-Koizumi et al. also used high-throughput Q-PCR to identify genes whose expression could predict response to docetaxel in 44 breast tumors [16]. The expression of 2453 genes was proﬁled in the tumors relative to controlling RNA from 78 primary breast cancers. WV, K-NN, and SVM approaches were used in data analyses to identify gene sets, which were then

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subjected to leave-one-out veriﬁcation studies. It was observed that the most consistent results were obtained with the use of the WV algorithm, which identiﬁed 85 genes that were capable of identifying response to docetaxel with 80.7% accuracy in veriﬁcation studies. To provide further evidence of their utility, expression vectors containing cDNAs for a number of the genes associated with resistance to docetaxel chemotherapy were transfected into MCF-7 cells and were found to increase survival in the presence of docetaxel compared to nontransfected controls. This suggests that observations from in vitro experiments using cell lines may have relevance to drug resistance in cancer patients. Interestingly, the genes determined to be associated with docetaxel response in this study differed greatly from those identiﬁed in the study by Chang and colleagues [10]. Only three genes were in common between the two studies. A number of differences in the studies may account for this discordance, including different patient populations, widely varying sets of genes being proﬁled, and differences in the algorithms used in data analysis. Use of a more quantitative method for gene proﬁling, the larger set of patients, and the higher prediction accuracy would suggest that the 85-gene set identiﬁed by Iwao-Koizumi has a higher probability of being replicated in other independent studies. Genome proﬁling was also used by Cleator et al. to identify genes whose expression is predictive of response to doxorubicin/cyclophosphamide chemotherapy in breast cancer patients [52]. In this study, RNA samples were isolated from 12 patients whose tumors responded to chemotherapy and from six patients whose tumors did not. After ampliﬁcation and labeling, the RNAs were hybridized to Affymetrix HgU133A arrays. After scanning the arrays, the output data were assessed using a “randomized variance model,” and 253 genes were identiﬁed to be differentially expressed between responding and nonresponding tumors. Leave-one-out cross-validation studies were then performed using the training set, and only 67% of samples were correctly classiﬁed as responders or nonresponders. This accuracy is only slightly better than chance. Thus, this approach could not be used to identify genes associated with response to doxorubicin/cyclophosphamide chemotherapy, or, more likely, the number of patients in the training set was of insufﬁcient size to reliably identify genetic biomarkers for prediction of response to doxorubicin/ cyclophosphamide chemotherapy. The authors of this study also tested the ability of the Chang 92-gene set for docetaxel responsiveness [10] (described above) to predict response to doxorubicin/cyclophosphamide treatment. The 92-gene proﬁle was unable to distinguish between responders and nonresponders of doxorubicin/cyclophosphamide chemotherapy, suggesting that genes for prediction of response to chemotherapy regimens are probably regimen-speciﬁc and not general biomarkers of drug response. A similar approach to that described above was used to identify 74 genes whose expression correlates positively or negatively with response to T/FAC chemotherapy (paclitaxel, followed by 5-ﬂuorouracil, doxorubicin, and cyclophosphamide). Accuracy at measuring response to this regimen was deter-

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mined to be 78% [53]. Independent expression of any one of the 74 genes could not be used to predict response to T/FAC chemotherapy. As mentioned previously, false discovery rates are often high in genome proﬁling experiments, particularly when tens of thousands of genes are surveyed. Thus, in the gene proﬁling studies above, the genes identiﬁed may correlate with drug response, but, in reality, may have no relationship to drug action. This would then explain the many failures that have been observed in identifying genes whose expression correlates with drug response across a wide variety of independent clinical studies. To circumvent this problem, it can often be advantageous to restrict the analysis of genes in patient populations to those that have demonstrated a clear relationship with drug response or resistance in in vitro studies. A number of studies have identiﬁed clear differences in gene expression between drug-sensitive and drug-resistant tumor cells. For example, Villeneuve et al. used microarray studies to identify a number of genes whose expression was altered in doxorubicin- or paclitaxelresistant MCF-7 breast tumor cells compared to wild-type cells [12]. The majority of genes (>98%) exhibited no difference in expression between the wild-type and drug-resistant cell lines, conﬁrming the isogenicity of the established cell line. One strength of this study was that 91 to 95% of the differences in gene expression identiﬁed by microarray analysis were corroborated through Q-PCR veriﬁcation experiments. In addition, control cell lines were created to account for differences in gene expression during selection for drug resistance that were simply due to extended propagation of cells in culture. The identities of the genes uncovered in this study were associated with drug transport, drug metabolism, growth promotion, and cell death inhibition, consistent with the types of genes that one might expect to observe in drugresistant cells. Thus, this approach has the prospect of providing signiﬁcant insight into the many and varied mechanisms by which tumor cells acquire resistance to chemotherapy drugs. The limitation of the study by Villeneuve et al. [12] is that gene expression was compared between single sets of isogenic wild-type and drug-resistant breast tumor cell lines. Although the genes identiﬁed may play a role in drug resistance in a single breast tumor cell line, some may have little relevance to drug resistance in other breast tumor cell lines or other cell lines of different tissue origin. In contrast, genes having altered expression in cell lines from several tissues upon acquisition of resistance to a speciﬁc drug have a higher likelihood of playing a bona ﬁde role in drug resistance, although probably not in a tissue-speciﬁc manner. Györffy et al. proﬁled gene expression across 30 different cancer cell lines using HGU133 Affymetrix arrays and identiﬁed genes whose expression correlated with sensitivity or resistance to 11 different chemotherapy drugs [24]. The accuracy of the gene sets to identify a cell line as sensitive or resistant to a given chemotherapy agent was 86%. Using this approach, 1481 genes were identiﬁed as playing a role in resistance to a diverse set of anticancer agents. Not surprisingly, the drug-resistance genes identiﬁed were different depending on the chemotherapy agent. Only 67 genes were

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observed to be associated with resistance to four or more anticancer agents, and the expression level of only two genes correlated with resistance to all 11 drugs. In a similar study [54], HGU133 Affymetrix arrays were also used in the study by Györffy et al. to identify genes associated with resistance in gastric cancer cells to three of the agents used. Despite similar array platforms, the number and identities of genes associated with resistance to 5-ﬂuorouracil, doxorubicin, or cisplatin varied dramatically between the two studies. A number of factors could account for the differences in the gene numbers and identities. Whereas the former study assessed a large number of wild-type and drug-resistant cell lines of varying tissue origin for sensitivity or resistance to 11 chemotherapy agents, the latter study examined only 14 gastric cancer cell lines, of which 10 were resistant to one of three chemotherapy agents. Thus, it is possible that the genes associated with drug resistance in gastric cancer cells are distinct from those that would have relevance across cell lines. Alternatively, variations in experimental design, the number of cell lines, and approaches to microarray data analysis resulted in the identiﬁcation of different gene sets, which may or may not be relevant to drug resistance.

STRATEGIES TO IMPROVE THE ACCURACY OF GENE PROFILING IN IDENTIFYING PROGNOSTIC AND PREDICTIVE BIOMARKERS As stated previously, very few gene sets have proven reliable in predicting response to chemotherapy agents across large groups of patients [10,11,16]. Numerous factors may contribute to the poor reproducibility of ﬁndings from gene proﬁling experiments, including differences in the tissues studied, the array platforms, the number and nature of genes on the microarrays, RNA extraction procedures, the number of replicate arrays used in experiments, and the data analysis and veriﬁcation methods. Simon and colleagues reviewed approaches to improving the accuracy of gene proﬁling experiments in the identiﬁcation of reliable predictive or prognostic biomarkers [26]. The ﬁrst limitation to the reliable identiﬁcation of predictive and prognostic biomarkers is the size of the training and validation sets, which in many studies is from 30 to 50 patients. Larger data sets of 500 patients or more have a much higher probability than small data sets of identifying useful biomarkers that can be validated across experiments. The use of more than one method for obtaining gene classiﬁers is also preferred. Moreover, in addition to larger data sets, there should be equal representation between patients from which the positive and negative classiﬁers will be developed. In addition, a number of studies only validate the utility of a gene set to positively identify patients with a particular attribute, without showing that the positive classiﬁer has no ability to identify patients without the attribute. Minna et al. [23] discussed further the reproducibly of genome proﬁling experiments across laboratories in an editorial written in response to a study

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by Hsu et al. [25] which identiﬁed a set of genes that could serve as a classiﬁer of cisplatin responsiveness. While acknowledging the promising prospects of the study by Hsu and colleagues, Minna et al. commented on what additional information could have been described or collected in this and other microarray experiments. These include the method used for classiﬁcation of patients, the analytical procedures used in the genome proﬁling experiments, and access to all microarray data so that it is available for analysis by researchers. The free availability of well-described data sets will enable future researchers to use this information to identify the best analytical methods for microarray data and to compare microarray data analysis tools effectively across experiments. This will enable researchers to determine if future gene sets can accurately predict drug response in groups of patients outside the initial training and validation sets. It will also enable researchers to repeat or apply new analytical procedures to previous data sets in order to make effective comparisons across experiments. Interestingly, it has been shown that with the use of identical gene arrays, extraction methods, and statistical analyses, identical gene signatures can be obtained across microarray experiments, despite differences in operators, sample handling, and time of extraction [55].

FUTURE DIRECTIONS There can be little doubt that genome proﬁling experiments are beginning to yield important new biomarkers of clinical relevance, particularly for the diagnosis of disease states. However, considerable variability exists in the performance of genome proﬁling experiments across studies, in particular for microarray studies. Differences in array platforms, the number and identities of genes on arrays, sample size, experimental design, methods of patient classiﬁcation and RNA extraction, and algorithms for data analysis all contribute to the wide variability observed in microarray ﬁndings. Only through comparisons across well-characterized microarray experiments can a clear conclusion be made as to the optimal design of a microarray experiment and optimal methods for both data analysis and patient classiﬁcation. Moreover, by describing array experiments in detail and ensuring full access to microarray data, it should be possible to repeat microarray data analyses using such optimal methods (once deﬁned). In the future, the preferred method of microarray experimental design and microarray data analysis will enable more accurate and reproducible ﬁndings to be achieved. This will dramatically increase the capacity of microarray studies to affect the clinical diagnosis and management of cancer patients. Microarray or PCR-based gene proﬁling experiments are powerful tools which, used optimally will dramatically increase the discovery of gene classiﬁers for the accurate diagnosis of cancer patients and for the optimal management of their care. This would probably include gene classiﬁers for the prediction of patient prognosis pre- and posttherapy, the optimal selection of

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chemotherapy agents, and the measurement of patient response and/or toxicity to these agents. Moreover, genome proﬁling is likely to have a major impact on drug discovery programs by enabling investigators to classify and characterize drugs and toxicants, with the goal of optimizing drug structures to achieve maximal therapeutic beneﬁt with minimal toxicity to patients. Gene proﬁling of patients and their tumors before and after drug treatment may also enable us to tailor our approaches to the management of cancer patients by accurately identifying those that will derive beneﬁt, with minimal toxicity, from a given chemotherapy regimen. For the pharmaceutical industry, this may also permit the introduction or reintroduction of highly effective drugs into the marketplace that exhibit toxicities in rare subsets of patients. Recent evidence conﬁrms that gene proﬁling experiments are beginning to revolutionize the practice of oncology and that physicians are prepared to embrace their use, provided that the utility of genetic biomarkers identiﬁed from gene proﬁling experiments are widely validated in appropriately conducted clinical trials. This appears to be the case for both Oncotype DX [15,41] and Mammaprint [56] gene sets for prediction of prognosis in women with breast cancer. The Oncotype DX gene expression assay will probably be widely used by breast oncologists for a number of reasons: 1. The recurrence score associated with the gene set used in this assay greatly aids the oncologist in assessing the prognosis of a patient and the need for adjuvant chemotherapy, such as MF/CMF [41]. This is particularly useful for low-risk patients with node-negative, ER-positive tumors, where the physician is likely to recommend adjuvant chemotherapy for patients with high recurrence scores but not for patients with low recurrence scores. 2. Respected clinical organizations such as the American Society of Clinical Oncology [57] and the National Comprehensive Cancer Network have established clear clinical guidelines for use of the Oncotype DX assay in patients with tumors of 0.6 to 1 cm with moderate to poorly differentiated or unfavorable features, and all tumors larger than 1 cm. 3. Gene expression is assessed by a highly quantitative procedure (Q-PCR) conducted centrally in a recognized laboratory following good laboratory practices (GLP) guidelines. 4. There is now evidence that an expanded Oncotype DX assay, which includes estrogen and progesterone receptor scores, provides a quantitative measure of the expression of these receptors with a high degree of concordance with standard immunohistochemical approaches [58]. This provides greater information to the physician on the degree of expression of the receptors and the conﬁdence that can be placed on the diagnosis and treatment plans based on receptor status. Such developments further increase the adoption of this assay by breast oncologists. In addition to the beneﬁts of the Oncotype DX assay, recent studies using the new molecular classiﬁcations of breast cancer (based on earlier genome

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proﬁling studies) suggest that responses to speciﬁc therapies may vary signiﬁcantly among the various subgroups. Successful adoption of the Oncotype DX assay into clinical trials and into clinical practice guidelines clearly illustrates that oncologists are willing to accept quantitative gene proﬁling of patient tumors as an important tool in the clinical management of a cancer patient, but this requires the successful translation of ﬁndings in research laboratories into validated robust assays that can be conducted in licensed clinical service laboratories. The support and cooperation of clinical societies is critical to the adoption of gene-based assays into the clinic, including the establishment of clear practice guidelines for the physician.

REFERENCES 1. Goldhirsch A, Glick JH, Gelber RD, Senn HJ (1998). Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer. J Natl Cancer Inst, 90:1601–1608. 2. Eifel P, Axelson JA, Costa J, et al. (2001). National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, Nov. 1–3. J Natl Cancer Inst, 93:979–989. 3. McGuire WL (1991). Breast cancer prognostic factors: evaluation guidelines. J Natl Cancer Inst, 83:154–155. 4. Bast RC, Ravdin P, Hayes DF, et al. (2001). 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol, 19: 1865–1878. 5. Gottesman MM, Fojo T, Bates SE (2002). Multidrug resistance in cancer: role of ATP-dependent transporters. Natl Rev Cancer, 2:48–58. 6. Jänicke F, Prechtl A, Thomssen C, et al. (German N0 Study Group) (2001). Randomized adjuvant chemotherapy trial in high-risk, lymph node-negative breast cancer patients identiﬁed by urokinase-type plasminogen activator and plasminogen activator inhibitor type 1. J Natl Cancer Inst, 93:913–920. 7. van Diest PJ, Michalides RJ, Jannink L, et al. (1997). Cyclin D1 expression in invasive breast cancer: correlations and prognostic value. Am J Pathol, 150:705–711. 8. Villeneuve DJ, Parissenti AM (2004). The use of DNA microarrays to investigate the pharmacogenomics of drug response in living systems. Curr Top Med Chem, 4:1329–1345. 9. ‘t Hoen PA, de KF, van Ommen GJ, den Dunnen JT (2003). Fluorescent labelling of cRNA for microarray applications. Nucl Acids Res, 31:e20. 10. Chang JC, Wooten EC, Tsimelzon A, et al. (2003). Gene expression proﬁling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet, 362:362–369.

152

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11. Korkola JE, Blaveri E, DeVries S, et al. (2007). Identiﬁcation of a robust gene signature that predicts breast cancer outcome in independent data sets. BMC Cancer, 7:61. 12. Villeneuve DJ, Hembruff SL, Veitch Z, Cecchetto M, Dew WA, Parissenti AM (2006). cDNA microarray analysis of isogenic paclitaxel- and doxorubicin-resistant breast tumor cell lines reveals distinct drug-speciﬁc genetic signatures of resistance. Breast Cancer Res Treat, 96:17–39. 13. van de Vijver MJ, He YD, van’t Veer LJ, et al. (2002). A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med, 347:1999–2009. 14. Brazma A, Hingamp P, Quackenbush J, et al. (2001). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Geneti, 29:365–371. 15. Paik S, Shak S, Tang G, et al. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med, 351:2817–2826. 16. Iwao-Koizumi K, Matoba R, Ueno N, et al. (2005). Prediction of docetaxel response in human breast cancer by gene expression proﬁling. J Clin Oncol, 23:422–431. 17. Chang JC, Makris A, Gutierrez MC, et al. (2007). Gene expression patterns in formalin-ﬁxed, parafﬁn-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients. Breast Cancer Res Treat, 108:233–240. 18. Kohlmann A, Schoch C, Schnittger S, et al. (2003). Molecular characterization of acute leukemias by use of microarray technology. Genes Chromosomes Cancer, 37:396–405. 19. Golub TR, Slonim DK, Tamayo P, et al. (1999). Molecular classiﬁcation of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531–537. 20. Zhao YP, Chen G, Feng B, et al. (2007). Microarray analysis of gene expression proﬁle of multidrug resistance in pancreatic cancer. Chin Medi J, 120:1743–1752. 21. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000). Support vector machine classiﬁcation and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16:906–914. 22. Pomeroy SL, Tamayo P, Gaasenbeek M, et al. (2002). Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415:436–442. 23. Minna JD, Girard L, Xie Y (2007). Tumor mRNA expression proﬁles predict responses to chemotherapy. J Clin Oncol, 25:4329–4336. 24. Györffy B, Surowiak P, Kiesslich O, et al. (2006). Gene expression proﬁling of 30 cancer cell lines predicts resistance towards 11 anticancer drugs at clinically achieved concentrations. Intl J Cancer, 118:1699–1712. 25. Hsu DS, Balakumaran BS, Acharya CR, et al. (2007). Pharmacogenomic strategies provide a rational approach to the treatment of cisplatin-resistant patients with advanced cancer. J Clin Oncol, 25:4350–4357. 26. Simon R, Radmacher MD, Dobbin K, McShane LM (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classiﬁcation. J Natl Cancer Inst, 95:14–18. 27. Haferlach T, Kohlmann A, Schnittger S, et al. (2005). Global approach to the diagnosis of leukemia using gene expression proﬁling. Blood, 106:1189–1198.

REFERENCES

153

28. Sørlie T, Perou CM, Tibshirani R, et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA, 98:10869–10874. 29. Perreard L, Fan C, Quackenbush JF, et al. (2006). Classiﬁcation and risk stratiﬁcation of invasive breast carcinomas using a real-time quantitative RT-PCR assay. Breast Cancer Res, 8:R23. 30. Hu Z, Fan C, Oh DS, et al. (2006). The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomi, 7:96. 31. Mullins M, Perreard L, Quackenbush JF, et al. (2007). Agreement in breast cancer classiﬁcation between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-ﬁxed, parafﬁn-embedded tissues. Clini Chem, 53: 1273–1279. 32. Cai B, Liu L, Xi W, et al. (2007). Comparison of the molecular classiﬁcation with FIGO stage and histological grade on endometrial cancer. Eur J Gynaecol Oncol, 28:451–460. 33. Seike M, Yanaihara N, Bowman ED, et al. (2007). Use of a cytokine gene expression signature in lung adenocarcinoma and the surrounding tissue as a prognostic classiﬁer. J Natl Cancer Inst, 99:1257–1269. 34. Tschoep K, Kohlmann A, Schlemmer M, Haferlach T, Issels RD (2007). Gene expression proﬁling in sarcomas. Critl Rev Oncol Hematol, 63:111–124. 35. Warnat P, Oberthuer A, Fischer M, Westermann F, Eils R, Brors B (2007). Crossstudy analysis of gene expression data for intermediate neuroblastoma identiﬁes two biological subtypes. BMC Cancer, 7:89. 36. van’t Veer LJ, Dai H, van de Vijver MJ, et al. (2002). Gene expression proﬁling predicts clinical outcome of breast cancer. Nature, 415:530–536. 37. Sotiriou C, Neo SY, McShane LM, et al. (2003). Breast cancer classiﬁcation and prognosis based on gene expression proﬁles from a population-based study. Proc Natl Acad Sci USA, 100:10393–10398. 38. Perou CM, Sorlie T, Eisen MB, et al. (2000). Molecular portraits of human breast tumours. Nature, 406:747–752. 39. Cronin M, Sangli C, Liu ML, et al. (2007). Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem, 53:1084–1091. 40. Habel LA, Shak S, Jacobs MK, et al. (2006). A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res, 8:R25. 41. Paik S, Tang G, Shak S, et al. (2006). Gene expression and beneﬁt of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol, 24:3726–3734. 42. Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA (1999). Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog, 24:153–159. 43. Batt AM, Ferrari L (1995). Manifestations of chemically induced liver damage. Clin Chem, 41:1882–1887. 44. Fielden MR, Eynon BP, Natsoulis G, Jarnagin K, Banas D, Kolaja KL (2005). A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol Pathol, 33:675–683.

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45. Martin R, Rose D, Yu K, Barros S (2006). Toxicogenomics strategies for predicting drug toxicity. Pharmacogenomics, 7:1003–1016. 46. Tarr M, van Helden PD (1990). Inhibition of transcription by adriamycin is a consequence of the loss of negative superhelicity in DNA mediated by topoisomerase II. Mol Cell Biochem, 93:141–146. 47. Zijlstra JG, de Jong S, de Vries EG, Mulder NH (1990). Topoisomerases, new targets in cancer chemotherapy. Med Oncol Tumor Pharmacother, 7:11–18. 48. Binaschi M, Bigioni M, Cipollone A, et al. (2001). Anthracyclines: selected new developments. Curr Medi Chem Anticancer Agents, 1:113–130. 49. Galmarini CM, Mackey JR, Dumontet C (2002). Nucleoside analogues and nucleobases in cancer treatment. Lancet Oncol, 3:415–424. 50. Chazard M, Pellae-Cosset B, Garet F, et al. (1994). [Taxol (paclitaxel), ﬁrst molecule of a new class of cytotoxic agents: taxanes.] Bull Cancer, 81:173–181. 51. Chang JC, Wooten EC, Tsimelzon A, et al. (2005). Patterns of resistance and incomplete response to docetaxel by gene expression proﬁling in breast cancer patients. J Clin Oncol, 23:1169–1177. 52. Cleator S, Tsimelzon A, Ashworth A, et al. (2006). Gene expression patterns for doxorubicin (Adriamycin) and cyclophosphamide (Cytoxan) (AC) response and resistance. Breast Cancer Res Treat, 95:229–233. 53. Ayers M, Symmans WF, Stec J, et al. (2004). Gene expression proﬁles predict complete pathologic response to neoadjuvant paclitaxel and ﬂuorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol, 22:2284–2293. 54. Kang HC, Kim IJ, Park JH, et al. (2004). Identiﬁcation of genes with differential expression in acquired drug-resistant gastric cancer cells using high-density oligonucleotide microarrays. Clin Cancer Res, 10:272–284. 55. Kohlmann A, Schoch C, Dugas M, et al. (2005). Pattern robustness of diagnostic gene expression signatures in leukemia. Genes Chromosomes Cancer, 42:299–307. 56. Wittner BS, Sgroi DC, Ryan PD, et al. (2008). Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clin Cancer Res, 14:2988–2993. 57. Harris L, Fritsche H, Mennel R, et al. (2007). American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol, 25:5287–5312. 58. Badve SS, Baehner FL, Gray RP, et al. (2008). Estrogen- and progesteronereceptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory. J Clin Oncol, 26:2473–2481.

8 USE OF HIGH-THROUGHPUT PROTEOMIC ARRAYS FOR THE DISCOVERY OF DISEASEASSOCIATED MOLECULES Douglas M. Molina, Ph.D., W. John W. Morrow, Ph.D., and Xiaowu Liang, Ph.D. Antigen Discovery, Inc., Irvine, California

INTRODUCTION The discovery of biologically relevant disease-state markers, or biomarkers, is a crucial step in the maturation of accurate disease diagnosis and appropriate treatment. With all the advances in modern research, the need for better diagnostics as well as markers of disease is paramount. The management of infection, cancer, and autoimmune disorders could beneﬁt greatly from new biomarkers, or combinations of biomarkers, that could provide quick and accurate identiﬁcation of the causative agent of disease, disease state, and even the stage of the disease. The lack of progress in identifying such molecules is due primarily to the way that, to date, researchers have approached the problem. When using traditional approaches to the discovery of serodiagnostic antigens, researchers have been limited to the number of molecules they could study. Typically, most laboratories have studied just a handful of proteins at any given time and therefore have only a limited understanding of the pathobiology of disease. Much like the children’s game Flip ‘n’ Match, inves-

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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Figure 1

DAY 4 Chip QC

QC with Anti-Tag Ab

DAY 5 Serology

DAY 6 Data Acquisition

Serum Sample Profile

High-throughput proteomics screening technique used by the authors.

DAY 3 Chip Printing

DAY 3 Expression

DAY 2 Plasmid DNA Miniprep

DAY 1 PCR & Transformation

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tigators have employed the “best guess” approach to which “pieces” (i.e., antigens or proteins) may be most appropriate as disease markers. Such bestguess hypotheses have usually been based on the body of literature available as well as extrapolations from our understanding of the immune response. Much as in the game, once in a while the correct choice is made. However, a much more productive approach would be to view all the pieces simultaneously and assess the complete picture, without prejudice, in the context of the immune response proﬁle. In this way we would be in a much better position to make determinations as to which proteins, or combinations of proteins, would allow us to diagnose and monitor disease accurately. Very recently, such an approach has been made possible, and in this chapter we discuss how researchers have taken advantage of the sequencing boom sparked by the human genome project. We also examine assay miniaturization technology, which has carried over from the nucleic acid microarray ﬁeld, and how new advances in cloning and gene expression have allowed the use of those resources to proﬁle the immune response to infection, cancer, and autoimmune disease.

OVERVIEW OF THE PRODUCTION AND USE OF MICROARRAYS FOR THE DISCOVERY OF ANTIGENIC BIOMARKERS To take advantage of genomic sequence data for the development of novel therapeutics and clinical diagnostics, high-throughput cloning and screening technologies are required. The authors and own collaborators have developed and described a gene cloning and expression platform that allows whole proteome microarrays to be constructed from genomic sequence information in a relatively short time. The platform can then be screened with large numbers of well-characterized sera to identify antibody targets. This powerful highthroughput approach lends itself to the rapid identiﬁcation of reactive antigens on an epidemiological scale, and should lead to the development of improved serodiagnostic tests for many diseases (Figure 1).

PROTEIN MICROARRAYS In the context of the discovery of disease-associated molecules, protein microarrays can be thought of as miniaturized dot blots or highly multiplexed enzyme-linked immunosorbent assays (ELISAs) for serology, and highly multiplexed sedimentation, co-immunoprecipitation assays, or enzyme function assays for protein biochemistry. They allow data collection from hundreds of parallel interaction studies in an area the size of a single well of a 96-well ELISA plate. To achieve this type of miniaturization, the chemistry of the surface being used for spotting the proteins is important. Reagents can

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be coated onto activated plastics or glass to accommodate the deposition and subsequent binding of the protein onto the surface. Some examples of the chemistries used to bind proteins to the substrates are described below.

Two-Dimensional Substrates Well-characterized protein-binding substrates, used extensively in other assays, have been transferred or “ported” for the use of microarray fabrication. Most of these surface substrates were used extensively in DNA microarray research and have been used with success with puriﬁed protein arrays. There are two principal ways that proteins bind to surfaces: (1) by the formation of covalent bonds with reactive groups on the surface of the slide, and (2) by noncovalent interactions with the slide substrate. In slide surface chemistry, the use of aldehyde and epoxy coatings allows for covalent bonds between the proteins by virtue of the amine group on amino acids and the activated aldehyde of the epoxy group on the slide [1,2]. The primary amine group reacts with the aldehyde, or epoxy, groups on the slide surface. The electrons on the nitrogen attach to the carbon with a partial positive charge on the reactive group and form a nitrogen–carbon bond. In protein–aldehyde coupling, the attachment is stabilized by a dehydration reaction. Another option for two-dimensional substrates is the coat the slide surface with a molecule that is part of a high-afﬁnity binding interaction. Binding-partner substrates are not as common but are still useful. There are examples of protein-binding surfaces that have been treated with avidin, streptavidin, glutathione, and monoclonal antitag antibodies. Unlike the aldehyde and epoxy examples, these are very speciﬁc and require that the appropriate binding partner be coupled to the protein(s) being spotted.

Three-Dimensional Substrates Polyacrylamide-coated slides form a three-dimensional substrate for protein microarrays. Much like the polyacrylamide gels used for electrophoresis, they are reported to provide high probe loading capacity and a hydrophilic, solution phase–like environment, which is thought to preserve protein probe function [3–6]. Consequently, this ﬂexible and versatile platform is well suited for many types of proteins and has been used to develop a variety of assays in microarray format. A three-dimensional hydrogel coating preserves the native three-dimensional structure of proteins, thereby maintaining stability and functionality. The reactive chemistry is stable and remains active even during very long spotting (also know as “printing”) runs. Agarose is an economical, protein-friendly medium that reduces the background of the slide signiﬁcantly, which can interfere with signal detection of low-abundance proteins or pep-

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tides. There are other proprietary three-dimensional porous polymer surfaces, such as those produced by Full Moon Biosystems, that claim to provide a high level of sensitivity and speciﬁcity for ﬂuorescence-based assays. All seem to have similar properties, which include covalent multifunctional bonding sites that provide high binding speciﬁcity by covalent attachment to surface functional groups. Nitrocellulose is the best-characterized protein-binding chemistry that has been adapted successfully to microarray fabrication. Nitrocellulose membranes are used for immobilizing a wide variety of molecules, such as nucleic acids in Southern and Northern blots, as well as proteins in Western blots. Nitrocellulose makes an ideal surface for a wide variety of assays, including antibody-based assays, due to its nonspeciﬁc afﬁnity for proteins. Nitrocellulose is usually coated onto a nylon backbone to produce membranes. It has also been coated on activated glass microscope slide, and as such has become a very popular tool for the fabrication of protein microarrays. A variety of nitrocellulose-coated slides are currently available which are ideal for microarrays because of their high binding capacity per unit volume. At one end of the spectrum, the nitrocellulose surface may be as thin as 5 μm (Path Slides, GenTel), and at the other, the membrane can be as thick as 150 μm (SuperNitro, Telechem). In the case of nitrocellulose-coated slides, it is critically important to pick the correct thickness for the application. The thickness of the coating chosen will be inﬂuenced greatly by the type of microarray printing that is to be used. Contact vs. Noncontact Printing Like DNA microarray fabrication, protein microarray fabrication is dependent on applying the protein onto the substrate reliably and reproducibly. To achieve this objective, two major classes of spotting, or microarray printing, have evolved. Noncontact printing is the mechanism of spotting onto the slide or plate surface without any contact. The volumes range from picoliters up to hundreds of microliters having coefﬁcients of variation (CVs) of less than 10%, depending on the technology used. One such technology, solenoid-based dispensing systems, relies on the rapid opening and closing of valves, which permits the ﬂow of pressurized liquid. Rapid opening and closing of a valve, or pulsing, results in a very small volume being deposited. Leaving the valve open longer results in the deposition of larger volumes. Solenoid-based systems typically spot between 10 nl and hundreds of microliters. Another noncontact technology, piezoelectric-based dispensing systems, can dispense as little as picoliter quantities of liquid. They rely on electrical pulses that create a pressure wave when a piezoelectric element is deformed and squeezes the tubing, the pressure created results in the displacement of a small volume of liquid. By opening the valve at the precise moment that the piezoelectric element is deformed, a small volume of liquid is deposited on the substrate.

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The main drawback of noncontact printing is that it is not the ideal tool for arraying more viscous or concentrated protein mixtures. Cell lysates immediately come to mind when considering heterogeneous materials that may cause problems for noncontact arrayers. There is a high potential for clogging not only the dispensing solenoids, but the tubing as well. Another drawback is that the noncontact dispensing robots available are limited by the number of valves that can be used to array spots onto a slide or plate; thus, throughput becomes a problem. When printing thousands of different samples, the number of solenoids becomes the limiting step. Contact printers address these shortcomings directly. Although there are numerous variants of contact printing technologies, the two most prevalent are the solid pin and split pin methods. Unlike the solenoid- and valve-based systems, pin-based printing systems are not limited by size. Some printers use the traditional 9-mm pin spacing used in 96-well microtiter plates, but the majority use 4.5-mm pin spacing. More recently, printers that use 2.25-mm pin spacing have become available. Depending on the size of the printhead, as many as 256 pins in an 8 × 32 conﬁguration can be ﬁtted when the 2.25-mm spacing is used. This results in 256 samples for every sample uptake, or over 1000 samples with only four sample uptakes. The basics are the same in solid pin and split pin printing. The robot dips the pins into the samples, the pins deposit the sample on a substrate by making contact, the pins re-dip when necessary, and the pins are washed before the next sample. Solid pins must re-dip after every deposit on a substrate; split pins act more like a quill and ﬁll up a reservoir, of which only a fraction is deposited every time the pins make contact. The number of spots that can be made after sample uptake by split pins varies greatly depending on a number of factors. The viscosity of the sample, the substrate type due to varying amounts of adsorption, the humidity, the pin design, and whether or not it has a reservoir are all factors and must be tested for empirically. When making DNA arrays, the samples are more homogeneous; they have similar viscosities, similar concentrations (moles of DNA), and similar molecule substrate interactions. When dealing with protein microarrays these characteristics are seldom the same. Proteins are usually dialyzed after puriﬁcation with whichever buffer will keep the protein in solution. This can very widely from protein to protein. This will contribute to potential viscosity differences. Protein concentrations can vary greatly depending on the success of expression and subsequent puriﬁcation of the protein. In case the concentrations are similar, proteins vary in size and there will be a heterogeneous distribution of the number of molecules per spot. The amino acid sequence of the protein(s) will also inﬂuence how it interacts with the substrate on which it is being spotted. In addition, the isoelectric point of the proteins will undoubtedly have an effect on how they bind to the substrate, and furthermore, hydrophilic proteins will behave differently than hydrophobic proteins. There is also the problem of protein aggregation, or too high a concentration, clogging a split pin. There are various-sized split pins with chan-

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nels up to 400 μm across which would accommodate most substances. As attempts to make higher-density arrays to increase throughput take place, this option become less feasible. When the substrate to be arrayed is not compatible with the split pins necessary to meet the array density required, solid pins must be employed. The need for solid pins is more of an issue when arraying cell lysates, cells of bacterial culture fractions. Solid pins are a better choice for these situations, but at the expense of throughput. Even the fastest solid pin machine, the Aushon 2470, requires twice the number of pins to outperform the fastest split pin systems, such as the Omnigrid line of microarray printers manufactured by Genomic Solutions. Throughput may or may not be an issue, but when dealing with proteins, most researchers need the process to take as little time as possible.

GENOME–PROTEOME CONTENT CURRENTLY USED FOR BIOMARKER RESEARCH There is a need for an expressible open reading frame (ORF) eome that can be made into an arrayable proteome in the search for disease biomarkers. The number of proteins that are available for screening limits disease biomarker discovery. For example, in the search for human autoantigens, the availability of proteins to use in screening studies has been a bottleneck. The National Institutes of Health (NIH) has a collection of approximately 29,636 human ORFs that contain 17,526 (source: http://mgc.nci.nih.gov/) nonredundant gene sequences. Although Invitrogen has cloned all the human ORFs into their Gateway system, the creation of an assayable human proteome has been limited by the protein puriﬁcation process. After almost a decade, the number of human proteins contained on their commercial Human ProtoArray chip remains at 8000 (<30% coverage). Recent work has shown that that these ProtoArray human protein chips, although limited in scope, have been used successfully to identify serodiagnostic autoantigens (cancer biomarkers). Hudson et al. proﬁled serum samples from 30 cancer patients and 30 healthy persons using microarrays containing 5005 human proteins. Ninety-four discriminatory serodiagnostic antigens were identiﬁed that exhibited enhanced reactivity from sera in cancer patients relative to control sera [7]. This is a good proof-of-principle experiment that should be expanded to verify that the 30 serum samples used for this experiment are actually representative of the ovarian cancer population as a whole, not just markers for families that are predisposed to this type of cancer but may never develop the disease. An additional control would have included serum samples from the sisters of the patients who did not have ovarian cancer. To assay even a small population with an appropriate number of controls is ﬁnancially prohibitive using the commercially available human ProtoArray. The array is available for $3000 each. For this reason the Human ProtoArray

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chip offered by Invitrogen has not been utilized for large-scale screening. Small-scale experiments may be ﬁne for a publication, but to identify robust biomarkers there needs to be a massive screening that allows proﬁling as many healthy, predisposed, and diseased persons as possible so that the data derived from the experiment are biologically relevant. It is only at this scale that we can truly make any conclusions about the immune response. The reason that we still have not found the ideal biomarkers is due to the complexity of immune response and its variability from person to person. The current human protein chip is being distributed as a consumable with a sizable price tag. To attempt a study of the size required to determine disease-state discriminatory antigens, or groups thereof, there needs to be a higher-throughput, more costeffective method of generating the human proteome chip. Infectious disease biomarker discovery faces challenges as well. Puriﬁed proteins of many infectious agents are scarce. Puriﬁed protein microarrays of pathogens usually include only a handful of proteins. The lack of content has been the major reason for the slow rate of emergence of new serodiagnostic markers. For example, only 149 out of the 4198 ORFs (3.5%) constituting the Yersinia pestis proteome were used to proﬁle the antibody response to live vaccine [8]. In addition, 156 out of approximately 1000 (15.6%) Chlamydia trachomatis proteins were used to proﬁle the human humoral immune response to urogenital tract infections in human subjects [9]. The ability to express and purify recombinant proteins in the lab limits the amount of unique content available for serological screening. This low percentage of proteome coverage is common, and most laboratories in this ﬁeld are studying a few antigens at a time from thousands of possible candidates. One of the innovative ways is which researchers have overcome the lack of coverage is to apply the lessons learned from whole-cell Western blots and two-dimensional gels to microarray research. Native protein microarrays are heterogeneous protein pools that have been fractionated using chromatography. Sartain et al. developed a technique for the separation of native Mycobacterium tuberculosis cytosol and culture ﬁltrate proteins that resulted in 960 unique protein fractions that were used to generate protein microarrays. These 960 fractions represented all the expressed proteins, having some proteins represented in different fractions. When these microarrays were used to proﬁle the reactivity of different disease states, various previously characterized proteins as well as some novel proteins were identiﬁed from these fractions using mass spectrometry [10]. A similar approach was utilized in a different ﬁeld to proﬁle the sera of prostate cancer patients and controls. Twodimensional liquid chromatography was used to separate proteins from the prostate cancer cell line LNCaP into 1760 fractions, which were subsequently spotted onto a microarray. The microarrays were probed with serum samples from 25 men with prostate cancer and 25 male controls. Statistical analysis revealed that 38 of the fractions showed signiﬁcantly more reactivity in the cancer group. Samples were classiﬁed with up to 98% accuracy using the same sera [11]. Although a good academic exercise, the protein fraction microarray

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needs further studies to uncover the proteins responsible for the reactivity. In the tuberculosis case, mass spectrometry was able to characterize a number of reactive proteins, but the authors conceded that this approach was a stopgap until the complete proteome is cloned, expressed, and arrayed. In the case of prostate cancer the reactive fractions must be analyzed further to determine which of the many proteins represented in the fractions are the important ones. Another challenge that faces users of protein fraction microarrays is that proteins are not all represented equally in these fractions. Antigens that potentially are extremely good biomarkers may be underrepresented, due to an expression level regulated by cell degradation pathways that may target these proteins.

HIGH-THROUGHPUT GENE CLONING AND EXPRESSION FOR THE FABRICATION OF PROTEOME MICROARRAYS The genomics era ushered in a new type of research. For the ﬁrst time in history the decoded “blueprint of life” was available for study. The Human Genome Project (HGP) was the ﬁrst major undertaking of its kind. The HGP was a 13-year project coordinated by the U.S. Department of Energy and the National Institutes of Health that was completed in 2003. The goal was to identify all of the approximately 20,000 to 30,000 genes in human DNA, determine the sequences of the 3 billion chemical base pairs that make up human DNA, and store this information in databases. This initiative sparked the boom in genome sequencing, and today there is a vast amount of publicly available data stored in numerous databases. The genomes of more than 180 organisms have been sequenced since 1995, and the data from these projects are available to the scientiﬁc community. The availability of genome sequence data leads to the ﬁrst big boom in microarray research. Researchers used this sequence to create libraries of oligo nucleotides representing all the ORFs of the organism being studied. Robotic machines were then used to transfer and arrange nanoliter amounts of thousands of gene sequences representing a cell expression state on a single microscope slide. These high-density, highly organized arrays of DNA are called microarrays. DNA microarrays allowed for the miniaturization and high-level multiplexing of Southern blots. Affymetrix and Agilent technologies pioneered the use of DNA microarrays for full genome gene expression analysis. Researchers were now able to track the expression of genes in response to various stimuli and incorporated this new tool in their existing research. They could track the synthesis and degradation at the mRNA level, to determine which genes are turned on and which are turned off in a given cell. We now knew the number and sequence of the expressible genes and the expression pattern of the genes in response to a particular stimulus. An area that still needs to be explored, however, is how the proteins performing their function once expressed. This conundrum has ushered in the age of protein microarrays.

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Currently, protein microarrays are being used for a variety of assays. There has been much trial and error when attempting to miniaturize binding assays, enzyme activity assays, and serological assays. The same tools that helped propel the era of the DNA microarray are being applied to protein microarrays. Protein microarrays permit the miniaturization and high-level multiplexing of protein-based assays. Companies like Protometrix cloned, expressed, and puriﬁed thousands of proteins to make the Yeast Proteome chip and the Human Partial Proteome chip. The Human chip, now on version 8, is available commercially from Invitrogen, with approximately 8000 human proteins that can be used for screening binding partners, substrates for enzymes, small molecules, and antibody proﬁling. That is the equivalent of 8000 cosedimentation assays, 8000 phosphorylation assays, or 8000 ELISA assays on a surface area of 3 inches × 1 inch. When all the approximately 29,000 ORFs that have been cloned are eventually expressed and printed on a microarray, all proteins expressed by human cells will be contained on two or three microscope slides and ready for assay. Traditional multistep cloning methods have been a major stumbling block because of their innate low throughput. Starting ﬁrst by amplifying the desired insert by polymerase chain reaction (PCR), then digesting it and the vector, followed by ligation, subsequent transformation, plating, colony picking, and veriﬁcation of clones, the volume of work is the rate-limiting step. Technical problems may be encountered in any of the aforementioned steps, and most often than not, researchers spend months cloning a few genes of interest. There are, however, ways to circumvent this laborious cloning methodology when attempting to manufacture a high-content microarray. One way is to take advantage of the vast genomic sequence data and the high-throughput friendly PCR reaction to amplify out all of the ORFs, followed by a second PCR step to make the products transcriptionally active. The secondary PCR step adds a promoter region at the 5′ end and a stop codon at the 3′ end of the PCR product, making a transcriptionally active PCR (TAP) product. The production of TAP fragments for expression can yield a large library of expressible ORFs in a relatively short amount of time. TAP fragments have been reported to express very well in a variety of expression systems [12]. Organisms with large genomes that would otherwise take decades to clone into an expressible form will now only take two rounds of PCR. A recent report by Regis et al. has shown that in the case of Plasmodium falciparum, TAP fragments could be used for a large-scale screening of the humoral immune response and eventual selection of antigens for further study [13]. While providing the throughput, TAP fragments are limited by the amount of product produced during the second PCR step. Recent research has shown that serological screening of cDNA libraries created from cancer cell lines can be used for the discovery of cancer biomarkers. Cancer is the result of a synergistic malfunctioning of multiple signaling pathways. This may or may not be the same set of pathways, proteins, and mutations in every cancer type and tissue, so it becomes even more critical to

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cast the widest net to ﬁnd the most potential biomarkers [14]. Lung cancer cell lines were used to make cDNA libraries that have been used successfully for serological identiﬁcation of serodominant antigens [16,17]. This is encouraging, but the task at hand with this technology is more daunting. After the creation of the reverse complement of the mRNA, there is still a need to use brute-force cloning techniques to accomplish the task. One must keep in mind that the majority of mRNAs in most tissues are in low abundance, and thus quite a few of the mRNAs will be underrepresented in the cDNA library [15,18]. Extremely large number of clones are required to ensure a good representation of these low-abundance genes. There is also the very real problem of partial cDNA clones, which do not have the complete sequence. If we are fortunate enough to minimize these two issues, there is still the very real bottleneck of screening the clones. High-density gridding is becoming more and more common and makes it possible to screen large amounts of mRNA complementary DNA. This technology is designed largely for gene discovery, but if reﬁned to the point that all mRNA transcripts are represented equally, we may have a tool for more reliable screening of human serum samples. The next rung on the ladder would be the creation of an expressible genome library that contains all the ORFs in the genome. To make the genome library quickly and efﬁciently, we need to consolidate all the traditional cloning steps into one efﬁcient step. Recombination in vivo provides the all-encompassing single step. Recombination cloning is widely used in research, but for a long time it was thought that bacteria lacked the mechanism to allow for recombination. However, bacterial in vivo homologous recombination has been an efﬁcient and heavily used tool in the genetic ﬁeld. Recombination-based cloning allows DNA sequences to be inserted or deleted without regard to location of restriction sites [19,20]. One very widely used methodology for recombination cloning is the Gateway system now offered commercially by Invitrogen. The ﬁrst step to Gateway cloning is inserting the gene of interest into the Gateway entry vector. There are two ways to clone your gene of interest into a gateway entry clone. The ﬁrst is the standard cut-and-paste protocol using restriction enzymes and ligase. The second way is to create a PCR product with terminal attB sites, using primers containing a 25-base pair attB sequence, plus four terminal G’s. This product will then be inserted in the entry clone using reconstituted recombination machinery. PCR-based inserts can be made using genomic DNA, a cDNA library, or an plasmid clone containing your gene of interest. An entry clone is a vector containing your gene of interest and ﬂanked by Gateway att recombination sites. Bacteriophage lambda att site recombination is a well-characterized phenomenon [21]. Bacteria have a stretch of DNA sequence called att encoded into their genome, and bacteria phage have the same stretch of DNA sequence. When the phage infects a bacterium, the lambda DNA injected recombines with the corresponding bacterial DNA via the att sites in the presence of integration-speciﬁc enzymes. The enzyme-assisted recombination results in integration of the phage DNA into the bacterial genome. Using the same reconstituted lambda

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att site recombination system, entry clones containing a gene of interest can then be transferred into any expression vector for subsequent protein expression. Although the Gateway System works and increases cloning throughput dramatically, it is not efﬁcient enough to work as a true high-throughput alternative. In vivo recombination cloning directly into an optimized expressible vector, however, appears to have solved the throughput dilemma. Recent advances in cloning methodologies have resulted in the ability to clone entire bacterial ORFeomes, comprising thousands of genes, in a relatively short time. Figure 2 illustrates the typical results one would see beginning with the PCR step followed by the in vivo recombination cloning, and checking the clones by PCR using sequence speciﬁc primers. Speciﬁcally, in vivo recombination cloning [22] in Erscherichia coli has broken the barriers posed by traditional brute-force cloning methodologies that rely on multiple-step cloning techniques: from PCR, restriction enzyme digestion of the PCR product and vector, ligation, to single colony screen and selection. Available sequence data are used to design 5′ and 3′ gene-speciﬁc primers for all ORFs encoded in the genome. The primers contain 53 nucleotides, which are comprised of a 33-nucleotide recombination adapter sequence and a 20-nucleotidegene speciﬁc sequence. The PCR products are then mixed with a T7-based linear expression vector as described previously [22] and transformed into supercompetent DH5a cells (Antigen Discovery Inc.). The cells transformed are grown overnight and are checked for turbidity the following day. DNA can then be puriﬁed from the mixed cultures using 96-well plate–based mini-prep protocols such as Qiaprep Turbo 96. The plasmid DNA puriﬁed from overnight culture can then be used to express the protein encoded by the insert. Proteins are expressed in a coupled in vitro transcription-translation (IVT) reaction in an E. coli–based cell-free

Figure 2 Synthesis and veriﬁcation of clones. Representative images of whole Vv ORFeome PCR (A), cloning (B), and QC-PCR (C).

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expression system such as RTS 100 from Roche. The unpuriﬁed proteins can then be printed directly to nitrocellulose-coated slides along with a set of negative and positive controls using a contact microarray printer. It is crucial that the mixture of unpuriﬁed proteins be spotted as quickly as possible. The quality of the arrays is inversely proportional to the amount of time from the end of the reaction to the deposition onto the nitrocellulose. Once the unpuriﬁed protein mixture is spotted and dry, it is very stable. In the high-throughput microarray fabrication ﬁrst reported by Davies et al. in 2005 [22], the cell-free expressed proteins can be detected using antibodies against the N-terminal polypolyhistidine (polyHis) tag and the C-terminal hemagglutinin (HA) tag directly on the chip. The antibodies were used to monitor expression of the large numbers of parallel reactions. The arrays probed using a mouse monoclonal antibody raised against the polyHis epitope and rat monoclonal antibody raised against the HA epitope are visualized using a ﬂuorescent-based microarray scanner. An example of one such hybridization and scanning output can be seen in Figure 3. The data can then be quantiﬁed using a microarray data analysis software package that can quantify the intensity of the spots on the microarray chip. Each array contains positive control spots printed from serial dilutions of whole immunoglobulin G (IgG). Each array also contains no DNA negative control spots, and the reactivity of

(A)

(B)

Figure 3 Quality assessment for protein expression. Images of whole Vv proteome microarray probed with mAb against polyhistidine (A) and hemoagglutinin tags (B). 99.0% of Vv proteins were reactive to antipolyhistidine antibody and 88.2% to antihemoagglutinin antibody. The images show four dilutions of human IgG (yellow box), positive controls (green box), and six negative controls (mock transcription/translation reaction) (red circles). The remaining spots are Vv proteins. (See insert for color reproduction of the ﬁgure.)

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these spots is low for both serum samples. The positive and negative controls are used to normalize the data using a modiﬁed VSN package in a statistical environment named R [23,24] from arrays that are probed on different days. There are also serially diluted EBNA1 protein control spots, which have been shown to be reactive to varying degrees in different human subjects [25–27]. Once an array has passed quality assurance, it is ready to be used in serological studies.

USING WHOLE PROTEOME MICROARRAYS TO SCREEN FOR DISEASE-STATE DISCRIMINATORY ANTIGENS The ability to ﬁnd biomarkers relies on the ability to proﬁle the immune response to entire proteomes. Antigens that are highly reactive to infection, or auto-antibodies, can be used in diagnostics and therapeutics. They are also potential candidates for the development of vaccines. Using the model organism vaccinia virus Western Reserve strain, researchers were able to use in vivo recombination cloning to clone the 185 Vv proteins, or greater than 90% of the proteome, from genomic DNA into an expressible vector. This proteome vector library was subsequently expressed and arrayed onto nitrocellulose-coated slides. Each slide contains 16 nitrocellulose pads with a Vv proteome array per pad. Each slide is capable of producing serological data for 16 serum samples interrogating approximately 200 spots (185 Vv proteins plus controls), yielding 3200 data points per slide, the equivalent of thirty-three 96-well ELISA plates. Davies et al. [22] showed that there are speciﬁc vaccination-state and disease-state proﬁles that are discernible using whole proteome screening. Comparing the pre- and postvaccination data for each species, naive mice, monkeys, and humans have a very different reactivity pattern than that of vaccinated mice, monkey, and humans. In Figure 4 we see one such example comparing the hyperimmune serum vaccinia immunoglobulin (panel A) from Cangene and a naive subject (panel B). These results veriﬁed that this platform was a rapid way to scan humoral immunity comprehensively from vaccinated or infected humans and animals on a whole proteome scale [22]. In 2008, Davies et al. showed the power of the technology for proﬁling the immune response. Figure 5 is a heat map of an experiment comparing the immune response of two different stains of vaccinia virus. The U.S. government is considering using modiﬁed vaccinia virus Ankara (MVA) as a smallpox vaccine to replace live vaccinia virus, otherwise known as Dryvax, because it is safer. The heat map is a global view that allows for comparison of the immune response elicited by MVA and Dryvax and can give us an idea of whether it elicits a similar response to confer protection. Barbour et al. recently published an article that takes advantage of this technology to clone and express 1292 Borrelia burgdorferi proteins. Employing enzymeless recombination cloning and E. coli cell-free expression, they produced a protein array representing approximately 80% of the genome. The

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(B)

Figure 4 Naive subject serum and VIG. Images of whole vaccinia virus proteome microarray probed with vaccinia immunoglobulin (Cangene) (A) and naive individual (B). The IgG control spots (shown in yellow) show signal intensities for both samples; both objects do not react with the no-DNA control spots (shown in red). The images show four dilutions of human IgG (yellow box), positive controls (green box), and six negative controls (mock transcription/translation reaction) (red circles). The remaining spots are Vv proteins. (See insert for color reproduction of the ﬁgure.)

antibody reactivities of sera from patients’ Lyme disease were compared to the antibody reactivities of sera from controls. Overall, approximately 15% of the (ORFs) of B. burgdorferi in the array elicited an antibody response in humans with natural infections. Among the immunogens, 103 stood out on the basis of statistical criteria [26]. In 2007 this same platform technology allowed fabrication of the Francisella tularensis proteome. The proteome was interrogated with serum samples to ﬁnd biomarkers that are speciﬁc to tularemia, the disease caused by F. tularensis. This particular study was looking at the efﬁcacy of subunit vaccines, and the proteome arrays were used to take a look at the humoral response to different vaccination protocols. The stimulation of a protective immune response against intracellular pathogens using nonreplicating vaccines was investigated. Many of today’s vaccines still use attenuated organism vaccines or a different less virulent strain as a vaccine. Eyles et al. showed the effect of different adjuvants on the response to the nonreplicating vaccine. The F. tularensis proteome chips were used to effectively differentiate which adjuvant was stimulating an IgG1 or IgG2 response. An IgG1 response is related to the TH2 receptor and does not confer protection. An IgG2 response is associated with the TH1 receptors and has been shown to be associated with protective immunity. Furthermore, these data give insight into the protective immune response

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Figure 5 Antibody proﬁling of human antibody proﬁles for pre- and postvaccination with MVA (A) and WR (B). For Dryvax responses, primary (n = 13) and secondary (n = 12) infections are shown. (From ref. 25.) (See insert for color reproduction of the ﬁgure.)

and have potentially important implications for the rational design of nonliving vaccines for tularemia and other intracellular pathogens. Recent developments in the fabrication of protein microarrays allow the multiplexing of the entire proteome in proﬁling the immune response. This approach is powerful enough to discover content to ﬁnally replace tests used currently to diagnose infectious disease, some of which were developed over a century ago. The diagnosis of tuberculosis (TB) is an excellent example of such an opportunity. TB is a major cause of illness and death worldwide. Globally, 9.2 million new cases and 1.7 million deaths from TB occurred in 2006 [28]. Multiple tests must be performed to diagnose TB. A person sus-

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pected of having TB is ﬁrst tested using the tuberculin skin test (TST), also known as the puriﬁed protein derivative (PPD) test, pioneered by French physician Charles Mantoux in 1907. The TST is used routinely in humans, but the results are subjective, variable, and their interpretation is rarely consistent [29]. Sputum testing identiﬁes M. tuberculosis directly, providing speciﬁcity, but current methodologies do not give good sensitivity [30,31]. The gold standard for the diagnosis of TB is a bacterial culture, which is a time-consuming process that requires a dedicated microbiology lab and several weeks to obtain the results [32]. More novel and sophisticated methods such as real-time PCR (RT-PCR) and cell-mediated-immunity–based detection assays, such as interferon gamma release assays (IGRAs), are accurate but require sophisticated equipment and highly trained personnel [33,34]. The limitations of the current TB diagnosis techniques have sparked renewed interest in antibody detection– based tests, which are relatively straightforward. The immune response to TB infection is variable enough that an infected population may not recognize a single antigen [35–40]. Methodology using previously characterized M. tuberculosis proteins using immunoassays such as ELISAs and microbead suspension assays for the purpose of TB diagnosis have shown the need of a multiplex approach at diagnosing TB [41,42]. There is a dire need for the discovery of an optimal panel of antigens that will allow accurate and reliable diagnosis. Cancer is another ﬁeld that is in urgent need of diagnostics with improved accuracy. We have known that cancer cells produce auto-antibodies since the 1950s [43]. Researchers found that these auto-antibodies were against proteins that were associated with these cells. Auto-antibodies against the tumor suppressor p53 have been found in breast cancer, lung cancer, and ovarian cancer [44–47]. Proteins that are overexpressed in a cancer cells also elicit the production of auto-antibodies. For example, the overexpression of c-Myc is linked to the presence of auto-antibodies against c-Myc in lung cancer [48]. There is evidence that the immune system begins to produce auto-antibodies against cancer cell–associated proteins years, perhaps even decades, before the onset of disease [49–51]. There is a dire need for earlier and more accurate cancer detection. The development of “early warning” tests will ensure a better prognosis for the patient. Serological biomarkers can also be a tool for the development of antibody-based therapeutics to treat cancer. To achieve this objective, it is important to identify sets of cancer-speciﬁc antigens, or biomarkers, that can differentiate disease states as well as different forms of cancer. Serum autoantibody proﬁling is a promising approach for early detection and diagnosis of breast cancer. It is becoming more and more clear that it will take more than one marker to achieve the early warning system that cancer clinicians so desperately seek, and it is likely that well-characterized panels of biomarkers will be required. Zong et al. recently tested a breast cancer T7 phage library with healthy control sera and breast cancer sera and concluded that a panel of auto-antibodies, not a single auto-antibody, is required for optimal accuracy of disease-state discrimination [49]. A similar study tested colon cancer sera

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with a colon cancer T7 phage library. Ran et al. were able to identify 24 autoantigens associated with colon cancer. A six-marker (six antigens) achieved 91.7% sensitivity and 91.7% speciﬁcity in the training set of sera. In a testing set with this marker panel, they predicted 85% of the samples correctly [52]. There are approximately 30,000 ORFs in the NIH human gene collection, representing 17,500 nonredundant genes and variants there of [53]. Using enzymeless recombination cloning, a human proteome chip could be made with upward of 90% of the ORFs represented. With the ability to assay 27,000 proteins at once, the discovery of new biomarkers for human disease will be limited only by the number of serum samples used to determine disease-state discriminatory sets of antigens, and potentially, antigen panels that differentiate between manifestations of the same disease, such as cancer. SUMMARY The high-throughput proteomics approach described here is the natural progression of the genome sequencing projects of the 1990s, but instead of sequencing the genome, we are fabricating the proteomes. Organisms such as M. tuberculosis with 4000 ORFs, or malaria (P. falciparum and P. vivax) with even larger genomes, can now be assayed on a whole-proteome scale. By providing a comprehensive tool to test all the proteins at once, we open the door to a new nonbiased approach in biomarker discovery. Instead of working with a handful of proteins and attaining very limited data sets, we can now look at these organisms from a whole-proteome perspective, allowing the immune system to tell us what is and is not important. Although this approach has been used primarily for infectious disease research, it also holds promise for the discovery and study of biomarkers for human disease. REFERENCES 1. Redkar RJ, Schultz NA, Scheumann V, et al. (2006). Signal and sensitivity enhancement through optical interference coating for DNA and protein microarray applications. J Biomol Tech, 17(2):122–130. 2. Angenendt P, Glökler J, Sobek J, Lehrach H, Cahill DJ (2003). Next generation of protein microarray support materials: evaluation for protein and antibody microarray applications. J Chromatogr A, 1009(1–2):97–104. 3. Chiari M, Cretich M, Corti A, Damin F, Pirri G, Longhi R (2005). Peptide microarrays for the characterization of antigenic regions of human chromogranin A. Proteomics, 5(14):3600–3603. 4. Brueggemeier SB, Wu D, Kron SJ, Palecek SP (2005). Protein–acrylamide copolymer hydrogels for array-based detection of tyrosine kinase activity from cell lysates. Biomacromolecules, 6(5):2765–2675. 5. Cretich M, Pirri G, Damin F, Solinas I, Chiari M (2004). A new polymeric coating for protein microarrays. Anal Biochem, 332(1):67–74.

REFERENCES

173

6. Brueggemeier SB, Kron SJ, Palecek SP (2004). Use of protein–acrylamide copolymer hydrogels for measuring protein concentration and activity. Anal Biochem, 329(2):180–189. 7. Hudson ME, Pozdnyakova I, Haines K, Mor G, Snyder M (2007). Identiﬁcation of differentially expressed proteins in ovarian cancer using high-density protein microarrays. Proc Natl Acad Sci USA, 104(44):17494–17499. 8. Li B, Jiang L, Song Q, et al. (2005). Protein microarray for proﬁling antibody responses to Yersinia pestis live vaccine. Infect Immun, 73(6):3734–3739. 9. Sharma J, Zhong Y, Dong F, Piper JM, Wang G, Zhong G (2006). Proﬁling of human antibody responses to Chlamydia trachomatis urogenital tract infection using microplates arrayed with 156 chlamydial fusion proteins. Infect Immun, 74(3):1490–1499. 10. Sartain MJ, Slayden RA, Singh KK, Laal S, Belisle JT (2006). Disease state differentiation and identiﬁcation of tuberculosis biomarkers via native antigen array proﬁling. Mol Cell Proteom, 5(11):2102–2113. 11. Bouwman K, Qiu J, Zhou H, et al. (2003). Microarrays of tumor cell derived proteins uncover a distinct pattern of prostate cancer serum immunoreactivity. Proteomics, 3(11):2200–2207. 12. Liang X, Teng A, Braun DM, et al. (2002). Transcriptionally active polymerase chain reaction (TAP): high throughput gene expression using genome sequence data. J Biol Chem, 277(5):3593–3598. 13. Regis DP, Dobaño C, Quiñones-Olson P, et al. (2008). Transcriptionally active PCR for antigen identiﬁcation and vaccine development: in vitro genome-wide screening and in vivo immunogenicity. Mol Biochem Parasitol, 158(1):32–45. 14. McCarrey JR, Williams SA (1994). Construction of cDNA libraries from limiting amounts of material. Curr Opin Biotechnol, 5(1):34–39. 15. Suzuki Y, Sugano S (2001). Construction of full-length-enriched cDNA libraries: the oligo-capping method. Methods Mol Biol, 175:143–153. 16. Ali Eldib AM, Ono T, Shimono M, et al. (2004). Immunoscreening of a cDNA library from a lung cancer cell line using autologous patient serum: identiﬁcation of XAGE-1b as a dominant antigen and its immunogenicity in lung adenocarcinoma. Int J Cancer, 108(4):558–563. 17. Li HH, Ma LJ, Wang QQ (2004). [Construction and characterization of cDNA library for lung cancer cell line YTMLC-90 isolated from Gejiu, Yunnan province]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi, 20(4):465–468. 18. Fulton LL, Hillier LD, Wilson RK (1995). Large-scale complementary DNA sequencing methods. Methods Cell Biol, 48:571–582. 19. Thomason L, Court DL, Bubunenko M, et al. (2007). Recombineering: genetic engineering in bacteria using homologous recombination. Curr Protoc Mol Biol, (1):16. 20. Wendland J (2003). PCR-based methods facilitate targeted gene manipulations and cloning procedures. Curr Genet, 44(3):115–123. 21. Groth AC, Calos MP (2004). Phage integrases: biology and applications. J Mol Biol, 335(3):667–678. 22. Davies DH, Liang X, Hernandez JE, et al. (2005). Proﬁling the humoral immune response to infection by using proteome microarrays: high-throughput

174

23.

24.

25.

26.

27.

28. 29.

30.

31.

32. 33.

34. 35. 36.

37. 38.

DISCOVERY OF DISEASE-ASSOCIATED MOLECULES

vaccine and diagnostic antigen discovery. Proc Natl Acad Sci USA, 102(3): 547–552. Sundaresh S, Doolan DL, Hirst S, et al. (2006). Identiﬁcation of humoral immune responses in protein microarrays using DNA microarray data analysis techniques. Bioinformatics, 22(14):1760–1766. Sundaresh S, Randall A, Unal B, et al. (2007). From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis. Bioinformatics, 23(13):i508–i518. Davies DH, Wyatt LS, Newman FK, et al. (2008). Antibody proﬁling by proteome microarray reveals the immunogenicity of the attenuated smallpox vaccine modiﬁed vaccinia virus ankara is comparable to that of Dryvax. J Virol, 82(2): 652–663. Barbour AG, Jasinskas A, Kayala MA, et al. (2008). A genome-wide proteome array reveals a limited set of immunogens in natural infections of humans and white-footed mice with Borrelia burgdorferi. Infect Immun, 76(8):3374–3389. Eyles JE, et al., (2007). Immunodominant Francisella tularensis antigens identiﬁed using proteome microarray. Crown Copyright 2007 Dstl. Proteomics, 7(13): 2172–2183. WHO (2008).Global Tuberculosis Control: Surveillance, Planning, Financing. WHO, Geneva, Switzerland. Eyles JE, Unal B, Hartley MG, et al. (2007). A systematic review of rapid diagnostic tests for the detection of tuberculosis infection. Health Technol Assess, 11(3):1–196. Gilpin C, Kim SJ, Lumb R, Rieder HL, Van Deun A (2007). Critical appraisal of current recommendations and practices for tuberculosis sputum smear microscopy. Int J Tuberc Lung Dis, 11(9):946–952. Steingart KR, Henry M, Ng V, et al. (2006). Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review. Lancet Infect Dis, 6(9):570–581. Baylan O (2005). [Culture based diagnostic methods for tuberculosis]. Mikrobiyol Bul, 39(1):107–124. Parashar D, Chauhan DS, Sharma VD, Katoch VM (2006). Applications of realtime PCR technology to mycobacterial research. Indian J Med Res, 124(4): 385–398. Hoffmann H, Loytved G, Bodmer T (2007). [Interferon-gamma release assays in tuberculosis diagnostics]. Internist (Berl), 48(5):497–498, 500–506. Steingart KR, Ramsay A, Pai M (2007). Commercial serological tests for the diagnosis of tuberculosis: do they work? Future Microbiol, 2:355–359. Steingart KR, Henry M, Laal S, et al. (2007). Commercial serological antibody detection tests for the diagnosis of pulmonary tuberculosis: a systematic review. PLoS Med, 4(6):e202. Bothamley GH (1995). Serological diagnosis of tuberculosis. Eur Respir J Suppl, 20:676s–688s. Raja A, Ranganathan UD, Bethunaickan R (2008). Improved diagnosis of pulmonary tuberculosis by detection of antibodies against multiple Mycobacterium tuberculosis antigens. Diagn Microbiol Infect Dis, 60(4):361–368.

REFERENCES

175

39. Wang EL, Liu WT, Li T (2006). [The serodiagnostic value of antigens secreted from Mycobacterium tuberculosis]. Zhonghua Jie He He Hu Xi Za Zhi, 29(7): 466–469. 40. Perkins MD, Conde MB, Martins M, Kritski AL (2003). Serologic diagnosis of tuberculosis using a simple commercial multiantigen assay. Chest, 123(1):107–112. 41. Khan IH, Ravindran R, Yee J, et al. (2008). Proﬁling antibodies to Mycobacterium tuberculosis by multiplex microbead suspension arrays for serodiagnosis of tuberculosis. Clin Vaccine Immunol, 15(3):433–438. 42. Verma RK, Jain A (2007). Antibodies to mycobacterial antigens for diagnosis of tuberculosis. FEMS Immunol Med Microbiol, 51(3):453–461. 43. Giraud G, Latour H, Levy A, Puech P, Roujon J (1955). [Cancer of pancreas with auto-antibodies in hemolytic anemia]. Montpellier Med, 48(3):465–467. 44. Bergqvist M, Brattström D, Larsson A, et al. (1998). P53 auto-antibodies in nonsmall cell lung cancer patients can predict increased life expectancy after radiotherapy. Anticancer Res, 18(3B):1999–2002. 45. Angelopoulou K, Diamandis EP (1997). Detection of the TP53 tumour suppressor gene product and p53 auto-antibodies in the ascites of women with ovarian cancer. Eur J Cancer, 33(1):115–121. 46. Regidor PA, Regidor M, Callies R, et al. (1996). Detection of p53 auto-antibodies in the sera of breast cancer patients with a new recurrence using an ELISA assay: Does a correlation with the recurrence free interval exist? Eur J Gynaecol Oncol, 17(3):192–199. 47. Green JA, Mudenda B, Jenkins J, et al. (1994). Serum p53 auto-antibodies: incidence in familial breast cancer. Eur J Cancer, 30A(5):580–584. 48. Yamamoto A, Shimizu E, Sumitomo K, et al. (1997). L-Myc overexpression and detection of auto-antibodies against L-Myc in both the serum and pleural effusion from a patient with non-small cell lung cancer. Intern Med, 36(10):724–727. 49. Zhong L, Ge K, Zu JC, et al. (2008). Autoantibodies as potential biomarkers for breast cancer. Breast Cancer Res, 10(3):R40. 50. Chapman CJ, Murray A, McElveen JE, et al. (2008). Autoantibodies in lung cancer: possibilities for early detection and subsequent cure. Thorax, 63(3):228–233. 51. Chapman C, Murray A, Chakrabarti J, et al. (2007). Autoantibodies in breast cancer: their use as an aid to early diagnosis. Ann Oncol, 18(5):868–873. 52. Ran Y, Hu H, Zhou Z, et al. (2008). Proﬁling tumor-associated autoantibodies for the detection of colon cancer. Clin Cancer Res, 14(9):2696–2700. 53. Zabarovskii ER, Allikmets RL (1989). [Construction of libraries of structural genes (cDNA) and “jumping” gene libraries in lambda vectors]. Mol Biol (Mosk), 23(5):1205–1220.

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9 CHARACTERIZATION AND VALIDATION BIOMARKERS IN DRUG DEVELOPMENT: REGULATORY PERSPECTIVE Federico Goodsaid, Ph.D. Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland

INTRODUCTION Biomarkers can be considered tools to be qualiﬁed in close association with individual submissions for drug approvals. The U.S. Food and Drug Administration (FDA) Drug-Diagnostic Co-development Concept Paper [1] described a speciﬁc example of this association, where the approval of a drug and a test are closely linked with each other in both their product concepts and the timelines of their regulatory approvals. Several examples of biomarkers approved through this co-development process are shown in the FDA’s Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels [2]. Biomarkers in this table include both genetic and translational entries. Genetic biomarkers are often integrated in drug development as clinical or nonclinical markers of drug efﬁcacy or safety for the purpose of patient selection in clinical trials, response prediction through stratiﬁcation or enrichment, or dose optimization. Translational biomarkers have applications similar to those of genetic biomarkers but may also be useful for response monitoring and as early indicators of toxicity or adverse reactions. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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Novel approaches are being tested continuously for the successful integration of biomarkers in drug development. Their applications range from early compound selection through postmarketing applications. However, integration of these novel biomarkers into routine nonclinical and clinical practice, and regulatory submissions, have often been slow. Hesitation in the application of these tests is often associated with fear not only about how comprehensive data supporting these applications are but also about the regulatory interpretation of the context of use for these applications [3]. Biomarker tests can be integrated into drug development when we have a consensus about the context in which we are measuring the biomarker and the evidence supporting this measurement. These levels of consensus need to be reﬂected in the regulatory review of biomarker data.

REGULATORY PATHS IN BIOMARKER EVALUATION AND QUALIFICATION The path from an exploratory biomarker to a biomarker qualiﬁed for a speciﬁc application context can be long and unpredictable [4]. Application of these biomarkers requires an objective record for their nonclinical or clinical context and supporting qualiﬁcation evidence. Information from the development of exploratory biomarkers has been shared between the pharmaceutical industry and the FDA through voluntary exploratory data submissions (VXDSs) [5]. Submissions of exploratory biomarker data have allowed reviewers at the FDA to share with scientists in the pharmaceutical industry study designs, sample isolation and storage protocols, technology platforms, analysis algorithms, biological pathway interpretation, and electronic data submission formats. This experience has been valuable in training our reviewers for the analysis and interpretation of biomarker data. VXDS have stressed the need for a regulatory path from exploratory biomarkers to biomarkers qualiﬁed for a speciﬁc context. Such a path is currently being tested at the FDA through a pilot process for biomarker qualiﬁcation [6]. This process is focused on the speciﬁc needs of the regulatory environment to ensure scientiﬁcally accurate and clinically (or preclinically) useful decision making. In the ﬁrst use of this new joint-agency review process put in place by U.S. and European drug regulators, the FDA and the European Medicines Association (EMEA) [7] allow drug companies to submit the results of seven new tests as evidence of nephrotoxicity by new drugs. At this time, the qualiﬁcation of these biomarkers covers voluntary submission of these data for rat studies. On a case-by-case basis, the FDA will also consider possible application of these biomarkers in phase I human trials. The tests measure levels of seven key proteins or biomarkers that scientists from the FDA and EMEA believe provide important new safety information about the effect of drugs on the kidney. When reviewing investigational new drug (IND) applications, new drug applications (NDAs), or BLAs, both regulatory agencies will now

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consider the test results in addition to blood urea nitrogen (BUN) and creatinine. The development of the new renal toxicity biomarkers was led by the Predictive Safety Testing Consortium (PSTC) [8], whose members include scientists from 16 pharmaceutical companies. The PSTC was organized and led by the Critical Path Institute [9]. Researchers from Merck and Novartis identiﬁed the new biomarkers, tested them to prove their sensitivity, speciﬁcity, and positive and negative predictive value, and then shared their ﬁndings with the other consortium members for further study. The consortium then submitted applications for their qualiﬁcation to FDA and EMEA. This is a unique example of how a group of drug companies can work together to propose and generate qualiﬁcation data for new safety tests and then present them jointly to the FDA and EMEA for qualiﬁcation. The FDA and EMEA laid the groundwork for such joint-agency reviews in 2004 with the development of the VXDS framework. The VXDS review served as the baseline model around which to design the pilot process for biomarker qualiﬁcation in 2006. A similar biomarker qualiﬁcation data submission (BQDS) meeting is held in this pilot process to allow an exchange of questions with the sponsor about scientiﬁc and clinical information submitted for qualiﬁcation. The pilot process for biomarker qualiﬁcation allowed the PSTC to submit a single application for biomarker qualiﬁcation to both regulatory agencies, and then to meet jointly with scientists from both agencies to discuss it in detail and to address additional scientiﬁc questions posed by the regulators. Each regulatory agency reviewed the application separately and made independent decisions on whether each would allow the new biomarkers to be used. The new biomarkers qualiﬁed by FDA and EMEA are KIM-1, albumin, total protein, β2-microglobulin, cystatin C, clusterin, and trefoil factor-3. Testing for these proteins will help scientists assess whether a drug is likely to cause damage to the kidneys, a toxic side effect of some drugs. At this time, both FDA and EMEA require drug companies to submit the results of two other tests, BUN and serum creatinine, to show whether such kidney damage has occurred. The seven new tests may provide important advantages over these two tests. For example, in the rat model, once kidney damage has begun to occur, it takes a week before the two current tests can detect it [10]. The new tests are more sensitive and can reveal cellular damage within hours [11]. BUN and serum creatinine show that damage has occurred somewhere in the kidneys, but the new tests can also pinpoint which parts of the kidney have been affected [12]. Although additional studies are needed, the new biomarkers may one day allow promising drugs to advance into clinical trials that otherwise would have been abandoned, because currently there are no tests available to detect early-onset renal injury. The seven new tests were developed and will be carried out initially in rats, but they were selected because other

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studies have shown that similar biomarkers are produced in human kidney cells [13]. Although initially the FDA and EMEA will consider only data from rat studies, the PSTC will begin work to qualify these biomarkers in human studies. If these studies are successful, the PSTC will present a new application seeking acceptance of the human biomarkers over the next two years. The need for an accurate, comprehensive, and efﬁcient process for biomarker qualiﬁcation is closely linked with our ability to quickly integrate new biomarkers in drug development and regulatory review. The biomarker qualiﬁcation pilot process at the FDA is testing the scientiﬁc, clinical, and regulatory components for a biomarker qualiﬁcation process. Experience gained with this pilot process will be useful in the development of a formal regulatory process for biomarker qualiﬁcation.

EVIDENTIARY RECOMMENDATIONS The most difﬁcult part of this process will be to deﬁne incremental contexts of use and the corresponding evidence with which biomarkers may be qualiﬁed. We should not misrepresent the industry goal: qualiﬁed biomarkers capable of managing new drugs in the clinic with nonclinical or clinical ﬁndings of nephrotoxicity somewhere in their previous developmental stages. We also cannot misrepresent the goal as far as public health is concerned, which is to obtain better biomarkers of nephrotoxicity for routine clinical use as quickly as the data will allow. Intermediate qualiﬁcation contexts and data need to be deﬁned so that investment in biomarker qualiﬁcation studies will be productive both for clinics and for the pharmaceutical industry. Initial studies proposed by consortia are unlikely to match a clear context for qualiﬁcation for a full clinical application of biomarkers. What intermediate contexts for qualiﬁcation can we deﬁne, and what study characteristics can we propose for qualiﬁcation in these intermediate contexts of use? Several authors [14,15] have proposed evidentiary recommendations for biomarker qualiﬁcation. Unlike the incremental process for biomarker qualiﬁcation embodied in the pilot process for biomarker qualiﬁcation at the FDA [7], papers on evidentiary recommendations often propose all-or-nothing qualiﬁcation contexts, where if the ultimate goal is a clinical qualiﬁcation, no intermediate qualiﬁcation contexts are expected to be deﬁned or qualiﬁed. This approach is not only time consuming but is also not likely to encourage the investment needed to generate data for biomarker qualiﬁcation. At each stage, whether the context of use for a biomarker is to be in vitro, in a nonclinical animal model, or in the clinic, a company or consortium proposing a qualiﬁcation will probably seek a quick return on the qualiﬁcation of a biomarker after data are available to qualify a biomarker in a speciﬁc context in drug development. An effective process for biomarker qualiﬁcation should include incremental application

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context steps, so that these incremental steps can quickly beneﬁt the drug development process.

HARMONIZATION The application of biomarkers to drug discovery and development has the potential of improving the efﬁcacy and speed of bringing more effective and safer new drugs to market. This requires that biomarkers which may be applicable to such uses be qualiﬁed for a speciﬁc application context. To achieve this, both the process of qualiﬁcation and the evidentiary criteria and standards for qualiﬁcation will need to be described and deﬁned. The International Committee on Harmonization’s (ICH) E16 [16] is a harmonization effort to deﬁne the context, structure, and format of the biomarker qualiﬁcation submission. It is based on the previous experience by the FDA and EMEA regarding biomarker qualiﬁcation. This harmonization effort does not address the evidentiary requirements for biomarker qualiﬁcation. The structure, format, and content of a submission of biomarker data for qualiﬁcation depend on the context in which the biomarker is intended to be used. The ﬁrst step in drafting a submission for qualiﬁcation of a biomarker is to determine its context of use, preceding speciﬁc decisions on applicable structure and format. The context of use for a biomarker is (1) the general area of biomarker application, (2) the speciﬁc applications/implementations, and (3) the critical factors that deﬁne where a biomarker is to be used and how the information from measurement of this biomarker is to be integrated in drug development and regulatory review. To demonstrate the alignment between proposed context and data, the initial context proposal must be supported by data available at the initial application step or expected to be available throughout the data evaluation process in biomarker qualiﬁcation. There is a convergent relationship between an initial qualiﬁcation context and the data supporting it. The initial gap between proposed context and data may need to be ﬁlled throughout the qualiﬁcation process. Initial context proposals, however, should project a signiﬁcant improvement over currently available biomarkers and/or endpoints. The context of a biomarker drives data requirements to demonstrate its qualiﬁcation for the intended application. The structure of a submission document ensures that the context and data can be submitted in a package consistent for consortia submitting qualiﬁcations as well as for reviewers in regulatory agencies evaluating a qualiﬁcation package. The structure of a qualiﬁcation submission is independent of the context of this submission, but must also be ﬂexible enough to deal with the speciﬁc requirements of each context. On the other hand, the format of data required to qualify a biomarker may vary signiﬁcantly with the context in which it is to be used. It is therefore only possible

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to harmonize general regulatory guidelines on data format for biomarker qualiﬁcation submissions.

SUMMARY The qualiﬁcation of novel biomarkers for drug development and regulatory review is made possible by the development and testing of regulatory mechanisms for biomarker qualiﬁcation. The urgency for getting more and better biomarkers to improve new drug development is clear to the pharmaceutical industry and regulatory agencies. Harmonized processes for biomarker qualiﬁcation are being actively developed through ICHs harmonization procedures.

REFERENCES 1. FDA. Drug-Diagnostic Co-development Concept Paper. http://www.fda.gov/cder/ genomics/pharmacoconceptfn.pdf (accessed Oct. 19, 2008). 2. FDA. Table of valid genomic biomarkers in the context of approved drug labels. http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm (accessed Oct. 19, 2008). 3. Wagner JA (2008). Strategic approach to ﬁt-for-purpose biomarkers in drug development. Annu Rev Pharmacol Toxicol, 48:631–651. 4. Rifai N, Gillette MA, Carr SA (2006). Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol, 24(8):971–983. 5. Goodsaid F, Frueh FW (2007). Implementing the U.S. FDA guidance on pharmacogenomic data submissions. Environ Mol Mutagen, 48(5):354–358. 6. Goodsaid F, Frueh F (2006). Process map proposal for the validation of genomic biomarkers. Pharmacogenomics, 7(5):773–782. 7. Goodsaid F, Frueh F (2007). Biomarker qualiﬁcation pilot process at the US Food and Drug Administration. AAPS J, 9(1):E105–E108. 8. Goodsaid FM, Frueh FW, Mattes W (2008). Strategic paths for biomarker qualiﬁcation. Toxicology, 245(3):219–223. 9. Anon. (2008). Public consortium efforts in toxicogenomics. Methods Mol Biol, 460:221–238. 10. Duarte CG, Preuss HG (1993). Assessment of renal function-glomerular and tubular. Clin Lab Med, 13:33–52. 11. Vaidya VS, Ramirez V, Ichimura T, Bobadilla NA, Bonventre JV (2006). Urinary kidney injury molecule: 1. A sensitive quantitative biomarker for early detection of kidney tubular injury. Am J Physiol Renal Physiol, 290(2):F517–F529. 12. Zhang J, Brown RP, Shaw M, et al. (2008). Immunolocalization of Kim-1, RPA-1, and RPA-2 in kidney of gentamicin-, mercury-, or chromium-treated rats: relationship to renal distributions of iNOS and nitrotyrosine. Toxicol Pathol, 36(3):397–409.

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13. Dieterle F, Maurer E, Suzuki E, Grenet O, Cordier A, Vonderscher J (2008). Monitoring kidney safety in drug development: emerging technologies and their implications. Curr Opin Drug Discov Dev, 11(1):60–71. 14. Altar CA, Amakye D, Bounos D, et al. (2008). A prototypical process for creating evidentiary standards for biomarkers and diagnostics. Clin Pharmacol Ther, 83(2): 368–371. 15. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther, 81(1):104–107. 16. International Committee on Harmonization (ICH) E16. Genomic biomarkers related to drug response: context, structure and format of qualiﬁcation submissions. http://www.ich.org/cache/html/4773-616-1.html. (accessed Oct. 19, 2008).

10 FIT-FOR-PURPOSE METHOD VALIDATION AND ASSAYS FOR BIOMARKER CHARACTERIZATION TO SUPPORT DRUG DEVELOPMENT Jean W. Lee, Ph.D., Yuling Wu, Ph.D., and Jin Wang, M.S. Amgen, Inc., Thousand Oaks, California

INTRODUCTION Novel biomarkers have been discovered from recent research in genomic and proteomic studies. Differences in gene expression and protein abundance or modiﬁcation provide the basis for novel biomarkers to be tested further with in vitro and preclinical disease models. Interactions of putative biomarkers at interwoven pathways and the intervening drug actions are relevant to polygenic disorders associated with the complex interplay between genetics, epigenetics, and the environment. Activities of novel biomarker development have increased, especially in diabetes, cancer, rheumatoid arthritis, and cardiovascular disease. In addition to clinical biomarkers proven previously, putative biomarkers have been included in clinical trials of new, mechanisticbased drug candidates for exploratory, demonstrative, or characterization applications in the process of developing mechanism-speciﬁc biomarkers toward the ultimate goal of surrogacy (Pepe et al., 2001; Wagner, 2002a; Bjornsson, 2005; Wagner et al., 2007). For example, to the commonly employed measurement of blood glucose, hemoglobin A1c, and circulating insulin, novel Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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biomarkers could be included in diabetes trials, depending on the mechanism of action. For peroxisome proliferator–activated receptor gamma (PPARγ) agonists in type 2 diabetes, free fatty acid was linked to insulin resistance as mechanism-speciﬁc, and adiponectin was identiﬁed as a proximal biomarker correlated with insulin sensitivity (Berger and Wagner, 2002; Wagner 2002b), while glycosylated hemoglobin (Hba1c) was qualiﬁed as a surrogate marker (Krishnamurti and Steffes, 2001). Other mechanisms and the relevant biomarkers (in parentheses), such as lipid and bone metabolism (free fatty acids, leptin, osteocalcin), incretins (glucagon, GLP-1), and inﬂammation (cytokines, hsCRP, PAI -1, ﬁbrinogen, adhesion molecules) are also involved in the complex disease of diabetes (Lee and Pratley, 2005; Chu et al., 2006; van Doorn et al., 2006; Haffner, 2007; Liu et al., 2007). In addition to circulating prostate-speciﬁc antigen (PSA), carcinoembryonic antigen (CEA), and other tumor markers, the mRNA of speciﬁc genes, various growth factors, and imaging are also used in clinical trials to monitor drug responses for cancer treatments (Sweep et al., 2003; Rubin, 2004; Quraishi et al., 2007; Yu and Veenstra, 2007; Khatami, 2007). The use of novel biomarkers has become a prominent component of decision-making processes in drug development. They are used in the in vitro and preclinical models and early clinical phase for quick-hit and early-attrition decisions (Bloom and Dean, 2003; Bjornsson, 2005; Lee et al., 2005; FDA CDER, 2006). Characterization of a novel biomarker in the translational phase requires data collection to show preclinical sensitivity and speciﬁcity, and linkage to clinical outcomes in multiple clinical studies in humans (Wagner et al., 2007). The purposes at this phase are different from the exploratory or demonstrative phase. In exploratory and demonstrative studies, pharmacodynamic (PD) correlations are typically unknown, data are used mainly for internal decision making, and the output will generally not be subject to regulatory review. The extent of method validation can thus be limited to a few basic components to expedite the process and preserve resources without unduly affecting commercialization (Lee et al., 2006). In contrast, the purposes of characterization application are to provide pivotal data to establish linkage to clinical outcome and to monitor patient progress upon treatment (Figure 1). The data are often used for critical decisions (such as supporting dose selection and patient stratiﬁcation, and demonstration of drug safety or efﬁcacy), submitted to review by regulatory agencies, or used for postmarketing patient monitoring (Kummar et al., 2007; Stoch and Wagner, 2007). In addition, the same method used for characterization is likely to be used in the qualiﬁcation phase toward surrogacy, conﬁrmed over multiple drugs of similar mechanism, and used during surveillance studies. Therefore, biomarker characterization studies would require more intense rigor in assay method validation and in traceable and more detailed documentation than that of previous phases to meet the study objectives in a deﬁned context of use. A biomarker is useful if there are distinctions in concentrations in the disease state from that of a healthy status (or disease stabilization, in the case

GENERAL PROCESSES

Biomarker Development

Discovery

Demonstration

Characterization

Qualification

Surrogacy

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Discover putative marker(s) of new mechanism. Quantitative and quantitative methods are used. Select biomarker with “druggable” mechanism. Develop quantitative bioanalytical method. Demonstrate proof of mechanism with exploratory studies of single or multiple drugs Validate quantitative bioanalytical method and apply to multiple confirmatory studies of single or multiple drugs. Translation from preclinical to clinical phase: • Dose selection • PK/PD modeling • Patient stratification • Efficacy or safety linkage to clinical outcome • Patient monitoring of treatment progress The same bioanalytical method is applied by multiple laboratories over drugs of similar mechanism through many late-phase and postapproval surveillance studies confirming linkage to clinical outcome.

Figure 1 Path from putative to conﬁrmed mechanism of a novel biomarker through the processes from discovery to surrogacy. The side boxes illustrate the relationship of the processes to drug development applications and the methods required.

of some oncology trials). The assay performance of the biomarker analytical method must be able to meet this goal of disease status distinction and estimation of the drug effect. Therefore, contrary to the general perception of many analytical laboratories, the required assay acceptance criteria do not depend solely on the deliverable method’s accuracy and precision. Instead, consideration should be on the assay suitability for the intended applications based on three major factors: (1) intended use of the data during various stages of drug development, (2) nature of the assay methodology and the different types of data that it provides, and (3) biological variability of the biomarker that exists within and between populations. The ﬁrst factor helps shape the assay tolerance or acceptance criteria for biomarkers. A ﬁt-for-purpose method validation approach at various phases of biomarker application has been described by Lee and colleagues (2006). In this chapter we focus on how the ﬁt-for-purpose method validation and assay application during drug development clinical trials can contribute to biomarker characterization.

GENERAL PROCESSES Quantiﬁcation of protein expression is important when considering the translation of a candidate protein biomarker. If there are changes in posttransla-

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tional modiﬁcation of the biomarker, it would be preferable to be able to quantify levels of both protein expression and posttranslational modiﬁcation, utilizing technologies such as ligand-binding assays (LBA), enzymatic assays, liquid chromatography–mass spectrometry (LC-MS) in multiple-reaction mode (MRM-LC-MS), protein assay, and real-time polymerase chain reaction (RT-PCR) genomics. LBAs can be performed in high throughput at a relatively low cost per samples, which is most advantageous for application through phases of characterization and qualiﬁcation development of a novel biomarker. Therefore, LBAs have been widely used for protein biomarker quantiﬁcation and will be the typical method for discussion in this chapter. The developmental processes of a novel biomarker and a new chemical drug entity are intertwined (Lee et al., 2003, 2007). Biomarker development may happen concurrently with the development of a single or multiple drug candidates, a reﬁned new chemical or biological entity, or for extended indications and/or additional mechanisms. The progress of a potential biomarker does not always coincide with or parallel that of a new drug candidate development. To utilize and integrate information from development of drugs in therapeutic programs and the development of relevant putative biomarkers, a detailed work plan can be prepared for the novel biomarker and the study objectives identiﬁed in the corresponding study protocols of each drug candidate. Innovative companies often organize biomarker work groups to facilitate timely input and communication among therapeutic areas and supporting teams. As depicted in Figure 2, novel target (BMKa) and/or proximal biomarkers (BMKb) of a speciﬁc mechanism can be used for multiple drug candidates to prove the mechanism of action by drug intervention during the exploratory and demonstration phases. If the circulatory biomarkers are reﬂecting their actions in the target cells/organs, measurements of blood levels of the target and/or proximal biomarkers can be included during clinical trials for further characterization purposes. In addition, distal biomarkers (BMKi,j) of a speciﬁc disease indication that have been used for other drug compounds can be applied for a new drug of a different mechanism toward the same indication. In general, distal biomarkers are closer to the disease symptom magniﬁcation for efﬁcacy and off-target indirect effects, while the target and proximal biomarkers are closer to the direct exposure effect. Thus, the establishment of PD relationships to pharmacokinetics (PK) is usually simpler for the target or proximal biomarkers, while linkage to a clinical outcome may be shown more readily by distal biomarkers. PK-PD modeling is used to correlate the dose– exposure effects. PK-PD modeling based on various mechanisms of action, direct and indirect drug effects, and reactions of biological cascades has been developed (Mager et al., 2003). For proof of biology, the biochemical coverage of biomarkers from target hit to distal to clinical outcome would provide a thorough understanding of drug effect and characterization of a novel biomarker, whether it is a target, proximal, or distal. Additionally, off-target negative effects should be monitored carefully by toxicity biomarkers (BMKt). Such information is vital in the selection of lead candidates and appropriate dose ranges for clinical trials (FDA, 2004).

METHOD VALIDATION AND ASSAY APPLICATION Specific target pathway BMKa’

On-target biomarker

Biomarkers from other pathways Drug Exposure

Proximal biomarker

BMKn, m Downstream Distal biomarkers

BMKa

BMKb

191

BMKi, j

BMKt Toxicity biomarkers

Direct effect

Disease outcome

Indirect, downstream effect

Figure 2 On- and off-target biomarkers of a speciﬁc mechanism of drug intervention. Possible relationship of biomarkers from a speciﬁc pathway to disease outcome. Other pathways may have common distal biomarkers that may be used as predictive index of the same disease outcome.

The application of a novel biomarker in clinical trials for the purpose of biomarker characterization is often included as a secondary objective in a clinical protocol, while toxicity and commonly recognized efﬁcacy biomarkers may be included in the primary objectives. Characterization of a speciﬁc novel biomarker may be carried out on different new chemical entity development programs over several therapeutic areas. Therefore, it is important for the assay laboratory to have both a “big picture” perspective on the development of a certain novel biomarker and the speciﬁc purpose of the application of that biomarker in a clinical trial. The time required for biomarker assay development and method validation, the operational and logistical issues, including preanalytical factors, and the limitations in data interpretation are key elements for careful planning and execution to support biomarker development and its applications.

METHOD VALIDATION AND ASSAY APPLICATION IN DRUG DEVELOPMENT Population Range and Assay Range In the process of biomarker assay development and validation, samples from the target populations of healthy persons and patients are used to establish expected ranges and biological variations for a given biomarker. Knowledge

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of these ranges and assay performance in these matrices are necessary for a number of reasons. The initial concentration ranges of healthy populations are usually available from literature or vendor brochures for previously identiﬁed biomarkers. During the characterization phase the range-ﬁnding experiments should include sufﬁcient samples from individuals of the target populations (and extended populations if applicable) to enrich the existing information. Biological variation exists in some biomarkers. It is important to note the variable levels over time (i.e., diurnal, seasonal, and food effects) to plan the correct conditions and time of sample collection appropriately. In addition, biological variation can come from the changes in the bindingprotein components in the biological matrices, affecting the biomarker measurement in some patient samples. The effect of matrix from patient samples is discussed further later in the chapter. The results of the range-ﬁnding studies and the expected drug effect on the biomarker are used in the design of method validation and assay application in clinical studies. Knowledge of variation and modulation will power the sample size necessary to conﬁdently detect relevant changes in a biomarker after therapeutic intervention. It is desirable to have the biomarker assay range cover the range of the target populations and the expected drug effect. However, this is often not possible. For example, most protein biomarkers are analyzed by LBA, with the assay working range dependent on the antibody reagents used, which is often different from the biological range. Additionally, the biomarker levels of one disease are often different from that of another. For example, inﬂammatory biomarkers such as IL-6 and TNFα in serum samples for healthy and rheumatoid arthritis patients are in pg/mL range, whereas those of sepsis patients are in the ng/mL range. Use of the inﬂammatory biomarkers for sepsis will require dilutions into the working range. Thus, application of these biomarkers to sepsis studies would require dilutional linearity during method validation, and the variability introduced by dilution should be monitored during in-study assays. Sample Integrity: Sample Collection at the Clinic Sample integrity means that the measurement of a biomarker after collection, transport, and storage should yield a result as close to that which could be measured in as fresh a sample as possible. Sample characteristics can vary over time, in the fed state, or by age- and gender-speciﬁc factors, and by the methods used to prepare the sample. Sample handling can alter a sample irrevocably, so care must be used in specimen collection. Delays in specimen processing, addition of anticoagulants and other additives, type of collection vessel and storage tube, and a myriad of other preanalytical circumstances can affect a biomarker. A careful review of potential confounding variables is essential prior to method validation. Preanalytical variables have been shown to compromise data utility in proteomic biomarker discovery and validation (Ferguson et al., 2007; Banks, 2008).

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Serum, plasma, or urine samples are the most common matrices during the characterization phase. For diseases involving the brain or central nervous system, cerebral spinal ﬂuids are commonly used. Accessible cells from sputum, bronchial lavage, and blood cells are used for receptor functional measurements. The preservation of samples must be explicitly deﬁned in the study protocol. Control and standardization of sample collection are especially necessary to minimize variability from multiple clinical sites. For both tissue collection and ex vivo stimulation assays, it is best to validate sample collection procedures using conditions that closely mimic the planned sampling protocol and to examine sample stability as soon as possible. If sample handling is complicated, it would be prudent to establish tolerance boundaries surrounding sample collection and processing for the clinical sites to abide and document. In addition, staff training and oversight procedures are essential for the monitoring and maintenance of sample integrity. In some biomarker studies, normalization is used to correct concentration variability. For example, urine creatinine concentration is used to normalize hydration variability in urine samples. In that case, the assay of the normalizing factor should also be validated on stability and reliability prior to the clinical study. During biomarker characterization, multiple clinical studies from multiple drug candidate development programs may be involved. It is important for the biomarker team to coordinate the study protocols to include standardized procedures for sample collection, shipping, and analysis and for sample storage and disposal. Sample collection and storage stability should be tested by the bioanalytical laboratory to cover the conceivable conditions during in-life study sample analysis. This includes process stability, assessing the impact of short- and long-term storage, and multiple freeze–thaw cycles. Method Development What Will Be Measured Many biomarkers of interest are proteins whose function is affected by a change in disease progression and drug intervention. The ultimate goal of a biomarker assay is typically to evaluate the effect on the biomarker activity in vivo, and a method that measures this activity accurately is most desirable. Direct measurement of changes to target cells, tissues, or organs in response to a drug is rarely feasible, and given current drug potencies, activity assays rarely have adequate sensitivity and precision. Moreover, cell-based and activity assays are usually laborious, low in throughput, and often lack deﬁnitive reference standards. LBA are often chosen for biomarker analysis over activity measurements because of their sensitivity, versatility in application, relatively high throughput, and low cost. Many biomarkers are endogenous protein that can be physicochemically heterogeneous, with multiple forms present simultaneously. The concentration and biological activity of each component are often unknown and may vary with health status, over time, and between individuals. However, it is

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desirable to know, even in the case of a well-deﬁned regulatory peptide, the related species of the analyte existing in the biological matrix of measurement. For example, measurements of proinsulin, insulin, and C-peptide provide different information for clinical interpretation and assessment. For large molecules, the heterogeneity of the molecules poses challenges to identiﬁcation of the exact species being measured. Therefore, various approaches in designing the analytical method, including choices of the reference standard and critical reagents, are worth considering. LBA methods measure the binding reaction of the mixed components in the matrix. The majority of the data generated by LBA is relative quantiﬁcation (Lee et al., 2006). Limitations of LBA methods are usually a lack of chemical information or correlation of the binding activity to the biological activity. In addition, the reference standard used for data regression may be a recombinant protein or one of several components which may or may not be representative of the entire endogenous mixture. However, as the purpose during biomarker characterization is to monitor population and longitudinal effects of drug treatment and disease progression, this type of relative quantiﬁcation is adequately “ﬁt for use.” Reference Standard and Alternative Matrix for Standard Preparation The reference material may not be fully representative of the endogenous analyte because the endogenous analyte may exist in various forms in the biological matrix. A well-deﬁned reference standard is used as a scalar for the relative measurement of the endogenous species. For example, the recombinant form of a biomarker can be produced and characterized with deﬁned molecular size and purity to enable concentration and molar equivalence calculations. Documentation of the reference material is necessary for its use. Most biomarkers are endogenous molecules with variable measurable amounts in the matrix. It is difﬁcult to ﬁnd “blank” control matrix for preparation of reference standards. An initial standard curve can be constructed by spiking the reference standard into a protein buffer solution. An initial screen on multiple matrix lots against the buffer standard curve may identify some blank matrix lots for standard preparation. When that is not possible, an alternative matrix can be prepared by depleting the endogenous analyte using methods such as charcoal stripping, high-temperature incubation, acid or alkaline hydrolysis, or afﬁnity chromatography. Commonly, a protein-containing buffer or the matrix from another species with a non-cross-reactive homolog is used. The use of alternative matrices require studies of matrix effects and parallelism during method development and validation to understand the impact that differences from patient samples will have on assay results. Parallelism experiments are performed through serial dilutions of a highconcentration sample with the standard matrix. Multiple individual matrix lots (three or more) should be tested to demonstrate lot-to-lot consistency. There are several ways to assess data of parallelism (Lee et al., 2006). The concentra-

Ratio of Conc./Mean Conc.

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1.4 1.2 1 0.8 0.6 0.00

0.20

0.40

0.60

0.80

1.00

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1/Dilution factor

Figure 3 Parallelism of three individual matrix lots. Each lot is represented by a separate symbol. The sample was diluted with the standard diluent at dilution factors of 1.5, 2.5, and 5. The concentrations of the diluted and undiluted samples observed were all within the standard curve assay range. The mean of the calculated concentrations (observed concentration times the dilution factor) of each lot was determined. The ratios of the individual calculated concentration over the mean of the lot were plotted against 1/dilution factor. The data showed that the ratios were all within the range 0.8 to 1.2, which was the deﬁned acceptance criterion of the assay. Therefore, the parallelism test results were acceptable for the test lots.

tions of diluted and undiluted samples observed should all be within the standard curve range. An example is depicted in Figure 3: The mean of the concentrations calculated (concentration observed times the dilution factor) of each lot was determined. The ratios of the individual concentrations calculated over the mean of the lot were plot against 1/dilution factor. Parallelism was demonstrated for the test lots because the ratio was not affected by the variable amounts of standard matrix introduced by dilution. When parallelism is not possible due to the unavailability of samples with sufﬁciently high concentrations of analyte, dilutional linearity should be tested in a manner similar to parallelism, except that high-concentration spike samples are used in place of the endogenous samples. The failure to demonstrate parallelism should be taken into consideration in data assessment and interpretation. For example, it may mean that the values obtained should be treated as quasiquantitative instead of as relative quantitative measurements (Lee et al., 2006). In addition, within-subject comparison on a longitudinal time sequence would be more useful than between subjects or populations, which affects the clinical study design. All of these considerations would be dependent on the a priori goals of the study. Selection of Critical Reagents in Ligand-Binding Assays For the development of a ﬁrst-in-class drug compound that targets a novel biomarker, assay reagents will need to be developed for both the drug candidate and the target protein biomarker. In the case of a macromolecular drug, often an analog of the drug candidate may be used as the binding reagent(s). On the other hand,

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reagents for most off-target biomarkers may be available from commercial sources. These reagents may be established assay kits [U.S. Food and Drug Administration (FDA) approved for diagnostic use] or kits for research use only. For the purpose of characterization of the biomarker of interest, ﬁt-forpurpose method validation should be conducted with sufﬁcient rigor to provide reliable data from multiple clinical studies. This should include assay range ﬁnding, accuracy and precision, selectivity, speciﬁcity, stability, and robustness (reproducibility) during prestudy method validation. Additional data will be collected from in-study assay performance, change control method validation, and long-term storage stability. Except in the case of FDA-approved diagnostic kits, many of the assay performance parameters will have to be established by the bioanalytical laboratory. For FDA-approved kits, because the application will be for drug development instead of diagnosis, some of the assay performance parameters will need to be established. These often include selectivity against the target patient sample matrices and speciﬁcity against the drug compound(s). If the drug is expected to decrease the biomarker to a level lower than the lowest standard of the kit, the method may need to be modiﬁed to increase assay sensitivity. Antibody pairs are typically chosen as capture and detection reagents. In general, the more selective antibody would be chosen as the capturing agent, especially if it is more readily available than the other member of the pair. A tertiary detector antibody can be used that is conjugated to a reporter enzyme such as horseradish peroxidase. Alternatively, a biotinylated detector antibody can be used together with a biotin-binding protein (e.g., antibiotin antibody or an avidin-type protein) conjugated to a reporter enzyme. The sensitivity of an assay can be increased by varying the number of reporter enzyme molecules on the detection reagents, or by using multivalent strategies to increase the effective signal from each analyte captured. Some assays use receptors or their fragments as binding partners, most often in concert with a speciﬁc second antibody. This arrangement can improve selectivity for speciﬁc ligands (e.g., a cytokine activated from a latent precursor, or a particular subtype of ligand with distinct binding characteristics from its homologs). The binding selectivity of such reagents can offer added biological relevance to quantiﬁcation, and can suggest results that might otherwise be obtained only via functional assays. Critical Reagents Characterization, Consistency, Stability, and Documentation Characterization of a novel biomarker often involves many clinical studies that last for several years. If the studies are to be conducted within the same company or from a joint program from different institutions, plans should be made to provide consistent supplies of the same reference material and assay reagents throughout the studies. If the materials are from in-house sources, sufﬁcient production of the reference standard and primary reagents should be assured. In addition, multiple preparations of labeled

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detector reagents should be assessed during method validation. If the materials are from commercial sources, negotiation with the vendor should take place to assure a consistent and sufﬁcient supply of the same batch material, if possible. Often, multiple batches of materials (reference standards and critical reagents) should be tested during method validation. Documentation should be obtained for the stability of the respective lots over the time span of a clinical study. Documentation of chain of custody should be similar to that of a good laboratory practices (GLP) study. For late-phase studies using multiple clinical sites, the use of a central sample repository offers numerous advantages in controlling the process of specimen collection and labeling of multiple biomarker assays. In-study sample control (SC) charts such as those shown in Figure 4 can be used for trend analysis of assay performance and stability. A big pool of endogenous sample at low, middle, and high concentrations of a biomarker were prepared during method validation and monitored throughout in-study.

1:2S 1:2S

LQC

3.5 3.0

4.0

UCL = 3.817

3.5 Avg = 2.814

2.5

LQC

4.0

UCL = 3.052

3.0

Avg = 2.635

2.5

2.0

LCL = 1.811

LCL = 2.217

3 6 9 12 15 18 21 24 27 30 33 36 39

3 6 9 12 15 18 21 24 27 30 33 36 39

2.0 12

MQC

8 Avg = 8.28

9

11

UCL = 11.23

10

MQC

1:2S 1:2S

11

10 8

7

Avg = 7.74

7

6

LCL = 6.67

LCL = 5.33

3 6 9 12 15 18 21 24 27 30 33 36 39

3 6 9 12 15 18 21 24 27 30 33 36 39

5 24 22

1:2S

UCL = 21.46

18 Avg = 16.63

16 14 12

LCL =11.80

22 21 20 19 18 17 16 15 14 13

UCL = 18.16 Avg = 15.80

3 6 9 12 15 18 21 24 27 30 33 36 39

LCL = 13.45

3 6 9 12 15 18 21 24 27 30 33 36 39

10

HQC

20 HQC

UCL = 8.81

9

Run Number

Run Number

(A)

(B)

Figure 4 Use of sample controls for trend analysis on variability. Low, middle, and high sample controls of a biomarker were monitored in Levey Jennings control charts. There was a noticeable shift in trend in all the sample control levels after run 27. The average, upper and lower control limits of analytical runs up to run 27 (B) were compared to the overall parameters (A). (See insert for color reproduction of the ﬁgure.)

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The Levey Jennings control charts showed a noticeable shift in trend at all the SC levels after run 27, indicating a systemic bias. The average, upper, and lower control limits of analytical runs up to 27 (right panels) were different from the overall values (left panels). This shift was traced to a change in commercial kit lot. Method Validation on Performance Parameters The foremost assay performance parameters to be established for quantitative methods are precision and accuracy. In addition to these two parameters, sensitivity, selectivity, speciﬁcity, stability, and reproducibility should also be demonstrated. These parameters are established during prestudy method validation to demonstrate reliably that the method is ﬁt for the purpose of biomarker characterization over multiple clinical studies. If more than one bioanalytical laboratory is used, each additional laboratory should also demonstrate its ability to perform by conducting a full or partial method validation. Accuracy and Precision Method performance is consistently better understood and validated through the appropriate use of control samples. To ensure data quality, assay performance is evaluated during method validation with validation samples (VSs) and monitored during sample analysis with quality control samples (QCs). VSs are used in method qualiﬁcation or validation to deﬁne intra- and interrun accuracy and precision, providing data to demonstrate the robustness and suitability of the assay for its intended application. On the other hand, QCs are essential in determining run acceptability during specimen analysis. However, many laboratories have not made the distinction, using the term QC for both VS (for prestudy method validation) and QC (for in-study run acceptance). For a well-deﬁned biomarker assay, at least six accuracy-and-precision validation runs should be performed to provide statistical data to calculate these assay parameters (DeSilva et al., 2003; Lee et al., 2008). Each run should consist of standards prepared by spiking the reference standard into blank biological matrix or an alternative blank matrix. QC/VS at low, middle, and high concentrations should be prepared in the biological matrix or alternative matrix by spiking the reference standard. In addition, sample controls (SCs) should be prepared by pooling authentic samples at the low and high levels of the biomarker to reﬂect the performance of the endogenous biomarker in assay precision, stability, and reproducibility. At least two sets of QC/VS and SC should be run with the standards in each accuracy and precision experiment. Accuracy and precision can be evaluated from the total error of VS data from the validation runs in a way similar to that of the macromolecular protein drug as analyte (DeSilva et al., 2003). However, given biological variability and other factors in biomarker research, more lenient acceptance criteria may

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be used for biomarker PD than that for PK studies. Still, it should be recognized that accuracy and precision data of VS in buffer provide only a relative quantiﬁcation, which may be quite different from measurements in the authentic matrix. Concentrations of the endogenous SCs will be determined by multiple validation runs. The true values are then deﬁned after sufﬁcient data collection from pre- and in-study validation. For example, the mean of 30 runs and 2 standard deviations can be used to deﬁne the target concentration and acceptable range of the SCs. Because the reference material may not fully represent the endogenous biomarkers, the SCs should be used for stability tests (such as minimum tests of exposure to ambient temperature and freeze–thaw cycle). In addition, the SC can be used for in-study, long-term storage stability analysis and for assessment of lot-to-lot variability of key assay reagents. Regression models are essential for data calculation from sigmoid curves like those in LBAs. The most commonly used four- or ﬁve-parameter logistic regression should be evaluated in conjunction with weighting factors during method development. Final decisions on which curve-ﬁtting model to use should rest on which offers the best ﬁt for all the standards in the precision proﬁle. In some cases, a less than optimal ﬁt will sufﬁce to allow for greater assay sensitivity. Sensitivity To provide data for PK/PD studies, assay sensitivity is usually deﬁned by the assay lower limit of quantiﬁcation (LLOQ), which is the lowest concentration that has been demonstrated to be measurable with acceptable levels of bias and precision and total error (the sum of bias and precision). However, low-concentration clinical samples may fall below the LLOQ (i.e., the method lacks the required sensitivity). In some instances, the investigator may want to use values below the LLOQ but above the limit of detection (LOD), to obtain a numerical estimate of the changes while recognizing the high variability below the LLOQ region. LOD is often used as the analytical “sensitivity” of the assay in a commercial diagnostic kit. A common practice of diagnostic kits to determine the LOD is the use of extrapolated concentrations from a response signal of +3 SD (or −3 SD for a competitive assay) of the mean background signal from 30 or more blank samples. The National Committee for Clinical Laboratory Standards (CLSI) has recommended an approach for determining LOD (National Committee for Clinical Laboratory Standards, 1999; Tholen et al., 2004). This statistically sound approach evaluates the limit of blank (LOB) as type I error and LOD as type II error from sufﬁcient numbers of blank and low concentration samples with normally distributed analyte concentrations. One approach of calculation is described brieﬂy as follows: LOB is estimated from the blank result at the 5th percentile position. LOD is calculated as LOD = LOB + cβ SDs

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SDs is the estimated standard deviation of the low-concentration sample measurement, and cβ is a correction factor for the population bias: cβ =

1.645 1 − 1 [ 4 ( N − K )]

Here N is the number of determinations and K is the number of sample sets. The LOD determined by this approach would avoid the inclusion of analytical false negative and false positive values. Due to assay conditions being variable, each bioanalytical laboratory should determine the LOD using the CLSI approach instead of using the LOD stated in a kit brochure. If the LOD is used as the lower limit for data inclusion instead of the LLOQ, the user should justify such a choice and be aware of the risk being taken with the higher variability at the LOD to LLOQ range, interpreting the data with caution. Selectivity and Matrix Effect Selectivity is the ability of the assay to discriminate the analyte unequivocally in the presence of components that may be expected to be present in the sample and alter assay results. Lack of selectivity can result in signal inhibition or enhancement. The results can appear as false or negative positives (an over or underestimation of the analyte concentration). In general, signal suppression from binding proteins occurs more often for LBAs than that of enhancement, resulting in a negative bias. Interference in an assay is the product of concentration of the interfering substance times the cross-reactivity (or binding inhibition). However, the concentration–response relationship of LBA is nonlinear, and often the magnitude of cross-reactivity (or inhibition) is not evenly dispersed over the entire assay range. Thus, a standard curve prepared in a protein buffer solution has higher optical density (OD) responses than that in biological matrices, while the differences in responses are variable over the standard curve. Moreover, a standard curve prepared in biological matrices from one person would have different responses than that of other people. Therefore, the estimate of interference should not simply extrapolate across the entire assay range or be represented by an IC50 value. Matrix interference should be surveyed in samples from normal and diseased donors, especially in samples from the anticipated patient population. Spike recovery using samples from persons in the target population should be performed. A later selectivity evaluation of LBA involves a parallelism test of high-concentration endogenous samples from many people diluted at ≥4 dilution factors with the standard diluent. Speciﬁcity Speciﬁcity reﬂects the ability of an assay to distinguish between the analyte of interest and other structurally similar components of the sample. For LBAs a speciﬁcity test should include test of interference from components that may speciﬁcally affect the binding reaction. For example, inter-

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ference from the drug compound may alter ligand interactions with the capture reagent. If the ligand reagent of an LBA is the target drug or its analog, the presence of the drug compound at certain concentrations can interfere with the assay. In addition, biomarker homologs or endogenous molecules of the same family may contribute to speciﬁcity problems. If reference material of the potential interferent is available, speciﬁcity can be tested using various amounts of the test material spiking into various levels of VSs (Lee and Ma, 2007). Speciﬁcity for the biomarker in the presence of the drug under study should also be tested, using samples from endogenous pools (such as the SCs), with and without the addition of the drug compound at various concentrations spanning the expected therapeutic range. Figure 5 illustrates a speciﬁcity experiment of a target biomarker (A) and its proximal biomarker (B), each tested with three sample pools at pM levels against various nanomolar drug concentrations. As expected, the test drug inhibited the quantiﬁcation of the target biomarker at concentrations greater than 2 nM. For the proximal

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Figure 5 Method validation speciﬁcity tests of a target biomarker (A) and a proximal biomarker (B) against a drug compound.

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biomarker, the test drug did not affect the assay at all test concentrations, which exceeds the highest expected concentration of the drug in the serum. The results illustrated that the drug affected only its target protein. Stability In addition to sample integrity during collection, stability of protein biomarkers and assay reagents is required to show that the assay is not compromised by this preanalytical factor. Stability information is gathered during method validation on short-term storage and process stability, and is extended through in-study validation for long-term storage stability. Reagent storage stability is tested on reference material stock solutions and ligand-binding reagent stock solution if information from the supplier is lacking. Working solutions will be prepared from the stored stock solution and assay performance compared to those from that of a freshly reconstituted solution. Process stability is evaluated at different time durations and various conditions to mimic possible sample handling in the laboratory from the results of SCs subjected to stress from multiple freeze–thaw cycles and temperature exposures (e.g., four cycles and up to 16 to 72 hours to at 2 to 8 °C and ambient temperature). Long-term storage stability of the SCs should be started during validation and continued to support the drug development program. The acceptance criteria of storage stability are according to those established during method validation, prior to conducting the experiment. For long-term stability evaluation, in addition to comparison against the initial concentration, trend analysis can be more useful than the acceptance criteria strictly applied. This is due to the challenges of a kit lot change, potentially shifting the mean value of the SCs, and a new preparation of the standard curve, introducing systemic bias to add an error component that is not caused by the storage conditions (similar to that illustrated in Figure 4). Assay Application Before sample analysis begins, a written method standard operating procedure (SOP) must be in place. Often, a validation report should also be issued. Documentation of sample assay should follow the SOP in a GLP compliance manner (see the discussion later on regulatory issues), with traceable records. The data should be stored in a secure place for auditing. Standard Operating Procedure A validated method should have deﬁned procedures, assay performance parameters collected from experiments using the same method during prestudy validation, and acceptance criteria established reﬂecting the acceptability of the method performance. These elements constitute a method SOP and are discussed in the following section. Method Procedures following:

A written method procedure should clearly describe the

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The intended purpose of the method The principle of the method A validation performance summary Materials and reagents, including lot numbers and expiration dates Solutions preparation Standards and QC, SC preparations Sample pretreatment (processing) Analytical steps Data regression Acceptance criteria References

During the development of a novel biomarker, a series of method modiﬁcations can take place. It is important that speciﬁc names (version number) and effective dates be given to the same method as that used in a clinical study. A historical log should be kept on the modiﬁcations made for each version, and its application in speciﬁc studies identiﬁed. Sometimes, for various reasons, if a method change has to happen within a clinical study, a crossover comparison of the old and new methods applied to a subset of clinical samples must be designed with a statistical approach to show method equivalence. Regression Model The response–concentration relationship of the LBA method is nonlinear. Curve ﬁtting would require regression model selection based on multiple runs of standard curves from the method validation accuracy and precision experiments. Typically, a four- or ﬁve-parameter logistic model will ﬁt most methods with a choice of weighting factors (such as 1/ response or 1/variance). The initial selection is based on the residual of the standard mean. It can be chosen to obtain the best ﬁt over the entire range and with regard to the low end if sensitivity is an issue. The minimal total error of the VS data generated in the validation batches should be used to determine the regression algorithm and weighting. Once the regression model is chosen, it is a basic element of the method that should not be changed during assay implementation. Justiﬁcation must be given and documented for a regression model change. Acceptance Criteria Without sufﬁcient data from the healthy and patient samples, the acceptance criteria for a novel biomarker can initially be set as determined by the assay performance from prestudy method validation. The biological data obtained from in-study validation from subject samples can then be used to reﬁne the initial acceptance criteria set by the prestudy validation. The process of setting acceptance criteria for a protein biomarker measurement follows an evolving path. During the exploratory phase, the acceptance criteria were initially set according to the initial assay performance

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of prestudy method validation. After use in pilot studies, assessment should be made on the suitability of the assay stringency vs. the effect observed. The biological data obtained from the in-study validation from subject samples during in-study phase can then be used to reﬁne the initial acceptance criteria set by the prestudy validation. For example, an assay with 50% total error may still be acceptable for detecting a twofold treatment effect observed in the in-study phase clinical trial. Setting the same acceptance criteria of a given method may be most convenient for an analytical laboratory. However, for a novel biomarker, it may not be the most appropriate for all of its applications. One could take into account the intended purpose of the application and the possible outcomes in the analytical phase. For example, the effect of one population or indication may be different from another (e.g., change from a twofold treatment effect into only 30%), which may require a more stringent method and/or more subjects to increase the predictive power in such an application for the other population. Controls in Clinical Studies For a well-planned assay application, both analytical quality controls and biological sample controls should be available to assess the assay variability. Data of analytical variability are gathered from standards, QC and SC of each analytical run of sample analysis. A common set of SCs (or pools of incurred samples from a previous study) analyzed by multiple bioanalytical laboratories can provide an assessment of assay performance among the laboratories. Variability of supplies in reagents and reference standard materials can be detected by tracking the assay performance of a common set of SCs used within and between studies. An example is shown by an SC chart for serum C-terminal telopeptides of type I collagen (CTx) assay in Figure 6, using the Westgard rule for monitoring (Westgard, 2003) as well as a priori acceptance criteria of 25%. The low and high SCs were prepared by pooling a large volume of authentic samples, and the mean values were established during method validation. Figure 6 shows the Levey Jennings charts of more than 117 analytical runs from ﬁve clinical studies (A to F) over more than 2.5 years. During this time span, two reference stock materials and three kit batches were used. The plots show a slight positive bias in the SC values after the initial sequence of studies B and C. In addition, a slight negative bias was observed prior to study A. However, since the SC values were still acceptable within ±25%, no action was taken. Other statistical analysis can be use to assess SC data on other assay variability sources, such as operator differences and sample storage stability trend. At the same time, data from predosed samples can furnish information on biological variability among the subjects and between studies. Figure 7 shows the baseline concentration distribution of TRACP 5b, a bone resorption biomarker, in various patient populations from four clinical studies. Statistical tools can be used to construct distribution charts of the predosed samples of each population, and cohorts can be compared. For pilot clinical trials with small number of cohorts, one should note that the data may not be normally

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Figure 6 Levey Jenning plots of low and high sample controls of serum CTx. Concentrations were plotted against run sequences labeled with the speciﬁed studies from A to F. UCL, upper conﬁdence limit; LCL, lower conﬁdence limit. Sample results exceeding the Westgard rule of 1:2 S (beyond 2 standard deviations) were marked.

distributed (example in Figure 7A and C) as compared to those with the latephase clinical trials (example in Figure 7B and D). In addition, multiple collections of predose samples from the same subject can provide sample collection variables of multiple clinical sites. Conﬁrming Selectivity in Patient Populations Biomarkers can reﬂect the dynamic changes in disease progression and response to therapy, but homeostatic and compensatory biological responses can alter the expected proﬁle in response to drug treatment and concomitant therapies (Heeschen et al, 2003; Vural et al., 2006). In the latter case, biomarker assay selectivity is a critical component for interpreting biomarker responses and clinical data, particularly when a concomitant therapy is a potential interferent in the assay. For example, the bone turnover biomarker, serum CTx, is being used in clinical studies involving many different populations. During method validation, serum from healthy males and females and from patients with breast cancer, multiple myeloma, osteoporosis, and rheumatoid arthritis were analyzed with and without the addition of CTx at 0.145 ng/mL. The spike recovery was calculated

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Figure 7 Baseline concentration distribution of TRACP5b in various patient populations from four clinical studies: (A) patients with advanced cancer (n = 105); (B) patients with prostate cancer (n = 1427); (C) postmenopausal patients (n = 222); (D) postmenopausal patients (n = 7828). The curved line represents a ﬁt by lognormal distribution. Outlier box plots are shown in the panels above the histograms. X-axis, concentrations of TRACP5b in ng/mL; Y-axis, relative frequency.

by subtracting the endogenous concentration of the unspiked sample from the spiked sample. The recovery data of the percent difference from the nominal value of the various populations were presented in Figure 8. The results show that all normal male and female serum recovered with the acceptance criteria (dashed line of ±25%). Recoveries were more variable with patient samples, although most of them were acceptable. The recovery data set of normal females had the best precision and was used as the control in Dunnett’s method to compare with the other groups. The Figure 8 side bar shows that the circles of all groups were overlapping with the control group (bold circle). All groups had p-values less than an α of 0.05, while that of the rheumatoid arthritis group was 0.0587, possibly due to a slight interference from the rheumatoid factor in the samples. Speciﬁcity and Sensitivity of Novel Biomarker on Drug Effect In-study data should be compiled and assessed to determine if (1) assay performances of

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Figure 8 Selectivity test of CTx in sera from healthy and patient populations. The populations included healthy male (normal M, n = 20) and female (normal F, n = 20); patients with breast cancer (BC, n = 20), multiple myeloma (MM, n = 10), osteoporosis (Osteo, n = 18) and rheumatoid arthritis (RA, n = 10). The normal male age range was 45 to 74, that of female was 38 to 67. Serum samples were analyzed without and with the addition of CTx at 0.145 ng/mL. The spike recovery was calculated by subtracting the unspiked sample concentration from that of spiked sample for each person. Percent (%) difference from the nominal value of 0.145 was calculated and the data presented in box plot for each group of population. Each box presents the mean, standard deviations, and range. Side panel: Comparisons with a control using Dunnett’s method.

standards and SCs are within acceptance criteria, (2) patient sample concentrations are in the ranges expected for predosed samples, and (3) postdosed patient sample concentrations are consistent with the values expected for previous pilot study (such as PD proﬁle, dose effect, and recovery to normal after treatment change, if applicable). Data should be evaluated for false positive (speciﬁcity) and false negative (sensitivity). For example, the variability of the SCs could reﬂect the assay noise of incurred samples. This variance should be smaller than that of the difference in disease subject vs. normal (speciﬁcity) and that of concentration change due to drug versus placebo effect (sensitivity). The data mining, interpretation, and linkage of biomarker data to clinical outcome is beyond the scope of this chapter. It should be borne in mind that a useful predictive index of a biomarker is dependent on whether it is used as one of a panel of biomarkers. The statistical power is affected by the sampling number (number of subject and time point) and assay stringency. Therefore, the bioanalytical laboratory should be included in the biomarker team to interpret data use, the model being tested, and the analytical component required for the predictive index desired. Regulatory Issues Regulatory guidance for bioanalytical validation indicates that the paramount objective of a bioanalytical method validation is to ensure

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that the assay is reliable for its intended use (ICH Guidelines, 1994; Shah et al., 1992). This philosophical viewpoint is central to many guidance documents, conference reports, and “white papers” for bioanalytical validation of assays for conventional small-molecule drugs and macromolecular therapeutics (Shah et al., 2000; Code of Federal Regulations, 2001a; FDA, 2001; Findlay et al., 2000; Miller et al., 2001; DeSilva et al., 2003). Currently, two basic bioanalytical approaches are being applied to biomarker validation. One is based on the FDA bioanalytical drug assay guidance and is generally referred to as “GLP-like” (Code of Federal Regulations, 2001a; FDA, 2001). The second is based on the CLSI and CLIA (National Committee for Clinical Laboratory Standards, 1999; Code of Federal Regulations, 2001b). Presently, no regulatory guidance has been given concerning what experiments should be performed and what data are necessary and appropriate for biomarker assay validation (Lee et al., 2007). This has led to inconsistent application of validation procedures and acceptance criteria for biomarkers in clinical trials. For biomarker assays to be characterized and used for drug development support, they should be GLP-like (Lee et al., 2005, 2006). However, the guidelines must be ﬂexible, to allow assay- and technology-speciﬁc applications to meet speciﬁc clinical study objectives on a case-by-case basis. Thus, general guidelines for bioanalytical laboratories should be developed to include both good scientiﬁc and clinical sense with ﬂexibility to develop speciﬁc applications (Lee et al., 2007). At the same time, documentation of change and version control of biomarker methods is important, especially to those who produce elements of the assay or kit. The FDA has set up an Interdisciplinary Pharmacogenomic Review Group (IPRG) to carry out speciﬁc review function for the assessment of biomarker qualiﬁcation data. The team will evaluate study protocols and review study results of novel biomarkers of drug safety, using appropriate preclinical, clinical, and statistical considerations. The team will then develop recommendations and guidance for the submission of biomarker data, assess the original biomarker context proposal through voluntary data submission (VXDS, where the X indicates a wide range of data sources), and then evaluate the qualiﬁcation study protocol together with the sponsor to reach a consensus protocol (Goodsaid and Frueh, 2007). Characterization data of novel biomarkers to support efﬁcacy can be categorically in the voluntary data submission, and discussion with the IPRG will clarify the data requirement for drug labeling. Documentation Documents and data on biomarker characterization to support drug development must be kept in a secure place and made accessible for internal or external audits. All processes and data generation should be traceable. It is critically important to deﬁne and document the reference standard. Because novel biomarkers do not usually have established reference standards, characterization of the standard material is dependent on the supplier laboratories from commercial or in-house sources. Documentation such as a certiﬁcate or record of analysis and a certiﬁcate of stability should be

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obtained. If these are not available, at least the source, identity, potency (or concentration), and lot number of the reference material should be documented. A consistent and sufﬁcient supply of the same reference material lot should be reserved and used for the duration of a study and program if interstudy comparisons are intended. Similar to the reference material, documentation of the source, identity, potency or concentration, and lot number of critical ligand reagents and their stability should be kept. If possible, the same lot of the capturing ligand should be used throughout a study. It is prudent to test for method robustness using multiple lots of reagents during advanced method validation. Partial Validation for Change Control The time span of biomarker characterization can be a long one. Changes in operators, assay equipment, reagent lots, and a variety of other unforeseen events usually happen and will require additional validation. The signiﬁcance of the alteration in a method will determine the extent of validation necessary, leading to cross-validation, partial validation, or complete revalidation of an assay to control for changes during or between studies. A validation plan should be prepared for partial validations, describing the intended purpose of change, scope, experiments to be carried out, data comparison, and a prior acceptance criterion for method equivalence. A common set of clinical samples analyzed by both methods can be used to show method equivalence. The sampling size should be powered by statistics based on the desired criterion of acceptance and the method variability. If the method change is expected to be extensive, a new validation would be conducted according to the new analytical procedure, followed by a new written validation report. CONCLUSIONS AND PERSPECTIVES In this chapter we have focused on a discussion of quantitative method validation and assay for biomarker characterization to support drug development using the ﬁt-for-purpose approach. The main processes include: deﬁning the purpose of application, assay development, validation and use meeting the purpose and statistical treatment, and interpretation of analytical and clinical data. Due to the heterogeneous nature of many protein biomarkers, discrepancies may exist between the endogenous forms and the reference standard. It is important to understand what is being measured and the clinical meaning of the moieties measured as related to disease progression and drug effect. For example, during the course of development of a LBA method, biophysical methods (such as mass spectrometry coupled to ligand binding solid-phase extraction) can be used to select reagents and identify the chemical species being captured in the ligand solid phase. In addition, there are differences in the standard curve blank control matrix from the intended biological matrix. Parallelism is a characteristic that must

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be tested using authentic samples from the target population. Another important consideration is that binding of the biomarker to endogenous binding proteins, receptors, and the xenobiotic drug candidate may cause assay anomaly. Different methods may measure different forms of the biomarker, which often leads to the confounding data in the literature. Speciﬁc binding reagents and processing steps can be designed and manipulated to measure “bound” or “free” species of the biomarkers. In contrast to the exploratory phase, biomarker characterization requires more intense rigor in a quantitative method. For example, method robustness and long-term storage stability must be established; sufﬁcient and consistent supplies of reagents should be assured to sustain the applications through multiple studies; and the same methods should be validated and applied in the participating clinical sites on sample collection and in bioanalytical laboratories on sample assays. Only when a robust method has been established and the assay performance deﬁned, is it possible for the biological variability (clinical variance) to be distinguished from analytical variability and be assigned its clinical signiﬁcance. Statistical treatment of biomarker assay performance enables assessing if the method will meet the study purpose before its application. Statistical data from in-study performance continue to add information to the method appropriateness, especially with data from the sample controls, which provide trends in authentic sample stability and reagent lot variability. Data from multiple studies and drug candidates (from the ﬁrst-in-class to later backups, or to another candidate of related pathways) contribute to the accumulated knowledge of the biomarker. Whereas the exercise of utilizing biomarkers to support drug development of a single or several candidates can be controlled within a pharmaceutical company, the movement of characterization and qualiﬁcations of a novel biomarker will depend on the concerted efforts of multiple institutes/organizations. The collaborative effort of sharing and using the same method and reagents may be a challenge. If different methods are to be used, the data interpretation and comparison will add complexity to this challenge. For example, metaanalysis of clinical biomarker data from publications has been problematic because method details and validation parameters were missing to enable proper comparisons. It will be helpful if a consortium organization will handle data repository and communication (such as standardization of publication) of novel biomarkers. This is the goal of the Biomarker Consortium (launched in October 2006) by the FDA, Foundation for the National Institutes of Health, and Pharmaceutic Research and Manufactures of America through their various working groups. Acknowledgments The authors are grateful to the Biomarker Subcommittee in the Ligand Binding Assay Bioanalytical Focus Group of the American Association of

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Pharmaceutical Scientists for ideas and collaborations in the ﬁt-for-purpose method validation concept. We thank Binodh DeSilva, Daniel Burns, Chad Ray, Han Gunn, and Lennie Uy for their contributions to the content and critical review of this manuscript. REFERENCES Banks RE (2008). Preanalytical inﬂuences in clinical proteomic studies: Raising awareness of fundamental issue in sample banking. Clin Chem, 54;16–17. Berger J, Wagner JA (2002). Physiological and therapeutic roles of peroxisome proliferator-activated receptors. Diabetes Technol Ther, 4:163–174. Bjornsson TD (2005). Biomarkers: applications in drug development. Eur Pharm Rev, 1:17–21. Bloom JC, Dean RA (eds.) (2003). Biomarkers in Clinical Drug Development. Marcel Dekker, New York. Chu CS, Lee KT, Lee MY, et al. (2006). Effects of rosiglitazone alone and in combination with atorvastatin on nontraditional markers of cardiovascular disease in patients with type 2 diabetes mellitus. Am J Cardiol, 97:646–650. Code of Federal Regulations (2001a). CFR Title 21, vol. 1, Good Laboratory Practice for Nonclinical Laboratory Studies, rev. Apr. 1. Code of Federal Regulations (2001b) CFR Title 42, vol. 3, Clinical Laboratory Improvement Amendment, rev. Oct. 1 DeSilva B, Smith W, Weiner R, et al. (2003). Recommendations for the bioanalytical method validation of ligand-binding assays to support pharmacokinetic assessments of macromolecules. Pharm Res, 20:1885–1900. FDA (2001). Guidance for industry on bioanalytical method validation: availability. Fed Reg, 66:28526–28527. FDA (2004). Innovation or stagnation? Challenge and opportunity on the critical path to new medical products. http://www.fda.gov/opacom/hpview.html. FDA (2006). Table of valid genomic biomarkers in the context of approved drug labels. http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm. FDA CDER (2006). Using disease, placebo, and drug prior knowledge to improve decisions in drug development and at FDA. Case Studies Across Companies Disease Models at FDA: Overview and Case Studies (Diabetes and Obesity), p. 65. Ferguson RE, Hoschstrasser DF, Banks RE (2007). Impact of preanalytical variable on the analysis of biological ﬂuids in proteomic studies. Proteom Clin Appl, 1:739–746. Findlay JWA, Smith WC, Lee JW, et al. (2000). Validation of immunoassays for bioanalysis: a pharmaceutical industry perspective. J Pharm Biomed Anal, 21: 1249–1273. Goodsaid F, Frueh F (2007). Biomarker qualiﬁcation pilot process at the US Food and Drug Administration. AAPS J, 9:E105–E108. Haffner SM (2007). Abdominal adiposity and cardiometabolic risk: Do we have all the answers? Am J Med. 120(9 Suppl 1):S10–S106.

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Heeschen C, Dimmeler S, Hamm CW, Boersma E, Zeiher AM, Simoons ML (2003). Prognostic signiﬁcance of angiogenic growth factor serum levels in patients with acute coronary syndromes. Circulation, 107:524–530. ICH Guidelines. (1994). Text on Validation of Analytical Procedures, Q2A. International Conference on Harmonization, Geneva, Switzerland. Khatami M (2007). Standardizing cancer biomarkers criteria: data elements as a foundation for a database. Inﬂammatory mediator/M-CSF as model marker. Cell Biochem Biophys, 47:187–198. Krishnamurti U, Steffes MW (2001). Glycohemoglobin: a primary predictor of the development or reversal of complications of diabetes mellitus. Clin Chem, 47:1157–1165. Kummar S, Kinders R, Rubinstein L, et al. (2007). Opinion: Compressing drug development timelines in oncology using phase ‘0’ trials. Nat Rev Cancer, 7:131–139. Lee JW, Ma H (2007). Speciﬁcity and selectivity evaluations of ligand binding assay of protein therapeutics against concomitant drugs and related endogenous proteins. AAPS J, 9:E164–E170. Lee JW, Smith WC, Nordblom GD, Bowsher RR (2003). Validation of assays for the bioanalysis of novel biomarkers. In Bloom JC, Dean RA (eds.), Biomarkers in Clinical Drug Development. Marcel Dekker, New York, pp. 119–149. Lee JW, Weiner RS, Sailstad JM, et al. (2005). Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res, 22(4):499–511. Lee JW, Devanarayan V, Barrett YC, et al. (2006). Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res, 23(2):312–328. Lee JW, Figeys D, Vasilescu J (2007). Biomarker assay translation from discovery to clinical studies in cancer drug development: quantiﬁcation of emerging protein biomarkers. In Hampton GM, Sikora K (eds.), Genomics in Cancer Drug Discovery and Development. Advances in Cancer Research. Elsevier, Amsterdam, pp. 269–298. Lee JW, O’Brien P, Pan Y, et al. (2008). Development and validation of ligand binding assays for biomarkers. In Khan M, Findlay JWA (eds.), Ligand-Binding Assays: Development, Validation and Implementation in the Drug Development Arena. Wiley, Hoboken, NJ. Lee YH, Pratley RE (2005). The evolving role of inﬂammation in obesity and the metabolic syndrome. Curr Diabetes Rep, 5:70–75. Liu S, Tinker L, Song Y, et al. (2007). A prospective study of inﬂammatory cytokines and diabetes mellitus in a multiethnic cohort of postmenopausal women. Arch Intern Med, 167:1676–1685. Mager DE, Wyska E, Jusko WJ (2003). Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos, 31:510–518. Miller KJ, Bowsher RR, Celniker A, et al. (2001). Workshop on bioanalytical methods validation for macromolecules: summary report. Pharm Res, 18:1373–1383. National Committee for Clinical Laboratory Standards (1999). Evaluation of Precision Performance of Clinical Chemistry Devices: Approved Guideline. CLSI Document EP5-A. Pepe MS, Etzioni R, Feng Z, et al. (2001). Phases of biomarker development for early detection of cancer. J Natl Cancer Inst, 93:1054–1061.

REFERENCES

213

Quraishi I, Rishi M, Feldman M, Wargovich MJ, Weber B (2007). Clinical validation of breast cancer biomarkers using tissue microarray technology. App Immunohistochem Mol Morphol, 15:45–49. Rubin MA (2004). Using molecular markers to predict outcome. J Urol, 172(5 Pt 2): S18–S21. Stoch SA, Wagner JA (2007). Biomarker analysis as a decision-making tool in drug discovery and development: implications for peroxisome proliferator–activator receptors. Int J Pharm Med, 21:271–277. Sweep FC, Fritsche HA, Gion M, Klee GG, Schmitt M (2003). Considerations on development, validation, application, and quality control of immuno(metric) biomarker assays in clinical cancer research: an EORTC-NCI working group report. Int J Oncol, 23:1715–1726. Shah VP, Midha KK, Dighe S, et al. (1992). Analytical methods validation: bioavailability, bioequivalence, and pharmacokinetic studies. Pharm Res, 9:588–592. Shah VP, Midha KK, Findlay JWA, et al. (2000). Bioanalytical method validation: a revisit with a decade of progress. Pharm Res, 17:1551–1557. Tholen DW, Linnet K, Kondratovich M, et al. (2004). Protocols for Determination of Limits of Detection and Limits of Quantitation; Proposed Guideline. NCCLS EP17-P. van Doorn M, Kemme M, Ouwens M, et al. (2006). Evaluation of proinﬂammatory cytokines and inﬂammation markers as biomarkers for the action of thiazolidinediones in type 2 diabetes mellitus patients and healthy volunteers. Br J Clin Pharmacol, 62:391–402. Vural P, Akgul C, Canbaz M (2006). Effects of hormone replacement therapy on plasma pro-inﬂammatory and anti-inﬂammatory cytokines and some bone turnover markers in postmenopausal women. Pharm Res, 54:298–302. Wagner JA (2002a). Overview of biomarkers and surrogate endpoints in drug development. Dis Markers, 18:41–46. Wagner JA (2002b). Early clinical development of pharmaceuticals for type 2 diabetes mellitus: from pre-clinical models to human investigation. J Clin Endocrinol Metab, 87:5362–5366. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharm Ther, 81:104–107. Westgard JO (2003). Internal quality control: planning and implementation strategies, Ann Clin Biochem, 40:593–611. Yu LR, Veenstra TD (2007). AACR-FDA-NCI cancer biomarkers collaborative. Expert Rev Mol Diagn, 7:507–509.

11 MOLECULAR BIOMARKERS FROM A DIAGNOSTIC PERSPECTIVE Klaus Lindpaintner, M.D., M.P.H.* F. Hoffmann–La Roche AG, Basel, Switzerland

INTRODUCTION Molecular biomarkers may be useful in a number of applications, including mechanistic studies, preclinical safety and efﬁcacy testing, early clinical pharmacodynamic assessment, dose-ﬁnding studies, and stratiﬁcation for efﬁcacy or safety and dose determination or dose guidance for a marketed pharmaceutical product. Although applications for a marketed drug product will pose the most demanding requirements in terms of the characterization of the test and its development into a commercially available product, all biomarker tests will need to satisfy certain requirements to be useful for derivation of the information they are intended to deliver. A broader deﬁnition of the term molecular biomarker comprises any and all in vitro testing for biologically informative molecules in tissues or body ﬂuids, be they for purposes of diagnosing disease, preventive health screening applications, prognostication of the natural history of a disorder, prediction of likely efﬁcacy or safety parameters of a treatment, monitoring of treatment efﬁcacy, or screening health care product manufacture or processing, including blood products. *This communication represents the author’s personal views and not necessarily those of any of his institutional or corporate afﬁliations, including F. Hoffmann–La Roche. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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For purposes of the discussion below, we focus primarily on the diagnostic perspective as relevant to novel biomarker tests that accompany the development of a drug, in particular those that will require the development of a commercially available diagnostic test. Since such tests, which are often referred to as companion diagnostics, represent the most demanding scenario, they serve as an example for the spectrum of possible considerations that may be applicable to any diagnostic test—even though, in reality, most tests that add value at various points during the discovery or development of a drug will not need to live up to such scrutiny. The fundamental question determining whether a biomarker is useful is the added information content that it conveys regarding the question it is intended to answer, and whether or to what extent this information adds value in the treatment of patients. Determination of these parameters will generally require characterization of the test on multiple levels that may be deﬁned, broadly, as analytical and clinical. Analytical performance includes a number of metrological aspects, and clinical performance is largely subdivided into validation and utility.

ANALYTICAL PERFORMANCE Describing and characterizing analytical performance is very much oriented toward metrology. The general conventions and standards to consider include precision, trueness, and accuracy, along with a number of other more technical characteristics that describe the performance of a test. Analytical Precision and Imprecision Precision is a measure of the random error or variability observed in measurement results and is a product of the sample handling and analytical process. Precision is typically expressed as the standard deviation found in sample results. Precision includes the deﬁnitions of repeatability and reproducibility. Repeatability is the precision with which, in a series of measurements carried out in sequence, results of a measurement agree with each other. Reproducibility, on the other hand, means the precision with which measurements of the same standard specimen carried out not in sequence, but under conditions that differ in some speciﬁed way (common examples are day to day, or lab to lab), agree with each other. The difference between actual and perfect agreement of these values is referred to as imprecision. Analytical Trueness and Bias Trueness describes the closeness of agreement of an average value from a large series of measurements with a “true value” or an accepted reference value. The numerical value that represents the difference between the two is gener-

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ally referred to as bias. Bias refers to systematic differences between measurement results and the true value of a parameter that is being measured. Bias in measurement results can be introduced in a number of ways, including through sampling, sample handling, sample preparation, matrix interference, cleanup, and determinative processes. Analytical Accuracy Accuracy refers to a composite assessment that comprises both random and systematic inﬂuences (i.e., both precision and trueness). Its numerical value is the total error of measurement. Other Metrics Describing Analytical Performance Aside from metrics for analytical accuracy, a number of parameters are of importance in deﬁning and/or determining the utility of a particular test: 1. The limit of detection (LOD) is the smallest amount of an analyte that can reliably be detected by an assay, with a stated conﬁdence limit. The deﬁnition includes a number of different detection limits that deﬁne different properties of the assay, including the lower limit of detection (LLOD), the instrument detection limit (IDL), the method detection limit (MDL), the (lower) limit of quantitation (LOQ or LLOQ), and the practical quantitation limit (PQL). The detection limits are estimated from the mean of a number of repeated measurements of the blank, the standard deviation of the blank measurements, and a deﬁned conﬁdence factor. The PQL is deﬁned simply as about ﬁve times the MDL. In practical terms, the lower limit of detection is the lowest level of analyte that can be statistically distinguished from the blank (i.e., from background noise). LLOD is a function of the variability of the blank and the sensitivity of the assay. The LLOD is usually considered to be a value that is 3 standard deviations above the mean of the blank. Using this formula, the chance of misclassiﬁcation is 7%. If 2 standard deviations are used, the chance of misclassiﬁcation is 16%. Values below the LLOD should be reported as “less than the LLOD value” rather than as a ﬁnite value. The LLOD can commonly be distinguished from an additional variable that is not assay-speciﬁc but instrument-speciﬁc. Most analytical instruments produce a signal even when a blank (matrix without analyte) is analyzed. This signal is referred to as the instrument detection level or instrument noise level. The IDL is the analyte concentration that is required to produce a signal greater than three times the standard deviation of the noise level. Ideally, this would be equivalent to the assay’s LLOD (as determined under optimal conditions), but is usually somewhat higher.

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2. The method detection limit (MDL) is a metric that is similar to the IDL, but is based on samples that have gone through the entire sample preparation scheme prior to analysis, such as extractions, digestions, concentrations or dilutions, or fractionations, as well as interference by other components present in a complex matrix. The recovery of an analyte in an assay is the detector response obtained from an amount of the analyte added to and extracted from the biological matrix, compared to the detector response obtained for the true concentration of the pure authentic standard. Recovery pertains to the extraction efﬁciency of an analytical method within the limits of variability. Recovery of the analyte need not be 100%, but the extent of recovery of an analyte and of the internal standard should be consistent, precise, and reproducible. Recovery experiments should be performed by comparing the analytical results for extracted samples at three concentrations (low, medium, and high) with unextracted standards that represent 100% recovery. 3. The limit of quantitation (or quantiﬁcation), also referred to as lower limit of quantiﬁcation (LLOQ), is set at a higher concentration than the LLOD; in the statistical method, it generally is deﬁned as 10 standard deviations above the mean blank value, thus presenting a greater probability that a value at the LLOQ is “real” and not just a random ﬂuctuation of the blank reading. The lowest standard on the calibration curve should be accepted as the limit of quantiﬁcation if the analyte response at the LLOQ is at least ﬁve times the response compared to the blank response, and if the analyte peak (the response) is identiﬁable, discrete, and reproducible with a precision of 20% and an accuracy of 80 to 120%. The LLOQ can differ drastically between laboratories, so another parameter for detection limit is commonly used, the practical quantitation limit (PQL). The PQL is commonly deﬁned as 3 to 10 times the MDL. 4. Selectivity is the ability of an analytical method to differentiate and quantify the analyte in the presence of other components in the sample. For selectivity, analyses of blank samples of the appropriate biological matrix (plasma, urine, or other matrix) should be obtained from a sufﬁciently large and representative number of sources. Each blank sample should be tested for interference, and selectivity should be ensured at the lower limit of quantiﬁcation (LLOQ). 5. The coefﬁcient of variation (CV) is a normalized measure of dispersion of a probability distribution. It is deﬁned as the ratio of the standard deviation to the mean. It is often reported as a percentage (%) by multiplying the calculation by 100. The coefﬁcient of variation is useful because the standard deviation of data must always be understood in the context of the mean of the data. The coefﬁcient of variation is a

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dimensionless number, so when comparing between data sets with different units or wildly different means, one should use the coefﬁcient of variation for comparison instead of the standard deviation.

DIAGNOSTIC ACCURACY AND CLINICAL VALIDATION Diagnostic accuracy, determined by a process commonly referred to as clinical validation, refers to the degree with which results of a test concur with what would be considered the current gold standard of clinical assessment of the question under study. This may be another biomarker (i.e., an established reference test) or—the real gold standard—a clinical outcome or endpoint, such as survival or death. Accuracy can be expressed through sensitivity and speciﬁcity, positive and negative predictive values, or positive and negative diagnostic likelihood ratios. Each measure of accuracy should be used in combination with its complementary measure: sensitivity complements speciﬁcity, positive predictive value complements negative predictive value, and positive diagnostic likelihood ratio complements negative diagnostic likelihood ratio [1,2]. All of these parameters are not intrinsic to the test and are determined by the clinical context in which the test is employed. A summary of the characteristics, and the strengths and weaknesses of these metrics, is presented in Table 1. Sensitivity The sensitivity (“positivity in disease”) of a test is the probability that it will produce a true positive result when used on a diseased population (compared to a reference test or gold standard assessment). For the purpose of biomarkers, this would translate to the probability of, or the frequency with which, a particular qualitative marker, or a certain value of a quantitative marker is observed in the group of people harboring the condition of interest. After inserting the test results into a table set up like Table 2, the sensitivity of a test can be determined by calculating TP/(TP + FN). High sensitivity corresponds to high negative predictive value and is the ideal property of a rule-out test. Speciﬁcity The speciﬁcity (“negativity in health”) of a test is the probability that a test will produce a true negative result when used on a nondiseased population (as determined by a reference test or gold standard assessment, such as clinical outcome). For the purpose of biomarkers, this would translate to the probability or the frequency with which a particular qualitative marker, or a certain value of a quantitative marker, is not observed in the reference group of nonaffected individuals. After inserting the test results into a table set up like

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TABLE 1 Summary of Characteristics for Various Parameters Used in Describing Clinical Test Performance Metric

Deﬁnitiona

Strengths

Weaknesses

Accuracy

(TP + TN)/N

Intuitive

Sensitivity

TP/(TP + FN)

Speciﬁcity

TN/(TN + FP)

Does not depend on prevalence Does not depend on prevalence

Positive predictive value Negative predictive value Positive likelihood ratio

TP/(TP + FP)

Clinical relevance

TN/(TN + FN)

Clinical relevance

[TP/(TP + FN)]/ [FP/ (TN + FP)] [FN/(TP + FN)]/ [TN/ (TN + FP)] (TP × TN)/ (FN × FP)

Does not depend on prevalence

Applies only to positive tests

Does not depend on prevalence

Applies only to negative tests

Does not depend on prevalence; combines sensitivity and speciﬁcity Does not depend on prevalence; combines sensitivity and speciﬁcity

Values FP and FN errors equally; not intuitive

Negative likelihood ratio Odds ratio

Area under curve

Area under ROC curve

Depends on prevalence Applies only to diseased persons Applies only to nondiseased persons Depends on prevalence Depends on prevalence

Lack of clinical interpretation

a

FN, false negative; FP, false positive; N, sample size; ROC, receiver operating characteristic; TN, true negative; TP, true positive.

Table 2, the speciﬁcity of a test can be determined by calculating TN/(TN + FP). High speciﬁcity corresponds to high positive predictive value and is the ideal property of a rule-in test. Positive Predictive Value The positive predictive value of a test is the probability that a person is diseased when a positive test result is observed. In practice, predictive values should only be calculated from cohort studies or studies that legitimately reﬂect the number of people in that population who are diseased with the disease of interest at that time [i.e., when the prevalence (or incidence, for predictive tests) of the disorder is known]. This is because predictive values are inher-

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TABLE 2 Basic Model for Clinical Test Performancea Gold Standard (Reference Test Results or Clinical Outcome) New Test Results + −

+

−

TP FN

FP TN

a TP, number of true-positive specimens; FP, number of falsepositive specimens; FN, number of false-negative specimens; TN, number of true-negative specimens.

ently dependent on the prevalence of disease, called the prior probability. After inserting results into a table set up like Table 2, the positive predictive value of a test can be determined by calculating TP/(TP + FP).

Negative Predictive Value The negative predictive value of a test is the probability that a person is not diseased when a negative test result is observed. Again, this measure of accuracy should be used only if prevalence is available from the data. After inserting test results into a table set up like Table 2, the negative predictive value of a test can be determined by calculating TN/(TN + FN).

Positive Diagnostic Likelihood Ratios Diagnostic likelihood ratios (DLRs) are not yet commonly reported in peerreviewed literature or in marketing information provided by test manufacturers, but they can be a valuable tool for comparing the accuracy of several tests to the gold standard, and they are not dependent on the prevalence of disease [3]. The positive DLR represents the odds ratio that a positive test result will be observed in a diseased population compared to the odds that the same result will be observed among a nondiseased population. After inserting test results into a table set up like Table 2, the positive DLR of a test can be determined by calculating [TP/(TP + FN)]/[FP/(FP + TN)], or it can also be expressed as sensitivity/(1 − speciﬁcity). Useful tests will therefore have larger positive DLRs, and less useful tests will have smaller positive DLRs. An example interpretation of a positive diagnostic likelihood ratio equal to 5.0 is that for every 1% of nondiseased subjects that test as positive, 5% of the diseased subjects will test as positive. DLRs leverage pretest into posttest probabilities of a condition of interest, and there is some evidence that they are more intelligible to users.

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Negative Diagnostic Likelihood Ratios The negative DLR represents the odds ratio that a negative test result will be observed in an diseased population compared to the odds that the same result will be observed among a nondiseased population. After inserting the test results into a table set up like Table 2, the negative DLR for a test can be determined by calculating [FN/(TP + FN)]/[TN/(FP + TN], or as false negative rate/true negative rate. Useful tests will therefore have negative DLRs close to zero, and less useful tests will have higher negative DLRs. As an example, interpretation of a negative diagnostic likelihood ratio equal to 2.5 is for every one false negative, we observe 2.5 true negatives. Receiver Operating Characteristic The determination of an “ideal” cutoff value of a quantitative test that delivers continuous values is almost always a trade-off between sensitivity (true positives) and speciﬁcity (true negatives). As both change with each cutoff value, it becomes difﬁcult to determine which cutoff is ideal. A graph of sensitivity against (1 − speciﬁcity) is called a receiver operating characteristic (ROC) curve. The ROC curve offers a graphical illustration of these trade-offs at each cutoff for any diagnostic test that uses a continuous variable. Ideally, the best cutoff value provides both the highest sensitivity and the highest speciﬁcity, easily located on the ROC curve by ﬁnding the highest point on the vertical axis and the farthest to the left on the horizontal axis (upper left corner) (Figure 1). ROC curves compare sensitivity versus speciﬁcity across a range

1

Sensitivity

0.8

0.6

0.4

0.2

0 0

Figure 1

0.2

0.4 0.6 1–Specificity

0.8

Receiver operating characteristic curve.

1

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of values of a quantitative biomarker for the ability to predict a dichotomous outcome. When the cutoff value for a continuous diagnostic variable is increased (assuming that larger values indicate an increased chance of a positive outcome), the proportions of both true and false positives decrease. These proportions are the sensitivity and 1 − speciﬁcity, respectively. A perfect test would have sensitivity and speciﬁcity both equal to 1. If a cutoff value existed to produce such a test, the sensitivity would be 1 for any nonzero values of 1 − speciﬁcity. The ROC curve would start at the origin (0,0), go vertically up the y-axis to (0,1), and then go horizontally across to (1,1), as shown in Figure 1 (dotted line). A good test would be somewhere close to this ideal. If a variable has no diagnostic capability, a test based on that variable would be equally likely to produce a false positive or a true positive: sensitivity = 1 − speciﬁcity, or sensitivity + speciﬁcity = 1. This equality is represented by a diagonal line from (0,0) to (1,1) on the graph of the ROC curve, as shown in Figure 1 (dashed line). The curve for a hypothetical test shown in Figure 1 (solid line) suggests that this marker does not provide a very good indication of the outcome of interest, but that it is better than a random guess. The performance of a diagnostic variable can be quantiﬁed by calculating the area under the ROC curve (AUROC) [4]. The ideal test would have an AUROC of 1, whereas a random guess would have an AUROC of 0.5. The AUROC can be calculated as a sum of the areas of trapeziums. For example, in Figure 1, the area under the curve between points (0.2, 0.5) and (0.4, 0.7) is given by (0.4 − 0.2) × (0.5 + 0.7)/2 = 0.12, or in other words, the difference between the x-values multiplied by half the sum of the y-values. In the example shown, the AUROC for the hypothetical marker is 0.68. This is interpreted as the probability that a patient who has the disease has a test value greater than that for a patient who does not. Like all summary measures, however, there are conﬁdence intervals around this value that must also be taken into consideration for proper interpretation. The area under the ROC curve has become a particularly important metric for evaluating diagnostic procedures because it is the average sensitivity over all possible speciﬁcities. Precision of Diagnostic Accuracy It is important to emphasize that as in other empirical studies, speciﬁc values of diagnostic accuracy are merely estimates. Therefore, when evaluations of diagnostic accuracy are reported, the precision of the sensitivity and speciﬁcity estimates or likelihood ratios should be stated. If sensitivity and speciﬁcity estimates are reported without a measure of precision, clinicians cannot know the range within which the true values of the indices are likely to lie. Evaluations of diagnostic accuracy should therefore be described with the inclusion of conﬁdence intervals. In the case of agreement with (an)other biomarker(s), the driving factor of such an evaluation may be the replacement of an existing, clinically validated

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test with a new one that may be operationally or economically more attractive, or one that may have greater diagnostic precision. Odds Ratio and Relative Risk The odds ratio is a way of comparing whether the relative odds of a certain event (e.g., of harboring a certain biomarker) is the same for two groups (e.g., those who have the disease as compared to those who do not). An odds ratio of 1 implies that the presence of the biomarker is equally likely in both groups (i.e., that the biomarker does not distinguish between the two). An odds ratio greater than 1 implies that the biomarker is more frequent or prevalent in the ﬁrst group, and may thus be a useful test to distinguish between the two groups. From Table 2, the odds ratio would be calculated as (TP × TN)/ (FN × FP). Odds ratios are applicable solely to retrospective case–control studies, whereas in prospective studies, randomized clinical trials, or cohort studies, calculation of relative risk is appropriate. The relative risk (sometimes called the risk ratio) compares the probability of disease in the biomarker-positive and biomarker-negative groups, rather than the odds. It is calculated as TP/(TP + TN)/FN/(FN + TN), which is the quotient of the positive and negative predictive values. It is applicable only to prospective studies, randomized clinical trials, or cohort studies (i.e., to situations where the incidence/prevalence of the outcome is not biased by retrospective selection). However, in rare conditions (i.e., conditions with low incidence or prevalence), where both TP and FN are very small, the odds ratio asymptotically approaches the relative risk and is therefore sometimes substituted for the latter. For small probabilities, TP and FN are very small compared to TN and FP. Translating Different Metrics of Information Content Academic biomarker research tends to be concerned primarily with the statistically determined reliability of a ﬁnding. Thus, if the association of a biomarker with a particular phenotype is reproducible, it is interpreted to convey real biological ﬁndings that may be of great interest to fundamental understanding of biological mechanisms, even if the magnitude of effect that can be seen under a particular set of experimental constraints is small. Similarly, the Geoffrey Rose paradox [5] explains that true, reproducible observations may be of great importance from the perspective of public health even if the overall magnitude of effect is modest. Thus, targeted modulation of variables that affect disease risk by small amounts may have important effects if applied to large populations and are thus commonly used to guide health policy. Academic epidemiological research evaluating associations of predictors for outcomes often uses odds ratios or relative risk as its primary parameters for reporting results, particularly in the ﬁeld of genetic association studies. In complex polygenic diseases, the magnitude of effects commonly found for associations

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for individual genetic variants generally ranges between odds ratios of 1 to 2. Demonstrating statistical signiﬁcance for such modest-sized effects requires usually fairly large studies, particularly if multiple comparisons are made (as is the case with genome-wide association studies). On the other hand, when tests are to be applied to clinical decision making in individual patients, information content with regard to the magnitude of association becomes critically important. Only reliable tests with an acceptably low rate of false positive and/or false negative results can responsibly be used in this setting. These performance parameters tend to be gleaned most directly from stating sensitivity and speciﬁcity (or positive predictive value and negative predictive value), and these are therefore the parameters commonly referred to in clinical diagnostics. In general, tests are not considered particularly useful if the area under the ROC curve does not exceed 0.8, which translates into balanced sensitivities and speciﬁcities of about 0.75. On the other hand, a balanced sensitivity and speciﬁcity of 0.75 translates into an odds ratio of about 9; if an effect of this magnitude is indeed present, it will be readily recognizable even in quite modestly sized studies.

CLINICAL UTILITY The term clinical utility, although used widely, is ill-deﬁned. It is commonly used as a synonym for studies of clinical effectiveness and/or economic evaluations. The most basic deﬁnition of clinical utility refers to an estimation of the respective beneﬁts and risks resulting from test use. Risks and beneﬁts are, at this higher-level perspective, to be seen as encompassing both medical and economic connotations and considerations, even though the discussion of beneﬁts and risks is often restricted to the former. As health care payers are becoming increasingly cost-conscious, and reimbursement decisions are being more commonly inﬂuenced by medical– economic considerations, clinical utility is quickly becoming the overriding consideration with regard to the introduction of a companion or other diagnostic. This adds signiﬁcantly to the burden of research and development expenses for diagnostic companies since reliable estimates of clinical utility will usually require prospective, controlled studies in which clinical endpoints are reached and where interventions are or are not guided by testing for the biomarker of interest. Medical Considerations On the level of medical considerations, in the narrowest sense of the term, clinical utility refers to the ability of a screening or diagnostic test to prevent or ameliorate adverse health outcomes such as mortality, morbidity, or disability through the adoption of efﬁcacious treatments conditioned on test results. A screening or diagnostic test in isolation does not have inherent

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utility; because it is the adoption of therapeutic or preventive interventions that inﬂuence health outcomes, the clinical utility of a test depends on effective access to appropriate interventions, or to the way it can beneﬁcially affect the choice of an intervention. This use of the term utility is consistent with standard practice in evidence-based medicine, which focuses on objective measures of health status to evaluate interventions. Clinical utility can more broadly refer to any use of test results to inform clinical decision making. Finally, in its broadest sense, the medical interpretation of clinical utility can refer to any outcome considered important to individuals and their families, as well as to other societal strata. Medical–Economic Considerations If economic considerations are included in the deﬁnition of clinical utility, the question of whether the actual medical beneﬁt derived from the use of the test indeed results in good value for the cost incurred becomes part of the consideration. Value in this context can be assessed by either cost–beneﬁt or costeffectiveness/utility analyses. In the case of a cost–beneﬁt analysis, costs and beneﬁts are expressed in the same units of measurement, usually monetary. Cost-effectiveness and cost-utility analyses compare the respective monetary cost of using or not using the test with deﬁned clinical outcomes, either in physical terms or by using an index that also includes quality-of-life aspects. As summarized in Table 3, when considering such cost–beneﬁt analysis to evaluate the utility of a companion diagnostic that aids in the selection of a particular treatment, one would compare the aggregate costs of testing all eligible patients, of treating test-positive patients with the associated therapy, and of treating those that are test-negative with the conventional standard-ofcare approach against the cost of using the standard of care, without any testing, on all patients. In a cost–beneﬁt analysis, the net (pecuniary) gain or loss of the two approaches would be calculated based on direct medical and nonmedical, and on indirect (productivity) costs of either alternative. As an example, if the test is intended to increase the efﬁcacy of a particular treatment, the cost–beneﬁt ratio may be advantageous if the savings in productivity losses due to faster recovery from an illness based on the test-guided stratiﬁcation of treatments offsets the added cost of testing and use of test-directed treatment alternatives (which may have a greater direct medical cost than the standard of care). Similarly, if a test is used to avoid serious adverse effects, the cost–beneﬁt analysis may indicate advantages if the cost savings realized by avoiding the treatment of adverse reactions outweighs the cost of testing. In a cost-effectiveness analysis, typically the overall cost per outcome unit (such as relapses or survival years), adjusted for a global quality of life in the case of a cost-utility analysis, is compared among the treatment alternatives. In the case of a companion diagnostic, the incremental cost of using the test in all patients and then treating only those in whom the test-driven therapy is either effective or safe may result in an acceptable cost-effectiveness ratio (as

CLINICAL UTILITY

TABLE 3

Cost–Beneﬁt Considerations for Companion Diagnosticsa Efﬁcacy Marker Direct Costs

Cost of testing plus Cost of diagnostics-driven therapy in selected group plus Cost of alternative therapy in de-selected group versus Cost of alternative therapy in all, no testing a

227

, increased cost;

Adverse Reaction Marker

Indirect Costs

Direct Costs

— Novel, presumFaster return to ably more productivity expensive drug —

No productivity loss due to ADR

—

Standard of care presumably cheaper

—

—

Later return to Cost of productivity treating ADR

Productivity loss due to ADR

, decreased cost.

No test Conventional chemo-Rx in all

Incr. QALYs

Hercep test in all→ Pos. test: Herceptin + conv.chemo-Rx Neg. test: conv. chemo-Rx

Incr. cost (in 10,000 UK £) Incr. cost/QALY (in 10,000 UK £)

No test Herceptin + conv. chemo-Rx in all 0

Figure 2

Indirect Costs

2

4

6

Incremental cost-effectiveness ratio Herceptin/HerCep test.

expressed cost per quality-adjusted years of life) for an expensive novel treatment where a less discerning use of the novel treatment may be associated with an unacceptable ratio based on the inclusion of nonresponders. An example for such an analysis was recently conducted for the case of Herceptin and its companion diagnostic test (HerCep test) (Figure 2). In this example

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[6], it could be clearly shown that the use of the test in all patients, followed by a targeted prescription of the new drug only to those in whom it was indicated based on the test, showed an acceptable incremental cost-effectiveness ratio, and one that was clearly superior to a more indiscriminate use of the drug. Cost-effectiveness calculations are affected by numerous factors, including the prevalence of a (positive) test result which materially affects the choice of therapy, the test performance as outlined above in terms of analytical performance and clinical validity, the context with regard to indication and intended use (see below), the magnitude of the advantage over conventional treatments that the test-guided alternative therapy provides, by the cost of tests and different treatments, and last but certainly not least, by the reimbursement environment, including pricing policies that will differ from market to market. Yet another way of looking at the question of clinical utility with regard to medical economics would be to calculate the number needed to screen (i.e., the number of patients that need to undergo the diagnostic test to achieve a particular outcome in at least one case). The outcome could either be deﬁned as the prevention of a death or as an adverse event. Such a deﬁnition could be reached for both efﬁcacy markers (where the choice of a certain drug based on screening may result in more successful treatment of eligible patients, and thus lives saved as compared with standard of care therapy) and—perhaps more obviously—for safety markers where the result would be the avoidance of an adverse event.

CONTEXTUAL CONSIDERATIONS FOR COMPANION DIAGNOSTICS’ CLINICAL UTILITY While clinical performance, expressed as sensitivity and speciﬁcity, is of obvious importance for the utility of any test, it is important to recognize that with regard to companion diagnostics, a set of speciﬁc considerations apply depending on the intended use of the test. In this respect, both the clinical indication and the magnitude of expected beneﬁt from the treatment, and the intended use to either enhance efﬁcacy or avoid adverse reactions, are variables that will greatly inﬂuence the requirements for sensitivity and speciﬁcity, respectively, of the desired test (Table 4). Thus, on one end of the spectrum, in life-threatening indications (e.g., oncology, HIV) and in the case of a drug that may be lifesaving, and for which feasible alternatives do not exist, an efﬁcacy marker would be expected to show a high sensitivity (i.e., few false negatives, as they would result in inappropriate withholding of the treatment), but may be acceptable at lower speciﬁcity (false positives will result, for an otherwise safe drug, in unnecessary and ineffective treatment). In contrast, a safety marker in such an indication would be required to demonstrate high speciﬁcity (i.e., few false positives, which again would result in unnecessary withholding of the treatment), while

NEW MEDICAL ENTITY AND COMPANION DIAGNOSTIC

TABLE 4

229

Contextual Considerations Regarding Companion Diagnosticsa Efﬁcacy Marker

Indication

Sensitivity

Speciﬁcity

Adverse Event Marker Sensitivity

Speciﬁcity

Serious or life threatening Less serious or trivial a

, High requirements, important;

, low requirements, less important.

sensitivity is less important [use of the marker would still be expected to result in fewer adverse reactions (ARs) than would otherwise be encountered; in indications where ARs are often viewed as necessary trade-offs]. On the other end of the spectrum, in more trivial indications where treatment is less urgent, the requirement for a safety marker would be very high sensitivity, as a false-negative test would result in the occurrence of an AR, which in this setting is much less acceptable than in a life-threatening illness. Speciﬁcity is less important, as “wrongful” withholding in a condition of modest severity is clearly less of an issue. Conversely, again, an efﬁcacy marker would be expected to show high speciﬁcity: If the added cost of the test is to be acceptable in such a condition, it would presumably need to be justiﬁed more by cost–beneﬁt considerations that would be set off by inappropriate inclusion in the treatment group of patients with false-positive test results. One may view these considerations from the standpoint of that old medical principle: ﬁrst, do no harm. In serious disease, the threat may come primarily from the illness, so harm would more likely result from inappropriate withholding of treatment based on a false test result. In contrast, in less serious disease, harm may, rather, come from inappropriate administration of a treatment, and one would perhaps be more aligned with the Hippocratic principle by avoiding noncritical treatments.

CO-DEVELOPMENT OF A NEW MEDICAL ENTITY AND COMPANION DIAGNOSTIC If the label of a new medicine is to include the obligatory use of a test, the manufacturer needs to take care that drug and test are developed in tandem and receive regulatory approval in such a fashion that they can reach the market simultaneously. Authorities are paying increasing attention to the challenges that this process may present. The U.S. Food and Drug Administration has issued a concept paper on “Drug–Diagnostic Codevelopment” (http://www.fda.gov/Cder/genomics/pharmacoconceptfn.pdf) that speciﬁcally addresses this issue. A number of working groups, including

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the International Conference on Harmonization, are also currently addressing aspects related to this issue, from coining uniﬁed terminology and language around biomarkers and companion diagnostics, to formal issues relating to regulatory submissions and the evidentiary standards that will be required to accept a companion diagnostic as valid and useful.

CONCLUSIONS At the transition of a biomarker discovery and assessment program into the development for the use of a biomarker as a clinically applicable decision tool, a number of considerations that are not applicable to the discovery stage must come into focus. Many of them represent well-established concepts in analytical metronomy but are not necessarily familiar to investigators at the cutting edge of biomedical research. It is critical that these concepts be considered carefully, as this will directly inﬂuence the feasibility of any biomarker test that is to be used in the setting of clinical practice. Although many of the considerations are of a technical nature, the ultimate driver of such workability is, of course, rooted in biology and in the information content conveyed by the data.

REFERENCES 1. Jaeschke A, Guyatt GH, Sackett DL (1994). Users’ guides to the medical literature: III. How to use an article about a diagnostic test. A. Are the results of the study valid? JAMA, 271:389–391. 2. Jaeschke A, Guyatt GH, Sackett DL (1994). Users’ guides to the medical literature: III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for patients? JAMA, 271:703–707. 3. Halkin A, Reichman J, Schwaber M, Paltiel O, Brezis M (1998). Likelihood ratios: getting diagnostic testing into perspective. Q J Med, 91:247–258. 4. Faraggi D, Reiser B (2002). Estimation of the area under the ROC curve. Stat Med, 21:3093–3106. 5. Rose G (1981). Strategy of prevention: lessons from cardiovascular disease. Br Med J, 282:1847–1851. 6. Elkin EB, Weinstein MC, Winer EP, et al. (2004). HER-2 testing and trastuzumab therapy for metastatic breast cancer: a cost-effectiveness analysis. J Clin Oncol, 22(5):854–863.

12 STRATEGIES FOR THE CO-DEVELOPMENT OF DRUGS AND DIAGNOSTICS: FDA PERSPECTIVE ON DIAGNOSTICS REGULATION Francis Kalush, Ph.D., and Steven Gutman, M.D., M.B.A.* U.S. Food and Drug Administration, Rockville, Maryland

INTRODUCTION The U.S. Food and Drug Administration (FDA) introduced its critical path initiative in 2004 as an effort to stimulate and facilitate the scientiﬁc process for the development of new drugs, biological products, and medical devices [1]. The initiative was grounded in the sobering reality that between 1993 and 2003, despite a doubling in research and development spending by both industry and the National Institutes of Health (NIH), the FDA observed a decrease in the number of new drug applications (NDAs) and an erratic but ﬂat curve in the approval of new molecular entities. Of particular concern was the dramatic increase in the developmental cost of new drugs and the high rate of new drug failures occurring relatively late in the developmental life cycle (phase III or phase IV drug failures). The critical path was developed to ensure that FDA fosters a collaborative and robust scientiﬁc environment to *The views presented in this chapter do not necessarily reﬂect those of the U.S. Food and Drug Administration. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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help strengthen the assortment of tools that might be used to bring new medical products to the marketplace in an expedited manner. Since its initiation in 2004, a new ofﬁce, the Critical Path Ofﬁce, has been created, a comprehensive and ambitious opportunities list has been generated [9], and numerous collaborative projects and initiatives have been born, all with the singular goal of using good science to address the challenge of timely and cost-effective product development. With the unveiling of the complete human genomic map in 2001–2003 [2,3] and the development of sophisticated new technologies for molecular diagnostics (including use of microarrays and bioinformatics to create integrated multiplex tests with complex molecular signals), considerable attention has been focused on biomarkers (especially those with a pharmacogenomics base) to help improve drug candidacy and to streamline drug studies. The introduction of a diagnostic biomarker to reﬁne drug discovery has obvious appeal. Although biomarkers in a broad sense can include both imaging technologies and in vitro diagnostic devices (IVDs), for the sake of this chapter the focus is on the latter. When used clinically, IVDs are subject to the same regulations as those applied to all other medical devices. Both the science and regulation required for proper deployment of these companion diagnostics products is quite different than the science and regulation of drugs. As a result, to bring closely linked products to the market in a timely manner, both FDA and sponsors must work collaboratively to address what may often be dual device and drug regulatory requirements.

FDA REGULATION OF IN VITRO DIAGNOSTIC DEVICES A medical device is deﬁned as an “instrument, apparatus, implement, machine, contrivance, implant, in vitro reagent, or other similar or related article, including a component part, or accessory which is: recognized in the ofﬁcial National Formulary, or the United States Pharmacopoeia, or any supplement to them, intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or intended to affect the structure or any function of the body of man or other animals, and which does not achieve any of its primary intended purposes through chemical action within or on the body of man or other animals and which is not dependent upon being metabolized for the achievement of any of its primary intended purposes” by the Federal Food, Drug, and Cosmetic Act [21 United States Code [321] (h)]. In vitro diagnostics (IVDs) are deﬁned as reagents, instruments, and systems intended for use in the diagnosis of disease or other conditions, including a determination of the state of health, in order to cure, mitigate, treat, or prevent disease or its sequelae. Such products are intended for use in the collection, preparation, and examination of specimens taken from the human body [21 CFR 809.3(a)]. IVDs are devices, as deﬁned in section 201(h) of the act, and may also be

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233

biological products subject to section 351 of the Public Health Service Act. While drugs are regulated in the Center for Drug Evaluation and Research (CDER) under the provisions of the state law, IVDs are regulated in the Center for Devices and Radiological Health (CDRH) under provisions of the Medical Device Amendments of 1976. Like the regulation of all medical devices, the regulation of IVDs by the FDA is risk-based with devices classiﬁed into low-risk (class I, e.g., adjunctive immunohistochemical stains used in conjunction with standard microscopic analysis to subtype tumors), moderate-risk (class II, e.g., prognosis, monitoring in already diagnosed cancer patients), or high-risk (class III, e.g., cancer diagnosis, screening) categories. The FDA regulatory program is comprehensive and includes requirements for registration and listing of products, for high-quality production using good manufacturing practices, and for postmarket reporting of adverse events. For some class I, most class II, and all class III devices, FDA review is required before a new medical device can enter the marketplace. • Class I devices: typically present minimal potential for harm to the user and the person being tested. They are subject to general controls, which include registration and listing, labeling, and adverse event reporting requirements [section 513(a)(1)(A) of the act]. Most class I devices are exempt from premarket notiﬁcation (see deﬁnition below), subject to certain limitations found in section 510(l) of the act and in 21 CFR 862.9, 864.9, and 866.9. An IVD example of class I devices is complement reagent (21 CFR 866.4100). • Class II devices: devices for which general controls alone are insufﬁcient to provide reasonable assurance of their safety and effectiveness and for which establishment of special controls can provide such assurances. Special controls may include special labeling, mandatory performance standards, risk mitigation measures identiﬁed in guidance, and postmarket surveillance [section 513(a)(1)(B) of the act]. Most class II devices require premarket notiﬁcation. IVD examples of class II devices include glucose test systems (21 CFR 1345), antinuclear antibody immunological test systems (21 CFR 866.5100), and coagulation instruments (21 CFR 864.5400). • Class III devices: devices for which insufﬁcient information exists to provide reasonable assurance of safety and effectiveness through general or special controls. Class III devices are usually those that support or sustain human life, are of substantial importance in preventing impairment of human health, or which present a potential, unreasonable risk of illness or injury [section 513(a)(1)(C) of the act]. Most class III devices require premarket approval (PMA), deﬁned below. IVD examples of these include automated PAP smear readers, nucleic acid ampliﬁcation devices for tuberculosis, and total prostate-speciﬁc antigen (PSA) for the detection of cancer (21 CFR 866.6010).

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Laboratory-developed tests (LDTs), in-house tests, and “home-brew” tests are tests developed in a laboratory for use only in that laboratory. Historically, FDA has applied enforcement discretion to most LDTs and not regulated them actively. Instead, FDA has generally chosen to regulate the components of LDTs, such as instruments and reagents. Considerable discussion has occurred over the past 10 years on the existence of two parallel but noncongruent regulatory pathways to market (IVD “kits” vs. LDTs); these have been the subject of a number of reports, including the Task Force on Genetic Testing and the Secretary’s Advisory Committee on Genetic testing [4]. The Secretary’s Advisory Committee on Genomics, Health and Society (SACGHS) is in the process of discussing the issue of genetic tests with regard to potential scientiﬁc or regulatory gaps and is currently seeking public comment on a draft report to the Secretary of Health and Human Services (HHS) on the oversight of genetic testing [5]. Major Elements of a Submission Major elements of a submission can be found by browsing the Ofﬁce of In Vitro Diagnostic Devices and Safety (OIVD) 510(k) data templates posted on the OIVD Web page [6]. These summarize information related to speciﬁc cleared products, including the intended use/indications for use, analytical and clinical validation information, device description including platform and software information, information on instrument and software validation when applicable, and labeling (package insert information). IVD labeling is unique among medical devices since it is described speciﬁcally in regulation [21 CFR 809.10(b)]. For PMAs, information describing manufacturing, design controls, and adherence to quality system regulations are also needed. The intended use should specify what analyte the test measures, the clinical indication for which the test is to be used, and the target population for which the test is intended. It should also indicate whether the test is qualitative and quantitative. Analytical validation includes precision (repeatability and reproducibility), accuracy, limit of detection, interferences, cross-reactivity, software, performance around the cutoff, carryover, cross hybridization, sample preparation/ conditions, and assay limitations. FDA recognizes dozens of standards by the Clinical Laboratory Standards Institute (CLSI) to assist companies in developing this information. A list of recognized standards [10] may also be found on the OIVD Web page. Clinical validation should be established in appropriate clinical studies to support the indication for use and claims of the device. These studies generally need to be provided by the sponsor or, if available, sponsors may also cite applicable clinical literature. For clinical literature to be acceptable for product clearance or approval, the quality must be carefully assessed and the link between the published studies and the device being reviewed must be carefully established. In instances where tests are developed based on sequential use of

REGULATORY TOOLS AND SOLUTIONS

235

training sets followed by independent test validation, the FDA encourages that rigorous and meticulous attention be paid to the training sets used for new diagnostic markers. However, FDA review is focused on the independent validation that supports the merits of the device itself based on feasibility studies and development of a clear hypothesis in a deﬁned population under established conditions of use. When the system includes software, software documentation including software design, hazard analysis, and complete veriﬁcation and validation should be provided [11]. Labeling needs to be detailed sufﬁciently to satisfy the requirements of 21 CFR 807.87(e). Final labeling for in vitro diagnostic devices must comply with the requirements of 21 CFR 809.10 before the device is introduced into interstate commerce. Among the particularly important components of required labeling are an intended use, device description, directions for use, quality control, precautions, warnings and limitations, performance characteristics, interpretation of results, and values expected.

REGULATORY TOOLS AND SOLUTIONS FOR PHARMACOGENOMICS MARKERS Over the past several years, the Ofﬁce of In Vitro Diagnosis (OIVD) has made ﬂexible use of its regulatory tools to help bring important new diagnostics to market. These include the use of the preinvestigational device exemption (pre-IDE process), expedited review, real-time work interactions, and de novo classiﬁcation of new devices into classes II and I under section 513(f)(2) of the FDCA. The purpose of a pre-IDE protocol review with the FDA is to assist the sponsor and familiarize FDA with cutting-edge technology in advance of the submission. Furthermore, assistance may be needed in deﬁning possible regulatory pathways, study design and analysis of complex data, and statistical approaches before initiation. This is a work process performed by review staff in the Center for Devices and Radiological Health at no cost to sponsors with a 60-day time line. OIVD now processes almost 300 pre-IDEs per year and ﬁnds that use of this process decreases uncertainty in the review process, provides an early opportunity for question and answers, and drives the least burdensome approach for sponsors. Both 510(k)s and PMAs for cutting-edge IVDs may also be designated for expedited review if the device is expected to provide unique public health beneﬁts. Expedited review ensures that at every stage in the review process, the device under consideration goes to the front of the review queue. Even so, the outcome of an expedited review, like the outcome of all reviews, will depend ultimately on the quality of the submission. A subcategory of PMA supplements is eligible for what is known as realtime review, which calls for rapid interactive review between the FDA and sponsors. Although OIVD gets only a small number of real-time applications

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per year, it has found rapid interactive review with sponsors to be so successful that it has been applied more broadly to review pre-IDEs and IDEs and processing of some devices subject to expedited review. In some cases, such interaction has facilitated review of cutting-edge new products in two weeks or less. OIVD has also made use of the de novo classiﬁcation process authorized by section 513(f)(2) of the FDCA. A device that measures a new analyte or has an intended use for which no submission has previously been cleared or classiﬁed as a class I or II device is automatically assigned a class III designation, which would require a PMA submission. Under the de novo process, a sponsor that receives a determination that its device is not substantially equivalent may petition for classiﬁcation directly into class II or class I if it can provide sufﬁcient scientiﬁc basis for the FDA to conclude that a reasonable assurance of the safety and effectiveness of that device can be assured without a PMA. In this case, the new device may be tested by the manufacturer in the same way as it would be for a PMA to establish that the test is safe and effective, but the review is performed following the administrative procedures commonly applied to the 510(k) process. As a result, many PMA requirements, including premarket review of manufacturing processes, premarket inspection of manufacturing facilities, and requirements for annual reports, are all not required. Mandatory time lines for review are also shorter (90 vs. 180 days). For de novo classiﬁcation requests, OIVD has been able to use clinical data and supportive literature information, current U.S. clinical guidelines and practices, as well as product-speciﬁc data generated by the sponsor to conclude that classiﬁcation into class II is appropriate and to establish special controls. This type of classiﬁcation occurs more quickly than other classiﬁcation processes and more quickly than review of a PMA, and the newly classiﬁed device not only may be legally marketed but becomes the ﬁrst predicate device for a new classiﬁcation, creating a means for subsequent similar devices to reach the market through the 510(k) process. Devices to detect cytochrome P450 DNA polymorphisms, cystic ﬁbrosis mutations, West Nile antibody, breast cancer gene expression signatures, and the hemagglutinin 5 (H5) subtype of inﬂuenza are among the products cleared using the 510(k) de novo process and the establishment of special control guidances.

REGULATORY FRAMEWORK TO ACCOMMODATE CO-DEVELOPING DRUG DIAGNOSTICS The lifecycle to obtain marketing approval for the development of a new drug is outlined in FDA law and regulation and progresses through a well-deﬁned multistep process: basic research, prototype design or discovery, preclinical development, clinical development (phases I, II, and III), FDA ﬁling/approval/ launch, and postlaunch (phase IV).

ACCOMMODATE CO-DEVELOPING DRUG DIAGNOSTICS

Basic research

Prototype design or discovery

Preclinical development

Clinical development Phase I

Phase II

Phase III

FDA filing approval launch

237

Phase IV post-launch

(A)

Basic research

Feasibility studies

Analytical validation

Clinical validation

Qualification of biomarker

FDA filing approval launch

(B)

Figure 1

(A) Drug and (B) device development pathways.

The development of a new diagnostic also goes through a well-deﬁned multistep process: basic research, feasibility studies, analytical validation, clinical validation, and FDA approval/clearance. The developmental pathways for a drug and device are illustrated in Figure 1. When use of a diagnostic test is identiﬁed as important in selecting which patients receive or avoid a new drug or are to be given a higher or lower dose of a drug, the efﬁcacy/ safety of the drug becomes inextricably linked to the effectiveness of the diagnostic. Drug performance will only be as good as the ability of the diagnostic to properly select patients for that drug treatment. Drug performance is judged based on an assessment of the drug to meet appropriate clinical endpoints or responses with a reasonable safety proﬁle. Diagnostic studies are based on demonstrating that a test properly identiﬁes an outcome or target of interest. When the diagnostic is used to detect drug response, the outcome of interest becomes the drug response or avoidance of drug toxicity. The test parameters of interest in the context of co-development with a new drug do not differ from those of ubiquitous interest in the study of a new diagnostic. Standardized techniques to establishing test performance have been promoted in the literature and in FDA guidances. Clinical parameters of importance include sensitivity—in this case the ability of the test to identify patients who will exhibit the desired drug response of interest—and speciﬁcity—in this case the ability of the test to identify patients who will not demonstrate the drug response of interest. Alternative useful techniques include a deﬁnition of the predictive value of a positive result (the fraction of test positives who respond), the predictive value of a negative result (the fraction of test negatives who do not respond), or the likelihood ratio of drug response after testing. The hazard ratio (HR) in prospective studies/analysis and the odds ratio (OR) in retrospective analysis could be used when interpreting probabilities in a different context of use.

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CHANGES IN STRATEGIC THINKING Because there are now two separate medical products under investigation which may have different life cycles, companies involved in co-development are facing new challenges and may have to create complex arrangements that allow for regulatory and business issues to be addressed. FDA is committed to approaching products being submitted as part of a co-development paradigm to assuring that good science and appropriate regulatory thresholds be met but also bringing to bear a regulatory framework that is as ﬂexible as possible. Companies may either wish to rethink their business models, shift strategies to accommodate new types of products, and change administrative structures to allow drugs and devices to be developed either in parallel or at least in a scientiﬁcally streamlined manner. Moreover, companies may be inclined to develop business alliances, seek out business partners and ﬁnancial solutions to developmental costs that will encourage product development, and demonstrate the proper premium on testing so that value-based reimbursement becomes possible. Some authors have called for health system reforms that promote value-based, ﬂexible reimbursement for innovative diagnostics and therapeutic products, and others have highlighted potential value to creating stronger economic incentives for the development of personalize medicine [7]. As health care costs continue to skyrocket, the role of FDA regulation appears to be a less important barrier to development of product than obtaining reimbursement or widespread clinical acceptance. Increasingly, both third-party payers and health care gatekeepers are demanding studies grounded in evidence-based medicine before making coverage decisions or before choosing to order new tests. There is also heightened interest among third-party payers to performing complex cost-effectiveness analysis to help make payment determinations.

PATHS IN CO-DEVELOPMENT There are several major paths of co-developing a drug and associated test: (1) drug and test are developed in parallel (true co-development); (2) drug is developed ﬁrst, followed by the diagnostic test(s) development (drug rescue in case there are safety issues discovered in phase IV); and (3) the already existing test can be used for drug(s) selection (patient enrichment/stratiﬁcation). Multiple tests may be developed for the selection of a certain drug, either in parallel with the drug or at a later date. Figure 2 illustrates major drug diagnostics collaboration scenarios. In the end, regardless of the path taken, co-development results in crosslabeling/relabeling of drug and/or the diagnostics. FDA drug package inserts frequently cite the relevant in vitro diagnostic test(s), and FDA cleared/ approved devices reference the targeted drug(s). Table 1 presents current

PATHS IN CO-DEVELOPMENT

239

Scenario 1 (Co-development) Drug development

Diagnostics development

Co-development

Scenario 2 (Drug Safety/Rescue) Development of diagnostics

Drug Scenario 3 (Drug Efficacy) Drug

Approved diagnostics

New drug

New diagnostics

Figure 2

TABLE 1

Drug-diagnostics collaboration scenarios.

Current Pharmacogenomics Examples

Drug

Efﬁcacy Test a

Trastuzumab (Herceptin)

Imatinib mesylate (Gleevec)a Cetuximab, Panitumumaba Imatinib mesylate (Gleevec)a Geﬁtinib Irinotecana 6MP and azathioprinea 5-HT3R antagonist, codeine derivative Warfarina

HER-2/neu IHC and FISHb C-kit IHCb EGFR IHCb bcr/abl or 9:22 tr EGFR mutations — — — —

Safety Test

UGT1A1 mutb TPMT CYP450(2D6) mutb CYP2C9&VKORC1b

a

FDA drug package insert cites the relevant test. FDA cleared/approved device in reference to the drug.

b

examples. This document is intended primarily to provide direction for the development of diagnostics that are used to select or avoid drugs (predictive tests). A predictive test/biomarker would be deﬁned as a single trait or a signature of traits that separates different populations with respect to the outcome of interest in response to a particular (targeted) treatment (e.g., response to Herceptin, Erbitux, Panitumumab). Predictive tests could be categorized into three classes: (1) a predictive test for efﬁcacy, to identify patients most likely to respond beneﬁcially to a

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targeted treatment; (2) a predictive test for safety, to identify patients most likely to respond adversely to a targeted treatment; and (3) a predictive test for dosing, to identify a patient’s speciﬁc dosing to optimize the beneﬁt of or minimize the risk of a targeted treatment. Following Sargent et al. [8], a predictive biomarker provides information about the effect of treatment and is useful for selecting patients for the drug. In this document, diagnostic tests are assumed to be predictive biomarkers. However, much of the discussion also applies to a prognostic test or biomarkers deﬁned as follows: A prognostic biomarker (e.g., risk for cancer recurrence) is a single trait or a signature of traits that separates different populations with respect to the risk of an outcome of interest in the absence of treatment or despite nontargeted “standard” treatment. Co-Development and Phase I and II Drug Studies Ideally, when a drug and a diagnostic are to be used in combination, the development of the drug and diagnostic should be coordinated as much as possible. For the drug candidate, CDER requires phase I studies to demonstrate drug safety and phase II studies to conﬁrm proof of concept, determine dosing and expand on drug safety. In cases where biomarkers are being studied for possible use with a drug, the diagnostic work is generally exploratory but, ideally, should include initial biomarker validation. Phase I and II drug studies do offer opportunities to gather preliminary data about associations between biomarkers and drug use. This information may include descriptive data on prevalence of the biomarker, association with adverse events or the safety proﬁle being identiﬁed with early drug use, and/ or information on possible ability of the test to predict drug response. In addition, the information gathered helps improve beneﬁt–risk analysis at an earlier stage of development. A number of interesting proposals have been made to streamline the path to market by expanding phase II studies to allow better selection and characterization of biomarkers for drug use. Jiang et al.’s [12] proposed design combines a test for overall treatment effect in all randomly assigned patients with the establishment and validation of a cut point for a prespeciﬁed biomarker for identifying the sensitive subpopulation. The procedure provides prospective tests of the hypotheses that the new treatment is beneﬁcial for the entire patient population or that it is beneﬁcial for a subset of patients deﬁned by the biomarker. The good news is that this allows an opportunity to introduce a drug without or with a biomarker as circumstances may dictate. However, the bad news is that each separate hypothesis cannot be performed without a p-value penalty, so a recommendation is made that testing for the drug affect in the all-comers population be with p = 0.04 and, if needed, follow-up testing in the biomarker positive population occurs with a p = 0.01. Proposals have also been made to ﬁnd mechanisms of adaptive design applying Bayesian statistics to allow phase II and III studies to be performed in a more seamless manner.

PATHS IN CO-DEVELOPMENT Prototype Assay Used

Final Locked Device

PMA or 510 (k) Application

Pre-IDE Process, IDE Review

Basic Research

Prototype Design or Discovery

241

Clinical Development* Preclinical Development Phase 1 Phase 2 Phase 3

Clinical Utility for Stratification Marker Contact OCP to Inform of Co-Development Plan

FDA Filing/ Approval & Launch

Drug/Diagnostic Labeling Consult

Clinical Validation for Stratification Marker

Co-Approval

*Adapted from Drug-Diagnostic Co-Development Concept Paper (draft).

Figure 3

Co-development strategy timing.

Co-Development and Phase III Drug Studies If a diagnostic is actually intended for use in patient selection, the safety and efﬁcacy of the drug may in fact depend on the ability of the test to identify patients properly. Clinical trials for drug approval are generally multicenter. Therefore, during co-development the test needs to be standardized and a consensus methodology for test validation among the centers should be established. Ideally, analytical validation should be performed prior to phase III trials, so that clinical validation can be established using an analytical robust test. When the drug approval is contingent on use of the diagnostic, the diagnostic should optimally be studied either at the time of the original phase III study or in a follow-up phase III–like study before drug approval can be obtained. These studies would most commonly be based on an appropriately designed and powered prospective blinded and randomized clinical trial usually associated with drug approvals and validated in follow-up trials. Codevelopment strategy timing is illustrated in Figure 3. Co-Development After Drug Approval In those cases in which the drug approval is not contingent upon the use of the diagnostic or when changes in drug labeling for improving safety or effectiveness proﬁles are being requested, studies may be performed during phase IV drug studies or at any interval following drug approval (e.g., postmarket

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commitments). In some cases, prospective studies may be needed; in others, use of banked samples from prospective studies, retrospective studies, and/or cross-sectional studies may be sufﬁcient to support diagnostic claims. If it is not possible to conduct coordinated studies of the drug and diagnostic, the phase III clinical trial may represent the only opportunity available to obtain and bank prospectively collected samples for future diagnostic test study. Particular attention should be paid to ensuring that samples are collected properly with appropriate informed consent to support future bridging studies (e.g., platform change), diagnostic studies, and evaluation of the analyte of interest. The samples collected should be banked in storage conditions that maintain sample integrity. Concerns about any bias, missing samples, or selection in accrual of banked samples should be addressed appropriately.

INTERCENTER REVIEW CONSIDERATIONS IN CO-DEVELOPMENT Overcoming potential regulatory barriers to speed up introduction of innovative technologies and products, while ensuring their safety and effectiveness, is a major challenge. Co-development, to be most efﬁcient, requires coordination and harmonization between FDA reviewing centers. The success of this coordination in part depends on the ability of all stakeholders to foster an increase in the dialogue between the FDA and industry and the ability of the FDA to engage sponsors in collaborative discussion early in the process as science develops. Co-developed products that would be used together may or may not be combination products as deﬁned in 21 CFR 3.2(e).1 FDA anticipates that many therapeutic drug and diagnostic test products will be marketed separately. For the purposes of this document, co-development refers to products that raise development issues that affect both the drug therapy and the diagnostic test, regardless of their regulatory status as a combination product or as a noncombination product. For example, when co-developed products are considered together, unique questions may arise that would not exist for either product alone. Scientiﬁc or technological issues for one product alone may be minimal, but they may have substantial implications for the other product. Also, postapproval changes in one may affect the safety and effectiveness of the other. The FDA has recently established the Ofﬁce of Combination Products (OCP)2 to work with sponsors and FDA work groups to ensure that proper regulatory tools are applied to products used in combination and to assist in addressing problems that joint reviews may encounter. If the diagnostic becomes integral to approval of the drug, diagnostic approval is needed in parallel with the drug. FDA review centers (CDER and CDRH) are willing to work with single sponsors or collaborating sponsors

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(drug–diagnostics companies) to coordinate review processes and have in the past had conjoint teams, tandem panel meetings, and same-day approval of linked products. If the diagnostic has broad use not speciﬁcally linked to the drug or if the drug has use not speciﬁcally linked to the diagnostic, approvals would follow usual CDER or CDRH procedures and timelines. In some cases, development of a diagnostic may follow development of a drug and be used to reﬁne or improve the safety or effectiveness proﬁle of the drug. CDER and CDRH will work collaboratively with OCP to determine best practices for addressing these situations in a timely manner.

CONSULTATION PROGRAMS The parallel development of a drug and a diagnostic is a relatively new aspect of drug development and calls for careful coordination. FDA has three mechanisms for an early interaction with companies about the use of drug–diagnostic combinations. CDER has developed a new approach for data review referred to as voluntary genomic data submissions (VGDSs). These submissions can be used throughout this development process to present and discuss data with the agency that are not used for regulatory decision making but could have an effect on the overall development strategy. Such data submitted voluntarily will not be used for regulatory decision making by the FDA and is not included in the evaluation of an investigational new drug (IND), investigational device exemption (IDE), or market application. Both CDER and CDRH representatives participate in VGDSs and will work with the sponsor to identify when pre-IDE interactions for development of the test in conjunction with the drug are advisable. CDER has also developed a pre-IND process for early interactions with a sponsor to improve a development program and expedite entry into clinical trials. Pre-IND advice may be requested for issues related to data needed to support the rationale for testing a drug in humans; the design of nonclinical pharmacology, toxicology, and drug activity studies, including design and potential uses of any proposed treatment studies in animal models; data requirements for an investigational new drug (IND) application; initial drug development plans; and regulatory requirements for demonstrating safety and efﬁcacy. Pre-IND interactions should be considered as preliminary communications based on early development information and will generally take the form of written comments that may be supplemented by teleconferences or meetings as needed and appropriate. The pre-IDE process is the term CDRH uses for a voluntary system available to sponsors for protocol review and has already been described in this chapter. Additional regulatory tools that CDRH uses to support introduction of new molecular diagnostics and co-development products are expedite review submissions (the front of the queue), the de novo process for regulating new unclassiﬁed devices (ﬁrst of a kind) and real-time reviews by reducing

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regulatory working time and improving the quality of results (see the Section “Regulatory Tools and Solutions for Pharmacogenomics Markers). By employing these types of review processes, the intent of CDER and CDRH is to provide useful information to sponsors as they work on codeveloped products. The FDA is willing to coordinate work between these reviews as appropriate. The FDA recommends that sponsors of new drug– diagnostic products interact early with the FDA to determine best practice information and current scientiﬁc and regulatory thinking.

SUMMARY One of multiple important parts of the critical path initiative at the FDA has been a growing interest and attention to the issue of biomarkers, particularly with regard to the role they play as tools to aid in development of drugs and other new therapies. The FDA appreciates that the coordination of reviews across these two product lines may be challenging and has both administrative and regulatory tools in place that can contribute to review successes for these products. Scientiﬁc models for optimal evaluation are currently a matter of great interest and discussion at the FDA, and scientists in all three centers regulating human products (CDER, CBER, and CDRH) have had multiple interactions with stakeholders and sponsors of new technologies. It is our belief that the FDA is well positioned to help foster the use of this new technology. We believe that the challenge at hand is taming the science itself and that the FDA can in a nimble manner review new products, assure they are safe and effective, and bring them to market in a manner consistent with the precepts of the critical path program.

NOTES 1. Under 21 CFR 3.2(e), a combination product is deﬁned to include: (1) A product comprised of two or more regulated components, i.e., drug/device, biologic/device, drug/biologic, or drug/device/biologic, that are physically, chemically, or otherwise combined or mixed and produced as a single entity; (2) Two or more separate products packaged together in a single package or as a unit and comprised of drug and device products, device and biological products, or biological and drug products; (3) A drug, device, or biological product packaged separately that according to its investigational plan or proposed labeling is intended for use only with an approved individually speciﬁed drug, device, or biological product where both are required to achieve the intended use, indication, or effect and where upon approval of the proposed product the labeling of the approved product would need to be changed, e.g., to reﬂect a change in intended use, dosage form, strength, route of administration, or signiﬁcant change in dose; or

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(4) Any investigational drug, device, or biological product packaged separately that according to its proposed labeling is for use only with another individually speciﬁed investigational drug, device, or biological product where both are required to achieve the intended use, indication, or effect. 2. Contact information and information on combination products and intercenter review processes may be found on the OCP Web site: http://www.fda.gov/oc/ combination.

REFERENCES 1. FDA (2004). Innovation or stagnation: challenge and opportunity on the critical path to new medical products. http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html. 2. Venter JC, Adams MD, Myers EW, et al. (2001). The sequence of the human genome. Science, 291(5507):1304–1351. 3. Lander ES, Linton LM, Birren B, et al. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822):860–921. 4. http://www4.od.nih.gov/oba/sacgt/reports/oversight_report.pdf. 5. http://www4.od.nih.gov/oba/sacghs/public_comments.htm. 6. http://www.fda.gov/cdrh/oivd. 7. Garrison LP, Austin F (2007). The economics of personalized medicine: a model of incentives for value creation and capture. Drug Inf J, 41. 8. Sargent DJ, Conley BA, Allegra C, Collette L (2005). Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol, 23(9):2020–2027. 9. http://www.fda.gov/oc/initiatives/criticalpath/reports/opp_report.pdf. 10. http://www.fda.gov/cdrh/oivd/regulatory-standards.html. 11. http://www.fda.gov/cdrh/ode/guidance/337.pdf. 12. Jiang W, Freidlin B, Simon R (2007). Biomarker-adaptive threshhold design: a procedure for evaluating treatment with possible biomarker-deﬁned subset effect. J Natl Cancer Inst, 99(13):1036–1043.

13 IMPORTANCE OF STATISTICS IN THE QUALIFICATION AND APPLICATION OF BIOMARKERS Mary Zacour, Ph.D. BioZac Consulting, Montreal, Quebec, Canada

INTRODUCTION Learning how to apply statistical analyses … may take a lifetime of trial (and sometimes error), as it has done in the author’s case. There is no evidence that biomedical investigators of the present generation are on a steeper learning curve. [1]

Sitting in the Drill Hall at the University of Toronto with row upon row of shifting, sighing, and coughing co-sufferers, cold ﬁngers clutching one of my two well-sharpened HB pencils while I carefully write out each step of my standard deviation calculation. Statistics 101 Christmas exam. One ﬁnger rasping down rows and across columns of tables, p-values, degrees of freedom, these are the memories I share with countless scientists who have studied statistics in preparation for their research careers. Although I left that course and others with respectable marks and a seemingly clear understanding of the null hypothesis and the normal curve, I found myself some years later, scratching my head as I considered a sheet whose bland columns of numbers belied the painstaking hours spent injecting, isolating, pipetting, incubating, and centrifuging, from which they had been distilled. Looked at one way, it seemed

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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to suggest one thing, but was that the correct approach? Looked at another way, the picture was different. The conclusion to be drawn would inﬂuence the direction that the entire project would take. How to apply the right statistical test correctly to a particular research question, given the many choices, assumptions, and caveats, is not always obvious; indeed “gross misunderstandings of the purpose and functions of statistical analysis are apparent in applications to research grant-giving bodies and ethics committees, in manuscripts submitted to journals and sometimes in published papers” [1]. In fact, there seems to be a growing ﬁeld of forensic biostatistics, pointing out data analysis errors that have crept into respected publications [2–4]. This is not surprising, considering the advent of genomics, proteomics, and other-omic-style research, in which the ability to produce huge quantities of complex data are outstripping the common biologist’s understanding of the mathematical issues concerning its interpretation. Scientists focused on biomarker research in the drug development industry face an additional set of challenges, where the pressures of the business world to use less time and fewer resources, to focus only on the immediate goal, and to quickly release positive ﬁndings to investors or other onlookers are often at odds with the expert, thorough, and careful consideration that the more challenging research problems may require. Understandably, biomedical scientists who are often highly expert in their scientiﬁc discipline are not necessarily so in the mathematical issues underlying analysis of their data, and many are also without the luxury of a biostatistician with whom to consult at times of indecision. Although some lucky others may feel that there is no need to master the ﬁeld when they can simply rely on in-house expertise, it is important to recognize that a working knowledge of the factors affecting data analysis facilitates better communication between the statistician and researcher, which, in turn, generally leads to better research design. Furthermore, the question of how to interpret data wisely is not limited to applying the correct statistical test in the correct manner, but also includes understanding the limits and pitfalls of the technical procedures used to generate the data. In this regard, biostatisticians, too, face challenges, such as being unfamiliar with or intimidated by the rapidly evolving and complex technical methods used in the lab. As so well voiced by Ransohoff [5], “how well do biologists … appreciate the nature or seriousness of problems that can be introduced by methods of handling data … ? How well do epidemiologists or biostatisticians understand technical details of specimen collection, handling, and analysis in a way they can use to anticipate and manage speciﬁc sources of bias?” In addition to an understanding of both technical and mathematical issues, there is a third element important to teasing out the elusive meaning of the data, which I like to think of as the HP factor (Hercule Poirot, that is). Born of intellect and experience, modeled by a higher-order view, a wider vision of extraneous factors that may play roles in the outcome, infused with a measure of skepticism and questioning of assumptions, this factor may more commonly be known as judgment.

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Now, I sincerely hope that I am not marked, Rushdie-like, by the statisticians guild for this comment, since this softer science is undoubtedly in the realm of the subjective, and as such, in danger of being classiﬁed in the same bin as bias, data selection, and the bubonic plague. To my mind, however, judgment is critical to the success of most endeavors, including data evaluation and statistical analysis. Even though the rules of application of such analyses may be straightforward (in our day, in fact, completed for us by a simple twitch of an index ﬁnger on a mouse), understanding potential errors in our data and/ or determining which of many analytical possibilities best ﬁt the situation presented by our own experimental paradigm and data set are not, and this is where judgment often comes in. There is certainly a large mass of statistical reference material already published and available to answer questions that most biologists might have, however, most texts and articles focused on statistical analysis issues are written by, well … statisticians. This is a good thing, of course, but sometimes brings with it the negative corollary of a specialized dialect and viewpoint. Joe or Josephine Biologist may ﬁnd these, at best, difﬁcult to adapt to their own needs, or, at worst, frankly unintelligible. This chapter is addressed to fellow Joes and Josephines. It is written in plain language by a nonstatistician who has often grappled at the interface where experimental results are transduced through an intermediate state as digits, before manifesting themselves, newly metamorphosed, into conclusions that ﬂutter their wings right into our collective scientiﬁc knowledge database. Whereas some basic statistics explanations may embellish its text, this is by no means intended to serve as a statistics manual, nor to explore the many intricacies of the ﬁeld; in fact, statisticians may well be appalled by its gaps and oversimpliﬁcations. The publisher, on the other hand, is likely to be pleased at the paucity of equations, since it is common knowledge in the industry that book sales drop with each equation in a text. This chapter addresses data analysis issues pertinent to biomarker research in drug development. More speciﬁcally, it is intended to clarify some common misconceptions or confusion concerning which situations are appropriate for alternative choices of statistical analysis and to integrate statistical issues with nonstatistical issues that also affect our data interpretation.

FUNDAMENTALS Is That Signiﬁcance Signiﬁcant? (And Conversely, the Signiﬁcant Insigniﬁcance) Statistics texts all deal with the issue of type I and type II errors, or, respectively, the false ﬁnding of a signiﬁcant difference where none exists and the false ﬁnding of no signiﬁcant difference where one indeed exists. They seem invariably to omit mention, however, of how a statistically signiﬁcant

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difference that truly exists and is truly shown can yet remain truly irrelevant. This particular possibility gets no press time in statistics manuals because it remains in the realm of that softer science mentioned above, judgment. As trained scientists sensitized to the dangers of making inferences based on anecdotal or potentially chance ﬁndings, we seem as a group to have acquired the tendency to overexalt the statistically signiﬁcant ﬁnding when it makes its appearance. A p-value of 0.05 or less has become our holy grail, the golden chalice that shines from the paragraphs of our grant and investigational new drug (IND) applications, presaging, we hope, the sweet wine to spill forth from them. This, of course, is not unnatural when one considers how often we are faced with budgetary or time constraints on the number of subjects that can be tested and therefore on the ease of statistically conﬁrming such differences when they truly exist. The converse situation of which we need to be mindful, however, is that with enough samples being evaluated, even tiny differences can be statistically signiﬁcant, and furthermore, even differences that are not tiny might still not be relevant. What constitutes a “signiﬁcant” increment of an analyte, in the physiological or clinical sense, is likely to vary with the situation; it is not necessarily on the same scale as a statistically signiﬁcant increment. An additional point to consider, addressed below in the section on measurement error, is whether the magnitude of a statistically signiﬁcant difference surpasses the magnitude of inaccuracy of measurement. A qualitative judgment on the signiﬁcance of signiﬁcance, rather than simply adjusting our blinders to keep out the sun while logging p-values, is certainly worth a moment of silence on our parts. Furthermore, the converse case is also relevant; for example, what if one’s inner Hercule is suspicious that a clinical signiﬁcance may be lurking behind the harmless façade of p > 0.05?. How could we reconcile the lack of statistical signiﬁcance in that case? One possibility is that too few samples were evaluated to achieve the statistical power to reveal signiﬁcance (discussed further later in the chapter). Another is that part of the group of samples tested reacts differently than (an) other part(s), resulting in a total effect across the disparate subgroups that does not reach statistical signiﬁcance, even though it reﬂects a clinical situation of great signiﬁcance. The latter concept is central to the development of personalized medicine, an emerging ﬁeld that seeks to improve disease treatment by targeting molecularly distinct subpopulations within a given group, such that response rates to treatments are increased and the administration of ineffective and potentially toxic treatments to inappropriate individuals is diminished. As an example, selection of breast cancer patients according to ErbB2 tyrosine kinase status has had a signiﬁcant impact on management of the disease; moderate, but signiﬁcant, response rates to ErbB2-targeted antibody therapeutics in some studies might have been overlooked had study groups not ﬁrst been selected for its overexpression [6]. Some basic concepts concerning statistical methods of relevance to pharmacogenomics are discussed later, in addition to some of the common errors in their application.

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Air the Error Because statistical descriptions and inferences are based on probability, a certain amount of chance (random) error is always inherent in any statistically based conclusion. This error is routinely quantiﬁed by upper and lower conﬁdence limits and mentioned in results sections. Unfortunately, such routine error expression seems to induce a false sense of security in readers that all the error inherent in the results has been taken into account. In fact, there are numerous sources of nonrandom error, generally covered by the blanket term bias (i.e., when subjects, specimens, or data in the groups being compared are inherently different or are handled differently in a way that systematically introduces a signal into data for one of the groups compared). Differences such as types of tubes used or length of time until spinning or time samples are maintained frozen prior to analysis would fall into this category. Bias has been deemed such a serious problem in nonexperimental research that some experts consider such studies guilty of bias and erroneous results until proven innocent [7]. The term nonexperimental research refers to research where the effects of some perturbation to the system are not being tested, but rather, information is simply gathered (i.e., in epidemiological studies) or quantiﬁed (i.e., in laboratory analyses, such as most proteomic analysis). Bias is also a serious and often overlooked problem in experimental research [i.e., where effects of deliberate maniuplation of (a) variable(s) are tested]. A recent perspective article on how potential ﬂaws in preclinical research may play a major role in late-phase drug failures discusses this in detail, particularly as it relates to animal models [8]. Measurement imprecision, included by some under the term bias, but by others considered separately, constitutes an additional signiﬁcant source of error. In the case of continuous data quantiﬁcations, although measurement error almost always occurs, it is almost never quantiﬁed, or at least such quantiﬁcation is in no way accounted for in the result reported. In the case of noncontinuous data, such as in diagnostic tests that may generate a yes/no dichotomy, measurement error can have a different but equally profound effect, potentially contributing to false positives or negatives. Continuous Data Quantiﬁcations Every assay result is only the best approximation to the true value that a particular assay is able to produce. This is readily acknowledged by all, and, in fact, it is standard practice in drug development to quantify the inaccuracy of assays using known amounts of a reference standard. How the quantiﬁcation should best be accomplished is beyond the scope of this chapter, but the total error approach wisely incorporates both the components of random error and bias/measurement error in a single measure and is discussed further elsewhere [9–11]. Despite the depth of consideration given to measurement error, there appears to be an element of lip service to its acknowledgment, since error quantiﬁcations are generally used only to determine acceptance or rejection

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of an assay’s results, rather than to modify or qualify in any way the numerical result produced by the assay. Measurement error is thus consistently brushed under the carpet, with results often reported in ways that dramatically overstate their precision. As an end result, differences from one measure to another in some cases become infused with spurious meaning, when they actually represent nothing more than noise in the signals. One can only speculate on how many drugs that failed during late-phase trials might never have gotten that far had the measurement error in their “promising” preclinical results been accounted for in the data analysis. Sometimes measurement error is compounded dramatically by introducing more than one erroneous measurement into a ﬁnal calculation of the result, and again the norm is to ignore this inﬂated error rather than account for it in the results reported. Let us consider the example of a clinical trial examining the effect of a treatment on the activity of a disease biomarker in white blood cells. In this example, two assays are required: one to quantify the disease biomarker in a lysate prepared from the cells, and one to quantify the amount of protein that was culled in each cell preparation. The activity result from the ﬁrst assay is then normalized against the protein result from the second assay, to give an activity per mass of protein as the ﬁnal assay result. Looking more closely at the potential error in this example, we should note that standard acceptance criteria for bioassays generally include speciﬁcation that a certain level of demonstrated measurement error will be considered acceptable, with data still included “as is” in the analysis. Assuming a typical acceptance criterion of up to ±20% inaccuracy, two measurements of a single identical sample of true value X could therefore quantify as differing from each other by 40% of X, and no one would raise an eyebrow. In fact, Example 1 shows how once you account for the compound error of the two assays in

Example 1. One assay for enzyme activity and one for protein content of sample are performed, with the ﬁnal activity reported as per mass of protein. Assuming ±20% measurement inaccuracy in each of the two assays, we see that if patient A has a true activity of 10 activity units, acceptably accurate activity assays would reveal values of 8 to 12 units, and each milligram of protein would be quantiﬁed as 0.8 to 1.2 mg. Therefore, within each assay, an identical sample could have acceptable measurement error, resulting in a 150% increase of one value relative to the other (12 units/8 units, or 1.2 mg/0.8 mg). Normalizing enzyme activity units to milligrams of protein inﬂates this possible discrepancy between two identical sample results from 150% to a 220% apparent increase in one sample versus another; that is, the most extreme acceptable values from each assay, normalized against each other, would be 8 units/1.2 mg and 12 units/0.8 mg, for a ﬁnal resulting range of 6.7 to 15 units/ mg as possible results from replicate aliquots of the same sample.

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this example, two assay results showing a 220% increase in one sample’s ﬁnal result versus another are possible when the two actually are replicates, having an identical amount of the biomarker. This example demonstrates the compounding of only two measurement errors. Imagine the case when more measurements go into a calculation, each having its own error: for example, when using reporter gene induction in a cell-based system as a model of induction of an in vivo response. In this case there are four measurements that need to be done to control properly, each with its own measurement error range: the reporter gene expression in transfected cells challenged with the experimental treatment, its expression in transfected cells challenged with only the vehicle in which the treatment compound is dissolved, and those same two conditions in cells that were transfected with empty vectors (as a control for the effects of the vector backbone on the system). All too often researchers using models of this sort tend to “simplify” by expressing induction of reporters as simply a fold-increase over the vehicle effect, essentially pretending that the complex and compounded error does not exist. Noncontinuous Data Measurement error can also have an important effect on noncontinuous data, an effect which, as noted above, is discrete from random error and is rarely taken into account. In this case, misclassiﬁcations can occur, such as false negatives or positives. We can all imagine the personal impact of such misclassiﬁcations, such as being falsely diagnosed with a deadly disease, or conversely, not receiving appropriate treatment because of a falsenegative test result. Misclassiﬁcation errors also decrease the power of studies to detect signiﬁcant differences and have the potential to obscure or even falsify study results, leading to equally devastating poststudy impact on a much wider scale. For example, successful efforts to develop an effective vaccine for malaria, considered to be the deadliest pediatric disease, have been hampered by measurement errors leading to misclassiﬁcations. In this case, the fact that vaccine efﬁcacy (VE) is a ratio of the number of infected, vaccinated persons over the number of infected, nonvaccinated persons gives rise to the misconception that false-positive or false-negative diagnosis in the numerator and denominator will cancel out or balance. Actually, since measurement error increases as the lower limit of detection is approached, a partial effect of vaccination causing lower-level signals at the time of measurement (such as a slowing of parasite growth, the biomarker for VE), can lead to systematic error in the measurement of the test group that is not matched in the control group, and an overestimation of VE [12]. The end effects of misclassiﬁcation error of this sort can vary substantially, depending not only on the goal of the testing and the decisions that are dependent on it, but also on the overall frequency of the disease being studied. For instance, elevated false-positive rates for a test may have a huge impact in the case of rare diseases, and conversely, elevated false-negative rates may have

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a bigger impact with very common diseases. Example 2 shows that if a test for rare disease X (occurring in 1 in 1000 people) gives 99% accurate positive results in people who truly have the disease and 95% accurate negative results in people who truly do not have it, any positive test result it gives will be false in 98% of the cases. Example 2. A diagnostic test has the following known accuracy: It returns a positive result in 99% of patients who truly have the disease and a negative result in 95% of patients who truly do not have the disease. The disease is rare, occurring in the general population at a rate of only 1/1000 people. Although you have no known predisposition or reason to think you have the disease, you decide to get tested and are shocked to learn that the result has come back positive. What are your chances that the diagnosis is actually a false positive? False positive probability = 1 − true positive probability probability of true positives total of true and false positive probabilities 0.99 × 0.001 = (0.99 × 0.001) + (0.05 × 0.999 ) = 0.019

True positive probability =

False positive probability = 1 − 0.019 = 0.981 Your chances are about 98% that the diagnosis of positive is false. The test would have to be 99.9% accurate for negative results before it would have a zero-rated false-positive detection rate. Whereas, intuitively, one might think of 95% and 99.9% accuracy as both sounding pretty good, you can see that the seemingly small gap between them could have a surprisingly important impact. What to Do? Samuel Taylor Coleridge once said that literary reviewers are usually people who would have been poets, historians, and biographers if they could, but having tried their talents at one or the other, and failed, they turn to critics. As in other areas of life, with the issue of measurement error, too, it is much easier to be a critic than to do something well oneself. What should one actually do? This is a very good question indeed, and, feeling a little uncomfortable under Samuel’s gaze from the sentence above, I shall attempt to come up with some useful suggestions. First, the appropriate approach is likely to vary according to the situation, and the most crucial issue to consider when choosing an approach to dealing

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with measurement error is the potential risk of the erroneous measurement to the end user of the drug, device, or process under study. Experiments and/ or data interpretation should then be designed to err on the side of caution, if risk is an issue. Other key determinants may include practicalities such as resources and time available to approach the problem, and questions of clinical or physiological relevance of the potential error margin, thresholds, or measurement ranges of interest. If, for example, a 10- to 100-fold increase in biomarker expression would be required for clinical relevance, and the maximum estimated measurement error is in the range of less than threefold, perhaps there is no issue. The discussion above, concerning whether signiﬁcant differences are clinically relevant also applies to the relevance of measurement errors. If measurement error is of a magnitude that could be clinically relevant, its quantiﬁcation becomes more important. Perhaps because of the status quo of ignoring measurement error quantiﬁcation in biomedical methods (for which, after all, error quantiﬁcation is much more challenging than for analysis of less complex models), this is not a widespread practice in the drug development industry. One way of acknowledging this error while avoiding complex quantiﬁcations might be to use the deﬁnition of maximum allowable measurement error that is already made routinely for the purposes of acceptance criteria as a threshold of level of difference, below which results would either not be considered to reﬂect anything but error, or would be more carefully scrutinized in some predeﬁned way. In other words, if you have determined that up to a doubling in endpoint values in a pretreatment versus posttreatment sample could be attributed solely to compounded measurement error rather than to treatment per se, one might decide not to consider effects to be a result of treatment unless they surpass this measurement error threshold. Of course, the fact that a method has been validated according to across-the-board standards such as having better than 15% or 20% total error does not mean that it actually produces results that are this far from the nominal values of the reference standards, but merely that it may do so at times, and that results of assays where these limits are exceeded will be rejected from the study. Error quantiﬁcations may well indicate much less error than the maximum allowed for assay acceptance, so given the circumstances it may be worth putting the time in to determine what the actual error is rather than simply assuming that the worst possible error has always occurred. Actual error might be revealed by data mining from previously amassed quality control data and/or on a perbatch basis using internal standards, for example. In these cases it is as simple as quantifying the proportional values obtained for accuracy rather than simply noting a pass or fail. In the case where biomarker analysis involves more complex sources of error than a single or a few analytical assays (such as for epidemiological studies or metaanalyses to qualify particular biomarkers for their intended use), more complex uncertainty quantiﬁcations would be required. Various such methods are available, including Bayesian [13] and Monte Carlo simula-

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tions [14]. Bayesian approaches, which stem from an alternative view of probability compared to the more common frequentist inferential statistics, are touched upon further later. Monte Carlo simulations represent the brute-force type of computation, basically executing a huge number of possible permutations using random draws from all the potential inputs, so as to spit out a ﬁnal probability function (made up of all the output values from the various random inputs) that comprises the compound error from all the unknown inputs. In a simple example, consider the ﬁlm Groundhog Day, where a man woke every morning to ﬁnd that it was the same day as the one before. The ﬁlm consisted of this man going through his interminable repeated days, making different choices when the ﬁxed events of the day occurred, as they did every day; you can think of this man as being stuck inside a Monte Carlo simulation (well, a simple one, since his own behavior choices were the only independent variable with randomly chosen modiﬁcations; the range of outcomes were consequences of his varied choices from that one variable). In questions of compound error, of course, there would be many more variables, also all being sampled randomly over and over again. One advantage of choosing a Monte Carlo approach to resolving complex error sources should your experimental paradigm present them, is that user-friendly software is readily available, due to its common use for risk analysis in ﬁnancial and engineering applications. Once measurement error has been quantiﬁed, by whatever means, error distributions could be used to express uncertainty limits around results, analogous to the random error-related conﬁdence intervals common in statistical analyses. These would help to clarify how much of the apparent differences between sets of results might be an artifact of the measuring tool(s) and not necessarily an effect of the treatment that one wishes to measure. In many cases, such as the reporter gene model example above, literature search will reveal readily applicable statistical models for speciﬁc types of assay that incorporate compound sources of error into the calculation of statistical signiﬁcance of differences [15]. Phillips and Lapole suggest a much simpler approach to partially quantify method error uncertainty without any complicated analysis [16]. Like Pablo Picasso, who once said it took him his entire life to learn how to paint like a child, these authors advocate a return to earlier ways: in this case, the gradeschool lesson of rounding ﬁgures. Rounding can adjust the measurement scale to reﬂect the limitations of the measurement error. In their words: “Rounding does not create imprecision—the imprecision exists even if we do not accurately report it.” Thus even though the measurement instrument may spit out values with several decimal places in them, samples with results in the range of 100 for a test demonstrating 2% inaccuracy (i.e., 98 to 102) might better be reported rounding to the nearest 5 rather than even to 1’s, let alone the decimal points reported by the instrumentation. Apart from attempting to quantify the error, contain it within a wider measurement scale, or set thresholds of interest that exceed it, as discussed

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above, we should also not forget to implement strategies to reduce it, when it is possible to do so. For instance, internal controls such as reference standards or paired samples can reduce the impact of the between-assay variance proportion of measurement error. For noncontinuous data, misclassiﬁcation rather than numerical differences are the end effect of measurement error. In this case, a suggested approach to avoid the negative impact of measurement error might be to assume that error will happen, and design studies to have higher power from the beginning, in order to minimize the impact of error on the power of the study. Statistical tests for noncontinuous data are already inherently less powerful than those for continuous data, and a lower initial power level in the experimental design can further magnify the impact of measurement error. For example, Gordon and Finch illustrate how the same misclassiﬁcation error rate causes much more power loss in a study that has lower power to start with than in a study that has higher power to start with. In their example, given exactly the same error rate, if the study is designed to have 99% power, it ends up with 95.5% after the error is taken into account, whereas if it is designed to have 80% power, it ends up with only 65.4% [17]. Therefore, specifying a higher power in the experimental design gives an advantage of robustness to the obscuring effects of misclassiﬁcation error. In real terms, designing an experiment with higher power most often translates into recruiting more subjects; these authors advocate that “paying the cost up front” strategy. Guilty by Association (and Other Misuses of Correlation Analysis) “Statistics show that of those who contract the habit of eating, very few ever survive.” This William Wallace Irwin quote [18] may bring a small smile to your lips. Should we conclude that eating is a bad habit that causes death? Ridiculous, right? Especially for well-educated biomedical scientists. How about this one: “Greater adiposity is associated with lower everyday physical functioning, such as climbing stairs or other moderate activities, as well as lower feelings of well-being and greater burden of pain” [19]. This quote from a respected medical journal does not seem quite so ridiculous, does it? Whereas the ﬁrst one is clearly a joke, we might be tempted to nod our heads at the second and experience discomfort in direct proportion to the increases we noted over the past few years in our own level of adiposity. If we take a closer look at both quotes we will notice that neither author actually claimed a cause-and-effect relationship. As we all know, however (especially those employed in the marketing department), just putting them together in a small room can’t help but to make everyone jump to conclusions. The strength of an association is indeed appropriately quantiﬁed by correlation analysis, but this analysis should not be misinterpreted to infer causation. Zou et al. hit the nail on the head when they note that rather than being misused to test hypotheses, correlation analysis should serve to generate them [20].

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The fact remains, however, that correlation is very commonly used to suggest that A causes B, or as in the second example above, being fat causes lower physical ability to function and lower feelings of well-being. Such guilt by association certainly is one possibility, but another possibility is that B causes A, or having lower ability to function and poor self-esteem induces weight gain. A third equally plausible hypothesis is that another cause or set of causes is (are) responsible for both the poor feelings/abilities and the weight gain. A fourth is that the observer has just observed and documented the effects of coincidence; if an insufﬁcient sample number were examined, for instance, a ﬂuke might be documented. Even with larger sample numbers, statistical conclusions are always subject to chance, with the type II error (or p-value) describing the percent chances that the “signiﬁcant difference” is actually just due to chance. Some extra consideration needs to be given to this when choosing an appropriate biomarker for one’s particular purposes. Biomarkers are, after all, by deﬁnition, a marker for something else, or an associative entity. If a particular biomarker has been qualiﬁed properly, its association with the disease of interest will not be due to chance, but that still does not imply causation. Whether a marker is simply associated, is a cause or an effect of disease, or is a cause or effect of something else causing a disease needs to be considered carefully in the experimental design. The issue of what considerations are important to determine causation is discussed in a landmark paper by Hill [21]. The reader is further referred to an interesting perspective commentary on this subject by Phillips and Goodman [22]. A further frequent misuse of correlation is to test agreement between methods. As pointed out by Bland and Altman [23], even poorly agreeing methods can produce quite high correlations, and for methods to be in perfect agreement their results would plot against each other along the line of equality, whereas results plotting on any straight line would give perfect correlation. Furthermore, the test of signiﬁcance may show that the two methods are related, but this is irrelevant to the question of agreement, and indeed, it would be amazing if two methods designed to measure the same quantity were not related. One can almost hear their teeth gnashing as they bemoan, “Why has a totally inappropriate method, the correlation coefﬁcient, become almost universally used for this purpose?” It seems clear that this is a case of copycat crime … perhaps a case for the remarkable Hercule Poirot?

Checking the Dessert Menu First In much the same way that Hercule Poirot might consider the temptations of the dessert menu before settling on his choice of main courses, we all need to consider potential data analysis issues before settling on our experimental design. Some questions need to be asked long before the data are in hand. For example:

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1. Is the assay to measure the biomarker as good as it can be, or the best type of assay given the circumstances? Can you make it better? All assays have error, but the more you ﬁne-tune the accuracy, precision, speciﬁcity/sensitivity, and robustness via appropriate optimization, the more your result will reﬂect for you the marker you wish to measure rather than the shortcomings of your technique. Assay optimization is beyond the scope of this chapter, but should deﬁnitely be completed before starting the study. Assay-speciﬁc parameters such as accuracy, precision, and speciﬁcity of the measurements all have a direct impact on the uncertainty of the statistical analysis outcomes. 2. Have you chosen the best biomarker, or should you rethink from the bottom up? To evaluate this, you must be well aware not only of the qualiﬁcation that a biomarker is relevant, linked appropriately, and speciﬁc to the pathology of interest (important issues, beyond the scope of this chapter), but also of quantitative issues of what to expect in the sample and control groups you have chosen. If one wishes to use a biomarker to differentiate between normal healthy persons and those with a disease, for example, one of the many questions to be considered prior to deciding which indeed is the most informative biomarker for the job concerns the potential degrees of measurement between them. In many cases the biomarker of interest is present in normal healthy persons as well as in those with disease, but with different typical levels in the two populations. In this case it is very important to establish a priori the descriptive characteristics of the two groups in order to deﬁne what a clinically relevant treatment effect might be and how one might judge that such an effect occurred. Published literature, unpublished previous data, and pilot studies all provide potential resource material for deﬁning these descriptive characteristics and should be evaluated carefully in advance. Since both populations will present with a range of values, relevant descriptive characteristics include not only mean values, but also the variances, ranges, and the difference between the population distributions (or extent of overlap, if that is the case). One should consider that the biomarker that is more consistent in its levels (i.e., less variance) may be a better choice than one that shows bigger mean differences between populations (but higher variability from person to person within a group). In the latter case, the variability can translate into overlap of two populations, which can be quite a complication when one wishes to discriminate between them. Another point for reﬂection in advance concerns integrating the quantitative ability of the measurement technique you intend to use with the quantitative characteristics of the populations you hope to discriminate between, or the effect size that you have determined you will need to achieve clinical relevance. For example, the smaller the differences are between expression levels of a biomarker that you wish to use to

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discriminate between two populations, the more sensitive the quantitation method you choose needs to be. Consider the example of wishing to measure the treatment effect of drug X on a disease by assessing whether a biomarker is increased from disease-typical levels to the level seen in healthy persons. Would one be able to accomplish this with statistical conﬁdence if the two populations showed close levels or overlap between their two distributions? If the difference between the populations is not big but the measurement error margin is, one may be doomed to an incapacity of discerning what one hopes to, regardless of the potential effects of the treatment. In this case it is wiser to rethink the fundamental premises of your study design and methodology before starting. Although this may sound like an obvious point, when one considers the common practice of ignoring measurement error and ignoring the compound error introduced by common study practices such as normalizing one assay result to another, it should perhaps give pause for thought. 3. Is your design sufﬁciently powered to detect what you wish to detect? Statistical power is the ability of a study to enable detection of a statistically signiﬁcant difference when there truly is one; power in the statistical sense is analogous to the sensitivity of a diagnostic test [24]. Eng says: “One could argue that it is as wasteful and inappropriate to conduct a study with inadequate power as it is to obtain a diagnostic test of insufﬁcient sensitivity to rule out a disease” [25]. In clinical trials, power often receives attention, perhaps thanks to ethics committees that have pointed out that it is frankly unethical to expose people to the risks and discomforts of research if the study has no potential for scientiﬁc gain, such as if a study was not designed to include enough people to adequately test the research hypothesis [26]. In laboratory experiments, on the other hand, statistical power is rarely estimated [1] and seems in general overlooked and poorly understood. To ensure sufﬁcient power, one needs to think ahead and choose an appropriate sample size. Factors playing a role in how one would deﬁne appropriate include: a. The smallest meaningful difference between the two means being compared that the investigator would like the study to detect (this is usually based on subjective criteria such as judgment of clinical importance; additionally, sensitivity and error margins of the measurement method may inﬂuence its choice). b. The estimated standard deviation within each comparison group (this can be based on a pilot study, prior literature that has been carried out with the same outcome measure, or subjective criteria). c. The desired power level (this is usually 80% or higher; however, although high power is desirable, it is always in a trade-off with a ﬁxed amount of time and resources for the study).

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d. The signiﬁcance criterion (usually set at 0.05). e. Whether one- or two-tailed analysis is planned. There are different formulas for computing power or sample size, depending on the experimental design and whether data are continuous or categorical; Example 3 shows an illustrative calculation using a formula suited to a standard design involving comparing means of two groups.

Example 3. You have qualiﬁed your biomarker X as an efﬁcacy marker in your disease model. In your studies qualifying X as a biomarker and developing a method to assay it, you found that its expression was low in rats with your disease of interest (10 ± 3 units; mean ± SD) but higher in healthy rats (100 ± 30 units). You also noted that interventions that increased X were associated with improvements in disease morbidity and that previous groups of animals studied tended to show biological variability of about 30% coefﬁcient of variation between rats in a treatment group. You wish to design an appropriately powered study comparing untreated diseased rats to those treated with a single dose of your new treatment Y, to determine if Y demonstrates efﬁcacy, and have decided that a mean 5-unit change in X is the lowest meaningful change that you wish to be able to detect. You will settle for standard levels of a signiﬁcance level of 0.05 and a power of 80%. How many rats do you need in each group? n of each group =

(SD12 + SD22 ) (zcrit + zpwr )2 D2

where SD1 is the standard deviation of group 1 and SD2 that of group 2 to be compared to each other (for a minimum difference from 10 to 15 units, this would be 3 and 4.5, respectively); the z-values are standard normal deviates corresponding to selected signiﬁcance criteria and statistical powers (zcrit for two-tailed p < 0.05 is 1.960, zpwr for 0.80 power is 0.842; these and other values are found in standard statistical tables and software packages); and D is the minimum difference that one wishes to detect (5 units). n=

( 32 + 4.52 ) × (1.960 + 0.842)2

52 29.25 × 7.851 = 25 = 9.2

Round up to 10 rats in each group.

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Numerous Web page calculators and statistical programs are available to perform this type of calculation for you; however, it is difﬁcult to use them properly without a working knowledge of the factors affecting power. This subject is well explained by Eng [25,26]. If your study is already complete and you found a signiﬁcant difference, you do not have to be concerned as to whether the study was sufﬁciently powered. This is akin to wondering if you put enough dynamite under the bridge after you have blown it to smithereens. If, however, you found no signiﬁcant difference and you are feeling uncomfortable about the fact that you did not consider calculating the power before starting the study, you may be tempted to do so after the fact, by plugging the values actually observed in your study for standard deviation and the difference between the populations into the power formula. This practice, dubbed retrospective power analysis, is problematic, because it does not tell you what the power to ﬁnd the smallest meaningful difference was, only what the power was to ﬁnd statistically signiﬁcant the difference that actually existed between the groups in your analysis; furthermore, this “observed” power will be inversely related to the pvalue observed, so it does not add meaningfully to the information already obtained with the p value [26]. Instead, approaches involving conﬁdence intervals or chi-square tests are recommended to guide the interpretation of negative results [27]. Some practical considerations about power and sample size are discussed further on page 264. 4. Have you planned in advance which groups you are going to compare to which, for signiﬁcant differences? Planned comparisons allow the researcher a greater rejection level α or p-value than do unplanned comparisons. This is because if you ﬁrst look at your data, then choose your comparisons, you are introducing bias into your analysis, and the rejection level will have to be tightened to compensate for that [28]. Planning ahead also allows you clear-headed time to consider which comparisons you really need when your design includes many different groups. The more comparisons you make, the more the p-value will have to be adjusted against false positives, in effect making it harder for you to see a true positive. Therefore, it is important not to compare groups that are irrelevant to your experimental goals. This is discussed further on page 272 and in Example 6.

Taking Care of Business As touched upon previously, the challenges faced by industry-based scientists often include business-related considerations that are not considered in most statistics manuals. In particular, time and resource constraints are often signiﬁcant, as well as pressures to proceed at a pace that is intended to satisfy management, investor, or proﬁt-related concerns, but is often incompatible with sufﬁcient depth of study or optimal experimental design. In short, between

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the business and science aspects of the industry, a strong pressure front constantly ﬂows from the business side to the science side. At times when shortsightedness and lack of understanding of the impact of ﬁnancial decisions on scientiﬁc practice threaten to nullify the potential uses of our scientiﬁc output, we should certainly consider exerting a pressure front in the opposite direction, from science to business. Since we cannot realistically hope to change the pressures of the business world, however, we need to at least consider modifying our experimental design and/or data interpretation approaches to take their effects into account. For example, let us consider the case of a study design for analysis of a biomarker as a pharmacodynamic (PD) endpoint in a clinical trial,where each patient provides a pretreatment control sample to be compared with a time series of their posttreatment samples. Given an endpoint assay with a high between-experiment variance component, the wisest experimental design might be to study all of a given patient’s posttreatment samples within the same assay, together with the matched pretreatment sample. Instead, scientists are often pressured to run assays and provide PD results as soon as the ﬁrst or ﬁrst few of several posttreatment time point samples become available in clinical trials. This business-driven decision is often made despite freezer stability of samples, simply because drug development companies are anxious to get the news about their product’s effects in clinical trial announced as quickly as possible; good news has an economic payoff, and similarly, the earlier that bad news is known, the better the chances for diverting precious resources before they are wasted. In the laboratory, however, the corollary of this pressure may be that precious volumes or tissue samples from a pretreatment control are used up in the ﬁrst analysis, and later analyses on samples not yet collected will have to be calculated in assays that lack this internal per-patient control. As well as introducing bias between the time points that were assayed within a single run and those that incorporate between-assay variability, the added component of variability between assays may add unnecessary noise, obscuring potential treatment effects. Certainly, as drug development scientists we cannot ignore concerns such as continued supply of cash by investors; indeed, such issues are supremely important to whether a scientiﬁc endeavor can even be continued. The point is that the effects of making less than ideal experimental design choices, too, should not be ignored or glossed over (which is, in fact, what tends to happen). Often, no consideration is given in the statistical analysis model to sample processing issues such as that described above, with raw values instead being logged into tables as if they had all been generated in a single assay of balanced design, regardless of the circumstances under which the data were generated. Obviously, there is no one correct approach to remedy such a situation, and the researcher must decide what best ﬁts the situation, hopefully with the help of a statistician who understands the sample processing issues. For instance, in some cases the best solution might be to convince decision makers to wait for a controlled comparison of pre- and posttreatment samples, in others it might

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be including an internal standard for normalization and changing to a ratiodata design, yet others might be amenable to another analysis strategy. The point is that analysis and/or interpretation of the data should be rethought in the light of particular business decisions that have affected their generation. In another example of rethinking assay design strategies to respond to the challenges of the business world, one might consider various strategies to increase statistical power in the face of time and resource constraints that typically limit the number of patients one can study. Browner et al. [29] and Eng [25] list a number of strategies for minimizing the sample size while maintaining power. 1. Use continuous measurements instead of categories; this is because, for a given sample size, statistical tests for continuous values are mathematically more powerful than those used for proportions. 2. Seek to decrease the variability of the measurement process (i.e., improve the precision of your processes, optimize your assays; the less standard deviation, the more power). 3. If possible, use paired measurements, matching each patient to his or her own control. Paired measurements are more powerful than unpaired measurements. 4. Add control subjects, even if case subjects are too difﬁcult to obtain. Although it takes more complex equations to calculate power in unbalanced designs than if both groups have the same size, this will still add power. 5. Expand the minimum expected difference-perhaps that which you have speciﬁed is unnecessarily small and a larger expected difference could be justiﬁed, especially if the study is preliminary, or intended as a screening test. The smaller the expected difference you want to pick up, the greater the number of samples needed to give an equivalent power. 6. Reﬂect on whether your hypothesis could have merit as a one-tailed, rather than a two-tailed one (although in most cases this is not an option). You can obtain equivalent power using less samples for a onetailed test. The sample size necessary for a one-tailed design with a 0.05 signiﬁcance criterion is the same as that for a two-tailed design with 0.10 signiﬁcance, if all other factors are equal. Many researchers think of power calculations as a futile exercise, likely to specify an unrealistically high sample size, given the resources on hand for the study. In fact, proper thought to power and the factors that contribute to it could, in many instances, lead to cost and resource savings. Rather than applying an across-the-board animal toxicity experimental design including, for example, 10 animals per group, Example 4 shows a case where ﬁve animals per group would be sufﬁcient; as this example is built on a data set similar to the one used in Example 3, which showed 10 animals per group as being

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appropriate for the experimental paradigm, it also illustrates that the number of samples that need to be in a test group for appropriate power depends strongly on issues speciﬁc to each particular research question. Thus, industry-based scientists grappling with business cases, proﬁtand-loss issues, how best to spread their research budget over the necessary items, and other ﬁnancial-based decisions with an impact on scientiﬁc quality would do well to manage their resources appropriately by incorporating power calculations into the design of their laboratory research. Example 4. Imagine an example similar to that described in Example 3, except that the biomarker is now a toxicity marker, which you have qualiﬁed and shown low under normal conditions in rats (10 ± 3 units; mean ± SD) but markedly increased in response to toxic reactions (10- to 100-fold). You wish to test a compound for toxicity using the same experimental design as in Example 3, but with a few different assumptions: 1. Because of being cautious about the risks of toxicity, you want to ensure higher than 80% power this time: You want to ensure 95% power, so you have more certainty that any ﬁnding of no signiﬁcant difference in the toxicity marker would not be attributable to too few animals being studied. 2. Because you are not concerned if the toxicity marker decreases in association with the treatment, but only if it increases, you adopt a one-tailed design. 3. Because toxicity so markedly increases the biomarker, you decide that the smallest meaningful increase you wish to detect can be greater than in Example 3. You decide that 20 units of biomarker would be greater than 3SD over the normal condition and is thus an acceptable threshold, so you set D at 20 − 10 = 10. How many rats do you need in each group? Using the same formula as in Example 3, but substituting a zpwr value of 1.645 (the value for 95% power), a zcrit value of 1.645 (the one-tailed value for a p < 0.05 signiﬁcance level), a D value of 10, and SD1 and SD2 values of 3 and 6, respectively: n=

( 32 + 62 )(1.645 + 1.645)2

10 2 45 × 10.824 = 100 = 4.87

Round up to 5 rats in each group.

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The Odd Risk Biomarkers are often used in studies that involve nominal data, such as a positive/negative dichotomy for a test result. There are various ways of quantifying the strength of the relationship between groups when it comes to this type of data, which is sometimes also referred to as proportional data, in reference to the fact that one measurement is often expressed as relative (proportional) to another (i.e., a percentage of patients responding, or the accuracy or sensitivity of a diagnostic test). Proportional data is often presented in a 2 × 2 contingency table. The chi-squared statistic is appropriate to test the null hypothesis of independence between row and column variables. Data are often described, however, in terms of difference of proportions, relative risk, or odds ratio. These are not statistical terms, but rather, quantiﬁcation terms. Often, these are misunderstood, sometime leading to serious confusion and misinterpretation of published research. When considering the use of diagnostic tests classifying disease states, based on biomarkers, the terminology of odds and risks is actually less useful than true and false positive and negative rates, since it does not capture the aspect of error in disease classiﬁcation. It is nonetheless important to be able to interpret research correctly where results are described in odds/risks terminology. This subject is well explained in a review by Sistrom and Garvan [30], from which most of the following is paraphrased. Assuming a data table comparing some outcome measure in two groups, one of which was exposed to some variable and the other of which was not, the difference in proportions is the proportion with the outcome in the exposed group minus the proportion with the outcome in the unexposed group. It is always between −1.0 and 1.0, and equals zero when the response Y is completely independent of the explanatory variable X. Relative risk is a measure of association between exposure to a factor and risk of a certain outcome, and is deﬁned as risk in the exposed groups divided by risk in the unexposed groups. Public health impact depends not just on relative risk but also absolute risk, so just looking at relative risk can be misleading. For example, the public health impact of a vaccine that halves the risk of an infection is very different for a rare (say, two cases per million people) or a common disease (say two cases per 10 people); in both cases the relative risk for vaccinated subjects would be 0.5 compared to unvaccinated subjects, but in the former case, for every million people vaccinated, only one would be saved, whereas in the latter case it would be 100,000 people saved [31]. Odds are the probability that the outcome does occur divided by the probability that it does not occur, with the odds ratio being the ratio of the odds of an outcome in one group (“exposed”) versus another (“unexposed”). A key concept about using odds to estimate risk of an event is that the relationship between the odds ratio and the risk ratio depends on the outcome frequency of the event. If the odds of an event are greater in one group than another, it does not mean that the risk ratio is increased by the same amount.

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This is a very common misunderstanding, but in fact, odds ratios and risk ratios are similar only when the outcome being studied is rare (i.e., about 10% or less probability in the unexposed group) [32]. Odds ratios are not a good estimate of risk ratio when the outcome is common in the population being studied. In that case, to obtain correct risks one needs to apply the equation relating odds to risks, as shown in Example 5. Example 5. The equation relating risk ratio (RR) to odds ratio (OR) is as follows: RR =

OR

(1 − Pr0 ) + ( Pr0 )(OR )

where Pr0 is the probability of the outcome in the unexposed group. Schulman et al. [33] compared the frequency of referral for cardiac catheterization for white men (exposed) to that for black men (unexposed). Black men were referred 90% of the time, and the odds ratio for black to white men being referred was 0.6 : 1. The risk ratio for black men to white being referred was 0.6 (1 − 0.9 ) + (0.9 × 0.6 ) 0.6 = 0.64 = 0.94

RR =

This example shows how an odds ratio of 0.6 for referral to cardiac catheterization for black men vs. white men (as reported by Schulman et al. [33]) does not mean that blacks were referred 40% less often than whites, as was reported in extensive media coverage following publication of that study. Once the correction for a 90% referral frequency for black men was applied, the risk ratio for black men to be referred was 0.94, or 6% less relative to white men rather than the 40% less that the media coverage of the original study had interpreted [34]. PRACTI(STI)CAL MAGIC: WHICH WITCH IS WHICH? During my time as a contract research organization scientist, I was astonished at the frequency of requests from pharmaceutical and biotechnology clients to perform inappropriate statistical treatment of the results; it seems that in the drug development industry at least, there is an appreciable level of confusion over when particular tests are appropriate and what underlying assumptions must be met for them to be valid. Accordingly, some refresher basic statistics background is summarized brieﬂy below.

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Parametric vs. Nonparametric Parametric tests are so named because they use parameters, such as mean and standard deviation, in their computations. Student’s t-test and the analysis of variance (ANOVA) are examples of this type of test. Parametric tests assume that the data sets are normally distributed and that the various groups have roughly equivalent variance between members of the group. They are prone to falsely indicating a signiﬁcant difference between the groups if these assumptions are not met. In that case, nonparametric tests provide an alternative. Nonparametric tests rely on ranking of samples relative to each other, and make fewer numerical assumptions. They are less likely to commit type I error, but usually also have less power to reveal true signiﬁcant differences. Researchers need to evaluate data with descriptive statistics in order to decide whether the assumptions of the more powerful parametric tests are met or whether nonparametric tests would be the more appropriate choice. Some assumptions apply to both types of test and must also be considered. In that regard, both parametric and nonparametric tests assume that samples included in the analysis are independent of each other. How does one judge whether these assumptions are met, and what should one do about it if they are not? Independence To judge whether samples are independent, one needs to ask if within any one group of data included in the analysis, the value of one data point is somehow related to another. If the answer is yes, these samples are not independent. For example, if biomarker expression is measured in two separate tissue samples three separate times each and you enter n = 6 data items into the analysis, you have violated the independence criterion. The three replicates of each sample are related to one another (from the same tissue sample); there are actually only two independent measures, not six. Since n = 2 is not enough samples to analyze statistically, potential plans to redress this analysis error could include either omitting statistical analysis in favor of descriptive results only, or redesigning the experiment to include additional tissue samples. Often, experiments are designed to measure biomarker expression repeatedly in a single subject, such as in the case of pharmacodynamic measures at different time points following administration of a drug in clinical trial patients. In this case, the independence assumption is not violated, since the related measures will be analyzed as different groups rather than being included within one group. Repeated-measure statistical tests are then applicable, such as the paired t-test (for two measures) or repeated-measure ANOVA (for more than two measures). To reiterate, then, if samples are not independent of one another and they do not constitute repeated measures, the correct statistical approach is to redesign your experiment. Although more complex analysis strategies do exist

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for clustering correlated samples within groups, there is no manipulation when using simple tests of signiﬁcance that will correct for samples in one data set being selected such that the choice of one sample depends on another sample being picked. Normally Distributed Data How do you judge if your data are normally distributed? The ﬁrst step is to perform descriptive statistics and plot the data out as a histogram. In a normal distribution, about 68% of the data falls within 1 standard deviation (1 SD) of the mean, and 95% within 2 SD of the mean. Many statistics programs will compute normality for you using statistical tests. The Kolmogorov–Smirnoff test, for instance, has nothing to do with martinis, being instead one such test for the normal distribution of data. If you have around n = 25 or more in each group, you can safely apply the simple eyeball test to your data, accepting data as normal if points fall into a roughly bellshaped curve when plotted as a histogram [35]. What if you are not sure whether your data are normally distributed? If you have small data sets, for instance, normality can be difﬁcult to determine visually. Similarly, statistical tests do not have much power to make the determination in this case. At this point, one’s own personal choice comes into play: If you are conservative, you might choose to play safe with a nonparametric test; if not, you might choose a parametric test. As with other life choices, there are risks associated with either: With parametric tests you risk error of type I, whereas with nonparametric ones you risk type II error. The best solution might be to collect reasonably large data sets so as to avoid this uncertainty. Assuming you do have a large enough data set to be certain, and what you are certain of is that the data are not distributed normally, well, what then? One solution might be to transform data mathematically such that the transformed data sets become normally distributed, in which case the more powerful parametric tests can then be used. Some commonly used transformations and appropriate types of data for their use [35–38] are listed in Table 1. If data are still not normally distributed with equal variances following mathematical transformation, a nonparametric test should be chosen for analysis. Some appropriate nonparametric equivalents of parametric tests are shown in Table 2. Equivalent Variances Between Groups As with the question of whether data are normally distributed, equivalence of variance between groups needs to be evaluated prior to choosing appropriate tests of signiﬁcance. Sometimes, homogeneity of variance from one group of data to another can be amply judged by plotting a frequency histogram of each group and seeing how spread out the data are, or calculating the variance for each group and qualitatively assessing if the numbers are very different or appear close. It is simple enough, however, to apply the appropriate statistical test, the F-test for two groups, or the Fmax-test for more than two.

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TABLE 1

Common Data Transformations

Type of Data

Example

Transformation

Proportional data

0–100%

Arcsin of the squareroot transformation

Poisson distributed (counts of random events)

Number of cells of a particular type in a given volume of blood

Square-root transformation

Variance proportional to mean squared (i.e., standard deviation proportional to mean)

Serum cholesterol levels in patients

Log transformation

Ratio data (normalized to internal standard on a per-assay basis)

Blots normalized to a housekeeping gene

Log transformation

Highly variable quantities, where variance is proportional to the mean to the fourth power (i.e., standard deviation proportional to mean squared)

Serum creatinine levels in patients

Reciprocal transformation

TABLE 2

Nonparametric Equivalents of Parametric Tests

Parametric Student’s t-test (unpaired) t-test (paired) t-test (paired), in cases where data are not symmetrical around the median ANOVA (without repeated measures) ANOVA (with repeated measures)

Nonparametric Mann–Whitney test (Wilcoxon rank sum) Wilcoxon signed-rank test Sign test Kruskal–Wallis test Friedman’s test

Source: Adapted from ref. 35.

If variances differ between groups and are not rendered equivalent by mathematical transformation (see Table 1), you may still be able to use parametric statistics. If, for example, the t-test is appropriate to your experimental design, and variances are unequal between groups but not extremely disparate, you may choose to use the t-test for unequal variances (in the case of extreme differences in variance, however, a nonparametric test is the recommended alternative). If your intended parametric test is ANOVA, on the other hand, the assumption of equivalent variance between test groups is essential [35]. If variances are not equivalent and transformation also does not render them equivalent, you must test statistically using a nonparametric equivalent (see Table 2).

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Simple Regression and Correlation Analysis At times there seems to be some confusion about simple linear regression vs. correlation analysis. This issue is well summed up in a review by Zou et al. [20], from which the following information is, for the most part, culled. The two are similar mathematically, but their purposes are different. Both relate to a function that describes the relationship between a given X and Y; however, regression analysis generally focuses on the form of that relationship and correlation generally on the strength. Furthermore, regression focuses on evaluating the relative impact of a predictor variable on a particular (dependent) outcome, whereas correlation’s purpose is to examine the strength and direction of the relationship between two random variables. Correlation analysis commonly involves either the Pearson coefﬁcient (r) or the Spearman coefﬁcient (r 2), with the former reﬂecting proportional changes in one variable when the other is changed, and the latter using ranks and reﬂecting instead a monotonic relationship between two variables (i.e., whether one tends to take either a larger or a smaller value than the other, but not necessarily with a proportional change in one variable when the other one is changed). If data sets are skewed or contain outliers, the Spearman coefﬁcient rather than the Pearson is the appropriate choice. Interpretation of correlation coefﬁcients is often rather qualitative, with the sign indicating the direction of the relationship (positive or negative). Values range from 0.0 (no correlation) to 1.0 (perfect correlation), with 0.5 generally being thought of as moderate, 0.8 as strong, and 0.2 as weak. Statistical signiﬁcance can also be computed, by formulating a null hypothesis of no correlation and a one-sided alternative hypothesis that the underlying value exceeds or is less than that value, then computing the z-test statistic and rejecting the null hypothesis based on the p-value. Simple linear regression analysis results in an r2 value that is calculated on the basis of Pearson r coefﬁcient and reﬂects the fraction of the variability in y that can be explained by the variability in x through their linear relationship (or vice versa). As with correlation analysis, a ﬁnding of strong linear relationship in a regression analysis does not mean that the variable causes the outcome (as discussed in the section on misuses of correlation), and should not be interpreted that way. Student’s t-test can be used, for example, to test if there is a linear relationship (i.e., null hypothesis of slope = 0) or whether the y-intercept is a particular value. As a salient point, no extrapolation outside the range of values of the independent variable in the regression analysis should be used to make any predictions.

t-Test vs. ANOVA Using inappropriate tests of signiﬁcance for the circumstances, such as testing differences between more than two groups by means of repeated t-tests rather than by ANOVA, is exceedingly common [35]. Don’t do it! Repeating

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pairwise comparisons ignores the experimental effects in all but the two groups being compared and increases the number of false-positive “signiﬁcant results” expected; multiple means comparisons following ANOVA correct for the increasing chances of false positives by adjusting the acceptable hypothesis rejection level. If you have only two groups to compare to each other and they meet the assumptions of parametric tests, use Student’s t-test. If you are testing more than two groups, however, use ANOVA. Since a standard ANOVA will tell you if there is a statistically signiﬁcant difference somewhere in your data sets but will not tell you where, you need to apply a multiple means comparison test after the ANOVA, to pinpoint the culprits. There are many tests available, each suited to different types of data, so it is advised that you consult the help menu of your own statistical program to decipher which one best ﬁts your own experimental design. As a rule of thumb, do not choose to make any more comparisons than are appropriate for the goals of the experiment, or you risk to lose the ability to detect signiﬁcant differences that may truly exist. This is because multiple means comparison tests apply corrections that tighten the rejection level in proportion to an increased group comparison number (i.e., effectively lowering the p-value required to ﬁnd a difference signiﬁcant). In general, you can make one less comparison between groups than the total number of groups, without affecting this rejection level; however, if you exceed this, the rejection level is adjusted. Dunnett’s test, for instance, is suited to designs such as a dose–response curve, which include a matched control sample and several different doses; this test compares each group only to the control (not to each other), to answer the question “Which dose(s) give(s) a response” but not “Was the response to one dose different than to another?” Example 6 shows how a signiﬁcant difference between the control and treatment groups could be missed if more comparisons were made than necessary. Example 6. You wish to test for whether any of several doses of your treatment has a signiﬁcant effect on your outcome. You have collected samples a, b, c, d, (i.e., four groups), where a is the control and b, c, d represent responses to three different concentrations of your treatment. Assuming that your highest dose, d, shows a p-value of 0.02 with respect to control, this difference would be reported as signiﬁcant by Dunnett’s test; that is, three comparisons would be made (b vs. a, c vs. a, d vs. a), one less than the total number of groups, so no adjustment to the rejection level would be necessary to compensate for multiple comparisons, and the d vs. a calculated p-value of 0.02 (≤0.05) would be interpreted as statistically signiﬁcant. If instead of Dunnett’s test, the Bonferroni test were chosen, six comparisons would be made (the same three as above, plus b vs. c, b vs. d, and c vs. d). Since this would be two more comparisons than the number of groups, the signiﬁcance threshold of 0.05 would be adjusted to compensate for multiple

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comparisons (0.05/6 = 0.008), such that a computed p-value of ≤0.008 would be required for signiﬁcance at the so-called 0.05 rejection level (i.e., p = 0.02 would not be considered signiﬁcant). One-Tailed vs. Two-Tailed The tails referred to in your statistical package are the nonwagging variety; the term represents the tail edges of the bell-shaped normal distribution. Tests are one- or two-tailed depending on whether they compute the probability of values exceeding the range expected from members of a population in only one direction or potentially in both directions (i.e., greater than or less than). Using a one-tailed test is justiﬁed only if there was an a priori determination that only differences in one speciﬁed direction would be assessed, not because one decides after seeing that the one-tailed p is signiﬁcant but the two-tailed is not, that one only really cares about one direction anyway. Furthermore, having an expectation that the difference to be found will go in a particular direction is not considered adequate justiﬁcation for performing one-tailed analysis; rather, one-tailed analysis could be considered appropriate if having a large difference in one direction vs. having no difference at all would have equivalent consequences [39]. Under most circumstances a two-tailed test is the appropriate choice, and if the researcher is in any doubt about which is appropriate, the two-tailed should be chosen. Paired vs. Unpaired Paired analysis is appropriate in the following circumstances: • When you do a before and after measurement in each subject. • When you match subjects in pairs (i.e., age, etc.), then treat one of the subjects and not the other. Pairs must be made before data are collected. • When you compare relatives (i.e., sibling studies). • When you perform an experiment many times, each time with the experimental and control sample treated in parallel (including log-transformed ratio data, as described in Example 7). Example 7. You have done Northern blots of gene expression in mutant animals vs. wild-type animals. Each of your blots shows wildly different signal magnitudes from other blots, due to technical reasons such as exposure time; having mutant and wild-type samples on every blot allows you to control for this variability by expressing data as a ratio of mutant to wild-type values. Ratios are not normally distributed, but logs of ratios are; therefore, you log-transform your data. Ratios become differences when log-transformed [i.e., log(mutant/wild type) = log(mutant) − log(wild type)], so to analyze this type of data you can take the log of each data point and perform a paired ttest on the transformed data.

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In judging whether distributions are normal in paired testing, it is the differences between the two members of each pair that must be approximately normally distributed rather than the two distributions themselves. If data from two sets to be compared do not meet one of the criteria above, they should be analyzed by unpaired testing [35]. Miscellaneous Data Outliers Within any set of observations it is not unusual to obtain occasional data points that vary substantially from others found in the same group of subjects. These outlier data may be the result of unknown errors or may represent genuine information, and indeed, whether such data should be included or rejected is the subject of much debate. Methods for detecting outlier data and the use of robust methods of statistical analysis to accommodate such data are complex issues, beyond the scope of this chapter; a thorough treatment of the subject is presented by Barnett and Lewis [40], to which the reader is referred. BLQ Data Statisticians sometimes warn against left-censored distributions, a term evocative of political intrigue and vaguely racy data sets that actually refers to the more bland reality of omitting results that are below the levels of quantitation of the assay (BLQ). BLQ results are fairly common in clinical and preclinical biomarker analyses and can cause serious problems with interpretation of the data if they are not taken into account, since unreported values are unusable in any statistical analysis of the data. Data imputation, a process to replace unreported BLQ values with valid estimates, can also affect the data interpretation, since all values in a data set affect the measures of central tendency, error, and statistical power. It seems clear that estimating a theoretical value for BLQ data points would allow for more accurate conclusions to be drawn from the data than simply removing data through leftcensoring, but what value should indeed be assigned to BLQ data points? Some researchers report BLQ values as zero, perhaps due to the inﬂuence of the sometimes black-and-white perspective of assay validation-style cutoffs. Assigning a zero value regardless of the sensitivity of a given test could be misleading, however, since zero is an absolute, whereas BLQ represents a scale from zero up to an upper limit that differs from one test to another in an assay-speciﬁc manner. Furthermore, assigning zero values creates other mathematical difﬁculties, such as not being able to compute fold-differences of another value with respect to the zero, or not being able to work with log transforms of data. One common approach to address these issues in the data imputation process has been to assign arbitrary nonzero values to BLQ data, most commonly either the limit of detection (LOD) divided by 2 or divided by the square root of 2, depending on the skewness of the data set in question [41]. Both appreciable bias and loss of power have been demonstrated with this

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type of approach, however [42,43]. Succop et al. compared the bias of alternative methods for imputing BLQ values and concluded that by imputing the values based on either median percentiles below the detection limit or based on predicting the BLQ value from an equation model ﬁt to the noncensored data set, both produced a good correlation between predicted and reported low values. In their example data set, bias between the predicted and true values was only 2.9% this way, compared to 348% overestimation bias when LOD/2 was used to impute the BLQ values [44]. Standard curves of analytical assays typically provide an equation model ﬁt that can be used to calculate values down to the zero level, but assay validation typically limits the data reported to levels that have met a cutoff level that is deemed to have accuracy and precision within proscribed acceptance limits (i.e., the lower limit of quantitation). The study above suggests that although equation-predicted values below the levels of quantitation of an assay may not meet acceptance criteria per se, they still represent more accurate estimations for BLQ data than either arbitrary imputation methods or left-censoring of data sets. Succop et al. recommended that analytical labs should provide a numerical result for all samples analyzed, with a ﬂag of those values that are below the detection limit [44].

ISSUES SPECIFIC TO SPECIALIZED FIELDS Genetic Association Studies Genetic association studies evaluate the association between speciﬁc genetic polymorphisms and disease. They often assess relatively small effects against a noisy background of biological and social complexity, and consequently, tend to lack statistical power [45]. Power calculations are critical for genetics researchers who wish to map susceptibility genes, with chi-square being the most commonly used test statistic for association studies. The noncentrality parameter for the chi-square asymptotic distribution developed by Mitra [46] is the key to computing power and sample size for this type of study. Gordon and Finch present detailed instructions (including step-by-step use of a Webbased calculator) for this, as well as reviewing other concepts of statistical genetics and the best study/analysis design to optimize power for this type of study [17]. Synthesizing evidence from multiple studies has been used as a means of increasing power; however, it seems that metaanalyses in this ﬁeld have been fraught with serious ﬂaws. As well as more general concerns, there are genetic issues particular to molecular association studies, including checking Hardy– Weinberg equilibrium (HWE, discussed below), handling data from more than two groups while avoiding multiple comparisons, and pooling data in a way that is sensitive to genetic models [47]. As regards the latter two points, there are always at least three possible genotypes to compare, but in practice

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most studies reduce the number of comparisons by assuming a speciﬁc genetic model, such as dominant or recessive, even though there are often no biological justiﬁcations for assumption of the model. Using an inappropriate genetic model or inappropriately pooled data produce misleading estimates of odds ratios. Appropriate metaanalysis methods and avoiding assumptions about genetic models are discussed further by Minelli et al. [45] and Thakkinstian et al. [48]. Bias seems to be another serious problem in this type of study. Some methods for detecting and correcting it are discussed by Sterne et al. [49]; publication bias, for example, may be detected by a simple funnel plot of the data. Not just the metaanalyses, but also single studies in this ﬁeld seem excessively prone to various sources of bias, with some critics going so far as to assert that most documented associations, even those that are replicated, represent nothing other than false positives based on bias [50]. The list of prevalent bias sources is impressive and includes biological plausibility bias, ascertainment bias, publication bias, selective reporting bias, spectrum of disease bias, population stratiﬁcation, biased selection of controls, lack of blinding in the genotyping process, and genotyping error. Some of these error sources can be addressed simply through researchers being cognizant of them and striving to avoid them. For example, one can choose to blind genotyping rather than failing to do so. In another example, ascertainment bias can be avoided by scrutinizing controls with the same intensity as cases; this type of bias relates to the situation where affected individuals (“cases”) have their DNA resequenced to identify rare variants. Identiﬁcation of such rare variants in an affected group does not necessarily signify a role in disease (as is often assumed when only cases are ascertained by resequencing) since sequencing DNA from any group tends to turn up a few rare mutations. Instead, a strong and statistically convincing preferential presence of variants in the cases compared to controls (that had been scrutinized in the same manner) would support the involvement of the variants in disease [51]. In cases where being cognizant of the potential for bias does not allow us to avoid it, designing experiments and/or data analysis with an expectation of inevitable bias can dampen its inﬂuence. For example, designing studies to have higher than desired power can combat the power loss expected from genotyping misclassiﬁcation errors, as discussed earlier. As another example, various statistical techniques that are robust to bias can be used, such as genomic control techniques or family-based methods to address problems of population stratiﬁcation [17]. Population stratiﬁcation refers to the situation in case–control studies where control groups are not well matched to case groups and in fact have different typical levels of what is being measured simply because they are from different populations. In this situation, association tests like chi-square may falsely indicate associations, even with as high as 100% probability.

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In fact, having appropriate control groups is a key determinant of the validity of genetics association studies, and determining whether controls deviate from HWE is a standard way of evaluating this. Theoretically, disease-free control groups from outbred populations should follow HWE, as should combined cases and controls if they both have a particular disease (i.e., such as in studies where different treatments are evaluated). If they do not, it is a signal of some peculiarity, error, or problem with the data sets that could invalidate the key inferences from a genetic association study [52]. For example, if there is a recessive model and the control group has an excess or deﬁcit of one group of homozygotes, this will directly affect calculation of the odds ratio (the control homozygotes divided by the other genotypes is the denominator of the odds ratio). Overall, if there is a signiﬁcant deviation from HWE it should induce some thinking about the study; deviations could result from genotyping errors but may also arise from other sources. For example, HWE deviation may suggest that allele-based estimates of genetic effects are biased, or may give further insight into the population from which the data are derived. A recent review of studies published in high-quality specialized genetics journals demonstrated that HWE is very commonly tested improperly or inadequately [3]. Of 776 associations tested, only 29% reported on HWE, introducing uncertainty as to if it was tested in the others. Furthermore, where HWE testing was described, the test was applied to the correct control group in only 50% of the associations tested. It is a common error to include the disease cases with the control cases in the HWE test when controls are diseasefree. Combined cases and controls should be tested only when the controls have the same disease as the cases; otherwise, only controls should be tested. These authors recalculated HWE for the studies reviewed and noted that in most of the samples where HWE was actually violated, this was either not mentioned in the original article or HWE conformity was actually claimed. Another common problem in the studies reviewed was the unjustiﬁed use of an inappropriate test. A number of different statistical tests could be used to test HWE (including chi-square, exact tests, and Bayesian methods), all based on the conditional probability that there would be the number of homozygotes that actually turned out to be in the sample. The chi-square test was the only test applied to HWE calculation in the studies reviewed, despite the fact that the chi-square asymptotic distribution is inadequate to deal with low genotype frequencies and is therefore not justiﬁed in studies involving them; in this case an exact test provides a simple and superior alternative [3,54]. A ﬁnal serious concern in the studies reviewed was that only 7% of the studies had an acceptable power to detect HWE deviation, with most studies being much too underpowered to make any claim of lack of deviations. Whereas power to detect HWE is of secondary importance compared to the prime consideration of power to detect a genetic association, undetected modest HWE deviations could affect considerably the inferences of many genetic association studies [3].

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Microarray or Other “Omics”-Type Data Relating “omics”-style biomarker data (gene/protein arrays, multiple m/z peaks from mass spectrometry, etc.) to clinical outcomes is the focus of a large body of research. Making statistically based decisions about what is expressed or produced differentially, and how this relates to outcomes is not simple for these types of studies. For one, the data are highly dimensional, with the number of variables often exceeding the number of samples by magnitudes (i.e., in contrast to the desirable format for statistical comparisons of a group of sufﬁcient numbers of samples for each variable tested). Furthermore, data tend to be very “noisy,” with widely different magnitudes of signals and the potential for large irrelevant signals to obscure lesser (but relevant) ones. This translates into an inherent tendency for algorithms ﬁt to the data to be more inﬂuenced by noise than by salient interest points (a phenomenon known as overﬁtting the noise). A recent review of analysis of genomics/proteomics data outlines the steps of quality control, clustering, classiﬁcation, feature ranking, and validation [55]. In Joe/Josephine terms these could be described as checking and normalizing the data, grouping like things together, statistically evaluating their likely involvement with the outcome, getting rid of the dead wood, and checking the conclusions on an independent data set. In the ﬁrst stage, quality control and normalization of signals reduce the technical variability and ensure that biomarkers discovered are statistically signiﬁcant. Cui and Churchill reviewed statistical tests that have been speciﬁcally adapted to cDNA microarray data analysis [56]; many of the issues discussed are similar for highly dimensional data from other study types. They conclude that fold-change is the simplest method for detecting differential expression of genes, but the arbitrary nature of assigned cutoff values, the lack of statistical conﬁdence measures, and the potential for biased conclusions all detract from its appeal. They also stress the need to normalize data properly, such as by using intensity-speciﬁc thresholds, since otherwise an excess of low-intensity genes may be identiﬁed as being expressed differentially simply because their fold-changes have larger variance than the fold-change values of high-intensity genes. Expression analysis steps following this generally fall into two categories, supervised or unsupervised, based on whether or not they are led by prior knowledge. Clustering is an unsupervised method that groups genes of similar expression proﬁle together; clustered genes may have diverse biological functions, but this is nonetheless useful for exploring new territory and seeing who is dancing together. Supervised classiﬁcation methods, on the other hand, train a mathematical model using prior biological knowledge of certain genes’ functional involvement with each other, then use the model for predictions on other genes. This is a useful approach for building on what is already known, but in cases of limited available information it is difﬁcult for a supervised method to achieve accurate predictions.

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Flawed statistical analysis is very common in this emerging ﬁeld, even in studies published in high-impact factor journals. Dupuy and Simon recently found that half of a survey of 90 microarray studies had at least one of three major analysis ﬂaws [2]. (These authors also present a list of analysis do’s and don’ts speciﬁc to this study type, to which the reader is referred for more detail.) Analysis ﬂaws like those discussed in this review and elsewhere [53,57] can lead to completely erroneous conclusions about the utility of given biomarkers or “signatures” of multiple biomarkers as prognostic or diagnostic tools, and may underlie some of the problems reproducing data in this ﬁeld. One of the most common ﬂaws present in the published studies was to perform multiple comparisons without making adequate adjustment for the expected increased false-positive rate. Although the standard of setting p at less than or equal to 0.05 is accepted as a reasonable compromise between type I and II errors when data sets are relatively small, in the relatively largeomic data sets a p-value this high is not sufﬁciently stringent to identify disease screening markers with an acceptably low false-positive rate. For example, a microarray containing 5000 genes examined for meaningful relationships between the genes and some outcome using this p-value could be expected to yield 250 false-positive relationships due simply to chance. Dupuy and Simon suggest that the simplest method to combat this type of error is to use a p-value of less than or equal to 0.001, which would give one false positive for every 1000 genes tested. Several other accepted methods are grouped under the term family-wise error-rate control (FWER), but they have been criticized for decreasing power substantially [58]; the Bonferroni correction (dividing the nominal signiﬁcance level by the number of tests) is the simplest of these. The false detection rate (FDR) test (or Benjamini–Hochberg test) is a favored control statistic for microarray analysis and is included in statistical analysis packages such as SAM (statistical analysis for microarray). The FDR achieves the same control of false signiﬁcant results while greatly improving the statistical power compared to FWER methods [59,60]. An FDR of 5% is not the same as a p-value of 0.05: p = 0.05 means that 5% of tested genes will be false positive, whereas 5% FDR means that 5% of positive genes detected will be false. For example, given a scenario of 5000 genes tested, with 20 of them being found positive, a p-value of 0.05 means that there could have been 250 false positives (due to chance alone), whereas an FDR of 0.05 means that only one false positive was attributable to chance alone. A second common ﬂaw was to make spurious claims of a meaningful correlation between clusters of genes and clinical outcome, when, in fact, correlation was detected after clustering using a selection of outcome-related differentially expressed genes. Therefore, genes being related to the outcome was a consequence of the selection bias, but was instead reported as independent proof of a correlation between the clinical outcomes and the gene clusters. A number of classiﬁcation methods decrease noise in the data by requiring selection of the relevant and informative predictor variables before modeling

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is performed [61], so users need to be aware that if they have used one of these methods there is no point in performing correlation analysis afterward. The third major ﬂaw concerned the mechanics of classiﬁcation model validation. Once a model is constructed it is generally validated using either an external test set or cross-validation, with the end goal being to use the validated model to predict outcomes for unknown samples. In the studies reporting supervised prediction, Dupuy and Simon noted the common ﬂaw of biased, overly optimistic estimation of the prediction accuracy through an incorrect cross-validation procedure. Different errors in cross-validation procedure were noted, but generally they were all related to failing to have test sets (data used to test the model’s predictive capacity) completely uninvolved in generating the algorithm in the ﬁrst place. Bayesian Information Integration for Personalized Medicine A key challenge for the success of pharmacogenomics lays in deﬁning analytical methods to integrate and interpret the increasingly complex information that it provides, so that decisions can be made about how people can best beneﬁt from certain therapeutic interventions. Bayesian statistics approaches, often favored by decision theorists, have proven useful in classiﬁcation models that integrate traditional clinical risk factors with complex genomics data. The point of doing this is to try to predict the outcome of future patients who share certain key clinicogenomic attributes with those from whom the model was derived and to use these predictions for patient-customized diagnostic, prognostic, and treatment purposes. Most of this chapter is based on the classical frequentist approach to inferential statistics with which most of us are familiar. In this approach, a single hypothesis (the null hypothesis) is formed about the effect of (or, rather, lack thereof) a variable or several variables that are tested using a fairly rigid and well-deﬁned format. The evidence against the hypothesis is all that is considered, and it is in the form of probabilities computed from the distribution of the outcome measure(s). A baseline assumption of randomness is assumed in deﬁning what the probabilities are, and rejection of a null hypothesis is founded on the experimental result being too different from the random probability function to actually be random (i.e., therefore it isn’t). Bayesian statistics, on the other hand, represent a fundamentally different way of thinking about probability, with statistical inferences being made using accumulated evidence of all kinds, both for and against a hypothesis, or several alternative hypotheses. These hypotheses do not even have to be testable, let alone be tested, and certainly not by any particular format. (The name of the ﬁrst publication of Bayes himself shows an example of such a hypothesis: Divine Benevolance, or an Attempt to Prove that the Principal End of Divine Providence and Government Is the Happiness of His Creatures [62].) Importantly, there is no baseline assumption of randomness: Instead, a “prior probability” is included in the paradigm. Although assigning it a value based

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on random chances is one option, another is that prior knowledge is incorporated into this value; in this way, Bayesian methods build on past experience to make statistical inferences. To compute the probability of each particular hypothesis being true, a calculation using Bayes’ theorem would multiply all the conditional probabilities that each is true by their prior probabilities and weight the ﬁnal values relative to each other. Conditional probabilities are those from each piece of evidence for or against the hypothesis, and prior probability is deﬁned in a very open-ended way; it could be pretty much anything, including subjective feelings, provided that it is decided upon in a consistent manner between all the hypotheses, it sums to 1 for all the hypotheses, and the information used to produce it is not used again later as evidence. As more evidence accumulates, it can be incorporated into updated models [63]. A simple Bayes calculation has already been shown (see Example 2); in that case, known disease frequency in the population constituted the prior probability, and the predictive values of the diagnostic test for true and false positives were the conditional probabilities. Biomarker researchers aiming to deﬁne predictive models for personalized medicine have showed how Bayesian methods can strengthen these models by integrating different types of information into them. For instance, Qi et al. noted that both supervised and unsupervised methods have different drawbacks for genomics data analysis, with the latter capturing relevant expression proﬁles but ignoring important a priori knowledge, and the former being limited where little prior knowledge exists. Accordingly, they used a Bayesian approach to perform accurate predictions even when scant prior knowledge is available; their integrated model achieved higher sensitivity and speciﬁcity compared to both unsupervised and supervised methods [64]. Similarly, Pittman et al. [65] used Bayesian tree modeling to integrate both clinical information and genomic biomarker status for a sample set of breast cancer patients. They showed that the integrated clinicogenomic model gave substantially greater log-model likelihood (>7) than the model based on genomic biomarker signatures alone, and blew the clinical predictors alone right out of the picture, with a weight of evidence for the clinicogenomic vs. the clinical predictors only of more than 26 log-likelihood units. Tree models in general involve successive splitting of a given patient sample group with certain known characteristics (i.e., gene signature, clinical risk factors) and outcomes (e.g., cancer status, survival, relapse) into more and more homogeneous subgroups. At each split, the collection of evidence (e.g., clinical or gene factors) is sampled to determine which of them optimally divide the patients according to their outcome, and a split is made if signiﬁcance exceeds a certain level. Multiple possible splits generate “forests” of possible trees. Some caveats noted for those using these methods include how to choose among alternative potential models that are identiﬁed as being of similar or signiﬁcant probability, and the issue of uncertainty. In that regard, whereas in

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some other applications Bayesian approaches are used to choose a single best hypothesis from among several, such an approach is warned against for pharmacogenomic modeling applications [61,65,66]. In this case it is typical to see multiple plausible tree models representing the data adequately, and this is consistent with the physical reality of multiple plausible combinations of genetic and clinical factors that could lead to the same outcome measures. Rather than choosing one of them, it is critical to deﬁne overall predictions by averaging across the multiple candidate models using appropriate weights that reﬂect the relative ﬁts of the trees to the data observed. The impact of averaging is seen in greater accuracy of the model predictive capacity and, importantly, in accurate estimation of the uncertainty about the resulting prediction uncertainty (i.e., the prediction uncertainty of such a model is conceptually akin to the measurement uncertainty associated with a laboratory method, as discussed earlier). Nevins et al. underlined well the importance of prediction uncertainty when they pointed out: “A further critical aspect of prognosis is the need to provide honest assessments of the uncertainty associated with any prediction. A predicted 70% recurrence probability, for example, should be treated quite differently by clinical decision makers if its associated uncertainty is ±30% than if it were ±2% …” [66].

SUMMARY: QUICK DO’S AND DON’TS The ﬁeld of biomarkers is a wide one, with a huge diversity of potential applications, all of which may have different and complex statistical analysis issues. This chapter has undoubtedly missed many of these and glossed over others, but summarizing concisely what it has covered is still a challenge. Instead, I present below a distillation in the form of one-liners addressing some of the more critical points (“do”) and more common misconceptions (“don’t”). Do: 1. Put on Hercule Poirot’s hat and use your judgment when considering analytical issues and your experimental situation. 2. Think ahead: Consider data analysis issues before settling on your experimental design (and long before having the data in hand). 3. Take the limitations of your techniques and experimental error into account in your interpretations. 4. Check that your design is powered appropriately to detect what you wish to detect. 5. Use appropriate tests (i.e., ANOVA when comparing means from more than two groups). 6. Use appropriate controls and check HWE in genetic association studies. 7. Make adequate adjustment for the elevated false-positive rates when dealing with-omics-style data.

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8. Average over multiple plausible candidate models with appropriate weights (rather than choosing a single one), for best predictive accuracy and uncertainty estimations in pharmacogenomic applications of Bayesian modeling strategies. Don’t: 1. Use parametric tests if data do not meet the assumptions of these tests (such as being normally distributed). 2. Use repeated t-tests when comparing means from more than two groups in one experimental design. 3. Compare more groups than are relevant to the goals of your experiment when applying multiple means testing. 4. Use correlation analysis to infer cause and effect or to test agreement between different methods. 5. Simply equate the odds ratio and risk ratio without considering the outcome frequency. 6. Use data to test correlation to outcomes or as a test set for validation of algorithms if they were already involved with those earlier in the process of mathematical modeling of genomics data (i.e., selected based on outcomes in the former case, or involved in algorithm generation in the latter).

REFERENCES 1. Ludbrook J (2001). Statistics in physiology and pharmacology: a slow and erratic learning curve. Clin Exp Pharmacol Physiol, 28(5–6):488–492. 2. Dupuy A, Simon RM (2007). Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Nat Cancer Inst, 99(2):147–157. 3. Salanti G, Amountza G, Ntzani EE, Ioannidis JP (2005). Hardy–Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power. Eur J Hum Genet, 13:840–848. 4. Attia J, Thakkinstian A, D’Este C (2003). Meta-analyses of molecular association studies: methodological lessons for genetic epidemiology. J Clin Epidemiol, 56:297–303. 5. Ransohoff D (2005). Lessons from controversy: ovarian cancer screening and serum proteomics. J Nat Cancer Inst, 97(4):315–319. 6. Goncalves A, Borg JP, Pouyssegur J (2004). Biomarkers in cancer management: a crucial bridge towards personalized medicine. Drug Discov Today: Ther Strategies, 1(3):305–311. 7. Ransohoff DF (2005). Bias as a threat to validity of cancer molecular-marker research. Nat Rev Cancer, 5:142–149. 8. Gawrylewski A (2007). The trouble with animal models. Scientist, 21(7):45–51.

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9. Hubert Ph, Nguyen-Huu JJ, Boulanger B, et al. (2004). Harmonization of strategies for the validation of quantitative analytical procedures: a SFSTP proposal— part 1. J Pharm Biomed Anal, 36(3):579–586. 10. Findlay JWA, Smith WC, Lee JW, et al. (2000). Validation of immunoassays for bioanalysis: a pharmaceutical industry perspective. J Pharm Biomed Anal, 21: 1249–1273. 11. Hubert Ph, Nguyen-Huu JJ, Boulanger B, et al. (2006). Validation des procédures analytiques quantitatives: harmonisation des démarches. Partie II—Statistiques. STP Pharma Prat, 16:30–60. 12. Prudhomme O’Meara W, Fenlon Hall B, Ellis McKenzie F (2007). Malaria vaccine efﬁcacy: the difﬁculty of detecting and diagnosing malaria. Malaria J, 6:136. 13. Carlin BP, Louis TA (1996). Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall, London. 14. Phillips CV, Maldonado G (1999). Using Monte Carlo methods to quantify the multiple sources of error in studies. Am J Epidemiol, 149:S17. 15. Plant N, Ogg M, Crowder M, Gibson G (2000). Control and statistical analysis of in vitro reporter gene assays. Anal Biochem, 278:170–174. 16. Phillips CV, LaPole LM (2003). Quantifying errors without random sampling. BMC Med Res Methodol, 3:9. 17. Gordon G, Finch SJ (2005). Factors affecting statistical power in the detection of genetic association. J Clin Invest, 115(6):1408–1418. 18. Cohen MJ (1998). The Penguin Thesaurus of Quotations. Penguin Books, Harmondsworth, UK. 19. Coakley EH, Kawachi I, Manson JE, Speizer FE, Willet WC, Colditz GA (1998). Lower levels of physical functioning are associated with higher body weight among middle-aged and older women. Int J Obes Relat Metab Disord, 22(10):958–965. 20. Zou KH, Tuncali K, Silverman SG (2003). Correlation and simple linear regression. Radiology, 227:617–628. 21. Hill AB (1965). The environment and disease: association or causation? Proc R Soc Med, 58:295–300. 22. Phillips CV, Goodman KJ (2006). Causal criteria and counterfactuals; nothing more (or less) than scientiﬁc common sense. BMC Med Res Methodol, 3:5. 23. Bland JM, Altman DG (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476):307–310. 24. Browner WS, Newman TB (1987). Are all signiﬁcant p values created equal? The analogy between diagnostic tests and clinical research. J Am Med Assoc, 257:2459–2463. 25. Eng J (2003). Sample size estimation: How many individuals should be studied? Radiology, 227:309–313. 26. Eng J (2004). Sample size estimation: a glimpse beyond simple formulas. Radiology, 230:606–612. 27. Detsky AS, Sackett DL (1985). When was a “negative” clinical trial big enough? How many patients you need depends on what you found. Arch Intern Med, 145:709–712. 28. Sokal RR, Rohlf FJ (1981). Biometry, 2nd ed. W.H. Freeman, New York.

REFERENCES

285

29. Browner WS, Newman TB, Cummings SR, Hulley SB (2001). Estimating sample size and power. In Hulley SB, Cummings SR, Browner WS, Grady D, Hearst N, Newman TB (eds.), Designing Clinical Research: An Epidemiological Approach, 2nd ed. Lippincott Williams & Wilkins, Philadelphia, pp. 65–84. 30. Sistrom CL, Garvan CW (2004). Proportions, odds, and risk. Radiology, 230:12–19. 31. Motulsky H (1995). Intuitive Biostatistics. Oxford University Press, Oxford, UK. 32. Agresti A (2002). Categorical Data Analysis. Wiley, Hoboken, NJ. 33. Schulman KA, Berlin JA, Harless W, et al. (1999). The effect of race and sex on physicians’ recommendations for cardiac catheterization. N Engl J Med, 340: 618–626. 34. Schwartz LM, Woloshin S, Welch HG (1999). Misunderstandings about the effects of race and sex on physicians’ referrals for cardiac catheterization. N Engl J Med, 341:279–283. 35. Ryder EF, Robakiewicz P (1998). Statistics for the molecular biologist: group comparisons. In Ausubel FM, Brent R, Kingston RE, et al. (eds.), Current Protocols in Molecular Biology. Wiley, New York, pp. A.31.1–A.31.22. 36. Bland JM, Altman DG (1996). The use of transformation when comparing two means. BMJ, 312(7039):1153. 37. Bland JM, Altman DG (1996). Transforming data. BMJ, 312(7033):770. 38. Ludbrook J (1995). Issues in biomedical statistics: comparing means under normal distribution theory. Aust N Z J Surg, 65(4):267–272. 39. Bland JM, Altman DG (1994). One and two sided tests of signiﬁcance. BMJ, 309:248. 40. Barnett V, Lewis T (1994). Outliers in Statistical Data, 3rd ed. Wiley, New York. 41. Hornung RW, Reed DL (1990). Appl Occup Environ Hyg, 5:46–51. 42. Hughes MD (2000). Analysis and design issues for studies using censored biomarker measurements with an example of viral load measurements in HIV clinical trials. Stat Med, 19:3171–3191. 43. Thiebaut R, Guedj J, Jacqmin-Gadda H, et al. (2006). Estimation of dynamic model parameters taking into account undetectable marker values. BMC Med Res Methodol, 6:38. 44. Succop PA, Clark S, Chen M, Galke W (2004). Imputation of data values that are less than a detection limit. J Occup Environ Hyg, 1(7):436–441. 45. Minelli C, Thompson JR, Abrams KR, Thakkinstian A, Attia J (2005). The choice of a genetic model in the meta-analysis of molecular association studies. Int J Epidemiol, 34:1319–1328. 46. Mitra SK (1958). On the limiting power function of the frequency chi-square test. Ann Math Stat, 29:1221–1233. 47. Attia J, Thakkinstian A, D’Este C (2003). Meta-analyses of molecular association studies: methodological lessons for genetic epidemiology. J Clin Epidemiol, 56:297–303. 48. Thakkinstian A, McElduff P, D’Este C, Duffy D, Attia J (2005). A method for meta-analysis of molecular association studies. Stat Med, 24(9):1291–1306. 49. Sterne JAC, Egger M, Smith GD (2001). Investigating and dealing with publication and other biases in meta-analysis. BMJ, 323:101–105.

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50. Ntzani EE, Rizos EC, Ioannidis JPA (2007). Genetic effect versus bias for candidate polymorphisms in myocardial infarction: case study and overview of largescale evidence. Am J Epidemiol, 165(9):973–984. 51. Hirschhorn JN, Altshuler D (2002). Once and again: issues surrounding replication in genetic association studies. J Clin Endocrinol Metab, 87(10):4438–4441. 52. Khoury MJ, Beaty TH, Cohen BH (1993). Fundamentals of Genetic Epidemiology. Oxford University Press, New York. 53. Baggerly KA, Morris JS, Edmonson SR, Coombes KR (2005). Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. J Nat Cancer Inst, 97(4):307–309. 54. Emigh T (1980). A comparison of tests for Hardy–Weinberg equilibrium. Biometrics, 36:627–642. 55. Phan JH, Quo CF, Wang MD (2006). Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics. Prog Brain Res, 158:83–108. 56. Cui X, Churchill GA (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 4:210. 57. Liotta LA, Lowenthal M, Mehta A, et al. (2005). Importance of communication between producers and consumers of publicly available experimental data. J Nat Cancer Inst, 97(4):310–314. 58. Dudoit S, Schaffer JP, Boldrick JC (2003). Multiple hypothesis testing in microarray experiments. Stat Sci, 18:71–103. 59. Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B, 57:289–300. 60. Benjamini Y, Drai D, Elmer GL, Kafkaﬁ N, Golani I (2001). Controlling the false discovery rate in behavior genetics research. Behav Brain Res, 125:279–284. 61. Tong W, Xie Q, Hong H, et al. (2004). Using Decision Forest to classify prostate cancer samples on the basis of SELDI-TOF MS data: assessing chance correlation and prediction conﬁdence. Toxicogenomics, 112:1622–1627. 62. Dale AI (2003). Most Honourable Remembrance: The Life and Work of Thomas Bayes. Springer-Verlag, New York. 63. Strachan T, Read AP (2003). Human Molecular Genetics. Garland Science, London. 64. Qi Y, Missiuro PE, Kapoor A, et al. (2006). Semi-supervised analysis of gene expression proﬁles for lineage-speciﬁc development in the Caenorhabditis elegans embryo. Bioinformatics, 22(14):e417–e423. 65. Pittman J, Huang E, Dressman H, et al. (2004). Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Nat Acad Sci USA, 101(22):8431–8436. 66. Nevins JR, Huang ES, Dressman H, Pittman J, Huang AT, West M (2003). Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Hum Mol Genet, 12:R153–R157.

PART IV BIOMARKERS IN DISCOVERY AND PRECLINICAL SAFETY

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14 QUALIFICATION OF SAFETY BIOMARKERS FOR APPLICATION TO EARLY DRUG DEVELOPMENT William B. Mattes, Ph.D., DABT The Critical Path Institute, Rockville, Maryland

Frank D. Sistare, Ph.D. Merck Research Laboratories, West Point, Pennsylvania

HISTORICAL BACKGROUND TO PRECLINICAL SAFETY ASSESSMENT It is often forgotten that the ﬁrst “blockbuster” drug, sulfanilamide, was discovered and developed in an era devoid of regulatory oversight and guided only by free-market forces. Domagk discovered the antibacterial properties of Prontosil in 1932, and with the subsequent discovery in 1935 that the active moiety was the off-patent and widely available substance sulfanilamide, a number of companies rushed to make preparations for sale to the public. This explosion of therapy options was unfettered by requirements for medicines to be tested for efﬁcacy or safety, although preparations could receive the endorsement of the American Medical Association [1]. Thus, in 1937, when the S.E. Massengill Company of Bristol, Tennessee, sought to prepare a ﬂavored syrup, it simply identiﬁed an appropriate excipient to dissolve the drug, prepared 240 gallons of the raspberry-tasting Elixir Sulfanilamide, and marketed it across the nation. Unfortunately, the excipient chosen was diethylene glycol, also used as an antifreeze. We now know this agent to be lethal to a Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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large number of species, causing acute kidney injury at relatively modest doses. Before Elixer Sulfanilamide was identiﬁed as the causative agent and pulled from pharmacies, 34 children and 71 adults died. A year later, Congress passed the 1938 Federal Food, Drug and Cosmetic Act, requiring pharmaceutical manufacturers to show product safety before distribution [1]. Thus, society transitioned from its former free-market approach to pharmaceutical development to one of safety testing (initially in animals), careful clinical trials, and government oversight. Safety testing as it is practiced today owes most of its form to procedures developed by the U.S. Food and Drug Administration (FDA) for testing food [2]. As early as 1949, the publication of “Procedures for the Appraisal of the Toxicity of Chemicals in Foods” began to formalize the practices the agency expected industry to follow in safety testing [3]. These practices included standard study designs and expectations as to what experimental observations would be recorded. They have evolved into the descriptive toxicity tests well known to modern toxicology [4]. Key to the value of these tests is not only their experimental design in terms of dose, route of administration, and duration, but also the endpoints evaluated, going beyond the observations of overall animal behavior and health. Thus, a battery of clinical pathology tests examining urine, hematological parameters, and serum chemistry is commonly evaluated [5]. Importantly, a number of tissues from the animal are examined both macroscopically and microscopically after sacriﬁce, and this histopathological examination allows for the identiﬁcation of unusual and subtle lesions and changes following compound treatment [6]. In the arena of pharmaceutical product development, these studies, carried out in at least two animal species, one rodent and one nonrodent, are used to assure the safety of human subjects exposed to experimental doses of novel compounds [7]. The types and durations of studies required to support safety in various types of clinical studies have been codiﬁed by the International Conference on Harmonisation (ICH) and described in their guidelines on nonclinical safety studies [8]. Even so, human subjects need to be monitored for “adverse events and/or laboratory abnormalities identiﬁed in the protocol as critical to safety evaluations” [9].

LIMITATIONS FACED IN PRECLINICAL SAFETY ASSESSMENT A critical problem faced by nonclinical safety assessment groups in pharmaceutical drug development is the disparity of responses sometimes seen between the two nonclinical test species in the tools used to assess these responses. Historically, microscopic histopathology is used as a primary tool for identifying compound-induced damage. When damage is identiﬁed only at exposures far exceeding those expected in clinical studies, clinical safety is expected. However, microscopic histopathology is not a tool generally applicable to human studies, where clinical pathology measurements play the criti-

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cal role in assessing adverse responses to drugs. Thus, if damage is observed in one nonclinical species at exposures close to those anticipated for human studies, the crucial question is whether the onset and reversibility of such damage could be monitored with clinical pathology or some other relatively noninvasive technology. Unfortunately, as described here, there are several types of drug-induced organ injury where current clinical pathology assays do not detect damage with sufﬁcient certainty at early stages, and where assurances are needed that discontinuation of drug treatment would be followed by a complete and swift return to normal structure and function.

Kidney Injury Kidney injury may be produced by a variety of insults, including those induced by drugs or toxicants [10]. Given the known morbidity and mortality associated with acute kidney injury [11,12], evidence of drug-induced kidney injury in preclinical studies is a matter of serious concern. While on one hand the kidney is capable of recovery from mild damage [13] if the injurious agent is removed, the very real clinical problem is that traditional noninvasive measures of kidney function are insensitive and confounded by many factors [13–16]. Thus, even modest increases in serum creatinine are associated with signiﬁcant mortality [12]. There is a real need for noninvasive markers that would detect kidney damage or loss of function at a stage before signiﬁcant and irreversible damage has occurred. Several markers with just such a potential have been described in numerous reviews [13–20]. However, many of these are described in relatively few clinical studies, most have not been examined carefully for their performance in animal models of drug-induced kidney injury, and no consensus understanding between drug development sponsors and regulatory review authorities had been reached as to their utility for regulatory decision-making purposes. Ultimately, if a microscopic histopathological examination shows evidence of even mild drug-induced kidney injury in a preclinical study at exposures close to those anticipated for clinical use, development of that compound may be stopped, even if human relevance may be questioned, yet unproven.

Liver Injury The fact that medicines can cause liver injury and failure has been appreciated for some time [21], and drug-induced liver injury remains a serious public health and drug development concern [22–25]. As with other drug-induced organ damage, it may be detected in preclinical studies through microscopic histopathology and clinical chemistry measurements. Since the late 1950s, serum transaminase measurements, in particular that of alanine aminotransferase (ALT), have served as a sensitive and less-invasive measure of liver damage in both animal and human settings [26]. In conjunction with serum

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cholesterol, bilirubin, alkaline phosphatase, and other factors, ALT has served as a translational biomarker for drug-induced liver injury [27–30]. However, ALT elevations are not always associated with clear evidence of liver injury [31–33], and ALT elevations cannot clearly indicate the etiology of damage [26,27,30,34]. Furthermore, ALT measurements either alone or with bilirubin cannot distinguish patients on a trajectory to severe liver disease and inability to heal or recover from injury, from patients with a full capacity to compensate and return ALT levels to normal despite continuation of drug dosing [35]. Combinations of clinical pathology changes have been used, including ALT and bilirubin, to assure safety in clinical trials [36] but there remains a need for diagnostic assays that reliably link and/or predict the histological observation of liver injury in both a preclinical and clinical setting, and discriminate the types and trajectory of apparent injury [30]. Vascular Injury Although injury to the vascular system is known to be caused by a variety of agents [37], many classes of therapeutic agents produce vascular lesions, in preclinical species with or without clinical signs, and with normal routine clinical pathology data [38]. Often, different preclinical species show a different level and type of response, and in many cases (e.g., minoxidil) the vascular injury reported in preclinical species is not observed in a clinical setting [39]. Drug-induced vascular injury in animals may result from altered hemodynamic forces, from a direct chemical-mediated injury to cells of the vasculature, and/or to an indirect immune-mediated injury of the endothelium and/ or medial smooth muscle. The conundrum faced in drug development is that there are no speciﬁc and sensitive biomarkers of endothelial and/or vascular smooth muscle injury that are clearly linked to the histological observations in animals and could be used to monitor for injury in clinical settings. Although it is assumed that an inﬂammatory component may be active at some stage in this process, and biomarkers are proposed for such processes [40], biomarkers that are sufﬁciently sensitive at early and fully reversible stages of vascular injury have not been fully evaluated [38,41]. Furthermore, speciﬁc markers of vascular injury/inﬂammation are sought that can discriminate from the multitude of other more benign causes for elevations of inﬂammatory biomarkers. Drug-Induced Skeletal Myopathy With the introduction of hydroxymethylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins), not only was there a successful treatment of hypercholesterolemia and dyslipidemia, but soon also a heightened awareness of the issue of drug-induced myopathy [42]. Statin-induced myotoxicity ranges from mild myopathy to serious and sometimes fatal rhabdomyolysis. Not surprisingly, a variety of drugs have been reported to induce myotoxicities

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[43]. While skeletal muscle toxicity may be monitored with elevations in serum creatinine kinase (CK), urinary myoglobin, and other markers [42], these biomarkers lack the sensitivity to deﬁnitively diagnose early damage or to distinguish the various etiologies of the muscle injury [43]. Thus, there is a need for markers of drug-induced muscle injury with improved sensitivity, speciﬁcity, and general utility.

WHY QUALIFY BIOMARKERS? More often than not, new biomarkers are judged on the basis of whether they have been subjected to a process of validation. Strictly speaking, validation is a process to “establish, or illustrate the worthiness or legitimacy of something” [44]. For judgments of biomarker worthiness or legitimacy, an assessment is needed of both (1) the assay or analytical method to measure the biomarker, and (2) the performance against expectations of the biomarker response under a variety of biological or clinical testing conditions. The term validation reasonably applies to the ﬁrst of these, the process by which the technical characteristics of an assay of a biomarker are deﬁned and determined to be appropriate for the desired measurements [45]. Thus, Wagner has deﬁned validation as the “ﬁt-for-purpose process of assessing the assay and its measurement performance characteristics, determining the range of conditions under which the assay will give reproducible and accurate data” [46]. Even for assay validation, the concept of ﬁt-for-purpose is introduced, which connotes that the process depends on context, and its level of rigor depends on the application of and purpose for the assay. Thus, a biomarker used for an exploratory purpose may not require the more rigorous analytical validation required of a biomarker used for critical decision making. The elements of biomarker assay validation that would be addressed for different categories of biomarker data and for different purposes have been discussed in this book and elsewhere, and they essentially constitute a continuum of bioanalytical method validation [45,47]. Such technical bioanalytical assay method validation is a familiar process and does not generally pose a problem for an organization embarked on assay development [45]. The term validation has also been applied to a process by which a new test method is conﬁrmed to be broadly applicable to interpretation of biological meaning in a wide variety of contexts and uses, such as in the validation of alternatives to animal tests as overseen by the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) [48]. This process involving assessment of biological performance expectations is in contrast to that of qualiﬁcation, which Wagner deﬁnes as “the ﬁt-for-purpose evidentiary process of linking a biomarker with biological processes and clinical endpoints” [46]. As for assay validation, this ﬁt-for-purpose biological qualiﬁcation concept marries the nature and extent of testing rigor to the intended application. In the case of biomarkers applied to predicting human

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outcomes, four general phases have been proposed [49], and as the level of qualiﬁcation progresses, the utility of a biomarker in clinical use increases [46]. In the case of biomarkers of safety (i.e., those that are used to predict or diagnose adverse responses to drug treatment), this qualiﬁcation process will necessarily involve certain steps. As with qualiﬁcation of a clinical disease or outcome marker, these steps link biomarker results with biological processes and endpoints. For example, in qualifying a biomarker for nonclinical use, the levels of a protein biomarker in urine may be correlated with certain chemically induced microscopic histopathology lesions in the kidneys of treated animals and thus serve as a noninvasive diagnostic of the appearance of that lesion. As with a clinical disease or outcome marker, qualiﬁcation of such a marker could proceed in stages. For the example given, an initial stage may be the correlation described, using a variety of treatments that produce only that lesion measured. Such a correlation could establish the sensitivity of the biomarker. However, establishing the speciﬁcity of that biomarker for that particular lesion would require a number of treatments that did not produce the lesion being monitored, but instead produce no lesions in the kidney or anywhere else, produced different lesions in the kidney, and/or produced lesions in different organs. Furthermore, the diversity of chemical treatments (i.e., structural and mechanistic diversity) would also need to be considered in the qualiﬁcation such that a variety of mechanisms underlying the genesis and progression of the lesion can be evaluated. Clearly, more data could support a higher level of qualiﬁcation and thus a higher level of utility. For biomarker qualiﬁcation, the highest phase or level of qualiﬁcation is the status surrogate endpoint, in which case the biomarker can, in fact, substitute for and serve as the new standard clinical endpoint of how a patient feels, functions, or will survive: for example, in efﬁcacy determinations to support marketing approval decisions. Qualiﬁed biomarkers that fall short as surrogate endpoints are nevertheless extremely valuable for both drug development and the general practice of medicine. The key to appreciating the value that will come from opportunities to deploy such biomarkers appropriately is in revealing a thorough understanding of their inherent strengths and limitations. In the early steps of designing studies to test sensitivity and speciﬁcity aspects of a biomarker’s performance to reveal that thorough understanding, the strategy may be relatively clear. For safety biomarker qualiﬁcation, the ﬁrst and most important attributes to benchmark are knowledge of the biomarker link to biology and outcome, sufﬁcient test sensitivity, and minimizing false test negatives. Evaluating the response of a new proposed biomarker in animals against biomarkers in conventional use using an agreed-upon set of well-recognized toxicants known to induce the desired organ injury is a fairly straightforward strategy. Pivotal, however, to the successful execution of such studies is to provoke a sufﬁcient number of cases where the timing of the samples taken and the choice of dose levels will yield subtle and mild treatment-related effects at the boundary between normal and abnormal. A full spectrum of histologic treatment-related lesions from very

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slight, to slight, mild, moderate, marked, and severe will be important in this regard for evaluating biomarker sensitivity. The approaches taken for qualiﬁcation of a safety biomarker for clinical uses will necessarily be different from those taken for nonclinical uses. Clearly, one cannot expect to have studies with healthy subjects intentionally exposed to a variety of toxicants, nor can one regularly use microscopic histopathology as a benchmark for clinical toxicity. Nonetheless, the goal is to reproducibly link the biomarker to a clinical outcome currently recognized and widely accepted as adverse. For certain types of drug-induced injury, there are standard-of-care treatments that unfortunately are associated with a known incidence of drug-induced injury. As an example, aminoglycoside antibiotic treatment is associated with a signiﬁcant incidence of nephrotoxicity [50], mirroring the effects seen in animal models. Similarly, isoniazid treatment has a known risk of hepatotoxicity [51]. Thus, one approach to safety biomarker qualiﬁcation in the clinic is to monitor novel biomarker levels longitudinally over the course of such a treatment and compare these with the current gold standard commonly used clinical biomarkers and outcomes [52]. Of course, one can complement these studies with those examining biomarker levels in patients with organ injury of a disease etiology [53]. The number of known agents appropriate for testing the sensitivity of safety biomarkers for certain target organ toxicities may be limited. It is generally expected that the number of studies conducted to evaluate sensitivity should reasonably represent a high percentage of the known but limited diverse mechanisms available for testing in animal and human studies. If the mechanisms are varied for each test agent and sensitivity performance remains high, the biomarker will probably ﬁnd strong use potential. Speciﬁcity tests then become very important considerations in a qualiﬁcation strategy. The number of test compounds that could be deployed to assess the false-positive test rate is far more expansive than the number of known compounds for testing sensitivity. Speciﬁcity testing therefore becomes a highly individualized dimension to a biomarker biological qualiﬁcation strategy for biomarkers that pass tests of sensitivity. The two critical questions to address are whether (1) there are alternative tissue sources to account for alterations in test safety biomarker levels, and (2) whether there are benign, nontoxicologic mechanisms to account for alterations in test biomarker levels. To evaluate speciﬁcity, therefore, a prioritized experimental approach should be taken using logical reasoning to avoid the testing burdens of an endless number of possible studies. The ultimate goal of these studies is the qualiﬁcation to the level of what Wagner et al. deﬁne as a characterization biomarker [49], a biomarker “associated with adequate preclinical sensitivity and speciﬁcity and reproducibly linked clinical outcomes in more than one prospective clinical study in humans.” Such a level of qualiﬁcation then supports the regulatory use of these biomarkers for safety monitoring in early clinical studies with a new pharmaceutical candidate. A strong case has been made that for safety biomarkers for regulatory decision-making purposes, where the rigor of

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supporting evidence would be expected to be high, that only fully qualiﬁed or “characterization” biomarkers are appropriate, and that there is really no regulatory decision-making role for exploratory and “emerging” or “probable valid” biomarkers. In this regard, measurements of such unqualiﬁed biomarkers in animal studies used to support the safe conduct of clinical trials would not be expected to contribute unambiguously and sufﬁciently to study interpretation, should not require submission to regulatory authorities, and therefore the exploration of their utility in such highly regulated studies should be encouraged [54] in order to accelerate the pace of biomarker evaluations and understanding.

COLLABORATION IN BIOMARKER QUALIFICATION Clearly, the number of preclinical and clinical studies and resources required to qualify a biomarker as a characterization biomarker appropriate for regulatory decision making are signiﬁcant. Thus, it is no surprise that in their Critical Path Opportunities List the FDA called for “collaborations among sponsors to share what is known about existing safety assays” [55]. Collaborations of this type have indeed played key roles in addressing technological problems common to a competitive industry. Thus, Sematech, a consortium formed in 1987 and made up of 14 leading U.S. semiconductor producers, addressed common issues in semiconductor manufacture and increased research and development (R&D) efﬁciency by avoiding duplicative research [56]. Sematech demonstrates that consortia can provide an opportunity for industry scientists to pool their expertise and experience to confront mutual questions collectively. The International Life Sciences Institute has for several years served as a forum for collaborative efforts between industry and academia [57], and for the past six years a Biomarkers Technical Committee has been pursuing assay development and evaluation of biomarkers of nephrotoxicity and cardiotoxicity [58]. Recently, the Critical Path Institute was incorporated as a “neutral, third party” to serve as a consortium organizer [59] and interface between industry members and the FDA. One of its ﬁrst efforts was the Predictive Safety Testing Consortium (PSTC) [60,61], with a speciﬁc focus on qualiﬁcation of biomarkers for regulatory use. The PSTC legal agreement addresses issues, such as intellectual property, antitrust concerns, and conﬁdentiality, and thus assures open collaboration in a manner consistent with applicable legal requirements. The PSTC solicited representatives from the FDA and the European Medicines Agency (EMEA) to serve as advisors. As experts in various areas of toxicity, these advisors bring not only their expertise but also the experience of how problems of a given target-organ toxicity are confronted and could be addressed in a regulatory setting. Thus, the development of qualiﬁcation data is targeted with a keen eye toward what will ultimately support safety decisions in the regulated drug development and regulatory review process.

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REFERENCES 1. Wax PM (1995). Elixirs, diluents, and the passage of the 1938 Federal Food, Drug and Cosmetic Act. Ann Intern Med, 122:456–461. 2. Miller SA (1993). Science, law and society: the pursuit of food safety. J Nutr, 123:279–284. 3. Stirling D, Junod S (2002). Arnold J. Lehman. Toxicol Sci, 70:159–160. 4. Eaton D, Klaassen CD (2001). Principles of toxicology. In Klaassen CD (ed.), Casarett and Doull’s Toxicology, 6th ed. McGraw-Hill, New York, pp. 11–34. 5. Weingand K, Brown G, Hall R, et al. (1996). Harmonization of animal clinical pathology testing in toxicity and safety studies. The Joint Scientiﬁc Committee for International Harmonization of Clinical Pathology Testing. Fundam Appl Toxicol, 29:198–201. 6. Bregman CL, Adler RR, Morton DG, Regan KS, Yano BL (2003). Recommended tissue list for histopathologic examination in repeat-dose toxicity and carcinogenicity studies: a proposal of the Society of Toxicologic Pathology (STP). Toxicol Pathol, 31:252–253. 7. FDA (1997). International Conference on Harmonisation; Guidance on General Considerations for Clinical Trials. Federal Register, p. 66113. 8. FDA (2008). International Conference on Harmonisation; Draft Guidance on M3(R2) Nonclinical Safety Studies for the Conduct of Human Clinical Trials and Marketing Authorization for Pharmaceuticals. Federal Register, pp. 51491–51492. 9. FDA (1997). International Conference on Harmonisation; Good Clinical Practice: Consolidated Guideline. Federal Register, pp. 25691–25709. 10. Schnellmann RG (2001). Toxic responses of the kidney. In Klaassen CD (ed.), Casarett and Doull’s Toxicology, 6th ed. McGraw-Hill, New York, pp. 491–514. 11. Hoste EA, Clermont G, Kersten A, et al. (2006). RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care, 10:R73. 12. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW (2005). Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol, 16:3365–3370. 13. Vaidya VS, Ferguson MA, Bonventre JV (2008). Biomarkers of acute kidney injury. Annu Rev Pharmacol Toxicol, 48:463–493. 14. Trof RJ, Di Maggio F, Leemreis J, Groeneveld AB (2006). Biomarkers of acute renal injury and renal failure. Shock, 26:245–253. 15. Molitoris BA, Melnikov VY, Okusa MD, Himmelfarb J (2008). Technology Insight: biomarker development in acute kidney injury—what can we anticipate? Nat Clin Pract Nephrol, 4:154–165. 16. Ferguson MA, Vaidya VS, Bonventre JV (2008). Biomarkers of nephrotoxic acute kidney injury. Toxicology, 245:182–193. 17. Devarajan P (2007). Emerging biomarkers of acute kidney injury. Contrib Nephrol, 156:203–212. 18. Bagshaw SM, Langenberg C, Haase M, Wan L, May CN, Bellomo R (2007). Urinary biomarkers in septic acute kidney injury. Intensive Care Med, 33:1285–1296.

298

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19. Nguyen MT, Devarajan P (2007). Biomarkers for the early detection of acute kidney injury. Pediatr Nephrol, 23:2151–2157. 20. Dieterle F, Marrer E, Suzuki E, Grenet O, Cordier A, Vonderscher J (2008). Monitoring kidney safety in drug development: emerging technologies and their implications. Curr Opin Drug Discov Dev, 11:60–71. 21. Zimmerman HJ (1999). Hepatotoxicity: The Adverse Effects of Drugs and Other Chemicals on the Liver, 2nd ed. Lippincott Williams & Wilkins, Philadelphia. 22. Maddrey WC (2005). Drug-induced hepatotoxicity: 2005. J Clin Gastroenterol, 39: S83–S89. 23. Arundel C, Lewis JH (2007). Drug-induced liver disease in 2006. Curr Opin Gastroenterol, 23:244–254. 24. Watkins PB, Seeff LB (2006). Drug-induced liver injury: summary of a single topic clinical research conference. Hepatology, 43:618–631. 25. Bleibel W, Kim S, D’Silva K, Lemmer ER (2007). Drug-induced liver injury: review article. Dig Dis Sci, 52:2463–2471. 26. Kim WR, Flamm SL, Di Bisceglie AM, Bodenheimer HC (2008). Serum activity of alanine aminotransferase (ALT) as an indicator of health and disease. Hepatology, 47:1363–1370. 27. Reichling JJ, Kaplan MM (1988). Clinical use of serum enzymes in liver disease. Dig Dis Sci, 33:1601–1614. 28. Ozer J, Ratner M, Shaw M, Bailey W, Schomaker S (2008). The current state of serum biomarkers of hepatotoxicity. Toxicology, 245:194–205. 29. Lock EA, Bonventre JV (2008). Biomarkers in translation; past, present and future. Toxicology, 245:163–166. 30. Amacher DE (2002). A toxicologist’s guide to biomarkers of hepatic response. Hum Exp Toxicol, 21:253–262. 31. Pettersson J, Hindorf U, Persson P, et al. (2008). Muscular exercise can cause highly pathological liver function tests in healthy men. Br J Clin Pharmacol, 65:253–259. 32. Giboney PT (2005). Mildly elevated liver transaminase levels in the asymptomatic patient. Am Fam Physician, 71:1105–1110. 33. Gaskill CL, Miller LM, Mattoon JS, et al. (2005). Liver histopathology and liver and serum alanine aminotransferase and alkaline phosphatase activities in epileptic dogs receiving Phenobarbital. Vet Pathol, 42:147–160. 34. Shapiro MA, Lewis JH (2007). Causality assessment of drug-induced hepatotoxicity: promises and pitfalls. Clin Liver Dis, 11:477–505. 35. Andrade RJ, Lucena MI, Fernandez MC, et al. (2005). Drug-induced liver injury: an analysis of 461 incidences submitted to the Spanish registry over a 10-year period. Gastroenterology, 129:512–521. 36. Hunt CM, Papay JI, Edwards RI, et al. (2007). Monitoring liver safety in drug development: the GSK experience. Regul Toxicol Pharmacol, 49:90–100. 37. Ramos KS, Melchert RB, Chacon E, Acosta D Jr (2001). Toxic responses of the heart and vascular systems. In Klaassen CD (ed.), Casarett and Doull’s Toxicology, 6th ed. McGraw-Hill, New York, pp. 597–651.

REFERENCES

299

38. Kerns W, Schwartz L, Blanchard K, et al. (2005). Drug-induced vascular injury: a quest for biomarkers. Toxicol Appl Pharmacol, 203:62–87. 39. Mesﬁn GM, Higgins MJ, Robinson FG, Zhong WZ (1996). Relationship between serum concentrations, hemodynamic effects, and cardiovascular lesions in dogs treated with minoxidil. Toxicol Appl Pharmacol, 140:337–344. 40. Blake GJ, Ridker PM (2001). Novel clinical markers of vascular wall inﬂammation. Circ Res, 89:763–771. 41. Louden C, Brott D, Katein A, et al. (2006). Biomarkers and mechanisms of druginduced vascular injury in non-rodents. Toxicol Pathol, 34:19–26. 42. Tiwari A, Bansal V, Chugh A, Mookhtiar K (2006). Statins and myotoxicity: a therapeutic limitation. Expert Opin Drug Saf, 5:651–666. 43. Owczarek J, Jasinska M, Orszulak-Michalak D (2005). Drug-induced myopathies: an overview of the possible mechanisms. Pharmacol Rep, 57:23–34. 44. Merriam-Webster Online Dictionary (2008). 45. FDA (2001). Guidance for Industry on Bioanalytical Method Validation. Federal Register, pp. 28526–28527. 46. Wagner JA (2008). Strategic approach to ﬁt-for-purpose biomarkers in drug development. Annu Rev Pharmacol Toxicol, 48:631–651. 47. Lee JW, Devanarayan V, Barrett YC, et al. (2006). Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res, 23:312–328. 48. Stokes WS, Schechtman LM, Rispin A, et al. (2006). The use of test method performance standards to streamline the validation process. Altex, 23 (Suppl):342–345. 49. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther, 81:104–107. 50. Wiland P, Szechcinski J (2003). Proximal tubule damage in patients treated with gentamicin or amikacin. Pol J Pharmacol, 55:631–637. 51. Tostmann A, Boeree MJ, Aarnoutse RE, de Lange WC, van der Ven AJ, Dekhuijzen R (2008). Antituberculosis drug-induced hepatotoxicity: concise upto-date review. J Gastroenterol Hepatol, 23:192–202. 52. Mishra J, Dent C, Tarabishi R, et al. (2005). Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet, 365:1231–1238. 53. Han WK, Bailly V, Abichandani R, Thadhani R, Bonventre JV (2002). Kidney injury molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int, 62:237–244. 54. Sistare FD, DeGeorge JJ (2008). Applications of toxicogenomics to nonclinical drug development: regulatory science considerations. Methods Mol Biol, 460:239–261. 55. FDA (2006). Critical Path Opportunities Report and List, p 28. 56. Irwin DA, Klenow PJ (1996). Sematech: purpose and performance. Proc Natl Acad Sci USA, 93:12739–12742. 57. ILSI (2007). ILSI About ILSI.

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QUALIFICATION OF SAFETY BIOMARKERS

58. ILSI and HESI (2007). ILSI: Development and Application of Biomarkers of Toxicity. 59. Woosley RL, Cossman J (2007). Drug development and the FDA’s Critical Path Initiative. Clin Pharmacol Ther, 81:129–133. 60. Marrer E, Dieterle F (2007). Promises of biomarkers in drug development: a reality check. Chem Biol Drug Des, 69:381–394. 61. Mattes WB (2008). Public consortium efforts in toxicogenomics. Methods Mol Biol, 460:221–238.

15 DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS AS CLINICAL BIOMARKERS FOR DRUG-INDUCED SYSTEMIC MINERALIZATION: CASE STUDY WITH A MEK INHIBITOR Alan P. Brown, Ph.D., DABT Pﬁzer Global Research and Development, Ann Arbor, Michigan

INTRODUCTION The mitogen-activated protein kinase (MAPK) signal transduction pathways control key cellular processes such as growth, differentiation, and proliferation, and provide a means for transmission of signals from the cell surface to the nucleus. As a part of the RAS–RAF–MEK–MAPK pathway, MEK (MAP kinase kinase) phosphorylates the MAPK proteins ERK1 and ERK2 (extracellular signal-regulated kinases) as a means for intracellular signaling (SeboltLeopold, 2000). Although MEK has not been identiﬁed as having oncogenic properties, this kinase serves as a focal point in the signal transduction pathway of known oncogenes (e.g., RAS and RAF) (Mansour et al., 1994). MEK exists downstream of various receptor tyrosine kinases (such as the epidermal growth factor receptor) which have been demonstrated to be important in neoplasia (Jost et al., 2001). RAS activation occurs ﬁrst, followed by recruitment of RAF (A-RAF, B-RAF, or RAF-1) proteins to the cell membrane through binding to RAS, with subsequent activation of RAF. RAF phosphorylates MEK1 and MEK2 on multiple serine residues in the activation Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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process. MEK1 and MEK2 phosphorylate tyrosine or threonine residues on ERK proteins in the signal transduction process, with phosphorylated ERK activating various transcription factors (Friday and Adjei, 2008). Aberrant activation of this pathway has been observed in a diverse group of solid tumors, along with leukemia, and is believed to play a key role in tumorigenesis (Hoshino et al., 1999; Milella et al., 2001). Based on a signiﬁcant amount of preclinical data, development of smallmolecule inhibitors of MEK appears to be a rational approach for treatment of various malignancies (Sebolt-Leopold et al., 1999; Dent and Grant, 2001). The ﬁrst MEK inhibitor to enter clinical trials was CI-1040 (also known as PD0184352), which was intended for oral administration. However, the level of antitumor activity in a multicenter phase II study in patients with various solid tumors was not sufﬁcient to warrant further development of this drug (Rinehart et al., 2004; Wang et al., 2007). CI-1040 exhibited low oral bioavailability and high metabolism, which were primary factors resulting in insufﬁcient plasma drug levels for antitumor activity. PD0325901 [Figure 1; chemical name of N-((R)-2,3-dihydroxypropoxy)-3,4diﬂuoro-2-(2-ﬂuoro-4-iodo-phenylamino)benzamide] is a highly potent and speciﬁc non-ATP competitive inhibitor of MEK (Ki of 1 nM against activated MEK1 and MEK2 in vitro), and demonstrated anticancer activity against a broad spectrum of human tumors in murine models (Sebolt-Leopold et al., 2004). Preclinical studies indicate that PD0325901 has the potential to impair growth of human tumors that rely on the MEK/MAPK pathway for growth and survival. PD0325901 inhibits the phosphorylation of MAPK proteins (ERK1 and ERK2) as a biochemical mechanism of action, and assays were developed to evaluate inhibition of protein phosphorylation in normal and neoplastic tissues (Brown et al., 2007). This compound has greatly improved pharmacologic and pharmaceutical properties compared with CI-1040 (i.e.,

OH OH

O HN

O H N

F

F

I

F

Figure 1

Chemical structure of PD0325901.

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greater potency for MEK inhibition, higher bioavailability, and increased metabolic stability) and has signiﬁcant promise for determining the therapeutic potential for treating cancer with an orally active MEK inhibitor (Rinehart et al., 2004; Sebolt-Leopold et al., 2004). PD0325901 was selected for development as a clinical candidate due to its superior preclinical proﬁle compared to CI-1040 (Wang et al., 2007). Toxicology studies were subsequently initiated to support the conduct of a phase I or II clinical trial in cancer patients with various solid tumors (advanced breast cancer, colon cancer, melanoma, nonsmall cell lung cancer) utilizing oral administration of the drug.

TOXICOLOGY STUDIES The nonclinical safety of PD0325901 was evaluated in Sprague–Dawley rats given single oral or intravenous (IV) doses, in beagle dogs given oral and IV escalating doses, and in cynomolgus monkeys given escalating oral doses to assess acute toxicity and assist in dose selection for subsequent studies. The potential effects of PD0325901 on central nervous system, cardiovascular, and pulmonary function were evaluated in single-dose safety pharmacology studies. Two-week dose-range ﬁnder (nonpivotal) oral toxicity studies were conducted in rats, dogs, and monkeys to assist in dose and species selection for the pivotal one-month oral toxicology studies. In addition, an investigative oral toxicity study was conducted in female Balb/c mice. The dog was selected as the nonrodent species for the pivotal toxicology study because of the following data. Metabolites of PD0325901 identiﬁed in human liver microsomal incubations were also present following incubation with dog liver microsomes, plasma protein binding of PD0325901 is similar in dogs and humans (>99%), and oral bioavailability in dogs is high (>90%). Finally, injury to the mucosa of the gastrointestinal tract occurred at lower doses and exposures in dogs than in monkeys (based on dose-range-ﬁnding studies), indicating the dog as the more sensitive nonrodent species. Pivotal one-month oral toxicity studies, including one-month reversal phases, were conducted in beagle dogs and Sprague–Dawley rats to support submission of an investigational new drug (IND) application to the U.S. Food and Drug Administration. A list of toxicology studies of PD0325901 conducted prior to initiation of human testing is presented in Table 1. Upon completion of the ﬁrst two-week dose-range-ﬁnding study in rats, a signiﬁcant and unique toxicity was observed that involved mineralization of vasculature (Figure 2) and various soft tissues (i.e., ectopic or systemic mineralization) as determined by routine light-microscopic evaluation. In a follow-up study in rats, dysregulation of serum calcium and phosphorus homeostasis, and systemic mineralization occurred in a time- and dosedependent manner. This toxicity was not observed in dogs or monkeys, despite systemic exposures to PD0325901 more than 10-fold higher than those associated with mineralization in rats and pharmacologic inhibition of

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TABLE 1

Summary of Toxicology Studies Conducted with PD0325901a

Acute and escalating dose Single dose in rats Single dose in rats, IVb Escalating dose in dogs Escalating dose in dogs, IV Escalating dose in monkeys Safety pharmacology Neurofunctional evaluation in rats Neurofunctional evaluation in rats, IV Cardiovascular effects in monkeys Pulmonary effects in rats Purkinje ﬁber assay HERG assay Nonpivotal repeated-dose studies 2-Week dose-range ﬁnder in rats Exploratory 2-week study in rats 2-Week dose-range ﬁnder in dogs 2-Week dose-range ﬁnder in monkeys Pivotal repeated-dose studies One month in rats (plus one-month reversal phase) One month in dogs (plus one-month reversal phase) Pivotal genetic toxicity studies Bacterial mutagenicity Structural chromosome aberration In vivo micronucleus in rats Special toxicity studies Pharmacodynamic and toxicokinetic in rats, oral and IV Time course and biomarker development in rats Serum chemistry reversibility study in rats Investigative study in mice Enantiomer (R and S) study in rats PD0325901 in combination with pamidronate or Renagel in rats a

All animal studies were conducted by oral gavage unless otherwise indicated. IV, intravenous (bolus).

b

phosphorylated MAPK in canine or monkey tissue (demonstrating biochemical activity of PD0325901 at the target protein, i.e., MEK). Various investigative studies were conducted to examine the time course and potential mechanism of systemic mineralization in rats and to identify biomarkers that could be used to monitor for this effect in clinical trials. Next we describe the key studies conducted to investigate this toxicity, the results obtained, and how the nonclinical data were utilized to evaluate the safety risk of the compound, select a safe starting dose for a phase I trial, and provide measures to ensure patient safety during clinical evaluation of PD0325901 in cancer patients.

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Figure 2 Mineralization of the aorta in a male rat administered PD0325901 at 3 mg/kg in a dose-range-ﬁnding study. Arrows indicate mineral in the aorta wall. Hematoxylin and eosin–stained tissue section. (See insert for color reproduction of the ﬁgure.)

At the beginning of the toxicology program for PD0325901, a two-week oral-dose-range-ﬁnding study was conducted in male and female rats in which daily doses of 3, 10, and 30 mg/kg (18, 60, and 180 mg/m2, respectively) were administered. Mortality occurred in males at ≥3 mg/kg and females at ≥10 mg/ kg, with toxicity occurring to a greater extent in males at all dose levels. Increased serum levels of phosphorus (13 to 69%), and decreased serum total protein (12 to 33%) and albumin (28 to 58%) were seen at all doses. Lightmicroscopic evaluation of formalin-ﬁxed and hematoxylin- and eosin-stained tissues was performed. Mineralization occurred in the aorta (Figure 2) and coronary, renal, mesenteric, gastric, and pulmonary vasculature of males at ≥3 mg/kg and in females at ≥10 mg/kg. Parenchymal mineralization with associated degeneration occurred in the gastric mucosa and muscularis, intestines (muscularis, mucosa, submucosa), lung, liver, renal cortical tubules, and/or myocardium at the same doses. Use of the Von Kossa histology stain indicated the presence of calcium in the mineralized lesions. Vascular/parenchymal mineralization and degeneration were generally dose related in incidence and severity. PD0325901 produced increased thickness (hypertrophy) of the femoral growth plate (physis) in both sexes at all doses, and degeneration and necrosis of the femoral metaphysis in males at ≥3 mg/kg and females at 30 mg/ kg. In addition, skin ulceration, hepatocellular necrosis, decreased crypt goblet cells, reduced hematopoetic elements, and ulcers of cecum and duodenum were observed. Systemic mineralization of the vasculature and soft tissues was

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the most toxicologically signiﬁcant ﬁnding of this study. At this time, it was not known whether the hyperphosphatemia was due to decreased renal clearance (York and Evans, 1996) and/or related to the mineralization. However, hyperphosphatemia and elevated serum calcium–phosphorus (Ca × P) product can result in vascular and/or soft tissue mineralization (Spaulding and Walser, 1970; Block, 2000; Giachelli et al., 2001). In addition, morphologic ﬁndings similar to those seen in this study are observed in various animal species (e.g., dogs, horses, pigs, rats) with vitamin D toxicosis and altered calcium homeostasis (Grant et al., 1963; Spangler et al., 1979; Harrington and Page, 1983; Long, 1984). Tissue mineralization is observed in the aorta, various arteries, myocardium, gastric mucosa, and renal tubules, along with other soft tissues in these animals. An exploratory two-week oral toxicity study was next conducted in male and female rats to further investigate the toxicities observed in the initial twoweek dose-range ﬁnder. The objectives of this study were to identify a minimal or no-adverse-effect level and to provide toxicity, toxicokinetic, and pharmacodynamic data to aid in dose selection for future studies. In addition, an attempt was made to assess whether alterations in phosphorus and calcium homeostasis occur and whether changes can be monitored as potential biomarkers of toxicity. Doses tested in this study were 0.3, 1, or 3 mg/kg (1.8, 6, or 18 mg/m2, respectively) and animals were dosed for up to 14 days. Cohorts of animals (5/sex/group) were necropsied on days 4 and 15, and hematology, serum biochemistry, plasma intact parathyroid hormone (PTH), and urinary parameters were evaluated. Urinalysis included measurement of calcium, phosphorus, and creatinine levels. Select tissues were examined microscopically, and samples of liver and lung were evaluated for total and phosphorylated MAPK (pMAPK) levels by Western blot analysis to evaluate for pharmacologic activity of PD0325901 (method described in Brown et al., 2007). Satellite animals were included for plasma drug-level analyses on day 8. In this study, systemic mineralization occurred at ≥0.3 mg/kg in a dosedependent fashion, was ﬁrst observed on day 4, and was more severe in males. By day 15, mineralization was generally more pronounced and widespread. Skeletal changes included hypertrophy of the physeal zone in males at ≥1 mg/ kg and at 3 mg/kg in females, and necrosis of bony trabeculae and marrow elements with ﬁbroplasia, ﬁbro-osseous proliferation, and/or localized hypocellularity at ≥1 mg/kg in males and 3 mg/kg in females. The minimal plasma PD0325901 AUC(0–24) values associated with toxicity were 121 to 399 ng · h/mL, which were well below exposure levels associated with antitumor efﬁcacy in murine models (AUC of 1180 to 1880 ng · h/mL). Pharmacologic inhibition of tissue pMAPK occurred at ≥1 mg/kg and was not observed in the absence of toxicity. The gastric fundic mucosa appeared to be the most sensitive tissue for evaluating systemic mineralization, which probably resulted from alterations in serum calcium and phosphorus homeostasis. This was based on the following observations. On day 4, serum phosphorus levels were increased 12

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TABLE 2 Mean Clinical Chemistry Changes in Male Rats Administered PD0325901 for Up to 2 Weeks PD0325901

Serum phosphorus (mg/dL) Serum calcium (mg/dL) Serum albumin (g/dL) Plasma PTH (pg/mL)b

Day

Control

0.3 mg/kg

1 mg/kg

3 mg/kg

4 15 4 15 4 15 4 15

12.90 11.30 10.58 10.38 2.74 2.56 492 1099

13.08 11.56 10.36 10.36 2.56 2.54 297 268

14.48 12.88 10.10 10.52 2.10a 2.36a 114a 457

16.24a 13.62a 10.16 10.36 2.04a 1.98a 155a 115a

p, < 0.01 vs. control; n = 5/group. Intact parathyroid hormone.

a

b

to 26%, and albumin was decreased 17 to 26% at ≥1 mg/kg (Table 2, male data only). In addition, PTH levels were decreased in a dose-dependent fashion (60 to 77%) at ≥1 mg/kg. On day 15, phosphorus levels were increased 21% in males at 3 mg/kg, and albumin was decreased 8 to 32% at ≥0.3 mg/kg. PTH levels were decreased 77 to 89% at 3 mg/kg. Changes in urinary excretion of calcium and phosphorus were observed in both sexes at ≥1 mg/kg and included increased excretion of phosphorus on day 15. Although increases in excretion of calcium were observed on day 4 in females, males exhibited decreases in urinary calcium. In this study, PD0325901 administration resulted in signiﬁcantly decreased levels of serum albumin without changes in serum (total) calcium levels (Payne et al., 1979; Meuten et al., 1982; Rosol and Capen, 1997). This indicates that free, non-protein-bound calcium levels were increased. Hyperphosphatemia and hypercalcemia result in an increased Ca × P product, which is associated with induction of vascular mineralization (Block, 2000; Giachelli et al., 2001). The changes observed in urinary excretion of calcium and phosphorus probably reﬂected the alterations in serum levels. After completion of the two studies in rats described above it was concluded that PD0325901 produces signiﬁcant multiorgan toxicities in rats with no margin between plasma drug levels associated with antitumor efﬁcacy, pharmacologic inhibition of pMAPK (as an index of MEK inhibition), and toxicity in rats. Systemic mineralization was considered the preclinical toxicity of greatest concern, due to the severity of the changes observed and expectation of irreversibility, and the data suggested that it was related to a dysregulation in serum phosphorus and calcium homeostasis. Furthermore, skeletal lesions were seen in the rat studies that were similar to those reported with vitamin D toxicity and may be related to the calcium–phosphorus dysregulation. In concurrent toxicology studies in dogs and monkeys, neither systemic

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mineralization nor skeletal changes were observed, despite higher plasma drug exposures, lethal doses, or pharmacologic inhibition of MEK. Therefore, the following questions were posed regarding PD0325901-induced systemic mineralization: (1) What is a potential mechanism? (2) Is this toxicity relevant to humans or rat-speciﬁc? and (3) Can this toxicity be monitored clinically? The ability of an anticancer agent that modulates various signal transduction pathways to produce dysregulation in serum calcium homeostasis is not unprecedented. 8-Chloro-cAMP is an experimental compound that has been shown to modulate various protein kinase signal transduction pathways involved in neoplasia. In preclinical models, this compound produced growth inhibition and increased differentiation in cancer cells (Ally et al., 1989). In a clinical trial, 8-chloro-cAMP was administered to patients with advanced cancer via intravenous infusion and resulted in dose-limiting toxicity of reversible hypercalcemia, as serum calcium was increased by up to approximately 40% (Saunders et al., 1997). This drug produced a parathyroid hormone-like effect in these patients, resulting in increased synthesis of 1,25-dihydroxyvitamin D (up to 14 times baseline value) as a mechanism for the hypercalcemia. Intravenous administration of 8-chloro-cAMP to beagle dogs also resulted in hypercalcemia (serum calcium increased 37 to 46%), indicating similar actions across species (Brown et al., 2000). Experience with this compound was important with respect to designing investigative studies with PD0325901 in which the hormonal control of serum calcium and phosphorus were evaluated. An investigative study was designed in rats to examine the time course for tissue mineralization in target organs and to determine whether clinical pathology changes occur prior to, or concurrent with, lesion development (Brown et al., 2005a). These clinical pathology parameters may therefore serve as biomarkers for systemic mineralization. Male rats (15/group) were used due to their increased sensitivity for this toxicity compared with females. Oral doses tested were 1, 3, or 10 mg/kg (6, 18, or 60 mg/m2, respectively). Five animals per group were necropsied on days 2, 3, or 4 following 1, 2, or 3 days of treatment, respectively. Clinical laboratory tests were conducted at necropsy that included serum osteocalcin, urinalysis, and plasma intact PTH, calcitonin, and 1,25-dihydroxyvitamin D. Lung samples were evaluated for inhibition of pMAPK, and microscopic evaluations of the aorta, distal femur with proximal tibia, heart, and stomach were conducted for all animals. Administration of PD0325901 resulted in inhibition of pMAPK in lung at all doses, demonstrating pharmacologic activity of the drug. On day 2, mineralization of gastric fundic mucosa and multifocal areas of necrosis of the ossifying zone of the physis were present only at 10 mg/kg. Necrosis of the metaphysis was present at ≥3 mg/kg. Serum phosphorus levels increased 33 to 43% and 1,25-dihydroxyvitamin D increased two- to sevenfold at all doses (Table 3). Osteocalcin increased 14 to 18%, and serum albumin decreased 8 to 14% at ≥3 mg/kg (Table 4). Osteocalcin is a major noncollagenous protein of bone matrix and synthesized by osteoblasts (Fu and Muller, 1999). Changes in serum osteocalcin can reﬂect alterations in bone turnover (resorption/

TOXICOLOGY STUDIES

309

TABLE 3 Mean Serum Phosphorus and Plasma 1,25-Dihydroxyvitamin D in Male Rats Administered PD0325901 for Up to 3 Days of Dosing PD0325901

Serum phosphorus (mg/dL) 1,25-Dihydroxyvitamin D (pg/mL)

Day

Control

1 mg/kg

3 mg/kg

10 mg/kg

2 3 4 2 3 4

12.06 11.48 11.34 309 257 191

16.10* 12.96* 13.18* M 856* 396 236 M

17.22* 15.62* M 15.40*M 1328* 776* M 604* M

16.84* Ma 19.02* M 21.70* M 2360* M 1390* M 1190* M

a

M, systemic mineralization observed. *, p < 0.01 vs. control; n = 5/group.

TABLE 4 Mean Serum Calcium and Albumin in Male Rats Administered PD0325901 for Up to 3 Days of Dosing PD0325901

Serum calcium (mg/dL)

Serum albumin (g/dL)

Day

Control

1 mg/kg

3 mg/kg

10 mg/kg

2 3 4 2 3 4

10.42 9.60 10.44 3.08 2.88 2.90

11.04 10.58 10.44 M 2.92 2.68 2.34** M

11.00 10.64 M 10.58 M 2.82* 2.62 M 2.34** M

10.66 Ma 10.58 M 7.24** M 2.66** M 2.34** M 1.98** M

a

M, systemic mineralization observed. *, p < 0.05 vs. control. **, p < 0.01 vs. control; n = 5/group.

formation). Serum osteocalcin appears to reﬂect the excess of synthesized protein not incorporated into bone matrix, or released protein during bone resorption (Ferreira and Drueke, 2000). The increases in osteocalcin seen in this study may have been reﬂective of bone necrosis. On day 3, mineralization of gastric fundic mucosa, gastric and cardiac arteries, aorta, and heart were present in all rats at 10 mg/kg. Myocardial necrosis was also seen at 10 mg/kg. Mineralization of gastric fundic mucosa was present in all rats at 3 mg/kg, and focal, minimal myocyte necrosis was present in one rat at 3 mg/kg. Thickening of the physeal zone of hypertrophying cartilage, and necrosis within the physeal zone of ossiﬁcation and in the metaphyseal region in femur and tibia were seen in all animals at 10 mg/kg. Necrosis within the metaphyseal region was also present at 3 mg/kg. Serum phosphorus increased 13 to 66% at all doses and 1,25-dihydroxyvitamin D increased twoto fourfold at ≥3 mg/kg. Osteocalcin increased 12 to 28% at ≥3 mg/kg and serum albumin was decreased (7 to 19%) at all doses. Urine calcium increased

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DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

ﬁvefold at 10 mg/kg, resulting in a ﬁvefold increase in urine calcium/creatinine ratio. This increase may have represented an attempt to achieve mineral homeostasis in response to the hypercalcemia. In addition, hypercalciuria can occur with vitamin D intoxication (Knutson et al., 1997). On day 4, mineralization of gastric fundic mucosa, gastric muscularis, gastric and cardiac arteries, aorta, and heart were present in the majority of animals at ≥3 mg/kg. Myocardial necrosis with accompanying neutrophilic inﬂammation was also seen in all rats at 10 mg/kg and in one animal at 3 mg/kg. Mineralization of gastric fundic mucosa was present at 1 mg/kg. Thickening of the physeal zone of hypertrophying cartilage, and necrosis within the physeal zone of ossiﬁcation and/or in the metaphyseal region in femur and tibia, were present at ≥3 mg/kg. At 1 mg/kg, thickening of the physeal zone of hypertrophying cartilage and metaphyseal necrosis were observed. Serum phosphorus increased 16 to 91% at all doses and 1,25-dihydroxyvitamin D increased twoto ﬁvefold at ≥3 mg/kg. Osteocalcin increased 14 to 24% at ≥3 mg/kg, and serum albumin decreased 19 to 32% at all doses. At 10 mg/kg, serum calcium was decreased 31% (possibly resulting from the hypercalciuria on day 3) and calcitonin was decreased by 71%. Calcitonin is secreted by the thyroid gland and acts to lower serum calcium levels by inhibiting bone resorption (Rosol and Capen, 1997). The decrease in calcitonin may have resulted from feedback inhibition due to low serum calcium levels at 10 mg/kg on day 4. Urine creatinine, calcium, and phosphorus were increased at 10 mg/kg. This resulted in decreases of 41% and 21% in the calcium/creatinine and phosphorus/creatinine ratios, respectively. This four-day investigative study in rats resulted in several very important conclusions which were critical for supporting continued development of PD0325901. In the study, PD0325901 at ≥1 mg/kg resulted in systemic mineralization and skeletal changes in a dose- and time-dependent fashion. These changes were seen after a single dose at 10 mg/kg and after 3 doses at 1 mg/ kg. Elevations in serum phosphorus and plasma 1,25-dihydroxyvitamin D occurred prior to tissue mineralization. Although serum albumin was decreased throughout the study, calcium remained unchanged, consistent with an increase in non-protein-bound calcium. This study set the stage for the proposal of using serum phosphorus and calcium measurements as clinical laboratory tests or biomarkers for PD0325901-induced systemic mineralization. Whereas measurement of plasma 1,25-dihydroxyvitamin D is technically complex and costly, evaluation of serum calcium and phosphorus is rapid and performed routinely in the clinical laboratory with historical reference ranges readily available. Although the data obtained with urinalysis were consistent with dysregulation of calcium and phosphorus homeostasis, concerns existed as to whether speciﬁc and reproducible urinalysis parameters could be developed for monitoring the safety of PD0325901. Based on the data obtained thus far, hyperphosphatemia appeared to be the primary factor for eliciting tissue mineralization, and serum phosphorus was proposed as the key analyte for monitoring.

TOXICOLOGY STUDIES

311

An investigative study was conducted in male rats to assess the reversibility of serum chemistry changes following a single oral dose of PD0325901 (Brown et al., 2005a). The hypothesis was that serum phosphorus levels would return to control levels in the absence of drug administration. Male rats (10/group) received single oral doses at 1, 3, or 10 mg/kg, with controls receiving vehicle alone. Blood was collected on days 2, 3, 5, and 8 for serum chemistry analysis. Hyperphosphatemia (serum phosphorus increased up to 58%) and minimal increases in calcium occurred at all doses on days 2 and 3. Albumin was decreased at 10 mg/kg. These changes were completely reversible within a week. This study demonstrated that increases in serum phosphorus and calcium induced by PD0325901 are reversible following cessation of dosing. Although a single dose of 10 mg/kg produces systemic mineralization in rats, withdrawal of dosing results in normalization of serum calcium and phosphorus levels, indicating that the homeostatic mechanisms controlling these electrolytes remain intact. The results of this study were not unexpected. Oral administration to dogs of the vitamin D analogs dihydrotachysterol and Hytakerol (dihydroxyvitamin D2-II) results in hypercalcemia that is reversible following termination of dosing (Chen et al., 1962). Reversal of hypercalcemia and hypercalciuria has been demonstrated in humans following cessation of dosing of various forms of vitamin D (calciferol, dihydrotachysterol, 1-α-hydroxycholecalciferol, or 1-α,25-dihydroxycholecalciferol) (Kanis and Russell, 1977). Another investigative study was conducted in male rats to determine whether pamidronate (a bisphosphonate) or Renagel (sevelamer HCl; a phosphorus binder) would inhibit tissue mineralization induced by PD0325901 by inhibiting hyperphosphatemia. Bisphosphonates inhibit bone resorption and in so doing modulate serum calcium and phosphorus levels. Renagel is a nonabsorbable resin that contains polymers of allylamine hydrochloride, which forms ionic and hydrogen bonds with phosphate in the gut. Rats received daily oral doses of PD0325901 at 3 mg/kg for 14 days with or without co-treatment with pamidronate or Renagel. Pamidronate was given twice intravenously at 1.5 mg/kg one day prior to PD0325901 dosing and on day 6. Renagel was given daily as 5% of the diet beginning one day prior to PD0325901 dosing. Treatment groups consisted of oral vehicle alone, PD0325901 alone, pamidronate alone, Renagel alone, PD0325901 + pamidronate, and PD0325901 + Renagel. PD0325901 plasma AUC(0–24) values were 11.6, 9.17, and 4.34 μg · h/mL in the PD0325901 alone, PD0325901 + pamidronate, and PD0325901 + Renagel groups, respectively. Administration of PD0325901 alone resulted in hyperphosphatemia on days 3 and 15, which was inhibited by co-treatment with pamidronate or Renagel on day 3 only. PD0325901 alone resulted in systemic mineralization and skeletal changes consistent with changes seen in previous rat studies. Coadministration with either pamidronate or Renagel protected against systemic mineralization on day 3 only. Bone lesions were decreased with the co-treatments. Inhibition of toxicity with Renagel may have been due in part to decreased systemic drug exposure. However, the inhibition of toxic-

312

DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

ity with pamidronate supports the role of a calcium–phosphorus dysregulation in PD0325901-induced systemic mineralization, because the inhibition of systemic mineralization observed on day 3 coincided with attenuation in the rise in serum phosphorus in these animals. A two-week oral dose range-ﬁnding study was conducted in dogs in which doses tested were 0.2, 0.5, and 1.5 mg/kg (4, 10, and 30 mg/m2, respectively). Also, a two-week oral-dose-range-ﬁnding study was conducted in cynomolgus monkeys at doses of 0.5, 3, and 10 mg/kg (6, 36, and 120 mg/m2, respectively). In addition to standard toxicology and toxicokinetic endpoints, determination of inhibition of tissue and peripheral blood mononuclear cell pMAPK was performed to assess pharmacologic activity of PD0325901 in both studies. PTH and 1,25-dihydroxyvitamin D were evaluated in the monkey study. In both studies, mortality occurred at ≥0.5 mg/kg (dogs) and at 10 mg/kg (monkeys) due to injury to the gastrointestinal tract mucosa, inhibition of pMAPK occurred at all doses, and systemic mineralization was not observed in either study. Increases in serum phosphorus were seen in moribund animals and/or associated with renal hypoperfusion (resulting from emesis, diarrhea, and dehydration). These elevations in phosphorus were considered secondary to renal effects and were not associated with changes in serum calcium. Toxicologically signiﬁcant increases in serum phosphorus or calcium were not evident at nonlethal doses in dogs or monkeys. In the two-week monkey study, a dose-related increase in 1,25-dihydroxyvitamin D was observed on day 2 only (after a single dose) at ≥3 mg/kg. This increase did not occur on days 7 or 15, and was not associated with changes in serum phosphorus or calcium, nor systemic mineralization. Therefore, there did not appear to be toxicologic signiﬁcance to the day 2 increase in 1,25-dihydroxyvitamin D in monkeys.

DISCUSSION Mineralization of vasculature and various soft tissues (systemic mineralization) was observed in toxicology studies in rats in a time- and dose-dependent manner. This change was consistent with the presence of calcium–phosphorus deposition within the vascular wall and parenchyma of tissues such as the stomach, kidney, aorta, and heart. The stomach appeared to be the most sensitive tissue, since mineralization of gastric fundic mucosa occurred prior to the onset of mineralization in other tissues. Male rats were consistently more sensitive to this toxicity than were female rats. In the pivotal one-month toxicity study in rats, the no-effect level for systemic mineralization was 0.1 mg/kg (0.6 mg/m2) in males and 0.3 mg/kg (1.8 mg/m2) in females, which were associated with PD0325901 steady-state plasma AUC(0–24) values of 231 and 805 ng · h/mL, respectively. Systemic mineralization was not observed in dogs or monkeys, despite pharmacologic inhibition of tissue pMAPK levels (>70%), administration of lethal doses, and exposures greater than 10-fold of those that induced mineralization in rats (10,600 ng · h/mL in dogs and

DISCUSSION

313

up to 15,000 ng · h/mL in monkeys). Systemic mineralization was not observed in mice despite administration of PD0325901 at doses up to 50 mg/kg (150 mg/m2). Systemic mineralization observed in rats following administration of PD0325901 is consistent with vitamin D toxicity due to dysregulation in serum calcium and phosphorus homeostasis (Grant et al., 1963; Rosenblum et al., 1977; Kamio et al., 1979; Mortensen et al., 1996; and Morrow, 2001). A proposed hypothesis for the mechanism of this toxicity is depicted in Figure 3. Elevated serum phosphorus levels (hyperphosphatemia) and decreased serum albumin were observed consistently in rats administered PD0325901. Although serum albumin levels are decreased in rats treated with PD0325901, calcium values typically remain unchanged or slightly elevated in these animals, indicating that free, non-protein-bound calcium is increased (Rosol and Capen, 1997; Payne et al., 1979; Meuten et al., 1982). Decreased parathyroid hormone levels (PTH) were observed in the rat studies. PTH plays a central role in the hormonal control of serum calcium and phosphorus. PTH is produced by the parathyroid gland and induces conversion of 25-hydroxyvitamin D (which is produced in the liver) to 1,25-dihydroxyvitamin D (calcitriol) in the kidney. 1,25-Dihydroxyvitamin D elicits increased absorption of calcium from the gastrointestinal tract. In addition, PTH mobilizes calcium and phosphorus from bone by increasing bone resorption, increases renal absorption of calcium, and increases renal excretion of phosphorus (in order to regulate

↑ [Ca] ¥ [P] = Systemic mineralization

↑P GI Tract ↑ Ca P, Ca ↑ Ca

Parathyroid

= ↓ PTH P

1, 25-Dihydroxyvitamin D PD0325901

Kidney

? 25-Hydroxyvitamin D

Figure 3 Hypothesis for the mechanism for systemic mineralization in the rat following PD0325901 administration.

314

DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

serum phosphorus levels). Elevations in serum calcium typically elicit decreased PTH levels as a result of the normal control (negative feedback loop) of this endocrine system (Rosol and Capen, 1997). The decreases in PTH observed in the rats were believed to be due to the elevations in serum calcium (hypercalcemia). Hyperphosphatemia in the presence of normo- or hypercalcemia can result in an increased Ca × P product, which is associated with systemic mineralization (Block, 2000). Hyperphosphatemia was also observed in rats administered PD176067, which is a reversible and selective inhibitor of ﬁbroblast growth factor receptor tyrosine kinase. In these animals, vascular and soft tissue mineralization also occurs (aorta and other arteries, gastric fundic mucosa, myocardium, renal tubules), probably due to increased Ca × P product (Brown et al., 2005b). Administration of PD0325901 to rats resulted in signiﬁcantly increased levels of plasma 1,25-dihydroxyvitamin D. The mechanism for this action is not known but is not believed to be due to a metabolite of PD0325901. This is the most potent form of vitamin D and the primary metabolite responsible for regulating serum calcium and phosphorus. Vitamin D is converted to 25-hydroxyvitamin D in the liver and then 1-hydroxylated to 1,25-dihydroxyvitamin D in renal tubules. 1,25-Dihydroxyvitamin D acts by increasing absorption of calcium and phosphorus from the gastrointestinal tract, and can increase calcium and phosphorus reabsorption by renal tubules. Hyperphosphatemia and increased plasma 1,25-dihydroxyvitamin D levels in rats occurred 1 to 2 days prior to the detection of tissue mineralization at doses ≤3 mg/kg (18 mg/m2). Administration of PD0325901 to rats resulted in bone lesions that included necrosis of the metaphysis and the ossifying zone of the physis, and thickening of the zone of hypertrophying cartilage of the physis. The expansion of chondrocytes in the physis may be a response to the metaphyseal necrosis and loss of osteoprogenitor cells. These changes are characterized by localized injury to bone that appear to be due to local ischemia and/or necrosis. Skeletal vascular changes may be present in these animals, resulting in disruption of endochondral ossiﬁcation. Skeletal lesions, including bone necrosis, can result from vitamin D intoxication (Haschek et al., 1978). The skeletal lesions observed in rats administered PD0325901 are similar to those reported with vitamin D toxicity, which provides additional evidence that toxicity occurred via induction of 1,25-dihydroxyvitamin D. Bone lesions similar to those observed in rats were not seen in dogs, monkeys, or mice administered PD0325901. In summary, PD0325901-induced systemic mineralization in the rat results from a dysregulation in serum phosphorus and calcium homeostasis. This dysregulation appears to result from toxicologically signiﬁcant elevations in plasma 1,25-dihydroxyvitamin D levels following drug administration. Based on the toxicology data, rats are uniquely sensitive to this toxicity. A summary of the primary target organ toxicities observed in the preclinical studies is presented in Table 5. Toxicity to the skin (epidermal lesions) and gastrointes-

DISCUSSION

TABLE 5

315

Primary Target Organ Toxicities Observed in Preclinical Studies Species

Organ System Gastrointestinal tract Skin Systemic mineralizationb Bone Liver Gallbladder

Rat

Dog

Monkey

×a × × × × n/a

× × — — — —

× × — — — ×

a

Toxicity observed. Includes vascular (aorta, arteries) and soft tissue mineralization (e.g., stomach, heart, kidneys).

b

tinal tract (primarily ulcers/erosions in the mucosa) were observed across species and may have resulted from inhibition of MEK-related signal transduction pathways in these tissues (Brown et al., 2006). Gastrointestinal tract toxicity is dose-limiting in dogs and monkeys and was anticipated to be the dose-limiting toxicity of PD0325901 in the clinic. Therefore, gastrointestinal tract toxicity may preclude the development of other potential adverse events in humans, including potential dysregulation in serum phosphorus or calcium. It is not known whether systemic mineralization is relevant to humans. However, if PD0325901 does induce a dysregulation in serum calcium– phosphorus metabolism in humans, monitoring serum levels would provide an early indication of effects and guide modiﬁcations to dosing regimens. To ensure patient safety in the phase I clinical trial with PD0325901, procedures were incorporated into the trial design to monitor for potential dysregulation in serum calcium–phosphorus homeostasis. Measurements of serum calcium, phosphorus, creatinine, albumin, and blood urea nitrogen were performed frequently during the initial treatment cycle (21 days of dosing in a 28-day cycle), with periodic measurement in subsequent cycles, and the serum Ca × P product was calculated. The serum Ca × P product has been determined to be a clinically useful value as a means for evaluating risk for tissue and/or vascular mineralization with the recommendation that the value not exceed 70 based on clinical use of vitamin D analogs such as Rocaltrol and Hectorol (Roche Laboratories, 1998; Bone Care International, Inc., 1999; Block, 2000). Serum calcium and phosphorus are readily measured in a clinical setting with well-established reference ranges available. The trial included a protocolspeciﬁc dose-limiting toxicity for a Ca × P product > 70, which required a conﬁrmatory measurement and dose interruption for that patient. In addition, serum vitamin D, PTH, alkaline phosphatase (total and bone), osteocalcin, and urinary C- and N-terminal peptide of collagen 1 (markers of bone resorption) were included for periodic measurement. Criteria considered for exclusion of candidate patients from the clinical trial included a history of

316

DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

malignancy-associated hypercalcemia, extensive bone metastasis, parathyroid disorder, hyperphosphatemia and renal insufﬁciency, serum calcium or phosphorus levels >1× the upper limit of normal, and/or concomitant use of calcium supplements and vitamin D in amounts exceeding normal daily allowances.

CALCULATION OF CLINICAL STARTING DOSE The currently accepted algorithm for calculating a starting dose in clinical trials with oncology drugs is to use one-tenth of the dose that causes severe toxicity (or death) in 10% of the rodents (STD10) on a mg/m2 basis, provided that this starting dose (i.e., 1/10 the STD10) does not cause serious, irreversible toxicity in a nonrodent species (in this case, the dog) (DeGeorge et al., 1998). If irreversible toxicities are induced at the proposed starting dose in nonrodents or if the nonrodent (i.e., the dog) is known to be the more appropriate animal model, the starting dose would generally be one-sixth of the highest dose tested in the nonrodent (the dog) that does not cause severe, irreversible toxicity. Calculation of the initial phase I starting dose of PD0325901 was based on the pivotal one-month toxicology studies in rats and dogs. Doses tested in the one-month rat study were 0.1, 0.3, and 1 mg/kg (0.6, 1.8, and 6 mg/m2, respectively), and doses in the one-month dog study were 0.05, 0.1, and 0.3 mg/kg (1, 2, and 6 mg/m2, respectively). Both studies included animals assigned to a one-month reversal phase, in the absence of dosing, to assess reversibility of any observed toxicities. In addition to standard toxicology and toxicokinetic parameters, these studies included frequent evaluation of serum chemistries, and measurement of vitamin D, osteocalcin, PTH, and inhibition of tissue pMAPK levels. In the one-month rat study, no drug-related deaths occurred and systemic mineralization occurred in multiple tissues in both sexes at 1 mg/kg. Hypocellularity of the metaphyseal region of distal femur and/or proximal tibia occurred in males at 1 mg/kg. Toxicologic ﬁndings at lower doses included skin sores (at ≥0.1 mg/kg) and mineralization of gastric mucosa in one male at 0.3 mg/kg. The ﬁndings at ≤0.3 mg/kg were not considered to represent serious toxicity. In previous two-week dose-range-ﬁnding studies in rats, death occurred at 3 mg/kg (18 mg/m2), indicating this to be the minimal lethal dose in rats. Based on these results, the STD10 in rats was determined to be 1 mg/kg (6 mg/m2) In the one-month dog study, doses up to 0.3 mg/kg (6 mg/m2) were well tolerated with minimal clinical toxicity. Primary drug-related toxicity was limited to skin sores in two animals at 0.3 mg/kg. One-tenth the STD10 in rats is 0.6 mg/m2, which is well below a minimally toxic dose (6 mg/m2) in dogs. These data indicate an acceptable phase I starting dose to be 0.6 mg/m2, which is equivalent to 1 mg in a 60-kg person. The relationships between the primary toxicities in rats and dogs with dose and exposure are presented in Figure 4.

CALCULATION OF CLINICAL STARTING DOSE Dog

Rat

1.8 Nonpivotal

6

18

1120– 2460 6

121– 399 1.8

60

180

8010– 19200

3850– 6070

0.6 Pivotal 195–231 733–805 2060–2820

Anticipated Phase 1 Starting Dose (0.6 mg/m2)

317

49600– 62100

Dose Range AUC (0–24) (ng·hr/mL)

Dose Range AUC (0–24) (ng·hr/mL) Skin Lesions Systemic Mineralization Bone Metaphyseal Hypocellularity Hepatocellular Necrosis GI Tract Lesions Death

4

10

Nonpivotal 971– 1860– 1850 3550 6

1 2 Pivotal 208–222 567–743 1440–1550

Dose Range

30

AUC (0–24) (ng·hr/mL) 4730– 10600 Dose Range AUC (0–24) (ng·hr/mL) Skin Lesions GI Tract Lesions Death

0.1

1

10

100

1000

PD 0325901 Dose (mg/m2)

Figure 4 Relationships between dose and exposure with the primary toxicities of PD0325901 in rats and dogs. Exposure is expressed as PD0325901 plasma AUC(0–24) in ng · hr/mL and dose in mg/m2. Results from nonpivotal (dose-range-ﬁnding) studies and the pivotal one-month toxicity studies are presented.

In the phase I trial of PD0325901 in cancer patients (melanoma, breast, colon, and non-small cell lung cancer), oral doses were escalated from 1 to 30 mg twice daily (BID). Each treatment cycle consisted of 28 days, and three schedules of administration were evaluated: (1) 3 weeks of dosing with one week off, (2) dosing every day, and (3) 5 days of dosing with 2 days off per week (LoRusso et al., 2005; Menon et al., 2005; Tan et al., 2007; LoRusso et al., 2007). Doses ≥2 mg BID suppressed tumor pMAPK (indicating biochemical activity of the drug) and acneiform rash was dose limiting in one patient at 30 mg BID. There were no notable effects on serum Ca × P product and the most common toxicities included rash, fatigue, diarrhea, nausea, visual disturbances and vomiting. Acute neurotoxicity was frequent in patients receiving ≥15 mg BID (all schedules) and several patients developed optic nerve ischemia, optic neuropathy, or retinal vein occlusion (LoRusso et al., 2007). In the phase I trial, there were three partial responses (melanoma) and stable disease in 24 patients (primarily melanoma) (LoRusso et al., 2007). In a pilot phase II study of PD0325901 in heavily pretreated patients with non-

318

DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

small cell lung cancer, 15 mg BID was given on various schedules over a 28-day cycle (Haura et al., 2007). The main toxicities were reversible visual disturbances, diarrhea, rash, and fatigue. The mean trough concentration of PD0325901 at 15 mg BID was 108 ng/mL (at steady state) and there were no objective responses (Haura et al., 2007).

CONCLUSIONS Tissue mineralization produced in rats administered the MEK inhibitor PD0325901 provides a case study of how a unique and serious toxicity observed in preclinical safety testing was effectively managed to allow progression of an experimental drug into human clinical trials. A number of key factors were critical for allowing continued development of this compound to occur, rather than early termination. PD0325901 represented a novel and targeted therapeutic agent for the treatment of various solid tumors, thereby allowing a high risk–beneﬁt ratio to exist due to the signiﬁcant unmet medical need posed by cancer. Phase I oncology trials typically occur in cancer patients with limited treatment options. Therefore, the barriers to entry for novel anticancer agents in the clinic are generally lower than for phase I trials involving healthy volunteers and therapies for non-life-threatening indications. Early in the toxicology program with PD0325901, lesions observed in rats were similar to those seen with vitamin D toxicity, and serum chemistry data indicated changes in phosphorus and calcium. This information provided the basis for the hypotheses to be proposed regarding the mechanism for vascular and soft tissue mineralization. Because mineralization occurred in rats administered PD0325901 rather than only in dogs or monkeys, an animal model suitable for multiple investigative studies was readily available. Despite the apparent species speciﬁcity for this toxicity, it was not appropriate to discount the risks toward humans because of a “rat-speciﬁc” ﬁnding. Rather, it was important to generate experimental data that characterized the toxicity and provided a plausible mechanism as a basis for risk management. Studies conducted with PD0325901 examined the dose–response and exposure–response relationships for toxicity and pharmacologic inhibition of MEK, the time course for lesion development, whether the changes observed were reversible or not, and whether associations could be made between clinical laboratory changes and anatomic lesions. We were able to identify biomarkers for tissue mineralization that were speciﬁcally related to the mechanism, were readily available in the clinical setting, noninvasive, and had acceptable assay variability. It is important that biomarkers proposed for monitoring for drug toxicity be scientiﬁcally robust and obtainable, and meet expectations of regulatory agencies. Finally, the data generated during the preclinical safety evaluation of PD0325901 were used to design the phase I–II clinical trial to ensure patient safety. This included selection of a safe starting dose for phase I,

REFERENCES

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criteria for excluding patients from the trial, and clinical laboratory tests to be included as biomarkers for calcium–phosphorus dysregulation and tissue mineralization. In conclusion, robust data analyses, scientiﬁc hypothesis testing, and the ability to conduct investigative work were key factors in developing a biomarker for a serious preclinical toxicity, thereby allowing clinical investigation of a novel drug to occur. Acknowledgments Numerous people at the Pﬁzer Global Research and Development (PGRD), Ann Arbor, Michigan, Laboratories were involved in the studies performed with PD0325901, including the Departments of Cancer Pharmacology and Pharmacokinetics, Dynamics and Metabolism. In particular, the author would like to acknowledge the men and women of Drug Safety Research and Development, PGRD, Ann Arbor, who conducted the toxicology studies with this compound and made signiﬁcant contributions in the disciplines of anatomic pathology and clinical laboratory testing during evaluation of this compound. REFERENCES Ally S, Clair T, Katsaros D, et al. (1989). Inhibition of growth and modulation of gene expression in human lung carcinoma in athymic mice by site-selective 8-Cl-cyclic adenosine monophosphate. Cancer Res, 49:5650–5655. Block GA (2000). Prevalence and clinical consequences of elevated Ca × P product on hemodialysis patients. Clin Nephrol, 54(4):318–324. Bone Care International, Inc., (1999). Package insert, HectorolTM (doxercalciferol) capsules. June 9. Brown AP, Morrissey RL, Smith AC, Tomaszewski JE, Levine BS (2000). Comparison of 8-chloroadenosine (NSC-354258) and 8-chloro-cyclic-AMP (NSC-614491) toxicity in dogs. Proc Am Assoc Cancer Res, 41:491 (abstract 3132). Brown AP, Courtney C, Carlson T, Graziano M (2005a). Administration of a MEK inhibitor results in tissue mineralization in the rat due to dysregulation of phosphorus and calcium homeostasis. Toxicologist, 84(S-1):108 (abstract 529). Brown AP, Courtney CL, King LM, Groom SL, Graziano MJ (2005b). Cartilage dysplasia and tissue mineralization in the rat following administration of a FGF receptor tyrosine kinase inhibitor. Toxicol Pathol, 33(4):449–455. Brown AP, Reindel JF, Grantham L, et al. (2006). Pharmacologic inhibitors of the MEK-MAP kinase pathway are associated with toxicity to the skin, stomach, intestines, and liver. Proc Am Assoc Cancer Res, 47:308 (abstract 1307). Brown AP, Carlson TCG, Loi CM, Graziano MJ (2007). Pharmacodynamic and toxicokinetic evaluation of the novel MEK inhibitor, PD0325901, in the rat following oral and intravenous administration. Cancer Chemother Pharmacol, 59:671–679.

320

DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

Chen PS, Terepka AR, Overslaugh C (1962). Hypercalcemic and hyperphosphatemic actions of dihydrotachysterol, vitamin D2 and Hytakerol (AT-10) in rats and dogs. Endocrinology, 70:815–821. DeGeorge JJ, Ahn CH, Andrews PA, et al. (1998). Regulatory considerations for preclinical development of anticancer drugs. Cancer Chemother Pharmacol, 41:173–185. Dent P, Grant S (2001). Pharmacologic interruption of the mitogen-activated extracellular-regulated kinase/mitogen-activated protein kinase signal transduction pathway: potential role in promoting cytotoxic drug action. Clin Cancer Res, 7:775–783. Ferreira A, Drueke TB (2000). Biological markers in the diagnosis of the different forms of renal osteodystrophy. Am J Med Sci, 320(2):85–89. Friday BB, Adjei AA (2008). Advances in targeting the Ras/Raf/MEK/Erk mitogenactivated protein kinase cascade with MEK inhibitors for cancer therapy. Clin Cancer Res, 14(2):342–346. Fu JY, Muller D (1999). Simple, rapid enzyme-linked immunosorbent assay (ELISA) for the determination of rat osteocalcin. Calcif Tissue Int, 64:229–233. Giachelli CM, Jono S, Shioi A, Nishizawa Y, Mori K, Morii H (2001). Vascular calciﬁcation and inorganic phosphate. Am J Kidney Dis, 38(4, Suppl 1):S34–S37. Grant RA, Gillman T, Hathorn M (1963). Prolonged chemical and histochemical changes associated with widespread calciﬁcation of soft tissues following brief calciferol intoxication. Br J Exp Pathol, 44(2):220–232. Harrington DD, Page EH (1983). Acute vitamin D3 toxicosis in horses: case reports and experimental studies of the comparative toxicity of vitamins D2 and D3. J Am Vet Med Assoc, 182(12):1358–1369. Haschek WM, Krook L, Kallfelz FA, Pond WG (1978). Vitamin D toxicity, initial site and mode of action. Cornell Vet, 68(3):324–364. Haura EB, Larson TG, Stella PJ, et al. (2007). A pilot phase II study of PD-0325901, an oral MEK inhibitor, in previously treated patients with advanced non-small cell lung cancer. Presented at the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapy, abstract B110. Hoshino R, Chatani Y, Yamori T, et al. (1999). Constitutive activation of the 41-/43kDa mitogen-activated protein kinase pathway in human tumors. Oncogene, 18:813–822. Jost M, Huggett TM, Kari C, Boise LH, Rodeck U (2001). Epidermal growth factor receptor–dependent control of keratinocyte survival and Bcl-XL expression through a MEK-dependent pathway. J Biol Chem, 276(9):6320–6326. Kamio A, Taguchi T, Shiraishi M, Shitama K, Fukushima K, Takebayashi S (1979). Vitamin D sclerosis in rats. Acta Pathol Jpn, 29(4):545–562. Kanis JA, Russell RGG (1977). Rate of reversal of hypercalcaemia and hypercalciuria induced by vitamin D and its 1α-hydroxylated derivatives. Br Med J, 1:78–81. Knutson JC, LeVan LW, Valliere CR, Bishop CW (1997). Pharmacokinetics and systemic effect on calcium homeostasis of 1α,25-dihydroxyvitamin D2 in rats. Biochem Pharm, 53:829–837. Long GG (1984). Acute toxicosis in swine associated with excessive dietary intake of vitamin D. J Am Vet Med Assoc, 184(2):164–170.

REFERENCES

321

LoRusso P, Krishnamurthi S, Rinehart JR, et al. (2005). A Phase 1–2 clinical study of a second generation oral MEK inhibitor, PD0325901 in patients with advanced cancer. 2005 ASCO Annual Meeting Proceedings. J Clin Oncol, 23(16S), abstract 3011. LoRusso PA, Krishnamurthi SS, Rinehart JJ, et al. (2007). Clinical aspects of a phase I study of PD-0325901, a selective oral MEK inhibitor, in patients with advanced cancer. Presented at the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapy, abstract B113. Mansour SJ, Matten WT, Hermann AS, et al. (1994). Transformation of mammalian cells by constitutively active MAP kinase kinase. Science, 265:966–970. Menon SS, Whitﬁeld LR, Sadis S, et al. (2005). Pharmacokinetics (PK) and pharmacodynamics (PD) of PD0325901, a second generation MEK inhibitor after multiple oral doses of PD0325901 to advanced cancer patients. 2005 ASCO Annual Meeting Proceedings. J Clin Oncol, 23(16S), abstract 3066. Meuten DJ, Chew DJ, Capen CC, Kociba GJ (1982). Relationship of serum total calcium to albumin and total protein in dogs. J Am Vet Med Assoc, 180:63–67. Milella M, Kornblau SM, Estrov Z, et al. (2001). Therapeutic targeting of the MEK/ MAPK signal transduction module in acute myeloid leukemia. J Clin Invest, 108(6):851–859. Morrow C (2001). Cholecalciferol poisoning. Vet Med, 905–911. Mortensen JT, Lichtenberg J, Binderup L (1996). Toxicity of 1,25-dihydroxyvitamin D3, tacalcitol, and calcipotriol after topical treatment in rats. J Inv Dermatol Symp Proc, 1:60–63. Payne RB, Carver ME, Morgan DB (1979). Interpretation of serum total calcium: effects of adjustment for albumin concentration on frequency of abnormal values and on detection of change in the individual. J Clin Pathol, 32:56–60. Rinehart J, Adjei AA, LoRusso PM et al. (2004). Multicenter phase II study of the oral MEK inhibitor, CI-1040, in patients with advanced non-small-cell lung, breast, colon, and pancreatic cancer. J Clin Oncol, 22(22):4456–4462. Roche Laboratories (1998). Package insert, Rocaltrol® (calcitriol) capsules and oral solution. Nov. 20. Rosenblum IY, Black HE, Ferrell JF (1977). The effects of various diphosphonates on a rat model of cardiac calciphylaxis. Calcif Tissue Res, 23:151–159. Rosol TJ, Capen CC (1997). Calcium-regulating hormones and diseases of abnormal mineral (calcium, phosphorus, magnesium) metabolism. In Clinical Biochemistry of Domestic Animals, 5th ed. Academic Press, San Diego, CA, pp. 619–702. Saunders MP, Salisbury AJ, O’Byrne KJ, et al. (1997). A novel cyclic adenosine monophosphate analog induces hypercalcemia via production of 1,25-dihydroxyvitamin D in patients with solid tumors. J Clin Endocrinol Metab, 82(12):4044–4048. Sebolt-Leopold JS, Dudley DT, Herrera R, et al. (1999). Blockade of the MAP kinase pathway suppresses growth of colon tumors in vivo. Nat Med, 5(7):810–816. Sebolt-Leopold JS (2000). Development of anticancer drugs targeting the MAP kinase pathway. Oncogene, 19:6594–6599. Sebolt-Leopold JS, Merriman R, Omer C, (2004). The biological proﬁle of PD0325901: a second generation analog of CI-1040 with improved pharmaceutical potential. Proc Am Assoc Cancer Res, 45:925 (abstract 4003).

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DEVELOPMENT OF SERUM CALCIUM AND PHOSPHORUS

Spangler WL, Gribble DH, Lee TC (1979). Vitamin D intoxication and the pathogenesis of vitamin D nephropathy in the dog. Am J Vet Res, 40:73–83. Spaulding SW, Walser M (1970). Treatment of experimental hypercalcemia with oral phosphate. J Clin Endocrinol, 31:531–538. Tan W, DePrimo S, Krishnamurthi SS, et al. (2007). Pharmacokinetic (PK) and pharmacodynamic (PD) results of a phase I study of PD-0325901, a second generation oral MEK inhibitor, in patients with advanced cancer. Presented at the AACRNCI-EORTC International Conference on Molecular Targets and Cancer Therapy, abstract B109. Wang D, Boerner SA, Winkler JD, LoRusso PM (2007). Clinical experience of MEK inhibitors in cancer therapy. Biochimi Biophys Acta, 1773:1248–1255. York MJ, Evans GO (1996). Electrolyte and ﬂuid balance. In Evans GO (ed.), Animal Clinical Chemistry: A Primer for Toxicologists. Taylor & Francis, New York, pp. 163–176.

16 BIOMARKERS FOR THE IMMUNOGENICITY OF THERAPEUTIC PROTEINS AND ITS CLINICAL CONSEQUENCES Claire Cornips, B.Sc., and Huub Schellekens, M.D. Utrecht University, Utrecht, The Netherlands

INTRODUCTION Therapeutic proteins such as growth factors, hormones, monoclonal antibodies (mAbs), and others have increased in use dramatically over the past two decades, although their ﬁrst use dates back more than a century, when animal antisera were introduced for the treatment and prevention of infections. Therapeutic proteins have always been associated with immunogenicity, although the incidence differs widely [1]. Granulocyte colony-stimulating factor (G-CSF) is the only protein in clinical use that has not been reported to induce antibodies. The ﬁrst proteins used in medicine around 1900 were of animal origin. As foreign proteins they induced high levels of antibodies in the majority of patients after a single or a few injections. The type of immunological response induced was identical to that seen with vaccines. Most of the therapeutic proteins introduced in recent decades are homologs of human proteins. However, in contrast to expectations, these proteins also appear to induce antibodies, and in some cases, in the majority of patients. Given that these antibodies are directed against auto-antigens, the immunological reaction involves breaking B-cell tolerance. Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

323

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MECHANISMS OF ANTIBODY INDUCTION There are two main mechanisms by which antibodies are induced by therapeutic proteins (Table 1). If the proteins are of completely of foreign origin, such as asparginase or streptokinase, and the ﬁrst generation mAbs derived from murine cells or partly foreign such as chimeric or humanized mAbs, the antibody response is comparable to a vaccination reaction. Often a single injection is sufﬁcient to induce high levels of neutralizing antibodies, which may persist for a considerable length of time. The other mechanism is based on breaking B-cell tolerance, which normally exists, to self-antigens, such as human immunoglobulins or products such as epoetins and the interferons. For breaking B-cell tolerance, prolonged exposure to proteins is necessary. In general, it takes months before patients produce antibodies, which are mainly binding and disappear when treatment is stopped. To induce a classical immune reaction a degree of nonself is necessary. The trigger for this type of immunogenicity is the divergence from the comparable human proteins. The triggers for breaking tolerance are quite different. The production of these auto-antibodies may occur when the self-antigens are exposed to the immune system in combination with a T-cell stimulus or danger signal such as bacterial endotoxins, microbial DNA rich in GC motifs or denatured proteins. This mechanism explains the immunogenicity of biopharmaceuticals containing impurities. When tolerance is broken with this type of mechanism, the response is often weak with low levels of antibodies with low afﬁnity. To induce high levels of IgG, the self-antigens should be presented to the immune system in a regular array form with a spacing of 50 to 100 Å, a supramolecular structure resembling a viral capsid [2]. Apparently the immune system has been selected to reach vigorously to these types of structures which

TABLE 1

Main Markers of Immunogenicity of Therapeutic Proteins Product

(Partly) foreign Level of nonself Presence of proteins T-cell epitopes inducing a Biological classical activity of the immune product response Self-protein Presence of breaking aggregates tolerance Biological activity of the product

Preclinical

Treatment

—

—

Induction of an immune response in immunetolerant mice

Chronic treatment

Patients Lack of immune tolerance Concomitant therapy Nonimmune compromised Concomitant therapy

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325

normally are found only in viruses and other microbial agents. The most important factor in the immunogenicity of therapeutic proteins which are human homologs has been the presence of aggregates [3]. Aggregates may be presenting the self-antigens in a repeating form which is such a potent inducer of auto-antibodies.

FACTORS INFLUENCING IMMUNOGENICITY So the primary factors inducing an antibody response are aggregates in the case of human proteins and the degree of nonself in (partly) nonhuman proteins. There are also cases in which the immune response cannot be explained. There are, however, a number of other factors that may inﬂuence the level of the immune response [4]: • Product characteristics 1. Biological function 2. Impurities and contaminants 3. Product modiﬁcation • Treatment characteristics 1. Length of treatment 2. Route of administration 3. Dosage 4. Concomitant therapy • Patient characteristics 1. Genetic background 2. Type of disease 3. Age 4. Gender • Unknown factors Product Characteristics The biological activities of the product are inﬂuencing the immune response. An immune stimulating therapeutic protein is more likely to induce antibodies than an immune suppressive protein. Monoclonal antibodies targeted to cellbound epitopes are more likely to induce an immune response than monoclonal antibodies with a target in solution. Also, the Fc-bound activities of monoclonal antibodies have an inﬂuence. Impurities may inﬂuence immunogenicity. The immunogenicity of products as human growth hormone, insulin, and interferon α-2 have declined over the years due to improved downstream processing and formulation, reducing the level of impurities. There are studies showing the induction of antibodies by

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oxidized protein which cross-reacted with the unmodiﬁed product [5] and host cell–derived endotoxin acting as an adjuvant. The probability of an immune response therefore increases with the level of impurities. Product modiﬁcations that are intended to enhance half-life potentially also increase the exposition of the protein to the immune system and may increase immunogenicity. In addition, the modiﬁcation may reduce biological activity necessitating more protein for the same biological effect. PEGylation (polyethylene glycol) is claimed to reduce the immunogenicity of therapeutic proteins by shielding. There is evidence that pegylation reduces the immunogenicity of nonhuman proteins such as bovine adenosine deamidase and asparginase. Whether pegylation also reduces the capacity of human proteins to break B-cell tolerance is less clear. There are reports of high immunogenicity of pegylated human proteins such as MDGF, but the immunogenicity of unpegylated MDGF products is unknown. Treatment Characteristics Foreign proteins such as streptokinase and asparaginase often induce antibodies after a single injection. Breaking B-cell tolerance by human protein takes in general more than six months of chronic treatment. The route of administration inﬂuences the likelihood of an antibody response independent of the mechanism of induction. The probability of an immune response is highest with subcutaneous administration, less probable after intramuscular administration, and intravenous administration is the least immunogenic route. There are no studies comparing parenteral and nonparenteral routes of administration. However, intranasal and pulmonary administration of therapeutic proteins may induce an immune response. Patient Characteristics Gender, age, and ethnic background have all been reported to inﬂuence the incidence of antibody response to speciﬁc therapeutic proteins. However, the only patient characteristic that has consistently been identiﬁed for a number of different products is the disease from which the patients suffer. Cancer patients are less likely to produce antibodies to therapeutic protein than other patients. The most widely accepted explanation for this difference is the immune-compromised state of cancer patients, both by the disease as by anticancer treatment. Also, the median survival of patients on treatment by therapeutic proteins may be too short to develop an antibody response. In any case, cancer reduces considerably the probability of an antibody response to a protein. As the experience in cancer patients shows, immune suppressive therapy reduces the probability to develop an immune response to proteins. In addition, immune-suppressive drugs such as methotrexate are used in conjunction with monoclonal antibodies and other protein drugs to reduce the immune reactions.

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327

PREDICTION OF IMMUNOGENICITY IN ANIMALS In principle, all therapeutic proteins are immunogenic in conventional laboratory animals, and their predictive value depends on the type of proteins [6]. The immune reaction in animals to biopharmaceuticals of microbial or plant origin is comparable to humans, as they are comparably foreign for all mammalian species. Animal studies in which the reduction of immunogenicity is evaluated therefore have a high degree of predictability for immunogenicity in humans. The development of antibodies has been observed regularly in preclinical studies in animals of biopharmaceuticals homologous to human proteins. Being considered a normal reaction to a foreign protein, it has led to the generally held assumption that immunogenicity testing and, in some cases, even preclinical testing in animals is irrelevant. However, not all antibodies interfere with the biological activity of a biopharmaceutical. And if there is a biological or clinical effect, these may help to identify the possible sequelae of immunogenicity, as has been shown with human epoetin in dogs. In the canine model human epoetin is immunogenic and it induces antibodies that neutralize the native canine epoetin, leading to pure red cell aplasia. This severe complication of antibodies to epoetin was later conﬁrmed in humans. Also, antibody-positive animals may provide sera for the development and validation of antibody assays; and the evaluation of an antibody response in animals is important to evaluate the safety and pharmacokinetic data in conventional laboratory animals. Nonhuman primates have been advocated as better models to predict the immunogenicity of human proteins because of a high sequence homology between the product and the monkey native molecule to which the animal is immune tolerant. Immunogenicity studies in nonhuman primates have, however, also shown mixed results. Products with a high immunogenicity in monkeys sometimes do not induce antibodies in human patients. The opposite has also been observed, although these studies may have been too limited in length of treatment or number of animals to be truly predictive. A good example of the possible use of monkeys was a study to determine the immunogenicity of different human growth hormone (hGH) preparations using a hyper-immunization protocol, including the use of adjuvant to provide a worst-case scenario of immunogenicity [7]. The monkeys were treated with cadaver-derived methionyl-hGH and natural sequence hGH. The antibody response was 81%, 69%, and 5 to 23%, respectively, which reﬂects the relative immunogenicity of these preparations in human patients. So in this example, rhesus monkeys predict relative immunogenicity. Also, the immunogenicity of lys-pro biosynthetic human insulin was compared with the immunogenicity of porcine insulin and native-sequence insulin in rhesus monkeys. Neither of these proved to induce antibodies. With tissue plasminogen activators the immunogenicity in nonhuman primates was reported by the same group to reﬂect the immunogenicity in patients. Rhesus monkeys (Macaca mulatta) and cynomolgus monkeys (M. fascicularis) were

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used to test antibody response to EPO/GM-CSF hybrids. Two of the three constructs tested produced high levels of neutralizing antibodies. In the rhesus monkeys these hybrids caused severe anemia. In cynomolgus monkeys no effect on hematological parameters was found, indicating lack of crossreactivity of the antibodies with the native cynomolgus erythropoietin. There are also reports on immunogenicity of products in monkeys such as IFN α B/D, diaspirin cross-linked hemoglobin, and IL-3, which did not induce antibodies in patients. So monkeys cannot be used as absolute predictors of immunogenicity in humans, and even the response within the macaque family seems to differ. Theoretically, the best predictive model for immunogenicity of human proteins is the transgenic mouse. These animals are immune tolerant for the human protein they express. The caveats are that the wild-type mouse strain used for transgenesis should be able to produce antibodies to the protein and that the transgenic should show immune tolerance for the native molecule and may be differences in the processing of antigens and epitope recognition between mouse and humans. Mice, transgenic for human insulin, showed that immunogenicity to variant insulin molecules was dependent on the number of substitutions. Mice that were made transgenic for human tissue plasminogen activator variants with a single amino acid substitution proved to be immunogenic, showing the discriminatory potential of the model [8]. The transgenic approach proved useful in ﬁnding a reason for the increased immunogenicity caused by a speciﬁc formulation. In mice transgenic for interferon α-2 only the batches immunogenic in patients produced antibodies [9]. The presence of aggregates and oxidized proteins proved the main cause of immunogenicity in the transgenic animals as in patients. In mice transgenic for hGH the immunogenicity of hGH encapsulated in microspheres for sustained release was tested and no enhanced immunogenicity was observed. We have used the transgenic mouse models to study the product characteristics capable of breaking tolerance. In these models, aggregates were shown to be the major factor. These models, however, have been used by us and others mainly in a yes-or-no fashion to study factors such as aggregates and sequence variations. To ﬁnd subtle differences that may exist between products and before these models can be used to fully predict the immunogenic potential of human therapeutic proteins, much more validation is necessary. With no models yet available with sufﬁcient predictive power, clinical studies are the only sure way to establish the induction of antibodies by therapeutic proteins.

ASSAYS FOR ANTIBODIES A cellular immune response to therapeutic proteins has never been established. Also, the biological and clinical consequences of an immune response to these products have always been associated with the presence of antibodies. So, by deﬁnition, a positive assay for antibodies is the biomarker for the immunogenicity of proteins. The lack of standardized assays and international

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329

reference sera presents a major problem in assessing immunogenicity. It makes a comparison of antibody data generated by different laboratories impossible; comparisons of products based on published data (e.g., using information on package inserts) are also meaningless. Recently, excellent reviews have been published regarding the development and validation of the various assay formats for antibodies to therapeutic proteins. There are two principles for the testing of antibodies: assays that monitor binding of antibody to the drug (EIA, RIA, BIAcore), or (neutralizing) bioassays. These assays are used in combination: a sensitive binding assay to identify all antibody-containing samples is often more practical, as bioassays are usually more difﬁcult and time consuming. If a native molecule is modiﬁed (e.g., pegylated or truncated) to obtain a new product with different pharmacological characteristics, both the “parent” and the new molecule should be used as capture antigens for antibody assays. A deﬁnition for the negative cutoff must be included in the validation process of this assay and is often based on the 5% false-positive rate. Such an analytical cutoff per se is not predictive of biological or clinical effect but rather, indicative of the technical limitations of the assay. And because the cutoff is set to include a relatively high number of false positives, all initial positive sera should be conﬁrmed either by another binding assay or by a displacement assay. The conﬁrmed positive samples should be tested in a bioassay for neutralizing antibodies, which correlate with a potential in vivo effect in patients, as these antibodies are usually neutralizing because they interfere with receptor binding. A conﬁrmatory step for neutralizing antibodies is not necessary because these antibodies are a subset of binding antibodies. In some cases it may be important to show the neutralization to be caused by antibodies if the presence of other inhibitory factors, such as a soluble receptor, has to be excluded. Further characterization of the neutralizing antibody response may follow, such as isotype, afﬁnity, and speciﬁcity assays. A positive or negative answer from the assays is not sufﬁcient; development of antibodies is a dynamic process, and therefore the course of antibody development (kinetics) must be plotted quantitatively over time. Usually, persisting titers of neutralizing antibodies correlate with some biological effect. An analysis of the biological effect based on incidence may also be misleading. Akin to population kinetics in pharmacology, a method that compares relative immunogenicity in patient groups as “mean population antibody titers,” taking into account both incidence and titers of neutralizing antibodies, has also been found more useful than a percentage of seroconversions for the purpose of comparing two products.

CONSEQUENCES OF ANTIBODIES TO THERAPEUTIC PROTEINS In many cases the presence of antibodies is not associated with biological or clinical consequences. The effects that antibodies may induce depend on their

330

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level and afﬁnity and can be the result of antigen–antibody reaction in general or of the speciﬁc interaction. Severe general immune reactions as anaphylaxis associated with the use of animal antisera have become rare because the purity of the products increased substantially. Delayed-type infusion-like reactions resembling serum sickness are more common, especially with monoclonal antibodies and other proteins administered in relative large amounts and the formation of immune complexes. Patients with a slow but steadily increasing antibody titer are reported to show more infusion-like reactions than patients with a short temporary response. The consequences of the speciﬁc interaction between protein drugs is dependent on the afﬁnity of the antibody translating in binding and/or neutralizing capacity. Binding antibodies may inﬂuence the pharmacokinetic behavior of the product, and both increases and reductions of half-life have been reported, resulting in enhancement or reduction in activity. Persisting levels of neutralizing antibodies in general result in a loss of activity of the protein drug. In some cases the loss of efﬁcacy can easily be monitored by the increase of disease activity. For example, in interferon alpha treatment of hepatitis C, viral activity can be monitored by transaminase activity. Loss of efﬁcacy is correlated directly by increased viral activity and increase in transaminase levels. In the case of interferon beta treatment of multiple sclerosis the loss of efﬁcacy is much more difﬁcult to measure because the mode of action of the therapeutic protein is not known and the disease progress is unpredictable and difﬁcult to monitor. The reduction of Mx induction which is speciﬁc for interferon activity has been used successfully to evaluate the biological effect of antibodies to interferon beta. The adverse effects of therapeutic proteins are in general the result of an exaggerated pharmacodynamic effect. So the loss of side effects may also be the result of the induction of antibodies and may be the ﬁrst sign of immunogenicity. For example, in patients treated with interferon the loss of ﬂulike symptoms is associated with the appearance of antibodies. Because by deﬁnition neutralizing antibodies interact with ligand– receptor interaction, they will inhibit the efﬁcacy of all products in the same class with serious consequences for patients if there is no alternative treatment. The most dramatic side effects occur if the neutralizing antibodies cross-react with an endogenous factor with an essential biological function. This had been described for antibodies induced by epoetin alpha [10] and megakaryocyte growth and differentiation factor (MGDF), which led, respectively, to life-threatening anemia and thrombocytopenia, sometimes lasting for more than a year. Skin Reactions Skin reactions are a common side effect of therapeutic proteins, and some of these reactions are associated with an immunogenic response. But can these skin reactions be used as a marker for the immunogenicity of therapeutic

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331

proteins? The hypersensitivity reactions are classiﬁed as type I, II, III, and IV reactions. The type I reaction is IgE mediated. The type II reactions are caused by activated T-killer cells and macrophages and complement activation. The type III hypersensitivity reaction is caused by the disposition of immune complexes [11]. Type IV is T-cell mediated. Type I hypersensitivity or IgE-mediated allergies are very rare, and most are related to the excipients present in formulation rather than the protein drug products. IgE-mediated reactions against human insulin have been reported, although is less common than with pork or beef insulin [12]. Theoretically, the type II hypersensitivity skin reaction may be a symptom of the immunogenicity of therapeutic proteins. During a type II hypersensitivity reaction, antibodies activate T-killer cells, macrophages, or complement factors to induce an immune response. However, the antibodies produced by most therapeutic proteins as a consequence of breaking B-cell tolerance and T-cells play only a minor role, if any. TNF inhibitors such as etanercept cause injection-site reactions by a T-cellmediated delayed-type hypersensitivity reaction. Antibodies against etanercept have been shown not to be correlated to adverse events. Skin reactions probably are a class effect of TNF inhibitors. Blockade of TNF can stimulate certain forms of autoimmunity by increasing T-cell reactivity to microbial and self-antigens [13]. The skin reactions that are part of the type III hypersensitivity reaction caused by the local disposition of immune complexes are seen after treatment with monoclonal antibodies, which are used in relative high and repeated doses. These immune complexes may lead to anaphylactoid reactions and serum sickness–like symptoms. Skin reactions such as urticaria and rashes are common symptoms of this complication [14]. Monoclonal antibodies may also lead to local reactions at the injection site. However, as shown in Table 2, there is no relation between immunogenicity and local skin reactions. So these local reactions cannot be used as an early marker for more serious symptoms of immunogenicity. Some skin reactions seen after treatment with therapeutic proteins are the result of their pharmacodynamics: for example, the epidermal growth factor

TABLE 2 Relation Between Local Skin Reactions and Immunogenicity of Monoclonal Antibodiesa

Humira Remicade Xolair Mabthera a

Based on SPCs.

INN

Incidence of Antibodies (%)

Incidence of Local Skin Reactions (%)

adalimumab inﬂiximab omalizumab rituximab

12 24 0 1

20 0 45 >1

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receptor (EGFR) inhibitors and beta interferon. EGFR plays a key role in normal skin function; thus, it is very likely that the rash and other skin reactions during therapy with EGFR inhibitors are caused by the inhibition of EGFR [14,15]. Interferon beta is known to induce high levels of neutralizing antibodies, but it seems that the adverse effects and skin reactions that occur during the ﬁrst months of treatment have already disappeared when patients develop antibodies. Most probably, these adverse effects are a direct pharmacodynamic effect of interferon. Indeed, patients with neutralizing antibodies have a smaller risk of adverse events such as injection site reactions than do patients without [16,17]. So both local skin reactions at the site of injection and generalized reactions can be seen after the use of therapeutic proteins. These skin reactions can have different causes and cannot be used as biomarkers for immunogenicity.

BIOMARKERS FOR THE IMMUNOGENICITY OF THERAPEUTIC PROTEINS Two classes of biomarkers are used to indicate the clinical consequences of immunogenicity of therapeutic proteins: 1. General: persisting levels of neutralizing antibodies 2. Speciﬁc: loss of activity of endogenous homolog; increase in speciﬁc disease marker; decrease in efﬁciency marker Biomarkers are also used to indicate the loss of efﬁciency of therapeutic proteins by antibodies: 1. Monoclonal antibodies: increase in side effects 2. Other therapeutic proteins: loss of side effects Structural properties are the main primary factors of the induction of antibodies [18]. Therapeutic proteins of nonhuman origin will induce antibodies in the majority of patients after a limited number of applications. The degree of nonself and the presence of T-cell epitopes and the relative lack of immune tolerance are predictors of the antibody response. Human homologs are less likely to be immunogenic. The best structural predictor of breaking tolerance is the presence of aggregates. The only animal model available for this type of immunogenicity is immune-tolerant transgenic mice. Induction of antibodies to human proteins usually occurs only after prolonged exposure. Independent of whether the protein is self or nonself, the possible immune modulating effect of the therapeutic protein, concomitant immune suppressive therapy, and the immune status of the patients are important predictors of an antibody response.

REFERENCES

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Antibody formation is by deﬁnition the marker for the immunogenicity of therapeutic proteins. The role of the cellular immunity is largely unknown and may be absent in the case of breaking B-cell tolerance. The occurrence of clinical consequences is in the majority of cases associated with relative high and persisting levels of neutralizing antibodies. In some cases the occurrence of neutralizing antibodies is preceded by binding antibodies, which may interfere with the pharmacokinetics of the proteins. As discussed extensively, skin reactions cannot be seen as signs of the immunogenicity of proteins. Often, the loss of efﬁcacy by neutralizing antibodies is difﬁcult to assess because the diseases involved are chronic diseases with an unpredictable development and the proteins have only a limited effect. In these cases surrogate markers for efﬁcacy may be monitored. Also, the effect on the side effects may be used as a marker. If the side effects are caused by the pharmacodynamic effect of the protein drugs, the loss of side effects is indicative of the development of neutralizing antibodies. If the side effect is the result of immune complexes, their appearance is associated with the induction of antibodies. REFERENCES 1. Schellekens H (2002). Bioequivalence and the immunogenicity of biopharmaceuticals. Nat Rev Drug Discov, 1(6):457–462. 2. Chackerian B, Lenz P, Lowy DR, Schiller JT (2002). Determinants of autoantibody induction by conjugated papillomavirus virus-like particles. J Immunol, 169:6120–6126. 3. Hermeling S, Schellekens H, Maas C, Gebbink MF, Crommelin DJ, Jiskoot W (2006). Antibody response to aggregated human interferon alpha2b in wild-type and transgenic immune tolerant mice depends on type and level of aggregation. J Pharm Sci, 95(5):1084–1096. 4. Kessler M, Goldsmith D, Schellekens H (2006). Immunogenicity of biopharmaceuticals. Nephrol Dial Transplant, 21(Suppl 5):v9–v12. 5. Hochuli E (1997). Interferon immunogenicity: technical evaluation of interferonalpha 2a. J Interferon Cytokine Res, 17:S15–S21. 6. Wierda D, Smith HW, Zwickl CM (2001). Immunogenicity of biopharmaceuticals in laboratory animals. Toxicology, 158(1–2):71–74. 7. Zwickl CM, Cocke KS, Tamura RN, et al. (1991). Comparison of the immunogenicity of recombinant and pituitary human growth hormone in rhesus monkeys. Fundam Appl Toxicol, 16(2):275–287. 8. Palleroni AV, et al. (1997). Interferon Immunogenicity: Preclinical Evaluation of Interferon-a2a. J Interferon Cytokine Res, 19(Suppl 1):s23–s27. 9. Stewart TA, Hollingshead PG, Pitts SL, et al. (1989). Transgenic mice as a model to test the immunogenicity of proteins altered by site-speciﬁc mutagenesis. Mol Biol Med, 6(4):275–281. 10. Casadevall N, Nataf J, Viron B, et al. (2002). Pure red-cell aplasia and antierythropoietin antibodies in patients treated with recombinant erythropoietin. N Engl J Med, 346:469–475.

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11. Janeway CA (2005). Immunobiology: The Immune System in Health and Disease, 6th ed. Garland Science, New York, pp. 517–555. 12. Frost N (2005). Antibody-mediated side effects of recombinant proteins. Toxicology, 209(2):155–160. 13. Thielen AM, Kuenzli S, Saurat JH (2005). Cutaneous adverse events of biological therapy for psoriasis: review of the literature. Dermatology, 211(3):209–217. 14. Lacatoure ME (2006). Mechanisms of cutaneous toxicities to EGFR inhibitors. Nat Rev Cancer, 6(10):803–812. 15. Robert C, Soria JC, Spatz A, et al. (2005). Cutaneous side-effects of kinase inhibitors and blocking antibodies. Lancet Oncol, 6(7):491–500. 16. Francis GS, Rice GP, Alsop JC (2005). Interferon beta-1a in MS: results following development of neutralizing antibodies in PRISMS. Neurology, 65(1):48–55. 17. Panitch H, Goodin D, Francis G (2005). Beneﬁts of high-dose, high-frequency interferon beta-1a in relapsing–remitting multiple sclerosis are sustained to 16 months: ﬁnal comparative results of the EVIDENCE trial. J Neurol Sci, 239(1):67–74. 18. Hermeling S, Crommelin DJ, Schellekens H, Jiskoot W (2004). Structure– immunogenicity relationships of therapeutic proteins. Pharm Res, 21(6):897–903.

17 NEW MARKERS OF KIDNEY INJURY Sven A. Beushausen, Ph.D. Pﬁzer Global Research and Development, Chesterﬁeld, Missouri

INTRODUCTION The current biomarker standards for assessing acute kidney injury (AKI), caused by disease or as a consequence of drug-induced toxicity, include blood urea nitrogen (BUN) and serum creatinine (SC). Retention of either marker in the blood is indicative of impedance in the glomerular ﬁltration rate (GFR), which if left untreated could escalate to serious kidney injury through loss of function and ultimately, death. Although the colorimetric assays developed for SC and BUN are relatively quick, seconds as compared to hours for antibody-based analysis platforms like enzyme-linked immunosorbent assay (ELISA), they are poor predictors of kidney injury because they both suffer from a lack of sensitivity and speciﬁcity. For example, SC concentration is greatly inﬂuenced by other nonrenal factors, including gender, age, muscle mass, race, drugs, and protein intake [1]. Consequently, increases in BUN and SC levels report out injury only after serious kidney damage has occurred. These shortcomings rather limit their clinical utility to patients who are at risk of developing drug-induced AKI or where instances of AKI have already been established, require frequent monitoring, and time to treatment is critical. Renal failure or AKI is often a direct consequence of disease, can result from complications associated with disease or postsurgical trauma like sepsis,

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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336

Heparin, warfarin, streptokinase

Aminoglycosides, radiocontrast media, cisplatin, nedaplatin, methoxyﬂurane, outdated tetracycline, amphotericin B, cephaloridine, streptozocin, tacrolimus, carbamazepine, mithramycin, quinolones, foscarnet, pentamidine, intravenous gammaglobulin, fosfamide, zoledronate, cidofovir,adefovir,tenofovir, mannitol, dextran, hydroxyethylstarch

Tubular toxicity

Discontinue medication, supportive care, plasmapheresis if indicated Drug discontinuation, supportive care

Fever, microangiopathic, hemolytic anemia, thrombocytopenia FENa > 2%, UOsm < 350 urinary sediment with granular casts, tubular epithelial cells

Discontinue medication, supportive care, plasmapheresis if indicated

Fever, microangiopathic, hemolytic anemia, thrombocytopenia

Ciclosporin, tacrolimus, mitomycin C, conjugated estrogens, quinine, 5-ﬂuorouracil, ticlopidine, clopidogrel, interferon, valaciclovir, gemcitabine, bleomycin

Intrinsic renal injury Vascular effects Thrombotic microangiopathy

Cholesterol emboli

Suspend or discontinue medication, volume replacement as clinically indicated

Benign urine sediment, FENa < 1%, UOsm > 500

Diuretics, NSAIDs, ACE inhibitors, ciclosporin, tacrolimus, radiocontrast media, interleukin-2, vasodilators (hydralazine, calcium-channel blockers, minoxidil, diazoxide)

Treatment

Prerenal injury

Clinical Findings

Medication

Common Medications Associated with Acute Renal Injurya

Pathoetiology

TABLE 1

337

Quinine, quinidine, sulfonamides, hydralazine, triamterene, nitrofurantoin, mephenytoin

Penicillin, methicillin ampicillin, rifampin, sulfonamides, thiazides, cimetidine, phenytoin, allopurinol, cephalosporins, cytosine arabinoside, furosemide, interferon, NSAIDs, ciproﬂoxacin, clarithromycin, telithromycin, rofecoxib, pantoprazole, omeprazole, atazanavir

Gold, penicillamine, captopril, NSAIDs, lithium, mefenamate, fenoprofen, mercury, interferon-α, pamidronate, fenclofenac, tolmetin, foscarnet

Aciclovir, methotrexate, sulfanilamide, triamterene, indinavir, foscarnet, ganciclovir

Methysergide, ergotamine, dihydroergotamine, methyldopa, pindolol, hydralazine, atenolol

Severe hemolysis

Immune-mediated interstitial inﬂammation

Glomerulopathy

Obstruction Intratubular: (crystalluria and/or renal lithiasis)

Ureteral (secondary to retroperitoneal ﬁbrosis)

Discontinue medication, supportive care

Edema, moderate to severe proteinuria, red blood cells, red blood cell casts possible

Benign urine sediment, hydronephrosis on ultrasound

Discontinue medication, decompress ureteral obstruction by intrarenal stenting or percutaneous nephrostomy

Discontinue medication, supportive care

Discontinue medication, supportive care

Fever, rash, eosinophilia, urine sediment showing pyuria, white cell casts, eosinophiluria

Sediment can be benign with severe obstruction, ATN might be observed

Drug discontinuation, supportive care

Drug discontinuation, supportive care

Treatment

High LDH, decreased hemoglobin

Elevated CPK, ATN urine sediment

Clinical Findings

Source: Adapted from ref. 2. a ACE, angiotensin-converting enzyme; ATN, acute tubular necrosis; CPK, creatinine phosphokinase; FENa, fractional excretion of sodium; LDH, lactate dehydrogenase; NSAIDs, nonsteroidal anti-inﬂammatory drugs; UOsm, urine osmolality.

Lovastatin, ethanol, codeine, barbiturates, diazepam

Medication

Rhabdomyolysis

Pathoetiology

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or is produced by drug-induced nephrotoxicity. Drug-induced renal injury is of great concern to physicians. Knowledge of toxicities associated with U.S. Food and Drug Administration (FDA)-approved compounds helps to guide product selection in an effort to manage risk and maximize patient safety. Drug-induced nephrotoxicity is of even greater concern to the pharmaceutical industry, where patient safety is the principal driver in the need to discover safer and more efﬁcacious drugs. Because BUN and SC are such insensitive predictors of early kidney injury, many instances of subtle renal damage caused by drugs may go unrecognized. Consequently, true estimates for druginduced nephrotoxicity are likely to be far lower than previously realized. For example, studies have indicated that the incidence of acute tubular necrosis or acute interstitial nephritis due to medication has been estimated to be as high as 18.6% [2]. In addition, renal injury attributed to treatment with aminoglycosides has been reported to approach 36% [3,4]. Not surprisingly, many common drugs have been associated with renal injury that cause site-speciﬁc damage (Table 1). Fortunately, most instances of drug-induced nephrotoxicity are reversible if discovered early and medication is discontinued. Collectively, the combined shortcomings of BUN and SC as predictors of nephrotoxicity and the propensity for many classes of medicines to cause drug-induced nephrotoxicity underscore the urgent need for the development and qualiﬁcation of more sensitive and speciﬁc biomarkers. The beneﬁts such tools will provide include predictive value and earlier diagnosis of drug-induced kidney injury before changes in renal function or clinical manifestations of AKI are evident. More important, biomarkers of nephrotoxicity with increased sensitivity and speciﬁcity will be invaluable to drug development both preclinically and clinically. Preclinically, new biomarkers will aid in the development of safer drugs having fewer liabilities with an ultimate goal to considerably lower or even possibly eliminate drug-induced nephrotoxicity. Clinically, the biomarkers will be used to monitor potential nephrotoxic effects due to therapeutic intervention or the potential for new drugs to cause renal toxicity in phase I to III clinical trials.

NEW PRECLINICAL BIOMARKERS OF NEPHROTOXICIY In recent years, two consortia led by the nonproﬁt organizations ILSI-HESI (International Life Sciences Institute, Health and Environmental Sciences Institute, http://www.hesiglobal.org) and C-Path (Critical Path Institute, http:// www.c-path.org) aligned with leaders in academia, industry, and the FDA with a mission to evaluate the potential utility of newly identiﬁed biomarkers of nephrotoxicity for use in preclinical safety studies and to develop a process for the acceptance of the new biomarkers in support of safety data accompanying new regulatory submissions. Several criteria for the evaluation and development of new biomarkers of nephrotoxicity were considered, including:

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339

TABLE 2 Biomarkers of Renal Injury by Region of Speciﬁcity, Onset, Platform, and Application Biomarker

Injury Related to:

Onset

Platforms

Application

Proximal tubular injury Tubular epithelial cells Tubular dysfunction

Early

Mouse, rat, human, chicken, turkey Mouse, rat, dog, monkey, human Mouse, rat, human

Proximal tubular injury Distal tubule

Early

KIM-1

General kidney injury and disease

Early

Luminex, ELISA Luminex, ELISA Luminex, ELISA Luminex, ELISA Luminex, ELISA Luminex, ELISA

Microalbumin

Proximal tubular injury Tubulointerstitial ﬁbrosis Proximal tubular injury Renal papilla and collecting ducts

Early

β2-Microglobulin Clusterin Cystatin-C GSTα GST Yb1

Osteopontin NGAL RPA-1

Early Late

Early

Late Early Early

Luminex, ELISA Luminex, ELISA Luminex, ELISA ELISA

Mouse, rat, human

Zebraﬁsh, mouse, rat, dog, monkey, human Mouse, rat, dog, monkey, human Mouse, rat, monkey, human Mouse, rat, human Rat

Source: Adapted from ref. 5.

• A preference for noninvasive sample collection. • New biomarkers developed for preclinical use should optimally be translated to the clinic. • Assays for new biomarkers should be robust and kits readily available for testing. • Assays should be multiplexed to minimize cost and expedite sample analysis. • Biomarkers should ideally predict or report out site-speciﬁc injury. • Biomarkers must be more sensitive and speciﬁc of kidney injury than existing standards. • Biomarkers should be predictive (prodromal) of kidney injury in the absence of histopathology. The preference for noninvasive sample collection made urine the obvious choice of bioﬂuid. Urine has proven to be a fertile substrate for the discovery of promising new biomarkers for the early detection of nephrotoxicity [5]. A number of these markers have been selected for further development and

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qualiﬁcation by the ILSI-HESI and C-Path Nephrotoxicity Working Groups in both preclinical and clinical settings, with the exception of RPA-1 and GST Yb1 (Biotrin), which are markers developed speciﬁcally for the analysis of kidney effects in rats (Table 2). The utility and limitations of each marker used in the context of early and site-speciﬁc detection are discussed below. β2-Microglobulin Human β2-microglobulin (β2M) was isolated and characterized in 1968 [6]. β2M was identiﬁed as a small 11,815-Da protein found on the surface of human cells expressing the major histocompatibility class I molecule [7]. β2M is shed into the circulation as a monomer, from which it is normally ﬁltered by the glomerulus and subsequently reabsorbed and metabolized within proximal tubular cells [8]. Twenty-ﬁve years ago, serum β2M was advocated for use as an index of renal function because of an observed proportional increase in serum β2M levels in response to decreased renal function [9]. It has since been abandoned due to a number of factors complicating the interpretation of the ﬁndings. More recently, increased levels of intact urinary β2M have been directly linked to impairment of tubular uptake. Additional work in rats and humans has demonstrated that increased urinary levels of β2M can be used as a marker for proximal tubular function when β2M production and glomerular ﬁltration are normal in a setting of minimal proteinuria [10–13]. Urinary β2M has been shown to be superior to N-acetyl-β-glucosaminidase as a marker in predicting prognosis in idiopathic membranous neuropathy [14]. In this context β2M can be used to monitor and avoid unnecessary immunosuppressive therapy following renal transplantation. β2M is being considered for evaluation as an early predictor of proximal tubular injury in preclinical models of drug-induced nephrotoxicity. Although easily detected in urine, there are several factors that may limit its value as a biomarker. For example, β2M is readily degraded by proteolytic enzymes at room temperature and also degrades rapidly in an acidic environment at or below pH < 6.0 [15]. Therefore, great care must be taken to collect urine in an ice-cold, adequately buffered environment with the addition of stabilizers to preserve β2M levels during the period of collection and in storage. It is unlikely that β2M will be used as a stand-alone marker to predict or report proximal tubule injury preclinically or in the clinic. Rather, it is likely to be used in conjunction with other proximal tubule markers to support such a ﬁnding. A brief survey for commercially available antibodies used to detect β2M indicates that most are species speciﬁc (http://www.abcam.com). Instances of cross-reactivity were noted for speciﬁc reagents between human and pig, chicken and turkey, and human and other primates. A single monoclonal reagent is reported to have cross-reactivity with bovine, chicken, rabbit, and mouse, and none were listed that speciﬁcally recognized dog β2M. Because both commonly used preclinical species rat and dog β2M proteins share only 69.7% and 66.7% amino acid identity with the human protein (http://www.expasy.org), it would be prudent to develop and

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characterize antibody reagents speciﬁc to each species and cross-reacting antisera to speciﬁc amino acid sequences shared by all three proteins. Clusterin Clusterin is a highly glycosylated and sulfated secreted glycoprotein ﬁrst isolated from ram rete testes ﬂuid in 1983 [16]. It was named clusterin because of its ability to elicit clustering of Sertoli cells in vitro [17]. Clusterin is found primarily in the epithelial cells of most organs. Tissues with the highest levels of clusterin include testis, epididymus, liver, stomach, and brain. Metabolic and cell-speciﬁc functions assigned to clusterin include sperm maturation, cell transformation, complement regulation, lipid transport, secretion, apoptosis, and metastasis [18]. Clusterin is also known by a number of synonyms as a consequence of having been identiﬁed simultaneously in many parallel lines of inquiry. Names include glycoprotein III (GPIII), sulfated glycoprotein-2 (SG-2), apolipoprotein J (apo J), testosterone-repressed message-2 (TRPM2), complement associated protein SP-40, 40, and complement cytolysis inhibitor protein (see Table 1). Clusterin has been cloned from a number of species, including the rat [19]. The human homolog is 449 amino acids in length, coding for a protein with a molecular weight of 52,495 Da [20]. However, due to extensive posttranslational modiﬁcation, the protein migrates to an apparent molecular weight of 70 to 80 kDa following sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Amino acid identity between species is moderate. Human clusterin shares 70.3%, 76.6%, 71.7%, and 77% with the bovine, mouse, pig, and rat homologs, respectively (http://www.expasy.org). Clusterin is a heterodimer comprised of an α and a β subunit, each having an apparent mass of 40 kDa by SDS-PAGE. The subunits result from the proteolytic cleavage of the translated polypeptide at amino acid positions 23 and 277. This eliminates the leader sequence and produces the mature 205amino acid β subunit and the remaining 221-amino acid α subunit. The α and β subunits are held together by ﬁve sulfhydryl bonds afforded by cysteine residues clustered within each of the subunits [21]. In addition, each subunit has three N-linked carbohydrates that are also heavily sulfated, giving rise to the higher apparent molecular weight observed following SDS-PAGE. Considerable evidence has been provided suggesting that clusterin plays an important role in development. For example, clusterin mRNA expression has been observed at 12.5 days’ postgestation in mice, where it is present in all germ cell layers [22]. Furthermore, stage-speciﬁc variations of the transcript have been observed, as have changes in speciﬁc localization during development. Similarly, changes in the developmental expression of clusterin in kidney, lung, and nervous system have also been reported [23]. These observations suggest that clusterin might play a role in tissue remodeling. In the developing murine kidney, clusterin is expressed in the tubular epithelium and later in development is diminished as tubular maturation progresses [24]. Interestingly, clusterin is observed in newly formed tubules

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NEW MARKERS OF KIDNEY INJURY

but appears to be absent in glomeruli. Of interest to many investigators of renal function is the reemergence of clusterin observed following induction of a variety of kidney diseases and drug-induced renal injury. Clusterin induction has been observed following ureteral obstruction [25] and ischemia reperfusion injury [26]. Elevations in the levels of clusterin have also been observed in the peri-infarct region following subtotal nephrectomy [27] and in animal models of hereditary polycystic kidney disease [28]. Marked increases of clusterin released in urine have also been recorded in animal models of aminoglycoside-induced nephrotoxicity [29–31]. Authors have opined that clusterin functions in either a protective role by scavenging cell debris or may play a role in the process of tissue remodeling following cellular injury based on these observations. Collectively, the body of work linking elevated levels of urinary clusterin to kidney damage has suggested that measurement of urinary clusterin may be useful as a marker of renal tubular injury. Indeed, an early study comparing urinary levels of clusterin against N-acetyl-β-glucosaminidase (NAG) following chronic administration of gentamicin over a two-month period demonstrated that while the urinary levels of both proteins rose rapidly, peaked, and then declined, clusterin levels remained signiﬁcantly higher than control values over the duration of the experiment. By contrast, NAG levels dropped to within control values within 10 days of treatment even though evidence of tubulointerstitial disease persisted [30]. More recent work examining the levels of urinary clusterin in the autosomal-dominant polycystic kidney disease (cy/+) rat model compared to the FHH rat model of focal segmental glomerulosclerosis following bilateral renal ischemia demonstrated that clusterin levels correlated with the severity of tubular damage and suggested use as a marker for differentiating between tubular and glomerular damage [32]. Although the value of clusterin as an early marker of tubular epithelial injury has not yet been established clinically, preclinical ﬁndings suggest that it is an ideal candidate for translation to the clinic as an early marker of nephrotoxicity. Cystatin-C Cystatin C (Cys-C) is a 13-kDa nonglycosylated protein belonging to the superfamily of cysteine protease inhibitors [33]. Cys-C is produced by all nucleated cells and, unlike SC, is unaffected by muscle mass. Serum Cys-C was suggested to be closer to the “ideal” biomarker reporting GFR because although freely ﬁltered by the glomerulus, it is not secreted. Instead, Cys-C is adsorbed by tubular epithelial cells, where it is catabolized and is not returned to the bloodstream, thus obviating the need to calculate urinary Cys-C to measure GFR [34]. Several studies have been designed to examine the usefulness of serum Cys-C as a measure or biomarker of GFR [35]. In one such study, serum Cys-C was shown to be a useful biomarker of acute renal failure and could be detected one to two days prior to the elevation in levels of SC, the accepted clinical diagnosis of AKI [36]. Although earlier in detection than

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SC, serum Cys-C levels were not predictive of kidney disease and, like SC, reported out kidney injury long after serious damage had occurred. In another study, investigators monitored and compared the levels of serum Cys-C and urinary Cys-C in patients following cardiothoracic surgery with and without complicating AKI [37]. The results clearly demonstrated that while plasma Cys-C was not a useful predictor of AKI, early and persistent increases in urinary Cys-C correlated with the development and severity of AKI. Another interesting but unexplained observation in this study was that women had signiﬁcantly higher postoperative levels of urinary Cys-C than did men even though preoperative Cys-C levels were similar. These data have prompted groups like ILSI-HESI and C-Path to examine the utility of urinary Cys-C as a preclinical biomarker of drug-induced renal injury in the hope that elevated levels of Cys-C can be detected in urine prior to the emergence of overt tubular dysfunction. Glutathione S-Transferases The glutathione S-transferases (GSTs) form a family of homo-and heterodimeric detoxifying enzymes [38] identiﬁed originally as a group of soluble liver proteins that play a major role in the detoxiﬁcation of electrophilic compounds [39]. They have since been shown to be products of gene superfamilies [40] and are classiﬁed into alpha, mu, pi, and theta subfamilies based on sequence identity and other common properties [41]. Tissue distribution and levels of GST isoform expression has been determined by immunohistochemical localization [42], isoform-speciﬁc peptide antibody Western blotting, and mass spectrometry [40]. Analysis of GST subunit diversity and tissue distribution using peptide-speciﬁc antisera has shown GST μ isoforms to be the most widely distributed class of GSTs, with expression evident in brain, pituitary, heart, lung, adrenal gland, kidney, testis, liver, and pancreas, with the highest levels of GST μ1 observed in adrenals, testis, and liver. Isoforms of the GSTα subfamily, also known by the synonyms glutathione S-transferase-1, glutathione S-transferase Ya-1, GST Ya1, ligandin, GST 1a-1a, GST B, GST 1-1, and GST A1-1 (http://www.expasy.org/uniprot/P00502), are more limited in distribution, with highest levels of expression observed in hepatocytes and proximal tubular cells of the kidney [42]. Indeed, proximal tubular GSTα levels have been reported to approximate 2% of total cytosolic protein following exposure to xenobiotics or renal toxins [43]. In the Rowe study [40], GSTα was found to be rather evenly distributed between adrenals, kidney, and pancreas, with highest levels observed in liver, whereas isoforms of the GSTπ subclass were expressed in brain, pituitary, heart, liver, kidney, and adrenals, with highest levels of expression observed in kidney. The high levels of expression and differential distribution of GST isoforms made them attractive candidates as biomarkers that could be used to indicate site-speciﬁc drug-induced nephrotoxicity. For example, development of a radioimmunoassay to quantify leakage of ligandin (GSTα)

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into the urine as a measure of nephrotoxicity in the preclinical rat model was reported as early as 1979 [44]. Subsequent work described the development of a radioimmunoassay for the quantitation of GSTπ in the urine [45a] later used as an indicator of distal tubular damage in the human kidney [45b]. Additional work described the development of a multiplexed ELISA for the simultaneous quantitation of GSTα and GSTπ to discriminate between proximal and distal tubular injury, respectively [46]. In terms of sensitivity, a study examining the nephrotoxic effects of the sevoﬂurane degradation product, ﬂuoromethyl-2,2-diﬂuoro-1-(triﬂuoromethyl) vinyl ether, in rats showed urinary GSTα to be the most sensitive marker of mild proximal tubular damage compared to other urinary markers measured, including protein and glucose [47]. A second study in which four human volunteers were given sevoﬂurane demonstrated abnormalities in urinary glucose, albumin, GSTα, and GSTπ, while levels of BUN or SC were unaffected, suggesting that the GSTs were more sensitive markers of site-speciﬁc dug-induced nephrotoxicity [48]. Immunohistochemical staining of the rat kidney with antibodies to different GST isoforms has shown that GSTα subunits are expressed selectively in the proximal tubule, whereas GSTμ and π subnits are localized to the thin loop of Henle and proximal tubules, respectively [38]. An examination of the distribution of the rat GSTμ equivalent, GSTYb1, in the kidney indicates that it is localized to the distal tubules. Simultaneous measurement of urinary GSTα and GSTYb1 has been used to discriminate between drug-induced proximal and distal tubular injury (cited by Kilty et al. [49]). The high levels of GSTs in the kidney and site-speciﬁc localization of different GST classes in addition to increased sensitivity in detecting drug-induced nephrotoxicity in humans make them ideal candidates for the development and testing of preclinical markers that predict or report early signs of nephrotoxicity to support preclinical safety studies and subsequent compound development. Kidney Injury Molecule 1 Rat kidney injury molecule 1 (KIM-1) was discovered as part of an effort to identify genes implicated in kidney injury and repair [50] using the polymerase chain reaction (PCR) subtractive hybridization technique of representational difference analysis originally developed to look at differences in genomic DNA [51] but adapted to examine differences in mRNA expression [52]. Complementary DNA generated from poly(A+) mRNA puriﬁed from normal and 48-hour postischemic rat kidneys was ampliﬁed to generate driver and tester amplicons, respectively. The amplicons were used as templates to drive the subtractive hybridization process to generate designated differential products, three of which were ultimately gel puriﬁed and subcloned into the pUC18 cloning vector. Two of these constructs were used to screen λZapII cDNA libraries constructed from 48-hour postischemic rat kidneys. Isolation and puriﬁcation of positively hybridizing plaques resulted in the recovery of a

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2.5-kb clone that contained sequence information on all three designated differential products. A BLAST search of the NCBI database revealed that the rat KIM-1 sequence had limited (59.8%) amino acid homology to HAVcr-1, identiﬁed earlier as the monkey gene coding for the hepatitis A virus receptor protein [53]. The human homolog of KIM-1 was isolated by low-stringency screening of a human embryonic liver λgt10 cDNA library using the same probe that yielded the rat clones [50]. The plaque of one of two clones puriﬁed from this exercise was shown to code for a 334-amino acid protein sharing 43.8% identity and 59.1% similarity to the rat KIM-1 protein. Comparison to the human HAVcr protein [54] revealed 85.3% identity demonstrating a clear relationship between the two proteins. Subsequent work has demonstrated that KIM-1 and HAVcr are synonyms for the same protein, also known as T-cell immunoglobulin and mucin domain–containing protein 1 (TIMD-1) and TIM-1. The TIMD proteins are all predicted to be type I membrane proteins that share a characteristic immunoglobulin V, mucin, transmembrane, and cytoplasmic domain structure [55]. It is not clear what the function of KIM-1 (TIMD-1) is, but it is believed that TIMD-1 is involved in the preferential stimulation of Th2 cells within the immune system [56]. In the rat, KIM-1 mRNA expression is highest in liver and barely detected in kidney [50]. KIM-1 mRNA and protein expression are dramatically up-regulated following ischemic injury. Immunohistochemical examination of kidney sections using a rat-speciﬁc KIM-1 antibody showed that KIM-1 is localized to regenerating proximal tubule epithelial cells. KIM-1 was proposed as a novel biomarker for human renal proximal tubule injury in a study that demonstrated that KIM-1 could be detected in the urine of patients with biopsy-proven acute tubular necrosis [57]. Human KIM-1 occurs as two splice variants that are identical with respect to the extracellular domains but differ at the carboxy termini and are differentially distributed throughout tissues [58]. Splice-variant KIM-1b is 25 amino acids longer than the originally identiﬁed KIM-1a and is found predominantly in human kidney. Interestingly, cell lines expressing endogenous KIM-1 or recombinant KIM-1b constitutively shed KIM-1 into the culture medium, and shedding of KIM-1 could be inhibited with metalloprotease inhibitors, suggesting a mechanism for KIM-1 release into the urine following the regeneration of proximal tubule epithelial cells as a consequence of renal injury. Evidence supporting KIM-1’s potential as a biomarker for general kidney injury and repair was clearly demonstrated in another paper describing the early detection of urinary KIM-1 protein in a rat model of drug-induced renal injury. In this study increases in KIM-1 were observed before signiﬁcant increases in SC levels could be detected following injury with folic acid and prior to measurable levels of SC in the case of cisplatin-treated rats [59]. In later, more comprehensive studies examining the sensitivity and speciﬁcity of KIM-1 as an early biomarker of mechanically- [60] or drug-induced renal injury [61], KIM-1 was detected earlier than any of the routinely used biomarkers of renal injury,

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including BUN, SC, urinary NAG, glycosuria, and proteinuria. Certainly, the weight of evidence described above supports the notion that KIM-1 is an excellent biomarker of AKI and drug-induced renal injury. The increasing availability of antibody-based reagents and platforms to rat and human KIM-1 proteins offer convenient and much needed tools for preclinical safety assessment of drug-induced renal toxicity and for aid in diagnosing or monitoring mild to severe renal injury in the clinic. Further work is required to determine if KIM-1 is a useful marker for long-term injury and whether it can be used in combination with other makers to determine site-speciﬁc kidney injury. Microalbumin The examination of proteins excreted into urine provides useful information about renal function (reviewed in [62]). Tamm–Horsfall proteins that originate from renal tubular cells comprise the largest fraction of protein excreted in normal urine. The appearance of low-molecular-weight urinary proteins normally ﬁltered through the basement membrane of the glomerulus, including insulin, parathormone, lysozyme, trypsinogen and β2-microglobulin indicate some form of tubular damage [63]. The detection of highermolecular-weight (40- to 150-kDa) urinary proteins not normally ﬁltered by the glomerulus, including albumin, transferrin, IgG, caeruloplasmin, α1-acid glycoprotein, and HDL, indicate compromised glomerular function [64]. Albumin is by far the most abundant protein constituent of proteinuria. Although gross increases in urinary albumin measured by the traditional dipstick method with a reference interval of 150 to 300 mg/mL have been used to indicate impairment of renal function, there are many instances of subclinical increases of urinary albumin within the deﬁned reference interval that are predictive of disease [65–67]. The term microalbuminuria was coined to deﬁne this phenomenon, where such increases had value in predicting the onset of nephropathy in insulindependant diabetes mellitus [68]. The accepted reference interval deﬁned for microalbuminuria is between 30 to 300 mg in 24 hours [69,70]. Because microalbuminuria appears to be a sensitive indicator of renal injury, there is a growing interest in the nephrotoxicity biomarker community to evaluate this marker in the context of an early biomarker predictive of drug-induced renal injury. Although microalbuminuria has traditionally been used in preclinical drug development to assess glomerular function there is growing evidence to suggest that albuminuria is a consequence of impairment of the proximal tubule retrieval pathway [71]. Evidence that microalbuminuria might provide value in diagnosing drug-induced nephrotoxicity was reported in four of 18 patients receiving cisplatin, ifosamide, and methotrextate to treat osteosarcoma [72]. Because microalbuminuria can be inﬂuenced by other factors unrelated to nephrotoxicity, including vigorous exercise, hematuria, urinary tract infection, and dehydration [5], it may have greater predictive value for renal

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injury in the context of a panel of markers with increased sensitivity and site speciﬁcity. Indeed, further evaluation of microglobulin as an early biomarker of site-speciﬁc or general nephrotoxicity is required before qualiﬁcation for preclinical and clinical use. Osteopontin Osteopontin (OPN) is a 44-kDa highly phosphorylated secreted glycoprotein originally isolated from bone [73]. It is an extremely acidic protein with an isoelectric point of 4.37 (http://www.expasy.org/uniprot/P10451), made even more acidic through phosphorylation on a cluster of up to 28 serine residues [74]. Osteopontin is widely distributed among different tissues, including kidney, lung, liver, bladder, pancreas, and breast [75] as well as macrophages [76], activated T-cells [77], smooth muscle cells [78], and endothelial cells [79]. Evidence has been provided demonstrating that OPN functions as a calcium oxalate crystal formation inhibitor in cultured murine kidney cortical cells [80]. Immunohistochemical and in situ hybridization examination of the expression and distribution of OPN protein and mRNA in the rat kidney clearly demonstrated that levels are highest in the descending thin loop of Henle and cells of the papillary surface epithelium [81]. Uroprontin, ﬁrst described as a relative of OPN was among the ﬁrst examples of OPN isolation from human urine [82]. Although normally expressed in kidney, OPN expression can be induced under a variety of experimental pathologic conditions [83,84], including tubulointerstitial nephritis [85], cyclosporine-induced neuropathy [86], hydronephrosis as a consequence of unilateral ureteral ligation [87], renal ischemia [88], nephropathy induced by cisplatin, and crescentric glomeulonephritis [89]. Up-regulation of OPN has been reported in a number of animal models of renal injury, including drug-induced nephrotoxicity by puromycin, cylcoaporine, strptozotocin, phenylephrine, and gentamicin (reviewed in [90a]). In the rat, gentamicin-induced acute tubular necrosis model OPN levels were highest in regenerating proximal and distal tubules, leading the authors to conclude that OPN is related to the proliferation and regeneration of tubular epithelial cells following tubular damage [90b]. Although osteopontin has been proposed as a selective biomarker of breast cancer [91] and a useful clinical biomarker for the diagnosis of colon cancer [92], OPN shows great promise and requires further evaluation as a clinical biomarker for renal injury. Certainly, the high levels of OPN expression following chemically or physically induced renal damage coupled with the recent availability of antibodybased reagents to examine the levels of mouse, rat, and human urinary OPN provide ample opportunity to evaluate OPN as an early marker of AKI in the clinic and a predictive marker of drug-induced nephrotoxicity preclinically. Further planned work by the ILSI-HESI and C-Path groups hope to broaden our understanding regarding the utility of OPN in either capacity as an early predictor of renal injury.

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Neutrophil Gelatinase–Associated Lipocalin Neutrophil gelatinase–associated lipocalin (NGAL) was ﬁrst identiﬁed as the small molecular-weight glycoprotein component of human gelatinase afﬁnity puriﬁed from the supernatant of phorbol myristate acetate stimulated human neutrophils. Human gelatinase puriﬁes as a 135-kDa complex comprised of the 92-kDa gelatinase protein and the smaller 25-kDa NGAL [93]. NGAL has subsequently been shown to exist primarily in monomeric or dimeric form free of gelatinase. A BLAST search of the 178-amino acid NGAL protein yielded a high degree of similarity to the rat α2 microglobulin-related protein and mouse protein 24p3, suggesting that NGAL is a member of the lipocalin family. Lipocalins are characterized by the ability to bind small lipophilic substances and are thought to function as modulators of inﬂammation [94]. More recent work has shown that NGAL, also known as siderocalin, complexes with iron and iron-binding protein to promote or accelerate recovery from proximal tubular damage (reviewed in [95]). RNA dot blot analysis of 50 human tissues revealed that NGAL expression is highest in trachea and bone tissue, moderately expressed in stomach and lung with low levels of transcript expression in the remaining tissues examined, including kidney [94]. Because NGAL is a reasonably stable small-molecular-weight protein, it is readily excreted from the kidney and can be detected in urine. NGAL was ﬁrst proposed as a novel urinary biomarker for the early prediction of acute renal injury in rat and mouse models of acute renal failure induced by bilateral ischemia [96]. Increases in the levels of urinary NGAL were detected in the ﬁrst hour of postischemic urine collection and shown to be related to dose and length of exposure to ischemia. In this study the authors reported NGAL to be more sensitive than either NAG or β2M, underscoring its usefulness as an early predictor of acute renal injury. Furthermore, the authors proposed NGAL to be an earlier marker predictive of acute renal injury than KIM-1, since the latter reports injury within 24 hours of renal injury compared to 1 hour for NGAL. Marked up-regulation of NGAL expression was observed in proximal tubule cells within 3 hours of ischemia-induced damage, suggesting that NGAL might be involved in postdamage reepithelialization. Additional work demonstrated that NGAL expression was induced following mild ischemia in cultured human proximal tubule cells. This paper also addressed the utility of NGAL as an early predictor of drug-induced renal injury by detecting increased levels of NGAL in the urine of cisplatin-treated mice. Adaptation of the NGAL assay to address utility and relevance in a clinical setting showed that both urinary and serum levels of NGAL were sensitive, speciﬁc, and highly predictive biomarkers of acute renal injury following cardiac surgery in children [97]. In this particular study, multivariate analysis showed urinary NGAL to be the most powerful predictor in children that developed acute renal injury. Measurable increases in urinary NGAL concentrations were recorded within 2 hours of cardiac bypass surgery, whereas

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increases in SC levels were not observed until 1 to 3 days postsurgery. Other examples demonstrating the value of NGAL as a predictive biomarker of early renal injury include association of NGAL with severity of renal disease in proteineuric patients [98] and NGAL as an early predictor of renal disease resulting from contrast-induced nephropathy [99]. NGAL has been one of the most thoroughly studied new biomarkers predictive of AKI as a consequence of disease or surgical intervention, and to a lesser extent, drug-induced renal injury. Sensitive and reliable antibody-based kits have been developed for a number of platforms in both humans and rodents (Table 2) and there is considerable interest in examining both the speciﬁcity and sensitivity of NGAL for acceptance as a ﬁt-for-purpose predictive biomarker of drug-induced renal injury to support regulatory submissions. Certainly, because NGAL is such an early marker of renal injury, it will have to be assessed as a stand-alone marker of renal injury as well as in the context of a larger panel of markers that may help deﬁne site speciﬁc and degree of kidney injury. Renal Papillary Antigen 1 Renal papillary antigen 1 (RPA-1) is an uncharacterized antigen that is highly expressed in the collecting ducts of the rat papilla and can be detected at high levels in rat urine following exposure to compounds that induce renal papillary necrosis [100]. RPA-1 was identiﬁed by an IgG1 monoclonal antibody, designated Pap X 5C10, that was generated in mice immunized with pooled fractions of a cleared lysate of homogenized rat papillary tissue following crude DEAE anion-exchange chromatography. Immunohistochemical analysis of rat papillae shows that RPA-I is localized to the epithelial cells lining the luminal side of the collecting ducts and to a lesser extent in cortical collecting ducts. A second publication described the adaptation of three rat papillaspeciﬁc monoclonal antibodies, including Pap X 5C10 (PapA1), to an ELISA assay to examine antigen excretion in rat urine following drug-induced injury to the papillae using 2-bromoethanamine, propyleneimine, indomethicin, or ipsapirone [101]. Of the three antibodies evaluated, PapA1 was the only antigen released into the urine of rats following exposure to each of the toxicants. The authors concluded that changes in the rat renal papilla caused by xenobiotics could be detected early by urinary analysis and monitored during follow-up studies. This study also clearly demonstrated that the Pap X 5C10, PapA1, RPA-1 antigen had the potential for use as a site-speciﬁc biomarker predictive of renal papillary necrosis. Indeed, the Pap X 5C10 monoclonal antibody was adapted for commercial use as an RPA-1 ELISA kit marketed speciﬁcally to predict or monitor site-speciﬁc renal injury in the rat [49]. The speciﬁcity and sensitivity of the rat reagent has generated a great deal of interest in developing an equivalent reagent for the detection of human papillary injury. Identiﬁcation of the RPA-1 antigen remains elusive. Early biochemical characterization of the antigen identiﬁed it as a large-

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molecular-weight protein (150 to 200 kDa) that could be separated into two molecular-weight species with isoelectric points of 7.2 and 7.3, respectively [100]. However, puriﬁcation and subsequent protein identiﬁcation of the antigen were extremely challenging. A recent attempt at the biochemical puriﬁcation and identiﬁcation of the RPA-1 antigen has been equally frustrating, with investigators providing some evidence that the antigen may be a large glycoprotein and suggesting that the carbohydrate moiety is the speciﬁc epitope recognized by the Pap X 5C10 monoclonal antibody [102]. This would be consistent with, and help to explain why, the rat reagent does not crossreact with a human antigen in the collecting ducts, as protein glycosylation of related proteins often differs dramatically between species, thereby precluding the likelihood of presenting identical epitopes. Nevertheless, continued efforts toward identifying a human RPA-1 antigen will provide investigators with a sorely needed clinical marker for the early detection of drug-induced renal papillary injury.

SUMMARY A considerable amount of effort has gone into identifying, characterizing, and developing new biomarkers of renal toxicity having greater sensitivity and speciﬁcity than the traditional markers, BUN and SC. The issue of sensitivity is a critical one, as the ideal biomarker would detect renal injury before damage is clinically evident or cannot be reversed. Such prodromal biomarkers would provide great predictive value to the pharmaceutical industry in preclinical drug development, where compounds could be modiﬁed or development terminated early in response to the nephrotoxicity observed. Even greater value could be realized in the clinic, where early signs of kidney injury resulting from surgical or therapeutic intervention could be addressed immediately before serious damage to the patient has occurred. Several of the candidate markers described above, including β2M, GSTα, microalbumin, KIM-1, and NGAL, have demonstrated great promise as early predictors of nephrotoxicity. Continued investigation should provide ample data from which to make a determination regarding the utility of these markers for preclinical and, ultimately, clinical use. The issue of biomarker speciﬁcity is also of great value because it provides information regarding where and to what extent injury is occurring. For example, increases in levels of SC and BUN inform us that serious kidney injury has occurred but does not reveal the precise nature of that injury, whereas the appearance of increased levels of β2M, GSTα, microalbumin, and NGAL indicate some degree of proximal tubule injury. Similarly, RPA-1 reports injury to the papilla, clusterin indicates damage to tubular epithelial cells, and GSTYb1 is speciﬁc to distal tubular damage. Low-level increases of early markers warn the investigator or clinician that subclinical damage to the kidney is occurring and provides the necessary time to alter or terminate a course of treatment or development.

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Monitoring toxicity is an important aspect of achieving a positive clinical outcome and increased safety in drug development. Incorporation of many or all of these markers into a panel tied to an appropriate platform allows for the simultaneous assessment of site-speciﬁc kidney injury with some understanding of the degree of damage. Several detection kits are commercially available for many of these new biomarkers of nephrotoxicity. For example, Biotrin International provides ELISA kits for the analysis of urinary GSTs and RPA-1, while Rules Based Medicine and Meso Scale Discovery offer panels of kidney biomarkers multiplexed onto antibody-based ﬂuorescence or chemiluminescent platforms, respectively. As interest in new biomarkers of kidney injury continues to develop, so will the technology that supports them. Presently, all of the commercial reagents and kits supporting kidney biomarker detection are antibody-based. The greatest single limitation of such platforms is how well the reagents perform with respect to target identiﬁcation, nonspeciﬁc protein interaction, and species cross reactivity. Although kits are standardized and come complete with internal controls, kit-to-kit and lab-to-lab variability can be high. Another technology being developed for the purpose of quantifying biomarkers in complex mixtures such as bioﬂuids is mass spectrometry–based multiple reaction monitoring. This technology requires the synthesis and qualiﬁcation of small peptides speciﬁc to a protein biomarker that can be included in a sample as an internal standard to which endogenous peptide can be compared and quantiﬁed. This platform is extremely sensitive (femtomolar detection sensitivity), requires very little sample volume, and offers the highest degree of speciﬁcity with very short analysis times. Limitations of the platform are related to the selection of an appropriate peptide and the expense of assay development and qualiﬁcation for use. For example, peptides need to be designed that are isoform speciﬁc, being able to discriminate between two similar but not identical proteins. Peptide design is also somewhat empirical with respect to ﬁnding peptides that will “ﬂy” in the instrument and produce a robust signal at the detector. The choice of peptides available in a particular target may be limiting given these design restrictions. Consequently, not all proteins may be amenable to this approach. In conclusion, continued improvement in technology platforms combined with the availability of reagents to detect new biomarkers of nephrotoxicity provides both the clinician and the investigator with a variety of tools to predict and monitor early or acute kidney injury. This will be of tremendous value toward saving lives in the clinic and developing safer, more efﬁcacious drugs without nephrotoxic side effects.

REFERENCES 1. Bjornsson TD (1979). Use of serum creatinine concentrations to determine renal function. Clin Pharmacokinet, 4:200–222.

352

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2. Choudhury D, Ziauddin A (2005). Drug-associated renal dysfunction and injury. Nat Clin Pract Nephrol, 2:80–91. 3. Kleinknecht D, Landais P, Goldfarb B (1987). Drug-associated renal failure: a prospective collaborative study of 81 biopsied patients. Adv Exp Med Biol, 212:125–128. 4. Kaloyandes GJ, et al. (2001). Antibiotic and Renal Immunosuppression-Related Renal Failure. Lippincott Williams & Wilkins, Philadephia. 5. Vaidya VS, Ferguson MA, Bonventre JV (2008). Biomarkers of acute kidney injury. Annu Rev Pharmacol Toxicol, 48:463–493. 6. Berggard I, Bearn AG (1968). Isolation and properties of a low molecular weight β2-globulin occurring in human biological ﬂuid. J Biol Chem, 213: 4095–4103. 7. Harris HW, Gill TJ III (1986). Expression of class I transplantation antigens. Transplantation, 42:109–117. 8. Bernier GM, Conrad ME (1969). Catabolism of β2-microglobulin by the rat kidney. Am J Physiol, 217:1350–1362. 9. Shea PH, Mahler JF, Horak E (1981). Prediction of glomerular ﬁltration rate by serum creatinine and β2-microglobulin. Nephron, 29:30–35. 10. Eddy AA, McCullich L, Liu E, Adams J (1991). A relationship between proteinuria and acute tubulointerstitial disease in rats with experimental nephritic syndrome. Am J Pathol, 138:1111–1123. 11. Holm J, Hemmingsen L, Nielsen NV (1993). Low-molecular-mass proteinuria as a marker of proximal renal tubular dysfunction in normo- and microalbuminuric non-insulin-dependent subjects. Clin Chem, 39:517–519. 12. Kabanda A, Jadoul M, Lauwerys R, Bernard A, van Ypersele de Strihou C (1995). Low molecular weight proteinuria in Chinese herbs nephropathy. Kidney Int, 48:1571–1576. 13. Kabanda A, Vandercam B, Bernard A, Lauwerys R, van Ypersele de Strihou C (1996). Low molecular weight proteinuria in human imminodeﬁciency virus– infected patients. Am J Kidney Dis, 27:803–808. 14. Hofstra JM, Deegans JK, Willems HL, Wetzels FM (2008). Beta-2-microglobulin is superior to N-acetyl-beta-glucosaminindase in predicting prognosis in idiopathic membranous neuropathy. Nephrol Dial Transplant, 23:2546–2551. 15. Davey PG, Gosling P (1982). Beta-2-microglobulin instability in pathological urine. Clin Chem, 28:1330–1333. 16. Blashuck O, Burdzy K, Fritz IB (1983). Puriﬁcation and characterization of cellaggregating factor (clusterin), the major glycoproteoin in ram rete testis ﬂuid. J Biol Chem, 12:7714–7720. 17. Fritz IB, Burdzy K, Setchell B, Blashuck O (1983). Ram rete testes ﬂuid contains a protein (clusterin) which inﬂuences cell–cell interactions in vitro. Biol Reprod, 28:1173–1188. 18. Rosenberg ME, Silkensen J (1995). Clusterin: physiologic and pathophysiologic considerations. Int J Biochem Cell Biol, 27:633–645. 19. Collard MW, Griswold MD (1987). Biosynthesis and molecular cloning of sulfated glycoprotein 2 secreted by rat Sertoli cells. Biochemistry, 26:3297–3303.

REFERENCES

353

20. Kirszbaum L, Sharpe JA, Murphy B, et al. (1989). Molecular cloning and characterization of the novel, human complement-associated protein, SP40,40: a link between the complement and reproductive systems. EMBO J, 8:711–718. 21. Kirszbaum L, Bozas SE, Walker ID (1992). SP-40,40, a protein involved in the control of the complement pathway, possesses a unique array of disulﬁde bridges. FEBS Lett, 297:70–76. 22. French LE, Chonn A, Ducrest D, et al. (1993). Murine clusterin: molecular cloning and mRNA localization of a gene associated with epithelial differentiation processes during embryogenesis. J Cell Biol, 122:1119–1130. 23. O’Bryan MK, Cheema SS, Bartlett PF, Murphy BF, Pearse MJ (1993). Clusterin levels increase during neuronal development. J Neurobiol I, 24:6617–6623. 24. Harding MA, Chadwick LJ, Gattone VH II, Calvet JP (1991). The SGP-2 gene is developmentally regulated in the mouse kidney and abnormally expressed in collecting duct cysts in polycystic kidney disease. Dev Biol, 146:483–490. 25. Pearse MJ, O’Bryan M, Fisicaro N, Rogers L, Murphy B, d’Apice AJ (1992). Differential expression of clusterin in inducible models of apoptosis. Int Immunol, 4:1225–1231. 26. Witzgall R, Brown D, Schwarz C, Bonventre JV (1994). Localization of proliferating cell nuclear antigen, vimentin, c-Fos, and clusterin in the post-ischemic kidney: evidence for a heterogeneous genetic response among nephron segments, and a large pool of mitotically active and dedifferentiated cells. J Clin Invest, 93:2175–2188. 27. Correa-Rotter R, Hostetter TM, Manivel JC, Eddy AA, Rosenberg ME (1992). Intrarenal distribution of clusterin following reduction of renal mass. Kidney Int, 41:938–950. 28. Cowley BD Jr, Rupp JC (1995). Abnormal expression of epidermal growth factor and sulfated glycoprotein SGP-2 messenger RNA in a rat model of autosomal dominant polycystic kidney disease. J Am Soc Nephrol, 6:1679–1681. 29. Aulitzky WK, Schlegel PN, Wu D, et al. (1992). Measurement of urinary clusterin as an index of nephrotoxicity. Proc Soc Exp Biol Med, 199:93–96. 30. Eti S, Cheng SY, Marshall A, Reidenberg MM (1993). Urinary clusterin in chronic nephrotoxicity in the rat. Proc Soc Exp Biol Med, 202:487–490. 31. Rosenberg ME, Silkensen J (1995). Clusterin and the kidney. Exp Nephrol, 3:9–14. 32. Hidaka S, Kranzlin B, Gretz N, Witzgall R (2002). Urinary clusterin levels in the rat correlate with the severity of tubular damage and may help to differentiate between glomerular and tubular injuries. Cell Tissue Res, 310:289–296. 33. Abrahamson M, Olafsson I, Palsdottir A, et al. (1990). Structure and expression of the human cystatin C gene. Biochem J, 268:287–294. 34. Grubb A (1992). Diagnostic value of cystatin C and protein HC in biological ﬂuids. Clin Nephrol, 38:S20–S27. 35. Laterza O, Price CP, Scott M (2002). Cystatin C: an improved estimator of glomerular function? Clin Chem, 48:699–707. 36. Herget-Rosenthal S, Marggraf G, Husing J, et al. (2004). Early detection of acute renal failure by serum cystatin C. Kidney Int, 66:1115–1122.

354

NEW MARKERS OF KIDNEY INJURY

37. Koyner JL, Bennett MR, Worcester EM, et al. (2008). Urinary cystatin C as an early biomarker of acute kidney injury following adult cardiothoracic surgery. Kidney Int, 74:1059–1069. 38. Rozell B, Hansson H-A, Guthenberg M, Tahir G, Mannervik B (1993). Glutathione transferases of classes α, μ and π show selective expression in different regions of rat kidney. Xenobiotica, 23:835–849. 39. Smith GJ, Ohl VS, Litwack G (1977). Ligandin, the glutathione S-transferases, and chemically induced hepatocarcinogenesis: a review. Cancer Res, 37:8–14. 40. Rowe JD, Nieves E, Listowsky I (1997). Subunit diversity and tissue distribution of human glutathione S-transferases: interpretations based on electospray ionization-MS and peptide sequence–speciﬁc antisera. Biochem J, 325:481–486. 41. Mannervik B, Awasthi YC, Board PG, et al. (1992). Nomenclature for human glutathione transferases. Biochem J, 282:305–306. 42. Sundberg AG, Nilsson R, Appelkvist EL, Dallner G (1993). Immunohistochemical localization of alpha and pi class glutathione transferases in normal human tissues. Pharmacol Toxicol, 72:321–331. 43. Beckett GJ, Hayes JD (1993). Glutathione S-transferases: biomedical applications. Adv Clin Chem, 30:281–380. 44. Bass NM, Kirsch RE,Tuff SA, Campbell JA, Saunders JS (1979). Radioimmunoassay measurement of urinary ligandin excretion in nephrotoxin-treated rats. Clin Sci, 56:419–426. 45a. Sundberg AG, Appelkvist EL, Backman L, Dallner G (1994). Quantitation of glutathione transferase-pi in the urine by radioimmunoassay. Nephron, 66: 162–169. 45b. Sundberg AG, Appelkvist EL, Backman L, Dallner G (1994). Urinary pi-class glutathione transferase as an indicator of tubular damage in the human kidney. Nephron, 67:308–316. 46. Sundberg AG, Nilsson R, Appelkvist EL, Dallner G (1995). ELISA procedures for the quantitation of glutathione transferases in the urine. Kidney Int, 48: 570–575. 47. Kharasch ED, Thorning D, Garton K, Hankins DC, Kilty CG (1997). Role of renal cysteine conjugate b-lyase in the mechanism of compound A nephrotoxicity in rats. Anesthesiology, 86:160–171. 48. Eger EI II, Koblin DD, Bowland T, et al. (1997). Nephrotoxicity of sevoﬂuorane versus desulfrane anesthesia in volunteers. Anesth Analg, 84:160–168. 49. Kilty CG, Keenan J, Shaw M (2007). Histologically deﬁ ned biomarkers in toxicology. Expert Opin Drug Saf, 6:207–215. 50. Ichimura T, Bonventre JV, Bailly V, et al. (1998). Kidney injury molecule-1 (KIM-1), a putative epithelial cell adhesion molecule containing a novel immunoglobulin domain, is up-regulated in renal cells after injury. J Biol Chem, 273:4135–4142. 51. Lisitsyn N, Lisitsyn N, Wigler M (1993). Cloning the differences between two complex genomes. Science, 259:946–951. 52. Hubank M, Schatz DG (1994). Identifying differences in mRNA expression by representational difference analysis of cDNA. Nucleic Acids Res, 22:5640–5648.

REFERENCES

355

53. Kaplan G, Totsuka A, Thompson P, Akatsuka T, Moritsugu Y, Feinstone SM (1996). Identiﬁcation of a surface glycoprotein on African green monkey kidney cells as a receptor for hepatitis A virus. EMBO J, 15:4282–4296. 54. Feigelstock D, Thompson P, Mattoo P, Zhang Y, Kaplan GG (1998). The human homolog of HAVcr-1 codes for a hepatitis A virus cellular receptor. J Virol, 72:6621–6628. 55. Kuchroo VK, Umetsu DT, DeKruyff RH, Freeman GJ (2003). The TIM gene family: emerging roles in immunity and disease. Nat Rev Immunol, 3:454–462. 56. Meyers JH, Chakravarti S, Schlesinger D, et al. (2005). TIM-4 is the ligand for TIM-1 and the TIM-1-TIM-4 interaction regulates T cell proliferation. Nat Immunol, 6:455–464. 57. Won KH, Bailly V, Aabichandani R, Thadhani R, Bonventre JV (2002). Kidney injury molecule-1(KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int, 62:237–244. 58. Bailly V, Zhang Z, Meier W, Cate R, Sanicola M, Bonventre JV (2002). Shedding of kidney injury molecule-1, a putative adhesion protein involved in renal regeneration. J Biol Chem, 277:39739–39748. 59. Ichimura T, Hung CC, Yang SA, Stevens JL, Bonventre JV (2004). Kidney injury molecule-1: a tissue and urinary biomarker for nephrotoxicant-induced renal injury. Am J Renal Physiol, 286:F552–F563. 60. Vaidya VS, Ramirez V, Ichimura T, Bobadilla NA, Bonventre JV (2006). Urinary kidney injury molecule-1: a sensitive quantitative biomarker for early detection of kidney tubular injury. Am J Renal Physiol, 290:F517–F529. 61. Zhou Y, Vaidya VS, Brown RP, et al. (2008). Comparison of kidney injury molecule-1 and other nephrotoxicity biomarkers in urine and kidney following acute exposure to gentamicin, mercury and chromium. Toxicol Sci, 101:159–170. 62. Lydakis C, Lip GYH (1998). Microalbuminuria and cardiovascular risk. Q J Med, 91:381–391. 63. Kaysen JA, Myers BD, Cowser DG, Rabkin R, Felts JM (1985). Mechanisms and consequences of proteinuria. Lab Invest, 54:479–498. 64. Noth R, Krolweski A, Kaysen G, Meyer T, Schambelan M (1989). Diabetic nephropathy: hemodynamic basis and implications for disease management. Ann Intern Med, 110:795–813. 65. Viberti GC (1989). Recent advances in understanding mechanisms and natural history of diabetic disease. Diabetes Care, 11:3–9. 66. Morgensen CE (1987). Micoalbuminuria as a predictor of clinical diabetic nephropathy. Kidney Int, 31:673–689. 67. Parving HH, Hommel E, Mathiesen E, et al. (1988). Prevalence of microalbuminuria, arterial hypertension, retinopathy, neuropathy, in patients with insulindependent diabetes. Br Med J, 296:156–160. 68. Viberti GC, Hill RD, Jarrett RJ, Argyropoulos A, Mahmud U, Keen H (1982). Microalbuminuria as a predictor of clinical nephropathy in insulin-dependent diabetes mellitus. Lancet, 319:1430–1432. 69. Morgensen CK, Schmitz O (1988) The diabetic kidney: from hyperﬁltration and microalbuminuria to end-stage renal failure. Med Clin North Am, 72:466–492.

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NEW MARKERS OF KIDNEY INJURY

70. Rowe DJF, Dawnay A, Watts GF (1990). Microalbuminuria in diabetes mellitus: review and recommendations for measurement of albumin in urine. Ann Clin Biochem, 27:297–312. 71. Russo LM, Sandoval RM, McKee M, et al. (2007). The normal kidney ﬁlters nephritic levels of albumin retrieved by proximal tubule cells: retrieval is disrupted in nephritic states. Kidney Int, 71:504–513. 72. Koch Nogueira PC, Hadj-Assa A, Schell M, Dubourg L, Brunat-Metigny M, Cochat P (1998). Long-term nephrotoxicity of cisplatin, ifosamide, and methotrexate in osteosarcoma. Pediatr Nephrol, 12:572–575. 73. Prince CW, Oosawa T, Butler WT, et al. (1987). J Biol Chem, 262:2900–2907. 74. Sorensen ES, Hojrup P, Petersen TE (1995). Posttranslational modiﬁcations of bovine osteopontin: identiﬁcation of twenty-eight phophorylation and three O-glycosylation sites. Protein Sci, 4:2040–2049. 75. Brown LF, Berse B, Van de Water L, et al. (1982). Expression and distribution of osteopontin in human tissues: widespread association with luminal epithelial surfaces. Mol Cell Biol, 3:1169–1180. 76. Singh PR, Patarca R, Schwartz J, Singh P, Cantor H (1990). Deﬁnition of a speciﬁc interaction between the early T lymphocyte activation 1 (Eta-1) protein and murine macrophages in vitro and its effect upon macrophages in vivo. J Exp Med, 171:1931–1942. 77. Weber GF, Cantor H. (1996). The immunology of eta-1/osteopontin. Cytokine Growth Factor Rev, 7:241–248. 78. Giachelli C, Bae N, Lombardi D, Majesky M, Schwartz S (1991). The molecular cloning and characterization of 2B7, a rat mRNA which distinguishes smooth muscle cell phenotypes in vitro and is identical to osteopontin (secreted phosphoprotein I, 2a). Biochem Biophys Res Commun, 177:867–873. 79. Liaw L, Lindner V, Schwartz SM, Chambers AF, Giachelli CM (1995). Osteopontin and beta 3 integrin are coordinately expressed in regenerating endothelium in vivo and stimulate ARG-GLY-ASP-dependent endothelial migration in vitro. Circ Res, 77:665–672. 80. Worcester EM, Blumenthal SS, Beshensky AM, Lewand DL (1992). The calcium oxalate crystal growth inhibitor protein produced by mouse kidney cortical cells in culture is osteopontin. J Bone Miner Res, 7:1029–1036. 81. Kleinman JG, Beshenky A, Worcester EM, Brown D (1995). Expression of osteopontin, a urinary inhibitor of stone mineral crystal growth, in rat kidney. Kidney Int, 47:1585–1596. 82. Shiraga H, Min W, Vandusen WJ, et al. (1992). Inhibition of calcium oxalate growth in vitro by uropontin: another member of the aspartic acid-rich protein superfamily. Proc Natl Acad Sci USA, 89:426–430. 83. Wuthrich RP (1998). The complex role of osteopontin in renal disease. Nephrol Dial Transplant, 13:2448–2450. 84. Rittling SR, Denhardt DT (1999). Osteopontin function in pathology: lessons from osteopontin-deﬁcient mice. Exp Nephrol, 7:103–113. 85. Giachelli CM, Pichler R, Lombardi D, et al. (1994). Osteopontin expression in angiotensin II–induced tubulointerstitial nephritis. Kidney Int, 45:515– 524.

REFERENCES

357

86. Pichler RH, Franseschini N, Young BA, et al. (1995). Pathogenesis of cyclosporine nephropathy: roles of angiotensin II and osteopontin. J Am Soc Nephrol, 6:1186–1196. 87. Diamond JR, Kees-Folts D, Ricardo SD, Pruznak A, Eufemio M (1995). Early and persistent up-regulated expression of renal cortical osteopontin in experimental hydronephrosis. Am J Pathol, 146:1455–1466. 88. Kleinman JG, Worcester EM, Beshensky AM, Sheridan AM, Bonventre JV, Brown D (1995). Upregulation of osteopontin expression by ischemia in rat kidney. Ann NY Acad Sci, 760:321–323. 89. Yu XQ, Nikolic-Paterson DJ, Mu W, et al. (1998). A functional role for osteopontin expression in experimental crescentic glomerulonephritis in the rat. Proc Assoc Am Physicians, 110:50–64. 90a. Xie Y, Sakatsume M, Nishi S, Narita I, Arakawa M, Gejyo F (2001). Expression, roles, receptor, and regulation of osteopontin in the kidney. Kidney Int, 60:1645–1657. 90b. Xie Y, Nishi S, Iguchi S, et al. (2001). Expression of osteopontin in gentamicininduced acute tubular necrosis and its recovery process. Kidney Int, 59: 959–974. 91. Mirza M, Shaunessy E, Hurley JK, et al. (2008). Osteopontin-C is a selective marker for breast cancer. Int J Cancer, 122:889–897. 92. Agrawal D, Chen T, Irby R, et al. (2002). Osteopontin identiﬁed as lead marker of colon cancer progression, using pooled sample expression proﬁling. J Natl Cancer Inst, 94:513–521. 93. Kjeldsen L, Johnson AH, Sengelov H, Borregaard N (1993). Isolation and primary structure of NGAL, a novel protein associated with human neutrophil gelatinase. J Biol Chem, 268:10425–10432. 94. Cowland JB, Borregaard N (1997). Molecular characterization and pattern of tissue expression of the gene for neutrophil gelatinase–associated lipocalin from humans. Genomics, 45:17–23. 95. De Broe M (2006). Neutrophil gelatinase–associated lipocalin in acute renal failure. Kidney Int, 69:647–648. 96. Mishra J, Ma Q, Prada A, et al. (2003). Identiﬁcation of neutrophil gelatinase– associated protein as a novel early urinary biomarker of renal ischemic injury. J Am Soc Nephrol, 14:2534–2543. 97. Mishra J, Dent C, Tarabishi R, et al. (2005). Neutrophil gelatinase–associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet, 365:1231–1238. 98. Bolignano D, Coppolino G, Campo S, et al. (2007). Urinary neutrophil gelatinase–associated lipocalin (NGAL) is associated with severity of renal disease in proteinuric patients. Nephrol Dial Transplant, 23:414–416. 99. Hirsch R, Dent C, Pfriem H, et al. (2007). HNGAL as an early predictive biomarker of contrast-induced nephropathy in children. Pediatr Nephrol, 22: 2089–2095. 100. Falkenberg FW, Hildebrand H, Lutte L, et al. (1996). Urinary antigens as markers of papillary toxicity: I. Identiﬁcation and characterization of rat kidney papillary antigens with monoclonal antibodies. Arch Toxicol, 71:80–92.

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101. Hildebrand H, Rinke M, Schluter G, Bomhard E, Falkenberg FW (1999). Urinary antigens as markers of papillary toxicity: II. Application of monoclonal antibodies for the determination of papillary antigens in rat urine. Arch Toxicol, 73:233–245. 102. Price S, Betton G. Personal communication, unpublished data.

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18 TRANSLATIONAL MEDICINE— A PARADIGM SHIFT IN MODERN DRUG DISCOVERY AND DEVELOPMENT: THE ROLE OF BIOMARKERS Giora Z. Feuerstein, M.D., Salvatore Alesci, M.D., Ph.D., Frank L. Walsh, Ph.D., J. Lynn Rutkowski, Ph.D., and Robert R. Ruffolo, Jr., Ph.D. Wyeth Research, Collegeville, Pennsylvania

DRUG TARGETS: HISTORICAL PERSPECTIVES Drugs are natural or designed substances used deliberately to produce pharmacological effects in humans or animals. Drugs have been part of human civilizations for millennia. However, until the very recent modern era, drugs have been introduced to humans by empiricism and largely by serendipitous events such as encounters with natural products in search of food or by avoiding hazardous plants and animal products. The emergence of the scientiﬁc era in drug discovery evolved alongside the emergence of physical and chemical sciences at large, ﬁrst as knowledge to distill, isolate, and enrich the desired substance from its natural environment, followed by deliberate attempts to modify natural substances to better serve the human needs and desires. Scientiﬁc evolution throughout the past two centuries enabled identiﬁcation of biologically active substances in humans (e.g., hormones) which were

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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manipulated chemically to improve (potency, duration of action, and exposure), or to mitigate or abrogate undesirable actions. The cumulative knowledge of human, animal, and plant biology and chemistry provided the scientiﬁc foundation and technical capabilities to alter natural substances purposely in order to improve them. Such evolution marked the era of forward pharmacology. The era of forward pharmacology is about drug design that emanates from primary knowledge of the action of the biological target that has clear biological action. The exponential progress in molecular biology since the mid-twentieth century, culminating in deciphering the complete human genome in the year 2000, brought the dawn of pharmacogenomics and the reverse pharmacology era. The reverse pharmacology era is deﬁned by the need, ﬁrst, to clarify the biology and medical perspectives of the target so as to qualify it as a drugable and pharmaceutically exploitable for drug discovery and development scheme. The pharmacogenomic era provides vast opportunities for selection of new molecular targets from a gamut of approximately 30,000 primary genes, over 100,000 proteins, and multiples of their translational and metabolomics products. Thus, the permutations in respect to opportunities for pharmacological interventions are unprecedented, vast, and most promising for innovative medicines. The pharmacogenomics era as a source for drug targets also poses unprecedented hurdles in selection, validation, and translation into effective and safe drugs. New technologies continue to drive efﬁciency and robustness of mining the genomic drug discovery opportunities, but physiological and integrated biology knowledge is lagging. In this perspective, translational medicine and biomarkers research have taken center stage in validation of the molecular target for pharmaceutical exploitation. In this chapter we offer a utilitarian approach to biomarkers and target selection and validation that is driven by the translational medicine prospect of the target to become a successful drug target. We hereby offer classiﬁcation and analytical process aimed to assess risk, innovation, feasibility, and predictability of success of translating novel targets into successful drugs. We provide clear deﬁnitions of the type of biomarkers that are the core of translational medicine and biomarkers research in modern pharmaceutical companies.

BIOMARKERS: UTILITARIAN CLASSIFICATION Biomarkers are the stepping-stones for modern drug discovery and development [1–4]. Biomarkers are deﬁned as biological substances or biophysical parameters that can be monitored objectively and reproducibly and used to predict drug effect or outcome. This broad deﬁnition is, however, of little utility to the pharmaceutical process since it carries no qualiﬁcation for the signiﬁcance and use of the biomarker. The following classes and deﬁnitions of biomarkers are therefore offered:

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1. Target validation: biomarkers that assess the relevance and the potential for a given target to become the subject of manipulation that will modify the disease to provide clear therapeutic beneﬁts while securing a sufﬁcient therapeutic index of safety and tolerability. 2. Compound-target interaction biomarkers: biomarkers that deﬁne the discrete parameters of the compound (or biological) interaction with the molecular target. Such parameters include binding of the compound to the target, its residency time on the target, the speciﬁc site of interaction with the target, and the physical or chemical consequences to the target induced by the compound (or biological). 3. Pharmacodynamic biomarkers: biomarkers that predict the consequence(s) of compound (biological) interaction with the target. The pharmacodynamic biomarkers include events that are desired therapeutically and adverse events based on mechanism of action. Pharmacodynamic biomarkers can report on discrete molecular events that are proximal to the biochemical pathway that is modiﬁed by the manipulated target or remote consequences such as in vivo or clinical outcomes (morbidity or mortality). Pharmacodynamic biomarkers are diverse and frequently nonobvious. Advanced and sophisticated bioinformatics tools are required for tracking the divergence and convergence of signaling pathways triggered by compound interaction with the target. A subset of the pharmacodynamic biomarkers are consequences induced by the compound outside its intended mechanism of action. Such pharmacodynamic effects are often termed “off-target” effects, as they are not the direct consequence of the compound interaction with the target. Usually, such pharmacodynamic events are due to unforeseen lack of selectivity or metabolic transformations that yielded metabolites not present (or detected) in the animals used for safety and metabolic studies prior to launch of the compound into human trials or into human use. These issues are not dealt with in this chapter. 4. Disease biomarkers: biomarkers that correlate statistically with the disease phenotype (syndrome) for which therapeutics are developed. Correlation of levels (in the circulation, other ﬂuids or tissue) or expression patterns (gene, protein) in peripheral blood cells or tissues should signify disease initiation, progression, regression, remission, or relapse. In addition, duration of aberrantly expressed biomarkers could also be associated with risk for disease, even if the level of the biomarker does not change over time. Since disease biomarkers are deﬁned by their statistical correlation to features of the disease, it is imperative that the clinical phenotyping is clearly deﬁned. Stratiﬁcation of all possible phenotypic variables is clearly a prerequisite for accurate assessment of the discrete relationships of the biomarker to the disease. Gender, age, lifestyle, medications, and physiological and biochemical similarities are

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often not sufﬁciently inclusive, resulting in a plethora of disease biomarker claims that are often confusing and futile. 5. Patient selection: biomarkers that are used for selection of patients for clinical studies, speciﬁcally proof-of-concept studies or conﬁrmation phase III clinical trials that are required for drug registration. These biomarkers are important in helping to select patients likely to respond (or conversely, not respond) to a particular treatment or a drug’s speciﬁc mechanism of action, and potentially predict those patients who may experience adverse effects. Such biomarkers are frequently genetic (single-nucleotide polymorphism, haplotypes) or pharmacogenomic biomarkers (gene expression), but could be any of the primary pharmacodynamic biomarkers. Biomarkers for patient selection are now mainstream in exploratory clinical trials in oncology, where genotyping of tumors in view of establishing the key oncogenic “driver(s)” are critical for the prediction of potential therapeutic beneﬁts of modern treatments with molecular targeting drugs. The success of the new era of molecular oncology (as compared to the cytotoxic era) will depend largely on the ability to deﬁne these oncogenic signaling pathways via biomarkers such as phosphorylated oncogenes, or the functional state due to mutations that cause gain or loss of function. 6. Adaptive trial design: The objectives of adaptive design trials are to establish an integrated process to plan, design, and implement clinical programs that leverage innovative designs and enable real-time learning. The method is based on simulation-guided clinical drug development. In a ﬁrst step, the situation is being assessed, the path forward and decision criteria deﬁned, and assumptions analyzed. Adaptive trials have become an enabler strategy, and they work to integrate competing positions and utilities into a single aligned approach and to force much clearer articulation and quantiﬁcation on the path forward. Once this framework is established, a formal scenario analysis that compares the ﬁngerprints of alternative designs through simulation is conducted. Designs that appear particularly attractive to the program are further subjected to more extensive simulation. Decision criteria steer away from doses that are either unsafe or nonefﬁcacious and aim quickly to hone in onto the most attractive dose range. Response-adaptive doseranging studies deploy dynamic termination rules (i.e., as soon as a noeffective-dose scenario is established, the study is recommended for termination). Bayesian approaches are ideally suited to enable ongoing learning and dynamic decision making [5]. The integrator role of adaptive trials is particularly strong in establishing links between regulatory accepted “conﬁrm”-type endpoints and translational medicine’s efforts to develop biomarkers. Search for biomarkers that may enable early decision making need to be read out early to gain greater conﬁdence in basing decisions on them. A biomarker can be of value even if it only

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allows a pruning decision. These considerations highlight the importance of borrowing strength from indirect observations and use mathematical modeling techniques to enhance learning about the research question. For example, in a dose-ranging study, it is assumed that there should be some relationship between the response of adjacent doses, and this assumption can be used to model an algorithm. Both safety and efﬁcacy considerations can be built into this model: ideally, integration of all efforts, from disease modeling in discovery to PK/PD modeling in early clinical development to safety/risk and business case modeling in late development [4–7]. The utility of this system is represented in Figures 1 and 2, which suggest a semiquantitative scoring system that helps assess the strength of the program

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Figure 1

Criteria for biomarker scoring.

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examples Class A

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CML: BCI/Abl DVT: FV Leiden MG: anti-AchR Ab

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Thrombosis: GPIIb/IIIa P-selectin

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Stroke: -glutamate Breast Cancer -GFRK

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Hypertension -renin -Ca+2 channel (L-type) -Cholesterol

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Figure 2 Type I biomarkers: target validation translational medicine perspectives. CMI, chronic myelocytic leukemia; DVT, deep vein thrombosis; FV, factor V; MG, myasthenia gravis; AchR, acetylcholine receptor; GPIIb/IIIa, platelet integrin receptor; GFRK, growth factor receptor kinase; MOA, mechanism of action; AE, adverse effects.

overall and identiﬁcation of the areas of weaknesses in each of the biomarkers needed along the compound (biological) progression path. Figure 3 illustrates the continuum of biomarkers research, validation, and implementation along the complete time line of drug discovery and development, including life-cycle management (phase IV) and new indication (phase V) when appropriate. Figure 3 illustrates the interfaces of translational medicine within the traditional drug discovery and development process, while Figure 4 represents the new model of “learn and conﬁrm,” where biomarkers ﬁgure prominently in driving the “learn” paradigm. A program for which a STRONG score is established across all ﬁve biomarkers’ speciﬁcations provides conﬁdence of the likelihood for success from the biological and medical perspectives and is likely to result in a more promising development outcome. Similarly, it would be prudent to voice concerns regarding programs that score WEAK, especially if low scores are assigned to target validation, pharmacodynamic, and in special cases, target-compound interaction (e.g., central nervous system target). This scoring system is complementary to other deﬁnitions of biomarkers based on certain needs. For example, surrogate biomarkers as deﬁned by the U.S. Food and Drug Administration (FDA) are markers that can be used for drug registration in

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Figure 3 Building translational medicine via biomarker research. (See insert for color reproduction of the ﬁgure.)

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Figure 4 Translational medicine: biomarker implementation along the pipeline. Exp, exploratory phase; Pre-Dev, predevelopment track; CR&D, clinical research and development; LCM, life-cycle management.

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lieu of more deﬁnitive clinical outcome data. Surrogate biomarkers are few and difﬁcult to establish (e.g., blood pressure and cholesterol; Figure 2).

PRINCIPLES OF TARGET SELECTION Two key guiding principles are essential in the early selection process of molecular targets: 1. Modulating the target carries the prospect of unequivocal medical beneﬁt (efﬁcacy) to patients beyond a standard of care. 2. Beneﬁts can be garnered while maintaining a sufﬁcient level of safety that can be realized within the attainable compound exposure. Such a mission is frequently unachievable, and hence establishing an acceptable therapeutic index is the practical goal for most drug development schemes. Commonly, a therapeutic index is established by calculation of the ratio of the maximum tolerated dose (MTD) and the minimum effective dose (MED) in animal efﬁcacy and safety studies. In this light, targets selected for drug development can be classiﬁed with respect to risk assessment based on the following categories (Figure 2): Class A. The target is only present and contributing to the disease process. Class B. The target is present physiologically but in a nonactive form, but then is activated and contributes to the disease. Class C. The target functions physiologically but in an augmented, uncontrolled fashion that contributes to the disease. Class D. The target functions in normal states and indiscriminately in disease (e.g., no difference in target expression, functions, or distribution can be identiﬁed in disease as compared to the normal physiological state). Class A: Disease-Speciﬁc Target A disease-speciﬁc molecular target should be a molecule that operates only in the disease state and does not participate in physiological (normal) functions. Drug interaction with such targets should provide efﬁcacy with the lowest chance for mechanism-based adverse effects when manipulated by drugs. Examples of such targets are genetic disorders, which result in either overactivity or loss of activity of the target. Such is the case in chronic myelogenous leukemia (CML), which results from aberrant recombination of DNA from chromosome 22 into chromosome 9 (Philadelphia chromosome), fusing the Bcr and Abl genes into an overacting tyrosine kinase, which drives oncogenic transformation. To cure the disease, potent and selective inhibitors of

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this aberrant kinase had to be discovered, a task that took over a decade to accomplish [8]. Such targets have the potential for a high safety proﬁle. It is however, important to note that this example may not necessarily represent the ultimate approach for this disease since the activity of the kinase (Bcr/Abl) is driven by the Abl kinase catalytic site, which is preserved in its physiological format. Thus, inhibitors of this target/kinase by drugs such as Gleevec may still carry the potential for interference in tissue and cells in which the Abl kinase is physiologically active. Another example that is applicable to this category is a disease such as myasthenia gravis, where speciﬁc antibodies that block the acetylcholine receptors cause progressive muscle weakness. Speciﬁc neutralizing agents to these antibodies are likely to provide high efﬁcacy in treating the disease, with the likelihood of fewer adverse effects [9] since such antibodies are not physiologically present in human beings. These examples are typical for type 1 class A target validation. The biomarkers that need to be established for this category should focus on validating the speciﬁcity of the target to the disease state. Class B: Target Present Physiologically in a Nonactive Form but Is Activated and Contributes to the Disease This class of targets has little or no discernible physiological activity in normal states, yet in certain pathophysiological situations, the target is presented, activated, and plays a role in a pathophysiological event. Example of such targets in the type 1 class B category is the P-selectin adhesion molecule. This adhesion molecule is normally cryptic within platelets and endothelial cells. Upon activation of these cells, P-selectin is presented on the surface of the cell and mediates adhesion interaction with its ligand, a mechanism believed to play a role in thrombosis and inﬂammation. Inhibitors of P-selectin binding to its ligand, the P-selectin glycoprotein ligand (PSGL-1), are expected to provide clinical beneﬁt with a lower likelihood of adverse events. To validate this situation, biomarkers that conﬁrm the preferential role of the activated target in a pathophysiological process while maintaining little physiological function are essential. However, one must be aware of potentially serious limitations to this approach where cryptic targets in the physiological state that are activated in pathological conditions and where inhibition of the target may not only provide for signiﬁcant therapeutic beneﬁt, but where inhibition of such a target may also expose the patient to some other risk, such as loss of host defense from injury. Such is the case of the platelet adhesion integrin molecule, GPIIb/IIIa, which serves as the ﬁnal common pathway for platelet aggregation. Interfering with activated GPIIb/IIIa binding to its ligand (e.g., ﬁbrinogen) provides effective and often lifesaving therapy in patients with acute risk for thrombosis; however, chronic treatment with GPIIB/IIIa antagonists have not been particularly effective in providing beneﬁts, due to the

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relatively high frequency of signiﬁcant adverse effects due to bleeding since platelet adhesion to matrix protein is essential to seal bleeding sites in trauma and disease conditions. Thus biomarkers for this class must establish the full physiological signiﬁcance of the target in order to assess the therapeutic index of tolerability (beneﬁts as well as risks). Class C: Target Functions Physiologically but in an Augmented, Uncontrolled Fashion That Contributes to the Disease This class of targets includes molecules that play an active role in normal physiological processes, some of which may be critical to health. Such is the neurotransmitter glutamate in the central nervous system, which is essential to cognition, memory, thought processes, and state of arousal. However, in ischemic stroke or asphyxia, glutamate release is uncontrolled and reaches to high levels over prolonged periods that are believed to be neurotoxic and likely contribute to neuronal death following a stroke. Inhibitors of glutamate release or antagonists of its action at various receptors are believed to carry the potential for effective treatment for stroke provided that the inhibition of excess release of the neurotransmitter can be achieved in a timely manner and only to an extent that preserves the physiological need of this transmitter, and over short periods (only that limited period where excess glutamate is neurotoxic). Such targets may be pharmaceutically exploitable when their manipulation is tuned carefully to the pathophysiological context. Another example of a target in this category includes the human growth factor receptor kinase (hGFRK) inhibitor, Herceptin, which in certain cancers (e.g., breast cancer) is constitutively activated and participates in the oncogenic drive. Inhibition of the hGFRK, while clearly of therapeutic value in breast cancer, has also been associated with heart failure due to the physiological role of GFRK in the cardiac myocyte survival signaling pathway [10]. Thus, the biomarker challenge in modulation of class C targets of this nature is in identifying biomarkers that assess the needed “titration” for inhibition of the target activity only to an extent that is necessary for the maintenance of normal physiological function. Class D: Target Maintains Physiological Functions in Normal and Disease States This class of targets encompasses the largest group of molecular targets exploited so far by modern drugs. Many members of this class have yielded highly beneﬁcial therapies. This class consists of molecular targets that are known to have important physiological functions, which cannot be differentiated within a disease context; that is, the target is not different in its expression levels (gene, protein) or signaling pathway in normal and disease states. A priori, such targets harbor the greatest risk for mechanism-based adverse effects, as there is no apparent reason to expect that modulation of the target in the disease state will spare the normal physiological function of the target.

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Examples of such targets include the coagulation factor inhibitors (e.g., FIX, FXa, and thrombin), which are critical to maintain a physiological level of homeostasis; hence, inhibition of these targets carries inherent bleeding liabilities. Similarly, all current antiarrhythmic drugs (e.g., amiodarone, lidocaine, dofetilide), while effective in treating life-threatening arrhythmias, all carry signiﬁcant liability for mechanism-based pro-arrhythmic effects and the potential for sudden death. The biomarker challenges for this class are deﬁning the ﬁne balance needed between efﬁcacy in the disease context and expected safety limitations. Biomarkers that deﬁne the acceptable therapeutic index are key to the successful utility of drugs that modulate such targets. However, targets in this class do not necessarily exhibit a narrow safety margin for clinically meaningful adverse effects. Signiﬁcant examples are the L-type Ca2+ channel blockers. The L-type Ca2+ channel is an essential conduit of Ca2+ needed for “beat by beat” Ca2+ ﬂuxes that secure precise rhythm and contractility of the heart, skeletal muscle, neuronal excitability, and hormone and neurotransmitter release. Yet L-type Ca2+ channel blockers are important and sufﬁciently safe drugs that are used to treat hypertension, angina, and cardiac arrhythmias with undisputable medical beneﬁts. However, inherent to the L-type Ca2+ channel blockers in this class of targets are mechanism-based adverse effects associated with rhythm disturbances, hypotension, edema, and other liabilities. Probably the best example for system speciﬁcity of physiological targets that provide major medical beneﬁts with a high safety margin is the renin– angiotensin–aldosterone (RAAS) system. The RAAS is an important blood pressure, blood volume, and blood ﬂow regulatory system, yet its manipulation by several different pharmacological agents (rennin inhibitors, angiotensin I converting enzyme inhibitors, angiotensin II receptors antagonists) has yielded highly beneﬁcial drugs that reduce risk of morbidity and mortality from hypertension, heart failure, and renal failure, despite the fact that the system does not demonstrate signiﬁcant operational selectivity between normal and disease states (especially hypertension). However, mechanismbased hypotension and electrolyte disturbances can limit the therapeutic beneﬁt of these drugs, and elicit signiﬁcant adverse effects when the RAAS is excessively inhibited [11]. The biomarker challenge for these targets is to deﬁne the relative or preferential role of the target in its various physiological activities, where minor manipulation in one organ might provide sufﬁcient therapeutic potential while providing a low likelihood for adverse effects that result from more substantial inhibition of the same target in other organs.

SUMMARY The analysis and classiﬁcation offered in this chapter regarding biomarkers in drug discovery and development aim to highlight the need for careful study and analysis of the signiﬁcance of the target selected for therapeutic intervention as the ﬁrst crossroad for success or failure in the development of effective

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and safe drugs [12]. The analysis and utility of biomarkers along the process of drug discovery and development have become an integral part of the “learn and conﬁrm” paradigm of drug discovery and development in leading pharmaceutical organizations such as Wyeth Research. Such analyses are useful to guide the “learn phase” in search for biomarkers that can better assess the beneﬁts and risks associated with manipulation of the molecular target. The scope of this chapter does not allow for a detailed review of the “learn and conﬁrm” paradigm, for which the readers are directed elsewhere [13,14]. Various technological and strategic activities are needed to establish the biomarker strategies for the various targets described. The need to address these issues via biomarker research, validation, and implementation beginning at the very early stages of the drug discovery and development process is emphasized. In the pharmaceutical setting, it means beginning efforts to identify biomarkers for all ﬁve categories listed above. Such efforts could begin even before a tractable compound (biological) is in hand, a time where target validation is a clear focus of the program. As a compound becomes available, compound–target interaction, pharmacodynamic (efﬁcacy and safety) biomarkers, and strategies for patient selection and adaptive design needs must be explored. At the onset of the ﬁrst-in-human studies, all strategies, plans, and biomarker research should be as well worked out as possible. We believe that fundamental changes in the structure, function, and interfaces of pharmaceutical R&D are urgently needed to provide a key role for translational medicine and biomarkers research toward more successful discovery and development of innovative medicines. REFERENCES 1. Biomarker Deﬁnition Working Group (2001). Biomarkers and surrogate biomarkers endpoints: preferred deﬁnitions and conceptual framework. Clin Pharmacol Ther, 69:89–95. 2. Trusheim R, Berndt ER, Douglas FL (2007). Stratiﬁed medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov, 6:287–293. 3. Feuerstein GZ, Rutkowski JL, Walsh FL, Stiles GL, Ruffolo RR Jr (2007). The role of translational medicine and biomarkers research in drug discovery and development. Am Drug Discov, 2:23–28. 4. FDA (2004). Challenge and opportunity on the critical path to new medical products. http:www.fda.gov/oc/initiatives/criticalpath/whitepaper.pdf. 5. Berry DA (2006). Bayesian clinical trials. Nat Drug Discov Rev, 5:27–36. 6. Gallo P, Chuang-Stein C, Dragalin V, Gaydos B, Krams M, Pinheiro J (2006). Adaptive design in clinical drug development: an executive summary of the PhRMA working group. J Biopharm Stat, 16:275–283. 7. Krams M, Lees KR, Hacke W, Grieve AP, Orgogozo J-M, Ford GA (2003). Acute stroke therapy by inhibition of neutrophils (ASTIN): an adaptive dose–response study of UK-279,276 in acute ischemic stroke. Stroke, 34:2543–2548.

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8. Kurzrock R (2007). Studies in target-based treatment. Mol Cancer Ther, 6(9):2385. 9. Hampton T (2007). Trials assess myasthenia gravis therapies. JAMA, 298(1):29–30. 10. Chien KR (2006). Herceptin and the heart: a molecular modiﬁer of cardiac failure. N Engl J Med, 354:789–790. 11. Hershey J, Steiner B, Fischli W, Feuerstein GZ (2005). Renin inhibitors: an antihypertensive strategy on the verge of reality. Drug Dev Today, 2:181–185. 12. Simmons D (2006). What makes a good anti-inﬂammatory drug target? Drug Discov Dev, 5–6:210–219. 13. Gombar C, Loh E (2007). Learn and conﬁrm. Drug Discov Dev, 10:22–27. 14. Sheiner LB (1997). Learning versus conﬁrming in clinical drug development. Clin Pharmacol Ther, 61:275–291.

19 CLINICAL VALIDATION AND BIOMARKER TRANSLATION David Lin, B.MLSc. University of British Columbia, Vancouver, British Columbia, Canada

Andreas Scherer, Ph.D. Spheromics, Kontiolahti, Finland

Raymond Ng, Ph.D. University of British Columbia, Vancouver, British Columbia, Canada

Robert Balshaw, Ph.D., and Shawna Flynn, B.Sc. Syreon Corporation, Vancouver, British Columbia, Canada

Paul Keown, M.D., D.Sc., MBA, Robert McMaster, D.Phil., and Bruce McManus, M.D., Ph.D. University of British Columbia, Vancouver, British Columbia, Canada

INTRODUCTION Throughout history, biological and pathogenic processes have been measured to monitor health and disease. The presence of “sweetness” in urine and blood was recognized thousands of years ago as an indication of the disorder now known as diabetes. By recent example, the characterization of infections has been achieved by performing cultures for microorganisms, both for identiﬁcation and to establish sensitivities to antibiotics. Any such measures, however variously quantitated, were forerunners of what are now popularly referred Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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to as biomarkers. When clinical laboratories became well established after the middle of the twentieth century, many components of body ﬂuids and tissues were assayed. These are all biomarkers of the physiological state of being. Modern biomarkers being discovered or sought are pursued and measured based on the same principles as the ﬁrst, most rudimentary of disease indicators. Hundreds of biomarkers are now used in modern medicine for the prediction, diagnosis, and prognostication about disease, as well as for monitoring preventive or therapeutic interventions. A sizable challenge now lies in selecting novel biomarkers that are most appropriate for clinical use in the face of many new candidates being suggested by various investigators. Biomarkers are often thought of as strictly molecular in nature, but in fact they include a vast range of objectively measurable features. Any characteristic that reﬂects a normal biological process, pathogenic process, or a pharmacologic response to an intervention can potentially become a clinically useful biomarker [1]. Thus, there are many different types of biomarkers, including molecular, physiological, or structural features of biological systems. However, how a speciﬁc measure reaches the status of customary has not been so clearly established. The focus on using biomarkers in clinical decision making and diagnosis has expanded, and similarly, biomarkers are playing an ever-increasing role in drug development processes and for regulatory decision making. As drug development costs continue to rise, biomarkers have become increasingly important, as they are a potential means to decrease the time for development, costs, and late-phase attrition rates in the regulatory approval process for new drugs: • Reduce clinical trial costs: • Decrease late-phase attrition rates. • Replace time-consuming clinical endpoints. • Reduce required clinical trial sample size by way of patient stratiﬁcation: • Identify the population most likely to beneﬁt. • Identify the population with a high level of risk for events of interest. • Provide more robust assays than some conventional clinical endpoints. • Help improve models for calculating return on investment. For example, cost and time can be reduced when biomarkers help to segment patient groups early in trials, and some new markers may be more timely than many of the more traditional clinical endpoints or outcomes used currently in assessing clinical trials, such as patient survival [2,3]. Biomarkers are also beneﬁcial tools for selecting the best candidates for a trial or to increase safety through more effective drug monitoring. Ultimately, by reducing the required time investment to demonstrate a drug’s safety and efﬁcacy,

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biomarkers may greatly reduce the costs and risks of performing a clinical trial.

BIOMARKER DISCOVERY, DEVELOPMENT, AND TRANSLATION Biomarker discovery can be performed using animal models, but it is now commonly carried out in humans from the very beginning stages of biomarker development. Biomarkers analyzed in preclinical animal studies are eventually transferred to the human, clinical settings. In a controlled laboratory environment the conditions are relatively constant, and “subjects” (i.e., the animals) are homogeneous and free of complicating co-morbidities. In these laboratory settings there are generally more options regarding the assays available, and the possibility of frequent and repeated testing in individual animals or groups of similar or identical animals allows for changes in the measured matrix of analytes to be detected with high precision and sensitivity. Ideally, a candidate biomarker discovered in this fashion would then be transferred into the clinical environment and evaluated further on human samples. The drawback of this approach is that many of the biomarkers discovered in animal models cannot be translated for use in humans. Animal models often do not accurately reﬂect human biology [4]. Further, the biomarker candidate may not achieve acceptable performance standards in heterogeneous patients with age, gender, and racial differences. This is why it has been suggested that pilot studies of biomarkers be conducted in humans ﬁrst, in early phase II clinical trials that incorporate the variability in various ambient inﬂuences, and then that the marker be validated in preclinical studies, and in a later stage, clinical trials in parallel. This approach expedites the biomarker development process and minimizes the attrition rate of biomarker candidates since they are developed from the beginning under human clinical conditions. In patients, biomarker candidate discovery is performed preliminarily in an internal primary cohort and is conﬁrmed subsequently in an external secondary cohort. These two discovery phases are typically performed in observational or retrospective cohorts, and less and less often in preclinical studies. Some companies are avoiding animal models for discovery all together to avoid spending time and money on animal-based biomarkers that can often lead to a dead end. It is not always practical to pursue validation of biomarker candidates identiﬁed in the discovery process. It is important to establish parameters for rational, statistically sound, and evidence-based selection and rejection of candidate biomarkers [5–7]. The decision to continue development of a biomarker candidate is largely based on its potential to contribute cost-effectively to disease management [8]. Biomarkers used in the early phases of clinical development may be useful in providing more timely proof of concept or dose-range information than a real clinical endpoint [9]. Biomarker development should also be driven by clinical need [8]. A clinically useful biomarker

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must favorably affect clinical outcomes such as decreasing toxicity or increasing survival [10]. Once cost-beneﬁt ratios are evident and more than one institution has consistently conﬁrmed a biomarker’s ability to perform at the requisite levels of sensitivity and speciﬁcity, a biomarker is ready for prospective testing in clinical trials [8]. The U.S. Food and Drug Administration (FDA) pharmacogenomic submission guidelines recommend that transferring genomic biomarker sets from microarrays to other platforms (such as quantitative real-time polymerase chain reaction) be attempted only once it has been demonstrated that the differential expression of such genes is sensitive, speciﬁc, and reproducible. Analytical quality assurance is performed continually throughout biomarker and drug development for most biomarkers; however, if a biomarker is to be used as an endpoint in a phase III efﬁcacy study, it should be validated beforehand [11]. Assay and analytical speciﬁcity and sensitivity should be established and validated prior to clinical phases such that clinical qualiﬁcation can be carried out using proven, analytically robust methods [12]. However, as biomarker and drug development are intertwined processes, they may often occur concurrently throughout the different stages of clinical trials.

BIOMARKER VALIDITY AND VALIDATION: THE REGULATORY PERSPECTIVE The FDA’s Guidance for Industry: Pharmacogenomic Data Submission, published in 2005, has helped introduce and deﬁne exploratory and valid biomarkers. The FDA deﬁnes a valid pharmacogenomic biomarker as one that is measured in an analytical test system with well-established performance characteristics and for which there is an established scientiﬁc framework or body of evidence that elucidates the toxicological, pharmacological, or clinical signiﬁcance of the test result. The FDA further classiﬁes valid biomarkers as “probable” and “known” in terms of the level of conﬁdence that they attain through the validation process. Probable valid biomarkers may not yet be widely accepted or validated externally but appear to have predictive value for clinical outcomes, whereas known valid status is achieved by those that have been accepted in the breadth of the scientiﬁc community. It is important to realize that the different classes of biomarkers reﬂect their levels of conﬁdence (Figure 1). This can be thought of in a hierarchical manner, with exploratory biomarkers being potential precursors of clinically useful (probable or known) valid biomarkers [13]. Integrating biomarkers into clinical trials for eventual clinical use by identifying the best or most valid biomarker candidates is not a clearcut process. The term validity, particularly in the ﬁeld of biomarkers research, is a broad concept that has been used to describe everything from the analytical methods to the characteristics of the biomarkers identiﬁed [14]. Validity is also used across multiple industries, not only medical or health disciplines. Therefore,

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Figure 1 Biomarker validation. Biomarker development and validation are driven by intended use or ﬁt-for-purpose (FFP). The principle of FFP validation is that biomarkers with false-positive or false-negative indications pertaining to high patient consequences and risks necessitate many phases of validation. There are ﬁve phases of validation that can be executed with the stratiﬁed use of various bioinformatical and bibliographical tools as well as different designs, statistical approaches, and modeling: (1) internal validation, (2) external validation, (3) clinical trials (phases I and II; checking for safety and efﬁcacy), (4) large clinical trials (phase III), and (5) continued surveillance. Sensitivity and speciﬁcity correlate with the intended purpose of the biomarker, and the level of conﬁdence that a biomarker achieves depends on the phase of validation that has been reached. In ideal cases biomarkers reach surrogate endpoint status and can be used to substitute for a clinical endpoint. This designation requires agreement with regulatory authorities, as the consequences of an ambiguous surrogate endpoint are high.

when referring to biomarkers, validation is sometimes termed qualiﬁcation for clarity. Biomarker qualiﬁcation has been deﬁned as a graded ﬁt-for-purpose evidentiary process linking a biomarker with biology and clinical endpoints [15,16]. Traditionally, the validity of clinical biomarkers has become established in a typically lengthy process through consensus and test of time [17]. Now more than ever, clear guidelines for validation are needed, as technological advances have drastically increased biomarker discovery rates. With the recent explosion of “omics” technologies and advancements in the ﬁelds of genomics, proteomics, and metabolomics, high-throughput biomarker discovery strategies are now widely used. This has created some unforeseen issues. Biomarker

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Figure 2

FDA biomarker qualiﬁcation pilot process. (Adapted from ref. 17.)

candidate discovery now commonly outruns the rate at which the candidates are being validated, creating a bottleneck in biomarker (assay) development [18,19]. Great efforts are now being undertaken to accelerate the acceptance of biomarkers from exploratory to valid, as the goal of many research teams and drug companies is to streamline the translation of biomarker from basic science and discovery to clinical use [12]. Despite the deﬁnitions provided by the FDA and the availability of FDA’s Guidance for Industry: Bioanalytical Method Validation in 2001, there is still a lack of sufﬁcient regulatory guidance for biomarker validation. The FDA has designed a qualiﬁcation process map that sets the foundational framework toward establishing validation guidelines (Figure 2). This pilot structure, to start qualiﬁcation processes for biomarkers in drug development, is designed around various FDA centers whereby the context and qualiﬁcation of new biomarkers is assessed. Ultimately they are rejected or accepted for suggested use relative to current biomarkers. This may not be ideal, as it may be problematic to establish new biomarkers accurately based on the current biomarkers, which are themselves often imperfect relative to a speciﬁc endpoint [17]. Nonetheless, this pilot framework will eventually enable more detailed biomarker translation models, which address some of the remaining issues with the current guidelines, to be developed.

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There remains a lack of speciﬁc guidelines on which validation process(es) are recommended or expected in order to transition effectively from exploratory to valid biomarkers, or from probable valid biomarkers into known valid biomarkers [13,20]. Confusion still exists with regard to analyses or experiments that need to be performed and data that are both appropriate and sufﬁcient for biomarker (assay) validation [20]. The confusion and inconsistency in the validation process are contributed to partially by the diverse nature of biomarker research [3,20]. Considering the large variety of novel biomarkers, their applications, and associated analytical methods, it is unlikely that FDA regulations or other available guidelines will easily be able to address validation issues associated with all possible research objectives [16,20]. Thus, it is incredibly difﬁcult to establish, let alone use, a speciﬁc detailed universal validation guideline [20]. FIT-FOR-PURPOSE STRATEGY Despite the lack of universal guidelines or agreement on the speciﬁc requirements for biomarker assay development and validation, there is a general consensus in the biomarker research community that the foundation of validation efforts is to ensure that the biomarker(s), or the assay, is “reliable for its intended use” [20]. This principle is now commonly referred to as the ﬁt-forpurpose validation strategy or guideline. This approach embraces the notion that depending on the intended purpose of the biomarker, the objectives and processes or types of validation will probably be different [21,22]. Dose selection, early efﬁcacy assessment or candidate selection, and surrogacy development are all examples of biomarker clinical purposes, each of which have differing levels of validation requirement. The risk and consequence may be different depending on the purpose, even when the same biomarker is used [4]. Further, the degree of stringency and phase of the validation, both of which are discussed later in the chapter, should be commensurate with the intended application of the validation data (Figure 1) [22]. In that sense, the term validity should be thought of as a continuum—an evaluation of the degree of validity—rather than an all-or-none state [14,23]. Therefore, biomarker utility is not measured dichotomously and should not be classiﬁed as simply good or bad. The level of a biomarker’s worth is gauged on a continuous scale, with some being much more valuable than others, depending on what they indicate and how they can be applied [24]. Validation of biomarkers and establishment of their “worth” or “value” is a continually evolving and often iterative process (Figure 1). Application of the Fit-for-Purpose Strategy The ﬁt-for-purpose (FFP) strategy is a ﬂuid concept that can be applied to any clinical trial to validate biomarkers of interest. As described earlier, the classiﬁcation and validation of a biomarker is context-speciﬁc, and the validation

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criteria required are dependent on the intended use of the biomarker. The concept of ﬁt-for-purpose validation is sensible, as biomarkers may be used to include or exclude patients from treatment and to determine dosing, toxicity, or safety. The consequence or risk of a false-negative or false-positive biomarker indication must be considered since even the most sensitive and speciﬁc assays are not perfect. A biomarker intended as a population screen must be very sensitive and extremely speciﬁc [25]. Any tests that are to be used in isolation for decision making require particularly stringent validation, whereas drug efﬁcacy biomarkers that are typically used in groups or involve subsequent testing may require less stringency since the consequence of a false indication of efﬁcacy is lower [26]. Biomarker Validation Process Utilizing the FFP strategy, various questions can be asked at the beginning of any biomarker research and development (R&D) project: For what reason are the biomarker(s) being identiﬁed? Is the clinical validation going to validate the biomarker for speciﬁcity, sensitivity, and reproducibility for diagnostic purposes, or to serve as a surrogate endpoint? What business-critical decisions will be made based on the biomarker data? These questions not only help determine the level of conﬁdence required for the biomarker, but also help strategize regarding the phases of validation. For example, in the case of developing surrogate biomarkers to replace clinical endpoints, candidate biomarkers would, theoretically, evolve over time toward the point of surrogacy as the research project moves through different phases of validation. For the purpose of this chapter, the validation process from initial biomarker development to postmarket surveillance has been broken down into ﬁve major phased strategies (Figure 1). It is important to note that the overall process is a continuous loop driven by the intended purpose of the biomarker data, but the ﬂow of the overall process may be subjected to change depending on the results generated at each phase [22]. In general, the process of biomarker validation can be described as a multifaceted process that includes determining sensitivity, speciﬁcity, and reproducibility of the assay and also clinical sensitivity and speciﬁcity [27]. This applies to both methodologic and clinical validation. Method validation pertains to the process by which the assay and its performance characteristics are assessed. This assessment is performed throughout biomarker translation and is based on several fundamental parameters: accuracy, precision, selectivity, sensitivity, reproducibility, and stability [16,22]. Method validation is not discussed in detail in this chapter.

PRECLINICAL AND CLINICAL VALIDATION PHASES Clinical validation is the documented process demonstrating the evidentiary link of a biomarker with a biological process and clinical endpoints [15,16].

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Figure 3 Biomarker development and translation. Biomarker translation from discovery to clinical use involves ﬁve general steps or phases. Biomarker candidates are initially discovered in a primary (internal) cohort and conﬁrmed in a secondary (external) cohort through clinical observations. Biomarker candidates that have the best ﬁt-for-purpose and also satisfy a clinical need then enter prospective phase I and II clinical trials. Once a biomarker is used in these early clinical trials to demonstrate safety and efﬁcacy, they may be considered “initially validated biomarkers.” Following large-scale clinical trials (phase III), and once biomarkers have been used for decision making, they may be considered valid and may be adopted for clinical use. Biomarker assessment continues in postmarket phase IV trials. SOP-driven analytical quality assurance is performed throughout all processes of biomarker translation.

The validation process whereby biomarkers are translated from discovery to clinical use should be customized according to biomarker type, use, variability, and prevalence. However, the general process for validating any biomarker is the same. Before any biomarker can be applied clinically, it is subjected to analytical/method validation and also clinical validation. Biomarker validation can be performed in ﬁve general translational steps: preliminary biomarker discovery, biomarker discovery, safety and efﬁcacy clinical trials, large-scale clinical trials, and continued surveillance (Figure 3). Clinical biomarkers are validated in retrospective or prospective analyses and biomarker trials, or drug trials [12]. The validation process should reﬂect the clinical performance of the biomarker(s), based on existing clinical data, new clinical data, literature review ﬁndings, or current clinical knowledge [12]. Moreover, it should be an evidentiary and statistical process that aims to link

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a biomarker to speciﬁc biological, pathological, pharmacological (i.e., drug effect), or clinical endpoints [22]. Internal Validation During the biomarker discovery phase, the main focus is to identify biomarkers that distinguish between the treatment and control groups or correlate with the clinical observation of interest. Prior to this process, and depending on the sample size, plans can be made to allocate the subjects into two individual cohorts for the purpose of internal validation. It is important, however, to distinguish this allocation/splitting process from that used for the purpose of internal validation of classiﬁers: • Internal validation of candidate biomarkers identiﬁed in the discovery cohort • Use of different platforms • Use of different statistical methods • Internal validation of classiﬁers • Split-sample method • Some form of cross-validation method There are several different approaches to separate the initial pool of patients or samples for internal validation and creating classiﬁers; the traditional and alternative approaches are outlined here. Traditionally, a discovery and a validation cohort are created (Figure 4). Genomic biomarker candidates may ﬁrst be identiﬁed in the discovery cohort using microarray analysis, for example. One way of identifying a panel of biomarkers from the candidates is by use of classiﬁcation methods. The samples from the discovery data set can be split into a training set, used to ﬁnd a classiﬁer, and a test set, used for internal validation of classiﬁers. The classiﬁer is an equation combining the expression values of some of the candidate markers that best distinguish the treatment from the control group. Once the classiﬁer has been developed, the panel of biomarkers may be validated again in the validation cohort before the external validation phase is carried out. In this sense, the data obtained in the validation cohort serves purely as an additional test set. Although the traditional internal validation model is simple and logical, it may not be the most applicable strategy in the real world, given the complexity of most biomarker research today. There are two potential weaknesses to this approach. First, separating available samples into discovery and validation cohorts from the outset might unintentionally restrict the use of the samples or data to their arbitrary labels. In reality, during the developmental phase of biomarker research, different statistical analyses are often carried out to identify potentially useful biomarkers. Depending on the types of comparisons being made, a sample (or portion of data) could be used for discovery pur-

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Figure 4 Traditional internal validation approach. Traditional approaches to internal validation rely on two cohorts: discovery and validation. The discovery cohort is typically separated into training and test sets for the development of classiﬁers based on biomarker(s) of interest. The classiﬁers are then tested in a separate cohort internally before external validation.

poses in one analysis while it is used for validation in another. Second, in the case where the classiﬁer fails to show acceptable accuracy in the initial discovery cohort testing set, one might consider incorporating additional data and reanalyzing the training set. However, collecting additional patient samples or information may not always be possible; this type of situation may warrant the reallocation of data from the validation to the discovery cohort training set, in order to redevelop the classiﬁer. An alternative model of internal validation may be particularly useful for smaller sample sizes (Figure 5). Similar to the traditional approach, the subjects are divided into two separate groups. However, neither cohort is marked strictly as discovery or validation, to minimize potential confusion in later analyses and maximize the utility of available data. As an example, a classiﬁer can be generated and internally validated by creating training and testing sets from cohort 1. The same classiﬁer can then be validated again in a separate cohort (cohort 2). Based on the outcome, the R&D team may decide that the classiﬁer is ready for external validation or that a more robust classiﬁer needs to be developed. In the latter case, a new classiﬁer can be created by utilizing cohort 2 as the discovery cohort. Once the new classiﬁer has been validated internally in cohort 2, it can be evaluated again in cohort 1, which is used

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Figure 5 Alternative internal validation approach, which may be particularly useful for smaller sample sizes. Like the traditional approach, the subjects are divided into two separate cohorts. However, in this model, a classiﬁer can be developed from either cohort, or whenever necessary, from two cohorts combined to improve the robustness of the classiﬁer.

entirely as another testing set. Finally, the same decision process is repeated: Is the classiﬁer robust enough to stand up to the scrutiny of external validation? Are additional data required to create a new classiﬁer? In the latter situation, it may be necessary to combine cohorts 1 and 2 to develop larger training and test sets for internal validation. This may, in turn, help create a new classiﬁer that is potentially more robust and applicable to a larger intended patient population. The main advantage of this model is that it provides many possible internal validation approaches, particularly in a project where sample size is small. This gives ﬂexibility to the overall validation process and allows decisions to be made based on the results generated at each step. During the development and testing of a biomarker panel, concurrent literature and technical validations may also take place. Depending on the characteristics of the biomarker, various technology and analytical techniques may be applied. For example, quantitative polymerase chain reaction (qPCR) may be used to validate the microarray data generated from cohort 1. Once the initial ﬁndings have been conﬁrmed, such as the differential expression of particular genomic biomarkers, qPCR can also be used to test the candidate biomarkers in cohort 2.

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There are a number of potential advantages to cross-platform or crosstechnology validations. Relative to high-throughput technologies such as microarray chips, which are typically used for identifying genomic biomarker candidates, the use of low-throughput techniques such as qPCR may help reduce the cost of the validation process. Also, by applying different platforms and analytical methods, biomarkers that are found to be statistically signiﬁcant across the various cohorts are less likely to be related to or inﬂuenced by platform-speciﬁc bias. More recently, studies have also suggested that the use of combined data from multiple platforms (i.e., genomics and proteomics) to assess potential biomarkers is far superior to those generated with one technical approach alone [21]. The internal validation strategy will be largely dependent on the characteristics of the biomarkers (i.e., genomic, proteomic, metabolomic), the intended use (i.e., prognostic or diagnostic), and the sample size (i.e., traditional or alternative model). Nonetheless, the general recommendation is that the preliminary or developmental studies should be large enough so that either split-sample validation or some form of cross-validation can be performed to demonstrate the robustness of the internally validated prediction [28,29]. External Validation The use of internal discovery and validation cohorts has helped studies to develop biomarker panels with impressive accuracy and precision for the predicted outcome [28]. However, internal validation should not be confused with external validation, which is typically performed on a cohort from a different geographical location and is meant to simulate a broader clinical application [28]. Internal validation, even with the use of “independent” cohorts, does not guarantee “generalizability.” In principle, the aim of external validation is to verify the accuracy and reproducibility of a classiﬁer in a truly independent cohort with similar underlying condition, given a deﬁned clinical context [30]. External validation is a crucial and essential step before a classiﬁer is implemented in a larger clinical setting for patient management [30]. Like internal validation, the design of external validation processes will depend on the intended use of the biomarker. For predictive biomarkers, prospective trials may be necessary, as they are considered the gold standard [28,30]. Moreover, it has been argued that a biomarker is more readily tested in a prospective clinical trial once retrospective studies conducted at external institutions have consistently shown the ability for the biomarker to perform at the required levels of sensitivity and speciﬁcity [8]. To accelerate the translational process from external validation to phase I and II clinical trials, partnerships and collaborations are often established. In some circumstances, classiﬁers may need to be tested in intended patients at a collaborators’ site or external institution in a prospective manner. In other cases, retrospective studies using patient samples may sufﬁce. There are

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several important factors to consider when conducting an external validation, regardless of the directionality of the study (i.e., prospective or retrospective). Many of these factors are also critical in designing clinical trials.

USE OF STATISTICS AND BIOINFORMATICS Due to the complexity and multifaceted nature of biomarker research, it is not uncommon, especially in the recent years, to see different “omics” techniques, bioinformatics, and statistical methods incorporated into biomarker research and development [21]. Thus, there has been a large increase in the number of possible approaches to analytical and clinical validations. As illustrated in Figure 1, the use of bioinformatics and statistics to validate biomarkers can accompany any preclinical or clinical stage. More speciﬁcally, they are applied to a greater degree of freedom during the initial phases of biomarker development. Ideally, by the time a biomarker enters large clinical trials or is put on the market under continued surveillance, its robustness should already have been tested with the use of a variety of statistical and bioinformatical approaches. Regardless, these computational approaches, although not very stringent, are fundamentally useful tools for ensuring the validity of results prior to using some of the more time-consuming and costly validation methods (i.e., external validation or clinical trials). Statistical Approaches High-throughput “omics” technologies such as microarray-based expression proﬁling generate massive amounts of data. As such, new statistical methods are continually being developed to deal with this challenging issue. The availability of a plethora of statistical techniques, when used with proper precautions, has provided a relatively quick and inexpensive way to validate biomarker candidates. The trial-and-error process with different statistical methods is especially common during early stages of biomarker development. In the exploratory phase of a biomarker project, various computational and mathematical techniques, such as multivariate analysis or machine learning, are often utilized to detect differences between treatment and control groups or between patients with different clinical presentations [18]. Statistically distinctive genomic biomarkers identiﬁed during the exploratory phase by one method may subsequently be subjected to a different technique. Similarly, given the same set of samples and expression measurements, permutation can be carried out on the data set prior to repeating an analysis. Congruency between the results generated using different methods may ultimately translate to an increase in biomarker conﬁdence. This is especially useful during the internal and external phases of validation, when greater statistical freedom is exercised for the purpose of identifying or ranking biomarkers. Ideally, by the time a panel of biomarkers is selected for use in

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a clinical trial, speciﬁc algorithms and statistical approaches should be established. It has been suggested in a recent FDA presentation [12] that the expression patterns or algorithms should be developed and conﬁrmed in at least two independent data sets (a training set and a test set, respectively) [12]. Bioinformatical and Bibliographical Approaches Potential biomarker candidates should be checked continuously using bioinformatical and bibliographical tools to provide biological and clinical context. This step should be performed in parallel to the stratiﬁed statistical approaches. Although bioinformatics originated from the ﬁeld of genomics, it now plays an important role in connecting and integrating biological and clinical data from a variety of sources and platforms [21]. Biomarkers that ﬁt accepted theory are more likely to be accepted readily by the research community and the public [14]. This is especially important when selecting and transitioning biomarkers from internal and external validation phases into clinical trials. Numerous bioinformatical tools, many of which are open-source, are available to accelerate this evidentiary process. Gene and protein biomarker candidates can be processed ﬁrst and then grouped through gene ontology using tools such as FatiGO and AmiGO [31,32]. More sophisticated pathway-oriented programs such as Ingenuity and MetaCore are also potentially useful in assisting the R&D team to zone in on biomarkers belonging to pathways and processes relevant to the clinical endpoints of interest [33,34]. Another advantage of the bioinformatical approach is the ability to link clinical measurements across platforms and/or identify a unique set of molecular signatures. For example, during external validation on a separate cohort of patients, results might indicate that the biomarkers identiﬁed initially as statistically signiﬁcant were individually unable to strongly predict the clinical endpoint. However, linking expression data across platforms (i.e., genomic and proteomic biomarkers) may help provide a more comprehensive understanding of the biology and establish a stronger correlation between the biomarker and the clinical presentation of the patient [21].

FROM EXTERNAL VALIDATION TO CLINICAL TRIALS: THE IMPORTANCE OF COHORT, TECHNICAL, AND COMPUTATIONAL FACTORS As biomarker candidates continue to pour out of research laboratories, it has become increasingly evident that validation is much more difﬁcult and complex than discovery. There are a multitude of general and speciﬁc considerations and obstacles to address in order to validate biomarker candidates clinically, some of which were discussed earlier and some of which we discuss now.

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Translation of a candidate biomarker from a discovery phase into the validation phase of clinical trials imposes huge organizational, monetary, and technical hurdles that need to be considered at the onset. The team needs to meet requirements in sample number and feature distribution (cohort factors), sample quality, accuracy and precision in handling and processing the samples (technical factors), and in the analysis (computational factors). As we will see, each of these steps is extremely important and very challenging at the same time. Cohort Factors During any phases of external or clinical validation of biomarkers, bias can be introduced unintentionally. This is a major concern in the design, conduct, and interpretation of any biomarker study [35,36]. Starting with the population selection process, variations at a biologically level can lead to discernible differences in body ﬂuid and tissue compositions and biomarker measurements [35]. As such, basic criteria such as gender, age, hormonal status, diet, race, disease history, and severity of the underlying condition are all potential sources of variability [35]. Moreover, patient cohort characteristics of the validation phase of a candidate biomarker must be representative of all patients for which the biomarker is developed. To reduce a bias requires the inclusion of hundreds of patients per treatment or disease arm. Not every patient is willing to give biosamples for a biomarker study, however, and only 30 to 50% of the patients in a clinical trial may have signed the informed consent for the biomarker analysis, presenting an important concern for statisticians. There is a risk for population bias, since a speciﬁc subset of patients may donate biosamples, hence skewing the feature distribution. Another risk factor that needs to be dealt with in some instances is the lack of motivation of clinical centers to continue a study and collect samples. Although it is sometimes possible to compensate for a smaller sample size through the use of different statistical methods, such as the use of local pooled error for microarray analysis, the analysis team needs to ensure that there is no patient selection bias by the center selection: Afﬂuent centers and their patients may have different characteristics from those with other social backgrounds. The lack of available samples or patient enrolment may ultimately translate to a decrease in biomarker conﬁdence or the generalizability of the intended biomarker. In reality, collaboration will probably make biomarker validation more robust and economically feasible than working independently [37]. Since the issue of intellectual property (IP) agreement is minimal for the biomarker validation process, as it is not patentable, open interactions among steering committees of large trials or cohort studies should be encouraged [37,38]. Technical Factors Sample Collection, Preparation, and Processing Even with the establishment of a relatively homogeneous cohort for external validation or

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clinical trials, results from the intended biomarker assays are valid only if sample integrity is maintained and reproducible from sample collection through analysis [22]. Major components of assay and technical validation are: • • • • • • •

Reference materials Quality controls Quality assurance Accuracy Precision Sensitivity Speciﬁcity

Prior to the start of a validation phase, the team needs to decide on the sampling procedure, sample quality parameters, and sample processing. Reproducibility is the key to successful biomarker validation. It is important that standardized operating protocols (SOPs) for sample collection, processing, and storage be established to provide guidance for the centers and the laboratory teams. Nurses and technicians should be trained to minimize the variability in sample collection and handling. Most biomarkers are endogenous macromolecules which can be measured in human biological ﬂuids or tissues [39]. The collection process for these specimens seems straightforward. However, depending on the type of biomarker (genomic, metabolomic, or proteomic) and the collection methods, various factors may need to be taken into account when designing the SOPs [22]. Several examples are given in Table 1. Processing the samples in a core facility reduces the risk of handling bias, since the same personnel would handle all samples in the most reproducible way possible. Once the samples are collected, systematic monitoring of their quality over time should also be established. Random tests can be conducted to ensure the short-term, benchtop, or long-term stability of the samples to uphold the integrity of the biolibrary [38].

TABLE 1

Sample Considerations

Collection (Biological Fluids or Tissues) Type of needle Type of collection tube or ﬁxation Location of collection Time of collection Status of patient

Preparation and Processing

Storage

Dilution Plasma or serum isolation

Type of storage containers Temperature of storage

Temperature of processing Reagents used

Duration of storage

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Unfortunately, during external and clinical validations, it is likely that the independent collaborators utilize a completely different set of SOPs. Even though this could ultimately contribute to the robustness of the validation process, it may be useful to establish speciﬁc guidelines that would help minimize site-to-site variability. There are several ways to achieve this. Provision to the laboratories and centers of kits that contain all chemicals needed for the processing of the sample can potentially reduce the risk of batch effects. Similarly, as pointed out in Figure 5, it may be feasible to collect a separate set of samples internally (to minimize sample collection variability) but send them to a collaborator’s site for external validation using independent processing and analytical SOPs. It is of utmost importance that any deviation from the SOPs is noted. These features can then later be accommodated by statisticians for better modeling. Any available information on bias or deviation from protocols or batch processing is useful in the computational process. Excluding them may inﬂuence the decision as to whether a biomarker was or was not validated. Sustained Quality Assurance, Quality Control, and Validation of the Biomarker Tests or Assays The aforementioned cohort (i.e., patient selection) and technical factors (i.e., sample collection, processing, and storage) can all have a signiﬁcant impact on any of the phased strategies to biomarker validation shown in Figure 1. However, in reality, validation of the biomarker test is just as important as the validation of the biomarker itself. To improve the chance of successful translation from external and clinical validation results to patient care, the analytical validity of the test (does the test measure the biomarker of interest correctly and reliably?) should be closely monitored along with the clinical validity of the biomarker (does the biomarker correlate with the clinical presentation?) [8,12]. Furthermore, to sustain the quality assurance and quality control between different stages of biomarker development, it may be necessary to carry out multiple analytical or technical validations when more than one platform is used. Computational Factors In addition to the statistical and bioinformatical factors in the validation process described earlier, the team must also be aware that a vast number of samples impose a huge computational burden on software and hardware. This is especially true when high-throughput and high-performance technologies or high-density arrays need to be used for the validation process. Not considering this potential issue may ultimately be costly in terms of money and time. To deal with the massive amount of data generated from these technologies, new statistical techniques are continuously being developed. Statistical methods should include those that were used successfully in the discovery phase. If the performance is not as good in prior analyses, the reﬁnement of algorithms will be necessary (“adaptive statistics”).

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Other Challenges of Clinical Biomarker Validation There are many additional barriers and concerns for clinical validation of biomarkers: • Choice of matrix (readily accessible, effect on biomarker concentrations) • Variability (interindividual and intraindividual) • Preparing calibration standards • Implementations of quality control to assure reproducibility • Limited availability of clinical specimens • Heterogeneity of biomarkers (isoforms, bound states) • IP protection (lack of collaboration) • Lack of clear regulatory guidance As discussed, the discovery and validation processes involve multicenter studies with large patient cohorts and technical equipment, steered by a vast number of staff members. The enormous costs for these studies can in most cases be covered only by consortia, consisting potentially of academic centers and pharmaceutical companies. The establishment of such consortia often has to overcome legal hurdles and IP issues, which may be a time-consuming process. As mentioned above, establishment of a biomarker may involve the recruitment of hundreds of patients per treatment arm. Both recruitment time and success rate are unpredictable at the beginning of the study phase. It is during the initial phase that the team needs to determine what really constitutes a good biomarker. Key questions in this decision include: What is the threshold for decision making that needs to be established to call a biomarker “useful”? How do we achieve a “robust” biomarker: a marker that is not easily inﬂuenced by such factors as location and personnel? How economical does the biomarker test need to be for it to be used? Which biomarker matrix should be selected, since this may affect future validation possibilities? Accessible matrices such as urine or blood with limited or known concentration variability are ideal.

BIAS AND VARIABILITY: KEY POINTS REVISITED To summarize the sections above, variability is a major obstacle in biomarker validation, regardless of the matrix, the type of biomarker, and its use. As noted above, there are two types of variability: intraindividual variability, which is usually related to lab techniques, sample timing, drug effects, and within-subject biological processes, and interindividual variability, resulting from different individual responses involving multiple genetic factors [40]. Biological variability may be difﬁcult to assess, but it is important to control

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TABLE 2

Fundamental Concerns in Biomarker Validation

Overﬁtting

Bias Generalizability

Concern

Solutions

False positives and negatives High sensitivity and speciﬁcity found but fail on independent validation sets Misidentiﬁcation of differences between samples Can results be applied to appropriate clinical populations?

Increase sample size Statistical approaches Receiver operator characteristic curves Control for confounding factors Representative validation cohorts

for this factor. A statistical correlation to a clinical endpoint for a candidate biomarker cannot be determined without assessing biological variability, as the overall noise of a sample is a sum of both analytical and biological variability [6]. A biomarker with wide biological variability or time ﬂuctuations that are difﬁcult to control may be rejected [6]. Diurnal variability may require sample pooling or collection at the same time of day [6]. Biomarkers have diverse molecular structures, including possible bound states, which also need to be considered as inﬂuences on variability. There are speciﬁc considerations for any biomarker validation study. Overﬁtting, bias, and generalizability are three of the most fundamental concerns pertaining to clinical biomarker validation (Table 2) [41]. With the introduction of high-throughput discovery strategies, overﬁtting has become a particular fear. These discovery platforms are designed to measure countless analytes, and there is therefore a high risk of false discovery. When a large number of variables are measured on a small number of observations to produce high sensitivity and speciﬁcity, the results may not be reproducible on independent validation sets. Some biomarker candidates may be derived simply due to random sample variations, particularly with inadequate sample sizes [26]. A false positive may be thought of as critical part of a disease process, when in fact it is either associated only loosely or coincided randomly with disease diagnosis or progression [3]. As mentioned earlier in the chapter, a biomarker may correlate with a disease statistically but not prove to be useful clinically [8,42]. Increasing sample size and use of receiver operator characteristic curves may help overcome this concern of overﬁtting. Bias is another major concern during biomarker validation, as there is often potential for misidentifying the cause of the differences in biomarkers between samples. Confounding variables such as age, race, and gender should be controlled for either through statistical modeling or validation study design to limit the effects of bias. Since validation cohorts require suitable diversity for widespread utility, bias may be difﬁcult to avoid entirely through study design.

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Similar to bias, most issues pertaining to the generalizability of a biomarker across clinical populations can be addressed through careful consideration of cohort selection. Cohort factors were discussed brieﬂy above. To increase generalizability, the later phases of validation should include more rigorous testing of potential interfering endogenous components by including more diverse populations with less control of these confounding variables [22]. For example, in later-stage clinical trials there should be less control of diet and sample collection and more concomitant medications and co-morbidities [22]. This will allow a biomarker to be used in more clinically diverse situations.

KEY MESSAGES Many of the biomarkers in current clinical use have become accepted via debate, consensus, or merely the passage of time [13]. This rather unofﬁcial establishment of biomarkers in the past has been very inefﬁcient. Importantly, biomarkers can no longer become accepted in this way, as they would fail to meet the current regulatory standards of modern medicine. Contemporary biomarkers must be tested in highly regulated human clinical trials [43]. To date, the clinical trial process has not been very efﬁcient, and a typical biomarker life cycle from discovery to clinical use may take decades. For example, the evolution of prostate-speciﬁc antigen (PSA) as a biomarker for prostate disease diagnosis and monitoring took 30 years for regulatory approval by the FDA [44]. In order to expand on the biomarker repertoire used currently in clinical practice, the acceptance process for new biomarkers needs to become much more efﬁcient and cost-effective. The general problem of lack of regulatory guidance is very likely to be addressed formally by regulatory bodies in the near future. Problems such as the availability of samples will also probably improve as collaborations are established and the ethical issues surrounding biobanks are clariﬁed. Even if a biomarker candidate fails during validation, much may be learned regarding the pathophysiology of the disease and the corresponding drug effects during the process [9]. However, without successful validation and integration of biomarkers into clinical use, much of the research effort, particularly in terms of biomarkers for drug development, can be futile.

REFERENCES 1. Biomarkers Deﬁnition Working Group (2001). Biomarkers and surrogate endpoints: preferred deﬁnitions and conceptual framework. Clin Pharmacol Ther, 69(3):89–95. 2. Colburn WA (1997). Selecting and validating biologic markers for drug development. J Clin Pharmacol, 37(5):355–362.

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CLINICAL VALIDATION AND BIOMARKER TRANSLATION

3. Colburn WA (2003). Biomarkers in drug discovery and development: from target identiﬁcation through drug marketing. J Clin Pharmacol, 43(4):329–341. 4. Boguslavsky J (2004). Biomarkers as checkpoints. Drug Discov Dev, Sept. 5. Hunt SM, Thomas MR, Sebastian LT, et al. (2005). Optimal replication and the importance of experimental design for gel-based quantitative proteomics. J Proteome Res, 4(3):809–819. 6. Lee JW, Figeys D, Vasilescu J (2007). Biomarker assay translation from discovery to clinical studies in cancer drug development: quantiﬁcation of emerging protein biomarkers. Adv Cancer Res, 96:269–298. 7. Listgarten J, Emili A (2005). Statistical and computational methods for comparative proteomic proﬁling using liquid chromatography–tandem mass spectrometry. Mol Cell Proteom, 4(4):419–434. 8. Bast RC Jr, Lilja H, Urban N, et al. (2005). Translational crossroads for biomarkers. Clin Cancer Res, 11(17):6103–6108. 9. Kuhlmann J (2007). The applications of biomarkers in early clinical drug development to improve decision-making processes. Ernst Schering Res Found Workshop, 59:29–45. 10. Mandrekar SJ (2005). Clinical trial designs for prospective validation of biomarkers. Am J Pharmacogenom, 5(5):317–325. 11. Lachenbruch PA, Rosenberg AS, Bonvini E, Cavaille-Coll MW, Colvin RB (2004). Biomarkers and surrogate endpoints in renal transplantation: present status and considerations for clinical trial design. Am J Transplant, 4(4):451–457. 12. Harper CC (2007). FDA Perspectives on Development and Qualiﬁcation of Biomarkers, in Rediscovering Biomarkers: Detection, Development and Validation, GTCbio; San Diego, CA. 13. Goodsaid F, Frueh F (2006). Process map proposal for the validation of genomic biomarkers. Pharmacogenomics, 7(5):773–782. 14. Bonassi S, Neri M, Puntoni R (2001). Validation of biomarkers as early predictors of disease. Mutat Res, 480–481:349–358. 15. Wagner JA (2002). Overview of biomarkers and surrogate endpoints in drug development. Dis Markers, 18(2):41–46. 16. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther, 81(1):104–107. 17. Goodsaid F, Frueh F (2007). Biomarker qualiﬁcation pilot process at the US Food and Drug Administration. AAPS J, 9(1):E105–E108. 18. Baker M (2005). In biomarkers we trust? Nat Biotechnol, 23(3):297–304. 19. Benowitz S (2004). Biomarker boom slowed by validation concerns. J Natl Cancer Inst, 96(18):1356–1357. 20. Lee JW, Weiner RS, Sailstad JM, et al. (2005). Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res, 22(4):499–511. 21. Ilyin SE, Belkowski SM, Plata-Salaman CR (2004). Biomarker discovery and validation: technologies and integrative approaches. Trends Biotechnol, 22(8): 411–416.

REFERENCES

397

22. Lee JW, Devanarayan V, Barrett YC, et al. (2006). Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res, 23(2):312–328. 23. Peck RW (2007). Driving earlier clinical attrition: If you want to ﬁnd the needle, burn down the haystack. Considerations for biomarker development. Drug Discov Today, 12(7–8):289–294. 24. Groopman JD (2005). Validation strategies for biomarkers old and new. AACR Educ Book, 1:81–84. 25. Normolle D, Rufﬁn MT IV, Brenner D (2005). Design of early validation trials of biomarkers. Cancer Inf, 1(1):25–31. 26. Jarnagin K (2006). ID and Validation of biomarkers: a seven-fold path for deﬁning quality and acceptable performance. Genet Eng Biotech News, 26(12). 27. O’Connell CD, Atha DH, Jakupciak JP (2005). Standards for validation of cancer biomarkers. Cancer Biomarkers, 1(4–5):233–239. 28. Simon R (2005). Roadmap for developing and validating therapeutically relevant genomic classiﬁers. J Clin Oncol, 23(29):7332–7341. 29. Simon R (2005). Development and validation of therapeutically relevant multigene biomarker classiﬁers. J Natl Cancer Inst, 97(12):866–867. 30. Bleeker SE, Moll HA, Steyerberg EW, et al. (2003). External validation is necessary in prediction research: a clinical example. J Clin Epidemiol, 56(9):826–832. 31. Al-Shahrour F, Diaz-Uriarte R, Dopazo J (2004), FatiGO: A Web tool for ﬁnding signiﬁcant associations of gene ontology terms with groups of genes. Bioinformatics, 20(4):578–580. 32. AmiGO. http://amigo.geneontology.org/cgi-bin/amigo/go.cgi. 33. Ingenuity Pathways Analysis. http://www.ingenuity.com/products/pathways_ analysis.html. 34. MetaCore Gene Expression and Pathway Analysis. http://www.genego.com/ metacore.php. 35. Moore RE, Kirwan J, Doherty MK, Whitﬁeld PD (2007). Biomarker discovery in animal health and disease: the application of post-genomic technologies. Biomarker Insights, 2:185–196. 36. Ransohoff DF (2005). Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer, 5(2):142–149. 37. McCormick T, Martin K, Hehenberger M (2007). The evolving role of biomarkers: focusing on patients from research to clinical practice. Presented at the IBM (Imaging) Biomarker Summit III, IBM Corporation; Nice, France. 38. Maruvada P, Srivastava S (2006). Joint National Cancer Institute–Food and Drug Administration workshop on research strategies, study designs, and statistical approaches to biomarker validation for cancer diagnosis and detection. Cancer Epidemiol Biomarkers Prev, 15(6):1078–1082. 39. Colburn WA, Lee JW (2003). Biomarkers, validation and pharmacokinetic– pharmacodynamic modelling. Clin Pharmacokinet, 42(12):997–1022. 40. Mayeux R (2004). Biomarkers: potential uses and limitations. NeuroRx, 1(2): 182–188.

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CLINICAL VALIDATION AND BIOMARKER TRANSLATION

41. Early Detection Research Network. Request for Biomarkers. Attachment 2: Concepts and Approach to Clinical Validation of Biomarkers: A Brief Guide. http://edrn.nci.nih.gov/colops/request-for-biomarkers. 42. Katton M (2003). Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst, 95:634–635. 43. NCI (2006). Nanotechnology-Based Assays for Validating Protein Biomarkers. NCI Alliance for Nanotechnology in Cancer, Bethesda, MD, Nov.–Dec. 44. Bartsch G, Frauscher F, Horninger W (January 2007). New efforts in the diagnosis of prostate cancer. Presented at the IBM (Imaging) Biomarker Summit III, IBM Corporation; Nice, France, Jan.

20 PREDICTING AND ASSESSING AN INFLAMMATORY DISEASE AND ITS COMPLICATIONS: EXAMPLE FROM RHEUMATOID ARTHRITIS Christina Trollmo, Ph.D., and Lars Klareskog, M.D., Ph.D. Karolinska Institute, Stockholm, Sweden

INTRODUCTION Chronic inﬂammatory diseases include a number of rheumatic, neurological, dermatological, and gastrointestinal diseases, which develop as a result of immune and inﬂammatory reactions. These perpetuating reactions ultimately cause the clinical symptoms, which are subsequently used to classify the symptoms as a “disease.” Analyzing the disease course longitudinally reveals several distinct steps during disease progression, for which the presence of biomarkers is of importance, both for identiﬁcation of disease status and for prediction of disease course and treatment options. However, biomarkers per se are not always available today. We have chosen here to discuss disease characteristics and potential biomarkers in one common chronic inﬂammatory disease, rheumatoid arthritis (RA), which affects approximately 0.5 to 1% of the population worldwide. In this chapter we focus on the following factors: 1. Onset of disease in order to discuss the questions in whom and why the disease occurs, and whether and how onset can be predicted

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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from biomarkers that are present before the occurrence of clinical symptoms 2. Progression of disease with respect to development of joint destruction, but also other with respect to complications such as extra-articular manifestations, cardiovascular events, and lymphoma development in this patient group 3. Treatment of the individual patient and of speciﬁc symptoms and disease manifestations 4. Selection of patients in clinical trials of new drugs

RHEUMATOID ARTHRITIS DISEASE PROCESS Rheumatoid arthritis (RA) is a disease deﬁned by seven criteria, with four that should be fulﬁlled to make the diagnosis (Table 1). These criteria have been useful in harmonizing clinical trials and clinical practice. However, they are not based on what is now known about etiology or pathogenesis, and they are not too helpful in selecting treatment for the single patient. Hence, there are needs to redeﬁne the diagnosis for RA and related diseases, ﬁrst to deﬁne entities more related to distinct etiologies and pathogenetic mechanisms, then to use such new entities for stratiﬁcation and selection of patients in clinical trials and clinical practice. Basic features of the immune and inﬂammatory process in RA are, on the one hand, processes that can be identiﬁed in the peripheral circulation, initially

TABLE 1

Classiﬁcation Criteria for Rheumatoid Arthritisa

1. Morning stiffness in and around joints lasting at least 1 hour before maximal improvement 2. Soft tissue swelling (arthritis) of three or more joint areas observed by a physician 3. Swelling (arthritis) of the proximal interphalangeal, metacarpophalangeal, or wrist joints 4. Symmetric swelling (arthritis) 5. Rheumatoid nodules 6. Presence of rheumatoid factor 7. Radiographic erosions and/or periarticular osteopenia in hand and/or wrist joints Source: Arnett FC, Edworthy SM, Bloch DA, et al. (American Rheumatism Association) (1988). The American Rheumatism Association 1987 revised criteria for the classiﬁcation of rheumatoid arthritis. Arthritis Rheum, 31:315–324. a Criteria 1 through 4 must have been present for at least six weeks. Rheumatoid arthritis is deﬁned by the presence of four or more criteria, and no further qualiﬁcations (classic, deﬁnite, or probable) or list of exclusions are required. These criteria demonstrated 91 to 94% sensitivity and 89% speciﬁcity for RA compared with non-RA rheumatic disease control subjects.

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bone Joint capsule

cartilage

Synovial membrane

Synovial fluid

Figure 1 Inﬂamed RA joint. In healthy joints a thin synovial membrane lines the joint capsule and the synovial ﬂuid. In the RA joint both the synovial membrane and the synovial ﬂuid are inﬁltrated by inﬂammatory cells, leading to tender and swollen joints. The synovial membrane also “grows” over the cartilage, aiding in the process of cartilage and bone desctruction.

rheumatoid factors (RFs), and on the other hand, processes in the inﬂamed tissue, mainly the joints (Figure 1). Rheumatoid factors, identiﬁed almost 70 years ago, are part of the diagnostic criteria for RA. Being present in some 50 to 60% on incident RA cases and increasing over time with active disease, these auto-antibodies are also seen in many non-RA conditions and are thus not very speciﬁc for the disease. Rheumatoid factors have never been shown to be pathogenic by themselves, neither in patients nor in experimental models. They are thus seen mainly as biomarkers of importance for diagnosis and prognosis of a more severe disease course, but not necessarily directly involved in disease pathogenesis. Joint inﬂammation in RA is focused on synovial inﬂammation, in many cases associated with cartilage destruction and concomitant erosions in bone. This inﬂammation has been studied in large detail over the years, demonstrating that its features are common to many other types of chronic inﬂammation in other tissues. No real RA pathognomonic features have yet been identiﬁed, the most unique feature identiﬁed so far being the way the inﬂammatory cells and molecules attack and destroy bone and cartilage. Having the major features of RA, the synovial joint inﬂammation and presence of RF being typical but by no means unique for RA, there is an obvious need to deﬁne more speciﬁc features of the disease. The identiﬁcation of such features would enable us to search for a better understanding of the pathogenesis of RA, a more accurate diagnosis based on biomarkers, and more speciﬁc treatments.

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STUDIES ON ETIOLOGY AND PATHOGENESIS AS A BASIS FOR DEVELOPMENT OF BIOMARKERS FOR DIAGNOSIS AND PROGNOSIS IN RA Any understanding of a complex, partly genetic disease is based on an understanding of how genes and environment interact in giving risk to immune reactions that contribute to the joint destruction and other inﬂammatory reactions in RA. In healthy subjects, the major role for the immune system and subsequent inﬂammatory reactions is to defend us against pathogens, but in RA the immune system has partly changed focus to attack our own tissues, primarily the joints, and is thus denoted as an autoimmune disease. Genes There is strong evidence to support a signiﬁcant genetic component to the susceptibility of RA. Twin studies clearly demonstrate an overrepresentation of disease concordance in monozygotic twins (12 to 20%, depending on study) compared to dizygotic twins (4 to 5%) and the general population (0.5 to 1%). Analysis on the gene level demonstrates the strongest genetic association with genes within the HLA region, speciﬁcally the HLA-DRB1 gene. Its gene products, the MHC class II molecules, were described in the 1970s to be present on cells in the inﬂamed joint, allowing antigens (part of proteins, generally pathogens, but in autoimmune diseases self-proteins) to be presented to the immune system and subsequently to trigger inﬂammatory reactions (Figure 2). Serologic and later genetic typing of the various MHC molecules revealed that a few allotypes of HLA-DRB1 were overrepresented in RA patients. A closer analysis demonstrated even identical amino acid

Figure 2 Inﬂammatory cells in the RA joint. A number of immune cells have inﬁltrated the joint and local production of inﬂammatory mediators, including cytokines and antibodies, occurs. The synovial ﬂuid, which functions as a cushion during joint movements, is in a healthy joint acellular. Illustrated to the left is the presentation of an antigen by the dendritic cell to a T-cell; the yellow connector is a MHC class II molecule. Cytokines, released from the immune cells, function as signaling molecules between cells. (See insert for color reproduction of the ﬁgure.)

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sequences in those regions of the MHC class II DRβ1 chain, which are in contact with the antigen and the T-cell receptor on the T-cell mediating the speciﬁc immune responses. This amino acid sequence is termed shared epitope. The nature of the speciﬁc immune reactions mediated by the MHC class II molecules has, however, been surprisingly difﬁcult to deﬁne. Identifying the speciﬁc antigens would help to identify the autoimmune trigger and make it possible to interfere speciﬁcally to break the autoimmune reactions. In total, the HLA region contributes 30 to 50% of the genetic component for RA in Caucasians. It appears that other MHC class II genes and allotypes may contribute to RA in other ethnic groups, and much is still not known about the contributions of different genes and allotypes within the MHC concerning susceptibility and disease course in RA. Only much more recently, a second genetic risk allele has been identiﬁed in populations of European descent. This minor allele of a nonsynonomous single-nucleotide polymorphism (SNP) in the protein tyrosine phosphatase nonreceptor 22 (PTPN22) gene confers the second-largest genetic risk to the development of RA, with an odds ratio of about 1.8. PTPN22 encodes the intracellular protein lymphoid tyrosine phosphatase, which plays a central role in immune responses by playing an integral part in signal transduction and T-cell receptor signaling pathway and inhibiting T-cell activation. This variant was ﬁrst demonstrated for type 1 diabetes, but was soon conﬁrmed in a number of autoimmune diseases, including RA, systemic lupus erythematosus (SLE), and Grave disease. However, other autoimmune diseases show no association with this SNP, suggesting subsets of autoimmune diseases to be deﬁned accordingly. The recent introduction of whole genome-wide association studies allowed, for the ﬁrst time, good coverage of common variations in the human genome. Hundreds of thousands of SNPs in thousands of samples were genotyped and compared. Interestingly, studies focusing on RA again demonstrated the strongest effects for the two well-documented RA susceptibility genes HLADRB1 and PTPN22. Other genes (Table 2) make a more modest contribution to susceptibility. Future studies have to identify remaining, probably smaller, genetic effects and how all the genetic effects interact with each other as well as with environmental factors in inducing and perpetuating the disease.

TABLE 2 Contribution of Genetic Risk Factors in Rheumatoid Arthritis Gene HLA PTPN22 6q23 STAT4 TRAF1/C5

Odds Ratio 6.4 1.8 1.2 1.2–1.4 1.1–1.4

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Environment Information on environmental factors important for the development, perpetuation, or course of RA is surprisingly scarce. Smoking is the only conventional environmental factor that has been linked reproducibly to an increased risk of developing RA. Other exposures, such as silica dust and mineral oils, have been reported in a few studies. It has not yet been possible to verify frequently hypothesized stimuli such as microbial infections with the methods used to date. Smoking was initially considered as an unspeciﬁc risk factor, of interest mainly from a public health perspective. However, newer studies indicate that smoking is a speciﬁc trigger of RA, as discussed in more detail below.

Immunity Studies on speciﬁc immune reactions in RA have been conﬁned almost entirely to those involving autoantibodies. As described above, they were initially restricted to the measure of rheumatoid factors, but more recently, antibodies speciﬁc for citrullinated proteins have been shown to be of great importance. This is discussed in detail below, since their presence covariates with genetic and environmental factors, providing an important tool in subgrouping patients into different entities of RA. In the process of joint inﬂammation and cartilage and bone destruction, many different cells and molecules of the immune system participate. Some of them are illustrated in Figure 2. However, even if some of these inﬂammatory processes are revealed in detail, a speciﬁc trigger has still not been identiﬁed. In recent years, advances within the ﬁeld of cytokine regulation and cytokine-directed therapy have largely dominated the research ﬁeld of RA, illustrating how therapeutic progress is possible even though the role of adaptive immunity in the disease is not fully understood. Cytokines are soluble molecules that mediate the communication between cells of the immune system but also with other cells of the body, such as the endothelium. Interestingly, the ﬁrst cytokine that was targeted, tumor necrosis factor (TNF), belongs to the innate immune system. Blocking IL-1, another cytokine belonging to the innate immune system, has not proven to be as effective as TNF blockade for the majority of the RA patients. Recent clinical trails blocking a third cytokine in this family, IL-6, show promising results. This cytokine exerts effects within both the innate and the adaptive immune systems. Temporarily eliminating B-cells, which are the producers of antibodies, has also proven a successful therapy. A third alternative, blocking the interaction between cells presenting antigens and T-cells, has resulted in an approved therapy. Together, these treatment-based data also demonstrate the signiﬁcant role of the various parts of the innate as well as adaptive immune system for disease progression.

BETTER DIAGNOSIS BY MEANS OF BIOMARKERS AND GENETICS

405

Citrulline Immunity The presence of antibodies speciﬁcally identifying citrullinated antigens, speciﬁcally termed antibodies to citrullinated protein antigens (ACPAs) are a strong predictor of developing RA. Approximately 60% of all RA patients carry such antibodies. These antibodies are highly speciﬁc for the disease; that is, they are rare in the normal population (<2%) and are also quite rare in other inﬂammatory conditions. Almost all patients who carry ACPAs developed them before disease onset. ACPAs are directed against proteins, which as the name implicates, contain the amino acid citrulline. Citrulline occurs only upon posttranslational modiﬁcation of the amino acid arginine by enzymatic conversion. Even if these auto-antibodies are more speciﬁc for RA than for RF, it is important to ask the question if their presence is an epiphenomenon or is causally related to the disease. This helps in further dissection of the pathophysiology of the disease and in the development of new treatments. Here, animal models provide a tool to test the hypothesis of citrullination to be causative of the disease. One example is arthritis induced in the rat by immunization with collagen type II, a protein expressed almost exclusively in the joint. Immunization with the citrullinated form of collagen type II resulted in a more severe arthritis than immunization with the same noncitrullinated protein. In a mouse model, where transfer of antibodies directed toward collagen induces a mild arthritis, the addition of antibodies directed against citrullinated ﬁbrinogen enhanced the arthritis. Thus, in experimental arthritis models citrullination can change the immunogenicity of self-antigens, and some citrullinated proteins may contribute to arthritis development. From the data above we can conclude that immunity to citrullinated proteins may play a pathogenic role in a subset of RA patients; however, so far we have not identiﬁed the speciﬁc proteins that are targeted by the immune system. How do we then measure ACPAs? Test kits have been developed which contain cyclic citrullinated peptides (CCPs), where the amino acid citrulline is ﬂanked by a variety of amino acids to mimic many different peptide sequences. Thus, the test measures anti-CCP antibodies, and this is what is generally reported in patient ﬁles. In summary, the presence of ACPAs in the circulation is more predictive for RA than high blood pressure is for cardiovascular disease and thus can be considered as a biomarker for a subset of RA patients.

BETTER DIAGNOSIS BY MEANS OF BIOMARKERS AND GENETICS By the late 1970s, the MHC class II genes were already identiﬁed as a major genetic risk factor for RA. However, only the presence of speciﬁc MHC class II alleles does not result in RA, since the disease-associated HLA-DRB1 alleles are common also in healthy subjects. But combining genetic studies

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with ACPA analysis, the genetic association of RA to HLA-DRB1 SE has recently been shown to be conﬁned entirely to the ACPA-positive subset of RA. In contrast, ACPA-negative RA may be associated with a unrelated HLA-DR allele, HLA-DR3. Also a second major genetic risk factor for RA, the polymorphism in the PTPN22 gene was also shown to be associated only with the ACPA-positive disease. In contrast, other genetic risk factors, in particular variations in the interferon regulating factor 5 (IRF-5), but also polymorphisms in a newly identiﬁed risk gene in the C-type lectin complex, were associated exclusively with ACPA-negative disease. Taken together, the descriptive studies of disease course and genetic linkages strongly indicate that ACPA reactivity splits RA into two major and clinically relevant subsets of disease and thus becomes an important biomarker for one subgroup of RA. Practically, ACPA-positive and ACPA-negative RA should be treated as separate entities when studying the molecular pathophysiology of RA and probably also when selecting treatment for some treatments for the single patient (Figure 3). Including smoking habits in these combined analyses provides an even clearer risk assessment for developing RA. The relative risk of developing ACPA-positive RA was shown to be over 20 times higher for smokers carrying two copies of the HLA-DRB1 SE alleles than for nonsmokers with no SE alleles. On the other hand, no increased risk was discerned from smoking concerning development of ACPA-negative RA. This leads directly to the question of whether smoking can trigger an ACPA immunity. Recent studies suggest that this could be the case since cells from bronchoalveolar lavage from smokers, but not from nonsmokers, contain large amounts of citrullinated proteins, and smoking can activate macrophages and thus enhance their antigen-presenting capacity. The disease onset of RA can be gradual, and thus patients can have swollen and tender joints without being given the diagnosis RA, which is based on

Pre-disease factors Genetic and environmental factors: MHC class II, PTPN22 Smoking ACPA

Diagnosis RA

Disease progression ACPA-positive RA ACPA-negative RA

Unspecific arthritis

Unspecific arthritis

Figure 3 Rheumatoid arthritis is a multifactorial disease. A number of predisease factors have been identiﬁed, which are of importance for the later disease course but not enough to initiate clinical disease. However, the trigger for clinical disease onset has still not been identiﬁed. The presence or absence of anticitrulline protein antibodies (ACPAs) distinguishes two forms of RA. Some patients will have swollen and tender joints but not fulﬁll four of the seven criteria for RA and thus carry an unspeciﬁc arthritis.

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407

achieving four of seven speciﬁc criteria. These patients are classiﬁed as undifferentiated arthritis (UA), having an inﬂammatory arthritis for which no speciﬁc diagnosis can be made. An important question is if these patients should be treated in order to ameliorate disease progression to RA and retard radiographic joint damage or even prevent the development of RA in these patients. A recent double-blind, placebo-controlled, randomized clinical trial indicated that methotrexate (MTX) treatment as used for RA patients can postpone progression to RA and retard radiographic progression but not prevent the development of RA from UA. Subgroup analysis revealed that the beneﬁcial outcome was most pronounced in patients with anti-CCP antibodies. In contrast, in the anti-CCP-negative subgroup the effect of MTX on the development of RA, the radiographic progression, and even the signs and symptoms was not demonstrable. Thus, this study provides evidence of distinguishing between anti CCP-positive and CCP-negative arthritis, even before the diagnosis RA is met, and therefore also becomes an important biomarker for undifferentiated arthritis. The identiﬁcation of predisease biomarkers might in the future lead to treatments which can be given to healthy subjects before the clinical diagnosis of RA and so prevent disease onset. An example already applied in medicine is the treatment of high blood pressure to prevent cardiovascular disease.

BETTER PROGNOSTIC TOOLS WITH THE HELP OF BIOMARKERS AND GENETICS Rheumatoid arthritis affects not only joints but also possibly, inﬂammation in other organs. Manifestations include rheumatoid nodules, secondary Sjögrens syndrome, rheumatoid lung disease, pleuritis, pericarditis, vasculitis, neuropathy, Felty syndrome, and severe eye disease, and are commonly summarized as extraarticular manifestations. Different co-morbidities also occur during the disease course. Cardiovascular morbidity and mortality occur at rates greater than would be expected from the proﬁle of established risk factors. The average risk of lymphoma is increased in RA; however, a more detailed analysis indicates that the risk is increased primarily in patients with the most severe disease (Figure 4). Extraarticular Disease Manifestations Extraarticular disease manifestations are more likely to occur in patients with more severe joint disease. Rheumatoid nodules often precede the onset of severe RA and thus can be considered a biomarker for severe disease. Also, their clustering with all different extraarticular manifestations indicate that vascular pathogenetic mechanisms are important in all types of extraarticular RA. This is of particular interest given the association of RA with cardiovascular co-morbidity. The different manifestations tend to cluster in speciﬁc

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atory

n

ase i

Incre

m inflam

en

burd

Lymphoma Cardiovascular disease Severe joint destruction

Figure 4 Inﬂammatory burden. With increased accumulated inﬂammatory burden the risk for additional disease states and complications increases for patients with RA. It is thus of importance to control the inﬂammation at all time points, a difﬁcult task when not all patients respond to the antirheumatic therapies available today.

patterns and suggest shared disease mechanisms in these systemic manifestations. There are very likely several genetic and environmental factors affecting the development of these manifestations, of which some are described below. An association of HLA-C3 with vasculitis has been described. HLA-C3 is the product of a speciﬁc MHC class I gene and thus with CD8+ cytotoxic T-cells and NK-cells, in contrast to MHC class II proteins, which interact with CD4+ T-helper cells. Smoking is an independent predictor of vasculitis and is probably involved in vascular damage by antigen modiﬁcation as described above. The increased number of patients with speciﬁc autoantibodies such as RF and ANAs (antinuclear antibodies) supports a role for immune complexes in the pathogenesis of vasculitis, which in turn indicates B-cell abnormalities in the development of extraarticular RA. In summary, extraarticular manifestation indicate a more severe disease; however, this risk group is today not routinely analyzed early in the disease course. Here, speciﬁc biomarkers could possibly aid an early aggressive treatment of this patient group in order to limit the progression of extraarticular manifestations. Cardiovascular Disease in RA Cardiovascular (CV) morbidity and mortality in RA occur at rates greater than would be expected from the proﬁle of established CV risk factors. Diabetes mellitus, hypercholesterolemia, hypertension, cigarette smoking, and obesity are generally powerful classiﬁers of CV risk and when comparing the proﬁles of these risk factors in people with and without RA, they are similar. Thus, there must be RA-speciﬁc factors explaining the increased risk. Dividing the patients in younger and older patients demonstrates that such factors are more common in the younger age group. In older patients, the

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proportion of atherosclerosis can be explained by established CV risk factors as described above. In contrast, in the younger age group, systemic inﬂammation seems to exert its atherosclerotic effects early. High-resolution carotid ultrasound is used to measure carotid intima media thickness (IMT). These measures may thus be used as a biomarker of subclinical atherosclerosis. Interaction calculations suggest that the ESR’s effect on IMT varies according to the number of CV risk factors; thus, higher ESR values, a measure for inﬂammation, are associated with greater IMT only in the presence of CV risk factors. In patients without CV risk factors, the ESR is not associated signiﬁcantly with IMT. Taking genetic and environmental components into consideration of CV disease in RA patients demonstrates that the presence of two SE alleles predicts death from cardiovascular disease in RA patients, and this is independent from autoantibody status. However, the greatest risk of death from CV disease is associated with an interaction among smoking, SE alleles, and anti-CCP antibodies. Interestingly, no association of the PTPN22 gene with mortality was detected. In summary, both established CV risk factors and RA manifestations account for a signiﬁcant proportion of atherosclerosis is RA. Factors related to RA may have a greater inﬂuence on the extent of atherosclerosis in young patients. The presence of established risk factors may be necessary for systemic inﬂammation to promote atherosclerosis; however, more studies are needed to understand these interactions. Lymphoma in RA Among RA patients there is in general a doubled risk for lymphoma. However, a closer analysis of disease severity, as measured by degree of joint destruction, ESR, number of affected joints, and accumulated inﬂammatory load, revealed a more pronounced relative risk in patients with the most severe disease and little or no increase in those with mild to moderate disease. This raises the question of whether lymphomas arise as a consequence of treatment or because of a lack thereof. Larger cohort studies have thus far not conﬁrmed any treatment-related excess of lymphoma in general. However, it is premature to make ﬁrm conclusions about the true lymphoma risk in patients treated with TNF antagonists. In summary, the data so far indicate that the inﬂammatory burden should be kept as low as possible to possibly reduce the risk for lymphoma development. Cartilage and Bone Destruction During the inﬂammation in the joint, cartilage and underlying bone are broken down. Matrix metalloproteinases (MMPs) are enzymes active in this process, which results in the release of a number of collagenous and noncollageneous cartilage and bone-derived molecules. Serum levels of MMP-3 and urinary

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levels of c-telopepetide of collagen type II (CTX II) have been identiﬁed as two independent baseline factors that predict radiographic progression. Upon successful MTX treatment, MMP-3 levels decrease. Also, serum cartilage oligomeric matrix protein (COMP) levels are predictive of radiographic damage. Proof-of-concept studies demonstrate that targeting RANK-mediated osteoclastogenesis prevents inﬂammatory bone loss, and clinical application has only just begun by blocking RANKL. Replacing radiographic measures with measures of biochemical molecules in serum and/or urinary samples might in the future become the preferred way to measure joint destruction speciﬁcally. Traditionally, high inﬂammatory burden and measures of CRP have been included in the prediction of joint destruction, but the concomitant measures of inﬂammation and joint destruction indicate that the inﬂammatory and destructive processes are uncoupled and therefore need to be identiﬁed and treated separately.

BIOMARKERS PROVING LEADS CONCERNING EFFECTS OF TREATMENT OF RA The drugs used in the treatment of RA are generally divided into three groups: nonsteroidal anti-inﬂammatory drugs (NSAIDs) to relieve pain, traditional disease-modifying antirheumatic drugs (DMARDs), and biologics. There are today some general guidelines for the treatment of RA; however, biomarkers guiding the most adequate selection for the single patient are missing. In the majority of RA patients, treatment with methotrexate or other DMARDs only partly reduces clinical and radiological progression of the disease. For many patients, therefore, the introduction of the biological drugs provided a major breakthrough. By blocking the proinﬂammatory cytokine TNF, joint progression could be halted much more efﬁciently than before in a large group of patients. More recent drugs eliminating circulating B-cells or blocking co-stimulation of T-cells also indicate a slowdown in joint destruction. Measuring levels of TNF in the inﬂamed tissue has been suggested to be a biomarker for treatment response to TNF blocking agents. Unfortunately, it has yet been possible to describe such a marker for serum samples. With the identiﬁcation of the association of smoking, SE alleles, and antiCCP antibodies with the greatest risk of death from CVD, it will be possible to be more active in treatment to prevent cardiovascular disease in these patients at high risk for CV disease. In the treatment of lymphoma, which in the majority of cases are B-cell derived, a speciﬁc treatment option exists for RA patients. Rituximab was established in 1998 as a drug to treat patients with B-cell lymphomas, and the indication was extended in 2006 to treat patients with moderate to severe RA, independent of lymphoma associations. Thus, in these cases both the lymphoma and the RA are treated with the same drug.

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CONCLUDING REMARKS In summary, RA is a heterogeneous disease with regard to severity of the joint inﬂammation and other inﬂammatory manifestations. To prevent disease onset or progression of disease in the single patient, distinct biomarkers are needed, and only a few have yet been identiﬁed. The fact that the presence or absence of ACPAs divides the disease into two distinct entities will very likely inﬂuence future treatments and clinical trials. Clinical trials, if not already focusing on a subgroup of patients, need also to be adequately powered to allow subgrouping patients according to genetic setup, environmental exposures, and different nonsynovial inﬂammations in order to ﬁnd biomarkers for individualized therapies. Today’s and tomorrow’s biological therapies targeting different parts of the immune and inﬂammatory pathways will also increase our understanding of disease etiology and progression and help identify new drugs.

RECOMMENDED READING Bowes J, Barton A (2008). Recent advances in the genetics of RA susceptibility. Rheumatology, doi10:1093. Ekström-Smedeby K, Baecklund E, Askling J (2006). Malignant lymphomas in autoimmunity and inﬂammation: a review of risks, risk factors, and lymphoma characteristics. Cancer Epidemiol Biomarkers Prev, 15:2069–2077. Farragher TM, Goodson NJ, Naseem H, et al. (2008). Association of the HLA-DRB1 gene with premature death, particularly from cardiovascular disease, in patients with rheumatoid arthritis and inﬂammatory polyarthritis. Arthritis Rheum, 58:359–369. Klareskog L, Rönnelid J, Lundberg K, Padyukov L, Alfredsson L (2008). Immunity to citrullinated proteins in rheumatoid arthritis. Annu Rev Immunol, 26:651–675. Romas E, Gillespie T (2006). Inﬂammation-induced bone loss: Can it be prevented? Rheum Dis Clin N Am, 32:759–773. Turesson C, Schaid DJ, Weyand CM, et al. (2006). Association of HLA-C3 and smoking with vasculitis in patients with rheumatoid arthritis. Arthritis Rheum, 54:2776–2783. Van der Helm-van Mil AHM, Huizinga TWJ, De Vries RRP, Toes REM (2007). Emerging patterns of risk factor make-up enable subclassiﬁcation of rheumatoid arthritis. Arthritis Rheum, 56:1728–1735. Van Dongen H, van Aken J, Lard LR, et al. (2007). Efﬁcacy of methotrexate treatment in patients with probable rheumatoid arthritis: a double-blind, randomized, placebo-controlled trial. Arthritis Rheum, 56:1424–1432. Young-Min S, Cawston T, Marshall N, et al. (2007). Biomarkers predict radiographic progression in early rheumatoid arthritis and perform well compared with traditional markers. Arthritis Rheum, 56:3236–3247.

21 PHARMACOKINETIC AND PHARMACODYNAMIC BIOMARKER CORRELATIONS J.F. Marier, Ph.D., FCP Pharsight, A Certara Company, Montreal, Quebec, Canada

Keith Gallicano, Ph.D. Watson Laboratories, Corona, California

INTRODUCTION The importance of a biomarker in drug development is based on the weight of evidence that changes in dose and/or drug concentration levels correlate strongly with the biomarker and the clinical outcome desired. The linkage between pharmacokinetics of a new chemical entity (NCE), the dynamics of a biomarker, and the ultimate clinical outcome should be based on robust theoretical considerations, prior therapeutic experience, well-understood pathophysiology, and knowledge of the drug’s mechanism of action. The validation of biomarkers is therefore very important, particularly if one considers their relevance for decision-making and regulatory purposes. The elements described below are crucial for the integration of a biomarker in a drug development program. 1. Identiﬁcation, quantitation, and validation of biomarker assays and drug concentrations. Reliable and selective assays should be validated under

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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a good laboratory practices (GLP)–like environment for quantitative methods for measuring both biomarker responses and drug concentrations. Most biomarkers are endogenous macromolecules that can be measured in biological ﬂuids. As for drug assays, biomarker assays should provide acceptable sensitivity, speciﬁcity, precision, and accuracy. For example, intra- and intersubject variability of the biomarker should be minimal and well understood, biomarker stability and sample storage conditions should be controlled, and quantitative assay should be standardized and validated with adequate power to distinguish between treatment and control groups [1–3]. Assays should be validated to meet study objectives at various drug development stages and possess adequate performance to quantify biochemical responses speciﬁc to the target disease progression and drug intervention. In addition, biomarker measurement should be technically practical, so that measurement can be obtained by minimally invasive techniques and that the sampling of the biomarker is minimal or is taken at the same time as other physiological, safety, or efﬁcacy measurements [4]. 2. PK/PD correlation and integration of model-based drug development. Biomarkers typically have different time courses from clinical endpoints and often are more directly related to the time course of drug concentrations. To evaluate potential correlations, adequate information should be available on the pharmacokinetics (PK) of the new chemical entity (NCE). Similarly, adequate information should be available on the pharmacodynamics (PD) (including PK) of the biomarker, with the expected effect on clinical outcome. Following the identiﬁcation of correlations between the NCE and speciﬁc biomarkers, PK/PD modeling may be performed in early stages of drug development to gain greater insight into the effect of drug concentrations on the biomarker as a function of time. PK/PD modeling involves a set of techniques using mathematical and mechanistic (as opposed to empirical) models derived from quantitative pharmacology. Biomarkers, together with PK/PD modeling and simulation, provide a continuous process to link what has been learned from today’s drug development cycle to the next generation of biomarkers, assays, and models [5–7]. 3. Correlation between biomarkers and clinical endpoint. The availability of a validated biomarker for a speciﬁc clinical endpoint may greatly facilitate the development of compounds that act via well-understood mechanisms of action. For example, biomarkers for HIV and AIDS include viral load (the number of free virus particles in the blood) and the count of CD4+ immune system cells. Fasting blood sugar and hemoglobin A1c, a protein that indicates a patient’s blood sugar history, are established biomarkers for diabetes treatments. The relationship of the biomarker to the therapeutic endpoint should

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be validated in early phase II trials; and, when possible, as early as phase I [8,9]. Many surrogate markers can be assessed in healthy volunteers and in the intended patient population. The ability to assess surrogate markers in healthy volunteers can bring the initial decision-making process into phase I rather than phase II or phase III of the NCE development process. This can be a signiﬁcant competitive advantage to those who use the opportunity. Table 1 presents widely used biomarkers according to disease and indications. Biomarkers can be elevated to the status of a surrogate endpoint based on the

TABLE 1 Widely Used Biomarkers for Speciﬁc Disease or Indications Biomarkers Pulmonary function tests, forced expiratory volume (FEV) PT (including INR), APTT, anti-Xa activity, fragment 1 + 2, plasminogen, plasmin inhibitor, D-dimer Leukotrienes, cytokines, and chemokines Epidermal growth factor (EGF), ﬁbroblast growth factor (FGF), human growth hormone (HGH), neopterin Viral load, CD4 count Cortisol, estradiol, estrone, follicle-stimulating hormone, luteinizing hormone, progesterone, testosterone Angiotensin-I, angiotensin-II, plasma renin, aldosterone, and angiotensin-converting enzyme (ACE) activity Eicosanoids (prostaglandins and leukotrienes) Interferons, interleukins, tumor necrosis factor (TNF), rheumatoid factor Cholesterol, fatty acids, HDL, LDL, phospholipids Cytidine deaminase activity, inosine, xanthine, hypoxanthine, uric acid 8-Hydroxy-dG, protein carbonyls, I-κB, NF-κB

Glucose, glucagon, fructosamine, glycosylated albumin, and hemoglobin A1c, insulin, C-peptide Stomach fullness, cholecystokinin, glucagon-like peptide 1, bombesin, somatostatin, ghrelin, leptin, glucose, insulin, diet-induced thermogenesis, temperature, ventilatory parameters ProMMP-1, ProMMP-3, tissue inhibitory of MMP-1

Disease or Indication Asthma Coagulation and ﬁbrinolysis Chronic obstructive pulmonary disease Growth modulation

HIV Hormonal control

Hypertension

Inﬂammation Immune modulation, rheumatoid arthritis Lipid metabolism Nucleotide metabolism Oxidative stress (diabetes, heart disease, arthritis, obesity, and cancer) Type 1 and 2 diabetes mellitus Satiety and satiation

Tissue remodeling

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weight of evidence that demonstrates that changes in the biomarker correlate strongly with the desired clinical outcome. The best known physiologic surrogate marker is blood pressure reduction as an indicator of reduced incidence of stroke and myocardial infarction. This surrogate marker is now used as a clinical endpoint, because a strong relationship between reduction in blood pressure and a reduction in the incidence of myocardial infarctions and strokes has previously been established. For innovative products, limited information may be available on the relationship between the PK of a drug, the effect on a candidate biomarker, and the ultimate clinical response. Although this may limit the use of predetermined biomarkers in the early development phases of a drug, the incorporation of recent advances in genomics, pharmacogenetics, and proteomics may help to qualify various candidate biomarkers. Pilot exploratory studies should therefore be conducted to qualify and validate candidate biomarkers for predictive clinical assessment of disease progression and the effect of drug intervention. Mechanistic approaches using new technological advances in target validation, functional biology, proteomics, genomics, and the monitoring of gene expression using DNA arrays, imaging, and the correlation of these effects to the disease state may hold promise for an earlier understanding of disease and toxicological processes, improve the predictiveness of preclinical pharmacology and toxicology data to humans, and ﬁnd suitable biomarkers for use in the early stage of drug development of innovative products [2,10]. The appropriate combination of biomarker identiﬁcation and selection, bioanalytical methods development and validation for drugs and biomarkers, and mechanism-based PK/PD models for ﬁtting data and predicting future clinical endpoints and outcomes may provide powerful insights and guidance for effective drug development, toward safe and efﬁcacious medicine for individual patients [6–8,11,12]. The U.S. Food and Drug Administration (FDA) is developing these concepts as part of its critical path initiative, where the agency recognizes that the major contributor to inefﬁciency in development was the absence of innovative new methods for preclinical and clinical testing of drugs. The FDA recommended new tools and various opportunities to improve drug development, including the use of validated biomarkers and the application of model-based drug development (MBDD). In the following sections we focus on the PK/PD correlation assessment of a drug and a biomarker, and the use of PK/PD modeling and simulation to optimize drug development and decision making.

PK/PD CORRELATION OF BIOMARKERS Plans to incorporate biomarkers for potential correlations in drug development programs should occur when a new candidate molecule is being identiﬁed and the discovery process is starting. To evaluate potential correlations between the drug and candidate biomarkers, adequate information should be

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available on the PK of the drug. Preliminary PK/PD correlation analyses may be performed using noncompartmental approaches (i.e., model independent) by deriving parameters such as the area under the concentration–time curve (AUC), maximum concentration (Cmax), time to maximum plasma concentrations (tmax), and terminal elimination half-life (t1/2). Exploratory data analyses (EDAs) between drug exposure data and candidate biomarkers are typically performed by plotting individual concentration data and/or PK parameters of the drug versus the candidate biomarker using appropriate data transformations and in appropriate subgroups (e.g., by gender, dose, or route of administration) in order to identify the impact of drug exposure on the source and magnitude of variability in biomarker response. During exploratory data analyses, questions in Table 2 should be answered to perform PK/PD correlations of biomarkers. In the simplest case, biomarkers will be related directly to the drug concentrations in the biological ﬂuid sampled; that is, the biomarker will respond in a graded fashion so that increasing drug concentrations will result in a gradual increase or decrease in the biomarker of interest over time. A temporal relationship between the time course of drug concentrations and the biomarker is observed. In some cases, the temporal delay may only be a few minutes to an hour, whereas in other cases it may be as long as several hours to days. Exploratory analyses may also provide valuable information on the pharmacodynamics of biomarkers as well as PK/PD correlations as presented in Table 3. Preclinical and clinical study designs should provide as much information on the nature of the PD response of candidate biomarkers. Depending on the PK of the drug and the dynamics of the biomarker, exploratory PK/PD correlations may be limited and likely to require a more mechanistic approach for an optimal assessment of PK/PD correlations. Mechanism-based PK/PD

TABLE 2

Pharmacokinetic Properties and Impact on PK/PD Assessment

Pharmacokinetic Property Absorption

Distribution

Metabolism

Elimination

Impact on Correlation Assessment Will rate and/or extent of drug exposure correlate with biomarkers? Is a minimum threshold likely to correlate with biomarker response? Will total (unbound plus bound) or unbound drug levels contribute to the overall effect on biomarkers? Are concentration levels in peripheral tissues likely to correlate with biomarkers? Will active metabolites contribute to the overall effect on biomarkers? Is there a temporal delay between concentrations and biomarkers (proteresis or hysteresis)? Will the rate of elimination of the drug correlate with biomarker response? Does drug elimination parallel the biomarker response?

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TABLE 3

Pharmacodynamic Properties and Impact on PK/PD Assessment

Pharmacodynamic Property Mechanism of action

Impact on Correlations Assessment Will the drug stimulate or inhibit the formation or degradation of the biomarker? Will the drug create a reversible or irreversible effect on the biomarker? Will the drug compete with endogenous, environmental, or dietary ligands for receptor binding? Are placebo data available to determine a diurnal/ circadian cycle of biomarkers? Will the disease state affect the biomarker response? Does drug elimination parallel the biomarker response?

Competitive ligands Diurnal variation Disease state

models differ from empirical descriptive models in that they contain speciﬁc expressions to characterize processes on the causal path between drug administration and effect [13–15]. In the next section we focus on important issues and concepts that apply to PK/PD modeling and the use of biomarkers to improve drug development processes. PK/PD Modeling of Biomarkers PK/PD modeling and simulation tools have continued to develop to a high level of sophistication over the past 20 years. Mechanism-based PK/PD modeling involves a set of techniques using mathematical and mechanistic models derived from quantitative pharmacology. The simplest compartmental PK model can be described as a one-compartment model following intravenous administration of a drug. A schematic representation of the compartmental model is presented in Figure 1. In this example, the removal of

PK Model

Equation

Pharmacokinetics

Dose (IV bolus)

dose C time = • exp − K10 • time Vc

Vc 1

K10

Drug Concentration

100

10

1.0 0.1 Time (h)

Figure 1

Pharmacokinetic modeling and equations.

PK/PD CORRELATION OF BIOMARKERS

419

drug from the central compartment (i.e., systemic circulation) was described using a ﬁrst-order elimination rate constant. Compartmental analyses can be performed routinely by ﬁtting the data to a model using a PK/PD software package such as WinNonlin (Pharsight, a Certara Company, St. Louis, Missouri), Kinetica (ThermoFisher, Waltham, Massachusetts), S-ADAPT (University of Southern California,) or NONMEM (University of California at San Francisco). The PK of drugs can be described using more advanced compartmental models and can be customized to describe any type of PK behavior. Case studies in which different types of compartmental models were used in preclinical and clinical drug development are presented later in the chapter. Modeling the concentration–effect relationship on the biomarker is very dependent on the mechanism of action of the drug. In vitro studies that may elucidate this mechanism are very important in the early stage of drug development. For example, drug-induced changes in biomarkers of interest are often modeled using classical drug receptor theory following the law of mass action. This theory predicts that as receptors in the target organ interact with the drug, the biomarker levels will increase with increasing interaction until all receptors have been occupied, at which time no further change in biomarker will occur. In its simplest form, the effect on the biomarker will be driven by the measured drug concentration over time, Ctime, assuming that drug concentrations in the systemic circulation is at equilibrium with the effect site. The classic and most commonly used PD model under these conditions is the Emax model, which is an empirical function for describing nonlinear concentration–effect relationships. It has the general form illustrated in Figure 2, where Emax represents the maximum effect on the biomarker, Ctime the concentrations of a drug predicted over time with a PK model, and EC50 the concentration of the drug that produces half of Emax. The PD model can be

Equation

Pharmacodynamics

Effectc = Emax •

C

n time

n C time + EC50n

Effect (Biomarker)

3

2

1 0 Drug Concentration

Figure 2

Pharmacodynamic modeling and equations.

PK AND PD BIOMARKER CORRELATIONS

Pharmacokinetics

C time =

Pharmacodynamics

⎛

dose • exp − K10 • time Vc

Effectc = Emax • ⎜

n n ⎝ Ctime + EC50

100

3 Effect (Biomarker)

Drug Concentration

n Ctime

⎛ ⎜ ⎝

420

10

1.0 0.1

2

1 0

Time (h)

PK/PD

Drug Concentration

Effect (Biomarker)

3

2

1 0 Time (h)

Figure 3

Pharmacokinetic/pharmacodynamic modeling.

related to receptor theory, where EC50 is the parameter characterizing the potency of the drug on the biomarker and n is a sigmoidicity factor describing the drug concentration vs. the effect on the biomarkers or the number of molecules interacting at the biomarker receptor. Although the Emax model is highly versatile for different situations, more sophisticated PD models have been developed over the last decades. PK/PD modeling builds the bridge between these two classical disciplines of pharmacology, as depicted in Figure 3. PK/PD modeling is performed to gain greater insight into how drug concentrations may affect the biomarker as well as how the models can be used to perform simulations in order to predict changes in biomarker levels under a variety of experimental conditions. Models can improve the prediction and assessment of patient response with the use of simulations, and therefore increase the success of a drug development program [13–15]. For example, the models can be used to reﬁne a dosing regimen in order to predict the drug exposure (PK modeling) and the resulting effect on the biomarker, to guide starting doses and regimens (as well as adjustments of dosage or dosing regimens in special populations), and to provide a better understanding of outcomes from clinical efﬁcacy studies. Case studies will be presented to demonstrates how biomarkers, together with PK/PD modeling and simulation, provide a continuous process to link what has been learned from today’s drug development cycle to the next generation of biomarkers, assays, and models.

PK/PD CORRELATION OF BIOMARKERS

421

Critical Path Initiative In the current health care environment, dominated by increasing expenses associated with drug development programs, rapidly changing technologies, generic competition, and therapeutic substitution, there is increased pressure to develop new therapies using effective drug development programs. The escalating costs and low productivity of drug development have been well documented over the past several years. In March 2004, the FDA recognized that drug development has become increasingly challenging, inefﬁcient, and costly [16]. The agency concluded that the major contributor to the inefﬁciency in development was the absence of innovative new methods for preclinical and clinical testing of drugs. In its critical path initiative, the agency called upon new, publicly available scientiﬁc and technical tools to improve drug development programs. These include the use of biomarkers and clinical trial endpoints as well as PK/PD modeling to improve decision making and make the development process more effective and more likely to result in safe products to beneﬁt patients [17–20]. The agency recommended reviewing the body of collective biomarker information to build a new framework for biomarker and surrogate endpoint use in drug development [16]. Part of this initiative is to complete an inventory of surrogate endpoints that have been used for past drug approvals and to evaluate how the decision was made to accept the respective surrogates. As part of this process there will be an attempt to identify widely used, diseasespeciﬁc biomarkers and determine the gap that exists to elevate these biomarkers to surrogate endpoints. The acceptance of CD4 cell counts and HIV plasma RNA as mechanism-based surrogate endpoints are probably the best examples. The elevation of a biomarker to a surrogate endpoint is particularly attractive because biomarkers can be measured more quickly and easily and at lower cost than can true clinical endpoints, or when clinical endpoints are excessively invasive or when their use would be considered unethical.

PK/PD Modeling of Biomarkers in the Early Phase of Drug Development (Colburn) There are numerous stages along the development cycle where the development of biomarkers and PK/PD modeling can add value. Incorporation of PK/ PD studies throughout preclinical and clinical development may lead to earlier identiﬁcation of optimal dosing regimens in clinical development and may reduce the overall time of drug development. Beyond the traditional paradigm of using fractions of the nonclinical toxic doses in animals to design ﬁrst-inhuman dosing schedules, the use of PK/PD modeling in early preclinical development allows one to deﬁne more precisely the dose–concentration– pharmacological effects and dose–concentration–toxicity relationships with the use of key efﬁcacy and safety biomarkers. The extrapolation of these results to humans using a combination of in vitro and in vivo data can be

422

PK AND PD BIOMARKER CORRELATIONS

particularly helpful in determining the appropriate dosing regimen for phase I studies and in guiding dose escalation in order to achieve the systemic exposure in humans that is expected to be associated with the desired effect on the biomarker and ultimately, the clinical outcome. Overall, the use of validated biomarkers and the application of PK/PD modeling and simulation principles represent an opportunity to identify optimal drug candidates and possibly to develop drugs in the shortest time frame possible [7–9]. PK/PD modeling output can be no better than the biomarkers or surrogate endpoints used for the modeling (i.e., the input). As we increase our understanding of biomarkers, surrogate markers, and disease and drug mechanisms, PK/PD modeling inputs and outputs will improve and, consequently, predictive power will improve. PK/PD Modeling of Biomarkers in Late Phases of Drug Development The usefulness and validity of PK/PD models for the evaluation of dose– concentration–effect relationships are not limited to well-designed clinical studies with relatively small groups of patients and frequent measurements of concentration and effect. It has also been shown for observational data obtained from large clinical trials with sparse and imbalanced sampling schedules by applying population modeling techniques. Population PK/PD modeling is based primarily on the nonlinear mixed-effects regression models introduced by Sheiner and co-workers and makes possible the characterization of dose–concentration effect relationships in populations rather than individuals, thereby providing the opportunity to identify and account for sources of interindividual PK and PD variability [21,22]. In addition, the increased understanding of drug action derived from biomarker response and PK/PD-based drug development may lead to a deﬁnition of strategies for individualization of drug dosage regimens to ensure optimal therapeutic outcome in subpopulations of patients. The building of a PK/PD database during drug development can provide an essential framework for continued reﬁnement and improvement during postmarketing drug use, allowing one to answer speciﬁc questions without the need to perform additional studies. Clinical Trial Simulation Population PK/PD modeling has been used successfully for numerous drugs. These models may be descriptive and/or predictive of the time course of PD effects. When properly developed, predictive models may be used to guide starting doses and regimens (as well as adjustments of dosage and dosing regimens in special populations), add evidence to the certainty of the decision to allow market access, and provide a better understanding of outcomes from clinical studies [23,24]. Clinical trial simulations are usually motivated as follows:

CASE STUDIES

423

• To improve the clinical trial process by maximizing the probability of trial objectives being attained with reduction of unnecessary costs • To improve decision making in drug development by providing an objective framework for safety, efﬁcacy, and commercial risk assessments • To improve the overall efﬁciency of drug development in terms of informativeness, economy, and speed Modeling and simulation may help predict the results of future drug trials and can be used at decision points in the drug development process to assess quantitatively the risk of moving forward with a candidate drug.

CASE STUDIES In recent years, PK/PD modeling has developed from an empirical descriptive discipline into a mechanistic science that can be applied at all stages of drug development. Preclinical PK/PD studies may prompt a series of important mechanistic studies to explore the relationship between plasma concentrations and the resulting effect on a biomarker in later stages of clinical drug development. The application of PK/PD modeling to preclinical pharmacology studies may help to provide information on drug effects and actions that would be difﬁcult to obtain in human subjects. As such, preclinical and clinical PK/PD studies constitute a scientiﬁc basis for rational drug discovery and development. The following preclinical and clinical cases studies are presented to demonstrate how modeling techniques have improved the overall understanding of PK/PD relationship of biomarkers. Case Study 1 (Preclinical): Pharmacokinetic–Pharmacodynamic Modeling of the Respiratory Depressant Effect of Norbuprenorphine in Rats The objective of this investigation was to characterize the PK/PD correlation of buprenorphine’s active metabolite norbuprenorphine for the effect on respiration in rats [25]. Following intravenous administration, the time course of plasma concentrations of buprenorphine and norbuprenorphine were determined in conjunction with the effect on ventilation as determined using plethysmography, a test used to measure changes in air volume. The PK of norbuprenorphine was best described by a three-compartment PK model with nonlinear elimination. A saturable biophase distribution model with a power PD model described the PK/PD relationship best as presented in Figure 4. No saturation of the effect at high concentrations was observed, indicating that norbuprenorphine acted as a full agonist with regard to respiratory depression. By simulation, it was shown that following intravenous administration of buprenorphine, the concentrations of norbuprenorphine reached values that were well below the values causing an effect on respiration. In

424

PK AND PD BIOMARKER CORRELATIONS Dose

Buprenorphine peripheral compartment (V2)

k12

Buprenorphine central compartment (V1)

k21

ke0 =

Norbuprenorphine biophase compartment

k13

Buprenorphine peripheral compartment (V3)

kconv

k10

Norbuprenorphine peripheral compartment (V5)

k31

k45

Norbuprenorphine central compartment (V4)

k54

V mke0 * K mke0 Ce + K m

ke0

k40 =

k64

k46

Norbuprenorphine peripheral compartment (V6)

V mk40 * K mk40

Pharmacodynamic model

Cp + K mk40

Respiratory depression

Figure 4 PK/PD model for respiratory depressant effect of norbuprenorphine and norbuprenorphine. (From ref. 25, by permission of the American Society for Pharmacology and Experimental Therapeutics.)

conclusion, the PD of the metabolite norbuprenorphine was determined to be markedly different from the PD of the parent compound buprenorphine with regard to the receptor association–dissociation kinetics, the in vivo potency, and the intrinsic efﬁcacy for the respiratory depressant effect. The values of these norbuprenorphine concentrations are well below values causing an effect on respiration. Therefore, it was concluded norbuprenorphine does not contribute to the overall respiratory depressant effect of buprenorphine. These results were consistent with the experience from clinical use of buprenorphine in patients. Case Study 2 (Preclinical): Pharmacokinetics and Pharmacodynamics of PEGylated IFN-β1a Following Subcutaneous Administration in Monkeys The purpose of this study was to characterize the PK/PD properties of a new polyethylene glycol (PEG) conjugate formulation of interferon-β1a (IFN-β1a) following subcutaneous (SC) administration in monkeys [26]. Single SC injections of 0.3, 1, and 3 MIU/kg of PEG-IFN-β1a were administered to three

CASE STUDIES

425

At Cp(t–τ) k12

Dsc k′ Asc

kin

k21

Ap V/F k′

N

kout

Figure 5 PK/PD model for the effect of IFN-β1a on neopterin. (From ref. 26, by permission of Springer Science and Business Media.)

groups of cynomolgus monkeys. Plasma concentrations of drug and neopterin, a classic biomarker for IFN-β1a, were measured at various time points after dosing using an ELISA assay. PK/PD proﬁles were ﬁrst described by noncompartmental methods, and then a pooled analysis was performed using an integrated mathematical model, where ﬁxed and delayed concentration–time proﬁles were used as a driving function in an indirect stimulatory response model as presented in Figure 5. The PK component of IFN-β1a was assessed using a standard linear twocompartment model with a ﬁrst-order rate constant for absorption to and elimination from the central compartment of IFN-β1a. The PD model was a modiﬁed stimulatory indirect response model, with a zero-order rate constant of neopterin production (kin), a ﬁrst-order rate constant of neopterin elimination (kout), where the driving function (i.e., concentrations of IFN-β1a) was delayed by a time-lag parameter (τ). Neopterin concentrations followed a typical dose-dependent biphasic pattern. Pooled PD proﬁles were well described by the PK/PD model, and the neopterin elimination rate was consistent with previous estimates. The PEG modiﬁcation of IFN-β1a provided enhanced drug exposure and comparable pharmacodynamics to unpegylated IFN-β1a. Case Study 3 (Clinical): Modeling of Brain D2 Receptor Occupancy–Plasma Concentration Relationships with a Novel Antipsychotic PK/PD modeling using functional imaging methodology such as positronemission tomography (PET) may be a very useful tool for early clinical development of antipsychotic compounds, especially in suggesting initial doses for further clinical studies. The purpose of this study was to assess PK/PD relationships of YKP1358 to guide decisions for further clinical study designs. YKP1358 is a novel serotonin (5-HT2A) and dopamine (D2) antagonist that

426

PK AND PD BIOMARKER CORRELATIONS Drug administration ka

V2 Rapidly equilibrating compartment

k12 k21

V1 Central compartment

k13 k31

V3 Slowly equilibrating compartment

k1e k10

dCe Ve Effect compartment

dt ke0

=

E=

k1eCV1 Ve

– ke0Ce

Emax CeH H EC50 + CHe

Figure 6 PK/PD model used to assess the relationship between YKP1358 and D2 receptor occupancy. (From ref. 27, by permission of Macmillan Publishers Ltd.)

ﬁts the general proﬁle of an atypical antipsychotic drug [27]. A D2 receptor occupancy study was conducted in healthy volunteers using PET to measure the D2 receptor occupancy of YKP1358 and to characterize its relationship to plasma drug concentrations. A single oral dose, parallel group, dose-escalation (100, 200, and 250 mg) study was performed in 10 healthy male volunteers with the PET radiotracer [11C]raclopride. The relationship between plasma concentration and D2 receptor occupancy was analyzed with an indirect link model that included an effect compartment, as well as an equilibrium rate constant (ke0), as presented in Figure 6. Results of the study demonstrated that YKP1358 possesses potential antipsychotic effects based on the D2 receptor occupancy data. Considering the D2 receptor occupancy data, effective doses in patients were predicted to be greater than 250 mg twice a day. This is the ﬁrst study in which the relationship between plasma concentration and the biomarker of D2 receptor occupancy was modeled using nonlinear mixed-effects modeling. It is anticipated that these results will be useful in estimating the initial doses of YKP1358 required to achieve a therapeutically effective range of D2 receptor occupancy in subsequent studies. Case Study 4 (Clinical): Semimechanistic and Mechanistic Population PK/PD Model for Biomarker Response to Ibandronate Biomarkers of bone turnover provide a rapid and accessible means of evaluating physiological responses to antiresorptive therapies. Recent evidence also suggests an association between the magnitude of biomarker suppression and the likely response in terms of bone mineral density (BMD) change and fracture risk reduction. This association suggests that these

CASE STUDIES

427

biomarkers are on the causal pathway for prevention of fracture following administration of bisphosphonates. As such, biochemical markers of bone turnover can offer a timely indication of the likely efﬁcacy associated with nominal dosing regimens. The aim of this project was to develop and validate a pharmacological model for ibandronate, a new bisphosphonate for the treatment of osteoporosis, capable of describing its PK in serum and urine, and the urinary excretion of the C-telopeptide of the α chain of type I collagen (uCTX), a sensitive biomarker of PD response to ibandronate [28]. A classical PK/PD model was developed that described accurately the PK of intravenously administered ibandronate and its effect on the biomarker, as presented in Figure 7. A four-compartment PK model was used, with an indirect PD response model with a uCTX formation rate (KS) and uCTX degradation rate constants (KD). Ibandronate in the “bone compartment” was assumed to inhibit osteoclast activity and hence the rate of synthesis (KS) and the urinary excretion of CTX. The complex physiological PK/PD model adequately described the PK of intravenously administered ibandronate in serum and urine as well as the time course of uCTX. To reduce processing times, the classical PK/PD model was simpliﬁed using a kinetics of drug action or kinetic (K)-PD model (i.e., a dose–response model, as opposed to a dose–concentration–response model). The model was subsequently extended to consider the inﬂuence of supplemental therapy on the PD response and subjected to external validation by retrospectively simulating the time course of uCTX change reported in a phase III study and in a phase II/III study of intravenous ibandronate. In summary, a pharmacostatistical model was developed and validated that adequately described the time course of uCTX change after oral and intravenous ibandronate therapy for osteoporosis. This model is currently being used to aid in the development of novel intermittent oral and intravenous regimens for ibandronate.

V2 Bone V1

Dose

V4

CL uCTX Ae

V3

KS

KD

Figure 7 PK/PD model used to assess the relationship between ibandronate in serum and urine and the urinary excretion of uCTX. (From ref. 28, by permission of Wiley-Blackwell.)

428

PK AND PD BIOMARKER CORRELATIONS

Case Study 5 (Clinical): Receptor Theory–Based Semimechanistic PD Model for the CCR5 Noncompetitive Antagonist Maraviroc Maraviroc (UK-427 857), a selective and reversible CCR5 co-receptor antagonist, has been shown to be active in vitro against a wide range of clinical isolates [29]. In human immunodeﬁciency virus type 1 (HIV-1)–infected patients, maraviroc given as monotherapy for 10 days reduced HIV-1 viral load dose dependently by up to 1.6-log10 copies, consistent with currently available agents that comprise the cornerstone of highly active antiretroviral therapy. The objective of this study was to develop a novel combined viral dynamics/ operational model of (ant-)agonism that describes the PD effects of maraviroc, a noncompetitive CCR5 inhibitor, on viral load. A common theoretical framework based on receptor theory and the operational model of (ant-) agonism has been developed to describe the binding of maraviroc to the CCR5 receptor and the subsequent decrease in viral load. The effect of maraviroc was modeled using an inhibitory Emax model in the PD model acting on the infection rate constant of the virus and target cells. With this parameterization, it was assumed that the antagonistic effect of maraviroc is noncompetitive, which is consistent with recent in vitro ﬁndings. In terms of receptor theory and an operational model of drug action, the viral replication process was modeled as a binding–stimulus–response cascade in which the virus acts as an agonist, as shown in the ﬁve steps in Figure 8. In the binding–stimulus–response cascade shown, Tmax is the maximum concentration of activated target cells that can be infected, V is the virus con-

Binding/Fusion/Entry

Reverse transcription

[S1] =

[S2] =

Integration/Replication

[S3] =

Protein synthesis

[S4] =

Protein cleavage Virus assembly

[S5] =

[TMAX].[V] KV + [V] [S1] KEI + [S1] [S2] KE2 + [S2] [S3] KE4 + [S3]

[S4] KE5 + [S4]

Figure 8 Viral dynamics–operational model (binding–stimulus–response). (From ref. 29, by permission of Wiley-Blackwell.)

SUMMARY

429

centration, Kv is the theoretical concentration of viruses that elicits half the maximum binding to the activated receptors on the target cells, and Kex is the theoretical stimulus (SX) at step X that elicits half the maximum response (SX+1) at step X + 1 (X = 1 to 5). This new model provided an explanation for the apparent discrepancy between the in vivo binding of maraviroc to the CCR5 receptor (KD = 0.089 ng/mL) and the estimated in vivo inhibition (IC50 = 8 ng/mL) of the infection rate. The estimated KE value of the operational model indicates that only 1.2% of free activated receptors are utilized to elicit 50% of the maximum infection rate. The model developed suggests that the target cells, when activated, express more receptors (spare receptors) than needed. In the presence of maraviroc, these spare receptors ﬁrst require blocking before any decrease in the infection rate, and consequently in the viral load at equilibrium, can be detected. The model allowed the simultaneous simulation of the binding of maraviroc to the CCR5 receptor and the change in viral load after both short- and long-term treatment. The current model will be used to guide the selection of the optimal dosage regimen for maraviroc in HIV-1–infected patients.

SUMMARY Considering the complexity, risk, and cost involved in drug discovery and development processes, it is imperative that companies adopt strategies and technologies that will facilitate the identiﬁcation and development of new therapies. The development of biomarkers and clinical trial endpoints as well as PK/PD modeling were recognized as key elements that can improve decision making and render development processes more effective and more likely to result in safe and beneﬁcial products for patients. A new framework for biomarker and surrogate endpoint use in drug development was recommended by the FDA. As part of this process, diseasespeciﬁc biomarkers will be evaluated to determine whether some biomarkers may be elevated to surrogate endpoints. This will be particularly attractive because biomarkers can be measured more quickly and easily and at lower cost than can true clinical endpoints. Appropriate PK/PD modeling of biomarkers and surrogate endpoints will facilitate proof-of-concept demonstrations for target modulation; enhance the rational selection of an optimal drug dose and schedule; aid decision making, such as whether to continue or close a drug development project, accelerate drug approval, minimize uncertainty associated with predicting drug safety and efﬁcacy, and decrease the overall costs of drug development. The future of PK/PD modeling of biomarkers holds great challenges and great promises. Pharmaceutical companies that implement PK/PD modeling of biomarkers in their early drug development strategy should have a distinct competitive advantage, with powerful insights and guidance for effective and efﬁcient rational drug development, and by providing safe and efﬁcacious

430

PK AND PD BIOMARKER CORRELATIONS

medicines for individual patients. Although the science of PK/PD modeling and biomarkers is constantly evolving, these disciplines provide a continuous process to link what has been learned from today’s drug development cycle to the next generation of biomarkers, assays, and mechanism-based models. REFERENCES 1. Gao J, Garulacan LA, Storm SM, et al. (2005). Biomarker discovery in biological ﬂuids. Methods, 35:291–302. 2. Goodsaid F, Frueh F (2007). Biomarker qualiﬁcation pilot process at the US Food and Drug Administration. AAPS J, 9:E105–E108. 3. Colburn WA (1997). Selecting and validating biologic markers for drug development. J Clin Pharmacol, 37:355–362. 4. Lee JW, Hulse JD, Colburn WA (1995). Surrogate biochemical markers: precise measurement for strategic drug and biologics development. J Clin Pharmacol, 35:464–470. 5. Aarons L, Karlsson MO, Mentré F, Rombout F, Steimer JL, van Peer A (2001). COST B15 experts: role of modelling and simulation in phase I drug development. Eur J Pharm Sci, 13:115–122. 6. Wagner JA, Williams SA, Webster CJ (2007). Biomarkers and surrogate end points for ﬁt-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther, 81:104–107. 7. Colburn WA, Lee JW (2003). Biomarkers, validation and pharmacokinetic– pharmacodynamic modelling. Clin Pharmacokinet, 42:997–1022. 8. Colburn WA (2000). Optimizing the use of biomarkers, surrogate endpoints, and clinical endpoints for more efﬁcient drug development. J Clin Pharmacol, 40(12 Pt 2):1419–1427. 9. Colburn WA (2003). Biomarkers in drug discovery and development: from target identiﬁcation through drug marketing. J Clin Pharmacol, 43:329–341. 10. Williams SA, Slavin DE, Wagner JA, Webster CJ (2006). A cost-effectiveness approach to the qualiﬁcation and acceptance of biomarkers. Nat Rev Drug Discov, 5:897–902. 11. National Institutes of Health (1999). Biomarkers and Surrogate Endpoints: Advancing Clinical Research and Applications. NIH, Bethesda, MD. 12. Rolan P, Atkinson AJ Jr, Lesko LJ (2003). Use of biomarkers from drug discovery through clinical practice: report of the Ninth European Federation of Pharmaceutical Sciences Conference on Optimizing Drug Development. Clin Pharmacol Ther, 73:284–291. 13. Meibohm B, Derendorf H (2002). Pharmacokinetic/pharmacodynamic studies in drug product development. J Pharm Sci, 91:18–31. 14. Derendorf H, Meibohm B (1999). Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives. Pharm Res, 16:176–185. 15. Derendorf H, Lesko LJ, Chaikin P, et al. (2000). Pharmacokinetic/ pharmacodynamic modeling in drug research and development. J Clin Pharmacol, 40:1399–1418.

REFERENCES

431

16. U.S. Department of Health and Human Services Food and Drug Administration (2004). Challenge and Opportunity on the Critical Path New Medical Products. FDA, Washington, DC, pp. 1–31. 17. Lalonde RL, Kowalski KG, Hutmacher MM, et al. (2007). Model-based drug development. Clin Pharmacol Ther, 82:21–32. 18. Miller R, Ewy W, Corrigan BW, et al. (2005). How modeling and simulation have enhanced decision making in new drug development. J Pharmacokinet Pharmacodyn, 32:185–197. 19. Gieschke R, Steimer JL (2000). Pharmacometrics: modelling and simulation tools to improve decision making in clinical drug development. Eur J Drug Metab Pharmacokinet, 25:49–58. 20. Zhang L, Sinha V, Forgue ST, et al. (2006). Model-based drug development: the road to quantitative pharmacology. J Pharmacokinet Pharmacodyn, 33:369–393. 21. Sheiner LB, Steimer JL (2000). Pharmacokinetic/pharmacodynamic modeling in drug development. Annu Rev Pharmacol Toxicol, 40:67–95. 22. Peck CC, Barr WH, Benet LZ, et al. (1992). Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development. Clin Pharmacol Ther, 51:465–473. 23. Holford NHG, Kimko HC, Monteleone JPR, Peck CC (2000). Simulation of clinical trials. Annu Rev Pharmacol Toxicol, 40:209–234. 24. Lockwood P, Ewy W, Hermann D, Holford N (2006). Application of clinical trial simulation to compare proof-of-concept study designs for drugs with slow onset of effect; an example in Alzheimer’s disease. Pharm Res, 23:2050–2059. 25. Yassen A, Kan J, Olofsen E, Suidgeest E, Dahan A, Danhof M (2007). Pharmacokinetic–pharmacodynamic modeling of the respiratory depressant effect of norbuprenorphine in rats. J Pharmacol Exp Ther, 321:598–607. 26. Mager DE, Neuteboom B, Jusko WJ (2005). Pharmacokinetics and pharmacodynamics of PEGylated IFN-beta 1a following subcutaneous administration in monkeys. Pharm Res, 22:58–61. 27. Lim KS, Kwon JS, Jang IJ, et al. (2007). Modeling of brain D2 receptor occupancyplasma concentration relationships with a novel antipsychotic, YKP1358, using serial PET scans in healthy volunteers. Clin Pharmacol Ther, 81:252–258. 28. Pillai G, Gieschke R, Goggin T, Jacqmin P, Schimmer RC, Steimer JL (2004). A semimechanistic and mechanistic population PK–PD model for biomarker response to ibandronate, a new bisphosphonate for the treatment of osteoporosis. Br J Clin Pharmacol, 58:618–631. 29. Jacqmin P, McFadyen L, Wade JR (2008). A receptor theory–based semimechanistic PD model for the CCR5 noncompetitive antagonist maraviroc. Br J Clin Pharmacol, 65(Suppl 1):95–106.

22 VALIDATING IN VITRO TOXICITY BIOMARKERS AGAINST CLINICAL ENDPOINTS Calvert Louden, Ph.D. Johnson & Johnson Pharmaceutical, Raritan, New Jersey

Ruth A. Roberts, Ph.D. AstraZeneca Research and Development, Macclesﬁeld, UK

INTRODUCTION Biomarkers have been used for many years to predict and track biological changes in tissue and organ systems. The conceptual framework for their use was captured in the output of the Biomarkers Deﬁnitions Working Group [1], which provided a valuable working deﬁnition and some challenges to the scientiﬁc community. Here, a biomarker was deﬁned as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention” [1]. The working group noted that biomarkers may have the greatest value in early efﬁcacy studies and safety evaluations with applications that could include prediction and monitoring of clinical response to an intervention [1]. Biomarkers in Drug Development Biomarkers have been used for many years as indicators of biological change. In the course of drug development, biomarkers enjoy utility both as predictors Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

433

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VALIDATING IN VITRO TOXICITY BIOMARKERS

Safety ‘What is the drug doing to the patient?’ Markers of tissue damage, adverse hematology, etc. Predict/report unwanted toxicity endpoint

Preclinical Preclinical Development

Clinical Development

Approval

Efficacy Biomarkers of Pharmacodynamic (PD) endpoints: ‘What is the drug doing to the patient?’ Receptor modification, altered glucose levels/blood lipids Commensurate with desired efficacy endpoint

Figure 1 Biomarkers in clinical medicine. Biomarkers of safety and efﬁcacy can be used together as part of the overall data set to assist in decision making.

of possible efﬁcacy and also as warning signs of potential toxicity (Figure 1). For efﬁcacy endpoints, biomarkers are used to address the pharmacodynamic question “What is the drug doing to the patient?” commensurate with desired outcomes such as receptor modiﬁcation or blood glucose changes. For safety endpoints, biomarkers may be used as indicators of unwanted endpoints such as tissue damage or adverse hematology. Although these two types of biomarker serve diverse purposes, the parameters dictating their identiﬁcation, development, and utility are very similar. However, there is one key aspect in which safety biomarkers differ; safety biomarkers are most useful when they predict rather than report damage. For example, an efﬁcacy biomarker can provide rapid conﬁrmation that the drug has hit its target, perhaps by conﬁrmation of growth factor receptor inhibition in a skin biopsy. However, a safety biomarker of liver damage would ideally precede the damage (either in dose or time) to be of maximum use to the clinician. Despite this, biomarkers that report rather than predict can still be useful if they prevent further damage or permit cessation of treatment and subsequent recovery. Toxicity Biomarkers In Vitro The driving force for better clinical biomarkers of potential adverse events is to monitor patient safety, and many such biomarkers will be discovered and developed within the context of human medicine. However, from a nonclinical

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perspective the discovery and development of toxicological biomarkers facilitates the translation to the clinic of knowledge that will avoid or minimize the risk of occurrence of a potential unwanted outcome identiﬁed in animal studies. Thus, it is key to identify preclinical biomarkers of the adverse response of animals to potential new drugs and to evaluate fully their relevance and applicability to humans. Potential biomarkers can be identiﬁed in vitro, and their translation to in vivo, either in animal or humans, evaluated at a later date. This concept is developed later in this review with speciﬁc examples and case histories. One argument against this approach is that ﬁndings in animals in vivo or in animalderived tissue in vitro may not be relevant for human safety; however, the job of the clinician is to manage the best possible therapy while minimizing the risk of adverse reactions. Thus, such data would form part of an integrated assessment of safety and efﬁcacy that can inform preclinical and clinical decisions. Clinically useful biomarkers of toxicity can be derived from appropriately designed preclinical experiments, and such biomarkers can signiﬁcantly assist safe progression into the clinic with an approach design to minimize patient risk. These biomarkers can be derived from in vitro or in vivo mode-of-action studies in preclinical species then translated to potential clinical use or, as we present here, could already be established in clinical practice. This translational science approach requires close collaboration between the clinical and the preclinical scientists. Characteristics of Biomarkers An important consideration in the use of biomarkers is the practicality of subsequent analysis, which is likely to be far more successful and robust if based on an uncomplicated physiological or biochemical assay that can be carried out with minimal training and preexisting equipment (Figure 2). Nonetheless, the need for specialist techniques or expertise should not exclude a potential biomarker, especially where it contributes to advancing therapies for a serious or unmet medical need. Other practical considerations include the need for assays to be transferrable across international boundaries, supported by low background and variability of the measured parameter coupled with a robust increase or decrease signifying the biological endpoint of interest. Indeed, lower and upper limits for normal values would be better if minimally inﬂuenced by age, gender, or race within the human population. Similarly, a biomarker of drug-induced toxicity would be better if it were largely unaffected by disease states expected in the patient population of interest or, indeed, any co-medication that might be present or recently administered. However, this is often not the case, and the best that can be expected is some understanding of these relationships to mitigate against difﬁcult or erroneous interpretation. In addition, an ideal biomarker should exhibit

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Parameter y (units)

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Figure 2 Technical aspects of biomarker utility. Biomarkers must be sufﬁciently robust biologically and technically to transfer across patient groups and across international boundaries. This is illustrated by the different variables that need to be overcome or accommodated in the assay.

sufﬁcient sensitivity (not too many false negatives) and speciﬁcity (not too many false positives) to underpin clinical decisions. As well as the technical aspects of biomarker assays, there are additional practical considerations to enhance appropriateness and usability. First, a biomarker needs to be accessible and ideally could be noninvasive. If this is not possible, a biomarker could be measurable in blood and other body ﬂuids obtained with minimal additional impact on the subject. If this cannot be achieved, biomarkers may be detectable with increasing impact on the patient via a peripheral or central biopsy. Finally, biomarker research for the advancement of medical knowledge can be conducted after autopsy. Examples of noninvasive methods would include standard physiological measures of cardiovascular parameters such as blood pressure, heart rate, and electrocardiogram, or could be ophthalmological. Fluids could include feces, saliva, and urine, in addition to standard blood sampling. Skin and hair follicles would be examples of peripheral biopsy and muscle and liver needle samples provide the most common route of more invasive biopsy. Biomarkers of Safety One key aspect where biomarkers of safety differ from those of efﬁcacy is that they preferably predict rather than report the biological effect they are ﬂagging. Thus the biomarker should ideally precede that biological effect either in dose (i.e., be detectable at doses below where the damage occurs) or in time

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(be detectable perhaps after one dose when the damage requires repeated dosing). These biomarker characteristics would provide support and conﬁdence to the clinician during dose escalation in early clinical trials or during the transition from single to multiple doses. During later clinical development, a predictive biomarker could monitor for onset in a patient who is beginning to develop toxicity perhaps after several months of drug use. In the absence of evidence that a biomarker can predict onset, an ideal biomarker of druginduced toxicity would characteristically show detectable changes prior to any serious or irreversible tissue injury and would also show reversibility of the biomarker signal upon removal of the toxicological insult.

IN VITRO TOXICITY BIOMARKERS Having considered some general points on the development and validation of toxicity biomarkers, we now present several working examples that explore the concept of in vitro toxicity biomarkers and their translation to the clinic. Opportunities and caveats are explored with an emphasis on assisting clinical progression and decision making. Toxicity biomarkers in the in vitro context are deﬁned as quantitative measurable characteristics that serve as indicators of a pathologic process or related biochemical or molecular events. Endocrine and Exocrine Pancreatic Toxicity The potential for development of clinical drug-induced diabetes is well recognized, and molecules such as cyproheptadine and structurally related compounds such as cyclizine and chlorcyclizine have been shown to cause pancreatic islet toxicity. However, this is not unique to this class of structurally related compounds because a similar ﬁnding has been observed following oral administration of some quinolone antimicrobial agents. In vivo, this effect is characterized by cytoplasmic islet vacuolation, decreased insulin secretory granules, decreased circulating insulin levels, intolerance to glucose challenge, and increased glycosylated albumin. The working hypothesis is that these compounds were associated with functional impairment of insulin synthesis and/or secretion, and an in vitro screen would be invaluable in identifying this liability early in the drug development process. The clonal insulin-producing cell line RINm5F is widely used as a model to detect compound-induced loss and/or reduction of insulin in vitro that is comparable to in vivo ﬁndings. Using this in vitro model, it was determined that cyproheptadine and selected analogs caused inhibition of insulin biosynthesis without any effects on pre-pro-insulin mRNA. These data suggested that inhibition of transcriptional regulation of insulin was an unlikely mode of action responsible for cyproheptadine-induced pancreatic islet cell toxicity. The limitation of the RINm5F cells is that they are not glucose-sensitive and as such, cannot be used to study glucose-dependent insulin secretion. When

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this effect is suspected, the use of Min 6 (mouse) and HT15 (hamster) cells are recommended. Isolated mouse pancreatic acini provide an ex vivo tool used to assess potential exocrine pancreatic toxicity. This is achieved by evaluating digestive enzyme secretion through an assessment of basal amylase and amylase release in response to carbachol and cholecystokinin octapeptide (CCK8). In vitro, cyproheptadine at high doses causes marked release of amylase without any breaks in structural integrity, thus ruling out cytotoxicity as the likely cause of the marked amylase release. Furthermore, cyproheptadine can abolish carbachol-stimulated amylase release, and this may be related to its antimuscarinic pharmacological property. This approach can serve as an in vitro toxicity screen to eliminate and/or reduce undesirable effects on the exocrine as well as endocrine pancreas and, additionally, the pathophysiology of these potential effects can be investigated. Bone Marrow Toxicity Within the last decade, clinical hematopoietic toxicity has accounted for 10 to 20% of approved drug withdrawals and is a major cause of early candidate drug attrition. Identiﬁcation of potential hematopoietic toxicity as a hazard is done only when in vivo studies are initiated even though there is strong evidence suggesting that in vitro assays could have a very good predictability. A three-tiered approach that enables a large number of compounds to be evaluated at the lead optimization phase was developed to improve the assessment of potential hematotoxicity. A key question in this approach is whether there is clinical translation and predictability of this combined in vitro, in vivo preclinical evaluation. This novel approach uses multiple assays, cell lines representative of broad bone marrow lineages (myeloid, erythroid, lymphoid, and stromal), and a nonhematopoietic cell line (tier 1). In general, there is a good correlation between the mouse myeloid cell line and the mouse colony-forming unit/GM (CFU-GM) assays and compounds with lineage-speciﬁc in vivo bone marrow toxicity showed similar lineage speciﬁcity in the tier 1 assays. An erythrotoxicant such as chloramphenicol has an IC50 value of 8 μM in the erythroid cell line assay and >100 μM for the other cell lines. CFU assays (tier 2) are then run to qualify and conﬁrm the ﬁndings of tier 1. Tier 2 consists of mouse, rat, dog, and human CFU-GM assays to predict species sensitivity, but these assays are tedious and time consuming, enabling only a limited number of compounds to be evaluated. Increased throughput was achieved when a 96-well plate CFU-GM assay was developed that yielded results that correlated well with the traditional CFU-GM assay. The data generated from the tiers 1 and 2 can trigger frontloading of in vivo studies to evaluate a more comprehensive hematology proﬁle and include use of additional technologies such as ﬂow cytometry (tier 3). In summary, a three-tiered approach using in vitro, ex vivo, and in vivo studies allows rapid, high-throughput assessment of compounds

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with potential chemistry-related bone marrow toxicity to be eliminated in the early preclinical stages of drug development. Liver Toxicity The overall goal of developing an in vitro toxicity testing system is for inclusion in a toolbox as an element of investigative toxicology. This should contribute to hypothesis-based research aimed at identiﬁcation of mode-ofaction-based toxicity biomarkers, which can extend to preclinical and clinical phases of drug development. The current preclinical and clinical diagnostic panel used to monitor hepatocellular toxicity has been quite reliable except in cases of human drug-induced liver injury, which occurs with a low incidence and prevalence, ranging from 1 in 10,000 to 1 in 100,000. This is also referred to as idiosyncratic hepatotoxicity, meaning that the adverse events are a consequence of individual and unpredictable patient responses. It is well recognized that there is an urgent need to identify and develop a more successful prediction model coupled with diagnostic and prognostic biomarkers for druginduced idiosyncratic liver injury in humans. This type of human liver toxicity is multifactorial, including individual-speciﬁc responses, and because of this, it is highly unlikely that any preclinical and/or clinical studies can be powered appropriately to unmask this risk. Furthermore, it is difﬁcult to assess the value of our preclinical toxicology studies and their relevance to predicting drug-induced idiosyncratic liver injury in humans, because we lack understanding of the mode of action and pathophysiology of this unique type of toxicity. However, there is extensive literature related to the predictability of hepatotoxicity, and there is good concordance with in vitro and in vivo assessments. Several different strategies have been employed to detect various forms of hepatotoxicity, including, necrosis, apoptosis, and phospholipidosis. The in vitro hepatotoxicity predictive strategy uses hepatic microsomes; immortalized cell lines; primary hepatocyte cell cultures from humans, rodents, and nonrodents; and liver slices. The latter, in particular, enables a better approximation and evaluation of metabolites that may be formed and an assessment of their hepatotoxicity potential determined and in vitro–in vivo comparisons made. For example, using this approach, studies were done to characterize the metabolic proﬁle of troglitazone, an approved drug that has now been withdrawn from the market because of hepatotoxicity and hepatic failure requiring liver transplants. Data from a series of studies using rat and human hepatic microsomes as well as in vivo rat studies suggested that troglitazone hepatotoxicity was caused by several reactive intermediates covalently binding to hepatic proteins that may undergo redox cycling and induce oxidative stress, causing cell damage. This hypothesis was strongly supported by the data suggesting that not only is troglitazone an inducer of P450 3A, but is also responsible for metabolism of the thiazolidinedione ring, a key structural element of many of the peroxisome prolifercator–activated receptor (PPAR) agonists.

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Taken together, troglitazone acts as an inducer of enzymes that catalyzes its biotransformation to chemically reactive intermediates; this autoinduction of its own metabolism is ultimately detrimental to the cells. Additionally, this combined approach was able to show that in humans these intermediates could be formed, thus identifying a potential etiology for troglitazone-induced hepatotoxicity in humans. Utilization of liver slice technology, co-cultures, and whole liver perfusion can be quite useful, but these are low-throughput assays that are not always suitable for screening. The advantage is that toxicity to the biliary and other hepatic cellular constituents can be assessed. Thus predicting the hepatotoxicity potential using in vitro cell culture systems can also employ the well-recognized preclinical and clinical biomarkers of hepatocellular damage, such as ALT (alanine aminotransferase), AST (aspartate aminotransferase), GLDH (glutamate dehydrogenase), MDH (malate dehydrogenase), PNP (purine nucleoside phosphorylase), and PON-1 (paraoxonase-1). Other enzymes, such as ALP (alkaline phosphatase), GGT (γ-glutamyl transferase) and 5′-nucleotidase (5′NT) can also be used when toxicity to the biliary tree is suspected. Cytotoxicity (necrosis and apoptosis) in vitro is the primary endpoint, and as such, biomarkers for potential hepatotoxicity clinical monitoring should include necrosis as well as apoptosis. Recent evidence suggests that this may be possible by measuring the soluble pool of cytokeratin 18 (CK18) and the caspase-cleaved CK18 fragments in conjunction with the traditional markers. The in vitro surrogate system can also provide valuable information on the suitability of the appropriate biomarker and potential mechanism of action related to toxicity, such as mitochondrial dysfunction, metabolic pathways, and/or CYP450 induction. In summary, in vitro hepatocyte cell culture systems and tissues derived from in vivo studies will identify the appropriate biomarker for monitoring preclinical and clinical hepatotoxicity but these data can also contribute to hypothesis-driven mode-of-action investigative toxicity studies. This will include molecular proﬁling, metabonomics, transcriptomics, and proteomics, all of which are useful tools that could aid in identiﬁcation of novel biomarkers of hepatotoxicity. Phospholipidosis Phospholipidosis (PLD) is characterized by concentric-layered multilamellar intracellular lysosomal inclusion bodies that are often composed of complex phospholipids, parent drug, and/or metabolites. Accumulation will often occur in several different cell types of the hepatobiliary, immune, and nervous systems. Typically, hepatocytes, biliary epithelial cells and macrophages of lymph nodes, and pulmonary alveoli and ganglia and nonganglia neuronal cell bodies of the central nervous system can be affected. The evidence to date suggests that phospholipidosis is a structural-related toxicity of cationic amphiphilic compounds irrespective of pharmacologic action. The ﬁnding of phos-

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pholipidosis may also be associated with inﬂammation, severe organ damage, and possibly impairment of immune function. Although drugs are marketed that cause phospholipidosis preclinically and clinically, this is an undesirable proﬁle for potentially new candidate drugs, and as such, this liability should be identiﬁed and avoided early in the drug discovery process. Therefore, highthroughput in vitro predictive screens can add value, particularly if phospholipidosis potency can be ranked and supported by in vivo data. Several methodologies have been evaluated to assess neutral and phospholipid content as an index of phospholipidosis in cells growing in culture. However, accumulation of NBD-PE as a result of cytotoxicity induces false-positive results, particularly at high concentrations. Recently, a highthroughput, validated, predictive, sensitive, and selective multichannel ﬂuorescence-based in vitro PLD assay was developed to reduce the false-positive limitation of cytotoxicity. This assay uses I-13.35 adherent mouse spleen macrophages cultured in 96-well plates with ﬂuorescent-tagged phospholipids. Cells with an intact nucleus were differentiated from dead cells using ethidium staining and cell gating that rejects dead cells. Using this improved technique, 26 of 28 positive phospholipidogenic compounds were identiﬁed. These ﬁndings aided application of this methodology to other techniques, such as ﬂow cytometry, which may be used in preclinical toxicology studies and clinical trials. For example, ﬂow cytometric analysis coupled with Nile Red staining was used to detect neutral and phospholipids in a monocyte cell line, U397. Application of this methodology was utilized in (in vivo) toxicology studies, and this raised the possibility that preclinical toxicology and clinical assessment of phospholipidosis could be done using peripheral blood cells and ﬂow cytometry.

CONCLUSIONS Overall, there is a compelling need for in vitro toxicity biomarkers for clinical endpoints. Toxicity biomarkers in the in vitro context are deﬁned as quantitative measurable characteristics that serve as indicators of a pathologic process or related biochemical or molecular events. Conceptually, bridging biomarkers of toxicity should include not only the traditional parameters of bioﬂuid and physiological measurements, but also measurable endpoints that could serve as indicators of potential adverse events preclinically and clinically even though they have been derived from in vitro, ex vivo, and in vivo studies. The use and application of this combination will undoubtedly improve the toxicologist’s ability to identify human hazards so that the appropriate risk assessment and management strategy can be developed. In developing in vitro assays to identify biomarkers with potential clinical application and utility, a clear understanding and determination of what that biomarker will assess must be deﬁned. For example, exaggerated pharmacologic action of a molecular target may be associated with an undesirable effect resulting in toxicity. In

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such cases, single/multiple markers and/or assays may be required for utilization in screens during the early drug discovery phase, with continued assessment in preclinical and clinical development. If successful, these data can be invaluable in deriving the therapeutic index of a drug. In the case of a speciﬁc target organ toxicity, the ultimate goal is to identify biomarkers of toxicity that can be used as a general screen that reﬂects cellular damage, regardless of mode of action in a high-throughput manner. Therefore, it is imperative that in vitro assays and the appropriate platforms be developed to identify relevant toxicity biomarkers that will be useful during preclinical and clinical development.

REFERENCE 1. Atkinson AJ Jr, Colburn WA, DeGruttola VG, et al. (2001). Biomarkers and surrogate endpoints: preferred deﬁnitions and conceptual framework. Clin Pharmacol Ther, 69:89–95.

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23 OPPORTUNITIES AND PITFALLS ASSOCIATED WITH EARLY UTILIZATION OF BIOMARKERS: CASE STUDY IN ANTICOAGULANT DEVELOPMENT Kay A. Criswell, Ph.D. Pﬁzer Global Research and Development, Groton, Connecticut

INTRODUCTION The cost of developing new drugs continues to rise and demands an everincreasing percentage of total research and development (R&D) expenditures [1]. Recent studies have shown that there is less than a 10% success rate between the ﬁrst human trials and the launch of a new product [1]. When coupled with an attrition rate of over 70% in phase II and nearly a third of all new compounds in phase III trials [1–3], the gravity of the need for reliable, early-decision-making capability is evident. All pharmaceutical companies are focused on ways to reduce the cost of discovering and developing new drugs. In general, three areas have received the greatest attention: (1) improving target validation, (2) selecting candidates with a greater chance of success, or (3) identifying those compounds that will fail earlier in their development. Inadequate efﬁcacy or poor safety margins are the two main reasons for compound attrition. Therefore, developing a strategy for development of safety

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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and efﬁcacy biomarkers is key to early prediction of compound success or failure. Utilization of biomarkers in preclinical testing and ex vivo human testing may provide valuable information for compound progression and success in the clinic, but it is not without problems. Studying the effects of markers on disease processes is not new. It has probably existed for centuries, as evidenced by such obsolete practices as tasting urine to determine the diabetic status of a patient. Utilization of biomarkers for disease diagnosis and prognosis has escalated dramatically during this century with the improved diagnostic power, reliability, and speed of routine clinical pathology, biochemical, and genomic data. However, when studying the speciﬁc effects of a drug on marker activity, utilization of biomarkers has a more recent history. One of the earliest documented cases may be the early trials of lamotrigine, which effectively utilized the electroencephalograph (EEG) as a biomarker and demonstrated decreased epileptiform activity [4]. Twenty years after the lamotrigine study the urgency for relevant and early biomarkers of safety and efﬁcacy during drug development has been recognized, and it is not uncommon that each new drug development program is accompanied by a biomarker request. Not only has the frequency of biomarker development exploded, but the timing of biomarker utilization has been pushed earlier in development. There is an ever-increasing demand to test and screen potential candidates before good laboratory practices (GLP) studies and commitment to human trials. Two problems inherently complicate the use of early biomarkers to preclinical outcomes: (1) truly understanding the disease process and the therapeutic intervention process, and (2) species–species differences that may alter the translation and application of the biomarker. Understanding the disease process appears obvious, but as already noted, understanding the target is still a key area for decreasing attrition. Regardless of the expanding body of knowledge surrounding disease processes, infectious diseases may be the lone area where the disease process is fully understood. Successful therapeutic intervention and drug sensitivity can be predicted accurately with well-characterized biomarkers of causal organism growth and survival [5]. For biomarkers to be truly effective in drug development, they need to reﬂect a highly speciﬁc biochemical or molecular change that occurs in the disease process, is altered by the therapeutic intervention, and occurs prior to any downstream effects on clinical endpoints. This is a lofty goal, as most disease processes affect multiple pathways, and feedback mechanisms further complicate the overall biology. The further impact of nontranslatable, speciesspeciﬁc characteristics makes daunting the task of providing biomarkers early in development. There are, however, diseases and biological pathways that have a fairly broad acceptance regarding the essential components. Additionally, certain areas of drug development target activation or inhibition of a highly speciﬁc molecule or receptor within those pathways. These conditions provide a unique opportunity for biomarker success when conducted early in the drug discovery process.

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The coagulation pathway is an example of a well-characterized model, and speciﬁc inhibitors of this pathway are being explored actively as new candidates for anticoagulant therapy. One of the documented areas is the utilization and challenge of early implementation of biomarkers to predict the clinical outcomes of factor Xa (FXa) inhibitor compounds. Venous and arterial thromboembolic disorders have a substantial impact on human health and morbidity [6]. Although these conditions have been treated for many years with the vitamin K antagonist warfarin (coumadin), or with intravenous or subcutaneous heparin administration, the opportunity to provide therapeutic intervention with a safer proﬁle, fewer side effects, less intra- and intersubject variability, a better route of administration, and less need for continuous coagulation monitoring is attractive. Novel coagulation therapeutics have generated tremendous interest due to the medical need and opportunity to improve on current treatment strategies. FXa occupies a pivotal position within the coagulation cascade and is a highly attractive candidate as a target for novel coagulation intervention. This enzyme links the intrinsic and extrinsic coagulation pathways and is the rate-limiting step in thrombin formation [7]. Changes within the intrinsic coagulation pathway are routinely monitored by a clotting assay called the activated partial thromboplastin time (aPTT), whereas changes in the extrinsic pathway are assessed by increases or decreases in another clotting assay called the prothrombin time (PT) (7, Figure 1). Both assays are assessed spectrophotometrically or mechanically and are expressed

Intrinsic Pathway (aPTT) Factor XII

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Intrinsic and extrinsic coagulation pathways.

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as the time to clot formation after addition of an exogenous clot-activating agent. The aPTT is used routinely to monitor the safety and level of anticoagulation of heparin therapy and coumadin in monitored via the PT assay. Safety proﬁles for these compounds have been established following years of use so that fold increases over the predose aPTT or PT can be used to predict therapeutically effective doses of these drugs versus levels associated with inadequate or excessive coagulation. Additionally, aPTT and PT are routine and highly standardized clinical assays. Testing reagents are standardized and instrumentation has undergone rigorous scrutiny to pass U.S. Food and Drug Administration (FDA) requirements because of its routine use in clinical diagnostics. The reliability of aPTT and PT as biomarkers of anticoagulant therapy safety is also fairly well established. Since activation of factor X is required for completion of both the intrinsic and extrinsic pathways, it appears logical that inhibition of factor X activation should prolong both PT and aPTT. It would be anticipated that early assessment of novel anticoagulants such as FXa inhibitors could readily be assessed with the aPTT and PT assays. Furthermore, if the new compound does not require metabolic activation, ex vivo incubation of human plasma with the compound of interest followed by aPTT and PT assessment may provide a reliable method to rapidly assess efﬁcacy. Despite their reliability and acceptability, PT and aPTT do not always reﬂect the anticoagulant activity of novel compounds. Rivaroxaban is an oral direct inhibitor of activated factor X [8]. In a rat venous model of the inferior vena cava, Rivaroxaban produced dose-dependent inhibition of FXa and an increase in PT [7]. An inhibition of 32% was associated with a 1.8-fold increase in PT, and nearly 100% inhibition produced a 3.2-fold increase in PT. However, in a rabbit model 92% inhibition of FX was associated with only a modest 1.2-fold increase in PT, demonstrating that species-speciﬁc sensitivity is one of the problems associated with monitoring the newer anticoagulants with standard coagulation parameters [8]. Even Coumadin provides a good example of species-speciﬁc effects that allow a useful therapeutic for humans but a lethal pest control for rodents [9]. Despite this challenge, utilization of coagulation testing for in vivo and ex vivo screening is well documented in the development of FXa inhibitors [8,10–12]. Rarely is a single biomarker considered deﬁnitive for evaluation of safety or efﬁcacy. The drug development approach for Otamixabin and DU-176b incorporated a series of clotting parameter assays and assays to measure the effects on thrombus formation [10–12]. Although clotting parameters would be the preferred biomarker based on the ability to monitor in plasma, use of fairly simple but reproducible assays, cost, and the ability to monitor a clinical population easily, evidence is not established that clotting assays and thrombus formation assays are interchangeable. For Otamixaban, in vitro coagulation parameters were assessed for their ability to produce a doubling of PT and aPTT. This testing allowed a rank ordering of anticoagulant effects per species of rabbit > human > monkey >

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rat > dog [10]. Additionally, aPTT appeared to be the more sensitive biomarker in all species, with aPTT doubling occurring at drug concentrations that were less than half the concentration required for PT doubling. Multiple pharmacological models of thrombosis in rats, dogs, and pigs were also conducted with Otamixaban. In rats, thrombus mass was markedly reduced by nearly 95%, with a corresponding increase in aPTT of 2.5-fold and in PT of 1.6-fold [10]. In contrast, intravenous administration of 1, 5, or 15 μg/mL Otamixaban in the pig model effectively eliminated coronary ﬂow reserves related to this stenosis model at the middle and high dose. PT was also prolonged at the middle and high dose, but aPTT was prolonged only at the high dose. Although pigs were not listed as assessed in the species-speciﬁcity model, it suggests that the clotting parameter of choice may vary per species and may not correlate well with thromobosis assays. Furthermore, clinical trial outcomes showed that at anticipated antithrombotic and therapeutic concentrations of 100 ng/mL Otamixaban, neither PT nor aPTT changed appreciably. In contrast, alternative clotting parameters such as the HepTest clotting time and the Russell viper venom clotting time, showed substantial prolongation, again suggesting that alternatives to standard PT and aPTT may be preferable [10]. Further work with the oral FXa inhibitor DU-176b provides additional evidence that selection of the right biomarker and appropriate correlation to functional assays is critical. This study was conducted in 12 healthy male volunteers [12]. The antithrombotic effect of DU-176b was assessed by measuring the difference in size of acutely formed, platelet-rich thrombus, pre- and postdrug administration using a Badimon perfusion chamber model under low and high shear force. Subjects received a single 60-mg dose of DU-176b, and pharmacokinetic and pharmacodynamic assessments were conducted at 1.5, 5, and 12 hours postdosing. Pharmacokinetic assessments included PT, international normalization ratio (INR), aPTT, thrombin generation, and anti-factor Xa activity. Drug levels were also assessed. Badimon chamber results demonstrated a strong antithrombotic effect at 1.5 hours with a progressive return toward baseline by 12 hours. All of the pharmacokinetic endpoints showed signiﬁcant change from pretreatment, suggesting that any of the parameters might be an effective biomarker of DU-176b safety and/or efﬁcacy. However, a close statistical look at this data raises some questions. A comparison of drug concentration level to anti-factor Xa activity and clotting parameters showed the strongest correlation with anti-factor Xa activity (r2 = 0.85), similar correlation with PT and INR (r2 = 0.795 and 0.78, respectively), but a fairly weak correlation with aPTT (r2 = 0.40). This suggests that although Otamixaban and DU-176b are both FXa inhibitors, arbitrary selection of PT or aPTT as a better predictor of drug concentration is problematic. Furthermore, when the antithrombotic effects of DU-176 assessed by Badimon chamber were compared to those obtained by clotting parameters, the correlation was even more challenging. Prothrombin time showed a correlation of r2 = 0.51 at both high and low stress and the correlation with aPTT was only r2 = 0.39 and 0.24 [12]. This suggests that although aPTT is used for monitoring of heparin therapy and PT

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is utilized for clinical safety of coumadin, their routine use as factor Xa inhibitors by themselves is insufﬁcient.

CASE STUDY DATA WITH A DEVELOPMENTAL FXa INHIBITOR Beyond the published literature that is available, data and personal observations collected during the development of another FXa inhibitor at Pﬁzer Global Research & Development are now provided to complete this case study approach to biomarker utilization during anticoagulant development. Development of this particular FXa inhibitor ultimately was discontinued, as this compound required intravenous administration and as such lacked marketability compared to oral FXa inhibitors. However, the lessons learned provide further documentation of species-speciﬁc and interpretational complications that arise with the utilization of accepted coagulation biomarkers to monitor anticoagulant efﬁcacy and safety for FXa inhibitors. Dose Selection for Ex Vivo Experiments In designing ex vivo experiments to evaluate potential biomarkers, selection of the appropriate drug concentration is critical. Furthermore, when experiments are conducted in multiple species, selection of the same drug concentration for all species is typically not ideal, due to species-speciﬁc drug sensitivity. Factor X concentrations vary by species, and the level of FXa inhibition is also variable. Therefore, the concentrations of FXa inhibitor utilized in this particular ex vivo evaluation were selected to achieve a range of FXa inhibition that was modest to nearly complete in all species examined. Pharmacology studies predicted that this FXa inhibitor would result in species-speciﬁc factor Xa sensitivity in the order human > dog > rat. Interestingly, this species speciﬁcity was not identical to that observed with Otamixaban [10], demonstrating that extrapolating biomarker data even between compounds in the same class may be misleading. For this developmental FXa inhibitor, human plasma was spiked to obtain ﬁnal drug concentrations of 0, 0.2, 0.6, 1.2, and 6.0 μg/mL. Drug concentrations of 0, 0.4, 2.0, 8.0, and 15.0 μg/mL were selected for dog assessments and 0, 1.0, 4.0, 12.0, and 24.0 μg/mL were used for ex vivo assessments in rats to achieve a comparable range of FXa inhibition compared to that observed in human samples. Thromboplastin is the reagent that induces clot formation in the PT assay. There is ample documentation that the type and sensitivity of thromboplastin is a critical factor in the effective and safe monitoring of coumadin administration [13–17]. To minimize this variability in PT assays a calibration system was adopted by the World Health Organization (WHO) in 1982. This system converts the PT ratio observed with any thromboplastin into an international normalized ratio (INR). This value was calculated as follows: INR = observed PT ratioc, where the PT ratio is subject PT/control PT and c is the power value

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representing the International Sensitivity Index (ISI) of the particular thromboplastin [18]. This system has proven to be an effective means of monitoring human oral anticoagulant therapy with coumadin and has been implemented almost universally. It allows individuals to be monitored at multiple clinics using varying reagents and instrumentation, while still achieving an accurate assessment of true anticoagulation. However, there is little or no information regarding selection of thromboplastin reagents or use of the INR for monitoring of FXa inhibitors. Typically, the higher the ISI value, the less sensitive the reagent, and the longer the PT time produced. The most commonly used thromboplastin reagents for PT evaluation are either rabbit brain thromboplastin (of variable ISI values, depending on manufacturer and product) or human recombinant thromboplastin, typically with an ISI of approximately 1.0. Use of the INR is accepted as a more relevant biomarker of anticoagulant efﬁcacy than are absolute increases in PT alone, at least for coumadin therapy [13]. To more fully evaluate the effect of this FXa inhibitor on INR, PT was evaluated using rabbit brain thromboplastin, with ISI values of 1.24, 1.55, and 2.21 and a human recombinant thromboplastin (0.98 ISI). Although either human recombinant thromboplastin or rabbit thromboplastin are considered acceptable reagents for the conduct of PT testing, it was unclear whether these reagents would produce similar results in the presence of an FXa inhibitor or whether the sensitivity of the thromboplastin itself would affect results. Effect on Absolute Prothrombin Time Prothrombin time data obtained using rabbit brain thromboplastin with the three increasing ISI values during these ex vivo studies are presented in Table 1. The source and sensitivity of thromboplastin used in the assay affected the absolute PT value in all species, clearly demonstrating the need to standardize this reagent in preclinical assessment and to be cognizant of this impact in clinical trials or postmarketing, when reagents are less likely to be standardized. As anticipated, addition of the FXa inhibitor to plasma under ex vivo conditions increased the PT in a dose-dependent manner. This increase in PT time length was observed regardless of the ISI value (sensitivity) of the thromboplastin used and occurred in all species (Table 1). Although the absolute time for clot formation generally increased with increasing ISI, this was not true for all assessments. Table 2 summarizes the maximum change in PT and the range of variability when rabbit brain thromboplastin of varying ISI values was compared to human recombinant thromboplastin. Again, in general, the higher the PT value, the larger the deviation between reagent types. For example, although there was a 2.1-second difference between human and rabbit thromboplastin in untreated human plasma, the difference increased from 5.5, 9.2, 12.0, 13.6, and 38.2 seconds in samples containing 0.2, 0.6, 1.2, 1.8, or 6.0 μg/mL FXa inhibitor, respectively. Dogs were much less sensitive to the type of thrombo-

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TABLE 1 In Vitro Effect of an Experimental Factor Xa Inhibitor on Absolute Prothrombin Time Using a Rabbit Brain Thomboplastin Concentration of Factor Xa Inhibitor (μg/mL) 0 0.2 0.6 1.2 1.8 6.0 0 0.4 2.0 8.0 15.0 0 1.0 4.0 12.0 24.0

International Sensitivity Indexa 0.98

1.24

PT (s)—Human Plasma 11.4 ± 0.11 13.4 ± 0.17* 18.3 ± 0.37 23.8 ± 0.50* 30.8 ± 0.76 37.3 ± 0.86* 47.0 ± 1.98 51.4 ± 1.51* 61.2 ± 2.04 61.8 ± 1.58 133.0 ± 3.82 111.4 ± 3.17* PT (s)—Dog Plasma 7.8 ± 0.14 8.4 ± 0.07* 11.0 ± 0.26 11.0 ± 0.13 17.6 ± 0.42 16.6 ± 0.23* 32.0 ± 0.98 27.6 ± 0.49* 45.4 ± 1.52 36.5 ± 0.73* PT (s)—Rat Plasma 9.1 ± 0.05 15.1 ± 0.07* 13.3 ± 0.06 20.7 ± 0.13* 20.1 ± 0.29 30.8 ± 0.23* 31.1 ± 0.72 46.2 ± 0.49* 42.7 ± 1.09 60.6 ± 0.73*

1.55

2.21

12.9 ± 0.13* 23.4 ± 0.44* 39.9 ± 0.70* 58.9 ± 1.08* 74.8 ± 1.54* 152.3 ± 3.08*

10.9 ± 0.14 16.8 ± 0.43* 27.0 ± 0.93* 38.2 ± 1.39* 48.3 ± 2.08* 94.8 ± 4.12*

7.1 ± 0.07 10.5 ± 0.19 17.9 ± 0.41 33.6 ± 0.92 46.7 ± 1.35

6.7 ± 0.06* 9.2 ± 0.15* 15.0 ± 0.31* 27.1 ± 0.63* 36.8 ± 0.88*

17.1 ± 0.15* 31.3 ± 0.27* 51.8 ± 0.52* 83.6 ± 0.84* 109.4 ± 0.79*

13.1 ± 0.07* 23.2 ± 0.19* 36.9 ± 0.52* 56.2 ± 0.84* 75.4 ± 1.60*

*, Mean value ± S.E.M. for 10 individual subjects signiﬁcantly different from 0.98 ISI thromboplastin means at 5% level by t-test, separately by increasing ISI value for individual rabbit thromboplastins. a

plastin used and showed smaller maximum changes in PT values. In contrast, rat PT values were highly dependent on the source of thromboplastin, and samples tested with rabbit brain thromboplastin were markedly longer than with human recombinant thromboplastin. Rats showed this high level of thromboplastin dependence even in untreated control samples. Variability of PT in FXa inhibitor-treated human and dog plasma was similar to that observed in controls and did not change appreciably with increasing concentration of drug (Table 2). FXa inhibitor-treated rat plasma showed an approximately twofold increase in variability compared to control. Effect on PT/Control Ratio and INR Generating a PT/control ratio by dividing the number of absolute seconds in the treated sample by the number in the control (untreated) sample provides a second method of assessing PT. If the ISI of the thromboplastin used is close to 1.0, the INR should be similar to the PT/control ratio (Table 3). The PT/

CASE STUDY DATA WITH A DEVELOPMENTAL FXa INHIBITOR

453

TABLE 2 Comparison of Human Recombinant Thromboplastin and Rabbit Brain Thromboplastin on Prothrombin Time in Plasma Samples Containing Increasing Concentrations of Factor Xa Inhibitora Species Human

Dog

Rat

Intended Drug Concentration (μg/mL) 0 0.20 0.60 1.20 1.80 6.00 0 0.40 2.00 8.00 15.00 0 1.00 4.00 12.00 24.00

Maximum Change in PTb

Range of Variabilityc (%)

2.1 5.5 9.2 12.0 13.6 38.2 1.1 1.8 2.5 4.9 8.9 8.0 18.0 31.7 52.5 66.7

−4 to +18 −8 to +30 −12 to +30 −19 to +25 −21 to +22 −29 to +14 −15 to +7 −7 to −5 −14 to +2 −15 to +5 −20 to +3 +43 to +88 +56 to +136 +53 to +158 +48 to +168 +42 to +156

a

Samples spiked with a factor Xa inhibitor in vitro. Maximum change in prothrombin time compared to 0.98 ISI human recombinant thromboplastin. c Variability of three increasing ISI levels of rabbit brain thromboplastin compared to human recombinant. b

control ratio could be used effectively to normalize thromboplastin differences in untreated human, dog, or rat samples. At predicted efﬁcacious concentrations of FXa inhibitor, the PT/control ratio effectively normalized reagent differences. However, at high concentrations of FXa inhibitor, particularly in the rat, this method lacked the ability to normalize results effectively. Table 4 shows the corresponding INR values obtained in human, dog, and rat plasma when assessed with rabbit brain thromboplastins of increasing ISI. As anticipated, the PT/control ratio and INR were similar when the ISI was approximately 1. In contrast to the modest differences in PT when expressed as either absolute seconds or as a ratio compared to control value, the INR showed dramatic increases (Table 3). The magnitude of the INR value rose consistently with increasing ISI value and was marked. At the highest dose tested, the INR ranged from 11.1 with the 0.98 ISI reagent to 121.9 with the 2.21 ISI reagent in human samples, 5.6 to 43.4 in dogs, and 4.6 to 48.1 in rats. Assessment of PT in human, dog, or rat plasma containing this developmental FXa inhibitor was affected by the ISI of the thromboplastin selected for the

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TABLE 3 In Vitro Effect of an Experimental Factor Xa Inhibitor on Prothrombin Time/Control Ratio Using a Rabbit Brain Thomboplastin Concentration of Factor Xa Inhibitor (μg/mL) 0 0.2 0.6 1.2 1.8 6.0 0 0.4 2.0 8.0 15.0 0 1.0 4.0 12.0 24.0

International Sensitivity Indexa 0.98

1.24

1.55

PT / Control Ratio (:1)—Human Plasma 1.0 ± 0.00 1.0 ± 0.00 1.0 ± 0.00 1.6 ± 0.02 1.8 ± 0.12 1.8 ± 0.21 2.7 ± 0.05 2.8 ± 0.04 2.9 ± 0.03 4.1 ± 0.15 3.8 ± 0.08 4.6 ± 0.06* 5.4 ± 015 5.6 ± 0.07 5.8 ± 0.08 11.7 ± 0.26 8.3 ± 0.17* 11.9 ± 0.16 PT / Control Ratio (:1)—Dog Plasma 1.0 ± 0.00 1.0 ± 0.00 1.0 ± 0.00 1.4 ± 0.01 1.3 ± 0.01 1.5 ± 0.02 2.2 ± 0.02 2.0 ± 0.02 2.3 ± 0.04 4.1 ± 0.07 3.3 ± 0.05* 4.3 ± 0.10 5.8 ± 0.14 4.4 ± 0.08* 6.5 ± 0.16* PT / Control Ratio (:1)—Rat Plasma 1.0 ± 0.00 1.0 ± 0.00 1.0 ± 0.00 1.5 ± 0.02 1.4 ± 0.02 1.6 ± 0.02 2.2 ± 0.03 2.0 ± 0.04 3.0 ± 0.02* 3.4 ± 0.08 3.1 ± 0.07 4.9 ± 0.06* 5.5 ± 0.17 7.3 ± 0.24* 15.3 ± 0.21*

2.21 1.0 1.5 2.5 3.5 5.4 8.7

± ± ± ± ± ±

0.00 0.02 0.06 0.09 0.15 0.29*

1.0 ± 0.00 1.4 ± 0.02 2.3 ± 0.04 4.1 ± 0.08 5.5 ± 0.11 1.0 1.6 2.8 4.3 11.3

± ± ± ± ±

0.00 0.01 0.03* 0.05* 0.28*

*, Mean value ± S.E.M. for 10 individual subjects signiﬁcantly different from 0.98 ISI thromboplastin means at 5% level by t-test, separately by increasingly ISI value for individual rabbit thromboplastins. a

assay. However, it was not affected to the same degree as was coumadin. Consequently, using the correction calculation designed for coumadin ﬂuctuations to obtain an INR with CI-1031 grossly exaggerated the INR value. Although INR has been used clinically to monitor anticoagulant status during coumadin therapy, it probably should not be used with FXa inhibitor administration. Coumadin therapy typically produces INR values of 2, 4, and 6 as therapeutic, above therapeutic, and critical levels, respectively. INR values of 10 to 15 may be observed in acute coumadin poisoning, but INR values higher than 15 rarely occur [19]. Clearly, the magnitude of the INR obtained in this experiment (>120 in humans), combined with the incremental increase that occurred with increasing ISI value, shows that INR values in these FXa inhibitor-treated samples were an artifact of the calculation and not associated with the true anticoagulant effects of the FXa inhibitor itself. This suggests that when INR is used in clinical trials, it is important to select a thromboplastin with an ISI value close to 1.0. In this manner, the INR will

CASE STUDY DATA WITH A DEVELOPMENTAL FXa INHIBITOR

455

TABLE 4 In Vitro Effect of an Experimental Factor Xa Inhibitor on International Normalization Ratio Using a Rabbit Brain Thomboplastin Concentration of Factor Xa Inhibitor (μg/mL) 0 0.2 0.6 1.2 1.8 6.0

International Sensitivity Indexa 0.98

1.24

1.55

2.21

International Normalized Ratio (:1)—Human Plasma 1.0 ± 0.10 1.0 ± 0.02 1.0 ± 0.02 1.0 1.6 ± 0.04 2.0 ± 0.06* 2.6 ± 0.07* 2.6 2.7 ± 0.07 3.6 ± 0.10* 5.7 ± 0.16* 7.5 4.0 ± 0.16 5.3 ± 0.19* 10.5 ± 0.29* 16.2 5.2 ± 0.17 6.7 ± 0.21* 15.3 ± 0.47* 27.4 11.1 ± 0.31 13.8 ± 0.49* 46.0 ± 1.41* 121.9

± ± ± ± ± ±

0.03 0.16* 0.57* 1.31* 2.618 11.54*

0 0.4 2.0 8.0 15.0

International Normalized Ratio (:1)—Dog Plasma 1.0 ± 0.03 1.0 ± 0.01 0.9 ± 0.02 1.4 ± 0.04 1.4 ± 0.01 1.7 ± 0.05* 2.2 ± 0.05 2.3 ± 0.04 3.8 ± 0.14* 4.0 ± 0.12 4.4 ± 0.10* 10.1 ± 0.43* 5.6 ± 0.18 6.2 ± 0.16* 16.7 ± 0.74*

1.0 ± 0.03 2.0 ± 0.08* 6.0 ± 0.28* 22.1 ± 1.19* 43.4 ± 2.31*

0 1.0 4.0 12.0 24.0

International Normalized Ratio (:1)—Rat Plasma 1.0 ± 0.03 1.0 ± 0.00 1.0 ± 0.02 1.5 ± 0.02 1.5 ± 0.02 2.6 ± 0.03* 2.2 ± 0.03 2.4 ± 0.06* 5.6 ± 0.11* 3.3 ± 0.07 4.0 ± 0.13* 11.7 ± 0.34* 4.6 ± 0.12 5.6 ± 0.22* 17.8 ± 0.20*

1.0 3.5 9.9 25.1 48.1

± ± ± ± ±

0.01 0.07* 0.31* 0.82* 2.27*

*, Mean value ± S.E.M. for 10 individual subjects signiﬁcantly different from 0.98 ISI thromboplastin means at 5% level by t-test, separately by increasingly ISI value for individual rabbit thromboplastins. a

closely approximate the PT/control ratio and give a true estimate of the anticoagulated state. Table 5 indicates the maximum change in PT/control ratio and INR using thromboplastins with increasing ISI values (1.24 to 2.21). Changes in the PT/control ratio were modest at drug concentrations that produced increases of fourfold or less, the maximum targeted therapeutic PT value for clinical trials. The mean PT/control ratio in human samples increased maximally from 2.7 to 3.1 at twice the therapeutic dose (0.6 μg/mL). Absolute PT and PT ratios compared to baseline values were only modestly different using thromboplastin from various manufacturers, sources (human recombinant versus rabbit), and ISI. This ﬁnding indicates that absolute PT or PT/control ratio were more effective biomarkers of FXa inhibitor concentration than was INR.

456

OPPORTUNITIES AND PITFALLS

TABLE 5 Comparison of PT/Control Ratio and International Normalization Ratio in Plasma Samples Containing Increasing Concentrations of Factor Xa Inhibitora

Species Human

Dog

Rat

Intended Drug Concentration (μg/mL) 0 0.2 0.6 1.2 1.8 6.0 0 0.4 2.0 8.0 15.0 0 1.0 4.0 12.0 24.0

PT/Control Ratio (0.98 ISI) 1.00 1.61 2.70 4.13 5.37 11.67 1.00 1.42 2.24 4.11 5.82 1.00 1.45 2.20 3.42 5.51

INR (0.98 ISI) 0.99 1.58 2.65 4.00 5.19 11.10 1.00 1.41 2.22 4.00 5.60 1.00 1.45 2.19 3.34 4.55

Maximum Change in PT/ Control Ratiob 0 0.22 0.38 0.65 0.97 3.39 0 0.10 0.27 0.81 1.46 0 0.38 0.82 1.48 9.82

Maximum Change in INRb 0.02 1.03 4.88 12.24 22.21 110.84 0.08 0.61 3.77 18.06 37.76 0.01 2.08 7.7 21.74 43.54

a

Samples spiked with a factor Xa inhibitor in vitro. Results obtained by selecting the maximum result obtained with rabbit brain thromboplastin and subtracting from result obtained with human recombinant thromboplastin. b

PURSUING BIOMARKERS BEYOND PT, INR, AND aPTT Values obtained with aPTT under ex vivo conditions were less sensitive than PT to FXa inhibitor-induced elevations and often underestimated drug concentration (data not shown). Beyond PT, INR, and aPTT, the most commonly used assay to evaluate FXa inhibitors is probably the anti-factor Xa assay (anti-FXa). It seems logical that a parameter named anti-Factor Xa assay should be the ideal biomarker for a FXa inhibitor. Additionally, this assay is used routinely in clinical settings to monitor the safety of heparin, a substance that also inhibits FXa production [20]. However, this assay is little more than a surrogate marker for drug concentration. A standard curve is prepared using the administered heparin (or other FXa inhibitor), and the chromagenic assay allows determination of the drug concentration in the plasma samples via production of FXa [21]. For heparin, the anti-FXa assay appears relevant. Years of use has allowed the development of a strong correlation between the number of international units of heparin determined via the assay and clinical safety. Reference ranges have been deﬁned for the assay and provide a rapid

PURSUING BIOMARKERS BEYOND PT, INR, AND aPTT

457

estimation of under, over, or therapeutic levels of heparin administration [22]. Still variability in the anti-FXa assay has been reported and is attributable to a number of factors, including instrumentation, assay technique, speciﬁcity of the commercially available kits, heparin preparations used in generating the standard curve, and approaches to data ﬁtting [21]. In contrast, this experience does not exist for anti-Xa values obtained during FXa inhibitor administration. Just as the PT and INR may not be as beneﬁcial for predicting FXa inhibitor effects as they are for coumadin, it should not be assumed that the anti-FXa assays have equivalent predictivity for heparin and other FXa inhibitors. For the Pﬁzer developmental FXa inhibitor, the anti-FXa assay offered little more than the PT as a monitor of drug concentration. An additional assay called the factor X clotting (FX:C) assay was also evaluated. This assay is conducted using genetically engineered factordeﬁcient plasma spiked with serial dilutions of puriﬁed human factor X [23,24]. Concentrations of factor X in plasma are then determined by extrapolation from the standard curve. Since factor X must be converted to factor Xa for clot formation to occur, a functional clotting assay for factor X can also be used to assess the effects of factor Xa inhibitors. The FX:C assay provides several unique features that may make it a valuable biomarker for monitoring factor Xa inhibitor therapy: (1) the assay provides a rapid, reliable assessment of drug concentration and the percent inhibition of FXa achieved during drug inhibitor administration; (2) the assay can be performed on a high-throughput automated platform that is available in most hospital-based coagulation laboratories; and (3) individual factor X concentrations range from 60 to 150% between subjects [25]. This fairly high level of baseline intersubject variability suggests that a standard dose of drug may have a substantially different impact on total factor X inhibition. The FX:C assay deﬁnes baseline factor X activity and thereby allows continued dosing to achieve a targeted factor X concentration [4]. Literature is available concerning factor X concentrations and bleeding history in patients with either inherited or acquired factor X deﬁciency, so minimally there is some understanding that correlates the impact of reductions in FX:C evaluations and bleeding potential [26–28]. By determining the actual concentration of functional factor X remaining, physicians may have increased conﬁdence in the administration of factor Xa inhibitors. As with all the other coagulation biomarkers used for monitoring FXa inhibition, it was not immediately clear whether the FX:C assay was applicable in multiple species. Ex vivo experiments allowed this evaluation. To provide effective anticoagulant activity a 30% reduction in FX:C activity was predicted to be the minimal requirement for this compound. The FXa inhibitor concentrations in the ex vivo experiments were selected to bracket a range of factor X inhibition predicted to range from approximately 30% to 100%. Table 6 shows the intended concentrations of this FXa inhibitor in each species, the resulting FX:C activity, and the percent inhibition achieved. Assessment of these drug concentrations induced factor Xa inhibition of approximately 20% to >90%, showing that the targeted range could be predicted and achieved in

458

OPPORTUNITIES AND PITFALLS

TABLE 6 Factor X Activity and Percent Inhibition in Plasma Samples Containing Increasing Concentrations of a Factor Xa Inhibitora Species Human

Dog

Rat

Intended Drug Concentration (μg/mL) 0 0.2 0.6 1.2 1.8 6.0 0 0.4 2.0 8.0 15.0 0 1.0 4.0 12.0 24.0

FX:C Activityb (%) 106.1 ± 1.9 64.3 ± 1.6 32.2 ± 1.0 16.5 ± 0.7 10.0 ± 0.5 2.3 ± 0.2 143.0 ± 4.5 112.9 ± 8.6 42.4 ± 4.3 11.1 ± 1.4 5.4 ± 0.8 84.8 ± 2.8 52.6 ± 1.8 26.2 ± 0.9 11.8 ± 0.6 6.7 ± 0.3

Percent Inhibitionc NA 39.4 69.7 84.4 90.6 97.8 NA 21.0 70.3 92.2 96.2 NA 38.0 69.1 86.0 92.1

a

Samples spiked with a Factor Xa inhibitor in vitro. Mean ± SD of 10 samples/concentration. c Calculated from species-speciﬁc control value. NA, not applicable. b

all species. These ex vivo experiments demonstrated that the predicted efﬁcacious dose of 0.2 to 0.3 μg/mL achieved the required 30 to 40% inhibition of FXa, providing early conﬁdence in the dose selection process for phase I human trials. Additionally, these early ex vivo studies conﬁrmed speciesspeciﬁc differences. The drug concentrations required to produce similar levels of FXa inhibition across species were markedly different. The FX:C assay was used effectively in preclinical rat and dog studies with this developmental FXa inhibitor. Knowledge of the species-speciﬁc concentration of drug required to induce the required 30% inhibition of FXa drove the selection of the low dose, whereas nearly complete inhibition of FXa drove the selection of the high dose. The FX:C assay helped determine the drug concentration required for complete inhibition of factor Xa in these species and the relative bleeding risk associated with a range of factor X concentrations. Prior knowledge of the impact of this drug on FXa inhibition through fairly simple clotting assessments helped eliminate undue risks of over-anticoagulation in preclinical studies, and there was no loss of animals due to excessive hemorrhage. It also addressed questions of whether dosing had been pushed to high enough levels when only minimal bleeding was observed at the highest dose. Since nearly 100% inhibition was achieved during the study, using higher doses was

CONCLUSIONS

459

not indicated and the lack of bleeding under conditions of complete FXa inhibition in rats and dogs suggested a strong safety proﬁle. Inclusion of these biomarkers in preclinical studies provided greater conﬁdence for selection of target stopping criteria for the ﬁrst-in-human trial. The FX:C assay was translated and used as part of the ﬁrst-in-human clinical trial with this compound. The FX:C assay provided data consistent with in vitro modeling, suggesting that it is predictive of drug concentration.

CONCLUSIONS One of the goals for new anticoagulant therapies is a superior safety proﬁle compared to marketed anticoagulants, thereby minimizing or eliminating the need for clinical monitoring. Although clinical monitoring with standardized coagulation assays may appear to be a simple solution to monitoring the safety and efﬁcacy of anticoagulants, there are inherent issues that make the elimination of clinical monitoring highly desirable. The obvious factors of cost and labor are minor in comparison to the problems associated with lack of patient compliance, delayed time to achieve therapeutic beneﬁt, and the high degree of variability in the assay itself due to instrumentation, reagents, technique, and the inherent variability among subjects. Phase I studies using biomarkers are generally cheaper than phase II clinical endpoint studies. Additionally, new anticoagulants pose a relatively undetermined safety risk, due to the possibility of excessive bleeding. Therefore, biomarkers will continue to be essential until safety proﬁles can be established for this newer generation of anticoagulants. Although PT and INR are an effective reﬂection of drug the safety and efﬁcacy for coumadins, they are less than ideal as biomarkers of new FXa inhibitor drugs. Assessing anti-FXa activity has been similar to drug concentration analysis for some inhibitors but variable for others [12; current case study]. FX:C clotting activity may be another alternative but remains largely unexplored. Regardless of the hope for the development of safer anticoagulants that are monitoring-free, the reality is that development of these drugs requires extensive patient monitoring to ensure safety. Compared to heparin and coumadin, which are monitored fairly effectively with aPTT and PT, respectively, development of the new FXa inhibitors are typically accompanied by a laundry list of probable biomarkers. This process is likely to continue until safety is ﬁrmly established through prolonged use and clinical experience with these agents. It seems likely that most of these coagulation assays could just as likely be a bioassay of drug concentration as an indicator of pharmacologic response. In Stern’s evaluation of biomarkers of an antithrombin agent, he concluded that “not all biomarkers are created equal” [29]. He suggested that “If a proposed biomarker measurement requires a drug and its molecular target to be combined in the same assay, it may be more a pharmacokinetic than a

460

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pharmacodynamic assessment. Also, such assays should not be assumed to demonstrate an in vivo effect” [29]. As such, these biomarkers face a lofty hurdle to replace such pharmacodynamic endpoints as the Badimon chamber. So what does this mean for the early use of biomarkers in the development of new anticoagulants? It suggests that the greatest beneﬁt for early utilization of coagulation biomarkers remains in allowing optimal selection of compounds, attrition of the right compounds, and the opportunity to provide an early ex vivo assessment against marketed competitors. It also demonstrates that efforts expended in understanding species-speciﬁc and reagent differences are critical in performing those early experiments.

REFERENCES 1. CMR International (2006). 2006/7 Pharmaceutical R&D Factbook CMR International, pp. 22–35. 2. Kola I, Landix J (2004). Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov, 3:711–715. 3. DiMasi JA (2001). Risks in new drug development: approval success rates for investigational drugs. Clin Pharmacol Ther, 69:297–307. 4. Jawad S, Oxley J, Yuen WC, Richens A (1986). The effect of lamotrigine, a novel anticonvulsant, on interictal spikes in patients with epilepsy. Br J Clin Pharmacol, 22:191–193. 5. Smith MB, Woods GL (2001). In vitro testing of antimicrobial agents. In Davey FR, Herman CJ, McPherson RA, Pincus MR, Threatte G, Woods GL (eds.), Henry’s Clinical Diagnosis and Management by Laboratory Methods, 20th ed. W.B. Saunders, Philadelphia, pp. 1119–1143. 6. Anderson FA, Wheeler HB, Goldberg RJ, et al. (1991). A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism. Arch Intern Med, 151:933–938. 7. Colman RW, Clowes AW, George JN, Hirsh J, Marder VJ (2001). Overview of hemostasis. In Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN (eds.), Hemostasis and Thrombosis: Basic Principles and Clinical Practice. Lippincott Williams & Wilkins, Philadelphia, pp. 3–16. 8. Kakar P, Watson T, Gregory-Lip YH (2007). Drug evaluation: Rivaroxaban, an oral, direct inhibitor of activated factor X. Curr Opin Invest Drugs, 8(3):256–265. 9. Guertin KR, Choi YM (2007). The discovery of the factor Xa inhibitor Otamixaban: from lead identiﬁcation to clinical development. Curr Med Chem, 14:2471–2481. 10. Zafar MU, Vorchheimer DA, Gaztanaga J, et al. (2007). Antithrombotic effect of factor Xa inhibition with DU-176b: phase-1 study of an oral, direct factor Xa inhibitor using an ex-vivo ﬂow chamber. Thromb Haemost, 98:883–888. 11. Hylek EM (2007). Drug evaluation: DU-176b, an oral, direct factor Xa antagonist. Curr Opin Invest Drugs, 8(9):778–783. 12. Crowther MA, Ginsberg JS, Hirsh J (2001). Practical aspects of anticoagulant therapy. In Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN, (eds.),

REFERENCES

13. 14. 15.

16. 17.

18.

19.

20.

21. 22. 23.

24.

25.

26.

27.

461

Hemostasis and Thrombosis: Basic Principles and Clinical Practice. Lippincott Williams & Wilkins, Philadelphia, pp. 1497–1516. Zucker S, Cathey MH, Sox PJ, Hall EC (1970). Standardization of laboratory tests for controlling anticoagulant therapy. Am J Clin Pathol, 52:348–354. Poller L (1987). Progress in standardization in anticoagulant control. Hematol Rev, 1:225–228. Bailey EL, Harper TA, Pinterton PH (1971). The “therapeutic range” of the one-stage prothrombin time in the control of anticoagulant therapy: the effect of different thromboplastin preparations. CMAJ, 105:307–318. Kirkwood TB (1983). Calibration of reference thromboplastin and standardization of the prothrombin time ratio. Thromb Haemost, 49:238–244. Jeske W, Messmore HL, Fareed J (1998). Pharmacology of heparin and oral anticoagulants, In Loscalzo J, Schafer AI (eds.), Thrombosis and Hemorrhage, 2nd ed. Williams & Wilkins, Baltimore, pp. 257–283. Crowther MA, Ginsberg JS, Hirsh J (2001). Practical aspects of anticoagulant therapy. In Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN, (eds.), Hemostasis and Thrombosis: Basic Principles and Clinical Practice. Lippincott Williams & Wilkins, Philadephia, PA, pp. 1497–1516. Levine MN, Hirsh J, Gent M (1994). A randominzed trial comparing activated thromboplastin time with heparin assay in patients with acute venous thromboembolism requiring large daily doses of heparin. Arch Intern Med, 154:49–56. Kitchen S, Theaker J, Preston FE (2000). Monitoring unfractionated heparin therapy: relationship between eight anit-Xa assays and a protamine titration assay. Blood Coagul Fibrinolysis, 11:55–60. Fifth ACCP Consensus Conference on Antithrombotic Therapy (1998). Chest, 119(Suppl):1S–769S. Bauer KA, Kass BL, ten Cate H (1989). Detection of factor X activation in humans. Blood, 74:2007–2015. Bauer KA, Weitz JI (2001). Laboratory markers of coagulation and ﬁbrinolysis. In Colman RW, Hirsh J, Marder VJ, Clowes AW, George JN, (eds.), Hemostasis and Thrombosis: Basic Principles and Clinical Practice. Lippincott Williams & Wilkins, Philadelphia, pp. 1113–1129. Fair DS, Edgington TS (1985). Heterogeneity of hereditary and acquired factor X deﬁciency by combined immunochemical and functional analyses. Br J Haematol, 59:235–242. Herrmann FH, Auerswald G, Ruiz-Saez A, et al. (2006). Factor X deﬁciency: clinical manifestation of 102 subjects from Europe and Latin American with mutations in factor 10 gene. Haemophilia, 12:479–489. Choufani EB, Sanchlorawala V, Ernst T, et al. (2001). Acquired factor X deﬁciency in patients with amyloid light-chain amyloidosis: incidence, bleeding manifestations, and response to high-dose chemotherapy. Blood, 97: 1885–1887. Mumford AD, O’Donnell J, Gillmore JD, Manning RA, Hawkins PN, Laffan M (2000). Bleeding symptoms and coagulation abnormalities in 337 patients with AL-amyloidosis. Br J Haematol, 110:454–460.

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28. Stern R, Chanoine F, Criswell K (2003). Are coagulation times biomarkers? Data from a phase I study of the oral thrombin inhibitor LB-30057 (CI-1028). J Clin Pharmacol, 43:118–121. 29. Stirling Y (1995). Warfarin-induced changes in procoagulant and anticoagulant proteins. Blood Coagul Fibrinolysis, 6:361–373.

24 INTEGRATING MOLECULAR TESTING INTO CLINICAL APPLICATIONS Anthony A. Killeen, M.D., Ph.D. University of Minnesota, Minneapolis, Minnesota

INTRODUCTION The clinical laboratory plays a critical role in modern health care. It is commonly estimated that approximately 70% of all diagnoses are to some extent dependent on a laboratory ﬁnding. The clinical laboratory has various roles in the diagnosis and treatment of disease, including determining disease risks, screening for disease, establishing a diagnosis, monitoring of disease progression, and monitoring of response to therapy. Not surprisingly, the size of the market is large. A 2004 report by S.G. Cowen on the global in vitro diagnostics (IVD) industry estimated it to be $26 billion in size, and molecular diagnostics was identiﬁed as being among the more rapidly growing areas. Today, molecular testing is used in many areas of the clinical laboratory, including microbiology and virology, analysis of solid and hematologic tumors, inherited disorders, tissue typing, and identity testing (e.g., paternity testing and forensic testing). The growth has occurred rapidly over the last 20 years. In this chapter we examine the principal issues surrounding the integration of molecular testing into the clinical laboratory environment.

Biomarkers in Drug Development: A Handbook of Practice, Application, and Strategy, Edited by Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, and Ramin Rahbari Copyright © 2010 John Wiley & Sons, Inc.

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CLINICAL LABORATORY REGULATION The clinical laboratory environment in the United States is one of the most extensively regulated areas of medical practice and comes under the federal Clinical Laboratory Improvement Amendments (CLIA) of 1988 (http://www. cms.hhs.gov/clia/). Any implementation of molecular diagnostic is therefore governed by the provisions of the CLIA. The history of the CLIA dates back to the 1980s, when public and congressional concern was raised by reports of serious errors being made in clinical laboratories. In response to these concerns, legislation was introduced with the intention of improving laboratory testing. These regulations cover most aspects of laboratory practice. Any laboratory testing that is performed in the United States for clinical purposes such as diagnosis, monitoring, deciding appropriate treatment, and establishing prognosis must be performed in a CLIA-certiﬁed laboratory. These regulations, however, do not apply to purely research studies or to early research and development work for molecular or other testing in a non-CLIA-certiﬁed environment, but as soon as testing that has genuine clinical utility is made available, it must be performed in a certiﬁed environment. The initial application for a CLIA certiﬁcate is usually made to the state ofﬁce of the Centers for Medicare and Medicaid Services (CMS). A successful application will result in a certiﬁcate of registration, which allows a laboratory to perform clinical testing pending its ﬁrst formal inspection. Depending on whether the laboratory is certiﬁed by CMS or by an accrediting organization, a successful inspection will result in a grant of either a certiﬁcate of compliance or a certiﬁcate of accreditation (Figure 1). These are essentially equivalent for Accreditation, 16,142

Compliance, 19,695

PPM,39,014

Waiver, 122,992

Figure 1 Distribution of CLIA certiﬁcates by type in non-CLIA-exempt states in 2007. (Data from the CLIA database, http://www.cms.hhs.gov/CLIA/.)

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the purposes of offering clinical testing. Accrediting organizations function as surrogates for CMS in the laboratory accreditation process and must be approved by CMS to accredit clinical laboratories. The accrediting organizations are the College of American Pathologists (CAP), the Council on Laboratory Accreditation (COLA), the Joint Commission, the American Association of Blood Banks (AABB), the American Society for Histocompatibility and Immunogenetics (ASHI), and the American Association of Bioanalysts (AAB). Some of these, such as the ASHI, accredit laboratories that perform only limited types of testing. Others, such as the CAP, accredit laboratories for all types of clinical testing, including molecular diagnostic testing. Clinical tests are categorized for the purposes of the CLIA into several levels of complexity. This categorization is the function of the U.S. Food and Drug Administration (FDA). The type of CLIA certiﬁcate that a laboratory requires parallels the complexity of its test menu. The lowest level of test complexity is the waived category. Tests in this category are typically simple methods with little likelihood of error or of serious adverse consequences for patients if performed incorrectly. Commonly, such tests are performed in physician ofﬁce laboratories. It should be noted that the term waived applies to a test, not to the need for the laboratory to have a CLIA certiﬁcate to perform any clinical testing. The next-highest level is the moderate-complexity test, including a category known as provider-performed microscopy. The highest level is the high-complexity test, which is applicable to most molecular tests. Laboratories that perform high-complexity testing must have a certiﬁcate to perform this type of testing. When the CLIA was written 20 years ago, there was relatively little molecular testing, and as a result, molecular diagnostics does not have speciﬁc requirements in the regulations as do most areas of clinical laboratory practice, such as clinical chemistry, microbiology, and hematology. Nevertheless, the general requirements of CLIA can be adapted to molecular testing. Accrediting organizations such as the CAP do have speciﬁc requirements for laboratories that perform molecular diagnostic testing. These are available in their laboratory inspection checklists [1]. Whereas the FDA is responsible for categorizing tests, the Centers for Medicare and Medicaid Services (CMS) are responsible for the oversight of the CLIA program, including granting certiﬁcates, approving accrediting organizations, approving proﬁciency testing (PT) programs, inspections, and enforcement actions. The CLIA is a federal law and applies to all clinical testing performed in the United States and in foreign laboratories that are certiﬁed under the CLIA. There are provisions in the CLIA under which individual states can substitute their own laboratory oversight programs if it is determined that such programs are at least as stringent as the federal program. Currently, such programs exist only in New York and Washington. These are known as “CLIA-exempt” states, although CMS reserves the authority to inspect any aspect of laboratory performance in these states. The CLIA includes the

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following areas of laboratory testing: proﬁciency testing, preanalytic testing, analytic testing, and personnel requirements. Proﬁciency Testing Proﬁciency testing (PT) is one external measure by which a laboratory’s performance can be judged. In a PT program, laboratories are sent samples for analysis and return their results to the PT program organizers. The correct result (or range of results) for these programs is determined by the organizers based on a comparison of participant results with results obtained by reference laboratories (accuracy-based grading), or by comparison with other laboratories that use the same analytical methods (peer-group grading). Ideally, all PT programs would use accuracy-based grading, but there are signiﬁcant practical limitations to this approach. One of the major limitations is the PT material itself. For many analytes it is not possible to obtain the necessary range of concentrations to test low, normal, and high concentrations using real human samples. This necessitates the use of artiﬁcial samples that have been spiked with the analyte or from which the analyte has been removed (or at least its concentration has been lowered). Such artiﬁcial samples may behave unexpectedly when tested using some analytical equipment and give higher or lower values that would be obtained in a native specimen containing the same concentration of the analyte. This is known as the matrix effect. Other limitations may require peer-group grading; for example, recombinant proteins may not be detected equally in different manufacturers’ immunoassays, making accuracy-based grading impossible. Enzyme concentrations may be determined by different manufacturers using different concentrations of cofactors, different temperatures, and different substrates, thus giving rise to such intermethod disagreement that accuracy-based grading is impossible. Molecular testing poses certain challenges to PT programs. It may not be possible to obtain real human specimens such as blood from subjects known to carry mutations of interest because of the quantities required for a large PT program. This necessitates the use of cell lines or even DNA aliquots for PT programs in genetics. Such samples cannot test all phases of the analytical process, including extraction of DNA from whole blood (the normal procedure for genetic testing). The same concern applies to molecular testing for infectious diseases such as HIV-1. For these reasons, it is not uncommon that PT samples do not fully mimic patient samples. Under the CLIA, laboratories are required to enroll in PT programs for a group of analytes speciﬁed in Subpart I of the regulations. These analytes were chosen based on clinical laboratory testing patterns that existed in 1988, and the list has not been updated since then. As a result, many newer tests, including molecular tests, are not on this list. For tests not on this list of “regulated” analytes, laboratories must verify the accuracy of their methods by some other method at least twice a year. This could include comparison of results with

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those obtained by a different method: sample exchange with another laboratory, or even correlation of results with patients’ clinical status. If formal PT programs exist, laboratories should consider enrolling in these. Several of the accrediting organizations do have requirements for participation in PT programs where these exist, including PT programs for molecular testing. Preanalytic Testing The CLIA has requirements that cover the preanalytic phase of testing. These include the use of requisition forms with correct identiﬁcation of the patient, the patient’s age and gender, the test to be performed, the date and time of sample collection, the name of the ordering provider or the person to whom results should be reported, the type of specimen (e.g., blood), and any other additional information needed to produce a result. All of these are critical pieces of information that should be provided to the laboratory. Many so-called “laboratory errors” actually arise at the time of sample collection, and specimen misidentiﬁcation is one of the most common types of error in the testing process. In addition to the patient’s age and gender, orders for molecular genetic testing should include relevant information about suspected diagnosis, clinical ﬁndings, and especially the family history. Many experienced clinical geneticists and genetic counselors will include a pedigree diagram on a requisition form for tests for inherited disorders. This practice is highly desirable and provides much useful information to the laboratory. As an example of the importance of this information, current practice guidelines in obstetrics and gynecology in the United States encourage the offering of prenatal testing to expectant Caucasian mothers to determine if they are carriers of mutations for cystic ﬁbrosis. A recommended panel of mutations to be tested by clinical laboratories covers approximately 80 to 85% of all mutations in this population. In general, a negative screening test for these mutations reduces the risk of being a cystic ﬁbrosis carrier from 1 in 30 to 1 in 141, and the laboratory would report these ﬁgures, or, if a mutation were identiﬁed, would report the speciﬁc mutation. However, these ﬁgures are based on the assumption that there is no family history of the disorder in the patient’s family. If there is such a history, the risk of being a carrier (both before and after testing) is substantially higher. It is therefore essential that the ordering physician inform the laboratory if there is a family history. Analytic Testing The CLIA has detailed requirements for the analytic phase of the testing process. These include the procedure manual, which is a step-by-step set of instructions on how the test should be performed, the process for method calibration, the procedures for preparation of reagents, the use of controls,

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establishment of the reference range, reporting procedures, and analytical parameters such as sensitivity and speciﬁcity. There are no speciﬁc CLIA requirements that are unique to molecular testing, and therefore the molecular diagnostics laboratory has to adapt requirements from related areas such as clinical chemistry and microbiology to molecular testing. Some of the accrediting organizations have checklists that include speciﬁc requirements for molecular testing. These can provide useful guidance on procedures even for a laboratory that is not accredited by one of these organizations. Postanalytic Testing Postanalytic testing refers to steps involved in reporting results to the ordering physician in a timely manner. The patient’s name and identiﬁcation information must be on the report, as should the name and address of the performing laboratory. In addition to the result, the report should include the reference interval and any relevant interpretive comments. The laboratory should be able to provide information on test validation and known interferences on the request of an ordering physician. Results must be released only to authorized persons. Although certain elements of the postanalytic phase of testing can be controlled by the laboratory, there are also critical elements that are beyond its control, notably the correct interpretation of the result by the ordering physician. Molecular diagnostics (and genetics in general) is an area in which many physicians and other providers never had formal training in medical school. Concern has been expressed about the need to improve genetics education for health care professionals. Where there is a gap in provider knowledge, the laboratory should be able to offer expert consultation on the interpretation of its results to primary care providers [2]. This requires time, patience, and good communication skills on the part of the laboratory director and senior staff. Although such activity may be reimbursable under some health plans, the primary incentives for providing this kind of consultation are good patient care and customer satisfaction. Personnel Qualiﬁcations Under the CLIA, requirements exist for laboratory personnel qualiﬁcations and/or experience. Perhaps the most important qualiﬁcation requirements apply to the laboratory director. The director of a high-complexity laboratory such as a clinical molecular testing laboratory must hold a license in the state in which he or she works (if the state issues such licenses) and be a physician or osteopathic physician with board certiﬁcation in pathology. Alternatively, the laboratory director can be a physician with at least one year of training in laboratory practice during residency, or a physician with at least two years of experience supervising or directing a clinical laboratory. A doctoral scientist holding a degree in a chemical, physical, biological, or clinical laboratory

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science ﬁeld with board certiﬁcation from an approved board may also serve as the laboratory director. There are also provisions that allow for grandfathering of persons who were serving as laboratory directors at the time of implementation of the CLIA. Currently, there are no speciﬁc CLIA-required qualiﬁcations for the director of a molecular diagnostics laboratory. There are, however, board examinations in this ﬁeld or similar ﬁelds that are offered by the American Board of Pathology, the American Board of Medical Genetics, the American Board for Clinical Chemistry, the American Board of Medical Microbiology, and the American Board of Bioanalysts. It is possible that individual states may begin to require speciﬁc qualiﬁcations in molecular diagnostics in the future or even that changes to the CLIA may require such qualiﬁcations. Other personnel and their qualiﬁcations described in the CLIA for high-complexity laboratories are technical supervisor, clinical consultant, general supervisor, cytology supervisor, cytotechnologist, and testing personnel.

GENETIC TESTING AND PRIVACY For many years there has been concern about the use of genetic information to discriminate against people with genetic diseases or those who are at risk of manifesting genetic disease at some time in the future. Although there are very few reported examples of such discrimination, the possibility of such misuse of genetic information by employers or insurance companies has received considerable attention by both the public and by legislative bodies [3]. A comprehensive analysis of applicable laws is beyond the scope of this chapter, but certain principles that apply to the clinical laboratory are worth mentioning. It is generally assumed, of course, that all clinical laboratory testing is performed with the consent of the patient. However, written consent is a legal requirement for genetic testing in some jurisdictions. The laboratory is generally not in a position to collect informed consent from patients, so it is usually obtained by some other health care worker, such as the ordering physician or genetics counselor. The laboratory director should be aware of applicable laws in this matter and determine, with legal advice if necessary, what testing is covered in his or her jurisdiction and ensure that appropriate consent is obtained. Genetic testing in its broadest meaning can cover more than just nucleic acid testing. For example, some laboratory methods for measuring glycohemoglobin, a test used for following diabetes control, can indicate the presence of genetic variants of hemoglobin, such as sickle-cell hemoglobin. Histopathologic examination of certain tumors can be strongly suggestive of an inherited disorder. Serum protein electrophoresis can reveal α-1 antitrypsin deﬁciency, an inherited disorder. The laboratory should consider how it reports such ﬁndings, which may contain genetic information that is unanticipated by both the ordering physician and the patient.

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The most signiﬁcant federal legislation in this area is the Genetic Information Nondiscrimination Act of 2008. This act offers protection against the use of genetic information as a basis for discrimination in employment and health insurance decisions. Under the provisions of this law, people who are healthy may not be discriminated against on the basis of any genetic predisposition to developing disease in the future. Health care insurers (but not life insurers or long-term care insurers) and employers may not require prospective clients or employees to undergo genetic testing or take any adverse action based on knowledge of a genetic trait. The beneﬁts of this legislation are that some people may feel less trepidation about undergoing genetic testing because of fear that such information could be used by an employer or insurance company to discriminate against them.

TESTING IN RESEARCH LABORATORIES As research laboratories report new molecular ﬁndings in inherited and acquired diseases, it is not uncommon for clinical laboratories to receive requests to send patient samples to research laboratories for testing. This is an area in which the clinical laboratory must be careful to avoid noncompliance with CLIA regulations. One of the requirements of the CLIA is that certiﬁed laboratories must not send samples for patient testing to a non-CLIAcertiﬁed laboratory. This rule applies even if the research laboratory is the only one in the world to offer a particular test. Such samples should not be handled by a CLIA-certiﬁed laboratory, and the ordering physician should ﬁnd some other means of arranging for testing if it is considered necessary. For example, it may be possible for testing to be performed under a research protocol. In this case the local institutional review board may be able to offer useful guidance on the creation and implementation of an appropriate protocol. There are good reasons to be cautious about performing clinical testing in a research setting. The standards that one expects in a CLIA-certiﬁed laboratory are designed to promote quality and improve the accuracy of patient testing. Laboratories that do not follow these extensive requirements may not have all of the necessary protocols and procedures in place to offer the same quality of test result. Research laboratories are often part of academic institutions that may or may not carry malpractice insurance coverage in the event that a reported test result is erroneous.

MOLECULAR TESTING FROM RESEARCH TO CLINICAL APPLICATION The usual progression of molecular testing begins with gene and mutation discovery, typically in a research laboratory setting. Publication of these early

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ﬁ