Risk Assessment of Phytochemicals in Food: Novel Approaches (Forschungsberichte (DFG))

Diese Seite wurde absichtlich leer gelassen

Risk Assessment of Phytochemicals in Food Novel Approaches Symposium

Diese Seite wurde absichtlich leer gelassen

Risk Assessment of Phytochemicals in Food Novel Approaches

Symposium Editors: DFG Senate Commission on Food Safety (SKLM) Editorial Committee: Gerhard Eisenbrand (Chairman), Jan Hengstler, Hans-Georg Joost, Sabine Kulling, Ivonne Rietjens, Josef Schlatter, Pablo€Steinberg and Doris Marko Scientists of the SKLM Secretariat: Sabine Guth, Michael Habermeyer and Barbara Kochte-Clemens

4 Deutsche Forschungsgemeinschaft German Research Foundation Kennedyallee 40 · 53175 Bonn, Germany Postal address: 53170 Bonn, Germany Phone: +â•›49 228 885-1 Fax: +â•›49 228 885-2777 [email protected] www.dfg.de

Alle Bücher von WILEY-VCH werden sorgfältig erarbeitet. Dennoch übernehmen Autoren, Herausgeber und Verlag in keinem Fall, einschließlich des vorliegenden Werkes, für die Richtigkeit von Angaben, Hinweisen und Ratschlägen sowie für eventuelle Druckfehler irgendeine Haftung.

Bibliografische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über abrufbar. ISBN 978-3-527-32929-8 © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Gedruckt auf säurefreiem Papier. Alle Rechte, insbesondere die der Übersetzung in andere Sprachen, vorbehalten. Kein Teil dieses Buches darf ohne schriftliche Genehmigung des Verlages in irgendeiner Form – durch Fotokopie, Mikroverfilmung oder irgendein anderes Verfahren – reproduziert oder in eine von Maschinen, insbesondere von Datenverarbeitungsmaschinen, verwendbare Sprache übertragen oder übersetzt werden. Die Wiedergabe von Warenbezeichnungen, Handelsnamen oder sonstigen Kennzeichen in diesem Buch berechtigt nicht zu der Annahme, dass diese von jedermann frei benutzt werden dürfen. Vielmehr kann es sich auch dann um eingetragene Warenzeichen oder sonstige gesetzlich geschützte Kennzeichen handeln, wenn sie nicht eigens als solche markiert sind. All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law. Corporate Design: besscom AG, Berlin Layout und Typografie: Tim Wübben, DFG Satz: Primustype Hurler GmbH, Notzingen Druck: betz-druck GmbH, Darmstadt Bindung: Litges & Dopf GmbH, Heppenheim Printed in the Federal Republic of Germany

5

Inhalt╛/↜Contents Vorwort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Preface . . . . . . . . . . . . . . . . . 尓. . . . . . . . . . . . . . . . . . 尓. . . . . . . . . . . . . . . . . 12 1 Bericht und Schlussfolgerungen . . . . . . . . . . . . . . . . . 尓. . . . . . . . . . . . . 13 1.1

Einleitung . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . 13

1.2

Methodenübergreifende Aspekte . . . . . . . . . . . . . . . . . å°“. . . . . . . . 14

1.3

Methoden . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . 15

1.4

Schlussfolgerungen und Empfehlungen . . . . . . . . . . . . . . . . . å°“. . . 22

1.5

Fazit . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . 25

2 Report and Conclusions . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . 26 2.1

Preface . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . 26

2.2

Transdisciplinary Aspects . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . 27

2.3

Methodologies . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . 28

2.4

Conclusions and Recommendations . . . . . . . . . . . . . . . . . å°“. . . . . . 34

2.5

Concluding Remarks . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . 37

3 Contributions . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . 38 3.1

Visions on Toxicity Testing in the 21st Century: �Reflections on a Strategy Document of the US€�National Research Council Marcel Leist, Thomas Hartung, and Pierluigi Nicotera . . . . . . . . . . . . . . . 38

3.2

Safety Assessment of Botanicals and Botanical Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach Gerrit Speijers, Bernard Bottex, Birgit Dusemund et al. . . . . . . . . . . . . . . 57

3.3

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure– Activity Relationship Analysis Luis G. Valerio Jr., Naomi L. Kruhlak, and R. Daniel Benz . . . . . . . . . . . 78

3.4

Testing Computational Toxicology Models with Phytochemicals Luis G. Valerio Jr., Kirk B. Arvidson, Emily Busta et al. . . . . . . . . . . . . . 93

3.5

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data Benoît Schilter, Manuel Dominguez Estevez, Myriam Coulet et al. . . . . . 110

6

Inhalt╃/╛Contents

3.6

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and DetoÂ�xification of Coumarin and Estragole: Implications for Risk Assessment Ivonne M.╃C.╃M. Rietjens, Ans Punt, Benoît Schilter et al. . . . . . . . . . . . . 124

3.7

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy? Pablo Steinberg, Carsten Müller, Kristina Ullmann et al. . . . . . . . . . . . . 149

3.8

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential? Hans-Jürgen Ahr, and Heidrun Ellinger-Ziegelbauer . . . . . . . . . . . . . . . 160

3.9

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach Anette Thiel, Jochen Bausch, Mareike Beck et al.╃ . . . . . . . . . . . . . . . . . . . 178

3.10 Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced Â�Toxicity Hennicke Kamp, Roland Buesen, Eric Fabian et al. . . . . . . . . . . . . . . . . . 189 3.11 Profiling Techniques in Nutrition and Food Research Hannelore Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 3.12 The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics Claudine Manach, Jane Hubert, Rafael Llorach et al. . . . . . . . . . . . . . . . 212 3.13 Anti-Oxidative and Antigenotoxic Properties of Â�Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology Theo M.╃C.╃M. de Kok, Pim de Waard, Lonneke C. Wilms et al.╃ . . . . . . . . 236 3.14 The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening Takeki Uehara, Atsushi Ono, Toshiyuki Maruyama et al. . . . . . . . . . . . . 254 3.15 Toxicology and Risk Assessment of Coumarin: Focus on Human Data Klaus Abraham, Friederike Wöhrlin, Oliver Lindtner et al. . . . . . . . . . . . 272 3.16 Risk from Furocoumarins in Food? An Exposure Â�Assessment Dieter Schrenk, Sabine Guth, Nicole Raquet et al.╃ . . . . . . . . . . . . . . . . . . 295 3.17 Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components Jaap Keijer, Yvonne G.╃J. van Helden, Annelies Bunschoten et al.╃ . . . . . . 309 3.18 Colorectal and Prostate Cancer: The Role of Â�Candidate Genes in Nutritional Pathways Ulrike Peters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 3.19 Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro Chimgee Baasanjav Gerber, Wolfram Engst, Simone Florian et al.╃ . . . . . 333

Inhalt╃/╛Contents

3.20 Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals Johanna Fink-Gremmels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 4 Posters . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . 361 4.1

Coumarin Risk Assessment: Lessons from Human Data Klaus Abraham, Klaus-Erich Appel, and Alfonso Lampen . . . . . . . . . . . 361

4.2

Coffee and Coffee Compounds are Effective �Antioxidants in Human Cells and In Vivo Tamara Bakuradze, Matthias Baum, Gerhard Eisenbrand et al.╃ . . . . . . 364

4.3

Studying Absorption, Distribution, Metabolism, and Excretion of a Complex Extract Mareike Beck, Martine Bruchlen, Volker Elste et al.╃ . . . . . . . . . . . . . . . . . 369

4.4

Polyphenolic Apple Extracts and their Constituents Modulate DNA Strand Breaks and Oxidation �Damage in Human Colon Carcinoma Cells Phillip Bellion, Frank Will, Helmut Dietrich et al.╃ . . . . . . . . . . . . . . . . . . 371

4.5

Comparative Evaluation of Experimental Data on α-Amylase Inhibition by Flavonoids Using Molecular Modelling Lisa M. Bode, Thomas Homann, Harshadrai M. Rawel et al.╃ . . . . . . . . . 376

4.6

Potential Risk of Furan in Foods J. Brück, Dieter Schrenk, U. Schauer et al.╃ . . . . . . . . . . . . . . . . . . . . . . . . 378

4.7

Comparative Study on the Toxicity of Alternariol and Alternariol Monomethyl Ether in Human Tumour Cells of Different Origin Julia Burkart, Markus Fehr, Gudrun Pahlke et al.╃ . . . . . . . . . . . . . . . . . 379

4.8

A Role for Resveratrol and Curcumine in Sensitization of Glioblastoma Cells to Genotoxic Stress Induced by Alkylating Chemotherapeutics Markus Christmann, N. Berdelle, G. Nagel et al.╃ . . . . . . . . . . . . . . . . . . . 381

4.9

BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids Birgit Dusemund, Klaus-Erich Appel, and Alfonso Lampen . . . . . . . . . . 382

4.10 Elucidation of the Genotoxic Activity of the Alkaloid Ellipticine in Human Cell Lines Eva Frei, Jitka Poljaková, Lucie Borˇek-Dohalská et al.╃ . . . . . . . . . . . . . . 391 4.11 Dietary Supplements and Herbal Medicinal Products – for a Clear Differentiation. Statement of the Â�Society for Phytotherapy (GPT) to the “Article€13 Health Claim List” of the EFSA Frauke Gaedcke, Bernd Eberwein, Olaf Kelber et al.╃ . . . . . . . . . . . . . . . . 393

7

8

Inhalt╃/╛Contents

4.12 Assessment of Genotoxicity of Herbal Medicinal Preparations According to the Guideline EMEA/HMPC/107079/2007 – A Model Project of Â�Kooperation Phytopharmaka, Bonn, Germany Frauke Gaedcke, Olaf Kelber, Karin Kraft et al.╃ . . . . . . . . . . . . . . . . . . . . 403 4.13 Implications for an Adverse Effect of Vitamin C in Photodynamic Therapy Stefanie Grimm, Nicolle Breusing, and Tilman Grune . . . . . . . . . . . . . . . 408 4.14 Using the Nematode Caenorhabditis elegans to Identify Mode of Action of the Flavonoid Â�Myricetin Gregor Grünz, B. Spanier, and Hannelore Daniel . . . . . . . . . . . . . . . . . . 409 4.15 Low-Temperature Plasma – Mild Preservation Â�Technology for Minimal Processed Fresh Food? Franziska Grzegorzewski, O.╃Schlüter, J.╃Ehlbeck et al.╃ . . . . . . . . . . . . . . 410 4.16 Influence of Fumonisin B1 on Gene Expression and Cytokine Production Dorothee C. Hecker, Christian Salzig, and Dieter Schrenk . . . . . . . . . . . . . 411 4.17 Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model Stefanie Hessel, Andrea John, Albrecht Seidel et al.╃ . . . . . . . . . . . . . . . . . 412 4.18 Risk Assessment of T-2 and HT-2 Toxin Using Human Cells in Primary Culture Dennis Mulac, Maika Königs, Gerald Schwerdt et al.╃ . . . . . . . . . . . . . . . 420 4.19 Pyrrolizidine Alkaloids in Honey Bee Products Michael Kempf, Till Beuerle, Annika Reinhard et al.╃ . . . . . . . . . . . . . . . 421 4.20 Identification of Molecular Determinants for Cytotoxicity of Isoliquiritigenin from Liquorice (Glycyrrhiza glabra) towards Leukemia Cell Lines V. Badireenath Konkimalla, Anne Kramer, Yujie Fu et al.╃ . . . . . . . . . . . 429 4.21 Functional Effects of Polyphenol Metabolites Produced by Colonic Microbiota in Colon Cells In Vitro Claudia Miene and Michael Glei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 4.22 Lifelong Exposure to Isoflavones Results in a Â�Reduced Responsivity of the Mammary Gland in Female Rats towards Oestradiol Almut Molzberger, Torsten Hertrampf, Frank Möller et al.╃ . . . . . . . . . . . 432 4.23 Derivation of Maximum Amounts for the Addition of Functional Ingredients to Foods Sina Tischer, Oliver Lindter, Almut Bauch et al.╃ . . . . . . . . . . . . . . . . . . . . 433 4.24 Constituents of Ginger Induce Micronuclei in Two Mammalian Cell Systems In Vitro Erika Pfeiffer, Julia S. Dempe, Marina J. Gary et al.╃ . . . . . . . . . . . . . . . . 434

Inhalt╃/╛Contents

4.25 Relative Photomutagenic Potency of Furocoumarins and Limettin Christiane Lohr, Dieter Schrenk, and Nicole Raquet . . . . . . . . . . . . . . . . . 435 4.26 Degradation of Green Tea Catechins Markus Schantz, Thomas Erk, and Elke Richling . . . . . . . . . . . . . . . . . . . 436 4.27 Evaluation of the Cytotoxic Effects of Herbal Homeopathic Extracts in Primary Human Â�Hepatocytes In Vitro Ulrike Sobeck, B. Rüdinger, F. Stintzing et al.╃ . . . . . . . . . . . . . . . . . . . . . . 437 4.28 Modulation of Antioxidant Gene Expression by Â�Apple Juice in Rats Bülent Soyalan, J. Minn, Hans-Joachim Schmitz et al.╃ . . . . . . . . . . . . . . 442 4.29 Predictivity Comparison between Screening Assays for Bacterial Mutagenicity for Natural Compounds: Micro-Ames vs. Ames Fluctuation Method Gerlinde Pappa, Tina Wöhrle, Anette Thiel et al.╃ . . . . . . . . . . . . . . . . . . . 449 4.30 Automated In Vitro Micronucleus Testing of Natural Compounds in Correlation with Hydrogen Peroxide Gerlinde Pappa, Tina Wöhrle, Anette Thiel et al.╃ . . . . . . . . . . . . . . . . . . . 450 4.31 Permeability of Apple Polyphenols in T84 Cell Model and their Influence on Tight Junctions Hannah Bergmann, Dorothee Rogoll, Wolfgang Scheppach et al.╃ . . . . . . 451 4.32 Influence of Apple Polyphenols on Inflammatory Gene Expression Sven Triebel, Ralph Melcher, Gerhard Erkel et al.╃ . . . . . . . . . . . . . . . . . . 452 4.33 Diethylstilbestrol-Like Effects of Genistein on Gene Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line Jörg Wagner and Leane Lehmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 4.34 Effect of Dietary Flavonoids in Different Cell Lines: Comparison of Uptake, Modulation of Oxidative Stress and Cytotoxic Effects Wim Wätjen, Sven Ruhl, Ricarda Rohrig et al.╃ . . . . . . . . . . . . . . . . . . . . 460 4.35 Risk–Benefit Considerations of Isoflavone Supplements in the Treatment of Menopausal Vasomotor Symptoms Uta Wegewitz, Klaus Richter, A. Jacobs et al.╃ . . . . . . . . . . . . . . . . . . . . . . 461 4.36 Effect of Different Catechins on the Growth of HT-29 Cells Stefanie Wiese, Tuba Esatbeyoglu, Peter Winterhalter et al.╃ . . . . . . . . . . . 463 4.37 Determination of the Isoflavone Content of Soy-Based Infant Formula of the German Market Using a Box-Behnken Experimental Design for Optimizing the Analytical Conditions Stefanie Witte, Hans-Peter Kruse, and Sabine E. Kulling . . . . . . . . . . . . . 465

9

10

Inhalt╃/╛Contents

5 Appendix . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . 467 Participants of the Symposium Risk Assessment of Phytochemicals in Food – Novel Approaches . . . . . . . . . . . . . . . . . å°“. . . . 467 Members of the DFG Senate Commission on Food Safety: Mandate 2007–2010 . . . . . . . . . . . . . . . . . å°“. . . . . . . . . . . . . . . . . . å°“. . . . . 475



Vorwort

Das Symposium „Risk Assessment of Phytochemicals in Food – Novel Approaches“ der Senatskommission zur gesundheitlichen Bewertung von Lebensmitteln (SKLM) der Deutschen Forschungsgemeinschaft (DFG) wurde vom 30.╃März bis 01.╃April 2009 in Kaiserslautern abgehalten. Die Senatskommission hat dabei mit international anerkannten Experten aus Akademie, Industrie und Behörden die Perspektiven innovativer “OMICS”-Techniken unter Einbezug verschiedener in-silico-, in-vitro- und in-vivo-Verfahren diskutiert. Diese neuen Techniken befinden sich in unterschiedlichen Stadien der Entwicklung und ihre Anwendbarkeit auf die Sicherheitsbewertung von Lebensmitteln ist derzeit noch offen. Die SKLM hat im Sinne ihres Beratungsauftrags für die DFG Schlussfolgerungen und Empfehlungen zum Forschungsbedarf erarbeitet, die gemeinsam mit den Einzelbeiträgen der Redner und den Posterbeiträgen in diesem Symposiums-Band veröffentlicht werden. Die SKLM dankt der DFG für die nachhaltige Unterstützung der Symposienreihe. Diese Symposien bieten ein exzellentes Forum zur Beratung aktueller Themen von besonderer wissenschaftlicher Bedeutung mit besonders ausgewiesenen Wissenschaftlern. Ich danke den Teilnehmern des Symposiums für ihre wissenschaftlichen Beiträge und den Mitgliedern und Gästen der Senatskommission für ihre Mithilfe bei der Abfassung der vorliegenden Veröffentlichung. Ebenso danke ich den Mitgliedern des Redaktionskomitees sowie den Vorsitzenden und Rapporteuren des Symposiums, Prof. Hengstler, Prof. Joost, Prof. Kulling, Prof. Rietjens, Prof. Schlatter, Prof. Steinberg und Prof. Marko für ihre Mitarbeit bei der Formulierung der Schlussfolgerungen und Empfehlungen. Das wissenschaftliche Sekretariat der SKLM mit Dr. Sabine Guth, Dr. Michael Habermeyer und Dr. Barbara Kochte-Clemens hat wesentlich zum Zustandekommen dieses Bandes beigetragen. Ihnen gilt mein herzlicher Dank. Besonders danke ich der Leiterin des Fachreferates Lebenswissenschaften I der DFG, Frau Dr. Heike Strelen, für ihre engagierte Unterstützung der Arbeit der SKLM. Die SKLM gibt der Hoffnung Ausdruck, dass dieser aktuelle Bericht mit Symposiumsbeiträgen, Schlussfolgerungen und Empfehlungen im forschungsund gesundheitspolitischen Raum Beachtung findet.

Prof. Dr. Gerhard Eisenbrand Vorsitzender der DFG-Senatskommission zur Bewertung der gesundheitlichen Unbedenklichkeit von Lebensmitteln

11

12



Preface

The Symposium “Risk Assessment of Phytochemicals in Food – Novel Approaches”, organized by the Senate Commission on Food Safety (SKLM) of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), was held from March 30th to April 1st 2009 in Kaiserslautern, Germany. The SKLM discussed with international experts from academia, industry and authorities the promise of innovative “OMICS” methodologies including various in silico, in vitro and in vivo approaches. These new methodologies are at different stages of development and their applicability for food safety assessment at present is still open. The SKLM, on behalf of the DFG, has prepared conclusions and recommendations for further research, published in this volume together with oral and poster contributions. The SKLM is grateful to the DFG for sustained support of the SKLM symposia series. These symposia represent an excellent forum to discuss with recognized scientific experts topics of particular importance. I would like to thank the participants for their scientific contributions, as well as the members and guests of the Senate Commission for their support in preparing this publication. I am also grateful to the members of the editorial committee, chairs and rapporteurs, Prof. Hengstler, Prof. Joost, Prof. Kulling, Prof. Rietjens, Prof. Schlatter, Prof. Steinberg and Prof. Marko, for their contributions to the conclusions and recommendations. Thanks are also due to Dr. Sabine Guth, Dr. Michael Habermeyer and Dr. Barbara Kochte-Clemens of the Scientific Office of the SKLM who substantially contributed to the preparation of this volume. I am indebted to Dr. Heike Strelen, Head of the DFG Life Sciences Division 1, for her sustained support of the Senate Commissions activities. The SKLM trusts that this report, encompassing contributions, conclusions and recommendations will find due attention in research and health policy.

Prof. Dr. Gerhard Eisenbrand Chair of the DFG Senate Commission on Food Safety

13

1

Bericht und Schlussfolgerungen

1.1 Einleitung Pflanzliche Lebensmittel sind ein wichtiger Bestandteil der Ernährung, einige Pflanzen jedoch enthalten Stoffe, die bestimmte gesundheitliche Risiken bergen. Solche „sekundären Pflanzenstoffe“ können unerwünschte Wirkungen über verschiedene Mechanismen auslösen. So wirken einige Stoffe auf das Hormonsystem, wie beispielsweise die in Soja enthaltenen Isoflavone. Andere Stoffe wirken lebertoxisch (Cumarin), neurotoxisch (Solanin), phototoxisch (Furocumarine) oder kanzerogen (Estragol). Bei einer normalen Aufnahme als natürliche Bestandteile von Obst, Gemüse, Kräutern und Gewürzen geht man von einem geringen Risiko aus. Potenziell problematisch jedoch ist eine erhöhte Exposition, z.╃B. bei einseitiger Ernährung oder bei Einnahme über Nahrungsergänzungsmittel in isolierter und konzentrierter Form. Für eine Risikoabschätzung sind Stoffe zu identifizieren, die aufgrund ihrer chemischen Struktur potenziell gesundheitsschädlich sind und deren Wirkungen im Organismus unter Bezug auf die Dosis zu klären sind. Hierfür werden zunehmend neuartige Profilierungstechniken und rechnergestützte Methoden eingesetzt – mit vielversprechenden Möglichkeiten. Die Senatskommission zur gesundheitlichen Bewertung von Lebensmitteln (SKLM) der Deutschen Forschungsgemeinschaft (DFG) hat ein Symposium zum Thema „Risk Assessment of Phytochemicals in Food – Novel Approaches“ organisiert, das vom 30.╃März bis 1.╃April 2009 in Kaiserslautern, Deutschland, stattfand. Potenziale, Auswirkungen und Perspektiven neuartiger Methoden für die Risikoabschätzung unter Einschluss von in-silico-, in-vitro- und in-vivoAnsätzen wurden diskutiert und der Stand der Technik unter Berücksichtigung spezifischer Beispiele aus dem Bereich der sekundären Pflanzenstoffe ermittelt. Ziel des Symposiums war, die Bedeutung neuer Methoden für die Risikoabschätzung dieser Stoffe herauszuarbeiten. Die SKLM hat hieraus Schlussfolgerungen und Empfehlungen abgeleitet und Wissenslücken sowie Forschungsbedarf identifiziert. Dieser Bericht basiert auf den Präsentationen von Marcel Leist (DE), Gerrit Speijers (NL), Luis Valerio (US), Benoît Schilter (CH), Ivonne Rietjens (NL), Pablo Steinberg (DE), Hans-Jürgen Ahr (DE), Anette Thiel (CH), Hennicke Kamp (DE), Hannelore Daniel (DE), Augustin Scalbert (FR), Theo de Kok (NL), Takeki Uehara (JP), Alfonso Lampen (DE), Dieter Schrenk (DE), Jaap Keijer (NL), Ulrike Peters (US), Hansruedi Glatt (DE), Johanna Fink-Gremmels (NL) sowie den Diskussionen in den anschließenden Workshops.

14

Bericht und Schlussfolgerungen

1.2 Methodenübergreifende Aspekte Nahrung und Lebensmittelinhaltsstoffe gelten, ganz gleich ob natürlichen oder synthetischen Ursprungs, als expositions-/ ernährungsbedingte Faktoren, die die menschliche Gesundheit über ihre Einwirkung auf das Genom, das Transkriptom, das Proteom und das Metabolom beeinflussen. Da solche Wechselwirkungen das ganze Leben hindurch bestehen, ist auch der physiologische und Entwicklungszustand eines Individuums zum Zeitpunkt der Exposition von Bedeutung. Neue Profilierungstechniken (profiling techniques) bieten die Möglichkeit, Wechselwirkungen zwischen Lebensmittelbestandteilen und Rezeptoren, Signalwegen und anderen Teilen des Genom-/ Transkriptom-/ Proteom-/ Metabolom-Systems zu identifizieren, die das physiologische Gleichgewicht sicherstellen. Die Risikobewertung von Lebensmitteln und ihren Inhaltsstoffen hat eine große Zahl natürlicher oder synthetischer Stoffe, die die menschliche Gesundheit beeinflussen können, in Betracht zu ziehen. Da für die meisten dieser Substanzen keine ausreichenden toxikologischen Daten zur Verfügung stehen, erhofft man sich von den neuen molekularen Techniken schnelle, effiziente und verlässliche Informationen, die es ermöglichen, sichere Expositionsgrenzwerte zu etablieren oder für (pflanzliche) Stoffe eine adäquate Risikoabschätzung auf der Basis angemessener Sicherheitsprüfungen durchzuführen. Eine Vielfalt neuer Methoden befindet sich derzeit in unterschiedlichen Stadien der Entwicklung und Anwendung. Die SKLM hält deren Einsatz zum Erreichen der folgenden Ziele für wesentlich: ►⌺ Aufklärung von Wirkmechanismen ►⌺ Entwicklung neuer Techniken, die eine umfassende Auswertung großer Datensätze erlauben ►⌺ Vergleich experimenteller Daten aus Tierversuchen oder in-vitro-Modellsystemen mit Daten aus Humanstudien ►⌺ Wissenschaftsgeleitete Entwicklung von Lebensmitteln und Lebensmittelinhaltsstoffen mit definierter Biofunktionalität ►⌺ Berücksichtigung individueller Variabilität und Prädisposition. Solche neuen Methoden könnten in klinischen und tierexperimentellen Studien dazu dienen, Daten höherer Aussagequalität zu generieren bzw. ein besseres Verständnis von scheinbar inkonsistenten Daten zu gewinnen. Durch die Einführung molekularer Marker können in künftigen epidemiologischen Studien biomolekulare Angriffspunkte und Effekte sekundärer Pflanzenstoffe eingehender untersucht werden. Hierdurch können Wechselwirkungen sekundärer Pflanzenstoffe mit biomolekularen Prozessen, aber auch die Rolle der genetischen Variabilität humaner Populationen bei der Auslösung heterogener Reaktionen durch sekundäre Pflanzenstoffe besser verstanden werden.

Methoden

1.3 Methoden 1.3.1 Rechnergestützte Toxikologie / in-silico-Modelle In-silico-Technologien verwenden rechnergestützte Informationen und Methoden, um das toxikologische bzw. Wirk-Profil einer Substanz zu prognostizieren. Sie ermöglichen die Überprüfung einer großen Zahl an Substanzen innerhalb einer relativ kurzen Zeit und sind sehr kosteneffizient verglichen mit konventionellen toxikologischen Tierstudien. Sie können darüber hinaus experimentell vielseitig eingesetzt werden, nicht nur für das Screening, sondern auch, um mittels adäquater Lernalgorithmen ihre Vorhersagekraft zu verfeinern. Dies gilt beispielsweise für Ansätze auf der Basis quantitativer Struktur-Aktivitäts-Beziehungen (quantitative structure–activity relationship, QSAR) oder des physiologiebasierten Biokinetik-Modellierens (physiologically-based biokinetic modelling, PBBK). Ein weiterer Vorteil von in-silico-Methoden zur Voraussage von Toxizität liegt im potenziellen Einsparungseffekt bei der Zahl der Versuchstiere, so dass Tierversuche eingeschränkt bzw. sogar ersetzt und damit ein Beitrag zum 3R-Prinzip (refinement, reduction, replacement) geleistet werden kann. Darüber hinaus erscheint der Einsatz von in silico rechnergestützten Toxikologie-Methoden (computational toxicology) für einzelne pflanzliche Substanzen zur Priorisierung der Risikoabschätzung auf chemischer Basis sehr vielversprechend.

1.3.1.1 Quantitative Struktur-Aktivitäts-Beziehungen (QSAR)

Definition: QSAR-basierte Methoden korrelieren quantitativ Parameter, die strukturelle chemische Eigenschaften von Molekülreihen beschreiben, mit deren biologischer Aktivität oder chemischer Reaktivität. Rechnergestützte prädiktive Modellierung auf der Basis von QSAR verwendet statistische Verfahren zur Korrelation von biologischer Aktivität von Molekülen mit Deskriptoren, die für eine Molekülstruktur repräsentativ sind. Stand der Technik: Rechnergestützte prädiktive Modellierung auf der Basis von QSAR liefert ein evidenzbasiertes Werkzeug zur Priorisierung und zur effizienten Gefährdungseinschätzung auf der Grundlage bereits vorhandener Testdaten zu unterschiedlichen Endpunkten. Solche Endpunkte umfassen z.╃B. das mutagene Potenzial von synthetischen und natürlichen Molekülen sowie pflanzlichen Stoffe, die in Pflanzenextrakten, Kräutern und natürlichen Nahrungsquellen zu finden sind. Prädiktive QSAR-Methodiken sind vielversprechende Entscheidungshilfen in der Sicherheits- und Risikobewertung. In dringenden Fällen könnten sie sich für eine schnelle Entscheidungsfindung als wertvoll erweisen und auch die Prioritätensetzung für zusätzliche Toxizitätstests unterstützen.

15

16

Bericht und Schlussfolgerungen

Limitierungen, Wissenslücken und Forschungsbedarf: Die Stoffevaluierung auf der Basis von QSAR erlaubt keine absolute Sicherheit der Aussage in Bezug auf die gesundheitliche Unbedenklichkeit des betreffenden Stoffes. Auch weisen die Programme nicht alle notwendigen Funktionen auf und bieten keine völlig umfassende Information zu den Stoffeigenschaften oder eine 100â•›%ige Spezifität. Die meisten QSAR-basierten prädiktiven Modelle der Stoffevaluierung sind außerdem begrenzt durch ihre inhärent geringe Empfindlichkeit in Bezug auf die Fähigkeit, echte Positive (true positives) richtig zu identifizieren. Exposition oder Spezifität des Angriffspunktes von Stoffen werden nicht gewichtet und mechanistische Gesichtspunkte, unter Einbezug klassenspezifischer Mechanismen, wie z.╃B. bei der Kanzerogenese die vielfachen Einzelschritte der Krebsstehung, Tumorpromotion oder Tumorart, spielen keine Rolle. Eine Kombination von Softwareprogrammen wäre die beste Möglichkeit, die Sensitivität und Spezifität der Vorhersage zu verbessern, da Datenbasen, Algorithmen und Methoden zur strukturellen Interpretation von Programm zu Programm variieren. Derzeit können Prognosen mit relativ guter Empfindlichkeit und Spezifität für bestimmte Endpunkte, wie etwa für die Mutagenität erstellt werden. Letztere hängt von einer begrenzten Zahl an Mechanismen ab, wie etwa der Fähigkeit von Stoffen, kovalent an die DNA zu binden. Dies gilt auch für die akute Toxizität in aquatischen Spezies, insbesondere dann, wenn ein „narkotischer“ Mechanismustypus verantwortlich ist. Sehr viel schwieriger ist es hingegen, komplexere Endpunkte wie Organtoxizität oder Kanzerogenität vorherzusagen, die auf einer Vielzahl möglicher Mechanismen beruhen. Derzeit sind Datenbanken limitiert und Daten über eine ausreichende Zahl an Substanzen nicht vorhanden. Diese Einschränkungen beruhen aber vor allem auf fehlenden rechnerischen Möglichkeiten, weniger auf den Einschränkungen von Datenbanken. Zum jetzigen Zeitpunkt kann man mit Hilfe rechnergestützter QSAR-basierter Toxikologie-Methoden einzelne chemische Substanzen analysieren. Komplexe Gemische, wie etwa pflanzliche Extrakte, können jedoch noch nicht auf diese Weise analysiert werden. Gleichwohl könnten sich in Zukunft rechnergestützte Techniken besonders bei der Bewertung pflanzlicher Gemische und der Voraussage additiver oder synergistischer Effekte als gewinnbringend erweisen.

1.3.1.2 Physiologie-basierte biokinetische Modelle (PBBK)

Definition: Das Physiologie-basierte biokinetische Modell (PBBK) umfasst einen Satz mathematischer Gleichungen, die auf der Grundlage dreier Parametertypen gemeinsam die Charakteristik von Absorption, Verteilung, Metabolismus und Exkretion (ADME; absorption, distribution, metabolism and excretion) eines Stoffes innerhalb eines Organismus beschreiben. Diese Parameter umfassen Physiologie (z.╃B. Herzleistung, Gewebevolumen und Gewebedurchblutung), Physiko-Chemie (z.╃B. Blut/Gewebe-Verteilungskoeffizienten) und Kinetik (z.╃B. kinetische Konstanten für metabolische Reaktionen).

Methoden

Stand der Technik: Ein generelles Problem bei der Risikoabschätzung ist die Notwendigkeit, tierÂ� experimentelle Daten bei hohen Dosen auf die humane Niedrigdosis-Situation zu extrapolieren. Solche Extrapolationen werden erschwert durch Unsicherheiten bezüglich der Dosis-Wirkungs-Kurve im Dosisbereich, wie er für die menschliche Ernährung relevant ist, sowie durch speziesspezifische Unterschiede im Metabolismus. PBBK-Gleichungen können z.╃B. den zeitlichen Verlauf der Gewebskonzentration eines Stoffes oder seiner Metaboliten in jedem Gewebe bei jeder Dosierung vorausberechnen und damit die Analyse von Effekten im niedrigen Dosisbereich, wie er für die humane in-vivo-Situation realistisch ist, ermöglichen. Modellvorhersagen können genutzt werden, um eine stärker mechanismusgetriebene Grundlage zur Einschätzung der Wirkungen in Tieren und Menschen bei niedrigen, nahrungsbezogenen Aufnahmemengen zu liefern, selbst wenn diese nur auf in-vitro-Daten beruhen. Darüber hinaus können PBBKModelle für verschiedene Spezies entwickelt werden, was die Extrapolation von Spezies zu Spezies erleichtert. Auch ist es möglich, durch Einbezug von Gleichungen und kinetischen Konstanten für metabolische Umsetzungen, die aus Proben einzelner humaner Individuen und/ oder spezifischer Isoenzyme gewonnen werden, eine Modellierung interindividueller Variationen und genetischer Â�Polymorphismen durchzuführen. Internationale Standardverfahren und Datenbanken, die eine Standardisierung und Transparenz der Erstellung von PBBK-Modellen gewährleisten, sind bereits verfügbar. Limitierungen, Wissenslücken und Forschungsbedarf: Einen besonderen Schwerpunkt in der Risikobewertung beansprucht die Beurteilung von Langzeiteffekten, wie z.╃B. Kanzerogenität. Tierexperimentelle Untersuchungen hierzu werden in der Regel mit einer einzigen definierten Substanz durchgeführt. Der Mensch ist aber gegenüber sekundären Pflanzenstoffen über die Nahrung exponiert, d.╃h. ein bestimmter Stoff wird innerhalb eines komplexen Gemisches mit anderen Inhaltsstoffen aufgenommen. In der Lebensmittelmatrix können verschiedene Interaktionen stattfinden, die die Bioverfügbarkeit bestimmter Lebensmittelbestandteile beeinflussen. Zudem können auf der Ebene der metabolischen Aktivierung und/ oder Entgiftung Wechselwirkungen mit anderen pflanzlichen Inhaltsstoffen stattfinden. Grundsätzlich sind PBBK-Modelle in der Lage, solche modulierenden Effekte pflanzÂ� licher Inhaltsstoffe in komplexen Gemischen mit einzubeziehen. Gegenwärtige Modelle berücksichtigen vorausberechnete Daten zu dosisabhängigen Effekten, Speziesunterschieden und interindividuellen Unterschieden in der Bioaktivierung. Für eine adäquate Abschätzung des Krebsrisikos bei Expositionen, die für den Menschen relevant sind, sind aber nach wie vor noch Zusatzinformationen erforderlich. Beispielsweise beeinflusst auch die Toxikodynamik die Risikobewertung. Dies lässt sich durch Erweiterung der PBBK-Modelle in so genannte Physiologie-basierte biodynamische Modelle (PBBD-Modelle, physiologically-based biodynamic models) untersuchen. Hierbei werden

17

18

Bericht und Schlussfolgerungen

Dosishöhe und Biotransformation verknüpft mit Biomarkern der Exposition oder der Toxizität bzw. letztlich der Krebsrate. Eine der stärksten Einschränkungen des PBBK-Modellierens besteht derzeit darin, dass ein Modell für jeden einzelnen Stoff aufwendig etabliert werden muss. Diese Validierung ist aber für eine ganze Reihe von Stoffen notwendig. Es lässt sich trotzdem absehen, dass die Bedeutung des PBBK-Modellierens mit sich allmählich erhöhender Verfügbarkeit von Modellen guter Qualität zunehmen wird. Insbesondere in der Kombination mit in-vitro-Tests kann die PBBK-Modellierung bei der Extrapolation von in-vitro-Konzentrations-Wirkungs-Kurven hin zu in-vivo-Dosis-Wirkungs-Kurven sehr hilfreich sein. Dies lässt sich dadurch bewerkstelligen, dass Konzentrations-Wirkungs-Kurven aus einem geeigneten in-vitro-Toxizitätstest als interne Konzentrationen im Modell verwendet werden. Mit Hilfe des PBBK-Modells lassen sich dann die in-vivoDosishöhen errechnen, die zum Erreichen der internen (toxischen) Konzentrationen erforderlich sind. Die so vorausberechneten Dosis-Wirkungs-Kurven können bei der Sicherheitsbewertung von Stoffen für die Bestimmung sicherer Expositionswerte verwendet werden. Auf diese Weise können PBBK-Methoden helfen, externe Dosen mit internen Konzentrationen (Zielgewebedosis) zu verknüpfen und diese internen Konzentrationen mit EC50-Werten (effective concentration 50â•›%) aus in-vitro-Studien zu verbinden und umgekehrt.

1.3.2 Techniken zur Profilerstellung: „OMICS“, in-vitro- und in-vivo-Modelle Der Ausdruck „OMICS“ bezieht sich auf eine Reihe von Disziplinen, die biologische Informationen zu Wechselwirkungen von Stoffen mit dem Genom, Proteom und dem Metabolom analysieren. Diese umfassen eine Vielfalt von Unterdisziplinen, die verschiedene Gruppen von Techniken, Reagenzien und Software verwenden, wie z.╃B. die DNA- und Protein-Microarray-Analyse, Gasund Flüssigchromatographie, Massenspektrometrie und eine Reihe anderer Methoden, die Analysen mit hohem Durchsatz ermöglichen. Eine der Stärken der „OMICS“ liegt darin, dass sie gleichermaßen gut sowohl für in-vitro- als auch für in-vivo-Studien angewendet werden können. Die Genomik umfasst sowohl die „Strukturelle Genomik“, DNA-Sequenzanalyse, Genomkartierung eines Organismus, als auch die „Funktionelle Genomik“, die Charakterisierung der Genwirkung, mRNA-Analyse und Proteinexpressionsprofilierung. Eine spezifischere Disziplin, die Proteomik, befasst sich mit der Untersuchung des vollständigen Proteinsatzes, der in einer Zelle, einem Gewebe, einer Körperflüssigkeit oder einem Organismus exprimiert wird. Im Unterschied zum Genom variiert das Proteom von Zelltyp zu Zelltyp. Die Proteomik versucht, Proteinprofile einzelner Zelltypen zu identifizieren und die Unterschiede im Proteinexpressionsmuster zwischen gesunden und kranken Zellen zu bewerten. Jedoch hat das Symposium den Schwerpunkt auf die Â�Transkriptomik und Metabolomik gelegt und die Proteomik nicht explizit behandelt.

Methoden

1.3.2.1 Transkriptomik

Definition: Die Transkriptomik umfasst die globale Analyse der Genexpression. Man nennt sie auch die „Genomweite Expressionsprofilerstellung“. Sie erfasst den relaÂ�tiven Gehalt an Boten RNA (mRNA), um Muster der Genexpression und der GenÂ� expressionshöhe zu bestimmen, und um die Genregulation zu analysieren. Stand der Technik: Zwar ist die m-RNA nicht das endgültige Produkt der Genexpression, die Transkription stellt aber den ersten Schritt in der Genregulation dar. Informationen bezüglich der Transkriptionshöhe werden benötigt, um Genregulationsnetzwerke besser zu verstehen und Ähnlichkeiten in Genexpressionsmustern aufzudecken, die funktional verbunden sind und dem gleichen genetischen Kontrollmechanismus unterliegen können. Die Analyse des gesamten Genoms/ Transkriptoms stellt dabei einen unvoreingenommenen Ansatz (unbiased approach) dar, der die Identifizierung aller potenziellen (erwarteten/ unerwarteten) Effekte erlaubt. RNA-Expressionssignaturen, die eine Unterscheidung zwischen verschiedenen Stoffklassen, wie etwa genotoxischen oder nicht genotoxischen Leberkarzinogenen erlauben, sind bereits identifiziert. Für eine Risiko-Nutzen-Analyse von sekundären Pflanzenstoffen können auch spezifische genetische Polymorphismen einbezogen werden. Darüber hinaus wird die Modulation der Expression von Genen, die an biologischen und genetischen Signalwegen beteiligt sind, welche für die Krebsentstehung Bedeutung haben, als eine wichtige Komponente des antikanzerogenen Effekts von Gemüse oder sekundären Pflanzenstoffen angesehen. Die Transkriptomik kann bei der Untersuchung solcher Effekte sehr hilfreich sein. Limitierungen, Wissenslücken und Forschungsbedarf: Die Modulation der Transkriptionshöhe ist nicht unbedingt mit entsprechenden Änderungen auf Proteinebene verbunden. Zumindest für spezifische Gene sollten Daten zur RNA-Expression durch die Bestimmung der entsprechenden Proteinkonzentrationen oder -aktivitäten ergänzt werden. Je nach dem Gewebskontext kann auch alternatives Spleißen die Funktionalität beeinflussen. Das Erstellen von Transkriptionsprofilen führt zu extrem großen Datensätzen und benötigt effektive Datenbankressourcen für Interpretation, Management und Analyse. Ob genomweite Expressionsdaten für Routineanwendungen erforderlich sind, muss noch untersucht werden. Möglicherweise benötigt man die Genomik nur für die Identifikation von Gengruppen, die die Etablierung eines Klassifikationsalgorithmus erlauben. Falls die Zahl an Genen, die für die Klassifikation benötigt werden, nicht zu hoch ist, könnten diese mittels quantitativer Techniken analysiert werden, wie z.╃B. qRT-PCR (quantitative real-time polymerase chain reaction).

19

20

Bericht und Schlussfolgerungen

Die Interpretation der Genexpressionsdaten sollte durch validierte Biomarker unterstützt werden. Zum besseren Verständnis der komplexen Information, die man normalerweise erhält, sollten integrierte systembiologische Ansätze entwickelt werden.

1.3.2.2 Metabolomik

Definition: Die Metabolomik hat zum Ziel, das Profil aller Metaboliten in einem einfachen System, z.╃B. einer Zelle, oder im komplexen System, z.╃B. Organ oder Organismus, zu untersuchen. Stand der Technik: Die Metabolomik verwendet hoch entwickelte instrumentell-analytische Techniken, wie Kernmagnetresonanzspektroskopie (NMR) und Massenspektrometrie (MS), meistens in Kopplung mit verschiedenen Trenntechniken wie Gaschromatographie (GC) oder Flüssigchromatographie (LC). Mit Hilfe dieses Instrumentariums kann das endogene und das mit einer Exposition verbundene Metabolom eines Organismus charakterisiert und die Bestandteile der verschiedenen Stoffwechselwege identifiziert werden. Eine Stärke der Metabolomik liegt darin, dass sie das Potenzial bestimmter Nährstoffe und von Xenobiotika erfasst, die Stoffwechseleigenschaften eines Organismus zu verändern. Dabei kann es sich beispielsweise um metabolische Leberfunktionen, Insulinempfindlichkeit von Geweben oder die Sekretion von Schilddrüsenhormonen handeln. Ein weiterer Vorteil liegt in der Möglichkeit der Analyse von Stoffwechselspektren in menschlichem Blut und Urin. So ist es möglich, mittels Metabolomik den Einfluss von Nahrungsbestandteilen im Menschen zu untersuchen. Limitierungen, Wissenslücken und Forschungsbedarf: Die Metabolomik deckt den Anteil am gesamten Metabolom ab, der mittels instrumenteller Analyse zugänglich ist. Allerdings bestehen trotz großer Fortschritte im Verständnis metabolischer Profile oft Unklarheiten bezüglich der Interpretation der komplexen Daten. Da viele Inhaltsstoffe über mehrere Mechanismen agieren, kann die Situation komplex werden. Darüber hinaus sind Effekte von Gemischen auf das Metabolom bisher nicht untersucht. Es besteht Bedarf, die quantitative Erfassung metabolischer Daten zu standardisieren. Zusätzlich sollte eine standardisierte Plattform für den Datenaustausch etabliert werden. Die statistische Auswertung muss soweit verbessert werden, dass induzierte Effekte klar von der physiologischen Streubreite im Metabolom unterschieden werden können. Für die Voraussage von Wirkmechanismen müssen generell einsetzbare Algorithmen etabliert und validiert werden.

Methoden

1.3.2.3 Toxikogenomik und Nutrigenomik

Definition: Toxikogenomik und Nutrigenomik sind Sammelbegriffe, welche die drei Unterdisziplinen Transkriptomik, Proteomik und Metabolomik mit einschließen. Diese Techniken zur Profilerstellung können aus ernährungsbezogenem oder aus toxikologischem Blickwinkel heraus betrachtet werden. Der Ausdruck Toxikogenomik wird verwendet, wenn die Toxikologie mit diesen neuen Methoden kombiniert wird, um die toxischen Wirkungen spezifischer Stoffe besser zu verstehen und zu erfassen, während die Nutrigenomik den Einfluss bestimmter Nährstoffe oder Ernährungsweisen auf die Genexpression untersucht. Dies sollte nicht mit dem Ausdruck „Nutrigenetik“ verwechselt werden, bei der untersucht wird, wie die genetische Variabilität die Reaktion des Körpers auf Nahrungsbestandteile bzw. auf die Ernährung beeinflusst. Stand der Technik: Die Toxikogenomik birgt großes Potenzial, spezifische toxische Effekte von Wirkstoff-Kandidaten und Stoffen vorherzusagen. Ziele der Toxikogenomik sind, Biomarkergene zu identifizieren und Zusammenhänge zwischen Änderungen in Genexpressionsmustern und bestimmten toxikologischen Endpunkten festzulegen. Man geht davon aus, dass eine Kurzzeitexposition Änderungen in den RNA-, Protein- und Metaboliten-Expressionsmustern verursacht, welche sich auch als hilfreich bei der Vorhersage von Langzeiteffekten erweisen könnten. Darüber hinaus konnte bereits überzeugend gezeigt werden, dass diese Techniken starke Hinweise auf toxische Mechanismen liefern können, die durch die zu prüfenden Verbindungen ausgelöst werden. Die Nutrigenomik birgt das Potenzial, innerhalb wohl definierter Versuchsbedingungen Hunderte von Messgrößen zu charakterisieren, die auf einen bestimmten Nährstoff oder Nichtnährstoff, auf eine Behandlung oder Ernährungsweise reagieren. Einschränkungen, Wissenslücken und Forschungsbedarf: Toxikogenomische Untersuchungen und daraus abgeleitete KlassifikationsÂ�Algorithmen beschränken sich bisher nur auf eine sehr begrenzte Zahl an Stoffen. Dosis-Wirkungs-Beziehungen sind bisher nur in sehr wenigen Fällen etabliert worden. Dies könnte sich als problematisch erweisen, da die gleiche Verbindung bei hoher bzw. niedriger Konzentration jeweils über unterschiedliche Mechanismen wirken kann. Für die Zukunft ist es wichtig, „OMICS“-Daten einer großen Zahl von Stoffen, die über gut definierte Mechanismen wirken, zu etablieren. Nur wenn Daten zu einer ausreichend großen Zahl von Stoffen derselben Stoffklasse verfügbar werden, können koordinierte Änderungen in den Transkripten, Proteinen oder Metaboliten identifiziert und mit gemeinsamen molekularen (toxischen) Wegen in Verbindung gebracht werden. Solche Schlüsselwege und deren Zusammenhänge mit biochemischen Daten müssen noch identifiziert werden. Ebenso müssen Änderungen im Expressionsmuster von Genen, Proteinen und Metaboliten sowohl mit nachteiligen, als auch posi-

21

22

Bericht und Schlussfolgerungen

tiven Effekten auf die menschliche Gesundheit verknüpft werden, um die bislang noch limitierte Aussagekraft für die Risikobewertung zu verbessern. Von wesentlicher Bedeutung ist der Aufbau einer öffentlich zugänglichen, umfassenden, gut strukturierten und harmonisierten Toxikogenomik-Datenbank mit Informationen zu Expressions- und toxikologischen Parametern. Für adäquate Ernährungsstudien (Nutrigenomik-Studien) bedarf es gut geplanter experimenteller Untersuchungen an Freiwilligen unter standardisierten Bedingungen in Bezug auf Ansatz, Ernährung und Profilerstellung. Die gegenwärtige Problematik gleicht derjenigen der Toxikogenomik und besteht vor allem darin, die durch die Nutrigenomik generierten großen Datensätze angemessen zu interpretieren. Koordinierte Änderungen bei Transkripten, Proteinen und Metaboliten können nur in wenigen Fällen und auf bekannte Regulationswege projiziert werden. Eine weitere Einschränkung rührt daher, dass sich die Expressionsprofile verschiedener Gewebe unterscheiden und oft nicht klar ist, welches Gewebe relevant ist. Während in Tiermodellen die Gewebe recht umfassend analysiert werden können, ist dies in Humanstudien nicht möglich.

1.4 Schlussfolgerungen und Empfehlungen In-silico- und in-vitro-Techniken zur Identifizierung von Gefährdungen können ebenso wie in-vitro-Techniken zur Prüfung bestimmter mechanistischer Endpunkte die Sicherheitsbewertung von pflanzlichen Stoffen mittels gezielten Studiendesigns verbessern. Darüber hinaus lässt sich mittels solcher in-silico- und in-vitro-Untersuchungen die Anzahl der für Wirksamkeits- und Sicherheitsstudien erforderlichen Tiere reduzieren. Hierzu können auch neue in-vivo-Techniken beitragen und ebenso die Versuchsdauer verkürzen, wenn auf der Basis von Ergebnissen aus „Kurzzeit“-Studien „Langzeit“-Effekte wie Kanzerogenität vorhergesagt werden können. Die SKLM vertritt die Ansicht, dass die neuen Methoden bislang noch nicht aussagekräftig genug sind, um die derzeitigen Verfahren der Stoffprüfung und Risikobewertung zu ersetzen. Sie werden aber als sehr nützlich angesehen, um umfassendere Daten zu erhalten und somit die Auswertung von klinischen und tierexperimentellen Studien zu verbessern bzw. inkonsistente Daten besser zu verstehen. Darüber hinaus können sie mechanistische Einblicke ermöglichen, die in die Risikobewertung einbezogen werden können. Gleichwohl ist eine stringente Validierung unter Einbezug des Vergleichs mit klassischen Verfahren erforderlich, um die Verwendbarkeit für einen bestimmten Zweck abzuÂ� sichern. Um größtmöglichen Erkenntnisgewinn zu erhalten, wird eine neue Teststrategie empfohlen, bei der verschiedene Methoden in einem kombinierten Ansatz integriert werden. Um Standardisierung, Vergleichbarkeit, Reproduzierbarkeit sowie die Zusammenarbeit zwischen verschiedenen Laboratorien zu gewährleisten, ist Planung, Design und Aufbau einer öffentlich zugänglichen, umfassenden, gut strukturierten und harmonisierten Datenbank notwendig. Dies beschleunigt den Validierungsprozess und stellt sicher, dass die neuen Â�Methoden

Schlussfolgerungen und Empfehlungen

angemessen angewendet werden. Bioinformatikexperten sind unerlässlich, um diese Ziele zu erreichen. Bei der Durchführung von Humanstudien muss der genetische Hintergrund der analysierten Population berücksichtigt werden. Der Aufbau einer umfassenden Datenbank zur physiologischen Hintergrundsituation von Individuen sowie der Allgemeinbevölkerung ist eine Voraussetzung für die Untersuchung des Einflusses von Stoffen auf humane Stoffwechselmuster. Die genetische Variabilität kann so besser berücksichtigt werden, und man kann möglicherweise Schlüsselfaktoren identifizieren, die für die Variabilität in der zu untersuchenden Population verantwortlich sind. Menschen sind normalerweise gegenüber pflanzlichen Stoffen in komplexen Stoffgemischen exponiert. Zum gegenwärtigen Zeitpunkt sind die meisten modernen Techniken aber nicht in der Lage, solche Gemische zu handhaben. Bis jetzt wurden „OMICS“-Techniken fast ausschließlich zur Analyse einzelner Substanzen und nicht von Gemischen angewendet. Das ist sinnvoll, da zunächst die bereits sehr komplexen Effekte einzelner Bestandteile identifiziert und verstanden werden müssen. Dennoch ist ein Vorteil der auf Mustern beruhenden „OMICS“-Technologien, dass es irrelevant ist, ob die Änderungen in komplexen Expressionsmustern durch Einzelsubstanzen oder Gemische verursacht werden. Daher wird mit fortschreitender Entzifferung der komplexen Sprache der „OMICS“-Muster für Einzelstoffe diese Methodik auch für Gemische anwendbar werden, was rasche Fortschritte auf dem Forschungsgebiet insgesamt mit sich bringen wird. Schließlich liegt ein einzigartiger Vorzug der „OMICS“-Techniken mit ihrem Zufalls- oder gar genomweiten Ansatz darin, auch solche Effekte zu entdecken, die gar nicht gesucht wurden. Daher ist es wahrscheinlich, dass sie bei zusätzlicher Anwendung gemeinsam mit konvenÂ� tionellen Toxizitätstests die Risikobewertung verbessern werden.

1.4.1 Spezifischer Forschungsbedarf in Schlagworten QSAR:

Verstärkte Erforschung der Möglichkeit einer Kombination von Softwareprogrammen zur Verbesserung von Sensitivität und Spezifität der Vorhersage ►⌺ Weiterentwicklung der Software, um Informationen aus Datenbanken intelligenter nutzbar zu machen ►⌺ Entwicklung von rechnergestützten Verfahren zur Bewertung von StoffÂ� gemischen. ►⌺

PBBK: ►⌺

Erweiterung der PBBK-Modelle in so genannte Physiologie-basierte biodynamische Modelle (PBBD-Modelle, physiologically based biodynamic models)

23

24

Bericht und Schlussfolgerungen

Entwicklung weniger aufwendiger Methoden, um eine große Zahl von Stoffen modellieren zu können ►⌺ Kombination von in-vitro-Toxizitätstests mit in-vivo- Dosis-Wirkungskurven. ►⌺

Transkriptomik:

Ergänzung von Daten zur RNA-Expression durch Bestimmung der entsprechenden Proteinkonzentrationen oder -aktivitäten, auch unter Berücksichtigung des alternativen Spleißens ►⌺ Entwicklung effektiver Datenbankressourcen zur Unterstützung von Interpretation, Management und Auswertung für die Erstellung von TranskripÂ� tionsprofilen; Klärung, ob genomweite Expressionsdaten für Routineanwendungen erforderlich sind ►⌺ Etablierung eines Klassifikationsalgorithmus ►⌺ Entwicklung von validierten Biomarkern zur Interpretation der Genexpressionsdaten ►⌺ Entwicklung von integrierten systembiologischen Ansätzen zum besseren Verständnis komplexer Daten. ►⌺

Metabolomik:

Standardisierung der Bestimmung metabolischer Daten Etablierung einer Plattform für den Datenaustausch ►⌺ Untersuchung der Effekte von Gemischen auf das Metabolom ►⌺ Verbesserung der statistischen Auswertung, so dass induzierte Effekte klar vom normalen Streubereich unterschieden werden können ►⌺ Etablierung generell einsetzbarer Algorithmen für eine Prognose von Wirkmechanismen. ►⌺ ►⌺

Toxikogenomik and Nutrigenomik:

Gewinnung und Nutzung von Daten zu toxikogenomischen Untersuchungen, abgeleiteten Klassifikations-Algorithmen und Dosis-Wirkungs-Beziehungen von einer großen Zahl von Stoffen zur Identifizierung toxikologischer Schlüsselwege und deren Zusammenhänge mit biochemischen Daten ►⌺ Beziehung zwischen Änderungen im Expressionsmuster von Genen, Proteinen und Metaboliten und nachteiligen oder positiven Effekten auf die menschliche Gesundheit ►⌺ Aufbau einer öffentlich zugänglichen, umfassenden, gut strukturierten und harmonisierten Toxikogenomik-Datenbank mit Informationen zu Expressions- und toxikologischen Parametern ►⌺ Durchführung gut definierter experimenteller Untersuchungen an Freiwilligen unter standardisierten Ernährungsbedingungen mit einem standardisier►⌺

Fazit

ten Ansatz zur Durchführung adäquater Ernährungsstudien (NutrigenomikStudien), ►⌺ Standardisierung der Anwendung der Profilerstellungstechniken.

1.5 Fazit Die hier diskutierten neuen Methoden sind erfolgversprechend und können Prioritäten- und Entscheidungsfindung im Prozess der Risikobewertung stützen. Sie können aber derzeit die klassischen Methoden noch nicht ersetzen. In-silico-Methoden, wie etwa QSAR- und PBBK-Modelle, sind in Bezug auf Â�regulatorische Auswirkungen und praktischen Einsatz am weitesten fortÂ� geschritten, da sie in-vivo-Dosis-Wirkungs-Beziehungen für die Toxizität prognostizieren können. Angesichts der immensen Datensätze, die anfallen, liegt eine Hauptaufgabe in der Interpretation. Eine hypothesen- und zweckgeleitete Methodenwahl ist daher, zusammen mit einer stringenten Validierung auf der Basis klassischer Methoden, unerlässlich. Die weitere Entwicklung dieser vielversprechenden neuen Technologien erfordert umfassende Langzeitforschung auf der Basis einer nachhaltigen Förderung.

25

26

2

Report and Conclusions

2.1 Preface Foods of plant origin are an essential part of the diet; however, some plants contain substances that hold certain health risks. Such “phytochemicals” can induce adverse effects by different mechanisms. Thus, some substances, for example soy isoflavones, affect the endocrine system. Other substances are hepatotoxic (coumarin), neurotoxic (solanine), phototoxic (furocoumarins) or carcinogenic (estragole). Normal intake of phytochemicals as natural components of fruits, vegetables, herbs, and spices is regarded to be of low risk. Increased exposure, however, poses a potential problem, for example in cases of unbalanced diets or uptake of dietary supplements in isolated and concentrated form. For risk assessment, it is necessary to identify potentially harmful substances based on their chemical structure. Also, their dose-dependent effects on the organism have to be described. For this purpose, novel profiling techniques and advanced computational methods are increasingly used, with promising possibilities. The Senate Commission on Food Safety (SKLM) of the Deutsche ForÂ� schungsgemeinschaft (DFG) organized a symposium entitled „Risk Assessment of Phytochemicals in Food – Novel Approaches“ that took place in Kaiserslautern, Germany from March 30th to April 1st 2009.╃Power, implications and promises of novel methodologies for risk assessment, including various in silico, in vitro and in vivo approaches were discussed and an evaluation of the state of the art for the various approaches was performed. Specific examples from the field of phytochemicals such as coumarin/ furocoumarins, beta-carotene, quercetin, glucosinolates and isoflavones were considered. The symposium addressed the relevance of the novel approaches for risk assessment of phytoÂ� chemicals. The SKLM has elaborated conclusions and recommendations, also identifying gaps in knowledge and research needs. This report is based on the presentations of Marcel Leist (DE), Gerrit Speijers (NL), Luis Valerio (US), Benoît Schilter (CH), Ivonne Rietjens (NL), Pablo Steinberg (DE), Hans-Jürgen Ahr (DE), Anette Thiel (CH), Hennicke Kamp (DE), Hannelore Daniel (DE), Augustin Scalbert (FR), Theo de Kok (NL), Takeki Uehara (JP), Alfonso Â�Lampen (DE), Â�Dieter Schrenk (DE), Jaap Keijer (NL), Ulrike Peters (US), Hansruedi Glatt (DE), Â�Johanna Fink-Gremmels (NL) and subsequent workshops.

Transdisciplinary Aspects

2.2 Transdisciplinary Aspects Diet and food components, irrespective of whether naturally occurring or manmade, are nowadays perceived as exposure/ nutrition related factors of influence on human health by affecting the genome, transcriptome, proteome and metabolome. In addition, since such interactions persist throughout life, the developmental and physiological status of an individual exposed at a given time in life is considered to be also of importance. New profiling techniques offer the possibility to identify interactions of food constituents with receptors, signalling pathways and other parts of the genome/ transcriptome/ proteome/ metabolome system involved in keeping our physiology in a balance. Risk assessment of food and its ingredients has to consider a large number of man-made chemicals, as well as various naturally occurring compounds which might influence human health. Since for the vast majority of these substances the available toxicological data are insufficient, novel molecular techniques are expected to provide rapid, efficient and reliable information to establish safe levels of exposure or to perform an adequate risk assessment on (phyto-) chemicals based on appropriate safety testing. In view of the large variety of new methodologies, which are at different stages of development and applicability at the present time, the SKLM underlines the importance of using these methodologies when pursuing a number of aims such as: The elucidation of mechanisms of action The development of novel techniques to enable comprehensive validation of large datasets ►⌺ The comparison of experimental data in laboratory animals or in vitro model systems with those obtained from studies on humans ►⌺ The science-driven development of food and food ingredients with a defined biofunctionality ►⌺ The consideration of individual variability and susceptibility. ►⌺ ►⌺

Such novel methodologies might be useful to generate data of improved quality in clinical and animal studies or to achieve a better understanding of seemingly inconsistent data. Biomolecular targets and biological effects of specific phytochemicals might be more adequately investigated by introducing molecular markers in future epidemiological studies. By doing so, the interaction of phytochemicals with biomolecular processes as well as the role of genetic variability in the heterogeneous response of different human populations towards certain phytochemicals may be better understood.

27

28

Report and Conclusions

2.3 Methodologies 2.3.1 Computational Toxicology╃/╃In Silico Models In silico technologies use computational information and strategies to generate a predictable toxicological and adverse effect profile for a given compound. They allow the rapid screening of high numbers of chemicals in a relatively short period of time, and are very cost-effective when compared to conventional animal-based toxicology studies. Moreover, they can also be used in multiple experiments, not only to screen large numbers of chemicals but also to refine their predictive power through adequate learning algorithms including for example quantitative structure–activity relationship (QSAR)-based strategies or so-called physiologically-based biokinetic (PBBK) modelling. A further benefit of in silico methods for predicting toxicity and aiding in risk assessment of chemicals is the potential net effect of reducing, refining or even replacing the number of animals used for laboratory testing, thus contributing to the 3R principle (refinement, reduction, replacement). Furthermore, the use of in silico computational toxicology methods with individual phytochemicals appears of great promise to generate priorities for risk assessment on a chemical basis.

2.3.1.1 Quantitative Structure–Activity Relationship (QSAR)

Definition: QSAR-based approaches quantitatively correlate parameters describing structural chemical characteristics of series of molecules with their biological activity or chemical reactivity. Computational QSAR-based predictive modelling applies statistical tools correlating biological activity of molecules with descriptors representative of a molecular structure. State of the Art: Computational QSAR-based predictive modelling provides an evidence-based tool for prioritization and an efficient estimate of hazard based on pre-existing test data regarding various end points. Such end points comprise e.╃g. the mutagenic potential of synthetic and naturally occurring molecules, including phytochemicals present in botanicals, herbs, and natural dietary sources. Predictive QSAR methodologies hold promise as a decision support tool in safety and risk assessment. They may be valuable in emergency situations to assist in fast decision making and may also help in defining priorities for additional toxicity testing. Limitations, Gaps in Knowledge and Research Needs: QSAR evaluation cannot ensure safety of a given substance with absolute certainty. Also, the programmes do not have all the needed functionalities nor do they give comprehensive information on the chemical properties or 100╛%

Methodologies

coverage or specificity. Furthermore, like most QSAR-based predictive models, the method suffers from inherent poor sensitivity, i.╃e. the ability to correctly identify true positives. It does not weigh exposure, or target specificity of compounds nor is it guided by mechanistic considerations such as class-specific mechanisms, for instance in carcinogenesis the multiple steps in tumorigenesis, tumor promotion, or tumor type. The use of a combination of computational software programs would be the best approach to maximize sensitivity and specificity of the prediction, since databases, algorithms and methods for structural interpretation vary between programs. Presently, predictions can be made with relatively good sensitivity and specificity for end points such as mutagenicity, which depends on a limited number of mechanisms, for instance on the capacity to covalently bind to DNA, or for acute toxicity in aquatic species, especially when a narcotic type of mechanism is responsible. However, more complex end points depending on a multitude of possible mechanisms, such as organ toxicity or carcinogenicity are much more difficult to predict. Currently, databases are limited and data on a sufficient number of compounds are missing. However, limitations are primarily due to current computational possibilities rather than to limited databases. At the present time computational QSAR-based toxicology methods can be used to analyze individual chemicals, but they do not address the screening of complex mixtures such as botanical extracts. However, particularly for the evaluation of mixtures and prediction of additive or synergistic effects computational techniques may be of value in the near future.

2.3.1.2 PBBK-Modelling

Definition: A physiologically-based biokinetic (PBBK) model is a set of mathematical equations that together describe the absorption, distribution, metabolism and excretion (ADME) characteristics of a compound within an organism on the basis of three types of parameters. These parameters include physiological parameters (e.╃g. cardiac output, tissue volumes, and tissue blood flows), physico-chemical parameters (e.╃g. blood/tissue partition coefficients), and kinetic parameters (e.╃g. kinetic constants for metabolic reactions). State of the Art: An overall problem in risk assessment is the need to extrapolate experimental data obtained in animal experiments at high dose levels to a low dose human situation. Uncertainties regarding the shape of the dose-response curve at dose levels relevant for dietary human intake and species differences in metabolism make such extrapolations difficult to be performed. PBBK equations predict, for example, the tissue concentration of a compound or its metabolites in any tissue over time at any dose, also allowing analysis of effects at low dose levels that are more realistic with respect to the human in vivo situation. Model predictions can be used to provide a more mechanism-

29

30

Report and Conclusions

driven basis for the assessment of the effects in animals and humans at low dose dietary intake levels, even based on in vitro data only. Furthermore, such PBBK models can be developed for different species, facilitating interspecies extraÂ� polation. In addition, by incorporating equations and kinetic constants for Â�metabolic conversions obtained from samples of individual humans and/or specific isoenzymes, modelling of interindividual variations and genetic polymorphisms is possible. International standardized procedures and databases to ensure standardization and transparency of the PBBK model building are already available. Limitations, Gaps in Knowledge and Research Needs: Risk assessment puts particular emphasis on long-term effects such as carcinogenicity. Whereas animal carcinogenicity experiments usually are conducted with a single defined compound, humans are exposed to phytochemicals via the diet, i.╃e. within a complex mixture of other ingredients. In a food matrix, various interactions might occur, thereby affecting the bioavailability of particular food components. In addition, interactions with other herbal ingredients might occur at the level of metabolic activation and/or detoxification. In principle, PBBK models can take into consideration such modulating effects of herbal ingredients for complex mixtures. Current models take into account the predicted data on dose-dependent effects, species differences and interindividual differences in bioactivation. However, for a complete assessment of the cancer risk under human-relevant intake conditions, additional information, such as toxicodynamic processes that might also affect the risk assessment, is still needed. This could be investigated by extending the PBBK models into so-called physiologically based biodynamic (PBBD) models, in which dose levels and biotransformation are coupled to biomarkers of exposure or toxicity and – ultimately – cancer incidence. One of the major limitations of PBBK modelling is that nowadays models have to be established in a laborious way for each individual compound. However, validation of a broad range of compounds is required. Nevertheless, the importance of PBBK modelling will certainly augment in the future, when high quality models become increasingly available. Particularly in combination with in vitro tests PBBK modelling might be of great help in extrapolating in vitro concentration–response curves to in vivo dose-response curves. This is achieved by using the concentration–response curves, acquired in an appropriate in vitro toxicity test, as internal concentrations in the model. By using the PBBK model, the in vivo dose levels that are needed to reach the internal (toxic) concentrations can then be calculated. The predicted dose–response curves thus obtained can be used to determine safe exposure levels in chemical safety assessment. Thus, PBBK approaches will help to link external doses to internal concentrations (target tissue dose) and to link these internal concentrations to EC50 values from in vitro studies and vice versa.

Methodologies

2.3.2 Profiling Techniques: “OMICS”, In Vitro and In Vivo Models The term “OMICS” refers to a field of disciplines analyzing biological information on the interaction of compounds with the genome, proteome and metabolome all of them ending in -omics (e.╃g. genomics, proteomics, metabolomics). It comprises a variety of subdisciplines using different sets of techniques, reagents and software like DNA and protein microarrays, gas and liquid chromatography, mass spectrometry and a number of other methodologies enabling high-throughput analyses. One of the strengths of “OMICS” lies in the fact that they can equally well be applied in studies being performed in vitro or in vivo. Genomics includes “structural genomics”, DNA sequence analysis and mapping of the genome of an organism, as well as “functional genomics”, the characterization of gene responses, analyzing mRNA and protein expression profiles. As a more specific discipline, the study of the complete set of proteins expressed in a cell, tissue, body fluid or organism is referred to as proteomics. Unlike the genome, the proteome varies between cell types. Proteomics attempts to identify the protein profile of each cell type, and to assess differences in protein expression patterns between healthy and diseased cells. However, proteomics was not explicitly discussed at the symposium which focussed on Â�transcriptomics and metabolomics.

2.3.2.1 Transcriptomics

Definition: Transkriptomics refers to the global analysis of gene expression, also called “genome-wide expression profiling”, measuring relative amounts of messenger RNA (mRNA) in order to determine patterns and levels of gene expression, and to analyze gene regulation. State of the Art: Although mRNA is not the ultimate product of a gene, transcription is the first step in gene regulation, and information regarding transcript levels is needed to better understand gene regulatory networks and to detect similarities in expression patterns of genes, which may be functionally related and under the same genetic control mechanism. Whole genome transcriptome analysis provides an unbiased approach to the identification of all possible effects, intended (expected) effects as well as unexpected ones. RNA expression signatures that allow differentiation between certain classes of compounds, such as genotoxic and non-genotoxic liver carcinogens, have already been identified. Specific genetic polymorphisms might be taken into account for risk–benefit analysis of phytochemicals. Furthermore, modulation of the expression of genes involved in biological and genetic pathways that are relevant to carcinogenesis is regarded as an important component of the anticarcinogenic effect of vegetables or phytochemicals. Transcriptomics can be helpful to study such effects.

31

32

Report and Conclusions

Limitations, Gaps in Knowledge and Research Needs: Modulation of transcription levels is not necessarily correlated to corresponding changes on the protein level. At least for specific genes, data on RNA expression levels should be complemented by determination of the respective protein levels or activity. Furthermore, alternative splicing may influence functionality, depending on the tissue context. Transcript profiling produces extremely large datasets and requires effective database resources for interpretation, management and analysis. It remains to be studied whether genome-wide expression data will be required for routine applications. Possibly, genomics will only be required to identify sets of genes that allow the establishment of a classification algorithm. If the number of genes required for classification is not too high, they could be analyzed by a quantitative technique, such as qRT-PCR (quantitative real-time polymerase chain reaction). Interpretation of gene expression data should be supported by validated �biomarkers. To better understand the complex information usually obtained, integrated systems biology approaches should be developed.

2.3.2.2 Metabolomics

Definition: Metabolomics intends to investigate the profile of all metabolites present in a simple system, e.╃g. a cell, or in a complex system, e.╃g. entire organ or organism. State of the Art: Metabolomics makes use of advanced analytical techniques including nuclear magnetic resonance (NMR) spectroscopy as well as mass spectrometry (MS) which is mostly coupled with various separation techniques like gas chromato� graphy (GC) and liquid chromatography (LC). These tools allow to characterize the endogenous and exposure related metabolome of an organism and to identify compounds of different metabolic pathways. A strength of metabolomics is its ability to identify the capacity of specific nutrients as well as xenobiotics to alter the metabolic properties of an organism such as for example metabolic liver functions, insulin sensitivity of tissues as well as thyroid hormone secretion. A further advantage is the possibility to analyze metabolic spectra in human blood and urine. Therefore, metabolomics can be applied to study the influence of dietary compounds in humans. Limitations, Gaps in Knowledge and Research Needs: Metabolomics covers the fraction of the entire metabolome accessible to instrumental analysis. However, although already much progress has been made in understanding metabolic profiles, it often is not clear how to interpret the complex data. Since many compounds act by several mechanisms the situation

Methodologies

may become complex. Moreover, analysis of the effect of mixtures on the metabolome has not been addressed so far. There is a need to standardize quantification of metabolomic data. In addition, a standardized platform for data exchange should be established. The statistical assessment needs to be improved to distinguish effects from the normal range of variations within the metabolome. Generalized algorithms for the prediction of the modes of action should be established and validated.

2.3.2.3 Toxicogenomics and Nutrigenomics

Definition: Toxicogenomics and nutrigenomics are collective terms that cover the three sub-disciplines of transcriptomics, proteomics and metabolomics. These profiling techniques can be looked at from a nutritional or toxicological point of view: The term toxicogenomics is used when toxicology is combined with these new methods to better understand and assess the toxic effects of specific compounds, whereas nutrigenomics addresses the impact of specific nutrients or diets on gene expression. It is not to be confused with the term nutrigenetics which investigates how genetic variability influences the body’s response to a nutrient or diet. State of the Art: Toxicogenomics has great potential to predict specific toxic effects of drug candidates and chemicals. Toxicogenomics aims to identify biomarker genes and to establish relationships between changes in gene expression patterns and certain toxicological end points. Short term exposure is expected to cause alterations in RNA, protein or metabolite expression patterns that could also help to predict long term effects. In addition, it has already been convincingly shown that these techniques can provide strong evidence regarding toxic mechanisms being activated by the test compounds. Nutrigenomics has the potential to easily identify hundreds of entities that respond to a given nutrient or non-nutrient, to a treatment or diet in a well defined experimental setting. Limitations, Gaps in Knowledge and Research Needs: Toxicogenomic studies and derived classification algorithms are as yet only based on a limited number of compounds. Dose–response relationships have only been established in a very limited number of cases. This could be critical because different mechanisms might be activated by low and high concentrations of one and the same compound. In future it will be important to establish “OMICS” data of a high number of compounds acting by well defined mechanisms. Only in the case that data on a sufficiently high number of compounds belonging to the same compound classes become available, coordinated changes in transcripts, proteins and metabolites could be identified and associated with common molecular (toxic) pathways. Such key pathways and their correlation

33

34

Report and Conclusions

with biochemical data still have to be identified. Gene, protein and metabolite expression pattern alterations also have to be linked to adverse and beneficial human health effects to increase its as yet limited value for risk assessment. It is essential to establish a harmonized, large-scale, well designed toxicogenomics database with information on gene expression and toxicological parameters that is publicly available. In order to adequately perform nutritional investigations (nutrigenomic studies), well defined experimental studies with volunteers kept under controlled dietary regimens and in a standardized setting are needed. Furthermore, there is a necessity to standardize the application of the profiling techniques. The current problem is in line with that of toxicogenomics, i.╃e. the huge datasets generated by nutrigenomics are difficult to interpret adequately. Coordinated changes in transcripts, proteins and metabolites can only be projected on common pathways of regulation in a limited number of cases. Another limitation is that expression profiles vary between different tissues and it often is uncertain which tissue may be relevant. Whereas in animal models tissues may be analyzed quite comprehensively, this is not possible for studies in humans.

2.4 Conclusions and Recommendations In silico and in vitro techniques for hazard identification as well as in vitro techniques for testing of certain mechanistic end points may facilitate a more efficient safety evaluation of phytochemicals by means of targeted study design. Furthermore, in silico and in vitro investigations may help to reduce the number of animals necessary for efficacy and safety studies. New in vivo techniques may also help to reduce animal numbers and the duration of experiments by predicting “long term” effects such as carcinogenicity based on the results obtained in “short term” studies. In the opinion of the SKLM the novel approaches are not yet powerful enough to replace the current testing and risk assessment procedures. They are, however, regarded as useful to obtain more comprehensive data and to improve the evaluation of clinical and animal studies or to understand inconsistent data. Furthermore, they might provide mechanistic insights to be included in the risk assessment procedure. However, these novel techniques need a stringent validation, including a comparison with classical methods, in order to ascertain their applicability for a given purpose. In order to achieve the maximum outcome it is suggested to develop a novel testing strategy by integrating different methodologies into a combined approach. The planning, design and establishment of harmonized, publicly available, high quality, large-scale databases is required to ascertain standardization, comparability and reproducibility and to facilitate cooperation between different laboratories. This will accelerate the validation process and make sure that the new approaches are adequately applied. Experts in bioinformatics are essential to achieve these goals.

Conclusions and Recommendations

It is essential to consider the genetic background of the analyzed population if human studies are performed. A precondition for studying the influence of compounds on metabolite patterns in humans is a comprehensive database on the physiological background situation of individuals and the general population. By doing so, genetic variability can be better taken into account and it might be possible to identify key factors responsible for variability in the study population. Humans are usually exposed to phytochemicals in complex mixtures of compounds. At present, most of the novel techniques are not able to deal with such mixtures of compounds. So far “OMICS” techniques have been applied almost exclusively to the analysis of individual compounds and not to mixtures. This is reasonable, because we first have to identify and understand the already very complex effects of single compounds. However, an advantage of pattern based “OMICS” technologies is that it is irrelevant whether alterations in complex expression patterns are caused by single compounds or by mixtures. Therefore, as soon as further research will have deciphered the complex language of “OMICS” patterns for individual compounds, this methodology will also be applicable to mixtures and may rapidly advance this field of research. Finally, the unique advantage of “OMICS” techniques due to their random or even genome-wide approach is that we may also find effects, which we were not looking for. Therefore, when applied in addition to our conventional toxicity tests, they probably will improve the risk assessment.

2.4.1 Specific Research Needs in Keywords QSAR:

Increased research into the possibility of combining software programmes to improve sensitivity and specificity of predictions ►⌺ Development of less complex, laborious software to make more intelligent use of databases ►⌺ Development of computational methods to assess mixtures of compounds. ►⌺

PBBK:

Extension of the PBBK models into so-called physiologically based biodynamic (PBBD) models ►⌺ Development of less laborious methods to allow for the modelling of a higher number of substances ►⌺ Combination of in vitro toxicity tests with in vivo dose–response curves. ►⌺

35

36

Report and Conclusions

Transcriptomics:

Complementation of data on RNA expression levels by determination of the respective protein levels or activities, also taking into account alternative splicing ►⌺ Development of effective databank resources to support interpretation, management and analysis for transcript profiling; Determination whether genome-wide expression data will be required for routine applications ►⌺ Establishment of a classification algorithm ►⌺ Development of validated biomarkers for the interpretation of gene expression data ►⌺ Development of integrated systems biology approaches to better understand complex data. ►⌺

Metabolomics:

Standardize the determination of metabolic data Establish a platform for data exchange ►⌺ Study the effects of mixtures on the metabolome ►⌺ Improve the statistical evaluation to distinguish effects from the normal range of variations ►⌺ Establish generalized algorithms for the prediction of modes of action. ►⌺ ►⌺

Toxicogenomics and Nutrigenomics:

Generation and use of data on toxicogenomic studies, derived classification algorithms and dose response relationships for a high number of compounds to identify toxicological key pathways and their correlation with biochemical data ►⌺ Relationships between gene, protein or metabolite expression pattern alterations with adverse or beneficial effects on human health ►⌺ Establish a harmonized, large-scaled, well designed public toxicogenomic database with information on gene expression and toxicological parameters ►⌺ Perform well designed experimental studies with volunteers under controlled dietary regimens to perform adequate nutritional studies (nutrigenomic studies) ►⌺ Standardization of the application of the profiling techniques. ►⌺

Concluding Remarks

2.5 Concluding Remarks At present, the above discussed novel methodologies are of great promise for prioritization and decision making in the process of risk assessment. However, these techniques are not yet able to replace classical methods. In silico methods such as QSAR and PBBK models are most advanced in terms of regulatory implications and use, since they may predict in vivo dose–response curves for toxicity. In view of the huge datasets generated interpretation becomes the major task. Therefore, hypothesis – and purpose-driven choice of methodology is essential, together with a stringent validation referring to classical methods. Further development of these largely promising novel technologies has to be based on comprehensive long-time research requiring sustained support.

37

38

3

Contributions

3.1 Visions on Toxicity Testing in the 21st Century: �Reflections on a Strategy Document of the US€�National Research Council Marcel Leist1,2, Thomas Hartung2, and Pierluigi Nicotera3

Abstract Toxicology faces enormous challenges in a world in which we are exposed to thousands of chemicals and especially millions of mixtures thereof. Radically new approaches to this problem need to be developed. A milestone in this direction is the vision of the US National Research Council (NRC) “Toxicity testing in the 21st century: A Vision and a Strategy”. Currently, an alliance formed by the National Toxicology Program (NTP) and the Chemical Genomics Centre (NCGC) of the National Institutes of Health (NIH) and the Computational Toxicology Centre (NCCT) of the Environmental Protection Agency (EPA) is testing whether this new strategy can realistically form the basis of future public health decisions. The vision requires a radical paradigm shift in the approach to safety assessments, and turns the traditional procedures upside down. Where animal experiments used to be the most important technology, the future is seen in the strength of in vitro and in silico approaches based on human material. Today’s toxicity testing starts with an initial black box screen on animals, sometimes followed by mechanistic studies, while the new vision approaches hazard assessment bottom-up. The procedure would begin with in vitro tests to define the affected pathways. To fill remaining gaps of knowledge, limited and targeted testing in animals would then be performed as a possible second step.

1

Correspondence to: Marcel Leist, University of Konstanz, Doerenkamp-Zbinden Chair for Alternative In Vitro Methods, D-78457 Konstanz, Germany, Tel: +╃49 7531 885037, Fax: +╃49 7531 885039, [email protected].

2

Johns Hopkins University, School of Environmental Health Sciences, Centre for Alternatives to Animal Testing, 615╃N. Wolfe St., W7035 Baltimore, MD, 21205, USA.

3

University of Leicester, MRC Toxicology Unit, Lancaster Road, Leicester LE1 9HN, UK / Scientific director of the German Centre of Neurodegenerative Disease (DZNE), LudwigErhard-Allee€2, 53175 Bonn, [email protected].

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

3.1.1 Introduction Toxicology is an exciting discipline that brings together specialists from vastly different areas. A picture that springs to mind is one of a body with three souls [1]: As for many other medical disciplines, one important aspect of toxicology is that its procedures and the specific knowledge are applied like a craft. In this first domain, which contains the translational aspects of the science, careful documentation, process-optimization and routine are of high importance. A second focus area is regulatory toxicology at the interface of industry and authorities, involved in setting and meeting guidelines and providing a basis for political decisions and legal requirements concerned with environmental health and consumer safety. The third soul of toxicology is its scientific basis. This area is concerned with the generation of new knowledge and is linked to other natural sciences. It appears as if the three souls have lost connection over the past decades and that a large part of toxicology became frozen in time, using and accepting the same old animal models again and again, often without stringent examination of their validity [2, 3]. In this situation, the overall discipline is strongly driven by the demand for protocols and data for regulatory action. Only few resources remain for generation of fresh, fundamental toxicological knowledge and scientific output. A lot of the remaining scientific progress of toxicology depends strongly on import from other biomedical fields [4]. The consequences are reduced innovation, followed by a loss of attractiveness of the field for talented workers, and finally an inability to meet newly arising challenges. Such new challenges are for instance the safety evaluation of compound mixtures in food or the environment, of biologics, of nanomaterials, of irradiated or genetically-altered food or of mobile phone radiation. None of them can be tackled adequately by classical animal-based methods. Huge challenges lie also in finding more predictive systems for developmental neurotoxicants [5] or non-genotoxic carcinogens [6, 7, and 8]. However, these current problems are also a huge opportunity for the future, to bring the domains of toxicology together again, to link the field more closely to progress in other areas of biomedical sciences, and to give it a new basis [1]. There is a vast body of evidence from mechanistic toxicology studies suggesting that the thousands of known noxious substances act by interfering with only a few (i.╃e. dozens) regulatory pathways of cells [9]. For instance, a variety of hepatotoxins act by enhancing TNF-induced apoptosis [10], various compounds are neurotoxic because of perturbed cellular calcium metabolism [11, 12, and 13], various immunotoxicants affect the cell cycle of lymphocytes via the Ah receptor [14], endocrine disrupters often bind to steroid receptors [15, 16, and 17], and interaction with the P450 system has been extensively examined as the basis of the toxicity of thousands of diverse compounds [18, 19, and 20]. Information on such affected pathways can nowadays be obtained rapidly by high-throughput screening systems using human cells, and then be further analyzed with modern methods of systems biology and bioinformatics. Such a new approach has recently been suggested by the US National Toxicol-

39

40

Contributions

ogy Roadmap “A national toxicology program for the 21st century” (http://ntp. niehs.nih.gov/files/NTPrdmp.pdf) and the NRC [9], and testing of its feasibility by major safety authorities has begun [21].

3.1.2 A New Vision of Toxicity Testing The NRC, the most prestigious scientific council of the USA, was funded some years ago by the EPA and the NTP to develop a long range vision and implementation strategy for modern toxicology (Fig.╃1; see list at the end of this text for abbreviations). The heart of the new vision of toxicity testing proposed by the NRC is the concept of “toxicity pathways” (Fig.╃2). As shown in Figure 2â•›a, the vision takes its starting point from the presumption that most toxicants will eventually act by interfering with pivotal cellular structures and regulatory pathways. This would result in a limited number of toxicity pathways (e.╃g. disturbed calcium regulation, triggering of apoptosis, cell cycle derangement …). It is then further presumed that knowledge of these pathways and knowledge of the action of toxicants on these pathways would allow predictions of toxicity on the level of the whole organism. This is a simple concept, but with huge implications. The practical consequence for toxicity testing would be no less than a turn-around of the currently used process from top to bottom (Fig.╃2â•›b, Fig.╃3). Currently, animal models are frequently used as black box system to identify problematic compounds. Only in few cases (e.╃g. for valuable compounds, or compounds leading to high human exposure) will toxicity data ever be followed up to understand why a compound is toxic and whether the effect is relevant to humans. The vision laid out by the NRC suggests a radical paradigm shift. The

Figure 1:€Parents and godfathers of the vision. At the end of 2007, the NRC published its report after the initial trigger by two important regulatory agencies a couple of years earlier. A pivotal strength of the procedure, compared to similar approaches, is the early involvement of and support by major stakeholders (academia, regulators, industry) and the coupling of a vision to an implementation strategy.

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

start of a safety evaluation would begin with the chemical properties of a compound and then proceed to the biological characterization in multiple in vitro systems (Fig.╃2). Bioinformatic procedures would transform this information into a hazard estimate. This procedure would prioritize a few compounds (e.╃g. unclear hazard estimate or biokinetic predictions and high exposure) for further animal testing, and be sufficient on its own to eliminate many compounds and mixtures. This would be a revolutionary approach if it was actually applied in practice, but is the idea really new? There is a saying that “success and good news have many parents, uncles, godfathers╃…, once they are apparent to everybody, while failure is an orphan, with an ugly mother-in-law, at best”. Accordingly,

Figure 2:€Approach to toxicity testing suggested by the NRC (USA). (A) Toxicity pathways lie at the heart of the approach of hazard evaluation and are examined with the help of in vitro models. Gaps of knowledge and uncertainties are addressed by targeted animal testing. Risk estimates are then based on the hazard evaluation, exposure data and the risk context. For evaluation of this approach, a number of important questions need to be addressed. (B) The new vision follows a bottom-up approach in contrast to the present approach.

41

42

Contributions

many will claim now, that they have worked on the same idea as promoted by the NRC [9] for years, or even decades. It is indeed true that in vitro toxicology is a firmly established and well-organized discipline which has produced similar ideas and also already some applications in the regulatory field and in applied research [22, 23, 24, 25, 26, and 27]. There has been a continuously good output over decades from laboratories interested in mechanistic toxicology, and many companies and regulatory toxicologists are deeply involved in the development of alternative methods, such as in silico and in vitro screens. For instance, G. Zbinden showed already 20 years ago the trend towards mechanistic models and the necessity for international regulators to follow this line and incorporate the ideas into the regulatory context [28, 29]. So again: What’s new? It is the way it is done. The determination to “think big”, the broad basis, the wide scope, the involvement of many stakeholders and drive by major authorities, the generation of open interfaces to the interested public (including accessible data bases) and the coupling of the vision to an implementation strategy that is robust enough to have a chance for success.

3.1.3 Testing the Vision A condensed overview of the initial phases of the implementation strategy was given recently by the involved US authorities [21]. Here, we want to outline the essential features (Fig.╃3), mainly as stimulation for the interested European readers and to provide a basis for potential interactions. Presently, the implementation strate5gy is being explored by three major players on the basis of a memorandum of understanding clarifying the roles and duties (Fig.╃2). One of the contributing institutions is the NCCT [30] under the roof of the EPA. The two other players are funded by the NIH: The NCGC contributes its screening infrastructure (robots, compound management, highthroughput measurement devices) and performs quantitative high-throughput screens (qHTS). The final player is the NTP which contributes with classical toxicological expertise, non-rodent animal models (for instance zebra fish embryos) and especially a screen programme for about 300 selected compounds run through hundreds of assays (Fig.╃3).

3.1.4 Steps toward a New Toxicology What happens with the data obtained? Here the idea of open public interfaces and generally accessible databases comes into play. This sounds like a relatively trivial issue, but it should by no means be underestimated. We all have witnessed how the free internet availability of literature references via NCBI’s PubMed has revolutionized the way scientific information is retrieved, and how Google has entirely changed the way general information is retrieved. Toxicology urgently needs a parallel effort. At present a number of interconnected databases is being developed (Fig.╃4) and expanded, but their user-

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

Figure 3:€Testing the feasibility of a new way of toxicity testing and reduction to practice. The vision and theoretical strategy were laid down by the NRC. Top: the paradigm shift according to this vision is outlined. Centre and bottom: In order to test whether the vision holds in the face of reality three major players agreed in a memorandum of understanding on a common test strategy. The three players are institutes and programmes of the EPA and the NIH, and contribute expertise as indicated.

friendliness is far from perfect. Much of the screening data will eventually end up in PubChem, which already harbours over 900 bioassays and will be fed directly with data from screens of the NCGC. Some of this data will also appear in classical journal publications, but in order to understand such publications one will have to be able to retrieve information from PubChem. An example is a publication describing the test of the cytotoxicity of about 1400 compounds on 13 different cell lines [31]. The publication compiles data from different screens and extracts information from comparisons of cell lines and compounds. However, the compounds themselves and the original data from the screens will have to be extracted from the database [32] – and, conversely, the database information may eventually be used again for new analyses and journal publications. DSSTox is another database with generally richer datasets than PubChem. Here, reviewed and quality-controlled classical toxicological information is added to the compounds. The DSSTox website provides a public forum for publishing downloadable, structure-searchable, standardized chemical structure files associated with toxicity data [33].

43

44

Contributions

Figure 4:€Databases to help computational toxicology and in vitro toxicity testing. Different interlinked databases allow public access to cpd (compound) and assay information. RefToxDB is presently not publicly accessible. Links to the other databases are indicated.

One important input for DSSTox is ToxCast [34]. This programme was designed to predict toxicity pathways and to characterize the hazard of a relatively small learning set of tool compounds (n=╃300) run through 400 different assays. ToxCast™ signatures will be evaluated by their ability to predict outcomes from existing mammalian toxicity testing and to identify toxicity pathways that are relevant to human health effects. High added value will be generated when this is linked to ToxRefDB, a database designed to contain data from huge historical animal testing efforts, including compounds selected for Toxcast. ToxRefDB is integrated into a more comprehensive data management system developed by NCCT called ACToR (Aggregated Computational Toxicology Resource) that manages the large-scale datasets of ToxCast™. The above databases are mainly compound and assay-focussed. For hazard assessment further dimensions are essential. We need to understand how the human body handles a given chemical, what the important toxicity pathways are, and how we deal with human genetic variability (Fig.╃5). An in vitro test strategy requires more than the test system and data analysis. It cannot function without a prediction model to make use of the data. This also applies to complex integrated test strategies, and here pharmacokinetic information and dose–response modelling become highly important issues for the construction of prediction models. During establishment of the test strategy, variations of the following problem are frequently encountered: “Pesticide X induces signs of toxicity (e.╃g. muscle paralysis) at a dose of Y mg/kg. Which concentrations should induce a positive readout in a corresponding in vitro toxicity test system in order to consider the test system relevant? In other words, which in vitro cytotoxic concentration would one predict from the in vivo data? Which would be a biologically relevant prediction model for in vitro concentrations, when in vivo doses are given?” Databases that translate such information are

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

Figure 5:€Identification of toxicity pathways and in vitro–in vivo extrapolation. To support the overall project to test a new vision for toxicity testing, compound information alone is not sufficient. An important accessory programme is the initiative to model pharmacokinetics and in vitro–in vivo extrapolations and dose–response relationships with the help of physiologicallybased pharmacokinetic modelling (PBPK). Another initiative makes use of the HapMap project. This is a multi-country effort to identify and catalogue genetic similarities and differences in human beings. Using this information, researchers will be able to find genes that affect health and individual responses to environmental factors. The tool library used by the NCGC (about 2800 compounds (cps) will be screened on cell lines with known haplotypes (i.╃e. known genetic variation). These compounds will also be tested within the HSP on transgenic mice and cells derived from them. This is expected to yield information on which genes have a major impact on adverse effects of environmental agents. The biological approach is complemented by the Virtual Liver Project, which plans to develop a database and algorithms able to predict liver toxicity and forms of liver carcinogenicity.

urgently required. During the application of an established test strategy to unknown compounds, a related problem occurs: “compound A triggers toxicity in vitro at concentrations higher than B micromolar. How much of the compound can be ingested safely?” PBPK databases will need to contain all the essential data on metabolism, protein binding and barrier permeation of compounds, in addition to suitable algorithms that will allow at least rough conversions of in vitro concentrations to in vivo doses. The setup of these databases is still in a very early phase. The HapMap project is attempting to map and understand human haplotypes (i.╃e. variants of a given gene that are found in different proportions of the population). This project can also be linked to toxicity testing strategies. Interesting information is expected from testing a set of 2800 compounds on human cell lines with known haplovariants. In a parallel approach taken by the host susceptibility programme (HSP), compounds are compared through a large number of transgenic mouse models and derived cell lines. These two

45

46

Contributions

programmes can contribute to the clarification of toxicity pathways and susceptibility genes and their respective effects. A different approach is taken by the EPA with the VirtualLiver project, which attempts to model the most important target organ of toxicity in its interaction with compounds. On its website it is stated ambitiously that “…the 5-year plan for the Virtual Liver Project is to develop a knowledgebase for qualitatively describing species-specific toxicity pathways due to exposure to chemicals, and to develop a virtual liver tissue that lays the foundation for quantitatively predicting the risk of non-genotoxic neoplastic lesions due to activation of certain genetic regulatory elements (i.╃e., nuclear receptors and other transcription factors) in humans”.

3.1.5 The Precautionary Principle Toxicological studies are designed to provide a basis for consumer protection by identifying hazardous compounds. The test systems will necessarily also produce false positives (compounds that are not hazardous to humans, but look hazardous in the test system) and false negatives (compounds that are hazardous to humans, but are not correctly identified by the test system) [1]. The latter class has been of particular concern. Therefore, the test systems and prediction models were tuned in a way to minimize this class as far as possible at cost of a largely increased class of false positives. This tuning of toxicity testing is called the precautionary principle and is one of the corner stones of toxicological thinking. Major changes in toxicity testing will always provoke fears in the public, in regulatory authorities and in other stakeholders that the precautionary principle may be violated. Therefore, one of the major tasks of the implementation strategy of a new vision is to address these worries and to generate confidence that the safety level will not be compromised. A first important issue to be considered is the understanding of the concept of “applicability domains”. All toxicological methods are not generally applicable, but have applicability domains, i.╃e. limitations as to for which part of the chemical universe their predictive value has been shown. For instance, “drugs” or “pesticides” are typical applicability domains. Test guidelines, legislation, authorities, and the questions asked are vastly different in these areas. Other applicability domains would be industrial chemicals, cosmetics, biologics, and food additives. The concept was taken from the field of (Q)SAR and translated to test methods first in ECVAM’s Modular Approach [35]. The vision discussed here applies mainly to the domain of environmental agents (i.╃e. pesticides or chemicals with relevant human exposure, for instance through the food chain). This is also reflected by different risk context scenarios that are explored and that are an important feature of the implementation strategy. Whether it can be translated to other domains without compromising the precautionary principle is one of the open questions for the future, and will certainly involve additional stakeholders. The key issue to consider is, what new methods of toxicity testing should be used for comparison? Can we expect a 100â•›% failsafe method? We know that

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

present animal-based testing does not guarantee absolute safety [36]. This is an obvious fact that is often forgotten in discussions on new approaches. New alternative tests are validated stringently [37], while many animal tests have never been formally validated [2, 3]. Even studies that address the question whether animal studies are of any toxicological use at all with respect to human safety are extremely scarce [38]. At least some doubt comes from the extreme variation of results when one and the same compound is used in different animal studies, and from the partially poor correlations between one species and the next, for instance between mouse and rat [2]. Thus, a fair and honest approach to alternative testing strategies would imply that one does not require a 100╛% safety level, but rather a safety level that is in the range of (or at least as good as) that of standard animal experimentation. This also implies that showing the one or other insufficiency of in vitro approaches, and of the cell culture technology in particular [39], does not invalidate the usefulness of a technology. The strengths and weaknesses of animal and non-animal test approaches will just lie in different areas. Only looking at the comparison of the overall performance with regards to human safety will allow a reasonable judgement of the value. In this context it is important to reconsider what the ultimate aim of the precautionary principle is: Human safety. Sometimes, more exact knowledge on toxicity does not contribute to higher safety, but precautionary measures, e.╃g. regarding the transport of chemicals, take this function. Extensive animal testing will often generate redundant information, and, in addition, we accumulate more false-positive results [40]. To trigger a certain and adequate set of measures, sometimes limited in vitro and in silico information may be sufficient [41].

3.1.6 The European Side The 3R principle (reduce, replace, refine), which already envisaged a combination of in vitro and in vivo approaches in the 1950’s was originally developed in Europe [42]. Is European toxicology less visionary now? What could be learned from the NIH/EPA approach? Europe has a different, more diversified, but also more fragmented political landscape and different countries have found their own ways. For instance, the MRC in the UK decided almost 10 years ago to restructure its entire central toxicology institute in Leicester. Already at that time the guiding principle was to promote research on bottom-up toxicology, taking its starting point from understanding toxicity pathways and common processes like apoptosis. In Germany, ZEBET, a federal institute, was established nearly 20 years ago to develop, test, and validate alternative methods to animal experimentation, and has been a major driver in the design of the first OECD toxicity testing guidelines based on in vitro testing only (for phototoxicity). On the EU level, the first major driver for a new vision of toxicity testing comes from a different applicability domain than in the US – from cosmetics products. Here, the vision was immediately reduced to practice by law. The 7th amendment of the Cosmetics Directive set a strict timeline, finally banning the

47

48

Contributions

use of cosmetics if their ingredients were tested on animals. The implementation strategy implies that industry will need to establish animal-free test methods or change the business model. This is an interesting test case for the whole world to follow. In order to guide the development of methods and to ensure their validity, the EU founded ECVAM in 1992, a research institute entirely devoted to the validation of alternative methods. Now corresponding agencies and institutes are also found in the USA, Japan and other countries [43]. ECVAM harbours also an important database, DB-Alm, which is a high-quality source for in vitro test protocols and alternative methods [44]. At present, the major driver for a rethinking of toxicity testing in Europe is the REACH legislation [45]. Over the last two years a revolution of the concept of how safety of chemicals is evaluated took place in Europe in this context: While in the past a (tonnage-triggered) set of mainly animal tests had to be provided in a tick-box manner, now (for both existing and new chemicals) integrated testing strategies making use of all information opportunities must be applied. A group of more than 200 experts from regulatory bodies, European Commission and industry developed these strategies (http://ecb.jrc.it/reach/ rip/) in REACH Implementation Project 3.3 under the coordination of CEFIC and ECVAM. New and existing approaches were combined in order to optimize information generation for REACH, making use also of in vitro, in silico and read-across data from similar compounds. This law is at the basis of an enormous effort to re-evaluate about 30,000 chemicals already marketed in the EU and generates major financial and logistic pressures in addition to the ethical problem of the requirement for millions or tens of millions of animals to fulfil the test requirements. Faced with this enormous challenge, industry and the European Commission formed a partnership in the form of the EPAA [46], which is working on new visions and implementation strategies. In parallel, the Directorate General of Research (DG Research) is heavily funding research consortia within the sixth and seventh framework programme to develop new in vitro test systems and strategies. The key feature of REACH in the context of new visions of toxicology is that it has been influenced by an important postulate of the European animal legislation from 1986 (Directive 609/86), which can be summarized as “when alternatives to animal experimentation are available, they must be used”; “more of these alternatives need to be developed”. More precisely, article 7.╃2.╃states: “An experiment shall not be performed if another scientifically satisfactory method of obtaining the result sought, not entailing the use of an animal, is reasonably and practicably available.” And in Article 23.1.: “The Commission and Member States should encourage research into the development and validation of alternative techniques which could provide the same level of information as that obtained in experiments using animals but which involve fewer animals or which entail less painful procedures, and shall

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

take such other steps as they consider appropriate to encourage research in this field.” Article 1.1 of the REACH regulation reads: “Aim and scope 1.╃The purpose of this regulation is to ensure a high level of protection of human health and the environment, including the promotion of alternative methods for assessment of hazards of substances, as well as the free circulation of substances on the internal market while enhancing competitiveness and innovation.” REACH is thus the first major legislation in the huge application domain of industrial chemicals that gives some space for “intelligent test strategies”, readacross between different information domains, the use of validated alternative methods, and also the use of non-validated alternative methods at least in a preliminary hazard evaluation [40, 47, 48, and 49]. Nevertheless, REACH will still require millions of animal experiments, and the free space given by legislation is still far away from the vision of toxicity testing in the 21st century laid out by the NRC. Whether this heavy animal testing effort will lead to a parallel increase of human safety with respect to chemicals already on the market has been doubted [50]. Thus, a new movement is presently forming that focuses on a more stringent validation of animal models and promotes an evidence-based toxicology, in which the best given test strategy is used instead of stringent adherence to only historically-legitimated animal models [51, 52, and 3]. A multitude of bottom-up movements are emerging at present, which include for instance ASAT, the NTC, and InViTech, to name a few.

3.1.7 Tasks Ahead We have tried here to survey exciting new developments and movements. Proofof-concept studies need to clearly demonstrate the predictive power gained from these new approaches. More researchers need to be attracted to join the efforts, and regulatory authorities must show a willingness to embrace the new approaches as they gain scientific acceptance. The next few years should witness the early fruits of such efforts, but the paradigm shift will require a long-term investment and commitment to reach full potential. In a brief last paragraph we want to summarize critical issues to be addressed by the scientific community, granting agencies and authorities: Databases: These require a change of attitude as they move more into the centre of the process instead of being a final end product. It often appears from the lack of care and the limited analysis and accessibility options that they are more or less considered a tiresome duty to those who have generated the data. It is not sufficient to simply “dump” the data somewhere, even if they are flexibly retriev-

49

50

Contributions

able and adequately quality-controlled. The science of visualization of data and especially visualization of large complex data spaces needs to be applied much more strongly here. The importance of this process and the need for users and developers to hold a constant dialogue during the design of analysis and visualization algorithms are still heavily underestimated. Another important issue is the cross-linking of information. For instance a number of databases have been generated in Europe on in vitro acute toxicity data. For instance the MEIC database (see list of abbreviations) covers a very large number of toxicity assays, and the Halle Registry [53] over 300 in vitro–in vivo comparisons, but cross-linking is limited, as is general and easy accessibility. Libraries: To fill the databases with information, real compounds are required. Especially in the proof-of-principle phase of testing, the selection of these compound libraries plays an important role and contributes to the success, the validity, and the general acceptance of validation efforts. Not only will the right “theoretical” composition of the libraries be of high importance, but also the physical composition and availability. Compound stability and purity, the general accessibility and continuous quality control are non-trivial issues, especially in the field of environmental chemicals, industrial chemicals and pesticides. Here, one solution to be considered is chemical reference laboratories making defined library copies available to others. This has been conceptualized on the European level in form of CORRELATE [54] and should also be considered as a great opportunity in the context of REACH (see [3]). Process: Many areas of basic biomedical research have experienced bumpy rides with periods of hype and disappointment. Toxicology has a continuous high responsibility for human safety and cannot, even transiently, simply drop the precautionary principle. However, it can ask critical questions on how it should best be applied in different situations, exposure scenarios and applicability domains. This provides a basis for a continuous, long-term effort to let toxicology evolve to a higher level than now. This process needs essentially to be global and involve all stakeholders [43]. Despite all enthusiasm, rapid success is not to be expected and all hype should rather be avoided as initial setbacks are likely to happen. This has to be accepted in the strategy. However, the determination to move on needs to be strong enough to attack problems with the right critical mass and impact right from the beginning and as they emerge. Chances: The process of putting regulatory toxicology and the process of toxicity testing on a more mechanistic basis provides a chance for toxicology to evolve as a discipline, and also contribute general biomedical knowledge. This closes the circle started at the beginning of this article. In the past, toxicology had the chance to promote the advance of biomedical sciences in general, for instance by discovering and driving the fields of apoptosis, toxinology or stress response.

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

However, these opportunities were not seized, and other sciences drove these fields instead. Now, new chances are arising, possibly in the fields of systems biology, DNA repair or pathological aging. Possibly also in the fields of chemical genetics and the introduction of chemical screens to non-pharmaceutical areas. To grasp any of these chances, it is important to dare to take the lead and not to lose touch with basic science. Application of HTS or qHTS as described above sounds fancy, but it is at the moment only a technology, not a science. This technology has brought a lot of disappointment in drug discovery, which one can learn from. It will be important in the future to avoid the mistakes of the past, and to incorporate the “technology” into a robust “scientific concept”, which combines brain with the muscles. Fairness and honesty: An unbiased approach, based on scientific evidence only, will be the best way to find solutions acceptable for all stakeholders. Presently one may wonder what the scientific basis for some animal experiments is. The lousy output and poor information from acute toxicity studies with lethality endpoint has been criticized for a long time [55, 56, 57, and 58], and now, at least in the application domain of drugs, there seems to be a broad agreement that the assay could easily have been abolished [59]. Why hasn’t this already happened? A similar situation can be found for two-generation studies for developmental toxicity testing, where the second generation apparently does not contribute with significant information [60]. Here, non-scientific reasons seem to prevail, and the argument may be expanded to more examples of animal toxicity testing [2]. It is also a sign of poor science that so little pharmacokinetic information is available from acute toxicity tests. This makes the present in vivo–in vitro comparisons very difficult and thus prevents a potential substitution of animal experiments by alternative methods. To be honest, the field of alternative methods also needs to look at obvious weaknesses of its own methods and establish itself as an academic discipline [61]. Many assays are still just as much black box systems as animal experiments and pharmacokinetic information has been terribly neglected. If all sides focus on a vision of best science for best toxicology, then the progress is sure.

Acknowledgements We gratefully acknowledge the valuable input and proofreading of S. Kadereit and S. Schildknecht, the secretarial help by B. Schanze and grant support by the Doerenkamp-Zbinden Foundation, the State of Baden-Württemberg and the European Union (ESNATS).

51

52

Contributions

Abbreviations and Glossary of Terms (Q)SAR: (quantitative) structure–activity relationship. A way to correlate chemical structural information with biological endpoints (e.╃g. receptor binding or toxicity). ASAT: EU initiative on “assuring safety without animal testing”; http://www. asat-initiative.eu. CEFIC: European Chemical Industry Council. DG RESEARCH: Directorate General of the EU for research (http://ec.europa. eu/dgs/research/index_en.html). In national terms this would correspond to the Ministry for Research. It is the major funding body for the large EU framework programme research projects. ECVAM: European Centre for the Validation of Alternative Methods. EPA: Environmental Protection Agency (of the USA). EPAA: European Partnership (of the European Commission and industry organisations) for Alternative Approaches to Animal Testing. InViTech: the EU high-throughput-high content centre; http://bms.jrc. ec.europa.eu/projects/InViTech.htm. MEIC: In 1989, Björn Ekwall and the Scandinavian Society for Cell Toxicology organised the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC). Fifty compounds were evaluated in dozens of cytotoxicity assays and the results were published in a series of papers in 1998 in ATLA. MRC: Medical Research Council of the UK; runs own research institutes, e.╃g. MRC Toxicology Unit in Leicester. NCBI: National Center for Biotechnology Information, a division of the National Library of Medicine (NLM) at the NIH. NCCT: National Center for Computational Toxicology (of the USA). NCGC: NIH Chemical Genomics Centre . NIH: National Institutes of Health (of the USA). NRC: National Research Council (of the USA), the principal operating agency of the National Academies of Sciences of the USA, the National Academy of Engineering and the Institute of Medicine. The National Academy of Sciences is known by many as publisher of the Proceedings of the National Academy of Sciences, USA. NTC: the Netherlands Toxicogenomics Centre; http://toxicogenomics.nl. NTP: National Toxicology Program (of the USA). PubChem: PubChem provides information on the biological activities of small molecules. It is a component of the NIH’s Molecular Libraries Roadmap Initiative. PubMed: Biomedical literature database at the NCBI. qHTS: quantitative high throughput screening. This technology allows the testing of thousands to ten-thousands of compounds in a single experiment. This compound number is 1–2 orders of magnitude lower than what would be used in industrial drug discovery screens. However, the data output is relatively rich, as compounds are screened at about 10 different concentrations and the shape of the resultant response curves yields additional information.

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

REACH: European Regulation (EC) No.╃1907/2006 on the Registration, Evaluation, Authorisation and Restriction of Chemicals, which entered into force on the 1st of June 2007. ZEBET: Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergänzungsmethoden zum Tierversuch am BfR; Centre for Documentation and Evaluation of Alternatives to Animal Experiments at the BfR (Federal Institute for Risk Assessment).

References 1. Leist, M., Hartung T., Nicotera P. (2008) The dawning of a new age of toxicology. ALTEX 25, 103–114. 2. Hartung, T. (2008â•›a). Food for thought… on animal tests. ALTEX 25, 3–10. 3. Hartung, T. (2008â•›b). Food for thought…on the evolution of toxicology and phasing out of animal testing. ALTEX 25, (this issue). 4. Lotti, M., Nicotera, P. (2002). Toxicology: a risky business. Nature 416, 481. 5. Grandjean, P., Landrigan, P.╃J. (2006). Developmental neurotoxicity of industrial chemicals. Lancet 368, 2167–2178. 6. Ashby, J. (1996). Alternatives to the 2-species bioassay for the identification of potential Â�human carcinogens. Hum. Exp. Toxicol. 15, 183–202. 7. Trosko, J.╃E., Upham, B.╃L. (2005). The emperor wears no clothes in the field of carcinogen risk assessment: ignored concepts in cancer risk assessment. Mutagenesis 20, 81–92. 8. Williams, G.╃M., Whysner, J. (1996). Epigenetic carcinogens: evaluation and risk assessment. Exp. Toxicol. Pathol. 48, 189–195. 9. NRC (2007). Committee on Toxicity Testing and Assessment of Environmental Agents, Â�National Research Council. Toxicity Testing in the 21st Century: A Vision and a Strategy. The national academies press. http://www.nap.edu/ catalog.php?record_id=╃11970. 10. Leist, M., Gantner, F., Naumann, H. et al. (1997). Tumor necrosis factorinduced apoptosis during the poisoning of mice with hepatotoxins. Gastroenterology 112, 923–934. 11. Nicotera, P. (1996). The Gerhard Zbinden Memorial Lecture. Alteration of cell signalling in chemical toxicity. Arch. Toxicol. 18 Suppl., 3–11. 12. Orrenius, S., Zhivotovsky, B., Nicotera, P. (2003). Regulation of cell death: the calcium-apoptosis link. Nat. Rev. Mol. Cell Biol. 4, 552–565. 13. Leist, M, Nicotera, P. (1998). Calcium and neuronal death. Rev. Physiol. Biochem. Pharmacol. 132, 79–125. 14. Kolluri, S.╃K., Weiss, C., Koff, A., Göttlicher, M. (1999). p27(Kip1) induction and inhibition of proliferation by the intracellular Ah receptor in developing thymus and hepatoma cells. Genes Dev.╃13, 1742–1753. 15. Vedani, A., Dobler, M., Lill, M.╃A. (2005). Virtual test kits for predicting harmful effects triggered by drugs and chemicals mediated by specific proteins. ALTEX 22, 123–134.

53

54

Contributions

16. Vedani, A., Dobler, M., Spreafico, M. et al. (2007). VirtualToxLab – in silico prediction of the toxic potential of drugs and environmental chemicals: evaluation status and internet Â�access protocol. ALTEX 24, 153–161. 17. Waring, R.╃H., Harris, R.╃M. (2005). Endocrine disrupters: a human risk? Mol. Cell Endocrinol. 244, 2–9. 18. Krebsfaenger, N., Mürdter, T.╃E., Zanger, U.╃M. (2003). V79 Chinese hamster cells genetically engineered for polymorphic cytochrome P450 2D6 and their predictive value for humans. ALTEX 20, 143–154. 19. Nussler, A.╃K., Wang, A., Neuhaus, P. et al. (2001). The suitability of hepatocyte culture models to study various aspects of drug metabolism. ALTEX 18, 91–101. 20. Ioannides, C., Lewis, D.╃F. (2004). Cytochromes P450 in the bioactivation of chemicals. Curr. Top. Med. Chem. 4, 1767–1788. 21. Collins, F.╃S., Gray, G.╃M., Bucher, J.╃R. (2008). Toxicology. Transforming environmental health protection. Science 319, 906–907. 22. Andersen, M.╃E., Dennison, J.╃E., Thomas, R.╃S. and Conolly, R.╃B. (2005). New directions in incidence-dose modeling. Trends Biotechnol. 23(3), 122– 127. 23. Hendriksen, C.╃F. (2006). Towards eliminating the use of animals for regulatory required vaccine quality control. ALTEX 23, 187–190. 24. Gruber, F.╃P., Hartung, T. (2004). Alternatives to animal experimentation in basic research. ALTEX 21 Suppl. 1, 3–31. 25. Hartung, T. (2001). Three Rs potential in the development and quality control of pharmaceuticals. ALTEX 18 Suppl. 1, 3–13. 26. Seiler, A., Buesen, R., Hayess, K. et al. (2006). Current status of the embryonic stem cell test: the use of recent advances in the field of stem cell technology and gene expression analysis. ALTEX 23 Suppl., 393–399. 27. Whitlow, S., Bürgin, H., Clemann, N. (2007). The embryonic stem cell test for the early selection of pharmaceutical compounds. ALTEX 24, 3–7. 28. Zbinden, G. (1988). Reduction and replacement of laboratory animals in toxicological testing and research. Interim report 1984–1987.╃Biomed. Environ. Sci.╃1, 90–100. 29. Zbinden, G. (1990). Alternatives to animal experimentation: developing invitro methods and changing legislation. Trends Pharmacol. Sci.╃11, 104–107. 30. Kavlock, R.╃J., Ankley, G., Blancato, J. et al. (2007). Computational Toxicology – A State of the Science Mini Review. ToxSci. Advance Access published on December 7, 2007.╃doi:10â•›1093/toxsci/kfm297. 31. Xia, M., Huang, R., Witt, K.╃L. et al. (2008â•›a). Compound cytotoxicity profiling using quantitative high-throughput screening. Environ. Health Perspect. 116, 284–291. 32. Xia, M. et al. (2008â•›b). The 1408 compounds of Environ Health Perspect 116, 284.╃http://www.epa.gov/ncct/dsstox/sdf_ntphts.html#DownloadTable. 33. Houck, K., Dix, D., Judson, R. et al. (2008). DSSTox EPA ToxCast High Throughput Screening Testing Chemicals Structure-Index File: SDF File and Documentation, Updated version: TOXCST_v2b_320_08Feb2008, http:// www.epa.gov/ncct/dsstox/sdf_toxcst.html.

Visions on Toxicity Testing in the 21st Century: Reflections on a Strategy Document of the US€�National Research Council

34. Dix, D.╃J., Houck, K.╃A., Martin, M.╃T. et al. (2007). The ToxCast program for prioritizing toxicity testing of environmental chemicals. Tox. Sci.╃95, 5–12. 35. Hartung, T., Bremer, S., Casati, S. (2004). A Modular Approach to the ECVAM Principles on Test Validity. ATLA 32, 467–472. 36. Zbinden, G. (1991). Predictive value of animal studies in toxicology. Regul. Toxicol. Pharmacol. 14, 167–177. 37. Hartung, T. (2007â•›a). Food for thought on… validation. ALTEX 24, 67–73. 38. Mathews, R.╃A.╃J. (2008). Medical progress depends on animal models – doesn’t it? J.╃R. Soc. Med.╃101, 95–98. 39. Hartung, T. (2007â•›b). Food for thought … on cell culture. ALTEX 24, 143– 147. 40. Bremer, S., Pellizzer, C., Hoffmann, S. et al. (2007). The development of new concepts for assessing reproductive toxicity applicable to large scale toxicological programmes. Curr. Pharm. Des.╃13(29), 3047–3058. 41. Rogers, M.╃D. (2003). Risk analysis under uncertainty, the precautionary principle, and the new EU chemicals strategy. Regul. Toxicol. Pharmacol. 37, 370–381. 42. Russell, W.╃M. and Burch, R.╃L. (1959). The Principles of Humane Experimental Technique. London: Methuen. 43. Bottini, A.╃A., Amcoff, P., Hartung, T. (2007). Food for thought… on globalization. ALTEX 24, 255–261. 44. DB-Alm (2007). Invittox protocol number 101.╃http://ecvam-dbalm.jrc. ec.europa.eu/public_view_doc2.cfm?id=╃6E7E72104B2DEFD6BE979B3B139176C67180BB0BC12CB10496CDA74B54630A05A3291B895581F634. 45. REACH (2006). REACH legislation under directive (EC) No€ 1907/2006, http://eur-lex.europa.eu/JOHtml.do?uri=OJ:L:2006:396:SOM:en:HTML. 46. EPAA (2006). European Partnership to Promote Alternative Approaches to Animal Testing http://ec.europa.eu/enterprise/epaa/conf_2006_presentationvdgraaf_unilever.pdf. 47. Combes, R., Grindon, C., Cronin, M.╃T. et al. (2008). Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation. ATLA 36(1), 45–63. 48. Grindon, C., Combes, R., Cronin, M.╃T. et al. (2008â•›a). An integrated decisiontree testing strategy for repeat dose toxicity with respect to the requirements of the EU REACH legislation. ATLA 36(1), 93–101.╃PMID: 18333717. 49. Grindon, C., Combes, R., Cronin, M.╃T. et al. (2008â•›b). Integrated decisiontree testing strategies for developmental and reproductive toxicity with respect to the requirements of the EU REACH legislation. ATLA 36(1), 65– 80. 50. Knight, A. (2007). Animal experiments scrutinised: systematic reviews demonstrate poor human clinical and toxicological utility. ALTEX 24(4), 320–325. 51. Hoffmann, S., Hartung, T. (2006). Toward an evidence-based toxicology. Hum. Exp. Toxicol. 25, 497–513.

55

56

Contributions

52. Guzelian, P.╃S., Victoroff, M.╃S., Halmes, N.╃C. et al. (2005). Evidence-based toxicology: a comprehensive framework for causation. Hum. Exp. Toxicol. 24, 161–201. 53. Halle, W. (2003). The Registry of Cytotoxicity: toxicity testing in cell cultures to predict acute toxicity (LD50) and to reduce testing in animals. ATLA 31, 89–198. 54. Correlate (2007). http://projects-2007.jrc.ec.europa.eu/show.gx?Object. object_id=PROJECTS0000000003008C51. 55. Tamborini P, Sigg H, Zbinden G. (19990). Acute toxicity testing in the nonlethal dose range: a new approach. Regul Toxicol Pharmacol. 12, 69–87. 56. Zbinden, G. (1986). Invited contribution: acute toxicity testing, public responsibility and scientific challenges. Cell Biol. Toxicol. 2, 325–335. 57. Paget, E. (1983). The LD50 test. Acta Pharmacol. Toxicol. (Copenh.) 52 Suppl.€2, 6–19. 58. Zbinden, G., Flury-Roversi, M. (1981). Significance of the LD50-test for the toxicological evaluation of chemical substances. Arch. Toxicol. 47, 77–99. 59. Robinson, S., Delongeas, J.╃L., Donald, E. et al. (2007). A European pharmaceutical company initiative challenging the regulatory requirement for acute toxicity studies in pharmaceutical drug development. Regulatory Toxicol. Pharmacol. (in press). doi:10â•›1016/j.yrtph.2007.11â•›009 60. Janer, G., Hakkert, B.╃C., Slob, W. et al. (2007). A retrospective analysis of the two-generation study: what is the added value of the second generation? Reprod. Toxicol. 24, 97–102. 61. Leist, M. (2006). What can a chair on alternatives to animal experimentation effectuate? ALTEX 23, 211–213.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

3.2 Safety Assessment of Botanicals and Botanical Â�Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety AuthorityTiered Approach Gerrit Speijers1, Bernard Bottex2, Birgit Dusemund3, Andrea Lugasi4, Â� Jaroslav Toth5, Judith Amberg-Müller6, Corrado Galli7, Vittorio Silano8, and Ivonne M.╃C.╃M. Rietjens9 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 175–185.

Abstract The present paper describes results obtained by testing the EFSA tiered guidance approach for safety assessment of botanicals and botanical preparations intended for use in food supplements. Main conclusions emerging are: (1) Botanical ingredients must be identified by their scientific (binomial) name, in most cases down to the subspecies level or lower. (2) Adequate characterization and description of the botanical parts and preparation methodology used is needed. Safety of a botanical ingredient cannot be assumed only relying on the long-term safe use of other preparations of the same botanical. (3) Because of possible adulterations, misclassifications, replacements or falsifications and restorations, establishment of adequate quality control is necessary. (4) The strength of the evidence underlying concerns over a botanical ingredient should be included in the safety assessment. (5) The matrix effect should be taken into

1

General Health Effects Toxicity and Safety Food, Winterkoning 7, NL-3435 RN Nieuwegein, Netherlands.

2

European Food Safety Authority, Scientific Committee and Advisory Forum Unit, Largo N. Palli 5A, I-43100 Parma, Italy.

3

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, D-14195 Berlin, Germany.

4

National Institute for Food and Nutrition Science, Gyáli út 3/a, H-1097 Budapest, Hungary.

5

Comenius University, Faculty of Pharmacy, Department of Pharmacognosy and Botany, Odbojárov 10, SK-83232 Bratislava, Slovak Republic.

6

Federal Office of Public Health, Food Safety Division, Nutritional and Toxicological Risks Section, Stauffacherstrasse 101, CH-8004 Zuerich, Switzerland.

7

University of Milan, Department of Pharmacological Sciences, Laboratory of Toxicology, Via Balzaretti 9, I-Milan, Italy.

8

National Institute for the Promotion of Migrant’s Health and the Control of Poverty-related diseases, I-Rome, Italy.

9

Correspondence to: Prof. Dr. ir. Ivonne M.╃C.╃M. Rietjens, Wageningen University, Division of Toxicology, Tuinlaan 5, NL-6703 HE Wageningen, The Netherlands, Tel: +╃31 317 483971, Fax: +╃31 317 484931, [email protected].

57

58

Contributions

account in the safety assessment on a case-by-case basis. (6) Adequate data and adequate methods for appropriate exposure assessment are often missing. (7) Safety regulations concerning toxic contaminants have to be complied with. The application of the guidance approach can result in the conclusion that safety can be presumed, that the botanical ingredient is of safety concern or that further data are needed to assess safety.

3.2.1 Introduction In June 2004 the Scientific Committee of the European Food Safety Authority (EFSA) adopted a discussion paper on botanicals and botanical preparations widely used as food supplements and related products [1]. First it was noted that the expanding market volume raises the need for a better characterization of botanicals and botanical preparations, and for harmonization of the scientific assessment of risks from exposure of consumers to these products. In addition EFSA launched, via its Advisory Forum, a questionnaire to the national food safety authorities of the European countries, to get a clearer picture of the extent of the issue in Europe. After responses to the questionnaire were received work was undertaken to (1) analyze the information provided by 25 European countries in response to the questionnaire; (2) prepare a guidance document on how to assess the safety of botanicals and botanical preparations intended for use in food supplements; and (3) establish a list of main categories of botanicals and used parts thereof (compendium) in order to prioritize the botanical preparations to be considered for a safety assessment. The draft safety guidance document and compendia thus prepared were revised following a public consultation and the updated draft guidance document was published on the EFSA website [2]. After the draft guidance document was published, EFSA concluded that it was necessary to test the proposed approach for the safety assessment of botanicals and botanical preparations to be used as ingredients in food supplements with a selected number of cases and to further update the compendium. To this end, on 15 April 2008, an EFSA Scientific Cooperation (ESCO) Working Group, composed of experts identified by EFSA and by the European Member States, was established, in order to (1) enlarge the information basis underlying the compendium of botanicals reported to contain toxic, addictive, psychotropic or other substances of concern; (2) test the proposed tiered approach for the safety assessment of botanicals and botanical preparations with a selected number of botanicals as real-case examples; and (3) provide a report summarizing the outcome of the case studies as well as to advise on the adequacy of the proposed approach for the safety assessment of botanicals and botanical preparations. The present paper aims at providing an overview of the outcome of the second above-mentioned task of this ESCO working group and describes especially the major issues emerging when testing the proposed tiered approach through a selected number of cases. Table 1 summarizes the case studies selected and

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach Table 1: Overview of the selected cases tested using the guidance document and the possible safety issues expected to be linked with these examples. Botanical

Preparation

Possible safety issue

Triticum aestivum L.

Wheat Bran

Low concern – presumption of safety

Citrus aurantium L. ssp. aurantium L.

Hydroalcoholic extract of dried peel

Misidentification/ adulteration

Camellia sinensis (L.) O. Kuntze

Dried green tea extract

Liver toxicity

Foeniculum vulgare Mill. ssp vulgare var. vulgare

Seeds and oil from the seeds

Carcinogenicity

Ocimum tenuiflorum L.

Dry leaves extract

Reproductive toxicity

Linum usitatissimum L.

Dried ripe seeds

Phytoestrogenic activity

presents an overview of the possible safety issues expected to be linked with these real case examples.

3.2.2 Materials and Methods Safety evaluation of the selected botanicals was performed using the proposed draft guidance document published on the EFSA website [2]. This guidance document indicates that data underlying a safety assessment of a botanical or botanical preparation should include technical data on the identity and nature of the source material, the manufacturing process, the chemical composition, specifications, stability of the botanical (preparation) used as ingredient in food supplements, proposed uses and use levels, information on existing assessments, exposure data including anticipated exposure and cumulative exposure, modality of use, as well as information on historical use and toxicological data. Data used when testing the guidance document for the safety evaluation of the selected botanicals were collected from the open literature and were not intended to be complete. The work aimed at testing the proposed tiered approach for the safety assessment of botanicals and botanical preparations with selected cases considering relevant constituents of concern within the botanical or botanical preparation. The evaluation was not aiming at providing a formal safety assessment of the botanical or its preparations, since each example focused on one type of preparation only. Once the outcome of this testing exercise has been considered for updating the draft guidance document for the safety assessment of botanicals and botanical preparations intended for use as ingredients in food supplements, EFSA published the reports summarizing the outcome of the case studies, together with the updated guidance document and the compendium on its website. The conclusions and recommendations of the present paper reflect those of its authors as individual scientists and not necessarily represent the views of EFSA.

59

60

Contributions

3.2.3 Results Triticum aestivum L. (wheat bran): Triticum aestivum L. (wheat bran) was chosen as an example of a botanical of low safety concern. Wheat bran is a by-product obtained in the manufacture of wheat flour from the grain. It consists mainly of the outer layers of the wheat kernel, including the aleuron layer i.╃e. the husk, seed, coat and germ. This example already reveals that when evaluating botanicals and botanical preparations it is essential though not always easy to adequately define and characterize the actual preparation being evaluated. Some selected wheat cultivars and varieties as well as genetically engineered wheat varieties exist [3]. The latter would fall under specific already existing European regulatory framework [4, 5]. Furthermore, selection of better tolerated wheat varieties by patients affected by gluten-induced enteropathy (celiac disease) has been described [6]. This illustrates that the botanical needs to be identified by its scientific name (binomial name, i.╃e. genus, species, subspecies, author), including the part of the plant used. Wheat bran may be obtained, in addition to hexaploid wheats, also from tetraploid wheats, namely Triticum turgidum L. ssp. durum (Desf.) Husn. (= syn. T. durum Desf., English common names: durum wheat, hard wheat), which further supports the need to adequately define the botanical evaluated by its full scientific name. Furthermore, there may be changes induced in bran composition by wheat growing conditions including high temperature stress and solar radiation [7, 8], and the composition of bran can vary depending on the milling process as well. Information on the milling process and the resulting size of the bran particles is of interest since these factors affect the biological properties, as illustrated by a particle size-dependency of the laxative effect and colonic fermentation [9]. This is why not only the scientific name but also information on the manufacturing procedure and chemical specifications of the botanical or botanical preparation are essential to adequately define the preparation to be evaluated. Another important issue emerging when evaluating this first example was the possible presence of contaminants in botanical preparations. Wheat bran must conform to the provisions of food regulations (Council Regulation 315/93/ EEC) [10], especially in terms of mycotoxins [11] arising from external fungal contamination (Fusarium spp.) [12], microbiology and pesticides. For instance, a maximum level for the trichothecene mycotoxin deoxynivalenol of 500╃µg/kg in cereal products was proposed by the Codex alimentarius [13], and the maximum level for sclerotia of Claviceps purpurea is set at 0.05â•›% m/m for wheat [14]. Fungal contamination and mycotoxin production cannot be totally eliminated at present [15]. In particular, mycotoxin contamination from Fusarium spp. is the result of a minor infection of grains and their envelopes by the fungi which may be transferred during the milling process [16–21]. Whereas for wheat bran the situation with respect to these contaminants may be well recognized and is even regulated at some extent, this may not hold true for other botanicals and botanical preparations.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

Citrus aurantium L. ssp. aurantium L. (bitter orange): Citrus aurantium L. ssp. aurantium L. (bitter orange) is an example of a botanical for which it is necessary to define the botanical or botanical preparation down to the subspecies level or even lower given that different subspecies may vary in the constituents and the level of substances of concern. Citrus aurantium L. ssp. aurantium L. (bitter orange) as compared to Citrus aurantium L. ssp. bergamia (Risso & Poit.) Engl. (bergamot orange) produces different fruits that contain different levels of biologically active principles such as furanocoumarins and para-synephrine (p-synephrine) [22, 23]. The case of Citrus aurantium L. ssp. aurantium L. was chosen since it represents the issue of misidentification and/ or adulteration which are matters for considerable concern with respect to the safety of botanicals and botanical preparations. Exposure to bitter orange peel and its constituents occurs primarily via ingestion of the fruit itself or its products (e.╃g. orange juice, marmalade, and dietary supplements). Bitter orange peel is added to various foods (e.╃g. beer, liquors and other beverages and cakes). Moreover, bitter orange juice may be added in limited amounts to sweet orange juice. Exposure can also result from peel oil used in aromatherapy and flavouring. Several evaluations of Citrus aurantium L. ssp. aurantium L. have concluded that there is no safety concern related to the regular food use of bitter orange [24, 25]. However, more recent evaluations concerning preparations containing high amounts of the sympathomimetic alkaloid p-synephrine concluded that there may be a possible safety concern [26, 27]. Bitter orange extracts used in food supplements, such as weight-loss pills, are possibly enriched in p-synephrine, typically to an amount of 6–10â•›% (but even extracts with a content of 95â•›% p-synephrine are documented) [23, 28– 32]. Thus, extracts used in many dietary supplements and herbal weight-loss formulas as an alternative to Ephedra have concentrations of p-synephrine that are often much higher than the p-synephrine concentrations reported for traditional extracts of the dried fruit or peel. This reflects another important issue to be taken into account when assessing the safety of botanical ingredients, i.╃e. that some preparations of a botanical may be marketed containing significantly higher levels of active (toxic) principles than those normally occurring in historical food uses of the same botanical. Furthermore, the position isomer of synephrine found in bitter orange peel is pâ•‚synephrine, not mâ•‚synephrine. Metaâ•‚synephrine (mâ•‚synephrine) and neo-synephrine are relatively rare synonyms of the compound named phenylephrine in the International Non-proprietary Name (INN) list of the WHO. Phenylephrine is used as a decongestant synthetic drug [33]. At least one product purportedly containing synephrine alkaloids from Citrus aurantium has been reported to contain both pâ•‚synephrine and mâ•‚synephrine [34, 35]. There is no evidence that octopamine or other phenethylamine alkaloids are present in bitter orange peel in any appreciable levels, although their increased content has been reported in some extracts and herbal products on the market [29–32]. The presence of any amounts of m-synephrine, higher amounts of the (+)-psynephrine stereoisomer or higher amounts of octopamine in food supplements

61

62

Contributions

supposedly containing only extracts or alkaloid fractions of Citrus aurantium L. ssp. aurantium L. should be considered undesirable, and suspicious of adulteration. The origin of these compounds is unlikely the natural botanical source (Citrus aurantium L. ssp. aurantium L.), thus strongly suggesting a requirement for a more efficient quality control. Camellia sinensis (L.) O. Kuntze (green tea): Dried green tea extracts prepared from young leaves and leave buds from Camellia sinensis (L.) O. Kuntze are used as food, including beverages and food supplements, and as pharmaceuticals. Uses as a food include a stimulant drink in the form of ready-to-drink beverages or of beverages prepared by the consumer from instant green tea powder. While the worldwide long-term consumption of traditional green tea infusions is assumed to be safe, a weight-loss product containing a high-dosed hydroalcoholic extract of green tea was marketed only until April 2003, when the French and Spanish authorities suspended the market authorization given its hepatotoxic side-effects [36, 37]. Some data point at epigallocatechingallate (EGCG) as the ingredient of concern in relation to the hepatotoxicity of green tea extracts but this relationship is not firmly established. In green tea, EGCG is a major constituent in terms of quantity and a constituent useful to characterize the quality of the preparation, besides caffeine, theanine and other catechins [38, 39]. Thus, the case of Camellia sinensis revealed that when evaluating the safety of a botanical or botanical preparation it can be difficult to identify the constituent or group of constituents of concern. In other cases it may be difficult to identify the active principle responsible for an effect, and therefore it is concluded that the strength of the evidence underlying the concerns over a compound being reason for concern should be given in a safety assessment of the respective botanical. Furthermore, the case of green tea reflects that different preparations from the same botanical source material can have a different outcome in the safety evaluation, especially since use of the different preparations may result in difference in composition and consequently in consumer exposure. Thus, regular intake of dried green tea extracts with food supplements or related products differs from the intake resulting from use of traditional green tea infusions (or beverages with identical composition). Dried aqueous green tea extracts, which are manufactured under the same extraction conditions as applied in the traditional preparation of green tea infusions and which are used to prepare solid or liquid food supplements may be evaluated based on their EGCG content and the daily exposure resulting from their proposed uses and use levels. In food supplements and related products the active green tea ingredients and particularly EGCG, which is associated with hepatotoxic concern, are available in a more concentrated form making higher dosage and bolus administration more likely than with the aforementioned beverages. Cases of liver disorders associated with intake of products containing dried aqueous green tea extract [37, 40, and 41] have to be taken into consideration. Moreover, the green tea example indicates that the matrix effect should be taken into account. When given in a green tea extract to rats EGCG appears to

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

be eliminated less readily from the body [42] and to have a higher toxicity than when given as a pure compound [43]. In addition, studies in healthy volunteers point at a reduced bioavailability of EGCG in the presence of a food matrix, showing that administration of concentrated green tea extracts under fasting conditions lead to a significant increase of plasma concentrations of EGCG compared to administration with food [44]. Thus the example of Camellia sinensis reflects the importance of the matrix effect that should be taken into account in the safety assessment of botanicals and botanical preparations. The use of dried green tea extracts in beverages or food supplements for weight reduction purposes under fasting conditions or reduced food intake might require adequate safety data accounting for the increased bioavailability in the absence of the food matrix effect. This applies as well to products containing dried green tea extracts as a part of the ingredient. Foeniculum vulgare Mill. ssp. vulgare var. vulgare: Foeniculum vulgare Mill. ssp. vulgare var. vulgare (bitter fennel) was selected as one of the real cases to be evaluated given that it contains estragole, an ingredient that in animal experiments and at certain concentrations showed both genotoxic and carcinogenic activity. Fruits from Foeniculum vulgare Mill. ssp. vulgare var. vulgare contain 2–6â•›% essential oil [45, 46]. The major constituent of the essential oil is trans-anethole at levels between 50–75â•›%, and the essential oil contains estragole at levels amounting to 3.5 to 12â•›% [46]. Estragole is an alkenylbenzene that is of safety concern given its reported carcinogenic effect at high dose levels [47]. In the safety assessment of botanicals and botanical ingredients a major issue is the question of how to deal with botanicals and botanical ingredients that contain chemicals that are both genotoxic and carcinogenic. The EFSA draft guidance document [2] states that in cases where the botanical ingredient contains substances that are both genotoxic and carcinogenic, the “Margin of Exposure” (MOE) approach [48] could be applied covering the botanical(s) under examination and any other dietary sources of exposure. The MOE approach compares animal toxic effect levels with human exposure levels. The guidance document states that alternatively, it could be evaluated whether the expected exposure to the genotoxic and carcinogenic ingredient will not be significantly increased compared to the intake from multiple sources. This implies that further data are required with respect to the assessment of the risk posed by the estragole levels present in bitter fennel fruits and their extracts including an estimate of the MOE. The MOE approach uses a reference point, usually taken from data from an animal experiment that represents a dose causing a low but measurable cancer response denoted the Benchmark Response (BMR). It can be for example the BMDL10, the lower confidence bound of the Benchmark Dose that gives 10â•›% (extra) cancer incidence (BMD10). The MOE is defined as the ratio between the BMDL10, and the estimated dietary intake (EDI) in humans. To date, carcinogenicity data for estragole from which a BMDL10, and thus a MOE, can be derived result from a long-term carcinogenicity study conducted in mice [49]. An accompanying paper of the present special issue reports a BMD analysis of these data using

63

64

Contributions

BMDS version 1.4.1â•›c software resulting in a BMDL10 value for estragole that varies between 9 and 33 mg/kg bw and day [50]. This value can be compared to, for example, estimated intake levels resulting from the use of bitter fennel fruits for the preparation of fennel tea. The exposure to estragole from bitter fennel fruits can be estimated based on the assumption that 4.5 to 7.5 gram (3 times 1.5 to 2.5â•›g) of fennel fruits per day would be used for the preparation of fennel tea. Assuming that fruits contain 5â•›% essential oil, that the extraction efficiency of the essential oil is 25–35↜狀%, and that there is 3.5 to 12â•›% estragole in the oil, this would imply an intake of 1.9 to 15.8 mg estragole per day. For a 60 kg person this amounts to an estragole exposure from tea consumption that amounts to 33 to 263╃µg/kg bw and day. Using the BMDL10 values of 9 to 33 mg/kg bw and day for female mice as derived from the Miller et al. study [49, 50] one can calculate a MOE in the range of 34 to 1000 which indicates that use of bitter fennel fruits for the preparation of fennel tea could be considered a high priority for risk managers [48]. In addition, the example of bitter fennel reflects the possibility to use, in cases where the botanical ingredient of concern is not genotoxic and carcinogenic, the acceptable daily intake (ADI) for the safety assessment. The safety of the intake of trans-anethole from use of bitter fennel fruits can be judged using the temporary ADI of 0–2.0 mg/kg bw and day for trans-anethole derived by JECFA [51]. The exposure to trans-anethole from bitter fennel fruits can be estimated based on the assumption that 4.5 to 7.5 gram (3 times 1.5 to 2.5â•›g) of fennel fruits per day would be used for the preparation of fennel tea. Assuming that fruits contain 5â•›% essential oil, that the extraction efficiency of the essential oil is 25–35â•›%, and that there is 50–75â•›% trans-anethole in the oil, this would imply an intake of 28 to 98 mg trans-anethole per day. For a 60 kg person this amounts to an intake of 0.5 to 1.6 mg trans-anethole/kg bw and day. This is below the above mentioned ADI established by JECFA. However, as the exposure to trans-anethole resulting from the use of bitter fennel fruits for the preparation of fennel tea already amounts to 25 to 80â•›% of the ADI, a possibility exists for exceeding the ADI due to other sources of trans-anethole. The case of bitter fennel further highlights the uncertainties associated with the kinetics as well as the expression of the inherent toxicity of a naturally occurring substance, i.╃e. estragole, possibly related to effects induced by the matrix. The question may be raised, whether studies with pure compounds dosed by gavage without the normal food matrix being present, represent a good starting point for the risk assessment of botanical ingredients. An illustrative example can be given for sweet basil which contains high amounts of estragole in the essential oil. Jeurissen et al. [52] demonstrated that the level of DNA binding of the proximate carcinogenic metabolite 1’-hydroxyestragole to DNA in vitro but also to DNA in intact HepG2 human hepatoma cells could be inhibited by a methanolic basil extract. It was demonstrated that the inhibition by the basil extract occurs at the level of the sulfotransferase mediated bioactivation of 1’-hydroxyestragole to 1-sulfoxyestragole [52]. Although it remains to be established whether a similar inhibition will occur in vivo, the inhibition of sulfotransferase mediated bioactivation of 1’-hydroxyestragole by basil ingredi-

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

ents suggests that the possibilities for bioactivation and subsequent adverse effects may be lower when estragole is dosed in a matrix of other basil ingredients than what would be expected on the basis of experiments dosing estragole as a single compound. Where a matrix effect is advocated to support the safety of specific levels of compounds (e.╃g. that data from a pure compound may overestimate effects of the compound in the botanical matrix), testing and/ or other data should be provided to demonstrate the occurrence of the matrix effect of the preparation and its magnitude. It is important to realize that when a matrix effect is demonstrated for an essential oil this matrix effect will not be similar for the intact botanical. Thus, the example of bitter fennel containing estragole supports that research on individual substance–matrix interactions cannot be used to draw general conclusions about intact botanicals, herbs and spices under all conditions of use, ingestion and metabolism and that the matrix effect should be judged on a case-by-case basis. Ocimum tenuiflorum L. (holy basil): Ocimum tenuiflorum L. (holy basil) was included in the evaluations representing an example of a botanical that may be of concern given its possible reproductive toxicity. There is, however, no information on actual constituents likely responsible for this effect. Only a few scientists attempted to look into the various changes in the reproductive system in detail after feeding Ocimum tenuiflorum L. leave extract and there is considerable debate regarding the histopathological changes in reproductive organs following the feeding of Ocimum tenuiflorum L. leaves [53–55]. In addition to the concerns over possible reproductive toxicity which need further testing, Ocimum tenuiflorum L. contains methyleugenol, an alkenylbenzene known to be both genotoxic and carcinogenic [56]. An Ocimum tenuiflorum L. leaf extract may contain up to 86â•›% methyleugenol [57–59]. Further details on an MOE assessment for methyleugenol, in line with what was done for estragole in the real-case example on Foeniculum vulgare Mill. ssp. vulgare var. vulgare, can be found in the literature [60]. Finally, the case of Ocimum tenuiflorum L. indicates once more the importance of defining the correct scientific name of a botanical to be evaluated. Ocimum tenuiflorum L. is the correct scientific name, but most publications still make use of the synonym Ocimum sanctum. Linum usitatissimum L. (flaxseed): Flax is known to be the richest food source of plant lignans including secoisolariciresinol diglucoside. This plant lignan is a precursor of the mammalian lignans, enterodiol and enterolactone and converted into these forms via the activity of colonic facultative aerobes [61]. Other lignans such as matairesinol and lariciresinol are also found in flaxseed. 100â•›g dry flaxseed contain about 300 mg lignans, including pinoresinol (∼870╃µg), syringaresinol (∼48╃µg), lariciresinol (∼1780╃µg), secoisolariciresinol (SEC, ∼165 mg), matairesinol (MAT, ∼529╃µg), and hydroxymatairesinol (HMR, ∼35╃µg), all expressed as aglycons [62, 63]. Phyto-oestrogens represent a family of plant compounds that have been shown

65

66

Contributions

to have both oestrogenic and anti-oestrogenic properties. Lignans, similarly to isoflavonoids and coumestans, are often referred to as phyto-oestrogens, and may possess oestrogen receptor agonistic or antagonistic properties, with unclear effects on hormone-sensitive cancers such as breast, uterine, and prostate cancer. Pharmacodynamic studies suggest that there might be an oestrogenic or anti-oestrogenic effect of flaxseed [64]. Some authors therefore call mammalian lignans modulators of endogenous sex steroid hormones. Since 1981, when mammalian lignans were identified in human urine, evidence supporting their role as modulators of endogenous sex steroid hormones has increased. However, the most convincing results have come from in vitro, animal and epidemiological studies, whereas results of the few intervention studies that have been conducted have been mixed [65–69]. Therefore, further research, including in particular long-term intervention trials, is needed to provide clarification for this relationship [70, 71]. The case of flax seed demonstrates that even when the compounds of concern are clearly identified the actual evidence for the effects may be controversial and may require further testing. So in this case it is clear that the strength of the evidence underlying the concerns over a botanical ingredient should be included in the safety assessment and that an evaluation based on the available knowledge can result in the conclusion that further data are requested.

3.2.4 Discussion Testing the EFSA draft guidance document for the safety assessment of botanicals and botanical preparations intended for use as food supplements [2], through its application to several selected real cases, has revealed many specific issues to be taken into account in the safety evaluation of botanicals and botanical preparations intended to be used as ingredients in food supplements and has led to a set of suggested amendments of the guidance document, in addition to its validation. The scheme proposed for safety assessment of botanicals and botanical preparations intended for use as ingredients in food supplements other than novel foods and GMOs (for which specific sectoral regulations exist) has been amended as shown in Figure 1.╃The safety assessment approach is a tiered approach starting with the evaluation on available knowledge (level A) in compliance with the criteria described in the EFSA guidance document as amended on the basis of the results of the tests described in the present paper. A level A assessment can result in the conclusion that safety can be presumed based on available knowledge (like for Triticum aestivum bran or some of the Camellia sinensis extracts), but it could also lead to the conclusion that the ingredient is of safety concern. If needed, the assessment should continue with further experimental studies, following guidance provided in the EFSA document, to obtain additional data required to reach a conclusion on safety (level B). The level B assessment may result in the conclusion that either the product is of safety concern or that the botanical or botanical preparation is not of safety concern.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

Figure 1: Scheme proposed for the safety assessment of botanicals and botanical preparations not regulated in the framework of specific regulations such as those on novel foods and GMOs. The safety assessment could include a tiered approach starting with a safety assessment based on available knowledge (level A) and the need to continue with further testing to obtain additional data (level B).

This guidance document is of importance to harmonize such an approach across Europe: In fact, in spite of the extensive harmonization that occurred through the EU Food Law, the safety assessment of food supplements based on botanicals and botanical preparations has remained a competence of each EU Member State. The outcomes of the safety evaluations of the selected cases using the EFSA guidance document will be published on the EFSA website as annexes of the advice of the ESCO Working Group on the adequacy of the proposed EFSA approach for the safety assessment of botanicals and botanical preparations. The most important issues emerging when performing the safety assessment on the selected botanicals can be summarized as follows (Tab.╃2). The botanical ingredient needs to be identified by its scientific name (binomial name, i.╃e. genus, species, subspecies, author), and the part of the plant used. In most cases it will be necessary to define the botanical down to the subspecies level or even lower given that different subspecies or varieties mostly vary in the constituents and the level of toxic principles. Examples are Foeniculum vulgare Mill ssp. vulgare var. dulce (sweet fennel) versus var. vulgare (bitter fennel) with the essential oil of the former containing about 10 times lower levels of estragole than the latter, and Citrus aurantium L. ssp. aurantium L. (bitter orange) versus Citrus aurantium L. spp. bergamia (Risso & Poit.) Engl. (bergamot orange) that produce fruits containing different levels of active principles such as furanocoumarins and p-synephrine. In other cases, however, it is possible to

67

68

Contributions Table 2: Overview of issues emerging when testing the tiered approach for the safety assessment of botanicals and botanical preparations used as ingredients in food supplements. Recommendation

Rationale

Botanical example

Botanical ingredients must be identified by their scientific (binomial) name, in most cases down to the subspecies level or lower.

Different subspecies or �varieties mostly vary in the constituents and the level of toxic principles.

Foeniculum vulgare Mill ssp. vulgare var. dulce versus var. vulgare Citrus aurantium L. ssp. aurantium L. versus Citrus aurantium L. spp. bergamia (Risso & Poit.) Engl.

Adequate characterisation and description of the botanical parts and preparation methodology used is needed. Each safety evaluation should focus on a well-defined species (or subspecies or variety), a well-defined part of the plant, and a well defined preparation.

Different preparations can be obtained from the chosen parts of a specific botanical. Composition of a botanical may vary significantly due to factors that cannot be easily controlled. Safety of a botanical ingredient cannot be assumed only relying on the long-term safe use of other preparations of the same botanical.

Camellia sinensis; different green tea preparations result in different outcome of the safety evaluation.

Establishment of adequate quality control is necessary.

Adulterations, misclassifications, replacements or falsifications and restorations may occur.

Citrus aurantium L. ssp. aurantium L. containing meta-synephrine which is not naturally occurring in bitter orange fruits.

The strength of the evidence underlying concerns over a botanical ingredient should be included in the safety assessment.

It is often difficult to identify the constituent or group of constituents in a botanical or botanical preparation that is responsible for the safety concern.

No firm link between hepatotoxicity and EGCG from leaves of Camellia sinensis. Controversial evidence adverse effects of phyto-oestrogens from seeds of Linum usitatissimum L. (flaxseed).

The matrix effect should be taken into account on a caseby-case basis.

The kinetics and toxicity of a naturally occurring substance can be modified by the surrounding.

Foeniculum vulgare Camellia sinensis.

Adequate data and methods for exposure assessment are needed.

Exposure assessment often plays a decisive role in the outcome of the safety assessment of the botanical or botanical preparations.

Margin of exposure (MOE) to the intake of estragole and margin of safety (MOS) to ADI for intake of trans-anethole from Foeniculum. vulgare

Safety regulations concerning toxic contaminants have to be complied with. Specifications should include maximum levels for possible contaminants, e.╃g. pesticide residues, mycotoxins, heavy metals and PAHs, according to existing guidelines for foods.

Some contaminants may arise from the manufacturing process and need to be kept within safety limits.

Presence of polycyclic aromatic hydrocarbons (PAHs) in dried preparations,

evaluate a variety of subspecies on the basis of one representative species. For example, this would be the case for rose hips, the spurious fruits of dog rose

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

(Rosa canina L.), alpine rose (Rosa pendulina L.) and other Rosa species, most commonly Rosa rugosa Thumb; the ripe hips of the different species are collected in late autumn and differ only slightly in their form as well as in the content of their main active constituent, ascorbic acid. Many different preparations can be obtained from the chosen parts of a specific botanical, depending on a number of factors including, for example, the solvents used and the extraction process. The example of green tea preparations from Camellia sinensis demonstrates that while the consumption of traditional infusions is assumed to be safe, toxicological concerns have been associated with certain extracts intended for weight-loss purposes. The composition of a botanical may vary significantly due to other factors that cannot be easily controlled; e.╃g. concentrations of active ingredients measured in the plant material may show significant variation with geographical origin, plant maturity at harvest, harvesting techniques, storage conditions, processing (e.╃g. drying) and method of detection. Therefore, adequate description is needed, not only of the botanical subspecies or variety evaluated, but also of the harvesting and manufacturing process. It is concluded that safety of a botanical ingredient cannot be assumed only relying on the long-term safe use of different preparations of the same botanical source, but that it is necessary to rely on well characterized preparations. Each safety evaluation should focus on a well-defined species (or subspecies or variety), a well-defined part of the plant, and a well defined preparation. Adulteration may occur. Manufacturers may add for example to Citrus aurantium L. ssp. aurantium L. preparations synthetic p-synephrine or isomers like meta-synephrine (also called phenylephrine or neosynephrine) which is not naturally occurring in bitter orange fruits. This will not become evident when in the specifications only known ingredients are listed and quantified. Furthermore, in some countries restoration of botanical preparations is allowed and may be part of the manufacturing process, i.╃e. addition of volatile ingredients lost in the manufacturing process to a dry extract. Given these aspects, the establishment of adequate quality control methods is necessary. It is often difficult to identify the constituent or group of constituents in a botanical or botanical preparation that is responsible for the safety concern. An example is EGCG from leaves of Camellia sinensis which is quantitatively a major constituent and useful to characterize the quality of the preparation, but for which no firm link has been established with the hepatotoxicity of the dried green tea extracts. Furthermore, the case of the seeds of Linum usitatissimum L. (flaxseed) indicates that, even when compounds of concern have been clearly identified, the evidence for their effects may be controversial and require further testing. The strength of evidence underlying the concerns over a botanical ingredient should be, therefore, included in the safety assessment. The matrix effect should be taken into account in the safety assessment of botanicals and botanical preparations. It is plausible that the kinetics as well as the expression of the inherent toxicity of a naturally occurring substance can be modified by the surrounding matrix. Depending on the mechanism of action of the substance and the nature of the matrix, this could result in the toxicity of

69

70

Contributions

the specific substance being unchanged, reduced or even increased. Research data on individual substance–matrix interactions, mainly available in vitro on specific botanical preparations, cannot be used to draw general conclusions applicable to intact botanicals or other preparations or in vivo. Where a matrix effect is advocated to support the safety of a botanical ingredient of specific levels of compounds, ad hoc test data should be provided to demonstrate the real occurrence of the matrix effect in that preparation and its magnitude. A matrix effect should be judged on a case-by-case basis as described in this paper for Foeniculum vulgare and Camellia sinensis. While working through the real cases the outcomes of the exposure assessment most often appeared to play a decisive role in the outcome of the safety assessment of the botanical or botanical preparations. For example, the margin of exposure (MOE) to the intake of estragole from the consumption of Foeniculum vulgare as well as judging whether the intake of trans-anethole from the use of bitter fennel fruits for the preparation of fennel tea would remain below the ADI for trans-anethole depends on the outcome of the exposure assessment. However, data on present uses and use levels of a botanical or botanical preparation may be sparse or lacking and other uncertainties may be in some cases unavoidable. Botanical food ingredients must be obviously in compliance with regulations on contaminants. Some contaminants such as for example polycyclic aromatic hydrocarbons (PAHs) in dried preparations may arise from the manufacturing process and need to be kept within safety limits. Therefore, specifications should include maximum levels for possible contaminants, e.╃g. pesticide residues, mycotoxins, heavy metals and PAHs, according to existing guidelines for foods. It should be pointed out that, although being outside the EFSA mission, there are other important issues that need to be considered to ensure safety of food supplements. These include: (1) the over-the-counter availability of food supplements through internet sites from countries where regulations are not in place or not aligned to European standards; and (2) the fact that the control systems in place to guarantee the safety and quality of botanical supplements are not well harmonized among different countries. The latter is of particular concern as some products on the market are known to be of variable quality with high variation in the content of the active and/ or the toxic principles, and due to the fact that already examples of replacement of a harmless variety with a toxic alternative have occurred [72–75]. Misidentification of plants harvested from the wild may add to the problem. The growing volume of products and sales call for a more formal pre-marketing assessment and better and stricter controls than at present. Regulatory bodies have become aware of the problem and are increasing their efforts to ensure the safety of botanical supplements€[1]. A last consideration is related to consumer information and empowerment which would make it possible to reduce phenomena such as over-consumption of food supplements by particular groups and the fact that many consumers equate “natural” with “safe” when considering botanical food supplements.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

Acknowledgement The work reported in the paper was carried out in the framework of the EFSA ESCO working group on Botanicals and Botanical Preparations with the support of the EFSA Scientific Committee & Advisory Forum Unit. The ESCO working group was composed of: Experts nominated by the EFSA Scientific Committee: Robert Anton, Angelo Carere, Luc Delmulle, Corrado L. Galli, Ivonne Rietjens, Vittorio Silano and Gerrit Speijers. Experts nominated by the members of the EFSA Advisory Forum: Ilze Abolina, Judith Amberg-Müller, Ulla Beckman-Sundh, Birgit Dusemund, MarieHélène Loulergue, Andrea Lugasi, Martijn Martena, Maria Nogueira, Kirsten Pilegaard, Mauro Serafini, Jaroslav Toth, Arnold Vlietinck and Magdalini Zika. The conclusions and recommendations of the present paper reflect those of its authors as individual scientists and not necessarily represent the views of EFSA.

List of Abbreviations ADI: Acceptable Daily Intake BMD: Benchmark Dose BMDL: Lower confidence bound of the Benchmark Dose BMR: Benchmark Response ECGC: Epigallocatechingallate EDI: Estimated daily Intake EFSA: European Food Safety Authority ESCO: EFSA Scientific Cooperation JECFA: Joint FAO/WHO Expert Committee on Food Additives MOE: Margin of Exposure PAHs: polycyclic aromatic hydrocarbons.

Conflict of Interest Statement The authors declare that there are no financial/commercial conflicts of interest.

References â•⁄ 1. Scientific Committee of the European Food Safety Authority, Discussion paper on “botanicals and botanical preparations widely used as food supplements and related products: coherent and comprehensive risk assessment and consumer information approaches”. Brussel 2004, pp.╃1–6. http://www.efsa.eu.int/science/sc_commitee/sc_documents/616/scdoc_advice03_botanicals_en1.pdf.╃

71

72

Contributions

â•⁄ 2. EFSA (European Food Safety Authority), Draft guidance document of the Scientific Committee for the safety assessment of botanicals and botanical preparations intended for use as ingredients in food supplements, 2008.╃http:// www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1178717026833. htm.╃ â•⁄ 3. Peterson, R.╃K., Shama, L.╃M., A comparative risk assessment of genetically engineered, mutagenic, and conventional wheat production systems. Transgenic Res., 2005, 14, 859–875. â•⁄ 4. EFSA (European Food Safety Authority), Guidance document of the Scientific Panel on Genetically Modified Organisms for the risk assessment of genetically modified plants and derived food and feed, EFSA Journal, 2006, 99, 1–100. http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_ 1178620775747.htm â•⁄ 5. Regulation (EC) 1829/2003 on GM food and feed, and Directive 2001/18/EC on the release of GMOs into the environment. http://eur-lex.europa.eu/JOIndex.do?year=╃2003&serie=L&textfield2╃=╃26 8&Submit=Search. â•⁄ 6. Spaenij-Dekking, L., Kooy-Winkelaar, Y., Van Veelen, P., Drijfhout, J.╃W., Jonker, H., Van Soest, L., Smulders, M.╃J., Bosch, D., Gilissen, L.╃J., Koning, F., Natural variation in toxicity of wheat: potential for selection of nontoxic varieties for celiac disease patients. Gastroenterology 2005, 129, 797–806. â•⁄ 7. Zhou, K., Su, L., Yu, L.╃L., Phytochemicals and antioxidant properties in wheat bran. J. Agric. Food Chem. 2004, 52, 6108–6114. â•⁄ 8. Zhou, K., Yu L., Antioxidant properties of bran extracts from Trego wheat grown at different locations. J. Agric. Food Chem. 2004, 52, 1112–1117. â•⁄ 9. Jenkins, D., Kendall, C., Vuksan, V., Augustin, L., Li, Y-M., Lee, B., Mehling, C., Parker, T., Faulkner, D., Seyler, H., Vidgen, E., Fulgoni III, V., The effect of wheat bran particle size on laxation and colonic fermentation. J.╃Am. Col. Nutr. 1999, 18, 339–345. 10. Council Regulation (EEC) No.╃315/93 of 8 April 1993 laying down Community procedures for contaminants in food. Official Journal of the European Union L 37, 13.╃2. 1993, p.╃1.╃http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1993R0315:20031120:EN:PDF 11.╃ WHO, Selected mycotoxins: ochratoxins, trichothecenes, ergot. Environmental Health Criteria, 1990, 105, 263 pages. 12.╃ Mueller, H.╃M., Metzger, K.╃U., Modi, R., Reimann, J., Ergosterol and Fusarium toxins in wheat bran and wheat. J. Anim. Physiol. Anim. Nutr. 1994, 71, 48–55. 13.╃Visconti, A., Haidukowski, E.╃M., Michelangelo Pascale, M., Silvestri, M., Reduction of deoxynivalenol during durum wheat processing and spaghetti cooking. Toxicol. Lett. 2004, 153, 181–189. 14.╃ Codex Alimentarius 1995, Codex standard for wheat and durum wheat. CODEX STAN 199–1995.╃www.codexalimentarius.net/download/standards/62/CXS_199â•›e.pdf. 15. Codex Alimentarius 2003, Code of practice for the prevention and reduction of mycotoxin contamination in cereals, including annexes on ochra-

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

toxin a, zearalenone, fumonisins and tricothecenes. CAC/RCP 51–2003: 1–8. 16. Brera, C., Catano C., De Santis, B., Debegnach, F., De Giacomo, M., Pannunzi, E., Miraglia, M. Effect of industrial processing on the distribution of aflatoxins and zearalenone in corn-milling fractions. J. Agricul. Food Chem. 2006, 54, 5014–5019. 17.╃ Brera, C., Debegnach F., Grossi S., Miraglia, M., Effect of industrial processing on the distribution of fumonisin B-1 in dry milling corn fractions. J. Food Protec. 2004, 67, 1261–1266. 18.╃ Broggi, L.╃E., Resnik S.╃L., Pacin, A.╃M., GonzaÂlez, H.╃H.╃L., Cano, G., Taglieri, D., Distribution of fumonisins in dry-milled corn fractions in Argentina. Food Add. Contam. 2002, 19, 465–469. 19.╃ Ryu, D., Jackson, L.╃S., Bullerman, L.╃B., Effects of processing on zearalenone. Mycotoxins Food Safety 2002, 504, 205–216. 20.╃ Trigo Stockli, D.╃M., Deyoe, C.╃W., Satumbaga, R.╃F., Pedersen, J.╃R., Distribution of deoxynivalenol and zearalenone in milled fractions of wheat. Cereal Chem.1996, 73, 388–391. 21.╃ Rafai, P., Bata, A., Jakab, L., Vanyi, A., Evaluation of mycotoxin-contaminated cereals for their use in animal feeds in Hungary. Food Add. Contam. 2000, 17, 799–808. 22.╃ Gardana, C., Nalin, F., Simonetti, P. Evaluation of flavonoids and furanocoumarins from Citrus bergamia (bergamot) juice and identification of new compounds. Molecules 2008, 13, 2220–2228. 23.╃ Avula, B., Upparapalli, S.╃K., Navarrete, A., Khan, I.╃A., Simultaneous quantification of adrenergic amines and flavonoids in C. aurantium, various Citrus species, and dietary supplements by liquid chromatography. J. AOAC Intern. 2005, 88, 1593–1606. 24.╃ Fugh-Berman, A., Myers, A., Citrus aurantium, an ingredient of dietary supplements marketed for weight loss: current status of clinical and basic research. Exp. Biol. Med.╃2004, 229, 698–704. 25.╃ Haaz, S., Fontaine, K.╃R., Cutter, G., Limdi, N., Perumean-Chaney, S., Allison, D.╃B., Citrus aurantium and synephrine alkaloids in the treatment of overweight and obesity: an update. Obesity Rev.╃2006, 7, 79–88. 26.╃ Calapai, G., Firenzuoli, F., Saitta, A., Squadrito, F., Arlotta, M.╃R., Costantino, G., Inferrera G., Antiobesity and cardiovascular toxic effects of Citrus aurantium extracts in the rat: A preliminary report. Fitoterapia 1999, 70, 586–592. 27.╃ Jack, S., Desjarlais-Renaud, T., Pilon, K. Bitter orange or synephrine: update on cardiovascular adverse reactions. Canadian Adverse Reaction Newsletter 2007, 17, 2–3.╃ 28.╃Avula, B., Upparapalli, S.╃K., Khan, I.╃A., Simultaneous analysis of adrenergic amines and flavonoids in Citrus peel jams and fruit juices by liquid chromatography: part 2.╃J. AOAC Intern. 2007, 90, 633–640. 29.╃ Pellati, F., Benvenuti, S., Melegari, M., Firenzuoli, F., Determination of adrenergic agonists from extracts and herbal products of Citrus aurantium L. var. amara by LC. J. Pharmac. Biomed. Anal. 2002, 29, 1113–1119.

73

74

Contributions

30.╃ Pellati, F., Benvenuti, S. Chromatographic and electrophoretic methods for the analysis of phenethylamine alkaloids in Citrus aurantium. J. Chromatog. A 2007, 1161, 71–88. 31.╃ NTP/NIEHS. Bitter orange (Citrus aurantium var. amara) extracts and constituents (±)-p-synephrine [CAS No.╃94–07–5] and (±)-p-octopamine [CAS No.╃104–14–3]. Review of toxicological literature. National Toxicology Program (NTP), 2004, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health, US Department of Health and Human Services, Research Triangle Park, North Carolina, VIII +╃73â•›p. http:// ntp.niehs.nih.gov/ntp/htdocs/Chem_Background/ExSumPdf/Bitterorange. pdf. 32. Blumenthal, M., Bitter orange peel and synephrine: Part 1 & Part 2.╃Herbal Gram 2005, 66, 0 (web-only exclusive). http://content.herbalgram.org/abc/ herbalgram/articleview.asp?a=╃2833&p=Y.╃ 33. Gaglia, C.╃A. Jr., Phenylephrine hydrochloride. Analyt. Prof. Drug Subst. 1974, 3, 483–512. 34.╃ Allison, D.╃B., Cutter, G., Poehlman, E.╃T., Moore, D.╃R., Barnes, S., Exactly which synephrine alkaloids does Citrus aurantium (bitter orange) contain? Intern. J. Obesity 2005, 29, 443–446. 35.╃ Santana, J., Sharpless, K.╃E., Nelson B.╃C., Determination of para-synephrine and meta-synephrine positional isomers in bitter orange-containing dietary supplements by LC/UV and LC/MS/MS. Food Chem. 2008, 109, 675–682. 36.╃ AFSSAPS, Agence Française de Sécurité Sanitaire des Produits de Santé, 2003, Communiqué de Presse. Suspension de l’autorisation de mise sur le ® marché de la spécialité pharmaceutique EXOLISE (gallate d’épigallocatéchol). 37.╃ Sarma, D.╃N., Barrett, M.╃L., Chavez, M.╃L., Gardiner, P., Ko, R., Mahady, G.╃B., Marles, R.╃J., Pellicore, L.╃S., Giancaspro, G.╃I., Dog, T.╃L., Safety of green tea extracts. A systematic review by the US pharmacopeia. Drug Safety 2008, 31, 469–484. 38.╃ Isbrucker, R.╃A., Edwards, J.╃A., Wolz, E., Davidovich, A., Bausch, J., Safety studies on epigallocatechin gallate (EGCG) preparations. Part 2: dermal, acute and short-term toxicity studies. Food Chem. Toxicol. 2006, 44, 636– 650. 39.╃ USDA database for the flavonoid content of selected foods. 2007, US Department of Agriculture, Agricultural Research Service. 40.╃ Kantelip, J.╃P., Laroche, D., Green tea and liver disorders. National Drug Surveillance survey submitted to the Technical Committee. Besancon: Besancon regional drug surveillance centre, 11 February, 2003. 41.╃ Canadian Adverse Reaction Newsletter Green tea extract (Green Lite): suspected association with hepatotoxicity. 2007,17, 1–3. 42.╃Chen, L., Lee, M-J., Li, H., Yang, C.╃C., Absorption, distribution, and elimination of tea polyphenols in rats. Drug Metab. Dispos. 1997, 25, 1045–1050. 43.╃ Johnson, W.╃D., Morrissey, R.╃L., Crowell, J.╃A., McCormick, D.╃L., Subchronical oral toxicity of green tea polyphenols in rats and dogs. The Toxicologist 1999, 48, 57–58.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

44.╃ Chow, H.╃H., Hakim, I.╃A., Vining, D.╃R., Effects of dosing condition on the oral bioavailability of green tea catechins after single-dose administration of Polyphenon E in healthy individuals. Clin. Cancer Res.╃2005, 11, 4627– 4633. 45.╃ European Pharmacopoeia (2005) Fennel, Bitter – Foeniculi amari fructus. Council of Europe. 5th ed., 01/2005:0824. 46.╃ Council of Europe, 2006, Active principles (constituents of toxicological concern) contained in natural sources of flavourings. Approved by the Committee of Experts on Flavouring Substances, October 2005, Health Protection of the Consumer Series. Council of Europe Press, Strasbourg. http://www.coe.int/t/e/social_cohesion/soc-sp/public_health/Flavouring_ substances/Active%20principles.pdf.╃ 47. SCF, 2001, Opinion of the Scientific Committee on Food on estragole (1-allyl-4-methoxybenzene). http://europa.eu.int/comm/food/fs/sc/scf/ out104_en.pdf. 48. EFSA, 2005, Opinion of the scientific committee on a request from EFSA related to a harmonised approach for risk assessment of substances which are both genotoxic and carcinogenic. http://www.efsa.europa.eu/en/science/sc_commitee/sc_opinions/1201.html. 49.╃ Miller, E.╃C., Swanson, A.╃B., Phillips, D.╃H., Fletcher, T.╃L., Liem, A., Miller, J.╃A., Structure-activity studies of the carcinogenicities in the mouse and rat of some naturally occurring and synthetic alkenylbenzene derivatives related to safrole and estragole. Cancer Res.╃1983, 43, 1124–1134. 50.╃ Rietjens, I.╃M.╃C. M., Punt, A., Schilter, B., Scholz, G., Delatour, T., Van Bladeren, P.╃J., In-silico methods for physiologically based biokinetics (PBBK models) describing bioactivation and detoxification of coumarin and estragole; implications for risk assessment. Mol. Nutr. Food Res., 2010, 54 (2), 195–207. 51.╃JECFA, 1998, trans-Anethole (addendum), In: Safety evaluation of certain food additives, prepared by the 51st meeting of JECFA, FAS 42-JECFA 51/5, p.╃5–32.╃http://www.inchem.org/documents/jecfa/jeceval/jec_137.htm. 52. Jeurissen, S.╃M.╃F., Punt, A., Delatour, Th., Rietjens, I.╃M.╃C. M., Basil extract inhibits the sulfotransferase mediated formation of DNA adducts of the procarcinogen 1’-hydroxyestragole by rat and human liver S9 homogenates and in HepG2 human hepatoma cells. Food Chem. Toxicol. 2008, 46, 2296–2302. 53.╃ Ahmed, M., Khan, M.╃Y. Khan, A.╃A. Effects of Ocimum sanctum (Tulsi) on the reproductive system, an updated review. Biomed. Res.╃2002, 13, 63–67. 54.╃ Ahmed, M., Ahamed, N., Aladakatti, R.╃H. Ghosesawar, M.╃G., Reversible anti-fertility effect of benzene extract of Ocimum sanctum leaves on sperm parameters and fructose content in rats. J. Basic Clin. Physiol. Pharmacol. 2002, 13, 51–59. 55.╃ Reghunandan, R., Sood, S., Reghunandan, V., Arora, B.╃B., Gopinathan, K., Mahajan, K.╃K., Effects of feeding Ocimum sanctum (Tulsi) leaves on fertility in rabbits. Biomed. Res. Aligarh, 1997, 8, 187–191.

75

76

Contributions

56.╃ SCF, 2001, Opinion of the Scientific Committee on Food on methyleugenol (4-allyl-1,2-dimethoxybenzene). http://ec.europa.eu/food/fs/sc/scf/out102 _en.pdf. 57. WHO, 2002, Folium Ocimi Sancti, WHO monographs on selected medicinal plants, 2 , 206–216.╃World Health Organization, Geneva, Switzerland. 58.╃ Blaschek, W., Hänsel, R., Keller, K., Reichling, J., Rimpler, H. , Schneider, G. (Eds.), 1998.╃Hager’s Handbuch der pharmazeutische Praxis. Folgeband 2: Drogen A-K, 5th ed. Springer-Verlag, Berlin, Germany. 59.╃ Lal, R.╃N., Sen, T.╃K., Nigam, M.╃C., Gas chromatography of the essential oil of Ocimum sanctum L., Parfümerie und Kosmetiks 1978, 59, 230–231. 60.╃ Rietjens, I.╃M.╃C. M., Slob, W., Galli, C., Silano, V., Risk assessment of botanicals and botanical preparations intended for use in food and food supplements: Emerging issues. Toxicol. Lett., 2008, 180, 131–136. 61.╃ Thompson, L.╃U., Robb, P., Serraino, M., Cheung, F., Mammalian lignan production from various foods. Nutr. Cancer 1991, 16, 43–52. 62.╃ Smeds, A.╃I., Eklund, P.╃C., Sjöholm, R.╃E., Willför, S.╃M., Nishibe, S., Deyama, T., Holmbom, B.╃R., Quantification of a broad spectrum of lignans in cereals, oilseeds, and nuts. J. Agric. Food. Chem. 2007, 55, 1337–1346. 63.╃ Milder, I.╃E.╃J., Arts, I.╃C.╃W., Van de Putte, B., Venema, D.╃P., Hollman, P.╃C.╃H., Lignan contents of Dutch plant foods: a database including lariciresinol, pinoresinol, secoisolariciresinol, and matairesinol. Br. J. Nutr., 2005, 93, 393–402. 64.╃ Adlercreutz, H., Phyto-oestrogens and cancer. Lancet Oncol, 2002, 3, 364– 373. 65.╃ Hutchins, A.╃M., Martini, M.╃C., Olson, B.╃A., Thomas, W., Slavin, J.╃L., Flaxseed consumption influences endogenous hormone concentrations in postmenopausal women. Nutr. Cancer 2001, 39, 58–65. 66.╃ Phipps, W.╃R., Martini, M.╃C., Lampe, J.╃W., Slavin, J.╃L., Kurzer, M.╃S., Effect of flax seed ingestion on the menstrual cycle. J. Clin. Endocrinol. Metab. 1993, 77, 1215–1219. 67.╃ Kurzer, M.╃S., Lampe, J.╃W., Martini, M.╃C., Adlercreutz, H., Fecal lignan and isoflavonoid excretion in premenopausal women consuming flaxseed powder. Cancer Epidemiol. Biomarkers Prev. 1995, 4, 353–358. 68.╃ Thompson L.╃U., Chen J.╃M., Li T., Strasser-Weippl K., Goss P.╃E., Dietary flaxseed alters tumor biological markers in postmenopausal breast cancer. Canada Clin. Cancer Res, 2005, 11, 3828–3835. 69.╃ Dodin S., Lemay A., Jacques H., Legare F., Forest J-C., Masse B., The effects of flaxseed dietary supplement on lipid profile, bone mineral density, and symptoms in menopausal women: a randomized, double-blind, wheat germ placebo-controlled clinical trial. J. Clin. Endocrin. Metab. 2005, 90, 1390–7. 70. Adlercreutz, H., Hoeckerstedt, K., Bannwart, C., Bloigu, S., Hämäläinen, E., Fotsis, T., Ollus, A., Effect of dietary components, including lignans and phytoestrogens, on enterohepatic circulation and liver metabolism of estrogens and on sex hormone binding globulin (SHBG). J. Steroid Biochem. 1987, 27, 1135–1144.

Safety Assessment of Botanicals and Botanical �Preparations Used as Ingredients in Food Supplements: Testing an European Food Safety Authority-Tiered Approach

71.╃ Adlercreutz, H., Does fiber-rich food containing animal lignan precursors protect against both colon and breast cancer? An extension of the „fiber hypothesis“. Gastroenterology 1984, 86, 761–764. 72.╃ Vanherweghem, J.-L., Depierreux, M., Tielemans, C., Abramowicz, D., Dratwa, M., Jadoul, M., Richard, C., Vandervelde, D., Verbeelen, D., Vanhaelen-Fastre, R., et al., Rapidly progressive interstitial renal fibrosis in young women: association with slimming regimen including Chinese herbs. Lancet 1993, 341, 387–391. 73.╃ Vanhaelen, M., Vanhaelen-Fastre, R., But, P., Vanherweghem, J.-L., Identification of aristolochic acid in Chinese herbs. Lancet 1994, 343, 174. 74.╃ Oudesluys-Murphy, A.╃M., Oudesluys, N., Tea: not immoral, illegal, or fattening, but is it innocuous? Lancet 2002, 360, 878. 75.╃ Johanns, E.╃S.╃D., van der Kolk, L.╃E., van Gemert, H.╃M.╃A., Sijben, A.╃E., et al., An epidemic of epileptic seizures after consumption of herbal tea. Ned. Tijdschr. Geneesk. 2002, 146, 813–816.

77

78

Contributions

3.3 In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis Luis G. Valerio Jr.1, Naomi L. Kruhlak2, and R. Daniel Benz2

Abstract In silico toxicology employs evidence-based methods using chemoinformatics and advanced computational analyses in evaluations by regulatory agencies done for safety assessments of drug-related substances (active pharmaceutical ingredients, metabolites, impurities), the industry drug discovery process, safety assessment of indirect food additives, environmental agents, and other applied uses of value for protecting public health. At the US Food and Drug Administration (FDA), in silico approaches harness vast experimental chemical toxicity test data that exist in regulatory archives to predict toxicological end points of regulatory interest on the basis of the classic structure–activity (SAR) paradigm, probabilistic evidence-based reasoning, machine learning methods, data mining, and human rules integrated into computational software programmes. The in silico approach can provide more affordable and time efficient alternatives to traditional studies (e.╃g., to support a decision in the event of equivocal evidence of toxic potential from laboratory results), and may reduce the use of animals in some circumstances and aid in risk management for the prioritization of chemicals requiring safety testing. The specific use of advanced in silico methods, including predictive quantitative structure–activity relationship (QSAR) analysis and chemoinformatic data mining software, for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources, is addressed in the context of an external validation study. External validation is the most stringent scientific method of measuring QSAR predictive performance. The external validation statistics measuring performance for predicting rodent carcinogenic potential of a dataset of phytochemicals is presented based on two different computational software programmes in current use at the FDA. How the FDA, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, uses chemoinformatics and computational toxicology software including QSAR modelling to predict the ability of phar-

1

Correspondence address and presentation: Luis G. Valerio, Jr., Ph.╃D., US Food and Drug Administration, Center for Drug Evaluation and Research, Science and Research Staff, Office of Pharmaceutical Science, White Oak 51, Room 4128, 10903 New Hampshire Ave., Silver Spring, MD 20993–0002, USA, Fax: +╃1 301 796 9997, [email protected].

2

US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Testing and Research, Office of Pharmaceutical Science, White Oak 64, 10903 New Hampshire Ave., Silver Spring, MD 20993–0002,╃USA.

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

maceuticals, their metabolites, impurities, and degradation products to cause toxicity in animals is also covered.

3.3.1 Introduction The Science and Research Staff (SRS) is a component of the Science and Research Staff of the Office of Pharmaceutical Science at the US FDA/ Center for Drug Evaluation and Research (CDER). SRS provides specialized support in the development and applied regulatory use of in silico (computer-based) toxicology methods at CDER. SRS has established a consortium of collaborators who are engaged in harvesting data from FDA archives and creating quantitative structure–activity relationship (QSAR) computational toxicology models of animal toxicological and human health effect end points. Computational databases of animal toxicology studies and human clinical trial and surveillance data have been compiled and the non-proprietary portions are being made publicly available through the collaborators. SRS provides computational toxicology safety evaluations of drugs, metabolites, contaminants, and degradants to CDER reviewers to support regulatory decision-making. SRS supports CDER regulatory decision-making by using computational information from chemically similar substances to generate a predicted toxicological and adverse effect profile for a compound to help support regulatory decision-making. Such practice provides additional scientific evidence to help in safety assessment and identify and eliminate compounds with potentially significant adverse effect properties early in the drug discovery and development process. Current SRS research is focused on developing strategies for the use of computational toxicology models for regulatory purposes, evaluating new computational software approaches, and validating predictive performance of the models. For a recent review of SRS computational toxicology activities at the FDA please refer to the article by Yang [1].

3.3.2 Why Use In Silico Predictive Models at FDA? One of SRS’ important roles at CDER is to provide predictions based on computational analyses of chemically similar substances that can help identify toxicity in the early stages of the drug review process. These responsibilities and activities are consistent with the FDA Critical Path Initiative [2]. Launched in March 2004, the FDA Critical Path Initiative is the agency‘s effort to stimulate and facilitate, via modernized approaches, the scientific process through which a potential human drug, biological product, medical device, or food additive is transformed from a discovery or “proof of concept” into a medical product or food ingredient (http://www.fda.gov/oc/initiatives/criticalpath/). Computational toxicology has been acknowledged as playing a role in FDA’s Critical Path.

79

80

Contributions

In 2007, the US National Research Council (NRC) published a report entitled, “Toxicity Testing in the 21st Century: A Vision and Strategy” at the request of the US Environmental Protection Agency (EPA) [3]. The report has sparked tremendous interest as it reviewed established toxicology methodologies, and discussed the use of alternative approaches and emerging technologies as a vision and strategy to increase efficiency and relevance of toxicity testing in risk assessment. In Chapter 4 of the report, computational toxicology and in silico SAR-based methods are specifically discussed as a future and long-term strategy for toxicity testing in risk assessment. Although addressing environmental agents, the report may also be applied across substances of regulatory importance including pharmaceutical molecules, food ingredients, and phytochemicals. Further rationale for use of in silico approaches includes the advantage of high efficiency. In silico technologies permit the rapid screening of thousands of chemicals in minutes, and are very cost-effective compared to conventional animal toxicology studies. For example, the cost to conduct the rodent 2-year cancer bioassay in two species and genders can run upwards of $╃3 million dollars, and this does not even take into consideration the time and resources involved in generating reports of results derived from a standardized protocol such as that of the US National Toxicology Program. By comparison, the acquisition of a battery of computational toxicology software may cost but a fraction of the amount of one 2-year bioassay and, moreover, can be used in multiple experiments to screen more chemicals. The other attraction of in silico methods for predicting toxicity and aiding in risk assessment of chemicals is the potential net effect of saving animals used for laboratory testing. The European Commission’s legislation known as REACH (Registration, Evaluation, Authorisation, and restriction of Chemicals) is a current and widely referred to example of the reality in reducing the use of animals in toxicity testing [4]. Under REACH, no animal test should be performed if it can be replaced with other techniques such as reliable in silico predictions. Through this law, millions of test animals may be spared if (Q)SAR methods performed by computers are accepted for REACH purposes. There are international standards in place in order to determine whether a QSAR method is acceptable for regulatory purposes. The Organisation for Economic Co-operation and Development (OECD) has established five principles that need to be addressed for a QSAR model to be acceptable: (1) A defined end point, (2) a defined (or unambiguous) algorithm, (3) a defined domain of applicability, (4) appropriate measures of goodness of fit, robustness, and predictivity, and (5) mechanistic interpretation, if possible [4]. Because of the aforementioned regulatory initiatives, and the increased usage and development of in silico approaches, improvements in predictive accuracy have been evolving. This is evident in recent external validation studies for predicting rodent carcinogenicity which have reported performance statistics above 90â•›% for sensitivity [5], while other models have been built and reported to perform more optimally with high specificity (>80â•›%) [6]. Rarely do models accomplish both high sensitivity and specificity, so a choice must be made about

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

which predictive parameters are most important for a particular regulatory application. Among other topics, this is being addressed by a newly formed CDER committee, the Pharmacology and Toxicology Coordinating Committee (PTCC) Computational Toxicology Subcommittee (CTCS). The purpose of the PTCC CTCS is to disseminate the appropriate guidance to CDER review staff and the pharmaceutical industry on the use and assessment of computational toxicology studies. The PTCC CTCS serves as an internal resource to the CDER Office of New Drugs and Office of Pharmaceutical Science on scientific and regulatory aspects of computational toxicology issues. Another reason to use in silico methods is that recently, it has been proposed that computational modelling can be a tool consistent with the principles and concept of evidence-based toxicology due to the objectivity with which chemoinformatic approaches can be applied [7]. The fact that computational approaches can provide a systematic analysis of high quality test data is recognized [7]. However, although in silico methods may be a useful tool for evidence-based toxicology for specific causation analysis, these methods cannot in themselves establish causation.

3.3.3 What In Silico Predictive Models does the FDA Use? In building computational models, non-proprietary and proprietary laboratory toxicity testing results have been mathematically transformed to construct global QSAR models that use 2-dimensional molecular fragment and descriptorbased approaches in the analysis of chemical structure to predict a battery of pre-clinical in vitro and animal end points important to regulatory safety assessments. In addition, recent work has centred on models designed to predict clinical end points and human adverse effects of pharmaceuticals [8]. The toxicological and clinical end points modelled and planned for development at CDER are listed in Table 1. The model validation methods that have been used in the put begin with conducting 100â•›% internal cross-validation. In internal cross-validation experiments, a 10╃×╃10â•›% procedure is conducted where chemicals that are part of the training dataset are randomly assigned to 10 equally-sized validation test Table 1: Computational modelling suites available and planned for development at the US FDA/CDER Office of Pharmaceutical Science. Non-clinical effect models

Human clinical adverse effect models

Carcinogenicity Genetic toxicity Reproductive toxicity Behavioural toxicity Phospholipidosis Quantitative MTD (vs. time)1 Subchronic toxicity2 Chronic rodent studies2

Hepatobililary Renal/ Bladder Cardiological Pulmonary1 Immunological1 Other organ systems2 Maximum recommended daily dose Molecular mechanism of action Human drug metabolism

1 under current development 2 planned for development

81

82

Contributions

sets (10â•›% each). In each iteration of the validation process, one of these sets of compounds is removed from the training dataset and a new model is constructed with the remaining 90â•›%. The compounds (10â•›%) are then used as a test set to run against the new model and the process is repeated 10 times until all of the validation compounds are used once as a test chemical. When additional data are available for a given end point, an external validation study is performed using a balanced set of compounds that were never part of the model. It is widely recognized that external validation studies are of the highest rigor and stringency in testing predictive performance of a QSAR model [5], but it is also acknowledged that previously unmodelled compounds can sometimes be poorly represented by the domain of applicability of the model. This reinforces the need for a suitable measure of molecular coverage. Data from external validation study of the predictive performance of models for the highly sought after toxicological end point of rodent carcinogenicity are addressed in this presentation.

3.3.4 What In Silico Predictive Software does the FDA Use? Through FDA-approved Cooperative Research and Development Agreements (CRADAs), Material Transfer Agreements (MTAs), Research Collaboration Agreements (RCA), and licensing, CDER is involved in long-term evaluation of and research with computational toxicology software for development of predictive (Q)SAR models, as well as their use in internal computational toxicology consultation services to CDER reviewers [1]. CDER’s current in silico toolbox of (Q)SAR predictive toxicity screens includes the software programmes listed in Table 2.╃It is important to point out that the FDA does not endorse any computational software product including the ones listed in Table 2.╃These software products are listed only because the FDA has entered into agency-approved collaborative research agreements such as RCAs, CRADAs, MTAs, or licensing with the companies supporting these software programmes. These agreements are intended to evaluate the utility of the software programmes and do not endorse any of them. Table 2: Computational toxicology software used in applied research at the US FDA/CDER Office of Pharmaceutical Science. Computational Toxicology Software Programme1

Product Developer

BioEpisteme® Derek for Windows and Meteor Leadscope Model Applier and Predictive Data Miner MC4PC and Meta MetaDrug™ and MetaTox™ SciQSAR (MDL-QSAR)

Prous Institute for Biomedical Research Lhasa Limited Leadscope® Inc.

1â•… No endorsement implied. Listed in alphabetical order.

MultiCASE Inc. GeneGo Inc. Scimatics Inc.

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis Table 3: Computational toxicology software platforms1 to estimate rodent carcinogenic potential. Leadscope Model Applier

MC4PC

SciQSAR

BioEpisteme

Derek for Windows

QSAR Methodology

Partial Logistic Statistical Regression (PLR) Analysis Algorithm/Statistical Analyses

Discriminant Analysis

Genetic Algorithm/ Statistical Analyses

Human Expert Rules

Structural Interpretation

Fingerprint Molecular Features/ Scaffolds

2–10 Atom Molecular Fragments

Molecular Descriptors

Molecular Descriptors

Structural Alert Molecular Features (Fragments)

2D/3D Molecular Descriptors

2D (n~10)

2D (n~6)

2D (n~200, Kier and Hall)

3D (n~126, 2D (n~4) 2D set of descriptors; 3D is a future functionality)

Training Data� set

FDA

FDA

FDA

FDA and PIBR

Private Industry, Government, Literature, and FDA

Coverage Measure

Presence in Molecular Feature Domain

Presence of 2–3 Atom Unknown Fragments

Descriptorbased Membership in Class

None

None

Operating System

Windows Desktop

Windows Desktop

Windows Desktop

Windows Windows Desktop Desktop (client server work station)

1â•… No endorsement implied.

The basis for selection and use of these computational platforms is principally driven by the scientific technical approach by each software programme as it relates to QSAR methodology and structural interpretation of chemical molecular features. Multiple approaches to the assessment of molecular features are necessary in making computational toxicology predictions that are reliable [6]. The more diverse, the more opportunity in general that computational analysis of a chemical or drug will lead to the correct prediction since chemical space itself is diverse. Thus, the use of multiple computational toxicology software applications is anticipated to be complementary from the technical standpoint. To help illustrate this point, Table 3 describes the varying computational approaches employed by the software that have been evaluated at the FDA. The main differences between the computational platforms are in the QSAR methodology and structural interpretation of molecular features in a chemical that is screened in silico. An important consideration is whether a computational software programme offers a means to assess molecular coverage. One common feature among all the computational software that FDA uses and for a large portion of other software programmes available otherwise, is that the molecular descriptors used to assess structure are two-dimensional. For some software

83

84

Contributions

programmes however, there are plans to add three-dimensional molecular descriptors. Another common feature of the computational software programmes is that they operate under a Windows Desktop environment.

3.3.5 Why Use In Silico Strategies as a Novel Approach to Assess Toxicity of Phytochemicals? Phytochemicals are often encountered as “active” (desirable or undesirable) constituents in mixtures for which there are little or no toxicological data. Yet given their high potential for human exposure through dietary sources in conventional food and supplement products, there is a practical need for assessing their toxicity using efficient and reliable methods [5, 9]. Moreover, natural products show a vast structural diversity and are being discovered at an accelerating pace. Phytochemicals are a class of substances that present a data-poor situation in assessment of toxicity. Thus, phytochemicals are a prime example of a need for efficient prioritization in testing and screening of chemical toxicity. In terms of chronic toxicity, one of the most important end points to consider for regulatory purposes and protection of human health is the carcinogenic potential of a chemical. Since carcinogenicity is an end point that cannot be tested in humans and is pivotal in regulatory decision-making for many types of substances, including phytochemicals, the in silico methods used by FDA/ CDER were tested to assess their utility for accuracy in screening naturally occurring carcinogens and non-carcinogens. In previous research studies by the FDA Center for Food Safety and Applied Nutrition (CFSAN) and CDER, a dataset of phytochemicals was tested in external validation studies to determine the performance characteristics and overall accuracy of computational toxicology software programmes for predicting rodent carcinogenicity [7, 9]. Results were very encouraging with one computational software programme demonstrating a high level of sensitivity (97â•›%) for predicting rodent carcinogenic phytochemicals, albeit marginal performance was reported for specificity (53â•›%) [7]. Given the practical interest and search for valid methods for high-throughput toxicity screening of phytochemicals, the desire to test multiple computational software platforms, and the lack of toxicological data in the public domain that is needed for safety and risk assessment of phytochemicals, this presentation provides the results from in silico screening of a dataset of non-proprietary phytochemicals for rodent carcinogenicity.

3.3.6 Prediction of Rodent Carcinogenicity of Phytochemicals in an External Validation Study 3.3.6.1 Background

Based on previous external validation testing for predicting rodent carcinogenicity of natural products using computational QSAR modelling at the FDA

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

[5, 9], our interest was to evaluate the performance of two statistical-based computational software platforms for their utility as a predictive tool of toxicity for phytochemicals. As a class, phytochemicals are plant-derived substances that are frequently lacking animal toxicology study data, thus presenting a challenge for risk assessors needing to evaluate their potential human health hazards and safety in consumer products. The hypothesis to be tested is that QSAR modelling could be useful as a decision support tool in safety assessment by providing accurate predictions on the rodent carcinogenic activity of phytochemicals. In addition, the in silico screening approach may serve as a useful tool for prioritizing which phytochemicals should undergo experimental testing for carcinogenicity in the absence of empirical data. The most rigorous method for determining if a QSAR model is performing satisfactorily in terms of its predictive performance for toxicity is to conduct an external validation study. External validation refers to in silico screening a dataset of chemicals that were never used to make the QSAR training dataset, yet the toxicity of the chemicals is known from experimental testing. Furthermore, there are retrospective and prospective types of external validation testing. In this presentation, data are presented from a retrospective external validation study of the rodent carcinogenic activity of phytochemicals.

3.3.6.2 External Validation Test Set

Using the same criteria for selection of phytochemicals as was described in a recent study using external validation [5], a dataset of 43 phytochemicals was screened as an external validation test set using two statistical-based computational software programmes, the Leadscope Model Applier (Leadscope Inc.) and MC4PC (MultiCASE Inc.). The predictive paradigms of these two computational software programmes have been described previously [6]. The external validation test set of 43 phytochemicals that were screened using these approaches is presented in Table 4, where a total of 24 active and 19 inactive phytochemicals are listed. It is notable that the phytochemicals may be found in the human diet as part of conventional foods, spices, flavouring agents, botanical extracts, and ingested ethnotraditional medicinal remedies, thus representing a wide variety of plant-derived natural products from different sources. Results of the external validation testing were used to evaluate the performance of the two software programmes to accurately screen the 43 phytochemicals for rodent carcinogenicity. Rodent cancer bioassay data exist for each of these chemicals and were identified from literature in the public domain as previously described [5]. In addition, a small positive control set of synthetic chemicals which can be found in the human diet mainly as contaminants was also added to the external validation test in order to follow predictive performance. These 10 synthetic chemicals were also not used to construct the QSAR model and are known rodent carcinogens based on bioassay results [5]. The synthetic chemicals are: 4-aminoazobenzene, dibutyltin diacetate, permanent orange, quinoline, trin-butyl phosphate, ammonium perfluorooctanoic acid, 1,3-dichloropropanol,

85

86

Contributions Table 4: Phytochemicals tested with computational toxicology software programmes in external validation. Phytochemical

Natural Occurrence

1‘-Hydroxyestragole

Basil, Ocimum basilicum; Metabolite of estragole

2-Ethyl-1-hexanol

Sassafras, Sassafras albidum; Metabolite of safrole

5-Methoxypsoralen

Parsley, Petroselinum sativum

6-Methylcoumarin

Oregano, Origanum vulgare

Capsaicin

Hot peppers, Capsicum annum

Dehydromonocrotaline

Russian comfrey, Symphytum uplandicum

Estragole

Basil, Ocimum basilicum

Heliotrine

Russian comfrey, Symphytum uplandicum

Vanillin

Vanilla, Vanilla planifolia

Ptaquilosin

Bracken fern, Pteridum aquilinum

Ptaquilosin (APT) dienone

Bracken fern, Pteridum aquilinum

Hydroxysenkirkine

Medicinal herb, Crotalaria laburnifolia

4-Methylphenylhydrazine

Edible mushroom, Agaricus bisporus; metabolite

Allyl hexanoate

Tea Tree oil, Melaleuca alternifolia; flavour

Anethole

Fennel, Foeniculum vulgare

β-apo-8’-carotenal

Carrot, Paucus carota

Citrate

Lemon, Citrus limon

Crotonaldehyde

Potato, Solanum tuberosum

Curcumin

Turmeric, Curcuma longa

Epicatechin

Green Tea, Camellia sinensis

Formic acid

Carrot, Paucus carota

Gallic acid

Mango, Mangifera indica

Indole

Corn, Zea mays

Indole-3-acetic acid

Strawberry fruit, Fragaria vesca

Linalool

Apricots, Prunus armeniaca

Lipoic acid

Spinach, Spinacia oleracea

Maltol

Roasted coffee, Coffea arabica

Piperonal

Vanilla, Vanilla planifolia

Piperine

Black pepper, Piper nigrum

Intermedine

Comfrey, Symphytum officinale

Isosafrole

Oil of Sassafras, Sassafras albidum

Jacobine

Ragwort herb, Senecio jacobaea

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

Lycopsamine

Comfrey, Symphytum officinale

Methylglyoxal

Roasted coffee, Coffea arabica

Parasorbic acid

Rowan berry, Sorbus aucubaria

Propionic acid

Tomato, Lycopersicon esculentum

Retronecine

Medicinal herb, Crotalaria laburnifolia

Senecionine

Ragwort herb, Senecio jacobaea

Seneciphyllinine

Ragwort herb, Senecio jacobaea

Tannic acid

Tea, Camellia sinensis

Hydroxymethylphenylhydrazine

Edible mushroom, Agaricus bisporus

1-Octacosanol

Perilla seeds, Perilla frutescens

Protocatechuic acid

Shallot onions, Allium cepa

2-chloro-1,2-propanediol, 4-hydroxyphenylacetamide, and diazoaminobenzene. The procedures used for in silico screening of the two-dimensional structures in the validation test set have been described previously [5, 6].

3.3.6.3 Rodent Carcinogenicity Models

A total of seven predictive QSAR models for the rodent carcinogenicity end point per software were used to in silico screen the phytochemical test set. The carcinogenicity QSAR models used in the external validation test are defined according to the species/ gender data that were used to construct the model as follows: rat, male rat, female rat, mouse, male mouse, female mouse, and rodent composite. Specific details as to how these QSAR models were built have been described previously [6, 10–11]. Generally, the models are comprised of over 24,500 study records, and contain over 1500 QSAR modellable chemicals from sources including the US National Toxicology Program, FDA/CDER archives, International Agency for Research on Cancer, the Gold Carcinogenicity Potency Database, and the published literature.

3.3.6.4 External Validation Predictive QSAR Performance Statistics

The results of the statistical analysis of the predictive performance of the Leadscope Model Applier software programme in the external validation test are presented in Table 5.╃The results show only marginal performance for predicting carcinogens (sensitivity) and non-carcinogens (specificity) using this data test set of 43 phytochemicals. Overall concordance followed suit with the performance for sensitivity and specificity. Table 6 presents the results of the statistical analysis of the predictive performance of the MC4PC software programme using the same external validation test set. Predictive performance for specificity

87

88

Contributions Table 5: External validation statistics for the Leadscope Model Applier computational software programme for predicting rodent carcinogenicity of 43 phytochemicals and 10 synthetic dietary chemicals based on QSAR analysis of seven models. Statistical Performance Parameters €

Experimental

€

+

–

+

16

7

–

14

9

€

Coverage

100.00â•›%

€

Specificity

56.25â•›%

€

Sensitivity

53.33â•›%

€

Concordance

54.35â•›%

€

False Positives

43.75â•›%

€

False Negatives

46.67â•›%

€

Positive Predictivity

69.57â•›%

Prediction€

€

Table 6: External validation statistics for the MC4PC computational software programme for predicting rodent carcinogenicity of 43 phytochemicals and 10 synthetic dietary chemicals based on QSAR analysis of seven models. Statistical Performance Parameters €

Experimental

€

+

–

+

14

1

–

16

15

€

Coverage

100.00â•›%

€

Specificity

93.75â•›%

€

Sensitivity

46.67â•›%

€

Concordance

63.04â•›%

€

False Positives

6.25â•›%

€

False Negatives

53.33â•›%

€

Positive Predictivity

93.33â•›%

Prediction€

€

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

was very high at 94â•›%, but there was poor sensitivity (47â•›%) with the phytochemical test set. Because of the high degree of specificity, a high false-negative rate occurred as expected. With the low performance for sensitivity, a low falsepositive rate was observed. However, high positive predictivity was observed in the statistical performance data suggesting that if there is a positive prediction it is likely to be accurate. Overall concordance was marginal with a value of 63â•›%. Coverage for both programmes was excellent meaning that the software programmes were able to screen all 43 phytochemicals and 10 synthetics run against the models, thus suggesting that the chemical space of the phytochemicals and synthetics was adequately represented by the rodent carcinogenicity QSAR training datasets that were used in the predictive modelling.

3.3.6.5 Discussion

The results from the external validation test of QSAR predictive performance for the rodent carcinogenicity of 43 phytochemicals and 10 synthetic chemicals using two different computational toxicology software programmes show that it was possible to have excellent predictive performance for specificity using one software programme but there was poor performance in sensitivity with both programmes. Taking a close look at the internal validation statistics for the software which performed well in predicting non-carcinogens, the predictive performance based on internal validation is also high for specificity (>90â•›%) but low for sensitivity (<60â•›%) [6, 10]. Thus, the internal validation statistics from the model construction seem to be consistent with the results from the present external validation test. In contrast to the present data, a previous external validation test with a larger dataset of phytochemicals proved to have high sensitivity and low specificity, with overall good concordance (80â•›%) [5]. There are several reasons that could explain the differences in predictive performance between the computational software. The QSAR training set in this previous study is different than the QSAR training set used in the current study. Moreover, multiple carcinogenicity models were tested in this study, whereas, only one model was tested previously. Also, the number of phytochemicals tested in this study is smaller than that of the previous work suggesting that in the case of the Leadscope Model Applier the predictive performance might improve if more phytochemicals were screened. The challenge with external validation studies is finding a sufficient number of chemicals that have the experimental data needed to determine concordance with the model, yet were never used to make the model, since most modelling building efforts end up gathering all available data leaving few chemicals available for testing purposes. When taken together with the previous validation work on phytochemicals, the results from this validation exercise suggest the usefulness of using multiple computational software programmes for predicting a critical toxicological end point such as rodent carcinogenicity, and also underscores the importance of examining how a QSAR model is built and its internal validation statistics, as these statistics will be indicative of how the model will perform when put into practical use.

89

90

Contributions

3.3.7 Conclusions Applied regulatory research is being conducted in the FDA CDER Office of Pharmaceutical Science that uses chemical structure-based in silico toxicology approaches (computational models) driven by algorithms and expert rules for making predictive toxicology assessments using available experimental toxicological studies and human clinical data that are integrated into high performance software programmes and databases. In silico approaches are an evidencebased method supported by FDA’s Critical Path Initiative. These programmes are often designed to predict the toxicity of chemicals with potential public health concern. There is widespread regulatory interest in the use of in silico approaches as a tool for helping to allay or raise concern for a chemical early in development, or when traditional toxicology studies are unavailable, equivocal or inadequate for safety assessment. Rodent carcinogenicity of a chemical is pivotal evidence for regulatory safety assessments, and thus is the most commonly in silico modelled toxicological end point. Phytochemicals are a class of natural products that are highly diverse in structural features and found in many different types of consumer products. They frequently present as data poor for empirical evidence needed in safety and risk assessments, and highlight a need for efficient prioritization in testing and screening of toxicity due to the high level of financial investment needed to conduct a rodent carcinogenicity study. It is proposed that computational toxicology approaches with phytochemicals may help overcome the challenge of missing or equivocal toxicology data. External validation studies are an excellent test method of determining if a computational model shows predictivity for its intended application. In this study, data were presented at the DFG symposium using a test set of phytochemicals and a small group of synthetic dietary chemicals in an external validation exercise with two different statistical-based computational software programmes in order to assess performance for predicting rodent carcinogenicity. Previous work using similar phytochemicals had indicated promising results using only one computational software programme. It appears from the current work that use of a combination of computational software programmes would be the best approach for supporting higher predictive accuracy since the algorithms and methods for structural interpretation vary between programmes. Thus it was concluded that screening phytochemicals using multiple computational toxicology programmes is anticipated to lead to a maximization of sensitivity and specificity. Currently, computational toxicology methods do not address screening complex mixtures such as botanical extracts, but can be used with individual phytochemicals for a risk assessment approach on a per chemical basis. It was concluded that natural carcinogens are not rare, and that computational QSAR predictive modelling methodologies hold promise as being a decision support tool in regulatory review of safety and risk assessment for both synthetic and naturally occurring molecules including phytochemicals, the subject of this conference.

In Silico Toxicology Screening of the Rodent Carcinogenic Potential of Phytochemicals Using Quantitative Structure–Activity Relationship Analysis

Acknowledgements The authors wish to gratefully thank Dr. Kirk Arvidson and Ms. Emily Busta of the FDA Center for Food Safety and Applied Nutrition for their assistance with some of the computational data that were presented at the conference. The authors thank Ms. Barbara Minnier for curating the molecular structures and running MC4PC predictions, and Dr. Edwin Matthews for building QSAR models.

Conflict of Interest Statement This research report is not an official US Food and Drug Administration guidance or policy statement. No official support or endorsement by the US Food and Drug Administration is intended or should be inferred.

References â•⁄ 1.╃ Yang, C., Valerio, L.╃G.╃Jr., Arvidson, K.╃B. (2009) Computational toxicology approaches at the U.╃S. Food and Drug Administration. ATLA. 37, 523– 553. â•⁄ 2. FDA (2004) Challenge and opportunity on the critical path to new medical products. Available at URL: Http://www.Fda.Gov/oc/initiatives/criticalpath/whitepaper.Html accessed March 1, 2009.╃US Department of Health and Human Services, US Food and Drug Administration, Rockville, MD.╃ â•⁄ 3.╃ RC (2007) Toxicity testing in the 21st century; a vision and a strategy. National Academy Press, Washington, D.╃C.╃ â•⁄ 4. Lahl, U., and Gundert-Remy, U. (2008) The use of (Q)SAR methods in the context of REACH. Toxicol. Mech. Methods. 18, 149–158. â•⁄ 5.╃ Valerio, L.╃G., Jr., Arvidson, K.╃B., Chanderbhan, R.╃F., Contrera, J.╃F. (2007) Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. Toxicol. Appl. Pharmacol. 222, 1–16. â•⁄ 6.╃ Matthews, E.╃J., Kruhlak, N.╃L., Benz, R.╃D., Contrera, J.╃F., Marchant, C.╃A., Yang, C. (2008) Combined use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows software to achieve high-performance, high-confidence, mode of action-based predictions of chemical carcinogenesis in rodents. Toxicol. Mech. Methods. 2–3, 189–206. â•⁄ 7.╃ Valerio, L.╃G. Jr. (2008) Tools for evidence-based toxicology: computationalbased strategies as a viable modality for decision support in chemical safety evaluation and risk assessment. Hum. Exp. Toxicol. 27, 757–760. â•⁄ 8.╃ Matthews, E.╃J., Contrera, J.╃F. (2007) In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects. Expert Opin. Drug. Metab. Toxicol. 3, 125–134.

91

92

Contributions

â•⁄ 9.╃ Arvidson, K.╃B., Valerio, L.╃G. Jr., Diaz, M., Chanderbhan, R.╃F. (2008) In silico toxicological screening of natural products. Toxicol. Mech. Methods. 2–3, 229–242. 10.╃ Matthews, E.╃J., Contrera, J.╃F. (1998) A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Reg. Toxicol. Pharmacol. 28, 242–264. 11.╃ Contrera, J.╃F., Matthews, E.╃J., Benz, D.╃R. (2003) Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. Reg. Toxicol. Pharmacol. 38, 243–259.

Testing Computational Toxicology Models with€�Phytochemicals

3.4 Testing Computational Toxicology Models with€Â�Phytochemicals Luis G. Valerio Jr.1, Kirk B. Arvidson2, Emily Busta2, Barbara L. Minnier3, Naomi L. Kruhlak4 and R. Daniel Benz4 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 186–194.

Abstract Computational toxicology employing quantitative structure–activity relationship (QSAR) modelling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological end points of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.╃g., active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources, is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programmes evaluated at the FDA, are discussed. One software programme showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other, rather than based on one software, the performance for sensitivity was enhanced. However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programmes with this particular dataset. This study suggests that complementary multiple com-

1

Correspondence to: Dr. Luis G. Valerio, Jr., US Food and Drug Administration, Center for Drug Evaluation and Research, Science and Research Staff, Office of Pharmaceutical Science, White Oak 51 Room 4128, 10903 New Hampshire Ave., Silver Spring, MD 20993–0002, USA. Fax: +╃1 301 796 9997, [email protected].

2

US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Division of Food Contact Notifications, Office of Food Additive Safety, HFS-275, 5100 Paint Branch Parkway, College Park, MD 20740, USA.

3

GlobalNet Services, Inc., 11820 Parklawn Drive, Rockville, MD 20852, USA.

4

US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Testing and Research, Office of Pharmaceutical Science, White Oak 64, 10403 New Hampshire Ave., Silver Spring, MD 20493–0002, USA.

93

94

Contributions

putational toxicology software has to be carefully selected to improve global QSAR predictions for this complex toxicological end point.

3.4.1 Introduction Phytochemicals are widely prevalent in normal dietary sources and can be found in many regulated consumer products. Phytochemicals are natural products and are present as constituents in conventional foods, components of natural mixtures (e.╃g., flavouring agents, botanicals) used as food ingredients, and botanical extracts used as ingredients in dietary supplements and botanical drug products. Unfortunately, a common problem with these substances is the lack of toxicology data that are useful for evaluating the safety of chronic human exposure. Chronic toxicity of a chemical is often pivotal evidence for regulatory decision-making on the safety of the product in which the chemical is present. The carcinogenicity end point is among the most important chronic toxicities used to assess risk for human exposure to chemicals and in safety evaluations of regulated products. Regulatory guidance recommends the use of 2-year rodent carcinogenicity studies in two species and sexes to support the safety of US Food and Drug Administration (FDA) regulated products [1], and there is internationally harmonized guidance [2]. Evidence that a chemical is a rodent carcinogen can adversely impact the regulatory approval of products and knowledge of carcinogenic potential is of great importance to protecting public health [3]. Although the need for rodent carcinogenicity study data for a chemical is great, relatively few substances, especially phytochemicals, have been tested for carcinogenicity. There are several practical reasons for the lack of carcinogenicity test data, including the exorbitant financial cost ($╃3 million for species/ sex standardized study), intensive resources (experts and review), and the long period of time required to conduct the study according to standardized protocols such as those described in US FDA guidance documents [1]. These challenges not withstanding, assessing cancer risk for chemicals humans are exposed to in regulated products is important in the context of hazard and risk characterization, which may lead to regulatory action, and for prioritization of these substances for further study in the interest of protecting public health [3–5]. In silico methods have been proposed as a way to predict both efficiently and accurately the outcome of rodent carcinogenicity studies on the scientific basis of structure–activity relationships [6]. The use of in silico methods is now supported in the EU by enacted legislation in reaction to public desire to reduce the use of animals in testing [7]. Moreover, these methods have been recommended by the US National Research Council [8], and are considered to be useful in setting testing priorities [8, 9]. In silico models of rodent carcinogenicity using quantitative structure–activity relationship (QSAR) analyses of phytochemicals have been previously reported to be a predictive tool indicating some degree of promise for predicting naturally occurring carcinogens derived from plants [6, 10]. At the US FDA Center for Drug Evaluation and Research (CDER), Office of

Testing Computational Toxicology Models with€�Phytochemicals

Pharmaceutical Science (OPS), and the Center for Food Safety and Applied Nutrition (CFSAN), Office of Food Additive Safety (OFAS), Division of Food Contact Notifications (DFCN), the use of computational toxicology software is being employed to help support regulatory decision-making in the safety evaluation of human pharmaceuticals, their metabolites, and impurities, and indirect food additives [6, 9–14]. Given the aforementioned rationale for regulatory use of in silico approaches, its potential application with diverse sets of chemicals, and the data poor situation for phytochemicals in terms of available chronic toxicology study data, the use of in silico modelling for predicting rodent carcinogenicity of these substances is of significant interest. The purpose of the present study was to examine the utility of in silico QSAR-based tools in current use at the FDA for predicting rodent carcinogenicity by employing two computational software programmes to screen a dataset of 43 phytochemicals. Predictive performance was validated by comparing computational predictions to empirical data. This analysis is considered to be an external validation study because the phytochemicals that were screened in silico were not used to construct the predictive QSAR models used by the computational software.

3.4.2 Materials and Methods 3.4.2.1 Computer Hardware and Computational Software Programmes

The computer hardware used for the computational toxicology software programmes in this study was a PC with Microsoft Windows XP Professional version 2002.╃The computational software programmes used were Leadscope Model Applier (LMA) version 1.0, available from Leadscope Inc. (www.leadscope. com) [15], and MC4PC version 2.1.0.11, available from MultiCASE Inc. (www. multicase.com) [16]. The following seven QSAR rodent carcinogenicity models were employed with the LMA in the Rodent Carcinogenicity suite: carc mouse, carc mouse female, carc mouse male, carc rat, carc rat female, carc rat male, and in the Miscellaneous Toxicity End point Suite: carc rodent. The QSAR prediction paradigm used in the LMA software has been described previously [17]. The following seven QSAR rodent carcinogenicity models were employed with the MC4PC software: carcinogenicity -rodents (+proprietary) AGU, carcinogenicity -rats (+proprietary) AGV, carcinogenicity -male rats (+proprietary) AG1, carcinogenicity -female rats (+proprietary) AG2, carcinogenicity -mice (+proprietary) AGW, carcinogenicity -male mice (+proprietary) AG3, carcinogenicity -female mice (+proprietary) AG4. Details on how the carcinogenicity models were built (including the acceptance criteria for rodent carcinogenicity data, classification, stratification, and scoring of rodent tumour findings) have been described previously [17, 18]. The predictive paradigm for the QSAR computational software has also been described previously [17].

95

96

Contributions

3.4.2.2 Dataset

Forty-three phytochemicals with rodent carcinogenicity studies were identified from sources in the public domain as described previously [6]. Briefly, these substances are organic molecules with a known activity or inactivity for rodent carcinogenicity. In addition, a dataset of ten synthetic chemicals with known rodent carcinogenic activity was added to increase the robustness of the study by adding statistical power to the analysis of predictive performance. The synthetic chemicals were a group of constituents obtained from CFSAN files that are known carcinogenic chemicals [6]. All molecules described in this study were non-proprietary and 2-dimensional molecular structures may be found in the public domain at the US National Library of Medicine’s PubChem web site (http://pubchem.ncbi.nlm.nih.gov/).

3.4.2.3 Assessment of Experimental Evidence for Rodent Carcinogenicity and �External Validation Experiment

The assessment of experimental evidence for rodent carcinogenicity was performed using published chronic bioassay data and conclusions published in the US National Toxicology Program, International Agency for Research on Cancer, Priority-based Assessment of Food Additives, and Gold Carcinogenicity Potency Database resources. Because of the small number of phytochemicals with rodent carcinogenicity study data, the carcinogenicity of the phytochemicals and synthetic chemicals was evaluated irrespective of target organs for tumour formation. Any positive or negative finding from the published literature of carcinogenicity studies was based on the author’s opinion in the published paper. Phytochemicals lacking chronic carcinogenicity studies were excluded from this study. In order to perform the external validation study, the predictive performance of the rodent carcinogenicity QSAR models was tested with a set of 43 phytochemicals, comprising 24 active (high carcinogenic potential) and 19 inactive (low carcinogenic potential) molecules, and a set of ten synthetic chemicals to increase the robustness of the statistical analysis. None of the 53 external validation chemicals were ever part of the QSAR model training datasets.

3.4.2.4 Statistical Analysis of Predictive Performance

The QSAR model performance for predicting the carcinogenicity end point was calculated according to the method of Cooper et al. [19]. The parameters include sensitivity, specificity, positive predictivity, negative predictivity, false positives, false negatives, concordance, and Chi-square (χ2). The sensitivity index is defined as the percentage of correctly predicted carcinogens from the total number of carcinogens. Specificity is defined as the percentage of correctly predicted non-carcinogens from the total number of non-carcinogens. Positive

Testing Computational Toxicology Models with€�Phytochemicals

predictivity is defined as the percentage of correctly predicted carcinogens from the total number of positive predictions from the test, and negative predictivity is defined as the percentage of correctly predicted non-carcinogens from the total number of negative predictions from the test. The false positives represent the percentage of incorrectly classified non-carcinogens from the total number of carcinogens. The false-negative parameter represents the percentage of incorrectly classified carcinogens from the total number of carcinogens. Concordance is defined as the percentage of correctly predicted non-carcinogens and carcinogens from the total number of chemicals tested. The χ2€ test is an overall measure of the association between the predicted and experimental results for the test chemicals, and is used to express the degree of association relative to a confidence level (p-value). Coverage is a measure of the percentage of chemicals in the test set for which the model is able to make a prediction, and is a reflection of the relationship of the molecular diversity of the training dataset with the test chemical being screened by the computational software programme. Test chemicals containing structural features not well represented in the training dataset are considered to be uncovered and, consequently, no prediction call can be made for them and these outputs by the computational software are referred to as “no call”.

3.4.2.5 Combining Predictions from two Software Programmes

Predictions from both MC4PC and LMA were combined to assess whether the overall predictive performance improved as compared to that of any single programme. The following protocol was used: If at least one of the two software programmes predicted a test chemical (i.╃e. phytochemical) to be positive then the molecule was given an overall positive call. The only situation leading to an overall negative call was consequently when both software programmes gave negative (consensus) predictions. In cases where a test chemical was covered in only one of the two software programmes, the prediction from the single software programme became the overall call. In the single case where a compound was uncovered by both software programmes, no overall call was made (i.╃e. “no call”). The χ2€test was used to determine whether the degree of association between the combined predictions and experimental calls was higher or lower than that of each software programme alone.

3.4.3 Results The 43 phytochemicals used for this validation study are presented in Table 1, along with their respective natural sources, and the set of ten synthetic chemicals, which are known rodent carcinogens, are listed in Table 2.╃Together these 53 compounds were run as a test set against the MC4PC and LMA computational software models in order to assess each programme’s respective performance in predicting rodent carcinogenicity.

97

98

Contributions Table 1: Forty-three phytochemicals tested with computational toxicology software programmes in external validation. Phytochemical

Natural Occurrence

1‘-Hydroxyestragole

Basil, Ocimum basilicum; Metabolite of estragole

2-Ethyl-1-hexanol

Sassafras, Sassafras albidum; Metabolite of safrole

5-Methoxypsoralen

Parsley, Petroselinum sativum

6-Methylcoumarin

Oregano, Origanum vulgare

Capsaicin

Hot peppers, Capsicum annum

Dehydromonocrotaline

Russian comfrey, Symphytum uplandicum

Estragole

Basil, Ocimum basilicum

Heliotrine

Russian comfrey, Symphytum uplandicum

Vanillin

Vanilla, Vanilla planifolia

Ptaquilosin

Bracken fern, Pteridum aquilinum

Ptaquilosin (APT) dienone

Bracken fern, Pteridum aquilinum

Hydroxysenkirkine

Medicinal herb, Crotalaria laburnifolia

4-Methylphenylhydrazine

Edible mushroom, Agaricus bisporus; metabolite

Allyl hexanoate

Tea-tree oil, Melaleuca alternifolia; flavour

Anethole

Fennel, Foeniculum vulgare

Beta-apo-8’-carotenal

Carrot, Paucus carota

Citrate

Lemon, Citrus limon

Crotonaldehyde

Potato, Solanum tuberosum

Curcumin

Turmeric, Curcuma longa

Epicatechin

Green Tea, Camellia sinensis

Formic acid

Carrot, Paucus carota

Gallic acid

Mango, Mangifera indica

Indole

Corn, Zea mays

Indole-3-acetic acid

Strawberry fruit, Fragaria vesca

Linalool

Apricots, Prunus armeniaca

Lipoic acid

Spinach, Spinacia oleracea

Maltol

Roasted coffee, Coffea arabica

Piperonal

Vanilla, Vanilla planifolia

Piperine

Black pepper, Piper nigrum

Intermedine

Comfrey, Symphytum officinale

Isosafrole

Oil of Sassafras, Sassafras albidum

Jacobine

Ragwort herb, Senecio jacobaea

Testing Computational Toxicology Models with€�Phytochemicals

Lycopsamine

Comfrey, Symphytum officinale

Methylglyoxal

Roasted coffee, Coffea arabica

Parasorbic acid

Rowan berry, Sorbus aucubaria

Propionic acid

Tomato, Lycopersicon esculentum

Retronecine

Medicinal herb, Crotalaria laburnifolia

Senecionine

Ragwort herb, Senecio jacobaea

Seneciphyllinine

Ragwort herb, Senecio jacobaea

Tannic acid

Tea, Camellia sinensis

Hydroxymethylphenylhydrazine

Edible mushroom, Agaricus bisporus

1-Octacosanol

Perilla seeds, Perilla frutescens

Protocatechuic acid

Shallot onions, Allium cepa

Table 2: Ten synthetic chemicals known to be rodent carcinogens tested with computational toxicology software programmes in external validation. Synthetic Chemicals 1,3-Dichloropropanol 3-Chloro-1,2-propanediol 4-Aminoazobenzene 4-Hydroxyphenylacetamide Ammonium perfluorooctanoic acid Diazoaminobenzene Dibutyltin diacetate Permanent orange Quinoline Tri-n-butyl phosphate

The predictive performance statistics for MC4PC on the 53 natural and synthetic compounds are summarized in Table 3.╃MC4PC made correct predictions for 94╛% of the phytochemicals with experimentally-determined low-risk for rodent carcinogenicity. This specificity value is considered to be of high performance. For phytochemicals with experimentally-determined high risk for inducing carcinogenicity in rodents, the programme predicted only 47╛% of the compounds correctly. This sensitivity value is considered of low or poor performance. Concordance for accurate predictions was 63╛%, a marginal value overall since it is only slightly above 50╛%. However, a low false-positive rate and high positive predictivity (94╛%) was observed. A total of four phytochemicals were not in the domain of applicability of the MC4PC QSAR training set,

99

100

Contributions Table 3: External validation statistics for the MC4PC computational software programme for predicting rodent carcinogenicity of 43 phytochemicals and ten synthetic dietary chemicals based on QSAR analysis of seven models. Performance Parameters: MC4PC

Value

Coverage

92â•›%

Specificity

94â•›%

Sensitivity

47â•›%

Concordance

63â•›%

False Positives

6â•›%

False Negatives

53â•›%

Positive Predictivity

94â•›%

Negative Predictivity

48â•›%

Chi-square

8.4835 (p=╃0.0036)

and thus a prediction could not be made for these molecules (i.╃e., no call). The Chi-square test gave a value of 8.4835 with p=╃0.0036, showing the overall predictive performance of this model to be highly statistically significant. Table 4 summarizes the performance parameters for the LMA when assessing the 53 natural and synthetic compounds. The LMA was able to correctly predict 59╛% of the phytochemicals with low-risk for rodent carcinogenicity and only 50╛% of the high-risk rodent carcinogens, leading to an overall concordance of 53╛%. Positive predictivity was higher at nearly 70╛% suggesting that when a positive prediction is made it will be made with a higher level of confidence for this class of chemicals; however, the negative predictivity was as low as 38╛% Table 4: External validation statistics for the Leadscope Model Applier computational software programme for predicting rodent carcinogenicity of 43 phytochemicals and ten synthetic dietary chemicals based on QSAR analysis of seven models. Performance Parameters: Leadscope Model Applier

Value

Coverage

92â•›%

Specificity

59â•›%

Sensitivity

50â•›%

Concordance

53â•›%

False Positives

41â•›%

False Negatives

50â•›%

Positive Predictivity

70â•›%

Negative Predictivity

38â•›%

Chi-square

0.3470 (p=╃0.5558)

Testing Computational Toxicology Models with€�Phytochemicals

suggesting that the opposite is true of negative predictions. A total of four phytochemicals were not in the domain of applicability of the LMA training set, and thus a prediction could not be made (i.╃e., no call). The Chi-square test resulted in a value of only 0.3470 with a p value of 0.5558, indicating a correlation between predicted and experimental values that is not statistically significant. Table 5: Concordance between the MC4PC overall rodent call and the Leadscope maximum positive prediction for rodent carcinogenic potential for the 43 phytochemicals and ten synthetic chemicals. Phytochemical

Experimental MC4PC Evidence based Overall Carcinogenic Rodent Call Risk

Leadscope Overall Rodent Call

Concordance (√) or nonconcordance (x) of MC4PC prediction with Leadscope prediction

1‘-Hydroxyestragole

high

-

-

√

1-Octacosanol

low

-

-

√

2-Ethyl-1-hexanol

high

+

-

x

4-Methylphenylhydrazine high

+

+

√

5-Methoxypsoralen

high

+

+

√

6-Methylcoumarin

high

+

-

x

Allyl hexanoate

low

-

-

√

Anethole

low

-

+

x

Beta-apo-8‘carotenal

low

No Call

-

x

Capsaicin

high

+

-

x

Citrate

low

-

-

√

Crotonaldehyde

high

-

No Call

x

Curcumin

low

+

+

√

Dehydromonocrotaline

high

+

No Call

x

Epicatechin

low

-

-

√

Estragole

high

+

-

x

Formic acid

low

-

+

x

Gallic acid

low

-

+

x

Heliotrine

high

-

+

x

Hydroxymethylphenyl� hydrazine

high

-

+

x

Hydroxysenkirkine

high

+

+

√

Indole

low

-

No Call

x

101

102

Contributions

Indole-3-acetic acid

low

-

-

√

Intermedine

high

-

+

x

Isosafrole

high

-

+

x

Jacobine

high

+

+

√

Linalool

low

-

-

√

Lipoic acid

low

-

-

√

Lycopsamine

high

-

+

x

Maltol

low

-

-

√

Methylglyoxal

high

-

-

√

Parasorbic acid

high

-

-

√

Piperine

low

-

+

x

Piperonal

low

-

-

√

Propionic acid

high

-

-

√

Protocatechuic acid

low

-

+

x

Ptaquilosin

high

-

-

√

Ptaquilosin (APT) dienone high

No Call

-

x

Retronecine

high

-

+

x

Senecionine

high

+

+

√

Seneciphyllinine

high

+

+

√

Tannic acid

low

No Call

No Call

√

Vanillin

low

-

+

x

1,3-Dichloropropanol

high

+

-

x

3-Chloro-1,2-propanediol

high

+

-

x

4-Aminoazobenzene

high

+

+

√

4-Hydroxyphenyl� acetamide

high

-

-

√

Ammonium �perfluorooctanoic acid

high

-

+

x

Diazoaminobenzene

high

+

+

√

Dibutyltin diacetate

high

No Call

-

x

Permanent orange

high

-

+

x

Quinoline

high

-

-

√

Tri-n-butyl phosphate

high

-

-

√

(-) indicates a negative prediction, low-rodent carcinogenic risk, and (+) indicates a positive Â�prediction, high-rodent carcinogenic risk, while “No Call” means the chemical is not in the Â�domain of applicability of the training set data.

Testing Computational Toxicology Models with€�Phytochemicals

A comparison of the predictions made by MC4PC and LMA software is provided in Table 5.╃Comparing the predictions between MC4PC and LMA, 51â•›% of the predictions were concordant between the two programmes while 49â•›% of the predictions were non-concordant between the two programmes. Of the concordant predictions, 63â•›% corresponded to low-risk predictions made by both of the programmes. Within the non-concordant predictions, MC4PC made a higher percentage of correct low-risk rodent carcinogenicity predictions. However, LMA correctly predicted a greater number of high-risk calls (54â•›%). An analysis of the predictive performance when combining the results was performed and is presented in Table 6.╃The protocol used for combining the results favoured sensitivity by accepting any single positive as the basis for an overall positive call. This led to more positive calls and an increase in sensitivity (to 68â•›%) as compared to the use of any one software programme alone. Specificity slightly increased compared to one software programme but by comparison decreased with the other programme. However, the level of negative predictivity was maintained upon combining results due the consensus-derived negative calls. False-positive and negative rates were comparable. The increase in sensitivity while maintaining negative predictivity when results from two different computational toxicology software programmes are combined supports the use of multiple (2 or more) computational software packages in applications where tolerance for risk is low. The association between experimental carcinogenicity and the combined predictions from the two software programmes gave a Chi-square index of χ2╃=╃3.9878 with a p value of 0.0458.╃This indicates a statistically significant association, but the association is lower than that of MC4PC used alone.

Table 6: External validation statistics for the consensus predictions made by the Leadscope Model Applier and MC4PC software programmes combined for predicting rodent carcinogenicity of 43€phytochemicals and ten synthetic dietary chemicals based on QSAR analysis. Performance Parameters: Consensus

Value

Coverage

98â•›%

Specificity

61â•›%

Sensitivity

68â•›%

Concordance

65â•›%

False Positives

39â•›%

False Negatives

32â•›%

Positive Predictivity

77â•›%

Negative Predictivity

50â•›%

Chi-square

3.9878 (p=╃0.0458)

103

104

Contributions

3.4.4 Discussion Human exposure to phytochemicals occurs on a daily basis, mainly through dietary sources. However, few chronic toxicity studies have been conducted for this class of chemicals, and this information is needed to help understand potential toxicological risks. Therefore, validated tools for risk assessment of these substances would be useful to help assess their safety. In the present study, the objective was to evaluate the utility of in silico predictive global QSAR-based tools currently used at the FDA for predicting rodent carcinogenicity of a set of phytochemicals. Rodent carcinogenicity is the most commonly computationally modelled end point for several reasons, including cost- and time-efficiency as compared to that of a traditional 2-year rodent bioassay, and its prominence as pivotal evidence for regulatory risk assessors and safety evaluators. In this study, two different in silico QSAR-based tools were employed to screen an external validation set of phytochemicals and synthetic dietary compounds in the interest of assessing the predictive performance of the software programmes and determining their usefulness as a substitute for in vivo testing. The results found excellent predictive performance with MC4PC in the context of predicting non-carcinogens (Tab.╃3); however, the performance for predicting carcinogens was poor for both MC4PC and LMA, with sensitivity of approximately 50â•›% (Tab.╃3 and 4). Examination of the internal validation statistics used to establish the models clearly demonstrates that performance was optimized for predicting non-carcinogens: The models were constructed to provide higher specificity with less concern for sensitivity or negative predictivity [17]. The best approach(es) in terms of model performance for sensitivity or specificity is still being evaluated at FDA/CDER and CFSAN. Thus, because of this fact and larger relative number of inactives in the QSAR training sets of these models, the expectation in predictive performance would be for high specificity not high sensitivity. The results of this study are consistent with other validation studies for these models [17, 18, and 20]. Overall, concordance was not optimal for both software programmes for predicting the known rodent carcinogenicity of the test set, but when results were combined between the two computational software programmes, the analysis yielded higher sensitivity at the expense of specificity (Tab.╃6). It could be argued that a larger training set of chemicals is needed in order to improve performance, or that there is a lack of appropriate descriptors in the QSAR programmes that are needed to identify SAR relevant oncogenic activity of these molecules. In addition, lack of natural product representation in the training dataset could be another point to consider. However, the training sets are considered “multipurpose” in that they contain pharmaceuticals, food additives, industrial chemicals, pesticides and some natural products, so a lack of phytochemical representation in the model may not be an issue. Other considerations are that the software evaluated in this study use global QSARs, pooling knowledge of carcinogenicity by multiple mechanisms of action, in contrast to mechanism-specific local QSARs. The latter often show good predictive performance for test compounds within a narrow applicability

Testing Computational Toxicology Models with€�Phytochemicals

domain but are limited when predictions are needed for a broad range of molecules. With respect to objectively assessing performance, the real dilemma in conducting external validation studies is that when new carcinogenicity studies are published, they are rapidly incorporated into predictive models rather than used in an external validation study. Thus, it is difficult to find a large dataset of chemicals with toxicology study data that were never used to construct the QSAR model. The calculated association between the carcinogenicity results and the predictions in this study, as measured by Chi-square values, indicated that one software programme had statistically significant discriminatory power over the other. When the predictions from the two software programmes were combined, the degree of association between experimental and predicted findings was still considered statistically significant but at a lower confidence level than when one of the software programmes was used alone. While this may suggest that there is no benefit to combining predictions from the two software programmes, the significant increase in sensitivity observed by applying this methodology supports its use in some situations, particularly in a regulatory environment where the goal is to minimize the risk to human health. A comparison of the predictions from the two software programmes one to another found a 50╛% concordance for the test set, and within the set of concordant predictions, specificity remained high at 90╛% (Tab.╃5). Over the entire test set, it was also notable that a high positive predictivity value was observed with both computational toxicology software programmes either when assessing the results from the perspective of each individual programme (Tab.╃3 and 4) or combined (Tab.╃6), giving the user a high degree of confidence in a positive prediction. These results taken together with previous external validation studies with natural products using other computational software suggest the desirability to use multiple computational platforms for high or low throughput screening. This notion is supported by the outcome of a previous external validation study with phytochemicals where high sensitivity (97╛%) was achieved for predicting rodent carcinogenicity but poor specificity (53╛%) was observed [6]. One logical approach given the limitations of computational predictive modelling is to combine techniques that perform well for specificity with those computational methods that have demonstrated high sensitivity, and then perform a weight of evidence approach for using multiple predictions from different software programmes. To take this one step further, added confidence can be assigned when predictions from different software programmes are in complete agreement, since each software programme is interpreting molecular structure using different parameters and arriving at the same conclusion. Some studies have tested this possibility with in silico methods showing some success; however, only a few phytochemicals were examined [10], or the chemicals were not natural products [17]. The problem arises in interpreting conflicting predictions and there is no clear level of confidence to assign to the weight of evidence. In such a scenario, the solution may be that it is simply not solv-

105

106

Contributions

able by the computational predictive software and based on current technology there is no way to address this conundrum. From the perspective of regulating food additives, the 1958 amendment to the US Food, Drug, and Cosmetic Act states that: ►⌺

“No additive shall be deemed to be safe if it is found to induce cancer when ingested by man or animal, or if it is found, after tests which are appropriate for the evaluation of the safety of food additives, to induce cancer in man or animal,╃…” (Delaney Clause).

Therefore from a regulatory standpoint, it would be more desirable to better predict possible carcinogens with the aim of identifying them and keeping them out of the food supply. Under this paradigm, the poor ability to predict potential rodent carcinogens by an in silico method would not be acceptable. Thus, the low sensitivity (poor ability for predicting potential rodent carcinogens) of both MC4PC and the LMA when used by themselves is not ideal for regulatory purposes. However, given the software programmes’ higher sensitivity with combined or consensus predictions, and the higher specificity when used alone (better predictions for rodent non-carcinogens), use of these programmes in drug discovery might be envisioned as a desirable predictive tool for filtering a high volume of molecules. This notion has also been recommended by a recent industry paper on the prediction of genotoxicity of human pharmaceuticals [21]. The study also asserted, however, that for predicting the potential of genotoxic impurities in the final drug product, a high sensitivity would be desired [21]. The whole issue of whether it is best to have a predictive model with higher specificity or sensitivity is paradoxical since arguments can be made for either, and it is likely most dependent upon the intended use of the in silico method and potential regulatory framework where it would be applied. One feasible solution suggested by this study is to employ multiple computational platforms that are complementary in the sense that each takes different approaches to molecular structure interpretation. One advantage of such an approach is if one falls short in covering an area of chemical space another can assist in giving a needed prediction for that area, as seen between MC4PC and the LMA where only a single test chemical was uncovered by both programmes (Tab.╃5). The net result in this study was that combined coverage increased by 6â•›%. Thus, the ability of one software package to complement another is an important consideration when screening chemicals. The low predictive performance for sensitivity with both computational software programmes can be related to the way the training sets were constructed for each model and so expectations may be tempered by model construction techniques. Perhaps with an expanded training set of chemicals the statistical parameter of specificity for the LMA might improve. The results of this study also indicate a need for the development of additional training sets emphasizing sensitivity, with follow-up studies to analyze their predictive potential within the regulatory framework. Depending on the anticipated application, screening of drug candidates or use in regulatory review for safety, one could select

Testing Computational Toxicology Models with€�Phytochemicals

the most appropriate set of models, or combination of models, for a particular need. Computational toxicology methods could potentially serve as part of an in silico toolbox of techniques for prioritizing further testing of phytochemicals when used in combination with other evidences (e.╃g., structural alert classification schemes and empirical data). Further research needs to be done with larger training and test datasets containing different classes of compounds, like industrial chemicals, pharmaceuticals, etc. to further investigate and establish computational toxicology software predictive performance. In addition, this study also suggests the need for construction of additional training sets emphasizing sensitivity in order to further evaluate their predictive potential within the regulatory frame for cases when consensus predictions are not adopted. In conclusion, the application of QSAR predictive computational models for rodent carcinogenic activity of phytochemicals is found to be useful for predicting non-carcinogens and, if a consensus approach is adopted, an improvement in sensitivity for predicting carcinogens is achieved. The deployment of multiple computational platforms may well be the optimal way of utilizing this type of predictive information in order to best fit into the safety and risk assessment paradigms in which these substances are encountered in; however, the software used for this approach must be carefully selected to ensure complementarity in predictive performance. Further research is needed to help make this determination.

Acknowledgements The authors acknowledge Dr. Edwin Matthews for building the QSAR models.

Abbreviations QSAR: quantitative structure–activity relationship ICSAS: Informatics and Computational Safety Analysis Staff.

Conflict of Interest Statement The authors have declared no conflicts of interest. This research report is not an official US Food and Drug Administration guidance or policy statement. No official support or endorsement by the US Food and Drug Administration is intended or should be inferred.

107

108

Contributions

References â•⁄ 1.╃ FDA, Guidance for industry: Carcinogenicity study protocol submissions. FDA Center for Drug Evaluation and Research (CDER), May. URL accessed May 10, 2009, http://www.fda.gov/downloads/chup/guidancecompliance regulatoryinformation/guidance/ucm078924.pdf. Department of Health and Human Services 2002. â•⁄ 2.╃ ICH, Guidance for industry: The need for long-term carcinogenicity studies of pharmaceuticals. ICH1A. http://www.fda.gov/downloads/chup/guidance complianceregulatoryinformation/guidance/ucm074911.pdf â•⁄ 3.╃ Jacobs, A., Jacobson-Kram, D., Human carcinogenic risk evaluation, Part III: Assessing cancer hazard and risk in human drug development. Toxicol Sci 2004, 81, 260–262. â•⁄ 4.╃ Contrera, J.╃F., Jacobs, A.╃C., DeGeorge, J.╃J., Carcinogenicity testing and the evaluation of regulatory requirements for pharmaceuticals. Regul Toxicol Pharmacol 1997, 25, 130–145. â•⁄ 5.╃ Jacobs, A., Prediction of 2-year carcinogenicity study results for pharmaceutical products: how are we doing? Toxicol Sci 2005, 88, 18–23. â•⁄ 6.╃ Valerio, L.╃G., Jr., Arvidson, K.╃B., Chanderbhan, R.╃F., Contrera, J.╃F., Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. Toxicol Appl Pharmacol 2007, 222, 1–16. â•⁄ 7. EU, Regulation (EC) No 1907/2006 of The European Parliament and of the Council concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Official Journal of the European Union 2006, L396, 1–843. â•⁄ 8. NRC, Toxicity testing in the 21st Century; a vision and a strategy, National Academy Press, Washington, D.╃C.╃2007. â•⁄ 9. Bailey, A.╃B., Chanderbhan, R., Collazo-Braier, N., Cheeseman, M.╃A., Twaroski, M.╃L., The use of structure–activity relationship analysis in the food contact notification program. Regul Toxicol Pharmacol 2005, 42, 225– 235. 10. Arvidson, K.╃B., Valerio, L.╃G., Diaz, M., Chanderbhan, R.╃F., In silico toxicological screening of natural products. Toxicology Mechanisms and Methods 2008, 18, 229–242. 11. Yang, C., Benz, R.╃D., Cheeseman, M.╃A., Landscape of current toxicity databases and database standards. Curr Opin Drug Discov Devel 2006, 9, 124–133. 12. Matthews, E.╃J., Contrera, J.╃F., In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects. Expert Opin Drug Metab Toxicol 2007, 3, 125–134.

Testing Computational Toxicology Models with€�Phytochemicals

13. Kruhlak, N.╃L., Contrera, J.╃F., Benz, R.╃D., Matthews, E.╃J., Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Adv Drug Deliv Rev 2007, 59, 43–55. 14. Mayer, J., Cheeseman, M.╃A., Twaroski, M.╃L., Structure–activity relationship analysis tools: validation and applicability in predicting carcinogens. Regul Toxicol Pharmacol 2008, 50, 50–58. 15. Yang, C., Hasselgren, C.╃H., Boyer, S., Arvidson, K., et al., Understanding genetic toxicity through data mining: the process of building knowledge by integrating multiple genetic toxicity databases. Toxicology Mechanisms and Methods 2008, 18, 277–295. 16. Saiakhov, R.╃D., Klopman, G., MultiCASE expert systems and the REACH initiative. Toxicology Mechanisms and Methods 2008, 18, 159–175. 17. Matthews, E.╃J., Kruhlak, N.╃L., Benz, R.╃D., Contrera, J.╃F., et al., Combined use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows software to achieve high-performance, high-confidence, ode of action-based predictions of chemical carcinogenesis in rodents. Toxicology Mechanisms and Methods 2008, 18, 189–206. 18. Matthews, E.╃J., Contrera, J.╃F., A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul Toxicol Pharmacol 1998, 28, 242–264. 19. Cooper, J.╃A., 2nd, Saracci, R., Cole, P., Describing the validity of carcinogen screening tests. Br J Cancer 1979, 39, 87–89. 20. Contrera, J.╃F., Kruhlak, N.╃L., Matthews, E.╃J., Benz, R.╃D., Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. Regul Toxicol Pharmacol 2007, 49, 172–182. 21. Snyder, R.╃D., An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity. Environ Mol Mutagen 2009, 50, 435–450.

109

110

Contributions

3.5 In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data Benoît Schilter1, Manuel Dominguez Estevez, Myriam Coulet, Anne Constable, and Paolo Mazzatorta

Abstract Specific in silico approaches for research and development (R&D) purposes have been designed and are successfully applied to pre-clinical screening of potential drugs in pharmaceutical discovery pipelines, where an early identification of toxicological hazard offers a clear competitive advantage. Applications of such an approach in the food sectors require the development of alternative models which should be global, quantitative and characterized by a high sensitivity. Currently, computational models are available to predict quantitatively chronic rodent and human toxicity. Validation studies indicate a reasonable performance of these models (>85â•›% of compounds tested within +/- 1 log). When taken together in an integrated approach, the models available provide a good coverage of the “world of chemicals”, including food additives, pesticides, environmental contaminants and biologically active substances (human and veterinary drugs). Their application in an integrated strategy is likely to allow establishing levels of safety concern of compounds for which no hard toxicological information is available. The application of such an approach can be valuable in emergency situations to support fast decision-making. It may also help in defining priorities for additional toxicity testing. Its application for botanical extract has still to be defined.

3.5.1 Introduction Over recent years, there has been mounting concern about food as a source of exposure to potentially toxic chemicals. It has been estimated that there are over five million man-made chemicals known, of which 70,000 are in industrial use today [1]. The application of continuously improving analytical methods has revealed that many of these chemicals can enter the food chain and result in human exposure, albeit often at low levels. Since for the vast majority of these chemicals, toxicological information is absent or limited, the assessment of their health significance is therefore difficult or impossible. Nevertheless, the detection of such chemicals in food may trigger not only heavy management actions (e.╃g. public recall) but also alarm resulting in loss of consumer confidence for the food supply. In such situations, the availability of reliable tools

1

Nestlé Research Centre, Quality and Safety Department, P.╃O. Box 44, CH-1026 Lausanne, Switzerland, [email protected].

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

allowing establishing levels of safety concern appear of particular importance to ensure adequate consumer protection without undue over-conservatism. Solutions to this general issue are not straightforward. Experimental toxicology is not a practical tool to deal with emergency situations requiring fast decisionmaking. In this context, in silico predictive models have obvious advantages in terms of time, cost and also animal protection. Ideally, such models should predict safe levels of exposure. A second potential use of predictive models under investigation relates to naturally occurring chemicals in foods. Because of potential health benefits, the application of traditional plants and plant extracts in food products is attracting a growing interest. For many of these ingredients, very limited toxicological information is available and their safety assessment is often based on traditional history of medicinal use, which may not be suitable to cover food applications. One challenge is to address the safety significance of inherent bioactives or toxicants occurring naturally in such plant materials [2]. A battery of in silico toxicology tools may be envisaged to establish the level of safety concern associated with key constituents of plant extracts. Such an approach may be valuable for early decision-making in new product development and to decide on the need for further toxicological studies. In addition, it could be applied to set up specifications and to define analytical requirements for compositional characterization and standardization. Specific in silico approaches for research and development purposes have been already designed and are successfully applied to pre-clinical screening of potential drugs in pharmaceutical discovery pipelines, where an early identification of toxicological hazard offers a clear competitive advantage [3]. Such efforts allow the exclusion of chemicals that could potentially reveal unacceptable based on further mandatory regulatory toxicological tests. In the present paper, in silico models developed to establish levels of safety concern of food chemicals are described. Their potential use in the safety assessment strategy of functional botanical products is briefly discussed.

3.5.2 Computational Toxicology Models Relevant for the Food �Sectors: Requirements As compared to the pharmaceutical area, the situation of the food sector requires the development of alternative models with the following specific characteristics. Quantitative rather than Qualitative Predictions: In the food context, the most likely application of computational toxicology models would be in the establishment of the level of safety concern associated with the inadvertent/accidental presence of a chemical in products. This requires not only qualitative information on the potential hazardous properties of the chemical (e.╃g., carcinogenicity) but also quantitative information (e.╃g., carcinogenic potency), allowing the derivation of a margin of exposure (MOE) with the estimated intake. The interpretation of the size of the MOE (e.╃g., al-

111

112

Contributions

lowing for various uncertainties such as inter- and intra-species differences) would likely help to make decisions at the management level. Reliable, High Sensitivity: Most quantitative structure–activity relationship (QSAR) predictive models suffer from inherent poor sensitivity, i.╃e., the ability to correctly identify true positives. Modellers, partially because they are often confronted with non-representative datasets, have focused their attention on identifying toxicophores that are overly general, and as a result, models tend to have many false positives. This has made computational toxicology a useful tool for high-throughput screening but different strategies should be developed if the aim is to have a low number of false negatives or a high concordance. Global Chemical Diversity: Compounds found in foods and food ingredients present a wide structural diversity and complexity that may be greater than synthetic pharmaceuticals targeted for a particular purpose, and, therefore, require the development of global in silico models (rather than local, referring to particular classes of chemical structures). Relevance and Transparency: Ideally, in silico toxicology strategies for food safety assessment should be able to predict adverse health effects in the human population. Because the toxicological training databases currently available are based on mainly in vitro and animal data with high limitations (species differences etc.) to directly predict the human situation, the development of such models will always constitute a significant challenge. Currently, their practical application in the food sectors will depend upon their potential to accurately predict biological end points/ hazards that are used in food chemical risk assessment. This includes the need to establish confidence limits. The acceptance of these models will be possible only if the analysis is fully transparent. Therefore, the promotion of validated, freely available tools based on open-source codes, such as those developed by the European Chemical Bureau and the US Environmental Protection Agency, is warranted.

3.5.3 Available Computational Toxicology Models for Food �Applications For most food-related compounds for which a complete toxicological database is available, chronic toxicity studies provide the most sensitive end point and usually the pivotal data to establish safe levels of exposure such as the acceptable or tolerable daily intakes [1, 4]. To predict chronic toxicity is therefore considered a first priority in the development of computational toxicology models. In this context the following models appear to be relevant:

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

3.5.3.1 Chronic oral Toxicity

3.5.3.1.1╇ Threshold of Toxicological Concern (TTC) TTC is a concept widely used in chemical food safety and is based on the decision tree developed by Cramer [5] for the estimation of chronic toxic hazard and the de minimis concept. Depending on its chemical features, a chemical can be assigned into one of three classes reflecting a presumption of low, moderate or serious toxicity corresponding to human exposure threshold values below which, there are no significant risks to human health [1, 4]. Although the TTC concept is not per se a computational predictive toxicity system, it has been the first attempt to formally combine structural chemical information with statistical processing of available oral toxicity data to establish levels of no safety concern for chemicals for which no hard toxicological data are available. Recently, additional applications have been proposed for botanical extracts [6], impurities in pharmaceutical preparations [7–9], occupational [10–12] as well as dermal [13–16] exposures. 3.5.3.1.2╇ Lowest Observed Adverse Effect Level (LOAEL) The effect of long-term exposure to chemicals is generally addressed by feeding rodents with various doses of test materials over long periods of time up to lifetime. These chronic studies are designed to obtain a dose-response including a dose producing severe toxicity, the lowest dose exhibiting overt toxic effects (Lowest Adverse Effect Level, LOAEL), and a dose without any toxic effects (No Observed Adverse Effect Level, NOAEL). These tests are fundamental for risk assessment, but the correlation between the chemical structures and the toxicological outputs has received only little attention. This is partly due to the complexity of such experimental tests that embrace a plethora of different biological effects and mechanisms of action, making QSAR studies extremely challenging. Often, it is considered that chronic toxicity is a far too heterogeneous end point to be encoded in a single predictive model. However, several models aimed at predicting chronic toxicity (LOAEL) have recently been developed. Published validation studies have indicated sufficient performance to justify applications in well selected and controlled situations. Commercially available models: Several commercially available systems can help risk assessors to predict a number of end points; however, the provision of a numerical value for chronic exposure is currently only supported by the TOPKAT package developed by Table 1: Performance of the TOPKAT chronic rat toxicity model. Percent of predictions within factors of:

10

100

Venkatapathy et al. [17]

72

98

Tilaoui et al. [18]

59

86

Cadmus Group, Inc. [19]

68

95

113

114

Contributions

Accelrys. This system, aimed at predicting Rat-LOAELs, has been challenged by several authors using different test sets. Results are summarized in Tab.╃1. Rat-LOAEL Model: We recently reported a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis [20]. The analysis used a highly homogenous LOAEL dataset restricted to chronic (defined as longer than 180 days), oral (gavage, diet and drinking water) rat studies. The root mean squared error of the predictive model was found to be 0.73 (in a logarithmic scale) on a leave-one-out cross-validation and is close to the observed variability of actual experimental values (0.64). More than 65â•›% of predictions fall within a factor of 5; 85â•›% within a factor of 10 and 99â•›% within a factor of 100 of empirical data. The analysis of the model revealed that the bioavailability of the compound drives chronic toxicity effects, constituting a baseline effect where additional toxicity is possibly described by a few specific chemical moieties. The results obtained give confidence that this model can be useful in supporting the prioritization of issues in food chemical toxicology research. Model Based on Human Data. (Maximum Recommended Therapeutic Dose, MRTD): The above mentioned models suffer from several limitations including the low number of chemicals in the database and consequently the limited coverage of the “world of chemical structures”, as well as the questionable relevance of the end point modelled to the human situation (i.╃e. the end point is not relevant for humans). Conversely, end points relevant for humans such as headache and nausea cannot be detected in animal tests or predicted from corresponding in silico models. To overcome these limitations, a model was developed [21] using Maximum Recommended Therapeutic Doses (MRTD) of a large number (over 1300) of drugs [22, 23]. The MRTD is an estimated upper dose limit beyond which a drug’s efficacy is not increased and/ or undesirable adverse effects begin to outweigh beneficial effects. The MRTD is essentially equivalent to the NOAEL in humans, a dose beyond which adverse (toxicological) or undesirable pharmacological effects are observed. It has been considered that with the exception of chemotherapeutics and immunosuppressants, the MRTD/10 would correspond to a dose exerting neither therapeutic nor chronic adverse effects in human [22, 23]. For non-pharmaceutical chemicals, there is no desired pharmacological effect and any compound-related effect could be interpreted as adverse or non-desirable effect. The MRTD is empirically derived from human clinical trials and is a direct measure of the dose-related effects of pharmaceuticals in humans. These data are of particular interest because they refer to the effect on humans of biologically active molecules, mainly marketed drugs. The model used spans nearly 9 orders of magnitude of dose variation and predicts 70â•›% of the compounds with q2╃=╃64â•›% and more than 82â•›% within 1 log unit of all experimental values (89â•›% within the applicability domain defined by a confidence interval >0.2) and has an overall mean log error of 0.╃51.╃These performances are comparable to those of published models based on the same

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

database and different commercial software: Matthews et al. [22] (mean log error=╃0.56, concordance=╃86â•›% on a two-class, high–low toxicity prediction) and Contrera et al. [23] (mean log error=╃0.58, concordance=╃71â•›% on a two-class, high–low toxicity prediction). The good performances of these models are an indication that it is indeed possible to predict human end points reliably with QSAR techniques, and suggest the importance of the quality of the dataset over the statistical techniques used to mine it.

3.5.3.2 Mutagenicity

The Ames test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity has been considered as an early alert for carcinogenicity caused by (direct and indirectly acting) DNA damaging agents. Based on experimental data generated over decades, several QSAR studies on this end point yielded enough information to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure. Combining a fragment-based (SA) model and an inductive database, it was possible to develop a predictive model [24] which is quantitatively similar to the experimental error of Ames test data (error on external test set compounds=╃15╛%, sensitivity=╃15╛%, specificity=╃15╛%). Sensitivity is defined as the ability to identify correctly true positives, while specificity is the ability to identify true negatives). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

3.5.3.3 Carcinogenicity

Carcinogenicity is among the toxicological end points raising the highest concern and scientific debate. Standard carcinogenicity bioassays in rodents used to study the carcinogenic effects of chemicals are long and costly, requiring the sacrifice of large numbers of animals. In addition, the extrapolation of animal carcinogenicity data to the human situation is highly challenging. Several attempts to develop alternative predictive models for carcinogenicity are available [3, 25]. Most local QSAR models for congeneric chemical classes agree with, and/ or support the available scientific knowledge, and exhibit good statistics. Local models that discriminate active and inactive chemical (qualitative prediction) are 70 to 100â•›% correct, whereas QSARs that estimate the potency of chemicals have considerably lower accuracy (30 to 70â•›%). In addition, the commonly used statistical internal cross-validation procedures resulted to be poorly correlated with external validation statistics. SA models have an accuracy of about 65â•›% for rodent carcinogens; however, these models do not discriminate well between active and inactive chemicals within individual chemical classes, suffering from poor sensitivity (specificity much higher than sensitiv-

115

116

Contributions

ity). Overall, the present QSAR-based predictions of carcinogenicity are more suited for preliminary (organization and rationalization of data, elucidation of mechanisms of action), or large-scale screenings and need to be complemented by data from other sources [3, 25].

3.5.4 Model Integration and Application Attempts to integrate currently available predictive toxicology models to optimize food applications are ongoing (see Fig.╃1). Preliminary thoughts are provided below. The minimum information required is chemical structure and an estimate of potential or actual exposures. Any new molecule entering the system is first tested for mutagenicity [24]. In absence of an alert for mutagenicity/ genotoxicity, the calculated exposure is compared to the relevant Threshold of Toxicological Concern (TTC) [1, 4]. Exposures lower than the relevant TTC are considered of low safety concern [1, 4]. In case of estimated exposure higher than the relevant TTC, margins of exposure (MOE) between the predicted toxicity values obtained from the TOPKAT, Rat-LOAEL and MRTD models are calculated. The interpretation of the different MOEs is not straightforward and should be done on a case-by-case basis. To conclude on low concern, the MOEs based on Rat-LOAELs should at least be large enough to account for potential inter- (UF=╃10) and intra- (UF=╃10) species differences and to allow for the conversion of LOAELs into a NOAELs (UF=╃3–10). Other inter-species uncertainty factors could be proposed based on allometric scaling. An additional factor would increase the confidence to fully cover the potential error of the models. The MOEs obtained with predicted

Figure 1: Model integration.

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

MRTD should be large enough to allow for the conversion of the MRTD into a safe level of exposure (UF=╃10). Additional factors (UF=╃2–10) may be necessary to cope with potential intra-human differences [22, 23]. Final interpretation will have to consider and compare the resulting three different and independent MOEs. In case of alerts for mutagenicity/ genotoxicity, the estimated exposure is compared to a TTC of 0.15 μg/pers [4]. Exposure below this TTC would be considered unlikely to be of any concern, even for compounds with mutagenic properties [1, 4]. Time adjustment may be envisaged in case of established shortduration exposure [8]. Additional development is necessary to handle chemicals with mutagenicity alerts at exposure levels significantly higher than the TTC of 0.15 μg/pers. Models predicting carcinogenicity are currently evaluated. A chemical with mutagenicity alert but negative in carcinogenicity predictive models would then enter the chronic toxicity prediction scheme as described above. A chemical positive in both mutagenicity and carcinogenicity predictions could theoretically be managed through the calculation of a MOE between a predicted carcinogenic potency (e.╃g. TD50) and the estimated exposure. However, no tools are currently available to undertake such an analysis.

3.5.5 Computational Toxicology and Safety Assessment of �Botanical Extracts 3.5.5.1 General Considerations

The use of botanical-derived functional/ medicinal ingredients in foods has raised several scientific and regulatory issues [2]. These products often fall into grey and undefined regulatory areas between foods and medicines. Furthermore, there is still debate regarding the information required to document beneficial effects and how to communicate such benefits through claims. In addition, there have been a number of recorded cases of intoxications with botanical products raising the issue of their safety [2]. However, whatever the regulatory status or claim, products must be safe for their intended use. The application of functional plant-derived ingredients in food products raises serious and complex questions. Plant misidentifications have been the source of severe intoxications, while the extreme compositional variability of botanical ingredients has often been highlighted. The biological activity of functional botanicals gives rise to other specific safety considerations. The doses producing either health benefits or adverse effects may not be so different and consequently the safety margins associated with the use of functional botanicals are often expected to be reduced as compared to for example food additives [2]. For many botanical extracts, safety assessments are based on history of medicinal use together with limited, often acute, toxicological data on the extracts and/ or extract constituents. One difficulty is to evaluate the applicability of medicinal data to back up the safety of food applications. Amongst the major differences between food and medicinal applications are the duration of exposure

117

118

Contributions

(often expected to be longer for food applications) and the doses (usually lower for food applications), and the acceptability of any risk–benefit consideration. A computational toxicology strategy as outlined above may be of value to address long-term concerns associated with exposure to the main identified constituents of the extracts. Although toxicological data on the extract themselves are considered more relevant for safety assessment (because of potential matrix effects, interaction of the different components), information on individual components may play a significant role to establish the level of safety concern and decide on further need for hard toxicological data.

3.5.5.2 Case Study

To study the potential value of the in silico strategy outlined above for establishing the level of safety concern associated with the chronic use of botanical extracts and phytochemicals, Ginkgo biloba leaf extract was chosen for a learning exercise. This material was selected because of the availability of a relatively large amount of both compositional and toxicological data allowing appropriate comparisons. Composition: For the present exercise, the application of a standardized commercial extract of the following composition was considered (26): ►⌺ 24â•› % flavonoid glycosides containing quercetin, kaempferol and isorhamnetin ►⌺ 6â•› % terpenoids, in which 3.0â•›% are ginkgolides A, D, C, M, and J, and 2.9â•›% bilobalide. The chemical structures are provided in Table 2. Exposures: In pharmaceutical applications 80 to 240 mg of standardized leaf extract are taken while food fortifications have proposed lower amounts, such as 10 to 60 mg/day. Considering the compositional data described above and extract intakes ranging from 10–240 mg/day, exposures to the different constituents were calculated assuming a standard consumer of 60 kg (Tab.╃3). Predictions: Mutagenicity predictions were negative for all chemicals tested. When available in both models, predictions based on TOPKAT and Rat-LOAELs were relatively similar (within a factor of 3). The Rat-LOAEL provides more conservative values. The predicted values were relatively high but compatible with available rat experimental data indicating low chronic toxicity for quercetin and the extract itself [26]. As expected, predicted MRTDs were significantly lower than predicted Rat-LOAELs.

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data Table 2: Structures of key Ginkgo Biloba constituents. Quercetin

Kaempferol

Isorhamnetin

Bilobalide

Ginkgolide A

Ginkgolide B

Ginkgolide C

Ginkgolide J

Ginkgolide M

To compare both types of parameters, animal LOAEL (from TOPKAT and RatLOAEL models) and human MRTD, is not simple. Levels of low safety concern based on MRTD could be estimated by dividing the predicted value by a factor of 10 (conversion of MRTD into no effect). An additional factor (2 to 10) may be applied for possible intra-specific differences [22, 23]. Low level of concern based on Rat-LOAEL predictions would require the application of a factor of 100 to cover inter- and intra-species differences and an additional factor of 3–10 to deal with the conversion of the LOAEL to NOAEL. For the flavonoids, the levels of low concern based on the three independent models were all within the same order of magnitude providing additional confidence for the predictions. Margins of Exposures: With the highest dose envisaged in human applications, MoEs lower than values considered necessary to provide a good confidence of safety were obtained. This is somehow expected since this dose is documented to be pharmacologically active. Much lower doses which have been sometimes proposed for food fortification are characterized by larger MoEs considered sufficient to provide a good guaranty of safety. In such case safety would likely be ensured at the expense of efficacy. The MoEs derived from this exercise in computational analysis are in good agreement with MoEs that can be derived from actual clinical and experimental data (26). Ginkgo biloba extract up to 240 mg/day is considered to be pharmacologically active with a good record of safety, although some possible side effects were reported in a limited number of exposed individuals.

119

120

Contributions Table 3: Exposure estimates, chronic toxicity and MRTD predictions and calculated margins of exposure (MOEs) for key Ginkgo biloba extract constituents. Name (%2)

Exposure1

TOPKAT3 (MOE)

Rat-LOAEL3 (MOE)

MRTD3 (MOE)

Quercetin (8)

13–320

371.8 (29,000–1200)

199.5 (15,000–625)

2.0 (154–6)

Kaempferol (8)

13–320

273.2 (21,000–875)

166.4 (13,000–540)

1.9 (146–6)

Isorhamnetin (8)

13–320

334.4 (26,000–1083)

127.4 (9800–400)

4.0 (307–13)

Bilobalide (2.9)

5–116

OPS(-)

252.2 (50,000–2100)

NA

Ginkgolide A (0.6)

1–24

OPS(-)

103.5 (103,500–4312)

NA (-)

Ginkgolide B (0.6)

1–24

OPS(-)

134.4 (134,400–5600)

NA (-)

Ginkgolide C (0.6)

1–24

OPS(-)

159.1 (159,100–6630)

NA (-)

Ginkgolide J (0.6)

1–24

OPS(-)

126.7 (126,700–5280)

NA (-)

Ginkgolide M(0.6)

1–24

OPS(-)

142.6 (142,600–5940)

NA (-)

Exposure in µg/kg/d based on intake of 10 to 240 mg/extract/day (assessing a 60╃kg bodyweight) Default contribution to the total extract mass (based on compositional data) 3 in mg/kg bw/d MOE: margin of exposure; OPS-: outside optimum prediction space; NA: not applicable, structure not recognized. 1 2

3.5.6 Discussion and Conclusion Computational models are available to predict quantitatively chronic rodent and human toxicity ►⌺ Validation studies indicate a reasonable performance of these models (>85â•› % of compounds tested within +/- 1 log) ►⌺ Ongoing research suggests a good inter-model correlation ►⌺ When taken together in an integrated approach, the models available provide a good coverage of the “world of chemicals”, including food additives, pesticides, veterinary drugs, environmental contaminants and biologically active substances (human drugs) ►⌺ This integrated approach is likely to allow establishing levels of safety concern of compounds for which no hard toxicological information is available ►⌺ The application of such an approach can be valuable in emergency situations to support fast decision-making. It may also help in defining priorities for additional toxicity testing ►⌺ As shown in the present paper, such an approach may also provide some valuable information to assess the level of chronic safety concern of functional botanical extracts ►⌺

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

The application of such an approach for botanical extract is mainly limited by the quality of the available compositional data. In addition, it does not allow addressing matrix effects ►⌺ Overall limitations of such models depend more on the experimental data than on the available computational methods which are now considered as mature ►⌺ Further work is necessary to optimize the interpretation of the information provided by the models ►⌺ The prediction of carcinogenicity and carcinogenic potency appear as the next big challenges ►⌺ Future efforts will have to focus on modelling mechanistic end points of higher relevance for low levels of human chemical exposures. ►⌺

References â•⁄ 1. Kroes R., Galli C., Munro I., Schilter B., Tran L.-A., Walker R., Würtzen G. (2000) Threshold of toxicological concern in the diet: a practical tool for assessing the need for toxicity testing. Fd. Chem. Toxicol. 38, 255–312. â•⁄ 2. Schilter B., Andersson C., Anton A. Constable R., Kleiner J., Korver O., O’Brien J., Renwick A., Smit F., Walker R. (2003) Guidance for the safety assessment of botanicals and botanical preparations with use in food and food supplements. Fd. Chem. Toxicol. 41, 1625–1649. â•⁄ 3. Benfenati, E., Benigni, R., Marini, D., Helma, C., Kirkland, D., Martin, T.╃M., Mazzatorta, P., Meunier, J.-R., Ouédraogo-Arras, G., Richard, A., Schilter, B., Schoonen, W.╃G.╃E.╃J., Snyder, R., Yang, C., Youne, D.╃M.(2009) Predictive models for carcinogenicity and mutagenicity. Frameworks, state-of-the art and perspectives. (submitted). â•⁄ 4. Kroes R., Renwick, A., Cheeseman, M., Kleiner, J., Mangelsdorf, I., Piersma, A., Schilter, B., Schlatter, J., van Schothorst, Vos J.╃G., Würtzen G. (2004) Structure-based thresholds of toxicological concern: guidance for application to substances present at low levels in the diet. Fd. Chem. Toxicol. 42, 65–83. â•⁄ 5. Cramer G.╃M.; Ford R.╃A. (1978) Estimation of toxic hazard – a decision tree approach. Fd. Cosmet. Toxicol. 16, 255–276. â•⁄ 6. Rietjens I.╃M., Slob W., Galli C., Silano V. (2008) Risk assessment of botanicals and botanical preparations intended for use in food and food supplements: emerging issues. Toxicol. Lett. 15, 131–6. â•⁄ 7. Bercu J.╃P., Hoffman W.╃P., Lee C., Ness D.╃K. (2008) Quantitative assessment of cumulative carcinogenic risk for multiple genotoxic impurities in a new drug substance. Regul. Toxicol. Pharmacol. 51(3), 270–7. â•⁄ 8. Müller L., Mauthe R.╃J., Riley C.╃M., Andino M.╃M., Antonis D.╃D., Beels C., DeGeorge J., De Knaep A.╃G., Ellison D., Fagerland J.╃A., Frank R., Fritschel B., Galloway S., Harpur E., Humfrey C.╃D., Jacks A.╃S., Jagota N., Mackinnon J., Mohan G., Ness D.╃K., O’Donovan M.╃R., Smith M.╃D., Vudathala G., Yotti L. (2006) A rationale for determining, testing, and controlling specific

121

122

Contributions

impurities in pharmaceuticals that possess potential for genotoxicity. Regul. Toxicol. Pharmacol. 44(3), 198–211. â•⁄ 9. Delaney E.╃J. (2007) An impact analysis of the application of the threshold of toxicological concern concept to pharmaceuticals. Regul. Toxicol. Pharmacol. 49(2), 107–24. 10. Carthew P., Clapp C. Gutsell S. (2009) Exposure based waiving: The applicaÂ� tion of the toxicological threshold of concern (TTC) to inhalation exposure for aerosol ingredients in consumer products. Fd. Chem. Toxicol. 46(6), 1287– 1295. 11. Hardin B.╃D., Robbins C.╃A., Fallah P., Kelman B.╃J. (2009) The concentration of no toxicologic concern (CoNTC) and airborne mycotoxins. J. Toxicol. Environ. Health A.╃72(9), 585–98. 12. Drew R., Frangos J. (2007) The concentration of no toxicological concern (CoNTC): a risk assessment screening tool for air toxics. J. Toxicol. Environ. Health A.╃70(19), 1584–93. 13. Safford R.╃J. (2008) The dermal sensitisation threshold- a TTC approach for allergic contact dermatitis. Regul. Toxicol. Pharmacol. 51(2), 195–200. 14. Kroes R., Renwick A.╃G., Feron V. Galli C.╃L., Gibney M., Greim H., Guy R.╃H., Lhuguenot J.╃C., van de Sandt J.╃J. (2007) Application of the threshold of toxicological concern (TTC) to the safety evaluation of cosmetic ingredients. Fd. Chem. Toxicol. 45(12), 2533–62. 15. Re T.╃A., Mooney D., Antignac E., Dufour E., Bark I., Srinivasan V., Nohynek G. (2009) Application of the threshold of toxicological concern approach for the safety evaluation of calendula flower (Calendula officinalis) petals and Â�extracts used in cosmetic and personal care products. Fd. Chem. Toxicol. 47(6), 1246–1254. 16. Blackburn K., Stickney J.╃A., Carlson-Lynch H.╃L., McGinnis P.╃M., Chappell L., Felter S.╃P. (2005) Application of the threshold of toxicological concern approach to ingredients in personal and household care products. Regul. Toxicol. Pharmacol. 2005 43(3), 249–59. 17. Venkatapathy R., Moudgal C., Swartout J., Bruce, R.╃M. (2004) Assessment of the rat chronic LOAEL model in TOPKAT, a QSAR software for toxicity prediction. J. Chem. Inf. Comput. Sci.╃44, 1623–1629. 18. Tilaoui L., Schilter B., Tran L.-A., Mazzatorta P., Grigorov M. (2997). Integrated computational methods for prediction of the lowest observable adverse effect level of food-borne molecules. QSAR Comb. Sci.╃26, 102–108. 19. The Cadmus Group, Inc. (2003) Evaluation of the use of QSAR models to generate data for use in screening the CCL universe to the PCCL. Discussion draft for NDWAC CCL workshop. 20. Mazzatorta P., Dominguez Estevez M., Coulet M., Schilter B. (2008) Modelling oral rat chronic toxicity. TJ. Chem. Inf. Mod.╃48, 1949–1954. 21. Maunz A., Helma C. (2008) Prediction of chemical toxicity with local support vector regression and activity-specific kernels. SAR QSAR Environ. Res.╃19, 413–431. 22. Matthews E.╃J., Kruhlak N.╃L., Benz R.╃D., Contrera J.╃F. (2004) Assessment of the health effects of chemicals in humans: I. QSAR estimation of

In Silico Models to Establish Level of Safety Concern in Absence of Sufficient Toxicological Data

the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data. Curr. Drug Disc. Tech. 1(1), 61–76. 23. Contrera J.╃F., Matthews E.╃J., Kruhlak N.╃L., Benz R.╃D. (2004) Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modelling of the human maximum recommended daily dose. Reg. Tox. Pharm. 40, 185–2006. 24. Mazzatorta P., Tran L.-A., Schilter B., Grigorov M. (2007) Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of Ames test mutagenicity. J. Chem. Inf. Mod.╃47, 34–38. 25. Benigni R., Bossa C. (2008) Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. Toxicol. Mech. Met.╃18, 137–147. 26. Chan P.-C., Xia Q., Fu P.╃P. (2007) Ginkgo Biloba leave extract: biological, medicinal, and toxicological effects. J. Environ. Sci. Health Part C 25, 211–244.

123

124

Contributions

3.6 In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and DetoÂ�xification of Coumarin and Estragole: Implications for Risk Assessment Ivonne M.╃C.╃M. Rietjens1,2, Ans Punt2, Benoît Schilter3, Gabriele Scholz3, Thierry Delatour3, and Peter J. van Bladeren2,3 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 195–207.

Abstract In chemical safety assessment, information on adverse effects after chronic exposure to low levels of hazardous compounds is essential for estimating human risks. Results from in vitro studies are often not directly applicable to the in vivo situation, and in vivo animal studies often have to be performed at unrealistic high levels of exposure. Physiologically based biokinetic (PBBK) modelling can be used as a platform for integrating in vitro metabolic data to predict dose- and species- dependent in vivo effects on biokinetics, and can provide a method to obtain a better mechanistic basis for extrapolations of data obtained in experimental animal studies to the human situation. Recently we have developed PBBK models for the bioactivation of the alkenylbenzene estragole to its DNA binding ultimate carcinogenic metabolite 1’-sulfooxyestragole in both rat and human, as well as rat and human PBBK models for the bioactivation of coumarin to its hepatotoxic o-hydroxyphenylacetaldehyde metabolite. The present paper presents an overview of the results obtained sofar with these in silico methods for physiologically based biokinetics, focusing on the possible implications for risk assessment, and some additional considerations and future perspectives.

1

Correspondence to: Prof. Dr. ir. Ivonne M.╃C.╃M. Rietjens, Division of Toxicology, Wageningen University, Tuinlaan 5, NL-6703 HE Wageningen, The Netherlands, Tel: +╃31 317 483971, Fax: +╃31 317 484931, [email protected].

2

Division of Toxicology, Wageningen University, Tuinlaan 5, NL-6703 HE Wageningen, The Netherlands.

3

Nestlé Research Centre, Quality and Safety Department, P.╃O. Box 44, CH-1026 Lausanne, Switzerland.

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

3.6.1 Introduction Estragole

Estragole is an alkenylbenzene that occurs in different herbs such as tarragon, basil, and fennel and is present in products derived from these herbs such as pesto and essential oils [1, 2]. Average daily intake of estragole was estimated to be 10–70╃µg/kg bw per day [1, 3]. There is interest in the safety assessment of estragole as a food constituent, since estragole has been identified to be genotoxic in vitro and carcinogenic in rodent studies performed at high dose levels [4–6]. Based on disposition studies of 14C-methoxy-labelled estragole in rats, mice, and humans and identification of the metabolites excreted, the principal metabolic pathways of estragole have been established [7, 8]. Figure 1 presents an overview of estragole metabolism including pathways for bioactivation to the proximate and ultimate carcinogenic metabolite and pathways for detoxification. The main phase I metabolic pathways include 1’-hydroxylation, O-demethylation, epoxidation and 3’-hydroxylation of estragole. The main metabolic pathways of the proximate carcinogen 1’-hydroxyestragole are sulfonation to the ultimate carcinogen 1’-sulfooxyestragole, and detoxification through glucuronidation to 1’-hydroxyestragole glucuronide, or oxidation to 1’-oxoestragole. Several evaluations have been performed to assess the safety of human exposure to estragole at low dietary intake levels. In an evaluation performed by the Scientific Committee on Food of the European Committee (SCF) in 2001, it was concluded that estragole is genotoxic and carcinogenic and restrictions in use were indicated [3]. The Expert Panel of the Flavor and Extract Manufacturers Association (FEMA) classified estragole in 1965 as GRAS (Generally Recognized as Safe) under conditions of intended use as flavouring substance in food [9]. In 2002, the FEMA re-evaluated the data available for estragole and concluded again that exposure to estragole from food, mainly as spices or added as such, does not pose a significant cancer risk to humans [1]. In this conclusion it was taken into account that there are experimental data suggesting a non-linear relationship between dose and profiles of metabolism and metabolic activation. In a more recent evaluation performed by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) in 2008, it was indicated that although evidence of carcinogenicity to rodents given high doses of estragole exists, further research is needed to assess the potential risk to human health from low-level dietary exposure to estragole present in foods and essential oils and used as flavouring agents [10]. Overall, these different expert judgments reflect a general problem in cancer risk assessment studies, which is a lack in scientific consensus on how to translate carcinogenicity data obtained in experiments with rodents at high levels of exposure to the situation for humans exposed to low levels. Determining the cancer risk in humans at low dose dietary intake levels requires extrapolation of the animal carcinogenicity data obtained with respect to species and dose.

125

126

Contributions

Figure 1: Metabolism of estragole with the bioactivation pathway proceeding by formation of the proximate carcinogen 1’-hydroxyestragole and the ultimate carcinogen 1’-sulfooxyestragole. Formation of the other metabolites eventually leads to detoxification and excretion.€

Uncertainties exist about the shape of the dose–response curve below the range of the animal experimental data, and about possible species differences in metabolism including metabolic activation and detoxification. The aim of our PBBK studies for estragole was to obtain quantitative insight into dose- and species-dependent differences in the bioactivation and detoxification of estragole.

Coumarin

Coumarin is a naturally occurring compound that was first isolated from Tonka beans, and is found at high levels in some essential oils, particularly cassia leaf

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

oil, cinnamon leaf oil, cinnamon bark oil and in lavender oil and peppermint oil. Coumarin is also found in fruits (bilberry), green tea and other foods, such as chicory, and in personal care products [11–13]. Chronic exposure to coumarin by the oral route has been reported to result in liver adenomas and carcinomas in rats and liver adenomas in mice [11–15]. Recently, the European Food Safety Authority (EFSA), based on results of a study on DNA adduct formation in kidney and liver of rats demonstrating that coumarin does not bind covalently to DNA, concluded that coumarin induces liver tumours by a non-genotoxic mode of action. A tolerable daily intake (TDI) of 0.1 mg coumarin/kg bw was established [13]. The theoretical maximum daily intake of coumarin was calculated to be about 4.1 mg/day or 0.07 mg kg bw per day for a 60 kg person [11, 13]. Figure 2 presents an overview of coumarin metabolism. The major route of coumarin bioactivation is 3,4-epoxidation to coumarin-epoxide, which is followed by subsequent rearrangement of the epoxide to o-hydroxyphenylacetaldehyde (oHPA) [16] which is considered to be the hepatotoxic intermediate [11, 17–19]. Coumarin epoxide may also be conjugated to glutathione both chemically and enzymatically, the latter route being especially efficient in rats and mice [18]. oHPA can be detoxified by reduction to o-hydroxyphenyletha-

Figure 2: Metabolism of coumarin with the bioactivation pathway proceeding by formation of oHPA. Formation of the other metabolites eventually leads to detoxification and excretion.€

127

128

Contributions

nol (oHPE), but especially by oxidation to o-hydroxyphenylacetic acid (oHPAA) [11, 18, 20–22]. Significant species differences between rat and human exist in coumarin bioactivation via the 3,4-epoxide pathway. In rats and mice the 3,4-epoxidation pathway appears to be the major route of coumarin biotransformation, whereas in humans the detoxifying coumarin 7-hydroxylation predominates, a reaction catalyzed by CYP2A6 [11, 16, 19, 20, 23–27]. Furthermore, detoxification of oHPA to oHPAA was shown to be more efficient in humans than in rats [18]. Based on these species differences in biotransformation Felter et al. [19] argued that the uncertainty factor for inter-species variation used for definition of the TDI could be reduced from 10 to 2.5 leaving only the factor 2.5 for toxicodynamic differences but taking out the factor 4 for toxicokinetics. However, also of importance is that in man a genetic polymorphism has been identified for CYP2A6, the P450 enzyme catalyzing the detoxifying 7-hydroxylation of coumarin [28–30]. The aim of our PBBK studies for coumarin was to quantify the metabolic pathway(s) replacing the 7-hydroxycoumarin formation in homozygous CYP2A6 deficient subjects and to estimate computationally the expected consequences of the CYP2A6 deficiency for oHPA formation in the liver of humans.

Physiologically Based Biokinetic (PBBK) Models

As outlined above an overall problem in current risk assessment strategies is the need to extrapolate experimental data obtained in animal experiments at high dose levels to a low dose human situation. Uncertainties about the shape of the dose–response curve at dose levels relevant for dietary human intake, and about species differences in metabolic activation and detoxification, make it difficult to perform such extrapolations. Physiologically based biokinetic (PBBK) modelling can provide a method to obtain a better mechanistic basis for extrapolations of data obtained in experimental animal studies to the human situation [31–33]. A PBBK model is a set of mathematical equations that together describe the absorption, distribution, metabolism and excretion (ADME) characteristics of a compound within an organism on the basis of three types of parameters [34–37]. These parameters include physiological parameters (e.╃g. cardiac output, tissue volumes, and tissue blood flows), physicochemical parameters (e.╃g. blood/tissue partition coefficients), and kinetic parameters (e.╃g. kinetic constants for metabolic reactions) [34–37]. Solution of the PBBK equations produces outcomes that are an indication of, for example, the tissue concentration of a compound or its metabolite in any tissue over time at any dose, allowing analysis of effects at both high but also more realistic low dose levels. Furthermore, such PBBK models can be developed for different species, which can facilitate inter-species extrapolation. In addition, by incorporating equations and kinetic constants for metabolic conversions by individual human samples and/

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

or specific isoenzymes, modelling of inter-individual variations and genetic polymorphisms becomes feasible [38]. For the development of a PBBK model for a specific compound, model parameters need to be obtained. The physiological parameters of a species (e.╃g. blood flow rates and tissue volumes) can be obtained from the literature [39]. Tissue-blood partition coefficients might be obtained experimentally in vitro using vial equilibration techniques or equilibrium dialysis techniques [40, 41], but can also be obtained using in silico methods. Several in silico models have been published by which tissue-blood partition coefficients of a compound can be calculated based on their octanol-water partition coefficients [42]. Biochemical parameters for PBBK models, including metabolic parameters, are most often obtained by making preliminary assumptions about metabolic routes and optimizing the kinetic constants by fitting the model to available in vivo data [34, 35]. Alternatively, metabolic parameters might also be derived from in vitro experiments with tissue fractions, primary cell cultures, or tissue slices of organs involved in the metabolism of the compound [43]. Lipscomb and Poet [43] have pointed out some advantages of using in vitro metabolic parameters to define PBBK models, which include the ability to separately define and analyze individual metabolic processes, such as phase I metabolism and phase II metabolism, or bioactivation and detoxification, and to compare contributions from individual conversions to the overall metabolism across species and between individuals, when limited in vivo data are available as is often the case for humans [43].

3.6.2 Methods In the present studies the metabolic parameters for the relevant biotransformation reactions for estragole and coumarin, depicted in Figure 1 and 2, were determined using in vitro experiments with tissue fractions [18, 20, 44–46]. PBBK models for estragole and coumarin in rat and human were developed based on these in vitro metabolic data. For estragole in rat and human the models defined consisted of seven compartments including blood, liver, kidney, lung, fat, richly perfused tissue and slowly perfused tissue [44, 47]. For coumarin in rat and human the models defined consisted of five compartments including blood, liver, fat, richly perfused tissue and slowly perfused tissue [45, 47]. A schematic diagram of both PBBK models is shown in Figure 3.╃The physiological parameters and partition coefficients used in the models can be found in the literature [44–46]. The physiological parameters were obtained from the literature [39]. The partition coefficients were estimated from the log Kow based on a method of DeJongh et al. [48]. Log Kow values were estimated with the software package ClogP version 4.0 (Biobyte, Claremont, CA). Model equations were coded and numerically integrated in Berkeley Madonna 8.0.1 (Macey and Oster, UC Berkeley, CA, USA), using the Rosenbrock’s algorithm for stiff systems. The Vmax values for the different phase I metabolic pathways in the liver, expressed as nmol/ min.mg microsomal protein, were scaled to the liver using

129

130

Contributions

Figure 3: Schematic diagram of the PBBK model for (a) estragole and (b) coumarin [44, 45].

a microsomal protein yield of 35 mg per g liver [49]. The Vmax values for the different phase I metabolic pathways in the lung and kidney were scaled accordingly using a microsomal protein yield of 20 mg per g lung, and 7 mg per g kidney [50–52]. The Vmax values for sulfonation, oxidation and glucuronidation of 1’-hydroxyestragole, expressed as nmol/ min.mg S9 or microsomal protein, were scaled to the liver using a S9 protein yield of 143 mg per g liver and a microsomal protein yield of 35€mg per g liver [50]. The apparent in vitro Km values were assumed to correspond to the apparent in vivo Km values. The uptake of estragole and coumarin from the gastro-intestinal tract was described by a firstorder process, assuming direct entry from the intestine to the liver compartment. The absorption rate constant (Ka) was set to 1.0â•›h-1, resulting in a rapid absorption of estragole or coumarin from the gastro-intestinal tract [7].

3.6.3 Results Estragole

As an example Figure 4 presents the estragole concentration dependent rate of formation of the different estragole phase I metabolites by rat and human liver microsomes. From these curves Vmax and Km values could be derived [44]. Vmax and Km values were also determined for glucuronidation, oxidation and sulfation of 1’-hydroxyestragole [44, 45] using rat as well as human samples [44, 47]. Based on the in vitro kinetic data for the different bioactivation and detoxification reactions catalyzed by rat and human tissue fractions PBBK models for estragole metabolism in rat and human were developed [45, 47]. With these models predictions were made on formation of different metabolites in human liver in time and at different oral doses. In male rat O-demethylation of estragole appeared to be a major metabolic route at low doses of estragole, occurring mainly in the lung and kidney. Due

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

Figure 4: Estragole concentration dependent rate of formation of 4-allylphenol (a), hydroxyestragole (b), estragole-2’,3’-oxide (c), and 3’-hydroxyestragole (d) by rat (▲) and human () liver microsomes. In the plots each point represents the mean (± SD) of three replicates.

to saturation of the O-demethylation pathway in lung and kidney, formation of the proximate carcinogenic metabolite 1’-hydroxyestragole, which was shown to occur mainly in the liver of male rat, becomes relatively more important at higher doses of estragole. The PBBK model predicted that formation of this metabolite increased from 16â•›% of the dose at a dose of 0.07 mg/kg bw to 29â•›% of the dose at a dose of 300 mg/kg bw. This relative increase in formation of 1’-hydroxyestragole leads to a relative increase in formation of 1’-hydroxyestragole glucuronide, 1’-oxoestragole, and 1’-sulfooxyestragole, the latter being the ultimate carcinogenic metabolite of estragole. The formation of 1’-sulfooxyestragole predicted by the PBBK model increased from 0.08â•›% of the dose at a dose of 0.07 mg/kg bw to 0.16â•›% of the dose at a dose of 300 mg/kg bw. Overall these results indicate that the relative importance of different metabolic pathways of estragole may vary in a dose-dependent way, leading to a relative increase in bioactivation of estragole at higher doses. The findings of the PBBK model for male rat were in good agreement with observations in the literature, revealing dose-dependent effects on the biokinetics for estragole in female Wistar rats in vivo. In these in vivo studies the proportion of O-demethylation was observed to decrease with increasing doses (as determined by the percentage of exhalation as 14CO2), whereas the proportion of the dose excreted as 1’-hydroxyestragole glucuronide in the urine increased from 1.3–5.4â•›% of the dose in the range of 0.05–50 mg/kg bw to 11.4–13.7â•›% in the dose range of 500–1000 mg/kg bw [7]. The PBBK model provided insight in

131

132

Contributions

the mechanism underlying this dose-dependent effect observed in vivo, which was identified to be a result of saturation of the O-demethylation pathway in the lung and kidney. Based on the PBBK model for estragole in human dose-dependent effects in bioactivation and detoxification of estragole in humans could be studied as well. In humans no relative increase in formation of 1’-sulfooxyestragole was identified to occur with increasing dose levels. The PBBK model even predicted that the relative formation of this metabolite decreased from 0.19â•›% of the dose at a dose of 0.07 mg/kg bw to 0.08â•›% of the dose at a dose of 300 mg/kg bw, due to saturation of the 1’-hydroxylation pathway in the liver. Further analysis revealed that this difference between the rat and human model, showing respectively an increase versus a decrease in the relative formation of 1’-sulfooxyestragole with increasing dose, was due to the fact that in the human model efficient O-demethylation in lung and kidney was absent, whereas in the rat these conversions reduced the relative formation of 1’-sulfooxyestragole at low dose levels. The human PBBK model also revealed that at a dose-range within one order of magnitude of the estimated average dietary human intake of 0.07 mg/kg bw, these dose-dependent effects on the relative percentage of the dose converted to 1’-sulfooxyestragole were not significant. The performance of the PBBK model defined for estragole in human could, to some extent, be evaluated against available in vivo data on the disposition of 0.001 mg/kg bw [methoxy-14C]-labelled estragole in two human volunteers obtained from Sangster et al. [8]. The PBBK model predicted formation of 1’-hydroxyestragole glucuronide, corresponding to 2.0â•›% of the dose after 24â•›h, is comparable to the reported in vivo level of this metabolite being ~0.5â•›% of the dose [8]. The predicted formation of 4-allylphenol, corresponding to 2.4â•›% of the dose after 8â•›h, is 4-fold lower than the reported in vivo level of ~10â•›% of the dose after 8â•›h [8]. These results indicate that the PBBK model predicts the formation of these metabolites within the same order of magnitude as the reported levels. Figure 5 presents an overview of the PBBK based predictions for the dose-dependent formation of 4-allylphenol, resulting from O-demethylation, 1’-hydroÂ� xyestragole, the proximate carcinogenic metabolite, 1’-sulfooxyestragole, the ultimate carcinogenic metabolite, 1’-hydroxyestragole glucuronide, and 1’-oxoestragole in the liver of rat and human at dose levels up to 300 mg/Â�kg bw. The results obtained clearly reflect significant species dependent differences in the relative importance of O-demethylation, being more important in male rat than in human (Fig.╃5â•›a), as well as in the major pathway for detoxification of 1’-hydroxyestragole, being glucuronidation to 1’-hydroxyestragole glucuronide in male rat (Fig.╃5â•›d) but oxidation to 1’-oxoestragole in human (Fig.╃5â•›e). These results also indicate that lower levels of urinary excretion of 1’-hydroxyestragole glucuronide in human than in male rat do not necessarily reflect lower levels of formation of the proximate and ultimate carcinogenic metabolites 1’-hydroxyestragole and 1’-sulfooxyestragole. The PBBK results obtained indicate that in spite of marked species differences in O-demethylation of estragole and in glucuronidation and oxidation of 1’-hydroxyestragole, the resulting species

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

Figure 5: PBBK model based predictions for the dose-dependent formation of (a) 4-allylphenol, (b) 1’-hydroxyestragole, (c) 1’-sulfooxyestragole, (d) 1’-hydroxyestragole glucuronide, and (e) 1’-oxoestragole in the liver of rat (─) and human (– – –) at dose levels up to 300 mg/kg bw.

differences in formation of 1’-hydroxyestragole and 1’-sulfooxyestragole up to dose levels of 50 mg/kg bw are moderate and amount to less than a 2-fold species dependent variation in bioactivation. Formation of 1’-oxoestragole has not been considered to be an important metabolic route of 1’-hydroxyestragole before, mainly because in rat only relatively small amounts of derivatives of this metabolite have been detected in the urine after exposure to estragole [53]. Based on the approach of identifying principal metabolic pathways of estragole in incubations with tissue fractions of relevant organs, it could be revealed that in human oxidation of 1’-hydroxyestragole to 1’-oxoestragole is a major metabolic pathway, which was predicted by the PBBK model to account for 62.7â•›% of the dose. Altogether it is concluded that the species dependent variation in bioactivation of estragole to 1’-sulfooxyestragole is smaller than the default factor of

133

134

Contributions

4€generally assumed to reflect inter-species variation in kinetics (assuming that the default factor of 10 can be divided into a factor of 4 for kinetics and 2.5 for dynamics) [54].

Coumarin

Figure 6 presents the HPLC chromatograms of incubations of coumarin with microsomes from pooled human and rat liver reflecting the species dependent

Figure 6: HPLC chromatograms of 30 min incubations of 1000╃µM coumarin with (a) pooled human liver microsomes and (b) pooled rat liver microsomes. An unidentified metabolite peak marked with a question mark [45].

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

Figure 7: PBBK model-predicted dose-dependent concentration of oHPA in the liver of (a) rat and (b) a human wild-type CYP2A6 subject (dotted line) and a human homozygous CYP2A6 deficient subject (Vmax for coumarin 7-hydroxylation set to zero, solid line).€

differences in formation of 7-hydroxycoumarin, the major metabolite formed by human liver microsomes (Fig.╃6╛a) and oHPA, the major metabolite formed by rat liver microsomes (Fig.╃6╛b). In rat microsomal incubations there was no formation of 7-hydroxycoumarin whereas in human microsomal incubations formation of oHPA was not observed to any significant extent. The PBBK model defined for coumarin included: (1) uptake of coumarin from the intestine by passive diffusion, (2) transport to the liver, fat and all other organs lumped together as either rapidly perfused tissue or slowly perfused tissue, (3) hepatic metabolism of coumarin to 7-hydroxycoumarin, oHPA, 3-hydroxycoumarin and 4-hydroxy-3-glutathionyl-coumarin (CE-SG) and (4) conversion of oHPA to oHPAA and oHPE. The PBBK model thus defined provided relative estimates of liver levels of oHPA, in man and rat, but also in humans deficient in coumarin 7-hydroxylation, at increasing levels of coumarin exposure (Fig.╃7). For rat liver a dose-dependent increase in the Cmax for oHPA formation is observed (Fig.╃7╛a). For human liver of wild-type CYP2A6 subjects (Fig.╃7╛b dotted line) a dose-dependent increase in oHPA formation is only observed at dose levels above 15 mg/kg bw when 7-hydroxylation of coumarin becomes saturated and additional amounts of coumarin start to be metabolized through alternative biochemical pathways. For homozygous CYP2A6 deficient subjects, with Vmax for coumarin 7-hydroxylation set to zero, there is a dose-dependent increase in the Cmax for oHPA in the liver without an apparent threshold (Fig.╃7╛b solid line). Nevertheless, comparison of Figure 7╛b to figure 7╛a reveals that along the whole dose range modelled the predicted oHPA levels in liver of CYP2A6 deficient subjects remain at least 10-fold lower than the Cmax values predicted for oHPA in rat liver at similar dose levels.

3.6.4 Discussion The results presented show that integrating in vitro metabolic parameters, using a PBBK model as a framework, provides a good method to evaluate the occurrence of dose-dependent effects and species differences in bioactivation and

135

136

Contributions

detoxification of estragole and coumarin. Using this approach, mechanisms underlying dose-dependent effects in bioactivation were revealed. Furthermore, insight was obtained in the occurrence of species differences in metabolism and metabolic activation.

Implications for Risk Assessment Coumarin

For coumarin significant species differences exist in metabolism. In man, the 3,4-epoxidation of coumarin leading to the hepatotoxic oHPA is a minor route, whereas in rats the detoxifying 7-hydroxylation appears to be a minor route. Whether these species differences in toxicokinetics may provide an argument for reduction of the inter-species safety factor when extrapolating from the animal studies to the human situation, as previously suggested by Felter et al. [19], is dependent on how these differences in kinetics together influence the levels of oHPA in the liver of rat and human. To provide some insight into this question a PBBK model for coumarin for both rat and human was developed, taking into account coumarin 7-hydroxylation, coumarin 3-hydroxylation, formation of the glutathione conjugate of coumarin 3,4-epoxide, formation of oHPA, detoxification of oHPA to oHPAA and conversion of oHPA to oHPE. The PBBK models presented may not give insight in the absolute formation of oHPA in the liver of rat and human in vivo since the models were not validated against in vivo data. Nevertheless, the model simulations give insight in the relative differences in oHPA formation in the liver of rat and human, in order to assist extrapolation of rat data to the human situation. The predicted Cmax for oHPA in the liver of the average CYP2A6 wild-type human subject was predicted to be about three orders of magnitude lower than the Cmax predicted for the liver of rats, representing a species sensitive to coumarin liver toxicity [19]. The PBBK models developed also allowed modelling of the kinetics in CYP2A6 deficient human subjects that are homozygous for the CYP2A6*2 allele (Vmax for coumarin 7-hydroxylation set to zero). The PBBK model thus defined revealed that for these CYP2A6 deficient human subjects the Cmax and AUC0–24â•›h for oHPA formation could amount to values that were respectively 70- and 500-fold higher than those predicted for the average CYP2A6 wild-type human subject. The increased AUC is in line with observations reported before for an individual who was homozygous for the CYP2A6*2 allele in which approximately 50â•›% of a 2 mg dose of coumarin was excreted as oHPAA [29], reflecting a significant increase in the percentage of coumarin excreted over time via the coumarin 3,4-epoxide pathway upon homozygous CYP2A6 deficiency. Assuming that all oHPAA formed will be excreted in the urine, the PBBK model predicts that upon dosing 2 mg (0.03 mg/kg bw for a 60 kg person), corresponding to the dose applied in the Hadidi et al. study [29] and within the range of estimated normal dietary exposure [15], the excretion of oHPAA will be 0.05â•›% of the dose for a wild-type human subject and

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

increases to 28â•›% of the dose for€a homozygous CYP2A6 deficient human subject. This increase in the predicted value of the dose excreted as oHPAA to 28â•›% approximately explains the 50â•›% reported by Hadidi et al. [29] and reveals that the model for the CYP2A6 deficient human subjects is in good agreement with the in vivo data obtained by Hadidi et al. [29]. Cmax values predicted for oHPA formation in the liver of CYP2A6 wild-type human and of homozygous CYP2A6 deficient subjects were, respectively, about 1000- and 10-fold lower than Cmax values predicted for rat liver. For wild-type human subjects and the subjects with completely deficient coumarin 7-hydroxylation the AUC0–24â•›h values for oHPA in the liver were also, respectively, about 1000- and 10-fold lower than that for rat liver. This points at reduced chances on oHPA liver toxicity in humans as compared to rat even for homozygous CYP2A6 deficient subjects. The results obtained also demonstrated that this holds over a dose range from 0.1 mg/kg bw (the TDI) to 50 mg/kg bw (Fig.╃7). It is concluded that even in human subjects with complete deficiency in detoxifying 7-hydroxylation the chances on formation of the hepatotoxic coumarin metabolite oHPA will be lower than those expected in the liver of rats when exposed to a similar dose on a body weight basis. Clinical data and case reports have been interpreted to indicate that a subgroup within the human population might be especially sensitive to the hepatotoxic effects of coumarin, occurring a few weeks after treatment [55–57]. Our modelling results corroborate that the CYP2A6 polymorphism is unlikely to be the factor underlying the higher sensitivity of these individuals. This outcome agrees with the lack of a correlation between hepatotoxic responses and the CYP2A6 genotype status of the patients [58] because the frequency of homozygous individuals with two defective alleles in the general population is estimated to be much lower than the frequency of study patients with elevated liver enzymes. The higher sensitivity in these individuals may be due to other as yet unsolved factors which may include the fact that these studies were generally not performed in healthy subjects, such as patients with chronic lymphedema [55], chronic venous insufficiency [56, 57], individuals with a history of hepatitis [56, 57], and/ or upon concomitant exposure to troxerutin [56, 57]. Increased toxicity could also be due to bolus dosing rather than dietary administration. Therefore, it can be concluded that the human studies include several confounding factors and that the reason for the increased susceptibility to liver damage of some individuals within the groups of patients treated with coumarin remains to be established. This could be a reason for taking not only these patient studies, but also animal studies into account in the safety assessment on coumarin. The PBBK results reveal that in human subjects, even when 7-hydroxylation is deficient, the chances on formation of the toxic oHPA metabolite will be significantly lower than those expected in the liver of rats when exposed to a similar dose on a body weight base. This conclusion should be taken into account when extrapolating data from experimental studies in sensitive animals, i.╃e. rats, to the general human population, and could be a reason to reduce the uncertainty factor for inter-species variation used for definition of the TDI from 10 to 2.5

137

138

Contributions

leaving only the factor 2.5 for toxicodynamic differences but taking out the factor 4 for toxicokinetics, as suggested before [60]. Estragole

Worldwide different approaches exist to assess the risk of compounds that are both genotoxic and carcinogenic. Numerical estimates of the risk associated with human exposure might be derived by extrapolation of carcinogenicity data obtained in animals at high dose levels to low dose levels relevant for the human situation. Many mathematical models have been proposed by which such an extrapolation below the available experimental data can be performed, of which linear extrapolation is the simplest form [59]. Extrapolating from animal tumour data at high doses using mathematical modelling in order to obtain estimates of the risk to humans at low dose exposure levels has been much debated, since it is not known whether or not the model chosen actually reflects the underlying biological processes. In addition, it is argued that species differences are not taken into account and that obtaining numerical estimates may be misused or misinterpreted in further risk management and risk communication, where the uncertainties and inaccuracy connected to the model may not be communicated [60]. Considering these disadvantages the Scientific Committee of the European Food Safety Authority (EFSA) recommends using a different approach, known as the Margin of Exposure (MOE) approach [60]. The MOE approach uses a reference point, usually taken from data from an animal experiment that represents a dose causing a low but measurable cancer response. It can be for example the BMDL10, the lower confidence bound of the Benchmark Dose that gives 10â•›% (extra) cancer incidence (BMD10). The MOE is defined as the ratio between this reference point, the BMDL10, and the estimated dietary intake (EDI) in humans. When this MOE is higher than 10,000, the compound is considered to be of low priority for risk management actions [60–62]. This safety margin of 10,000 is applied to adequately allow for various uncertainties in the MOE approach, including: 1. A factor of 100 for species differences and human variability in biokinetics and biodynamics. 2. A factor of 10 for inter-individual human variability in cell cycle control and DNA repair. 3. A factor of 10 for uncertainties in the shape of the dose-response curve outside the observed dose range. To date carcinogenicity data for estragole from which a BMDL10, and thus a MOE, can be derived result from a long-term carcinogenicity study conducted in mice [5]. Table 1 presents the carcinogenicity data obtained for estragole in female mice in this study. A BMD analysis of these data using BMDS version 1.4.1â•›c software was performed of which the results are given in Table 2.╃Based on the results presented in Table 2 it is concluded that the BMDL10 value varies between 9 and 33 mg/kg bw per day.

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment Table 1: Overview of the data from Miller et al. [5] on the incidence of haematomas in female mice exposed for 12 months via the diet to estragole. Dose

Estimated dose No. of animals No. of mice with mg/kg bw per day haematomas

Incidence

0

0

43

0

0

0.23â•›% in diet

150–300

48

27

56

0.46â•›% in diet

300–600

49

35

71

Table 2: Results of a BMD analysis of the data from Miller et al. [5] on the incidence of hepatomas in female mice exposed for 12 months via the diet to estragole (Tab.╃1), using BMDS version 1.4.1╛c and the default settings of extra risk, a Benchmark Response (BMR) of 10╛% and a 95╛% confidence limit. To make a worst case estimate the lowest dose levels of the range were used (i.╃e.╃150 and 300 mg/kg bw per day respectively). Mice gender

Model

No. of parameters

Log likeli- Accepted hood

BMD10 mg/kg bw per day

BMDL10 mg/kg bw per day

Females

null

1

–96â•›1243

Females

full

3

–62â•›2103

Females

two-stage

1

–62â•›7403

yes

22.4

18.1

Females

gamma

1

–62â•›7403

yes

22.4

18.1

Females

log-logistic

1

–62â•›2124

yes

13.1

9.2

Females

log-probit

1

–62â•›7928

yes

40.7

32.7

Females

Weibull

1

–62â•›7403

yes

22.4

18.1

The average per capita daily intake of estragole was estimated by the Scientific Committee on Food of the European Union (SCF) to amount to about 4.3 mg per day (corresponding to 0.07 mg/kg bw per day for a 60 kg person) [3]. This estimation is based on a relative conservative method using theoretical maximum use levels of estragole in 28 food categories and consumption data for these food categories based on seven days dietary records of adult individuals [3]. Using a different method, a lower average per capita daily intake of estragole was estimated by the Expert Panel of the Flavor and Extract Manufacturers Association (FEMA) [1]. This estimation was performed using production volume data of herbs, essential oils, and flavour substances containing estragole in the US [1]. The FEMA estimated the daily per capita intake to be less than 10╃µg/ kg bw per day [1]. Using the exposure assessment provided by the SCF [3] of 0.07 mg/kg bw per day and the BMDL10 of 9 to 33 mg/kg bw per day, the MOE value would amount to 129 to 471, which is lower than 10,000, indicating that the consumption of estragole at these intake levels might be a high priority for risk management. Using the exposure assessment provided by Smith et al. [1] of 0.01/kg bw per day and the BMDL10 of 9 to 33/kg bw per day, the MOE value would amount to 900 to 3300.╃Comparison of this MOE value to the value of 10,000 indicates

139

140

Contributions

that at these intake levels the use of estragole containing spices and their essential oils might also be considered a priority for risk management. In the opinion of the EFSA it has been stated that the default MOE of 10,000 can be reduced or increased when appropriate chemical specific data are available [60]. The results of our PBBK modelling can provide insight in especially the applicability of the default safety factor for species differences in biokinetics used to define the value of 10,000.╃The outcomes of the PBBK models of the present work reveal that species differences in bioactivation of estragole were observed to be about 2-fold and thus smaller than the default factor of 4 generally assumed to reflect inter-species variation in kinetics. However, a 2-fold reduction of the default value of 10,000 would not lead to a different conclusion on the priority for risk management. A similar conclusion emerges from the approach in which linear extrapolation from a defined point of departure is used to derive a so-called virtual safe dose (VSD) at which the additional cancer risk upon life-time exposure would be one in a million and considered negligible [63]. Using the data and BMD analysis of the study of Miller et al. [5] (Tab.╃1 and 2) it can be concluded that in mice, a BMR (Benchmark Response) of 10â•›% extra tumour risk is observed at a BMD10 value of 13 to 41 mg/kg bw per day. By linear extrapolation from this point of departure, the VSD that results in an additional cancer risk of 1 in a million is calculated to amount to 0.13 to 0.41╃µg/kg bw per day. Comparison of this estimated VSD to the estimated dietary human intake of 10–70╃µg/kg bw per day [1, 3] indicates that dietary intake levels are about two orders of magnitude above the VSD, indicating a priority for risk management. The results of the PBBK models developed for estragole for male rat and human indicate that kinetic data do not provide a reason to argue against such a linear extrapolation from the rat tumour data to the human situation. This is illustrated in Figure 8, in which the PBBK model-predicted dose-dependent formation of 1’-sulfooxyestragole in the liver of rat and human is displayed. Both curves appear to be quite linear from doses as high as the BMD10 at which actual increased tumour incidences are observed in rodent bioassays, down to as low as the VSD, when plotted on a log-log scale as done in Figure 8 as well as on a linear scale (Figure not shown). Since the BMD10 appears to be within the linear part of the curve and since the rat and human curves do not differ substantially, the PBBK results of the present study support that possible non-linear kinetics and species differences in kinetics should not be used as arguments against using this linear low dose extrapolation from high dose animal data to the low dose human situation. Altogether, the results presented demonstrate that PBBK models provide a useful tool in risk assessment of food-borne chemicals when evaluating human risks.

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

Figure 8: PBBK model-predicted dose-dependent formation of 1’-sulfooxyestragole in the liver of rat (─) and human (– – –).

Additional Considerations

Whereas the risk assessments outlined above for estragole and coumarin take into account the predicted data on dose-dependent effects, species differences and inter-individual differences in bioactivation, it should be noted that other factors might affect the risk assessment as well. The carcinogenic effects of estragole and coumarin will for instance also depend on toxicodynamic processes (i.╃e. processes of importance for the ultimate formation and development of tumours). This could be investigated in further detail by extending the PBBK models to so-called physiologically based biodynamic (PBBD) models in which dose levels and 1’-sulfooxyestragole or oHPA formation should be coupled to DNA adduct formation, considered a biomarker of exposure, or to toxicity and – ultimately – cancer incidence. In addition, it should be noted that whereas animal carcinogenicity experiments are conducted with a pure compound, human dietary exposure to estragole or coumarin occurs in a complex food matrix containing other (herbal) ingredients. In a complex food matrix, interactions can occur that can affect the bioavailability of food components [46, 64]. For example, a slow or incomplete release of estragole or coumarin from the matrix could result in a reduced bioavailability as compared to the bioavailability when dosed as a pure compound by oral gavage. In addition to the effect of the food matrix on the bioavailability, interactions with other herbal ingredients might occur at the level of metabolic activation and/ or detoxification [46, 64]. It was for instance observed by Jeurissen et al. [65] that a methanolic basil extract is able to efficiently inhibit the sulfotransferase mediated DNA adduct formation in HepG2 human hepatoma cells exposed to 1’-hydroxyestragole. These results suggest that the bioactivation of estragole and subsequent adverse effects of estragole are prob-

141

142

Contributions

ably lower when estragole is consumed in a matrix of other basil ingredients than would be expected on the basis of experiments using estragole as a single compound. Whether this inhibition of DNA adduct formation by matrix ingredients could also occur in vivo was, however, not yet established and should be further explored. In conclusion, the data presented show that PBBK modelling provides a good method to evaluate the occurrence of dose-dependent effects, species differences, and human variability in bioactivation and detoxification. The model predictions obtained can be used to provide a more mechanistic basis for the assessment of the effects in humans at low dose dietary intake levels based on data obtained in experiments with rodents at high dose levels. However, for a complete assessment of the cancer risk at low dose human intake scenarios additional information is still needed. For example more insight will be needed in toxicodynamic processes that can affect the risk assessment, and more insight is needed in the modulating effects of herbal ingredients on the carcinogenic process resulting in a so-called matrix effect.

Acknowledgements Part of the work on coumarin was supported by the Dutch Ministry of Economic Affairs (Innovation Vouchers G071064 and G062238), J.╃S. Polak Koninklijke Specerijenmaalderij b.╃v., and the Vereniging voor de Bakkerij- en Zoetwarenindustrie (VBZ). Part of the work on estragole was supported by the Nestlé Research Center Lausanne, Switzerland.

Abbreviations BMD: Benchmark Dose BMDL: lower confidence bound of the Benchmark Dose BMR: Benchmark Response CE-SG: 4-hydroxy-3-glutathionyl-coumarin EDI: Estimated daily Intake EFSA: European Food Safety Authority FEMA: Flavor and Extract Manufacturers Association JECFA: Joint FAO/WHO Expert Committee on Food Additives MOE: Margin of Exposure oHPA: o-hydroxyphenylacetaldehyde oHPAA: o-hydroxyphenylacetic acid oHPE: o-hydroxyphenylethanol PBBK model: physiologically based biokinetic model SCF: Scientific Committee on Food VSD: Virtual Safe Dose

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

Conflict of Interest Statement The authors declare that there are no financial/ commercial conflicts of interest.

References â•⁄ 1. Smith, R.╃L., Adams, T.╃B., Doull, J., Feron, V.╃J., Goodman, J.╃I., Marnett, L.╃J., Portoghese, P.╃S., Waddell, W.╃J., Wagner, B.╃M., Rogers, A.╃E., Caldwell, J., Sipes, I.╃G., Safety assessment of allylalkoxybenzene derivatives used as flavouring substances – methyl eugenol and estragole. Food Chem. Toxicol. 2002, 40, 851–870. â•⁄ 2. Siano, F., Ghizzoni, C., Gionfriddo, F., Colombo, E., Servillo, L., Castaldo, D., Determination of estragole, safrole and eugenol methyl ether in food products. Food Chem. 2003, 81, 469–475. â•⁄ 3. SCF (Scientific Committee on Food, Opinion of the scientific committee on food on estragole (1-allyl-4-methoxybenzene). 2001 http://ec.europa.eu/ food/fs/sc/scf/out104_en.pdf.╃ â•⁄ 4. Drinkwater, N.╃R., Miller, E.╃C., Miller, J.╃A., Pitot, H.╃C., Hepatocarcinogenicity of estragole (1-allyl-4-methoxybenzene) and 1’-hydroxyestragole in the mouse and mutagenicity of 1’-acetoxyestragole in bacteria. J. Natl. Cancer Inst. 1976, 57, 1323–1331. â•⁄ 5. Miller, E.╃C., Swanson, A.╃B., Phillips, D.╃H., Fletcher, T.╃L., Liem, A., Miller, J.╃A., Structure–activity studies of the carcinogenicities in the mouse and rat of some naturally occurring and synthetic alkenylbenzene derivatives related to safrole and estragole. Cancer Res.╃1983, 43, 1124–1134. â•⁄ 6. Wiseman, R.╃W., Miller, E.╃C., Miller, J.╃A., Liem, A., Structure–activity studies of the hepatocarcinogenicities of alkenylbenzene derivatives related to estragole and safrole on administration to preweanling male C57BL/6J x C3H/HeJ F1mice. Cancer Res.╃1987, 47, 2275–2283. â•⁄ 7. Anthony, A., Caldwell, J., Hutt, A.╃J., Smith, R.╃L., Metabolism of estragole in rat and mouse and influence of dose size on excretion of the proximate carcinogen 1’-hydroxyestragole. Food Chem. Toxicol. 1987, 25, 799–806. â•⁄ 8. Sangster, S.╃A., Caldwell, J., Hutt, A.╃J., Anthony, A., Smith, R.╃L., The metabolic disposition of [methoxy-14C]-labelled trans-anethole, estragole and p-propylanisole in human volunteers. Xenobiotica 1987, 17, 1223–1232. â•⁄ 9. Hall, R.╃L., Oser, B.╃L., Recent progress in the consideration of flavoring ingredients under the food additives amendment III. GRAS substances. Food Technol. 1965, 253, 151–197. 10.╃ JECFA Sixty-ninth meeting, 2008 Rome, Italy, 17–26 June 2008.╃http:// www.who.int/entity/ipcs/food/jecfa/summaries/summary69.pdf.╃ 11. Lake, B.╃G., Coumarin metabolism, toxicity and carcinogenicity: relevance for human risk assessment. Food Chem. Toxicol. 1999, 37, 423–453. 12. SCF (Scientific Committee on food), Opinion on coumarin (a constituent of natural flavouring source materials limited by annex II of flavourings

143

144

Contributions

directive 88/388/EC) expressed on 16 December 1994.╃Reports of the Scientific Committee for Food. Thirty-sixth series, 1997, 13–20. 13. EFSA, Opinion of the Scientific Panel on Food additives, flavourings, processing aids and materials in contact with food (AFC) on a request from the commission related to coumarin. Question number EFSA-Q-2003–118.╃EFSA J.╃2004, 104, 1–36. 14. NTP (National Toxicology Program), Toxicology and carcinogenesis studies of coumarin (CAS No.╃91–64–5) in F344/N rats and B6C3F1 mice (gavage studies). Technical Report No. NTP TR 422.╃NIH Publication No.╃93–3153, US Department of Health and Human Services, NIH, Research Triangle Park, NC 1993. 15. Carlton, B.╃D., Aubrun J-C., Simon G.╃S., Effects of coumarin following perinatal and chronic exposure in Sprague-Dawley rats and CD-1 mice. Fundam. Appl. Toxicol. 1996, 30, 145–151. 16. Born, S.╃L., Rodriguez, P.╃A., Eddy, C.╃L., Lehman-McKeeman, L.╃D., Synthesis and reactivity of coumarin 3,4-epoxide. Drug Metab. Dispos. 1997, 25, 1318–1324. 17. Lake B.╃G., Gray, T.╃J.╃B., Evans, J.╃G., Lewis, D.╃F.╃V., Beamand, J.╃A., Hue, K.╃L., Studies on the mechanism of coumarin-induced toxicity in rat hepatocytes: comparison with dihydrocoumarin and other coumarin metabolites. Toxicol. Appl. Pharmacol. 1989, 97, 311–323. 18. Vassallo, J.╃D., Hicks, S.╃M., Daston, G.╃P., Lehman-McKeeman, L.╃D., Metabolic detoxification determines species differences in coumarin-induced hepatotoxicity. Toxicol. Sci.╃2004, 80, 249–257. 19. Felter, S.╃P., Vassallo, J.╃D., Carlton, B.╃D., Daston, G.╃P., A safety assessment of coumarin taking into account species-specificity of toxicokinetics. Food Chem, Toxicol. 2006, 44, 462–475. 20. Born, S.╃L., Caudill, D., Smith, B.╃J., Lehman-McKeeman, L.╃D., In vitro kinetics of coumarin 3,4-epoxidation: application to species differences in toxicity and carcinogenicity. Toxicol. Sci.╃2000, 58, 23–31. 21. Fentem J.╃H., Fry, J.╃R., Whiting, D.╃A., o-Hydroxyphenylacetaldehyde: A major novel metabolite of coumarin formed by rat, gerbil and human liver microsomes. Biochem. Biophys. Res. Commun. 1991, 179, 197–203. 22. Lake, B.╃G., Osborne, D.╃J., Walters, D.╃G., Price R.╃J., Identification of o-hydroxyphenylacetaldehyde as a major metabolite of coumarin in rat hepatic microsomes Food Chem. Toxicol. 1992, 30, 99–104. 23. Born, S.╃L., Api, A.╃M., Ford, R.╃A., Lefever, F.╃R., Hawkins, D.╃R., Comparative metabolism and kinetics of coumarin in mice and rats. Food Chem. Toxicol. 2003, 41, 247–258. 24. Van Iersel, M., Walters, D.╃G., Price, R.╃J., Lovell, D.╃P., Lake B.╃G., Sex and strain differences in mouse hepatic coumarin 7-hydroxylase activity. Food Chem. Toxicol. 1994, 32, 387–390. 25. Van Iersel, M.╃L., Henderson, C.╃J., Walters, D.╃G., Price, R.╃J., Wolf, C.╃R., Lake, B.╃G., Metabolism of [3–14C]coumarin by human liver microsomes. Xenobiotica 1994, 24, 795–803.

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

26. Draper, A.╃J., Madan, A., Parkinson, A., Inhibition of coumarin 7-hydroxylase activity in human liver microsomes. Arch. Biochem. Biophys. 1997, 341, 47–61. 27. Bogan, D.╃P., Deasy, B., O’Kennedy, R., Smyth, M.╃R., Interspecies differences in coumarin metabolism in liver microsomes examined by capillary electrophoresis. Xenobiotica 1996, 26, 437–445. 28. Fernandez-Salguero, P., Hoffman, S.╃M.╃G., Cholerton, S., Mohrenweiser, H., Raunio, H., Rautio, A., Pelkonen, O., Huang J.╃D., Evans, W.╃E., Idle, J.╃R., Gonzalez, F.╃J., A genetic polymorphism in coumarin 7-hydroxylation: sequence of the human CYP2A genes and identification of variant CYP2A6 alleles. Am. J. Human Genet. 1995, 57, 651–660. 29. Hadidi, H., Zahlsen, K., Idel, J.╃R., Cholerton, S., A single amino acid substitution (Leu160His) in cytochrome P450 CYP2A6 causes switching from 7-hydroxylation to 3-hydroxylation of coumarin. Food Chem.Toxicol. 1997, 35, 903–907. 30. Hadidi, H., Irshaid, Y., Vågbø, C.╃B., Brunsvik, A., Cholerton, S., Zahlsen, K., Idle, J.╃R., Variability of coumarin 7- and 3-hydroxylation in a Jordanian population is suggestive of a functional polymorphism in cytochrome P450 of CYP2A6. Eur. J. Clin. Pharmacol. 1998, 54, 437–441. 31. Andersen, M.╃E., Krishnan, K., Physiologically-based pharmacokinetics and cancer risk assessment. Environ. Health. Persp. 1994, 102, 103–108. 32. Clewell, H.╃J., Gentry, P.╃R., Gearhart, J.╃M., Allen, B.╃C., Andersen, M.╃E., Comparison of cancer risk estimates for vinyl chloride using animal and human data with a PBPK model. Sci. Total Environ. 2001, 274, 37–66. 33. Clewell, H.╃J., 3rd, Andersen, M.╃E., Barton, H.╃A., A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment. Environ. Health Perspect. 2002, 110, 85–93. 34. Krewski, D., Withey J.╃R., Ku L.╃F., Andersen M.╃E., Applications of physiologic pharmacokinetic modeling in carcinogenic risk assessment. Environ. Health. Perspect. 1994, 102 (Suppl 11), 37–50. 35. Krishnan, K., Andersen, M.╃E., Physiologically-based pharmacokinetic modeling and toxicology. In: Principles and methods of toxicology (A.╃W. Hayes, Ed.), Raven Press, N.╃Y.╃2001, 193–241. 36. Chiu, W.╃A., Barton, H.╃A., DeWoskin, R.╃S., Schlosser, P., Thompson, C.╃M., Sonawane, B., Lipscomb, J.╃C., Krishnan, K., Evaluation of physiologically based pharmacokinetic models for use in risk assessment. J. Appl. Toxicol. 2007, 27, 218–237. 37. Clewell, R.╃A., Clewell, H.╃J., 3rd, Development and specification of physiologically based pharmacokinetic models for use in risk assessment. Regul. Toxicol. Pharmacol. 2008, 50, 129–143. 38. Bogaards, J.╃J.╃P., Hissink, E.╃M., Briggs, M., Weaver, R., Jochemsen, R., Jackson, P., Bertrand, M., and van Bladeren, P.╃J. Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling. Eur. J. Pharm. Sci. 2000, 12, 117–124.

145

146

Contributions

39. Brown, R.╃P., Delp, M.╃D., Lindstedt, S.╃L., Rhomberg, L.╃R., Beliles, R.╃P., Physiological parameter values for physiologically based pharmacokinetic models. Toxicol. Indust. Health 1997, 13, 407–484. 40. Gargas, M.╃L., Burgess, R.╃J., Voisard, D.╃E., Cason, G.╃H., Andersen, M.╃E., Partition coefficients of low molecular-weight volatile chemicals in various liquids and tissues. Toxicol. Appl. Pharmacol. 1989, 98, 87–99. 41. Jepson, G.╃W., Hoover, D.╃K., Black, R.╃K., McCafferty, J.╃D., Mahle, D.╃A., Gearhart, J.╃M., Partition coefficient determination for non-volatile and intermediate volatility chemicals in biological tissues. Toxicologist 1992, 12, 262. 42. Payne, M.╃P., Kenny, L.╃C., Comparison of models for the estimation of biological partition coefficients. J. Toxicol. Environ. Health A 2002, 65, 897–931. 43. Lipscomb, J.╃C., Poet, T.╃S., In vitro measurements of metabolism for application in pharmacokinetic modeling. Pharmacol. Ther. 2008, 118, 82–103. 44. Punt, A., Delatour, T., Scholz, G., Schilter, B., van Bladeren, P.╃J., Rietjens, I.╃M.╃C. M., Tandem mass spectrometry analysis of N2-(trans-isoestragole3’-yl)-2’deoxyguanosime as a strategy to study species differences in sulfotransferase conversion of the proximate carcinogen 1’-hydroxyestragole. Chem. Res. Toxicol. 2007, 20, 991–998. 45. Punt, A. Freidig, A., Delatour, T., Scholz, G., Schilter, B., van Bladeren, P.╃J., Rietjens, I.╃M.╃C. M., A physiologically based biokinetic (PBBK) model for estragole bioactivation and detoxification in rat. Toxicol. Appl. Pharmacol. 2008, 231, 248–259. 46. Rietjens, I.╃M.╃C. M., Zaleska, M., Boersma.M.╃G., Punt A., Differences in simulated liver concentrations of toxic coumarin metabolites in rats and different human populations evaluated through physiologically based biokinetic (PBBK) modeling. Toxicol. in Vitro 2008, 22, 1890–1901. 47. Punt, A., Paini, A.╃A., Boersma, M.╃G., Freidig, A.╃P., Delatour, T., Scholz, G., Schilter, B., van Bladeren, P.╃J., Rietjens, I.╃M.╃C. M., Use of physiologically based biokinetic (PBBK) modeling to study estragole bioactivation and Â�detoxification in human as compared to male rat. Toxicol. Sci. 2009, 110, 255–269. 48. Ramsey, J.╃C., Andersen, M.╃E., A physiologically based description of the inhalation pharmacokinetics of styrene in rats and humans. Toxicol. Appl. Pharmacol. 1984, 73, 159–175. 49. DeJongh, J., Verhaar, H.╃J., Hermens, J.╃L., A quantitative property–property relationship (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans. Arch. Toxicol. 1997, 72, 17–25. 50. Barter, Z.╃E., Bayliss, M.╃K., Beaune, P.╃H., Boobis, A.╃R., Carlile, D.╃J., Edwards, R.╃J., Houston, J.╃B., Lake, B.╃G., Lipscomb, J.╃C., Pelkonen, O.╃R., Tucker, G.╃T., Rostami-Hodjegan, A., Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver. Curr. Drug Metab. 2007, 8, 33–45.

In Silico Methods for Physiologically Based Biokinetic (PBBK) Models Describing Bioactivation and Deto�xification of Coumarin and Estragole: Implications for Risk Assessment

51. Medinsky, M.╃A., Leavens, T.╃L., Csanády, G.╃A., Gargas, M.╃L., Bond, J.╃A., In vivo metabolism of butadiene by mice and rats: a comparison of physiological model predictions and experimental data. Carcinogenesis 1994, 7, 1329–1340. 52. Beierschmitt, W.╃P., Weiner, M., Age-related changes in renal metabolism of acetaminophen in male Fischer 344 rats. Age 1986, 9, 7–13. 53. Solheim, E., Scheline, R.╃R., Metabolism of alkenebenzene derivatives in the rat. I. p-Methoxyallylbenzene (Estragole) and p-methoxypropenylbenzene (Anethole). Xenobiotica 1973, 3, 493–510. 54. WHO, International Programme on Chemical Safety (IPCS): Assessing human health risks of chemicals: Principles for the assessment of risk to human health from exposure to chemicals. Environmental Health Criteria 210, World Health Organisation, Geneva. 1999, http://www.inchem.org/ documents/ehc/ehc/ehc210.htm. 55. Loprinzi, C.╃L., Kugler, J.╃W., Sloan, J.╃A., Rooke, T.╃W., Quella, S.╃K., Novotny, P., Mowat, R.╃B., Michalak, J.╃C., Stella, P.╃J., Levitt, R., Tschetter, L.╃K., Windschitl, H., Lack of effect of coumarin in women with lymphedema after treatment for breast cancer. New Engl. J. Med.╃1999, 340, 346–350. 56. Schmeck-Lindenau, H.╃J., Naser-Hijazi, B., Becker, E.╃W., Henneicke-von Zepelin, H.╃H., Schnitker, J., Safety aspects of a coumarin-troxerutin combination regarding liver function in a double-blind placebo-controlled study. Int. J. Clin. Pharmacol. Therapeut. 2003, 41, 193–199.€ 57. Vanscheidt, W., Rabe, E., Naser-Hijazi, B., Ramelet, A.╃A., Partsch, H., Diehm, C., Schultz-Ehrenburg, U., Spengel, F., Wirsching, M., Gotz, V., Schnitker, J., Henneicke-von Zepelin, H.╃H., The efficacy and safety of a coumarin-/ troxerutin-combination (SB-LOT) in patients with chronic venous insufficiency: a double blind placebo-controlled randomised study. Vasa 2002, 31, 185–190. 58. Burian, M., Freudenstein, J., Tegtmeier, M, Naser-Hijazi, B., Henneicke-von Zepelin, H.╃H., Legrum, W., Single copy of variant CYP2A6 alleles does not confer susceptibility to liver dysfunction in patients treated with coumarin. Int. J. Clin. Pharmacol. Therapeut. 2003, 41, 141–147. 59. COC (Committee on Carcinogenicity of chemicals in food, consumer products and the environment), Guidance on a strategy for the risk assessment of chemical carcinogens. 2004, http://www.advisorybodies.doh.gov.uk/coc/ guideline04.pdf.╃ 60. EFSA, Opinion of the scientific committee on a request from EFSA related to a harmonised approach for risk assessment of substances which are both genotoxic and carcinogenic. EFSA J.╃2005, 282, 1–31. 61. Barlow, S., Renwick, A.╃G., Kleiner, J., Bridges, J.╃W., Busk, L., Dybing, E., Edler, L., Eisenbrand, G., Fink-Gremmels, J., Knaap, A., Kroes, R., Liem, D., Muller, D.╃J., Page, S., Rolland, V., Schlatter, J., Tritscher, A., Tueting, W., Wurtzen, G., Risk assessment of substances that are both genotoxic and carcinogenic, report of an International Conference organized by EFSA and WHO with support of ILSI Europe. Food Chem. Toxicol. 2006, 44, 1636– 1650.

147

148

Contributions

62. Dybing, E., O’Brien, J., Renwick, A.╃G., Sanner, T., Risk assessment of dietary exposures to compounds that are genotoxic and carcinogenic, an overview. Toxicol. Lett. 2008, 180, 110–117. 63. EPA, Guidelines for carcinogen risk assessment and supplemental guidance for assessing susceptibility from early-life exposure to carcinogens. Washington, DC, US Environmental Protection Agency, 2005, http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=╃116283. 64. Schilter, B., Andersson, C., Anton, R., Constable, A., Kleiner, J., O’Brien, J., Renwick, A.╃G., Korver, O., Smit, F., and Walker, R., Guidance for the safety assessment of botanicals and botanical preparation for use in food and food supplements. Food Chem. Toxicol. 2003, 41, 1625–1649. 65. Jeurissen, S.╃M.╃F., Punt, A., Delatour, Th., Rietjens, I.╃M.╃C. M., Basil extract inhibits the sulfotransferase mediated formation of DNA adducts of the procarcinogen 1’-hydroxyestragole by rat and human liver S9 homogenates and in HepG2 human hepatoma cells. Food Chem. Toxicol. 2008, 46, 2296–2302.

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

3.7 In Vitro Models for Carcinogenicity Testing – Reality or Fantasy? Pablo Steinberg1,2, Carsten Müller1, Kristina Ullmann1, and René Thierbach1,2

Abstract In the present article two experimental systems to evaluate the carcinogenic potential of chemicals are presented. On the one hand, the BALB/c 3T3 cell transformation has been optimized in such a way that it can detect compounds with tumour initiating and tumour promoting activities within 3 to 3.5 weeks. On the other hand, an automated version of the soft agar assay in a 96-well format is now available that allows determining within one week whether a cell line treated with a chemical is able to grow in an anchorage-independent way. At the present time an assay, in which the two above-mentioned experimental systems are combined, is being developed. Although the in vitro test systems already available or under development nowadays will not substitute the carcinogenicity testing in whole animals, they very well could help to significantly reduce the number of animals needed for the in vivo testing of carcinogenicity in the near future.

3.7.1 Introduction Up to the present time the “gold standard” method to prove whether a chemical is carcinogenic or not is to test the chemical in whole animals. However, this procedure is extremely time-consuming, makes use of a high number of animals and cannot be used to screen a high number of compounds at a time. Because of these limitations in the last few years great efforts have been undertaken to develop test systems that could be used to evaluate the carcinogenic potential of chemicals in vitro. In the present article two important advances in this research field will be described.

3.7.2 BALB/c 3T3 Cell Transformation Assay Several different in vitro transformation assays have been established for the detection of carcinogenic compounds. Three of them, the Syrian hamster embryo cell assay (the so-called SHE assay), the C3H10T1/2 cell assay and the

1

University of Potsdam, Chair of Nutritional Toxicology, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany.

2

University of Veterinary Medicine Hannover, Institute for Food Toxicology and Analytical Chemistry, Bischofsholer Damm 15, D-30173 Hannover, Germany.

149

150

Contributions

BALB/c 3T3 cell assay, have recently been reviewed in detail by the OECD [1], whereby the most promising one is the BALB/c 3T3 cell assay. A twostage protocol for the BALB/c 3T3 cell assay was described by Sakai and Sato in 1989 [2] and optimized over the years by Makoto Umeda and colleagues at the Hatano Research Institute in Japan [3–5]. The protocol used nowadays in our laboratory is shown in Figure 1.╃The initiating activity of a compound can be tested by incubating the BALB/c 3T3 clone A31–1-1 cells between day 1 and 4 with the compound to be evaluated and then treating the cells with the strong tumour promoting agent 12-O-tetradecanoyl-phorbol-13-acetate for two weeks (between day 7 and 21). At the end of the 6th week the number of foci formed is determined. When wanting to determine the tumour promoting activity of a compound the cells are first treated with the strong initiating agent 3-methylcholanthrene between day 1 and 4.╃Thereafter, the cells are incubated for two weeks with the compound being evaluated and the number of cell foci is determined at the end of the 6th week. In order to shorten the duration of the cell transformation assay BALB/c 3T3 cells transfected with v-Ha-ras [6], so-called Bhas 42 cells, are now being used [7]. By doing so transformed foci can develop within 3 to 3.5 weeks. Interlaboratory studies have clearly shown that the Bhas 42 cell-based transformation assay is able to detect chemicals with tumour initiating and/ or tumour promoting activity [5]. A scheme of the Bhas 42 cell-based transformation assay for the detection of compounds with tumour initiating and/ or tumour promoting activity is depicted in Figure 2.╃In order to detect compounds with initiating activity in this assay, Bhas 42 cells must be seeded at a low density (2·103 cells/ ml). At this density it is guaranteed that cells carrying DNA adducts can divide, thereby “perpetuating” the DNA damage in form of mutations in the daughter cells. These cells in turn will proliferate and later give rise to the foci. The results of a cell transformation assay with Bhas 42 cells, in which the initiating activity of aflatoxin B1 was tested in the absence and in the presence of a rat liver homogenate, are exemplarily shown in Table 1.╃Aflatoxin B1 exerts its initiating activity only after having been metabolized to aflatoxin B1–8,9epoxide (e.╃g. by hepatic cytochromes P450). In line with this concept aflatoxin B1 only induced the formation of transformed foci in the presence of a rat liver homogenate (i.╃e. the S9 mix; Tab.╃1), Furthermore, the initiating activity of aflatoxin B1 was first observed when incubating the cells with concentrations of aflatoxin B1╃≥╃1 mg/ml.

Figure 1: Diagram of the two-stage protocol for the BALB/c 3T3 cell transformation assay according to Sakai and Sato [2].

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

Figure 2: Scheme of the Bhas 42 cell-based transformation assay for the detection of tumour initiating (A) and tumour promoting chemicals (B) according to Asada et al. [5].

The data of a Bhas 42 cell-based transformation assay, in which the tumour promoting activity of 12-O-tetradecanoyl-phorbol-13-acetate and lithocholic acid were tested, are shown in Table 2.╃A concentration-dependent increase in the tumour promoting activity of both compounds was observed. An assay to test the tumour-initiating activity of the two chemicals was performed in parallel and revealed, as expected, that 12-O-tetradecanoyl-phorbol-13-acetate and lithocholic acid do not possess tumour initiating activity at all (data not shown) [5]. The BALB/c 3T3 cell transformation assay can also be used to analyze the cancer-preventive activity of natural compounds and extracts. The protocol used in our laboratory to test this activity is shown in Figure 3. As shown in Figure 4 resveratrol (trihydroxystilbene), a polyphenol with anti-oxidative and cancer-preventive activities present in red wine, is able to inhibit the chemically-induced malignant transformation of BALB/c 3T3 cells. Table 1: Initiating activity of a flatoxin B1 in the Bhas 42 cell-based transformation assay (data from [5]). Aflatoxin B1 concentration (µg/ml)

S9 mix

Initiation assay (foci/well)

0 0.05 0.1 0.2 0.5 1 2 0 0.05 0.1 0.2 0.5 1 2

– – – – – – – + + + + + + +

â•⁄ 3.0╃±â•ƒ1.1 â•⁄ 2.5╃±â•ƒ1.1 â•⁄ 1.8╃±â•ƒ1.2 â•⁄ 3.0╃±â•ƒ1.7 â•⁄ 3.5╃±â•ƒ1.5 â•⁄ 2.5╃±â•ƒ0.5 â•⁄ 0.8╃±â•ƒ1.0 â•⁄ 3.2╃±â•ƒ1.5 â•⁄ 3.7╃±â•ƒ1.6 â•⁄ 4.2╃±â•ƒ1.5 â•⁄ 4.8╃±â•ƒ3.3 â•⁄ 4.7╃±â•ƒ1.8 14.3╃±â•ƒ2.4* 11.7╃±â•ƒ2.8*

*Significantly different from the solvent control (p<0.01).

151

152

Contributions Table€2: Promoting activity of 12-O-tetradecanoyl-phorbol-13-acetate and lithocholic acid in the Bhas 42 cell-based transformation assay(data from [5]). Chemical

Concentrationa

Promotion assay (foci/well)

12-O-tetradecanoylphorbol-13-acetate

0 2 5 10 20 50 100 0 0.5 1 2 5 10 20

â•⁄ 2.8╃±â•ƒ0.4 â•⁄ 9.0╃±â•ƒ1.3** 12.7╃±â•ƒ1.0** 17.2╃±â•ƒ3.0** 26.0╃±â•ƒ0.5** 28.7╃±â•ƒ3.3** 27.3╃±â•ƒ2.9** â•⁄ 4.2╃±â•ƒ0.8 â•⁄ 6.5╃±â•ƒ1.9* â•⁄ 7.8╃±â•ƒ1.0** â•⁄ 7.2╃±â•ƒ2.2* 11.7╃±â•ƒ1.4** 16.5╃±â•ƒ1.0** 31.8╃±â•ƒ3.1**

Lithocholic acid

The concentration units for 12-O-tetradecanoyl-phorbol-13-acetate are ng/ml and those for lithocholic acid µg/ml. * Significantly different from the solvent control (p<0.05). ** Significantly different from the solvent control(p<0.01). a

Figure 3: Scheme of the two-stage protocol for the BALB/c 3T3 cell transformation assay used to prove the chemopreventive activity of nature compounds/ extracts.

Figure 4: Influence of Resveratrol on the malignant tranformation of BALB/c 3T3 cells. The malignant transformation of BALB/c 3T3 cells was induced by first treating them with 3-methyl� cholanthrene (MCA) and then with 12-O-tetradecanoyl-phorbol-13-acetate (TPA). Resveratrol (Resv) was present at the given concentration in the cell culture medium from the beginning of the experiment, as shown in Figure 3.

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

Figure 5: Influence of Vineatrol®30 on the malignant tranformation of BALB/c 3T3 cells. The malignant transformation of BALB/c 3T3 cells was induced by first treating them with 3-methylcholanthrene (MCA) and then with 12-O-tetradecanoyl-phorbol-13-acetate (TPA). Vineatrol®30 (Vin) was present at the given concentration in the cell culture medium from the beginning of the experiment, as shown in Figure 3.

In the last few years the health promoting effects of a grapevine-shoot extract named Vineatrol®30, which contains resveratrol as well as considerable amounts of resveratrol oligomers, have been investigated. In this context it has been shown that Vineatrol®30 exerts antiproliferative and pro-apoptotic effects when added to chronic B lymphocytic leukaemia cell cultures [8] and inhibits the proliferation of human colon carcinoma cells by inhibiting cell cycle progression [9]. As shown in Figure 5 Vineatrol®30 is also able to inhibit the malignant transformation of BALB/c 3T3 cells. The inhibition of the formation of transformed foci was statistically significant at a concentration of 2.3╃µg/ml Vineatrol®30.

3.7.3 Automated Soft Agar Assay One of the characteristics of malignantly transformed epithelial cells held in culture is their anchorage-independent growth (i.╃e. their ability to grow in a semi-solid medium). This widely accepted criterion for the identification of malignantly transformed epithelial cells can be demonstrated by using the soft agar assay [10]. This assay delivers results that are comparable to those obtained when injecting tumourigenic cells into nude mice and is regarded as the standard method when wanting to test the tumourigenicity of cells in vitro [11]. In this context the soft agar assay can be used to determine whether a cell line (e.╃g. the above-mentioned BALB/c 3T3 cells) has been malignantly transformed after incubating the cells with a test compound. Furthermore, the soft agar assay is nowadays also being performed to (1) test the chemosensitivity of tumour cells towards established antitumoural agents [12], (2) to identify new anticancer compounds [13] and (3) to establish new therapeutic strategies to control cancer cell outgrowth, e.╃g. modification of cancer cell behaviour by gene transfection [14, 15]. Major drawbacks of the classical SAA are that it requires a long time to be performed (i.╃e. two to four weeks), it makes use of a great amount of plastic material and the quantification of the number of colonies growing in the semi-

153

154

Contributions

solid medium is difficult. Furthermore, the agar must be heated and mixed with living cells, whereby the temperature of the agar must be strictly controlled: If the temperature were too high the cells would be damaged, if it were too low the agar would prematurely solidify. In the last few years many of these problems have been resolved. First, to enable a higher throughput the SAA has been miniaturized from the classical 6-well format down to the 384-well microtiter plate format [13] and the length of the experimental procedure has been shortened down to one week [13,15]. Second, the extremely laborious and inconsistent cell colony counting has been substituted by a staining of the cells with Alamar Blue™ and their subsequent quantification with a plate reader [13, 15]. The only remaining bottleneck in the development of a true high-throughput soft agar assays was its automation. This obstacle has very recently been overcome by our research group by making use of a commercially available liquid handling system, the epMotion® 5075 LH from Eppendorf (Hamburg, Germany) [16]. Figure 6â•›a shows the work flow scheme of the automated soft agar assay. A detailed protocol of the assay has previously been described by Thierbach and Steinberg [16]. In order to demonstrate the utility of the automated soft agar assay, the sensitivity of three human colon cancer cell lines (MIP-101, DLD-2, and HT-29) towards seven different concentrations of the widely used chemotherapeutic agent 5-fluorouracil in a single 96-well microtiter plate was determined by making use of the newly developed experimental system. The distribution of the three cell lines as well as that of the different 5-fluorouracil dilutions on the 96-well microtiter plate is schematically displayed in Figure 6â•›b. Each 5-fluorouracil concentration is tested in quadruplicate (e.╃g. in the case of the DLD-2 cells the highest 5-fluorouracil concentration is added to wells 1–4 in row A, Fig.╃6â•›b). Approximately 40 min after programme start the 96-well plate is removed from the epMotion® 5075 LH and incubated at 37╃°C, 5â•›% CO2 and 95â•›% humidity. The only steps that have to be performed manually in between is the addition of agar and cell suspensions to the corresponding reservoirs in the epMotion® 5075╃LH. The final concentration of the agar in the top agar and in the base layer is 1 and 0.35â•›% w/v, respectively, as in the case of the classical soft agar assay [10]. After six days the individual cells form colonies, which are then quantified by adding Alamar Blue™ (resazurin) to each well. The non-fluorescent resazurin is metabolized to the red fluorescent resorufin by reductive processes in living cells. After three hours the amount of resorufin generated in this way is measured fluorometrically. If one assumes that the amount of resorufin generated is directly proportional to the number of living cells in the individual wells, the fluorescence measurement provides an estimate of the extent of cell growth in soft agar [13, 15]. Figure 7â•›a clearly shows that the use of 5-fluorouracil leads to a concentration-dependent reduction of colony formation in all three cases, each point of the curves representing the mean ± standard deviation of six independent experiments. The microphotographic documentation of representative colonies (Fig.╃7â•›b) makes clear that the data obtained with the automated method (Fig.╃7â•›a) excellently correlate with those obtained by manually meas-

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

Figure 6: Scheme of the automated SAA work flow on the epMotion® 5075╃LH. (a) Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), penicillin/streptomycin (P/S), complete medium and distilled water were mixed to yield the base-premix and the top-premix. The base-premix and the Bacto™ agar build the base layer, which coats the bottom of the plate wells. The top-premix, the cells, the Bacto™ agar and the compound(s) constitute the top agar. (*) The complete medium can be replaced by conditioned medium, if only one cell line is seeded on the plate. (b) The described experiment combines 3 cell lines with a dilution series of 5-fluorouracil.

155

156

Contributions

uring the colonies. Each experimental condition was tested in quadruplicate and showed a high reproducibility (data not shown). The automated method presented here considers all the options of classical soft agar assay protocols. One part of the culture medium can be replaced by conditioned medium and the substances to be tested come directly into contact with the cells. On each 96-well plate 3 different cell lines or 3 different cell con-

Figure 7: Automated soft agar assay results obtained after incubating the six 96-well microtiter plates for six days. (a) The fluorescence obtained is taken as a measurement for the ability of the cells to proliferate in soft agar (untreated cells=╃100â•›%). A 5-fluorouracil concentration-dependent inhibition of cell growth is evident for the three cell lines tested (MIP-101 [blue], DLD-2 [green] and HT-29 [red]). The mean ± standard deviation of six independent experiments per 5-fluorouracil concentration are shown. (b) Microphotographic documentation of representative MIP-101 cell colonies on day six after incubating the cells with increasing concentrations of 5-fluorouracil (phase contrast, 20â•›x objective).

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

centrations with a substance diluting series (7 concentrations + solvent control) or up to 8 different substances can be tested at the same time on a single 96-well microtiter plate. Taken together, the method presented here allows for the first time to test the cancer-inhibiting potential of a high number of compounds in the soft agar assay in an automated and highly reproducible way within a short period of time (40 min for processing in the epMotion® 5075 LH, 6 days incubation at 37╃°C, 3â•›h for the Alamar Blue™ staining) and does not require special cell culture practice. In this context it should be mentioned that only 70â•›% ethanol is used to disinfect the different parts of the pipetting system and that no single bacterial or fungal contamination was evident in any of the more than 100 automated soft agar assays performed in our laboratory up to now.

3.7.4 Concluding Remarks 1. The transformation process in the BALB/c 3T3 cell line parallels the induction and progression of tumours in vivo. As shown in the present article the cell line can be used to identify tumour initiators, tumour promoters as well as compounds with cancer chemopreventive activity. However, the BALB/c 3T3 transformation assay does not offer any advantage to the well established mammalian cell mutagenicity assays performed in vitro for the detection of genotoxic chemicals. 2. A true high-throughput soft agar assay is now available. The assay delivers highly reproducible results within a short period of time (40 min for the liquid handling process, 6â•›d incubation at 37╃°C, 3â•›h for the Alamar Blue™ staining) and can be used to determine whether cell lines treated with a test compound grow in an anchorage-independent manner as well as to identify compounds with cancer-inhibiting activity. 3. At the present time a combination of the two procedures mentioned above, i.╃e. exposure of cultured cells to chemicals according to the protocols developed by M. Umeda and colleagues [3–5] and testing of the treated cells in the automated soft agar assay [16] is being tried out. The two main obstacles to be overcome are the miniaturization of the cell transformation assay and the shortening of the time needed to perform the combined test. If the obstacles were overcome and the combined test were successfully validated, it could be included as part of an in vitro test battery to detect chemicals with a carcinogenic potential at an early stage of their development in the near future. 4. Although the in vitro test systems already available or under development will not substitute the carcinogenicity testing in whole animals, they very well could help to significantly reduce the number of animals needed for the in vivo testing of carcinogenicity in the near future.

157

158

Contributions

Acknowledgments We gratefully acknowledge the excellent technical assistance of Mrs. Babette Wagenhaus.

References â•⁄ 1. OECD (2006) Detailed review paper on cell transformation for detection of chemical carcinogens. OECD Environment, Health and Safety Publications. Series on Testing and Assessment No.╃31. â•⁄ 2. Sakai, A., Sato, M. (1991) Improvement of carcinogen identification in BALB/c 3T3 cell transformation by application of a 2-stage method. Mutat. Res.╃214, 285–296. â•⁄ 3. Tsuchiya, T., Umeda, M. (1995) Improvement in the efficiency of the in vitro transformation assay method using BALB/3T3 A31–1-1 cells. Carcinogenesis 16, 1887–1894. â•⁄ 4. Umeda, M. (2004) Cell transformation assay using BALB/c 3T3 cells or Bhas 42 cells for the efficient detection of tumour promoters. ATLA 32 (Suppl. 1), 673–677. â•⁄ 5. Asada, S., Sasaki, K., Tanaka, N., Takeda, K., Hayashi, M., Umeda, M. (2005) Detection of initiating as well as promoting activity of chemicals by a novel cell transformation assay using v-Ha-ras-transfected BALB/c 3T3 cells (Bhas 42 cells). Mutat. Res.╃588, 7–21. â•⁄ 6. Sasaki, K., Mizusawa, H., Ishidate, M. (1988) Isolation and characterization of ras-transfected BALB/3T3 clone showing morphological transformation by 12-O-tetradecanoyl-phorbol-13-acetate. Jpn. J. Cancer Res.╃79, 921–930. â•⁄ 7. Ohmori, K., Sasaki, K., Asada, S., Tanaka, N., Umeda, M. (2004) An assay method for the prediction of tumor promoting potential of chemicals by the use of Bhas 42 cells. Mutat. Res.╃557, 191–202. â•⁄ 8. Billard, C., Izard, J.-C., Roman, V., Kern, C., Mathiot, C., Mentz, F., Kolb, J.-P. (2002) Comparative antiproliferative and apoptotic effects of resveratrol, ε-viniferin and vine-shots derived polyphenols (vineatrols) on chronic B lymphocytic leukemia cells and normal human lymphocytes. Leukemia & Lymphoma 43, 1991–2002. â•⁄ 9. Marel, A.-K., Lizard, G., Izard, J.-C., Latruffe, N., Delmas, D. (2008) Inhibitory effects of trans-resveratrol analogs molecules on the proliferation and the cell cycle progression of human colon tumoral cells. Mol. Nutr. Food Res.╃52, 538–548.

In Vitro Models for Carcinogenicity Testing – Reality or Fantasy?

10. Macpherson, I., Montagnier, L. (1964) Agar suspension culture for the selective assay of cells transformed by polyoma virus. Virology 23, 291–294. 11. Freedman, V.╃H., Shin, S.╃I. (1974) Cellular tumorigenicity in nude mice: correlation with cell growth in semi-solid medium. Cell 3, 355–359. 12. Hamburger, A., Salmon, E. (1977) Primary bioassay of human myeloma stem cells. J. Clin. Invest. 60, 846–854. 13. Anderson, S.╃N., Towne, D.╃L., Burns, D.╃J., Warrior, U. (2007) A high throughput soft agar assay for identification of anticancer compound. J. Biomol. Screen. 12, 938–945. 14. Figini, M., Ferri, R., Mezzanzanica, D., Bagnoli, M., Luison, E., Miotti, S., Canevari, S. (2003) Reversion of transformed phenotype in ovarian cancer cells by intracellular expression of anti folate receptor antibodies. Gene Ther. 10, 1018–1025. 15. Ke, N., Albers, A., Claassen, G., Yu, D.╃H., Chatterton, J.╃E., Hu, X., Meyhack, B., Wong-Staal, F., Li, Q.-X. (2004) One-week 96-well soft agar growth assay for cancer target validation. BioTechniques 36, 826–833. 16. Thierbach, R., Steinberg, P. (2009) Automated soft agar assay for the high throughput screening of anticancer compounds. Anal. Biochem. 387, 318– 320.

159

160

Contributions

3.8 Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential? Hans-Jürgen Ahr1, and Heidrun Ellinger-Ziegelbauer2 Application of gene expression analysis using microarrays in toxicological studies allows an unprecedented hypothesis free insight into mechanisms underlying toxicological effects and thus may facilitate the interpretation of a toxic compounds mode of action. Transcriptomic biomarkers may further be used to predict a toxicological outcome earlier or with higher sensitivity. The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time-consuming and expensive. Since the results of these 2 year bioassays are known only quite late during development of new chemical entities, the identification of early biomarkers for carcinogenicity would be a big step forward. We therefore treated male Wistar rats with selected genotoxic and non-genotoxic carcinogens at doses known to induce tumours in the two year bioassays for up to 14 days and analyzed the early events in the liver following treatment by global expression analysis on Affymetrix RAE 230A microarrays. We identified distinct cellular pathways to be affected for both types of carcinogens. Characteristic to genotoxic carcinogens were DNA damage response and the activation of proliferative and survival signalling. Non-genotoxic carcinogens showed responses to oxidative stress, cell cycle activation and signs of regeneration. In analogy, similar signatures were also found in the kidney after treatment with selected nephrocarcinogens. We further used the expression profiles to extract multi-gene biomarkers discriminating genotoxic from non-genotoxic carcinogens and to train classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with good accuracy. This gives some hope, that pathway associated gene expression profiles may proof to be useful in the future to predicting a genotoxic or non-genotoxic carcinogenic potential of chemicals from short-term studies.

1

Bayer Schering Pharma AG, Aprather Weg 18â•›a, D-42096 Wuppertal, hans-juergen.ahr@ bayerhealthcare.com.

2

Bayer Schering Pharma AG, Aprather Weg 18â•›a, D-42096 Wuppertal, [email protected].

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Carcinogen specific expression profiling: Prediction of carcinogenic potential? H. J. Ahr, H. Ellinger-Ziegelbauer Bayer Schering Pharma AG Germany SKLM Symposium Kaiserslautern, March 30. – April 1., 2009

Kaiserslautern 03/2009

1

Toxicogenomics of Carcinogens DNA damage

Chromosome damage Point mutations

Cancer

Carcinogenesis Multistage process

Genotoxicity testing • Genetox battery • Cost: $60K/cmpd • Time: 1-3 month • High sensitivity • Low specificity

Short term studies Kaiserslautern 03/2009

Non-genotoxic mechanisms ¾Proliferation ¾Direct/regenerative hyperplasia ¾Hormone changes ¾Nuclear hormone receptor activation ¾Epigenetics ¾DNAImproved Methylationprediction by ¾Histone modifications

Toxicogenomics

?

2

Carcinogenicity testing • 2-year bioassay • Cost: $3M/cmpd • Time: 3 years

Life time bioassay

(Fig:J.Aubrecht ,modified)

161

162

Contributions

Toxicogenomics of Carcinogens Mechanistic and Predictive Toxicogenomics Mechanistically based Biomarker Toxmechanism correlated

Compound specific

Pathway analysis → Mechanistic investigation

Tox-effect/ Phenotype correlated

Mode of action correlated

Target validation → Drug discovery

Kaiserslautern 03/2009

“Phenotypic anchoring”

“Biomarker” → Classification/prediction

3

Toxicogenomics of Carcinogens

Outline: • Short term study for predicting long term effects? • Characteristic expression signals for hepatocarcinogens? • Characteristic expression signals in other target organs? • Prediction of hepatocarcinogenic potential? • Contribution to “weight of evidence“?

Kaiserslautern 03/2009

4

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Short vs. Long Term Study NNM-Stop design: Time-matched controls Treatment period NNM

120 mg/L (drinking water) Samples: 1d 1

3

7 8

early Foci

12

20

Preneoplastic progression

30

50 weeks

Neoplastic progression

• Collaborative Project (BfR Berlin, Merck, Bayer, DKFZ) funded by BMBF • In vivo dosing (Merck KGaA Darmstadt): – Male Wistar rats, 7 animals per timepoint, treatment for 7 weeks, samples over 50w • Liver: – Histopathology (Merck) – Proteomics (BfR, Merck) – RNA isolation and gene expression analysis (Bayer) (Oberemm et al, TAAP submitted) Kaiserslautern 03/2009

5

Short vs. Long Term Study

12_U9_SOM2(153)

NNM-Study PCA – Analysis:

20-50w

NNM 12w

Control 12-50w

8w

- Increasing deregulation during treatment 1d

3w 1w

Kaiserslautern 03/2009

- Comparison NNM treated versus control samples (significantly deregulated genes)

1d

- Fades out in the progression phase - Few expression differences at 50 weeks

6

163

164

Contributions

Toxicogenomics of Carcinogens

Outline: • Short term study for predicting long term effects?

9

• Characteristic expression signals for hepatocarcinogens?

Kaiserslautern 03/2009

8

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Mechanistic Toxicogenomics in Liver Study Design: • 5 genotoxic and 5 nongenotoxic carcinogens, 3 noncarcinogens • In vivo dosing:

Vehicle

Compound

– Male Wistar rats – 5 animals per dose-timepoint – Time course: 1d, 3d, 7d, 14d – Dosing: daily by gavage – Vehicle (time-matched controls) AAAA AAAA AAAA AAAA

• Liver:

– Histopathological and serum analysis – Gene expression analysis on Affymetrix RAE230A chips (3 livers/selected timepoint) – Data analysis

Scores for different modes of hepatotoxicity (Ellinger-Ziegelbauer et al, Mutation Research (2005))

Kaiserslautern 03/2009

9

Mechanistic Toxicogenomics in Liver Compounds and Selected Time Points Class Genotoxic carcinogen

Compound 2-Nitrofluorene Dimethylnitrosamine N-Nitrososmorpholine Aflatoxin B1 C.I Direct Black Nongenotoxic carcinogen Methapyrilene HCl Thioacetamid Wy-14643 Diethyl-stilbestrol Piperonyl-butoxide Nonhepatotoxicant Cefuroxime Nifedipine Propranolol

Abbrev. 2-NF DMN NNM AB1 CIDB MPy TAA Wy DES PBO CFX Nif Prop

Dose Vehicle Time course 44 mg/kg/d CO po 3, 7d 4 mg/kg/d CO po 3, 7d 3.5 mg/kg/d MC po 3, 7d 0.24 mg/kg/d MC po 3, 7d 146mg/kg/d CO po 3, 7d 60 mg/kg/d MC po 1, 3, 7d 19.2 mg/kg/d MC po 1, 3, 7, 14d 60 mg/kg/d MC po 1, 3, 7d 10 mg/kg/d MC po 1, 3, 7d 1200 mg/kg/d MC po 1, 3, 7d 250 mg/kg/d MC po 3, 7d 3 mg/kg/d MC po 3, 7d 40 mg/kg/d MC po 3, 7d

Vehicle Abbrev. Corn Oil CO Methylcellulose MC

Kaiserslautern 03/2009

10

165

Contributions

Mechanistic Toxicogenomics in Liver Characteristic Genes

PCA – Analysis - carcinogens versus non carcinogens / controls (significantely deregulated genes) - carcinogen treated samples are distinct from control / non-hepatoxin samples - no separation of control and non hepatotoxic samples Vehicle controls Nonhepatotoxicants Genotoxic carcinogens Nongenotoxic carcinogens

- clear separation of genotoxic and non-genotoxic carcinogens

Kaiserslautern 03/2009

11

Mechanistic Toxicogenomics in Liver

genes

1D Cluster analysis for genes using relative expression data

Ratio treated-replicate vs control-mean

166

samples

Oxidative stress / DNA damage response Nongenotoxic carcinogens NonNon-hepatotox C N P P D T W

Genotoxic carcinogens M

CCNG1 p21 MGMT

DNA damage response: ↑ p21, CycG1, MGMT → Genotoxic carcinogens

APEX1

Oxidative DNA damage: ↑ APEX, some p53 target genes → Nongenotoxic carcinogens

MDM2

Cell cycle progression

Cell cycle progression • Early + transient: Wy, PBO, DES, TAA • Regenerative hyperplasia: MPy, DMN → n ongenotoxic carcinogens

CycB1 CDC2 CycD1

PCNA

Kaiserslautern 03/2009

12

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Mechanistic Toxicogenomics in Liver Mapping onto cellular pathways ↑ by genotoxic carcinogens ↑ by nongenotoxic carcinogens ↑ by both types

APEX1

DNA repair (oxidative damage)

MGMT

DNA repair (adduct damage)

DNA damage ROS

DNA damage response

Mdm2

p53

ATM

BTG2 p21

Bax

Cell cycle arrest

Cell cycle progression

MCM6 PCNA S (DNA TOP2A synthesis)

Apoptosis

G2

CycB1 Cdc2

CDC20 Securin G1

Kaiserslautern 03/2009

Replication checkpoint

Cyclin G1

Mitosis

TUBA1 TUBB5

13

Toxicogenomics of Carcinogens

Outline: • Short term study for predicting long term effects?

9

• Characteristic expression signals for hepatocarcinogens? • Characteristic expression signals in other target organs?

Kaiserslautern 03/2009

14

9

167

168

Contributions

Mechanistic Toxicogenomics in Kidney Characteristic expression profiles in rat kidney? •

Control

Study design and analysis

Compound

– Male Wistar rats, 3 animals per dose-timepoint – Time course: 3d, 7d, 14d – Carcinogenic compound (kidney and/or liver) ¾ Genotoxic: 2-Nitrofluorene (2-NF): 44 mg/kg/d (both) Aristolochic acid: 9.6 mg/kg/d (kidney) ¾ Non genotoxic: Ochratoxin A: 3 mg/kg/d (kidney) – Time-matched controls

AAAA AAAA AAAA AAAA

– Kidney and / or liver, heart • Kidney cortex • Liver • Heart – RNA isolation and gene expression analysis (RAE230A)

Kaiserslautern 03/2009

15

Mechanistic Toxicogenomics: Kidney vs. Liver PCA – Analysis

Characteristic genes

Kidney

ols n tr Co

n tio ula eg r De

•

Genotoxic carcinogens in liver and kidney

•

Liver: 2NF, DMN, AB1 Kidney: 2NF, AA

•

Gene categories (liver selected): DNA damage response survival / proliferation

•

Evidence for clear treatment effects in both organs

Liver

Kaiserslautern 03/2009

16

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Mechanistic Toxicogenomics: Kidney vs. Liver: 2-NF

Gene categories deregulated by 2-NF in rat kidneys and liver Oxidative stress |Ox. Ox.Stress/DNA Stress/DNAdamage damageresponse response Cellsurvival survival/ |proliferation proliferation Cell Detoxification reponse Stress response Tissue organization Signaling

% Kidney % Liver

Apoptosis Inflammation RNA damage response Dedifferentiation Dedifferentiation Regulation of gene expression Other Unknown

0

5

10

15

20

% Kaiserslautern 03/2009

18

25

30

169

Contributions

DNA damage response

samples p21

Ratio treated-replicate vs control-mean

genes

Mechanistic Toxicogenomics: Kidney vs. Liver Liver 2-NF DMN AB1

2-NF

Kidney

AA

Genotoxic carcinogens • Genes:

– Characteristically deregulated by genotoxic liver carcinogens

• Samples:

CCNG1 MGMT

Cell survival / proliferation

– Liver: 2-NF, DMN, AB1 – Kidney: 2-NF, AA

DNA damage response: • ↑ p21, CycG1, MGMT • These “marker” genes are upregulated in both liver and kidney

SCYB10

Survival / proliferation signaling: • Only few genes also ↑ in the kidney (SCYB10, GDF15, PLK2) by both 2-NF and AA

PLK2 GDF15

Kaiserslautern 03/2009

19

genes

Mechanistic Toxicogenomics: Kidney vs. Liver Ratio treated-replicate vs control-mean

170

samples

Cell cycle progression MPy

CDC20

CDC2 CycB1

Liver Wy

PBO

Kidney OTA

(Non) genotoxic carcinogens • Genes: - Characteristically deregulated by non genotoxic carcinogens in liver and OTA in kidney • Samples: - Liver: MPy. Wy, PBO - Kidney: OTA

PCNA

Cell cycle progression: up regulation of cell cycle genes in liver and kidney

Mitotic spindle genes

Kaiserslautern 03/2009

20

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Toxicogenomics of Carcinogens

Outline: • Short term study for predicting long term effects?

9

9 in other target organs? 9

• Characteristic expression signals for hepatocarcinogens? • Characteristic expression signals

• Prediction of hepatocarcinogenic potential?

Kaiserslautern 03/2009

21

Predictive Toxicogenomics Supervised Learning

Expression profiles of training compounds (3 classes) Gene Ranking: ANOVA, Kruskal Wallis, RFE,.... Crossvalidation with several classifiers (KNN, SVM, SLDA)

“Biomarker”: Top-scoring genes with lowest error rate at lowest number of genes Classification of unknown samples using classification algorithms (SVM)

Prediction of toxicant class / classification of unknowns (Ellinger-Ziegelbauer et al. Mutat.Res. 2008) Kaiserslautern 03/2009

22

171

Contributions

Predictive Toxicogenomics • Tox classes (relative data) – 5 Genotoxic carcinogens 2NF, DMN, NNM, AB1,CIDB (d3+d7)

– 5 Nongenotoxic carcinogens Wy, DES, PBO (d1+3), MPy,TAA (d3+7)

– 3 Nonhepatocarcinogens: CFX, Nif, Prop: d1+d3+d7+d14

• Genes: all RAE230A • Ranking method: Recursive Feature Elimination (RFE) • Classifier: – SVM, SLDA, KNN (K=1, PC) – Test set fraction 25%

Error Rate

172

128

256

Number of genes

• Error rates for different classifiers: 128 – KNN: 0.21% – SVM: 0.08% – SLDA:0.13% Kaiserslautern 03/2009

256 0.04% 0% 0.04%

Selected Biomarker: 256 genes

23

Predictive Toxicogenomics • Representation of gene categories (Fisher‘s Exact Test)

(256 gene biomarker) Kaiserslautern 03/2009

24

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Predictive Toxicogenomics Validation compounds Class Genotoxic carcinogen

Compound 4-(methylnitrosamino)-1-(3-pyridyl)-1butanone 2-Acetylaminofluorene N-Nitrosopiperidine Methylendianiline Methylcarbamate Acetamide Dehydroepiandrosterone Ethionine Acetaminophen Cyproterone acetate Clonidine Prazosin Ibuprofen Allyl alcohol 1,4-Dichlorobenzene 3-Methylcholanthrene

Nongenotoxic carcinogen

Non-hepatocarcinogen

Abbrev. NNK

Dose 20 mg/kg/d

Vehicle Time course MC po 7, 14d

2-AAF NPip MDA Mcarb AA DHEA ETH Aap CPA Clon Praz Ibup AlAl DCB 3-MC

1.2 mg/kg/d 20 mg/kg/d 50 mg/kg/d 400 mg/kg/d 3000 mg/kg/d 600 mg/kg/d 200 mg/kg/d 4.25 g/kg po 100 mg/kg/d 0.1 mg/kg/d 1 mg/kg/d 94 mg/kg/d 36 mg/kg/d 300 mg/kg/d 25 mg/kg/d

MC po 1, 3, 7, 14d MC po 1, 3, 7, 14d CO po 1, 3, 7, 14d MC po 1, 3, 7, 14d MC po 1, 3, 7, 14d MCpo 1, 3, 7, 14d MC po 1, 3, 7, 14d Sal po 6, 12, 24, 48h CO po 1, 3, 7, 14d MC po 1, 3, 7, 14d MC po 1, 3, 7, 14d MC po 1, 3, 7, 14d MC po 1, 3, 7, 14d CO po 1, 3, 7, 14d COpo 1, 3, 7, 14d

Vehicle Abbrev. Corn Oil CO Methylcellulose MC

Kaiserslautern 03/2009

25

Predictions of Selected Validation Compounds (SVM, 256 genes)

Nongenotoxic carcinogens

Nontox

Geno NonG

Geno

3-MC

NonG Nontox

Geno NonG

Nontox

Affin. Assign.

Ibup

Nontox

ETH

Geno

Geno NonG

Nontox

Affin. Assign.

DHEA

Nontox

Geno

Non-carcinogens

MDA: False negative Weak deregulations Influence of dose? Kaiserslautern 03/2009

ETH: False positive for genotoxic carcinogenicity? AMES –, MNT +, 26

Nontox

Geno NonG

Geno

NonG Nontox

Geno NonG

NonG Nontox

Geno

Geno NonG

NonG Nontox

MDA

Geno

Affinity

NonG Nontox

Affin. Assign.

NPip

NonG Nontox

Genotoxic carcinogens

173

174

Contributions

Predictive Toxicogenomics Summary of Predictions for the Validation Compounds Gene group: GR-RFE(256) Class Genotoxic carcinogen (G)

Nongenotoxic carcinogen (NG)

Non-hepatocarcinogen (NC)

Compound NNK 2-AAF NPip MDA

CLassifier: SVM

Predictions 6 of 6 G 2 of 12 G 9 of 12 G 6 of 12 NC

Deregulation

Mcarb AA DHEA ETH Aap CPA

6 of 12 NG 11 of 12 NG 12 of 12 NG 3 of 12 G 6 of 12 NG 6 of 12 NG

weak

Clon Praz Ibup AlAl DCB 3-MC

12 of 12 NC 12 of 12 NC 12 of 12 NC 5 of 12 NC 6 of 12 NC 11 of 12 NC

weak weak

weak

affinity ≥ 0 Prediction √ √ √ false negative

3/4

√ √ √ false positive G √ √

5/6

√ √ √ √ √ √

6/6

weak

Kaiserslautern 03/2009

27

Predictive Toxicogenomics Prediction statistics: 3 x 3 contingency table for all chemicals (n=30) No. of test chemicals

TXG Prediction

Life time bioassay

Precision (%)

Genotoxic Non Genotoxic Non carcinogens

Genotoxic

9

8

0

1

89

Non genotoxic

12

1

11

0

91

Non carcinogens

9

0

0

9

100

Predictivity 89 (%)

100

90

Kaiserslautern 03/2009

28

93 % Accuracy

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Toxicogenomics of Carcinogens

Outline: • Short term study for predicting long term effects?

9

9 Characteristic expression signals in other target organs? 9 Prediction of hepatocarcinogenic potential? 9

• Characteristic expression signals for hepatocarcinogens? • •

• Contribution to “weight of evidence“?

Kaiserslautern 03/2009

29

Predictive Toxicogenomics

genes

samples

Genotoxic carcinogen 2-NF DMN

NNM

AB1

CIDB

NNK

ETH

Ratio treated-replicate vs control-mean

Nontox

p21 CycG1 MGMT

Kaiserslautern 03/2009

Affin. Assign Geno NonG

ETH

Geno

Prediction:

NonG Nontox

Ethionine, genotoxic in the liver?

CDC2

PCNA

CycB1

ETH: False positive for genotoxic carcinogenicity? AMES –, MNT +,

DNA damage response: ↑ p21, CycG1, MGMT → Genotoxic carcinogen Cell cycle progression, found characteristic for non-genotoxic carcinogens: •↑ of PCNA,CDC2, CycB1 • Not found for Ethionine

30

175

Contributions

Predictive Toxicogenomics

genes

Nontox

Oxidative stress / DNA damage response Nongenotoxic carcinogens Coumarin P W M

Affin. Assign. Geno NonG

Coum

Geno

samples

Prediction:

NonG Nontox

Coumarin: Non-genotoxic Carcinogen ?

Ratio treated-replicate vs control-mean

176

Genotoxic carcinogens

p21 CCNG1 MDM2 MGMT APEX1

(Male Wistar Rats, 150 mg/kg/d) Coumarin: Ames +, MNT -

Cell cycle progression

DNA damage response: ↑ p21, CycG1, MGMT → Genotoxic carcinogens Not found for Coumarin

CycB1 CDC2

Cell cycle progression, found characteristic for non-genotoxic carcinogens. PCNA, CDC2, CycB1 transiently up → n ongenotoxic carcinogen

CycD1 PCNA

Kaiserslautern 03/2009

31

Toxicogenomics of Carcinogens

Conclusion • Characteristic pathway associated gene expression signatures were identified in the liver of rats after short term treatment with carcinogens, which fit very well to the known mechanisms of carcinogenesis • They can discriminate genotoxic and non-genotoxic carcinogens from non carcinogens and will support weight of evidence considerations • Comparable signatures were also found in kidney • Pathway associated gene signatures (biomarker) may then help to identify a carcinogenic potential in different organs already after short term treatment Kaiserslautern 03/2009

32

Carcinogen Specific Expression Profiling: Prediction of Carcinogenic Potential?

Acknowledgements • Heidrun Ellinger-Ziegelbauer

• Scientific guidance and biological interpretation

• • • •

Juergen Hellmann, Michaela Kröger (Merck KGaA) Axel Oberemm (BfR, Berlin) Carina Ittrich (DKFZ, Heidelberg) Gabriele Scholz (Bayer HealthCare, now at Nestle, Geneva) • Collaborative NNM project

• Barry Stuart (Bayer Crop Science) • Brad Wahle (Bayer Crop Science) • Werner Bomann (Bayer Crop Science)

In vivo studies

• Hans Gmuender (GeneData) • Arnd Brandenburg (GeneData) • Guidance in data analysis

Thank you for your attention! Kaiserslautern 03/2009

33

177

178

Contributions

3.9 Safety and Biological Efficacy Testing of PhytoÂ�chemicals: An Industry Approach Anette Thiel1, Jochen Bausch, Mareike Beck, Paul Beilstein, James Andrew Edwards, Gerlinde Pappa, Robert Rümbeli, and Michael Török

Abstract When considering current requirements for assessing biological efficacy and safety of phytochemicals as food ingredients or dietary supplements one faces numerous challenges for both efficacy and safety. Biological efficacy screening and safety testing should be fast, efficient, targeted and ideally using only few animals. Facing these challenges one may use modern in vitro techniques like automated high-content high-throughput fluorescence microscopy, screening batteries on receptor/ ion-channel activation, or “OMICS” technologies for both efficacy and safety. Ideally, the efficacy and safety screening should incorporate the same techniques and instruments. This combination would result in insight into the mechanisms of toxicities and in a reduction of animal studies thereby contributing to animal welfare principles. We integrate early safety investigations in the screening process by using in silico and in vitro techniques to evaluate and rank screening hits. The selected non-genotoxic candidate compounds being part of biological efficacy experiments can be assessed in an early phase on in vitro absorption, metabolite profile, inter-species comparison, inhibition or induction of cytochrome P-450 isoenzyme activities for interaction with other compounds, or interaction with nuclear receptors. During this process, however, we face the question on the relevance of the collected in vitro data and the challenge to extrapolate them into the human situation and risk assessment. The safety profile of a development compound is then investigated in bioavailability and metabolism (ADME) studies giving also insight in accumulation potential. Further, standard regulatory, OECD guideline-compliant toxicological studies including genotoxicity, general toxicity (28-day and 90-day repeated administration) to derive a NO(A)EL for risk assessment, and a developmental toxicity study are performed. In addition, information on previous use situations including exposure estimates or case reports on adverse effects if available are collected and included in the safety evaluation. We think that non-genotoxic and non-developmentally toxic substances with a known history of (safe) use showing no accumulation potential with time, no differences in toxicity between the repeated toxicity studies, nor progressive change in histopathology can be assessed using an uncertainty factor

1

DSM Nutritional Products Ltd, Wurmisweg 576, CH-4303 Kaiseraugst, [email protected].

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach

of 100.╃We consider that with this approach the safety of our products is adequately evaluated.

3.9.1 Introduction Consumers expect not only tasty and convenient but also healthy food thereby acknowledging that life-style and thus food consumption may play a critical role in sustaining health. Therefore, food industry is challenged by providing adequate intake of macro- and micronutrients as well as beneficial constituents from food. These constituents must be identified by appropriate techniques from compound databases. Food industry has adapted approaches used by pharmaceutical research with the difference that nutritional screening does not focus on pathophysiological parameters but looks primarily for physiological changes triggered by food constituents. In addition, food industry wants to supply consumers with safe food not posing risks to consumers. Therefore, both sides, the efficacy as well as safety of the products, needs to be assessed thoroughly within challenging circumstances that biological efficacy screening and safety testing should be fast, non-expensive, targeted, and thereby using only low numbers of animals. The question arises how to align efficacy screening and safety testing in the best way, thereby using all information coming out of experimental work. Can this information be used to adjust the regulatory safety package and be used for safety assessment purposes?

3.9.2 Screening Process Identification of new nutritional bioactives can be done by screening pure compounds and afterwards identifying food sources with high content. Another possibility is to use existing knowledge on beneficial effects of certain plants or constituents thereof e.╃g. Traditional Chinese Medicine (for an overview please refer to [1]). Both processes tend to result in numerous compounds. Therefore, an early safety evaluation is implemented/ under development to help in the selection of the candidate compounds (see Fig.╃1). This initial early safety evaluation is done in silico using DEREK for Windows and MeteOr both developed by LHASA Ltd and by literature searches. DEREK for Windows with its substructure-alert principle recognizes known structural alerts within the molecule. We usually search using all so-called super end points2. However, for the purpose of early safety evaluations we focus on mutagenicity, genotoxicity, chromosome aberration, and carcinogenicity end

2

Carcinogenicity, chromosome damage, genotoxicity, hepatotoxicity, hERG channel inhibition, irritation, miscellaneous end points, mutagenicity, ocular toxicity, reproductive toxicity, respiratory sensitization, skin sensitization, thyroid toxicity.

179

180

Contributions

Figure 1: Screening process including early safety evaluation steps. In the first instance this early safety evaluation is focused on inherent potential mutagenic/ genotoxic activity of the screening hits because this is the most critical end point.

points. These end points within DEREK for windows have been the focus of several scientific publications [2–4]. The applicability of DEREK for Windows as currently used during early safety evaluation is limited to guide the assessor for ranking and screening purposes with regard to bioactive compounds. Consequently, substances showing a respective alert will be ranked at lower position. The absence of a certain genotoxicity/ mutagenicity alert will, however, not indicate absolute certainty that this end point is not of relevance for the toxicity of the respective compound. Therefore, we usually perform literature searches and in addition conduct confirmatory in vitro screening assays on mutagenicity (micro-Ames) and genotoxicity (in vitro MNT) with the best screening hits. The validation experiments of the micro-Ames revealed excellent concordance to the OECD€471 guideline-compliant Ames test for selected food specific validation compounds (genistein, resveratrol, epigallocathechin gallate (EGCG), or lycopene, see [5]). The established in vitro MNT is a version in multi-well format with fully-automated scoring of stained micronuclei by use of a high content analysis system. The in vitro MNT, however, is characterized by a low specificity, meaning that this assay is prone to false-positive results: Natural, anti-oxidative compounds like polyphenols have been shown to become pro-oxidative under certain culture conditions leading to the formation of peroxides, which may result in a positive in vitro MNT. This effect is an in vitro artefact and does not occur in vivo. Probably, the most best-known food-derived compound in this context is EGCG [6, 7]. We have developed a setup to assess the potential of substances to induce micronuclei in vitro and concurrently to assess the potential to develop hydrogen peroxide under the conditions of the assay (see [8]). In addition, the metabolism prediction programme Meteor (LHASA Ltd.) is also used as part of the initial safety assessment. The information on potential metabolites guides the targeted search during early investigations on metabo-

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach

lism, either in vitro or ex vivo in urine collected from first animal efficacy studies. Meteor gives good prediction on metabolism of xenobiotica occurring in mammalian liver where often a functionalization reaction is needed prior to conjugation and subsequent excretion. For plant constituents, however, Meteor prediction is less accurate. For instance, Meteor does not predict the 15–15’-monooxygenase cleavage reaction of carotenoids. Reductive metabolism e.╃g. reduction of trans-resveratrol to dihydroresveratrol catalyzed by gut microflora was also not predicted. In addition to early investigations on genotoxicity and metabolism, we currently work on the implementation of certain relevant mechanistic information to be performed already during early safety evaluation, here again using techniques in place from efficacy evaluations. We work on assays for interaction with nuclear receptors e.╃g. oestrogen and androgen receptors, pregnane X receptor (PXR) by using stably transfected HEK 293 cells and luciferase activity determination (transactivation assay). For measurement of potential induction or repression of metabolizing phase I and II enzymes in human liver cell lines such as HepG2, mRNA levels may be determined by RT-PCR. Currently, the Luminex technology allowing the concurrent determination/ quantification of these mRNAs within one sample is also evaluated. Another safety relevant application of the high content screening technique recently established is the cell health profiling where HepG2-cells are incubated with substances of interest [9]. We can assess such end points as standard cytotoxicity by means of YOYO-1 fluorescence dye as a measure of plasma membrane integrity. Changes in mitochondrial membrane potential can be assessed using Mitotracker Deep Red fluorescence dye, and thirdly we can measure oxidative stress by means of Dihydroethidium bromide which becomes oxidized by superoxide and peroxy-nitrite. The resulting ethidium subsequently binds to DNA and can be measured. We are in the process of trying to integrate such mechanistic in vitro data into the safety assessment of phytochemicals in the way that they can lead to a more efficient evaluation with the identification of possible mechanistic end points which can then be evaluated in the in vivo safety studies. Since often in vitro effects are not reproduced in vivo, it is important to confirm the relevance in in vivo experiments. Examples for such situations are the genotoxicity observed in vitro for EGCG or the reports on oestrogenic activity of trans-resveratrol in vitro which could not be confirmed in vivo [7, 10, 11]. In conclusion, the early evaluations (see Fig.╃2) done: 1. Allow the evaluation of large sets of food constituents with regard to efficacy as well as safety relevant parameters without using animals 2. Reduce the number of substances to those screening hits which can afterwards be further assessed in in vivo efficacy experiments 3. Allow selection of candidates with low potential of toxicity 4. Ensure that only non-genotoxic compounds enter development 5. Enable the design the regulatory safety package in a very straight forward way already implementing relevant mechanistic examinations.

181

182

Contributions

Figure 2: Techniques used/ in development for early safety evaluation.

3.9.3 Regulatory Pre-Clinical Safety Plant derived substances, compositions or extracts for food or supplements are subject of regulatory pre-clinical safety assessment. The regulatory safety evaluation is built on four pillars (see Tab.╃1), namely ADME3, mutagenicity and genotoxicity assessment, repeated general toxicity, and developmental toxicity. In addition, relevant acute toxicity, irritation as well as skin sensitization studies are performed for worker safety as well as classification and labelling purposes. The first pillar comprises ADME, with in vitro experiments with microsomal and/ or hepatocyte incubations and in vivo studies mainly in rats. Species comparison on metabolite profile is done with respective liver preparations obtained from laboratory animals and man. Compounds or plant extracts selected for development in vivo are studied if possible by the use of 14C-labelled material to study absorption, distribution, metabolism and excretion at the recommended intake level. Extracts may be spiked with the 14C-label of the main constituent [12]. In addition, plasma kinetics for the parent compound and/ or main metabolites is investigated. This information is especially useful for comparison

3

Absorption, Distribution, Metabolism, and Excretion.

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach Table 1: Basic Regulatory Safety Package according to accepted international guidelines.1 Worker Safety2

(acute toxicity3) In vitro skin and eye corrosion In vitro skin and eye irritation4 Skin sensitization (LLNA) (Phototoxicity5)

ADME

Excretion balance and plasma kinetics6 at recommended dose level Distribution using radioactive material Metabolism in vitro and in vivo Inter-species comparison based on metabolism in vitro Inhibition/ induction of drug metabolism

Genotox package in vitro

Reverse mutation in bacteria and chromosome Aberration in mammalian cells and/ or forward mutation in mammalian cells (Photomutagenicity5)

Genotoxicity in vivo7

In vivo MNT in mouse/ rat bone marrow or In vivo UDS (if in vitro test is positive)

Repeated toxicity

4-week subacute tox study7 and 13-week subchronic tox study with 4-week recovery period8 including exposure control

Developmental toxicity

Dose-range finder in pregnant animals (rat) Main developmental toxicity study (rat)

1 E.╃g. OECD. 2 In vivo testing for worker safety purposes for possible cosmetic ingredients complicated due to 7th amendment of Cosmetic Directive 76/768/EEC. 3 Needed for classification and labelling purposes. 4 No final OECD guidelines available yet. 5 In case substance absorbs UV-light and is present in/ on skin. 6 Also obtained in man. 7 Recent tendency to add micronuclei analysis in bone marrow in 4-week study to avoid use of extra animals in separate in vivo genotoxicity study. 8 Including sperm analysis, analysis of oestrous cycle, functional observation battery, motor activity.

with human plasma kinetic profile (available at later stages of product development) with regard to curve shape, Cmax, Tmax, and AUC. In the second pillar, for the genotoxicity studies done during early safety evaluation are repeated according to GLP and international accepted guidelines (see Tab.╃1). In the third pillar, the toxicity after repeated administration is investigated. As a standard extra parameters are included in the 90-day repeated toxicity study in addition to the current OECD€408 guideline: The addition of oestrous cycle analysis as well as sperm analysis as required in the 2-generation reprotoxicity study (OECD 416) enables to study a limited part of the requirements given on reprotoxicity and enables us a small insight in potential interference with reproduction and fertility. Optionally, determination of thyroid hormone plasma levels or assessment of immunotoxicological relevant parameters may be also added. If evidence exists from early safety evaluation on induction of microsomal enzymes e.╃g. CYP enzymes, determination of respective enzyme

183

184

Contributions

activities is included into the 90-day study thereby avoiding later sacrifice of further animals for mechanistic purposes. In the fourth pillar, the potential interference of the test substance with in utero development, starting with implantation to delivery is studied in a developmental toxicity study according to OECD 414. We consider that chronic toxicity data may not be necessary for hazard/ risk assessment especially for substances without accumulation potential, comparable toxicity profile obtained in 28-day and 90-day study and with a known history of (safe) use. During conduct of the safety package, special emphasis is given to accumulation potential by multiple plasma level determination of the active compounds and/ or its major metabolites during the conduct of the 90-day study. Considering plant constituents (e.╃g. ResvidaTM, a high-purity synthetic trans-resveratrol, Fig.╃4) usually being prone to efficient elimination by rapid metabolism and excretion, no increase in plasma levels with time is observed indicating no accumulation potential in plasma (see Fig.╃3). The ADME data of ResvidaTM in addition indicate rapid absorption from the gut with peak levels observed 10 to 30€min followed by a second peak at 4 to 8╛h (indicating enterohepatic recirculation). ResvidaTM is efficiently metabolized via conjugation in the gut and liver prior to rapid excretion. Conjugation reaction showed no evidence of saturation even at high dose levels (see Fig.╃3). Major metabolites in animals and humans are the phase II enzyme products but also

Figure 3: Comparison plasma levels of free and total (conjugated) trans-resveratrol at multiple time points during a 90-day repeated toxicity study (taken from [13]).

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach

Figure 4: Proposed metabolic pathways of resveratrol in rats before conjugation with sulfate and glucuronide.

conjugates of dihydro-resveratrol coming from reduction of aliphatic doublebond by gut microflora (see Fig.╃4). We often do not observe differences in toxicity when comparing 28-day and 90-day repeated dose toxicity studies. For example ResvidaTM, showed no adverse effects to groups in Wistar rats (5 per sex per group) when administered for 28 days as feed admix up to a dose level of 500 mg/kg bw/d. In the subsequent 90-day study including 4-week recovery period, even with dose levels up to 750 mg/kg bw/d, ResvidaTM produced no toxicologically relevant changes especially no pre-neoplastic lesions [13]. Thus, the NOAEL in this study was 750 mg/kg bw/d. Comparison with published data showed that toxicological data obtained with ResvidaTM are in line with published data on resveratrol: ►⌺ 1250 mg/kg bw/day was the NOAEL in a 14-day subacute oral gavage toxicity study in the rats; this dose corresponding to the highest dosage used [14] ►⌺ 2500 mg/kg bw/day was the NOAEL in a 14-day subacute oral gavage toxicity study in mice; this dose also corresponding to the highest dosage used [15] ►⌺ 300 mg/kg bw/day was the NOAEL in a 28-day oral gavage study in rats. 1000€mg/kg€bw/day produced reduced body weights in females and at the high dosage of 3000 mg/kg bw/day the kidney was identified as the target organ [16]. ►⌺ 1000 mg/kg bw/day was the NOAEL (highest dose) in a 28-day oral capsuleadministration study in dogs [17] ►⌺ 200 mg/kg bw/day was the NOAEL in a 90-day oral gavage toxicity study in rats, and no adverse histopatholgoical effects were seen at the high dose of 1000 mg/kg bw/day [18] ►⌺ 1000 mg/kg bw/day was well tolerated in a 6-months oral gavage study in p53-deficient mice. Increased liver weight was observed. Mortality occurred in the 2000 and 4000€mg/kg bw/day groups but was attributed to oral administration of large quantities of resveratrol administered in methylcellulose rather than due to resveratrol-mediated toxicity per se. There was an increase

185

186

Contributions

in epithelial hyperplasia of the bladder at 2000€mg/kg bw/d. However, incidences of benign or malignant tumours were similar to controls at 2000 mg/ kg bw/day, the highest dose examined histopathologically [19]. In addition, ResvidaTM showed no adverse effect on pregnant rats and their unborn progeny up to and including the highest dose level administered (750 mg/kg bw/d). Based on this information, the overall relevant NOAEL for safety assessment was considered to be 750 mg/kg bw/d. An uncertainty factor to adjust for intraand inter-species differences of 100 was applied for the proposed ADI of 7.5€mg/ kg bw/d. An additional adjustment factor for extrapolation from subchronic to chronic exposure is suggested by several authors and may vary between 2 and 10 (see for example [20, 21]). However, recently it was suggested that additional adjustment factors for extrapolation of duration may not be necessary especially when there is evidence “that increasing exposure duration does not increase the incidence or severity of adverse effects” [22]. We consider that this may be shown by comparison of respective adverse effects (occurrence, severity and incidence) observed in 28-day and 90-day studies. In the case of ResvidaTM, an additional uncertainty factor for extrapolation of duration from subchronic to chronic exposure was not considered necessary due to the absence of accumulation potential in plasma as well as no differences in toxicity between subacute and subchronic toxicity studies (see above).

3.9.4 Overall Conclusion In silico and in vitro techniques for hazard identification as well as in vitro techniques for testing of certain mechanistic end points may facilitate a more efficient safety evaluation of phytochemicals by means of targeted study design. Thus, in silico and in vitro examination help in the reduction of animal number used for efficacy studies and regulatory safety studies. For substances without accumulation potential, showing no increase in incidence or severity of adverse effects with time in independent 28-day and 90day studies and importantly not showing pre-neoplastic changes in histopathology, an uncertainty factor of 100 for deduction of ADI is considered adequate.

References â•⁄ 1. Schwager J, Mohajeri MH, Fowler A, Weber P (2008) Challenges in discovering bioactives for the food industry, Current Opinion in Biotechnology 19: 66–72. â•⁄ 2. Snyder RD, Smith MD (2005) Computational prediction of genotoxicity: room for improvement, Drug Discovery Today 10(16): 1119–1124.

Safety and Biological Efficacy Testing of Phyto�chemicals: An Industry Approach

â•⁄ 3. Snyder RD, Pearl GS, Mandakas G, Choy WN, Goodsaid F, Rosenblum IY (2004) Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules, Environmental and Molecular Mutagenesis 43:143–158. â•⁄ 4. Cariello NF, Wilson JD, Britt BH, Wedd DJ, Burlinson B, Gombar V (2002) Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity, Mutagenesis 17(4): 321–329. â•⁄ 5. Pappa G, Woehrle T, Thiel A, Toeroek M (2009â•›a) Predictivity comparison between screening assays for bacterial mutagenicity for natural compounds: micro-Ames vs. Ames fluctuation method, poster presentation for SKLM Symposium, see chapter 4.29 in this book. â•⁄ 6. Long LH, Kirkland D, Whitwell J, Halliwell B (2007) Different cytotoxic and clastogenic effects of epigallocatechin gallate in various cell-culture media due to variable rates of its oxidation in the culture medium, Mutation Research 634:117–183. â•⁄ 7. Takumi-Kobayashi A, Ogura R, Morita O, Nishiyama N, Kasamatsu T (2008) Involvement of hydrogen peroxide in chromosomal aberrations induced by green tea catechins in vitro and implications for risk assessment, Mutation Research 657:13–18. â•⁄ 8. Pappa G, Woehrle T, Thiel A, Toeroek M (2009â•›b) Automated in vitro micronucleus testing of natural compounds in correlation with hydrogen peroxide, poster presentation for SKLM Symposium, see chapter 4.30 in this book. â•⁄ 9. Woehrle T, Toeroek M, Seifert N, Fowler A (2006) A combined high content cytotoxicity and oxidative stress assay for early safety profiling of plant derived natural products, poster presentation MIPTEC 2006. 10. Isbrucker RA, Bausch J, Edwards JA, Wolz E (2006) Safety studies on epigallocatechin gallate (EGCG) preparations. Part 1: Genotoxicity, Food Chem Tox 44(5): 626–635. 11. Ashby J, Tinwell H, Pennie W, Brooks AN, Lefevre PA, Beresford N, Sumpter JP (1999) Partial and weak oestrogenicity of the red wine constituent resveratrol: consideration of its superagonist activity in MCF-7 cells and its suggested cardiovascular protective effects, J Appl Toxicol 19: 39–45. 12. Beck M, Bruchlen M, Elste V, Mair P, Rümbeli R (2009) Studying absorption, distribution, metabolism, and excretion of a complex plant extract, poster presentation for SKLM Symposium, see chapter 4.3 in this book. 13. Williams LD, Edwards JA, Beck M (2008) Toxicological Evaluation of ResvidaTM, a High Purity trans-Resveratrol, poster presentation SOT 2008. 14. Dalefield RR, Wheat TM, Athey PM, Colleton CA, Burback BL, Hejtmancik M. (2006â•›a) The 14-day gavage toxicity study of resveratrol (RES) (CAS No.╃501–36–0) in Fischer 344 rats (G823522-B). Final report prepared by Battelle, Columbus, Ohio, December, 2006.╃National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC. 15. Dalefield RR, Wheat TM, Athey PM, Skowronek AS, Burback BL, Hejtmancik M. (2006â•›b) The 14-day gavage toxicity study of resveratrol (RES) (CAS No.╃501–36–0) in B6C3B6C3F1 mice (G823522-C). Final report prepared

187

188

Contributions

by Battelle, Columbus, Ohio, December, 2006.╃National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC.╃ 16. Crowell JA, Korytko PJ, Morrissey RL, Booth TD, Levine BS (2004) Resveratrol-associated renal toxicity, Toxicological Sciences 82: 614–619. 17. Crowell J, McCormick D, Cwik M, Kapetanovic I (2007) Toxicokinetics of resveratrol in dogs (Abstract No. Q08 from Abstracts of the 44th Congress of the European Societies of Toxicology), Toxicology Letters 172 (Supplement 1): S102. 18. McCormick D, Johnson WD, Morrissey RL, Kapetanovic IM, Crowell JA (2008) Cardioprotective activity of resveratrol in rats, poster presentation at SOT 2008. 19. Horn TL, Cwik MJ, Morrissey RL, Kapetanovic I, Crowell JA, Booth TD, McCormick DL (2007) Oncogenicity evaluation of resveratrol in p53(+/-) (p53 knockout) mice, Food and Chemical Toxicology 45: 55–63. 20. FAO/WHO (2006) A model for establishing upper levels of intake for nutrients and related substances. Report of a Joint FAO/WHO Technical Workshop on Nutrient Risk Assessment, Geneva, 2005.╃World Health Organisation. ISBN 92 4 159418 7. 21. Falk-Filipsson A, Hanberg A, Victorin K, Warholm M, Wallén M (2007) Assessment factors – Applications in health risk assessment of chemicals, Environmental Research 104: 108–127. 22. ECHA (2008) Guidance on information requirements and chemical safety assessment Chapter R.╃8: Characterisation of dose [concentration]-response for human health, May 2008, http://guidance.echa.europa.eu/docs/guidance_document/information_requirements_en.htm?time=╃1236336030, accessed on 06 March 2008.

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

3.10 Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity Hennicke Kamp1, Roland Buesen1, Eric Fabian1, Michael Herold2, Gerd Krennrich3, Edgar Leibold1, Ralf Looser2, Werner Mellert1, Tsuyuki Nishino1 , Alexander Prokoudine2, Volker Strauss1, Tilmann Walk2, Jan Wiemer2, and Bennard van Ravenzwaay4

Abstract Metabolite profiling, the study of endogenous metabolites and their changes under toxic effects, is a methodology arising from the post-genomics era. This technique has already been applied in many fields and has, compared to other “OMICS” technologies the advantage that metabolite changes are determined as a direct consequence of biological effects. BASF has established a large metabolite profiling dataset on the basis of up to now more than 400 chemicals, agricultural chemicals and pharmaceuticals tested in repeated-dose toxicity studies in rats. In addition, studies were performed that address the metabolic profile in rat plasma in dependency of different nutrition scenarios. The purpose of the project is to establish defined relationships (metabolite patterns) between metabolic profiles in plasma (and/ or urine) of rats and known toxicological modes of action. Therewith, it is possible to generate a comprehensive database for the detection of toxicological modes of action by test substances based metabolic profiles. Wistar rats were administered test substances at a high dose level (overt toxicity) and a low dose level (slight toxicity) for 28 days (5 animals per dose group). Blood samples were taken on days 7, 14 and 28.╃The metabolic profiles in these blood samples were determined using multiple mass spectrometry-based technologies (LC-MS and GC-MS). The levels of endogenous metabolites were compared to the levels of untreated controls and the metabolic profile was expressed as fold-change of control and subjected to different tests for statistical significance. Taken together, metabolic profiling of reference compounds combined with their toxicity data enables early predictions of toxicological profiles of new test compounds and can be applied in screening tests as well as mechanistic research in toxicology: It should also allow for a better (biologically-based) grouping of chemicals for the purpose of read-across strategies

1

BASF SE, Experimental Toxicology and Ecology, Z 470, D-67056 Ludwigshafen, Germany.

2

metanomics GmbH, Tegeler Weg 33, D-10589 Berlin, Germany.

3

BASF SE, Scientific Computing, B009, D-67056 Ludwigshafen, Germany.

4

Correspondence to: Dr. B. van Ravenzwaay, BASF SE, Experimental Toxicology and Ecology, Z 470, D-67056 Ludwigshafen, Germany, Tel. +╃49 621 605 64 19, Fax: +╃49 621 605 81 34, [email protected].

189

190

Contributions

within the REACH framework, i.╃e. from QSAR (quantitative structure–activity relationship) to QBAR (quantitative biological activity relationships).

3.10.1 Introduction Metabolite profiling describes the analysis of endogenous low molecular compounds such as carbohydrates, amino acids, lipids, organic acids etc. Metabolite profiling has a long history of application in the plant sciences [1, 2] but it is a relatively new technology in toxicology studies to elucidate changes in biochemical pathways after administration of test compounds [3–5]. The analysis is performed routinely by using blood or urine applying mainly two different technologies, namely profiling by NMR [6, 7], and profiling via chromatography coupled with MS detection systems [8–11]. For a more detailed comparison of the two techniques see van Ravenzwaay et al.╃2010 [12] and Gomase et al.╃2008 [13]. It is well known that the levels of endogenous metabolites are influenced by various physiological factors like sex, circadian rhythm, oestrous cycle, strain, genetic mutations, stress etc. [14–22], but also as a result of toxicological responses. For example, specific metabolite changes have been described in the context of liver and kidney toxicity as well as for other toxicological targets [8, 23–25]. For the purpose of establishing a large metabolite profiling database for chemicals, agricultural active ingredients and pharmaceuticals, BASF has established a specific and highly standardized 28-day testing procedure in rats with sampling of blood and urine at several time points [8]. Here, we describe the current status of our work and the applicability of metabolite profiling and the BASF database MetaMap®Tox within toxicological research.

3.10.2 Materials and Methods Animals and Maintenance Conditions: Wistar (CrI:WI(Han)) rats were supplied by Charles River Laboratories, Germany. The animals were housed under acclimatized conditions as described in [26]. At the beginning of the study the animals were 10–11 weeks old. Animal Treatment: The studies were performed according to the German Animal Welfare legislation (approved by the local authorizing agency for animal experiments (Landesuntersuchungsamt Koblenz, Germany) as referenced by the approval number 23 177–07/G 08–3-001) in an AAALAC (Association for Assessment and Accreditation of Laboratory Animal Care) certified laboratory. Each dose group in the studies consisted of five rats per sex, and was compared with controls (10 rats per sex). Preferably, the test substances were administered via feed or gavage, but intra-peritonial, subcutaneous and intra-muscular

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

injections were also used according to the formulation of the compounds. The dose levels were chosen to show the typical overt (high dose) and slight (low dose) toxicological symptoms of the substances as described in the literature or in BASF internal study reports. Blood Sampling: Blood samples were withdrawn from the retro-orbital sinus in all rats under isoflurane anaesthesia on study days 7, 14 and 28 after a fasting period of 16–20 hours. Plasma samples were prepared [26] and used for analysis. Metabolite Profiling: For mass spectrometry-based metabolite profiling analysis, plasma samples were extracted by a proprietary method which delivers a polar and a non-polar fraction. For GC-MS analysis, the non-polar fraction was treated with methanol under acidic conditions to yield the fatty acid methylesters. Both fractions were further derivatized with O-methyl-hydroxyamine hydrochloride and pyridine to convert oxogroups to O-methyloximes and subsequently with a silylating agent before analysis. In LC-MS/MS analysis, both fractions were reconstituted in appropriate solvent mixtures. HPLC was performed by gradient elution on reversed phase separation columns. For mass spectrometric detection metanomics proprietary technology was applied, which allows target and high sensitivity MRM (Multiple Reaction Monitoring) profiling in parallel to a full screen analysis. The method resulted in 269 unique analytes for semi-quantitative analysis, 187 of which were chemically identified and 82 were unknown. Moreover, several hundred additional analytes giving a fingerprint of the sample were included in the methods. Following comprehensive analytical validation steps, the data for each analyte were normalized against data from pool samples. These samples were run in parallel through the whole process to account for process variability. Statistics: Metabolic profiles: The data were analyzed by univariate and multivariate statistical methods. The sex- and day-stratified heteroscedastic t-test (“Welch test”) was applied to compare treated groups with respective controls, p-values and ratios of corresponding group medians were collected as metabolic profiles and fed into a database. The univariate analysis was complemented by multivariate Principal Component Analysis (PCA) taking the covariance structure of the entire dataset into account. PCA was performed using SIMCA-P+ Version 11 (Umetrics AB, Umea, Sweden).

191

192

Contributions

3.10.3 Results and Discussion Data on untreated control animals: The metabolite profile in the plasma of a high number of untreated control animals was regularly analyzed within the MetaMap®Tox project over a time period of 3 years. The metabolite profiles consist of approximately 300 different endogenous metabolites, which can be assigned to amino acids, small carbohydrates, complex lipids, fatty acids, intermediates of energy metabolism, small hormones, signal substances, nucleobases, vitamins, and cofactors as well as miscellaneous. Some metabolites, that are reliably measurable, are still of unknown chemical structure (Fig.╃1). These data were used to establish base line values for variation and to assess the stability and reproducibility of metabolite profiling (Fig.╃2). The results of this analysis showed the absence of seasonal effects as well as the stability of the test system and the applied methodology [27]. Additionally, one can see that the biological variability as represented by the coefficient of variation (CV) for male (blue line) and female (red line) animals is greater than the technical variability as represented by the CV for the technical pool (green line). Moreover, the variability in the metabolite profile for male animals is comparable to that of female animals. A more detailed discussion on the variability can be found in [12]. Taken together, the robustness of the test system allows the application of metabolite profiling for toxicity studies, such as the 28 day repeated-dose toxicity study according to OECD TG 407. A number of investigations with control animals have addressed the influence on the metabolite profile dependent on nutritional status, handling, and route of administration including the use of different vehicles for oral administration. The evaluation of these data is currently being finalized, a publication is under preparation. Data on reference compounds in the database MetaMap®Tox: The variability in the metabolite profile of treated animals is usually slightly higher than in control animals (data not shown). Nevertheless, we have shown

Figure 1: Relative number of >300 metabolites used for metabolite profiling in the respective substance classes (amino acids and related; carbohydrates and related; complex lipids, fatty acids and related; intermediates of energy metabolism and related; hormones, signal substances, and related; nucleobases and related; vitamins, cofactors and related; miscellaneous; unknown).

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

Figure 2: Technical and biological variation during the MetaMapTMTox project development. Metabolite specific coefficient of variation (CV) was determined for reference samples (technical pool – green line) as well as for male and female control samples (male controls – blue line and female controls – red line). The plot shows median values (circles) and 1st and 3rd quartiles (error bars) of the CV distribution of 233 metabolites across 61 separate studies run in about 3 years.

that it is possible to reliably distinguish the metabolite profile in plasma of animals treated with test compounds from the respective control groups [8, 26, and 27]. Using more than 400 compounds, a metabolite profiling database (MetaÂ� Map®Tox) was established at BASF. The compounds currently comprise chemicals (approximately 37â•›%), agricultural active ingredients (approximately 20â•›%), pharmaceuticals (approximately 41â•›%), and nutritionals (approximately 2â•›%) (Fig.╃3). For all compounds, the toxicity profile is known from literature or BASF internal studies and covers the vast majority of toxicological modes of action relevant for the safety assessment of chemicals or agricultural chemicals.

Figure 3: Relative distribution of reference compounds used for the establishment of the MetaMap®Tox database.

193

194

Contributions

A set of reference compounds has been repeatedly used in different studies within this project in order to show the reproducibility and predictivity of the methodology (publication in preparation). Moreover, the effect of two reference compounds on the metabolite profile in different rat strains (Crl:WI(Han), Han:RCC:WIST(SPF), Sprague-Dawley (Crl:CD), F-344/Crl) has been investigated [26]. Applicability of the MetaMap®Tox database: The MetaMap®Tox database is the technical setup to generate specific metabolite profiles of test substances or classes, and identify toxicological modes of action or target organs. An automated tool is implemented in the database for correlation analysis based on the whole metabolite profiles of the test substance and the whole metabolite profiles of the reference compounds. With this correlation analysis, reference compounds can be identified that share similar changes in the metabolite profile with the test substance. This tool could assist in identifying groups of chemicals showing the same effects in repeated-dose toxicity studies. Therewith, a grouping approach based on similarity of biological activity is possible, providing an enhanced scientific basis for read-across approaches, i.╃e. from QSAR (quantitative structure–activity relationship) to QBAR (quantitative biological activity relationship). This could reduce animal studies, time as well as costs for the required testing under the new European Chemicals Policy REACH. The reference compounds present in the database can further be used to identify patterns of metabolite changes, which are specifically indicative for a certain toxicological mode of action (MOA) and thus can serve as biomarkers for this particular MOA. To achieve this goal, statistical methods (univariate statistical analysis, panellized logistic regression, etc.) are used to identify metabolite changes that are common for a given set of reference compounds representing a certain MOA. The resulting patterns can be validated through cross-validation or against additional reference compounds of this particular MOA. The patterns can be used to identify the toxicological MOA or possible target organs of test compounds. Figure 4 shows the pattern ranking for the fungizide vinclozolin, known for liver toxicity, inhibition of the steroid biosynthesis and antiandrogenic activity. Further examples for the usability of such specific patterns for the safety assessment of test substances are shown for hepatotoxicity, liver enzyme induction, peroxisome proliferation and liver carcinogenicity in [12]. Currently, specific toxicity patterns have been established for toxicological effects on liver, kidney, thyroid, blood, sex hormones, testes, bones, and the nervous system (Tab.╃1). Furthermore, patterns for specific biological effects such as inhibition of the hydroxyphenylpyruvate dehydrogenase (HPPD) are available [8]. Challenges and chances concerning the use of metabolite profiling in toxicology: Metabolite profiling is discussed as being complementary to other “OMICS” technologies such as genomics, transcriptomics, and proteomics, though it might have some advantages compared to the other “OMICS” technologies [7, 12, 28,

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

Figure 4: Pattern ranking for the fungizide vinclozolin. Shown are the top- and bottom-correlations of the metabolite profile of vinclozolin with the pattern specific for toxicological MOAs. Red dots and error bars represent the changes including variability induced by vinclozolin whereas black boxes indicate the changes including variability of the reference compounds used to establish the specific pattern.

Table 1: Specific metabolite patterns for toxicological modes of action (MOA) established in the MetaMap®Tox database. Target Organ

Toxicological Mode of Action (MOA)

Liver

Enzyme induction Peroxisome proliferation Liver toxicity

Kidney

Tubular toxicity Organic anion transporter inhibition

Thyroid

Direct: hormone synthesis inhibition Indirect: increased metabolism

Blood

Porphyrine synthese inhibition Aplastic anemia Haemolytic anemia Platelet aggregation inhibition

Sex hormones

Steroid biosynthesis inhibition Aromatase inhibition Anti-androgenic effect Oestrogenic effects Oestrogen receptor modulation

Testes

Impaired spermatogenesis

Bones

Osteoblast inhibition Mineralization

Nervous system

Dopamine agonism/ antagonism Noradrenaline agonism Acetylcholinesterase inhibition Nicotinic receptor agonist Serotonin receptor antagonist

195

196

Contributions

and 29]. The chain of events after a toxic insult starting with the interaction of the test substance with components of cells and tissues (e.╃g., receptors, membrane activity), followed by reactive changes in gene expression, and protein levels is resulting in altered physiology as displayed by changes in the reaction products of biological pathways. One can suggest that the metabolite profile provides a snapshot of the physiology of an organism more closely related to the phenotype than other “OMICS” technologies. Moreover, the knowledge concerning structure and function of metabolites is significantly greater than that of genes and their corresponding proteins. In addition, the number of metabolites is lower than those of genes and proteins. Another advantage of metabolite profiling is the use of non- or minimally invasively accessible biological matrices like blood or urine, besides the use of organ tissue which is also possible. Therewith, it is possible to obtain information for toxicological evaluation by inducing reduced distress for the animals used (refinement). Moreover, time courses can be observed without the necessity to use satellite groups of animals [8]. However, it has to be elucidated in the future whether metabolite profiling alone or in combination with other “OMICS” technologies as well as classical toxicological investigations will be able to give maximum understanding of the biological, pharmacological and toxicological effects caused by test substance administration. The early recognition of toxic effects caused by test substances is of major importance in the development of substances with a desired activity, such as agricultural active ingredients and pharmaceuticals. Besides, other technologies like in vitro tests, metabolite profiling in combination with the comparison against appropriate databases may serve to aid decision processes during substance development and thus focus the development on candidates which show not only desirable target activity but also a non-critical toxicity profile. The challenges and the opportunities of using a combination of data from genomics, proteomics and metabolite profiling for the toxicity assessment of test substances have been demonstrated by Craig et al. [30]. Approaches taken in the past are limited to the search for correlations between specific genes and defined metabolite changes. For the future comprehensive multivariate statistical analysis seems to be the path to gain significant knowledge through integrated approaches [6, 31]. Nevertheless, in the years to come, the “OMICS” technologies including metabolite profiling will likely have a “research status” [32]. Especially for defining safe dose levels, such as the NOAEL (no observed adverse effect level), “OMICS” data alone will not be sufficient. Such findings would have to be combined with other observable changes at the microscopic and macroscopic level [27]. Through gaining knowledge and experience by using such new techniques and approaches as described above, it should be possible to make “OMICS” approaches and – for the reasons discussed – especially metabolite profiling a powerful tool for toxicological and pharmacological research in the next couple of years – for exploratory as well as regulatory testing.

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

3.10.4 Concluding Remarks Metabolite profiling is starting to demonstrate its potential in toxicology. With metabolite profiling still being a new field, there are only a few databases available that offer comprehensive pathway contents. However, with the current pace of progression within metabolomic research and with the databases that are currently being developed, it is highly likely that metabolite profiling will gain enhanced recognition and assessment of toxicological modes of action. We have shown that metabolic profiling of reference compounds combined with their toxicity data enables fast predictions on toxicological profiles of new test compounds. Potential future applications are early toxicological screenings, mechanistic research in toxicology as well as grouping and read-across strategies within the REACH framework.

Acknowledgements The authors are grateful to Gertrud Skawran and Gunter Rank for their skilful technical assistance performing the animal study as well as Irmgard Weber for doing the clinical pathology work and the lab and data analysis teams for performing the extensive metabolite profiling analyses. Meyasse Bugay and Marina Herbst are acknowledged for their assistance in the preparation of the manuscript.

References â•⁄ 1. Trethewey RN, Krotzky AJ, Willmitzer L (1999) Metabolic profiling: a rosetta stone for genomics?, Curr Opin Plant Biol 2, 83–85. â•⁄ 2. Sauter H, Lauer M, Fritsch H (1991) Metabolic profiling of plants – a new diagnostic-technique, ACS Symp. Ser.╃443, 288–299. â•⁄ 3. Lindon JC, Holmes E, Nicholson JK (2004â•›a) Toxicological applications of magnetic resonance. Progress in Nuclear Magnetic Resonance Spectroscopy 45, 109–143. â•⁄ 4. Robertson DG (2005) Metabonomics in toxicology: a review. Toxicological Sciences 85, 809–822. â•⁄ 5. Keun HC (2006) Metabonomics modelling of drug toxicity. Pharmacology & Therapeutics 109, 92–106. â•⁄ 6. Griffin JL, Bonney SA, Mann C, Hebbachi AM, Nicholson, JK, Shoulders CC, Scott J (2004) An integrated reverse functional genomic and metabolomic approach to understanding orotic acid-induced fatty liver. Physiol Genomics 17, 140–149. â•⁄ 7. Lindon JC, Holmes E, Bollard ME, Stanley EG, Nicholson JK (2004â•›a) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9, 1–31.

197

198

Contributions

â•⁄ 8. van Ravenzwaay B, Coelho-Palermo Cunha G, Leibold E, Looser R, Mellert W, Prokoudine A, Walk T, Wiemer J (2007) The use of metabolomics for the discovery of new biomarkers of effect. Toxicology Letters 172, 21–28. â•⁄ 9. Looser R, Krotzky AJ and Trethewey RN (2005) Metabolite profiling with GC-MS and LC-MS – a key tool for contemporary biology. In Metabolome analyses – Strategies for systems biology, S. Vaidyanathan, G.╃G. Harrigan, & R. Goodacre, eds., Springer, New York, pp.╃103–118. 10. Weckwerth W, Morgenthal K (2005) Metabolomics: from patter recognition to biological interpretation. Drug Discovery Today 10, 1551–1558. 11. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EM (2005) HPLCMS-based methods for the study of metabonomics. Journal of Chromatography B 817, 67–76. 12. van Ravenzwaay B, Coelho-Palermo Cunha G, Fabian E, Herold M, Kamp HG, Krennrich G, Krotzky A, Leibold E, Looser R, Mellert W, Prokoudine A, Strauss V, Trethewey R, Walk T, Wiemer J (2010) The use of metabolomics in cancer research. In Cho W.╃C.╃S. (ed.) An Omics Perspective on Cancer Research, Springer, Dordrecht Heidelberg London New York, pp.╃141–166. 13. Gomase VS, Changbhale SS, Patil SA, Kale KV (2008) Metabolomics. Current Drug Metabolism 9, 89–98. 14. Li H, Ni Y, Su M, Qiu Y, Zhou M, Qiu M, Zhao A, Zhao L, Jia W (2007) Pharmacometabonomic phenotyping reveals different responses to xenobiotic intervention in rats. Journal of Proteome Research 6, 1364–1370. 15. Teague CR, Dhabbar FS, Barton RH, Beckwith-Hall B, Powell J, Cobain M, Singer B, McEwen, BS, Lindon JC, Nicholson JK, Holmes E (2007) Metabonomic studies on the physiological effects of acute and chronic psychological stress in Sprague-Dawley rats. Journal of Proteome Research 6, 2080–2093. 16. Wang Q, Jiang Y, Wu C, Zhao J, Yu S, Yuan B, Yan X, Liao M (2006) Study of a novel indolin-2-ketone compound Z24 induced hepatotoxicity by NMRspectroscopy-based metabonomics of rat urine, blood plasma, and liver extracts. Toxicology and Applied Pharmacology 215, 71–82. 17. Robosky LC, Wells DF, Egnash LA, Manning ML, Reily MD, Robertson DG (2005) Metabonomic identification of two distinct phenotypes in SpragueDawley (Crl:CF(SD)) rats. Toxicological Sciences 87 (1), 277–284. 18. Stanley EG, Bailey NJC, Bollard ME, Haselden JN, Waterfield CJ, Holmes E, Nicholson JK (2005) Sexual dimorphism in urinary metabolite profiles of Han Wistar rats revealed by nuclear-magnetic-resonance-based metabonomics. Analytical Biochemistry 343, 195–202. 19. Plumb RS, Granger JH, Stumpf CL, Johnson KA, Smith BW, Gaulitz S, Wilson ID, Castro-Perez J (2005) A rapid screening approach to metabonomics using UPLC and oa-TOF mass spectrometry: application to age, gender and diurnal variation in normal/ Zucker obese rats and black, white and nude mice. Analyst 130, 844–849.

Metabolite Profiling in Rat Plasma as a Potential New Tool for the Assessment of Chemically Induced �Toxicity

20. Bollard ME, Holmes E, Lindon JC, Mitchell SC, Branstetter D, Zhang W, Nicholson JK (2001) Investigations into biochemical changes due to diurnal variation and estrus cycle in female rats using high-resolution 1H-NMR spectroscopy of urine and pattern recognition. Analytical Biochemistry 295, 194–202. 21. Gavaghan CL, Nicholson JK, Connor SC, Wilson ID, Wright B, Holmes E (2001) Directly coupled high-performance liquid chromatography and nuclear magnetic resonance spectroscopic with chemometric studies on metabolic variation in Sprague-Dawley rats. Analytical Biochemistry 291, 245–252. 22. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK (2000) An NMRbased metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Letters 484, 169–174. 23. Boudonck KJ, Mitchell MW, Nemet L, Keresztes L, Nyska A, Shinar D, Rosenstock M (2009) Discovery of metabolomics biomarkers for early detection of nephrotoxicity. Toxicologic Pathology 37, 280–292. 24. Clarke J, Haselden JN (2008) Metabolic profiling as a tool for understanding mechanisms of toxicity. Toxicologic Pathology 36, 140–147. 25. Holmes E, Nicholson JK, Tranter G (2001) Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chem. Res. Toxicol. 14, 182–191. 26. Strauss V, Wiemer J, Leibold E, Kamp H, Walk T, Mellert W, Looser R, Prokoudine A, Fabian E, Krennrich G, Herold M, van Ravenzwaay B (2009) Influence of strain and sex on the metabolic profile of rats in repeated dose toxicological studies. Toxicology Letters 191, 88–95. 27. ECETOC (2008) Workshop on the application of OMICS technologies in toxicology and ecotoxicology: case studies and risk assessment 6–7 December 2007, Malaga. ECETOC, Brussels, Workshop Report No.╃11. 28. Bilello JA (2005) The agony and ecstasy of “OMIC” technologies in drug development. Curr. Mol. Med.╃5, 39–52. 29. Griffin JL, Bollard ME (2004) Metabonomics: its potential as a tool in toxicology for safety assessment and data integration. Curr. Drug Metab. 5, 389– 398. 30. Craig A, Sidaway J, Holmes E, Orton T, Jackson D, Rowlinson R, Nickson J, Tonge R, Wilson I, and Nicholson J (2006) Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat. Journal of Proteome Research 5, 1586–1601. 31. Thomas CE and Ganji G (2006) Integration of genomic and metabonomic data in systems biology – are we “there” yet? Current opinion in Drug Discovery & Development 9(1): 92–100. 32. Dix DJ, Gallagher K, Benson WH, Groskinsky BL, McClintock JT, Dearfield KL and Farland WH (2006) A framework for the use of genomics data at the EPA. Nature Biotechnology 24 (9).

199

200

Contributions

3.11 Profiling Techniques in Nutrition and Food Research Hannelore Daniel1

Abstract High-throughput profiling technologies are now being exploited by the nutrition and food science disciplines. These technologies allow for the first time the interaction of foods and individual food constituents with the biological system to be defined on a molecular basis. However, both, academia and the food industry face a new challenge when applying transcriptomics, proteomics and metabolomics techniques in view of the huge datasets generated and the limited knowledge on human nutrition and responses to food. Applications of the techniques are almost unlimited when used in cell culture or animal studies and may be helpful in identifying individual biomarkers or clusters of markers linked to the activity of a dietary component. However, when taken into human trials for studying the responses to dietary interventions, the profiling techniques are limited by obtainable biosamples and the rather large variation generally found in humans. What seems important to be taken into consideration as well is the role of the gut microbiota that constitute a metagenome with quite different metabolic capacities and that with the production of distinct metabolites may substantially contribute to the biological effects of food constituents on human metabolic responses.

3.11.1 Introduction Diet and food components are prime environmental factors that affect the genome, transcriptome, proteome and metabolome and this life-long interaction largely defines the health or disease state of an individual. With transcriptomics, proteomics and metabolomics technologies at hand, the interaction of foods and individual food constituents with the mammalian system can now be defined on a molecular basis. “OMICS” profiling platforms are deployed in basic science applications for identifying the mode of action of dietary constituents and are also taken into the science-driven development of foods with a defined biofunctionality. However, foods deliver hundreds or thousands of compounds simultaneously and cause organ-specific responses with a highly dynamic change over time and in space, and even within individual organs different cell types react differently to the nutritional challenge. Understanding this complexity of interactions of foods with the mammalian system and their feed-back and feed-forward loops is therefore most challenging. In this respect 1

Technische Universität München, Nutrition and Food Research Centre, Molecular Nutrition Unit, Gregor-Mendel-Str.╃2, D-85350 Freising-Weihenstephan, Germany, Tel: +╃49 816 17â•›13â•›400, Fax: +╃49 816 17 13 999, [email protected].

Profiling Techniques in Nutrition and Food Research

the metabolic response to complex foods assessed with the profiling techniques is quite different from similar approaches applied in pharmacology or toxicology, in which usually a single drug/ xenobiotic is studied. The present section utilizes examples to provide insights into the power and the limits of transcript, proteome and metabolite profiling technologies when taken into human studies targeted to the assessment of bioactive food ingredients and for characterizing variations in human metabolism. We will use “nutritional genomics” as a collective term that covers the three subdisciplines of transcriptomics, proteomics and metabolomics and describe the use of these profiling technologies to assess the response of humans to a dietary treatment or particular food constituents. With complete genomes at hand, transcriptomics can generate a rather holistic view of all molecular changes that occur in response to a dietary treatment. Proteomics is most challenging due to the higher complexity of the protein complement of the genome and a wider dynamic range in protein concentration. Metabolomics tries to identify and quantify all small molecules in a biological sample. The most important techniques for metabolomics in human biofluids are gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. Each of these platforms has its advantages and limitations but most are currently also limited to metabolites with higher concentrations in a sample. There are many intrinsic and extrinsic factors affecting the metabolome and every sample taken represents just a snapshot of an ever-changing metabolite profile. Since every technique has its strength and pitfalls the methods should be deployed in an integrated fashion to elucidate the nutritionally most relevant biomarkers that define an individual’s susceptibility to diet-induced metabolic perturbations in intervention trials or for assessing food ingredient efficacy. Nutritional genomics approaches are also thought to deliver the basis for the development of sciencebased dietary recommendations to the individual or at least target groups of individuals with similar metabolic phenotypes and genetic risks.

3.11.2 Genomics Applications in Basic and Pre-Clinical Nutrition and Food Research In cell cultures or animal models the applications of transcriptomics, proteomics and metabolomics technologies seem unlimited. The techniques have the potential to easily identify hundreds of entities that respond to a given nutrient, non-nutrient compound, treatment or diet in a well defined experimental setting. Whereas in the past, biomarkers have been identified mainly by rational approaches based on the knowledge of metabolism, the new approaches are non-logical by their nature as screening processes, displaying up to several thousands of potentially affected indicators of the metabolic status simultaneously. This might not mean that changes in individual mRNA or protein levels can be taken as causal markers it might rather be a pattern of expressed mRNAs or a pattern of proteins that changes in a characteristic but reproducible way. The current problem is that the huge datasets generated are hard to interpret

201

202

Contributions

and only in rare cases coordinated changes in transcripts, proteins and metabolites can be identified that can be projected on common pathways of regulation. In view of risk–benefit analysis on ingredient actions there is currently also no tool available that allows data generated by the profiling techniques to be judged for “the good or the bad”. But there is a growing number of studies that take the profiling techniques into the assessment of the effects of dietary ingredients and this builds a knowledge base that may allow a more scientifically sound interpretation and judgment of the findings in the future. Examples on the use of DNA microarray and proteome profiling technologies used to identify the cellular responses to dietary constituents such as polyunsaturared fatty acids, zinc or vitamin E or secondary plant constituents such as the green tea catechins, soy isoflavones and flavones [1–10] have been published and demonstrate the pleiotropy of the effects that individual nutrients and non-nutrient components of foods can possess. We have used transcriptome, proteome and metabolite profiling approaches to define the molecular mechanisms by which the flavonoid flavone affects colonic tumour cells. Flavone was shown to be a potent apoptosis inducer in human colon cancer cells [11] but did not show any effects in a non-transformed human colonocyte cell line NCOL-1 [12] or in primary mouse colonocytes [13]. Analysis of changes in transcript and protein levels by oligonucleotide arrays and proteome analysis applied to both, cell cultures as well as animal studies, revealed numerous changes in gene and protein expression related to cellular signalling, transcription, cancer development but also of metabolism [14]. Pathway analysis tools revealed a complex interconnected network of genes derived from distinct signalling pathways. PI-3-kinase related pathways with various members of the insulin-signalling cascade changed by flavone treatment but also members of the epidermal growth factor and G-protein coupled-receptor pathways and those of TGF-β, IL-6 related mechanisms and p53-signalling. But there was also regulation found in several pathways of intermediary metabolism. In contrast to mRNA-profiling, proteome analysis in its current state is less comprehensive and sensitive as only the proteins with high abundance can easily be identified. Consequently, proteome analysis applied to the same samples in which transcript profiling was performed, revealed only some 20 protein spots with altered levels upon flavone treatment [15]. These proteins were identified by MALDI-TOF mass spectrometry and also submitted to pathway analysis. A network of interactions was identified with protein kinase Cbeta (PKC-β) in central position. Six proteins displayed a direct or intermediate interaction with PKC-β and four of these proteins are known to be involved in the removal of superoxide anions. Moreover, when transcription factors as targets of PKC-β were included into the analysis, it became evident, that the same pathways could be affected by PKC-β at the protein level and altered by flavone when assessing the mRNA-level changes. PKC-β thus was identified by various means as the prime target of flavone action in colonic tumour cells and PKC-β has been proposed to serve as a valid biomarker in colon cancer development [16].

Profiling Techniques in Nutrition and Food Research

However, flavone treatment also affected succinate dehydrogenase (SDH) levels and other tricarboxylic acid cycle enzymes. This was in accordance with experimental findings in flavone-treated tumour cells that revealed changes in protein levels of a number of enzymes of intermediary metabolism [14]. These results obtained for flavone effects on apoptosis in colonic tumour cells and in particular on the levels of metabolic enzymes were mirrored by an impaired energy status of the cells [14]. Flavone seems to induce respiratory metabolism leading to a huge increase in ROS production based on increased influx of energy substrates into mitochondria in vitro but also in vivo in a mouse model of colonic cancer and treatment with flavones [17, 18, and 19]. Production of superoxide anions initiates gene expression of ROS-dependent pathways and activates via caspases the apoptosis cascade. A second pathway involves altered gene expression of a variety of pathways centred around PKC-β including genes that are also needed for altered energy metabolism including TCA-cycle and respiratory chain activity considered to represent a compensation mechanism to reduce the ROS-burden again. For identification of these putative mechanisms by which flavones can induce apoptosis in cells in culture in vitro and in mice transformed colonocytes in vivo, the profiling methods have been very instrumental. There can be no doubt that the near future will overwhelm us with similar studies that investigate the biological actions of individual food constituents by using these genomics tools.

3.11.3 Profiling Technologies Applied in Human Studies Although unlimited when utilizing cells in culture or model organisms, there are clearly restrictions towards the application of the profiling methods in human studies as displayed with the options in Figure 1.╃Expression profiling at the mRNA or protein level is limited by the availability of vital cells or tissues for analysis. Although hair follicle cells, skin cells or even exfoliated intestinal cells can be obtained in sufficient quantities by non-invasive techniques, proteome or metabolome analysis can’t currently be carried out on the single cell level whereas expression analysis using quantitative PCR or even array techniques can be applied when only low cell numbers are available. Different types of blood cells are therefore used as the source of biological material in human studies. They do respond to dietary manoeuvres, display different gene expression profiles and control systems and can reach and occupy different body compartments. In particular, peripheral blood mononuclear cells (PBMC) are the cells of choice for identification of mRNA or protein biomarkers in human studies by employing DNA-arrays or proteome analysis. Other biological samples that can be obtained most easily in the required amount are urine and saliva by noninvasive sampling and blood serum or plasma by minimal invasive approaches. Given that these biological fluids are essentially devoid of cells only proteins and metabolites can be analyzed with the required robustness and sensitivity. However, proteomic profiling of blood, blood cells and urine from human intervention trials is in need of further refinements [20–25]. The techniques

203

204

Contributions

Figure 1: Restrictions in the use of the profiling techniques in human trials. Although biopsy samples may be obtained from muscle or adipose tissue with ethical restrictions almost all analyses rely on use of body fluids and circulating cells in blood that can be obtained by non-invasive or minimally invasive sampling techniques. Transcriptome and proteome analysis may be performed with samples obtained from circulating blood mononuclear cells whereas plasma/serum, saliva and urine may be submitted to proteome and metabolite profiling.

employed are liable to large within- and between-subject background variation because of rapid changes in response to external signals, due to differences in methods of blood sampling and sample preparation, and a relatively high level of technical variation inherent in the use for example of 2D-gel electrophoresis technology. A number of methods to optimize the depletion of human plasma of its most abundant proteins using immuno-affinity columns prior to proteome analysis and for isolation of PBMC with minimal platelet contamination have been proposed [22, 23]. The amount and the variability of proteins available from platelets, PBMC, plasma, urine and saliva from healthy volunteers for proteomics analysis was shown do provide decent yields and variability [26, 27] but urine and saliva samples were extremely variable within and between subjects questioning their utilization in biomarker discovery studies. Plasma or serum represents the fluid compartment that carries metabolites and signalling molecules from and to cells in the various organs and therefore serves as a distribution system in inter-organ metabolism. By contrast, urine excretion serves the elimination of metabolic end products of no further use

Profiling Techniques in Nutrition and Food Research

and contributes to electrolyte and water homeostasis. Nutrients and metabolic intermediates are found in urine only in trace quantities due to very efficient reabsorption mechanisms in tubular cells along the nephrons in kidney and their appearance in higher concentrations in urine might reflect a disturbed homeostasis or impaired renal function. However, urine metabolite profiling has revealed a lot of attention recently for characterization of normal human variation [28, 29] as well as a tool for diagnostics in disease states such as celiac disease [30] or cancers [31, 32]. Most interestingly, a few low molecular weight metabolites in urine can identify an individual suggesting discrete phenotypes with defined patterns in renal loss of metabolites [28]. Metabolomics studies in humans for assessing the effects of diet [33] or food constituents are currently sparse and are mainly based on nuclear magnetic resonance spectroscopy applications and a few examples with use of mass-spectrometry technologies. Recent studies employing different metabolomics tools to define the human response to a standard oral glucose tolerance test in healthy [34, 35] and pre-diabetic volunteers [35] have identified subset metabolites that define the insulin action on proteolysis, lipolysis, ketogenesis, and glycolysis with the induced switch from catabolism to anabolism and a blunted response in pre-diabetics on these processes [35]. In a similar setting based on plasma metabolite profiling in 15 healthy human volunteers after an oral glucose tolerance test (75â•›g of glucose) with or without 30â•›g of protein we observed that all volunteers showed fairly

Figure 2: Principal component analysis of some 60 metabolites obtained from plasma over 3 hours after an oral glucose tolerance test (75â•›g of glucose orally) with or without 30â•›g of protein in the same 15 healthy volunteers after an overnight fast. Whereas all volunteers respond homogenously to the test with glucose alone or with glucose plus protein, one volunteer moves out of space when receiving additional protein but not when receiving glucose alone. This strongly suggests a distinct perturbation induced by the protein intake affecting essentially all measured metabolites and arguing for a distinct metabolic phenotype.

205

206

Contributions

homogenous changes in some 60 metabolites – except in one volunteer. Figure 2 shows the corresponding principal component analysis of the post-prandial changes based on all time points and metabolites in which one volunteer moves out of the space in one condition but remains in space with all other volunteers under the other condition. This indicates a distinct metabolic abnormality under one feeding condition and raises the question on the cause of this unusual response. It can be expected that the near future will provide a large number of similar studies with more physiological and biochemical evidence for the heterogeneity of humans in their response to food intake and hopefully will also define the genetic and molecular basis of this variation.

3.11.4 Evidence for a Significant Role of the Gut Microbiota in Â�Human Responses to Dietary Interventions The gut microbiota has recently achieved a huge interest and humans have been coined “superorganisms” [36] by hosting a microbial metagenome in the large intestine. Any dietary intervention can significantly affect the microbial community and its metabolic activity and thereby alter nutrient bioavailability and host metabolism. The challenge is to establish coherent links between the bioconversion of food ingredients, their bioavailability and their downstream effects on the host metabolism and for those studies the profiling techniques may be particularly useful. Recent metabolomics studies addressing the microbe–host interplay have demonstrated that metabolomics can detect diverse microbial metabolites from different foods or food ingredients. It seems particularly relevant when studying the effects of non-nutrient components such as polyphenols which may undergo metabolic conversion by the microbiota prior to absorption into the human body (Fig.╃3). Well studied examples are the soy isoflavones for which daidzein metabolism by the microbiota defines the plasma concentration of equol in humans who can be categorized as equolproducers and non-producers [37]. In a similar fashion the flaxseed lignans are converted to enterolactone by the intestinal microbiota and enterolactone has been suggested to be the prime active compound mediating the atherosclerosis protective effects of flaxseed [38]. When assessing the biological functions of these dietary constituents for which interesting health promoting effects have been proposed [40], it seems most crucial to separate the volunteers in those that produce the “active compound” and the non-responders by assessing the corresponding plasma levels of the crucial metabolites. It has been shown in biomarker studies that the soy isoflavones affect a variety of processes that are associated with protective effects on the endothelial cells [41] and the cardiovascular system and the isoflanones are considered to provide protective effects based on their weak oestrogenic and or anti-oestrogenic activities in particular in post-menopausal women [37] However, intervention trials in post-menopausal women with soy preparations have frequently also failed to demonstrate the clinical benefits [37, 42]. Nevertheless, it has been shown that certain polymorphisms in oestrogen receptor genes may be required to show the health-

Profiling Techniques in Nutrition and Food Research

Figure 3: Schematic representation of the crucial interplay of the gut microbiome with its heterogenetic metabolic capacity and the host responses when assessing in particular the effects of non-nutrient components of foods in human intervention trials. As shown for soy daidzein that can be converted by the microbiota into equol and classifies producers from non-producers or for flaxseed lignans that can undergo conversion to enterolactone, the corresponding plasma concentrations of the “active metabolites” may determine whether a volunteer responds to treatment or not. In addition, the genetics of the host with single nucleotide polymorphisms or haplotypes in target genes/ proteins of the compound may critically affect the outcome of the intervention.

promoting effects of the isoflavones. For example, the plasma vascular cell adhesion molecule 1 (VACM-1) concentrations in post-menopausal volunteers receiving soy did show a significant positive effect only in those women with an oestrogen receptor βAluI genotype and in the equol-producers [37]. S-equol has been shown to be largely oestrogen receptor β selective and binds to the receptor with an affinity of around 20â•›% of that of 17â•›β-estradiol. These findings demonstrate that the genetics of the host in combination with the genetic and metabolic constitution of the microbiota are defining whether for a given food constituent a health-promoting effect can be observed or not. In the first place it may be a sole scientific challenge to define this interplay, but when it comes to substantiated health-claims for corresponding food ingredients it generates a huge challenge for food industry in driving the need for huge human trials with embedded comprehensive geno- and phenotyping.

3.11.5 Summary The interactions of food constituents with the mammalian genome and its inherent microbiota-metagenome are possibly the most complex biological processes to be studied in life sciences. However, knowledge on the response of

207

208

Contributions

mammalian organisms to changes in diet or in response to individual nutrients and non-nutrient components of foods can now be gathered by expressionarrays, proteome analysis and metabolite profiling technologies and it is becoming evident that nutrients and other food components are key factors in altering gene transcription, protein levels and functions, and the metabolome. The variability in transcriptome, proteome and metabolome profiles appears much greater in humans than in model systems or laboratory animals and there are many intrinsic and extrinsic factors affecting these entities. Genotype, gender, hormonal status and age as well as dietary variation, physical activity, socioeconomic status, cultural influences, medication, smoking, stress, pathologies and the microbiota with an enormous metabolic capacity – all can affect the read-outs. What is needed, are therefore extremely well defined experimental studies with volunteers kept under controlled feeding regimens and in a standardized setting. Moreover, there is a necessity to standardize the applications of the profiling techniques in nutrition studies in terms of sample collection and preparation of fluids, times, volumes and processing aids. These studies will be tedious and costly but seem worth a collaborative effort of the nutrition and food science community.

References â•⁄ 1. Kitajka K, Sinclair AJ, Weisinger RS, Weisinger HS, Mathai M, Jayasooriya AP, Halver JE, Puskás LG. Effects of dietary omega-3 polyunsaturated fatty acids on brain gene expression. Proc. Natl. Acad. Sci. USA.╃2004, 101(30): 10931–10936. â•⁄ 2. Lapillonne A, Clarke SD, Heird WC. Polyunsaturated fatty acids and gene expression. Curr. Opin. Clin. Nutr. Metab. Care. 2004, 7(2): 151–156. â•⁄ 3. Kindermann B, Döring F, Fuchs D, Pfaffl MW, Daniel H. Effects of increased cellular zinc levels on gene and protein expression in HT-29 cells. Biometals. 2005, 18(3): 243–253. â•⁄ 4. tom Dieck H, Döring F, Fuchs D, Roth HP, Daniel H. Transcriptome and proteome analysis identifies the pathways that increase hepatic lipid accumulation in zinc-deficient rats. J. Nutr. 2005, 135(2): 199–205. â•⁄ 5. Johnson, A. and Manor, D. The transcriptional signature of vitamin E. Ann. NY Acad. Sci.╃2004, 1031: 337–338. â•⁄ 6. Vittal R, Selvanayagam ZE, Sun Y, Hong J, Liu F, Chin KV, Yang CS. Gene expression changes induced by green tea polyphenol (-)-epigallocatechin3-gallate in human bronchial epithelial 21BES cells analyzed by DNA microarray. Mol. Cancer Ther. 2004, 3(9): 1091–1099. â•⁄ 7. McLoughlin P, Roengvoraphoj M, Gissel C, Hescheler J, Certa U, Sachinidis A. Transcriptional responses to epigallocatechin-3 gallate in HT 29 colon carcinoma spheroids. Genes Cells. 2004, 9(7): 661–669. â•⁄ 8. Fuchs D, Erhard P, Rimbach G, Daniel H, Wenzel U. Genistein blocks homoÂ� cysteine-induced alterations in the proteome of human endothelial cells. Proteomics. 2004, 5(11): 2808–2818.

Profiling Techniques in Nutrition and Food Research

â•⁄ 9. Fuchs D, Erhard P, Turner R, Rimbach G, Daniel H, Wenzel U. Genistein reverses changes of the proteome induced by oxidized-LDL in EAhy926 human endothelial cells. J. Proteome Res.╃2005, 4(2): 369–376. 10. Wenzel U, Herzog A, Kuntz S, Daniel H. Protein expression profiling identifies molecular targets of quercetin as a major dietary flavonoid in human colon cancer cells. Proteomics. 2004, 4(7): 2160–2174. 11. Herzog A, Kindermann B, Döring F, Daniel H, Wenzel U. Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mRNA expression profiling. Proteomics. 2004, 4(8): 2455–2464. 12. Wenzel U, Kuntz S, Daniel H. Nitric oxide levels in human preneoplastic colonocytes determine their susceptibility toward antineoplastic agents. Mol. Pharmacol. 64, 1494–1502. 13. Wenzel U, Kuntz S, Brendel MD, Daniel H. Dietary flavone is a potent apoptosis inducer in human colon carcinoma cells. Cancer Res.╃2000, 60: 3823– 3831. 14. Herzog A, Kuntz S, Daniel H, Wenzel U. Identification of biomarkers for the initiation of apoptosis in human preneoplastic colonocytes by proteome analysis. Int. J. Cancer. 2004, 20; 109(2): 220–9. 15. Herzog A, Kindermann B, Döring F, Daniel H, Wenzel U. Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mRNA expression profiling. Proteomics. 2004, 4: 2455–2464. 16. Yu W, Murray NR, Weems C, Chen L, Guo H, Ethridge R, Ceci JD, Evers BM, Thompson EA, Fields AP. Role of cyclooxygenase 2 in protein kinase C beta II-mediated colon carcinogenesis. J. Biol. Chem. 2003, 278: 11167–11174. 17. Wenzel U, Schoberl K, Lohner K, Daniel H. Activation of mitochondrial lactate uptake by flavone induces apoptosis in human colon cancer cells. J. Cell Physiol. 2005, 202(2): 379–90. 18. Wenzel U, Nickel A, Daniel H. Increased mitochondrial palmitoylcarnitine/ carnitine countertransport by flavone causes oxidative stress and apoptosis in colon cancer cells. Cell. Mol. Life Sci.╃2005, 62(24): 3100–5. 19. Winkelmann I, Diehl D, Oesterle D, Daniel H, Wenzel U. The suppression of aberrant crypt multiplicity in colonic tissue of 1,2-dimethylhydrazine-treated C57BL/6J mice by dietary flavone is associated with an increased expression of Krebs cycle enzymes. Carcinogenesis. 2007, 28(7): 1446–54. 20. Bottini PV, Ribeiro Alves MA, Garlipp CR. Electrophoretic pattern of concentrated urine: comparison between 24-hour collection and random samples. Am. J. Kidney Dis.╃2002, 39, E2. 21. Coombes KR, Morris JS, Hu J, Edmonson SR, Baggerly KA. Serum proteomics profiling – a young technology begins to mature. Nat. Biotechnol. 2005, 23:291–292. 22. Echan LA, Tang HY, Ali-Khan N, Lee K, Speicher DW. Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics. 2005, 5: 3292–3303.

209

210

Contributions

23. Huang L, Harvie G, Feitelson JS, Gramatikoff K, Herold DA, Allen DL, Amunngama R, Hagler RA, Pisano MR, Zhang WW, Fang X. Immunoaffinity separation of plasma proteins by IgY microbeads: meeting the needs of proteomic sample preparation and analysis. Proteomics. 2005, 5: 3314– 3328. 24. Omenn GS, States DJ, Adamski M, Blackwell TW, Menon R, Hermjakob H, Apweiler R, Haab BB, Simpson RJ, Eddes JS, Kapp EA, Moritz RL, Chan DW, Rai AJ, Admon A, Aebersold R, Eng J, Hancock WS, Hefta SA, Meyer H, Paik YK, Yoo JS, Ping P, Pounds J, Adkins J, Qian X, Wang R, Wasinger V, Wu CY, Zhao X, Zeng R, Archakov A, Tsugita A, Beer I, Pandey A, Pisano M, Andrews P, Tammen H, Speicher DW, Hanash SM.Omenn GS. Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics. 2005, 5: 3226–3245. 25. Rai AJ, Gelfand CA, Haywood BC, Warunek DJ, Yi J, Schuchard MD, Mehigh RJ, Cockrill SL, Scott GB, Tammen H, Schulz-Knappe P, Speicher DW, Vitzthum F, Haab BB, Siest G, Chan DW. HUPO Plasma proteome project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics. 2005, 5: 3262–3277. 26. Crosley LK, Duthie SJ, Polley AC, Bouwman FG, Heim C, Mulholland F, Horgan G, Johnson IT, Mariman EC, Elliott RM, Daniel H, de Roos B. Variation in protein levels obtained from human blood cells and biofluids for platelet, peripheral blood mononuclear cell, plasma, urine and saliva proteomics. Genes Nutr. 2009, 4(2): 95–102. 27. de Roos B, Duthie SJ, Polley AC, Mulholland F, Bouwman FG, Heim C, Rucklidge GJ, Johnson IT, Mariman EC, Daniel H, Elliott RM. Proteomic methodological recommendations for studies involving human plasma, platelets, and peripheral blood mononuclear cells. J. Proteome Res.╃2008, 7(6): 2280–90. 28. Assfalg M, Bertini I, Colangiuli D, Luchinat C, Schäfer H, Schütz B, Spraul M. Evidence of different metabolic phenotypes in humans. Proc. Natl. Acad. Sci. USA.╃2008, 105(5): 1420–4. 29. Mi Park E, Lee E, Jin Joo H, Oh E, Lee J, Lee JS. Inter- and intra-individual variations of urinary endogenous metabolites in healthy male college students using (1)H NMR spectroscopy. Clin. Chem. Lab. Med.╃2009, 47(2): 188–194. 30. Bertini I, Calabrò A, De Carli V, Luchinat C, Nepi S, Porfirio B, Renzi D, Saccenti E, Tenori L. The metabonomic signature of celiac disease. J. Proteome Res.╃2009, 8(1): 170–177. 31. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM.

Profiling Techniques in Nutrition and Food Research

Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009, 12, 457(7231): 910–914. 32. Woo HM, Kim KM, Choi MH, Jung BH, Lee J, Kong G, Nam SJ, Kim S, Bai SW, Chung BC. Mass spectrometry based metabolomic approaches in urinary biomarker study of women’s cancers. Clin. Chim. Acta. 2009, 400(1–2): 63–69. 33. Walsh MC, Brennan L, Pujos-Guillot E, Sébédio JL, Scalbert A, Fagan A, Higgins DG, Gibney MJ. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am. J. Clin. Nutr. 2007, 86(6): 1687–93. 34. Zhao X, Peter A, Fritsche J, Elcnerova M, Fritsche A, Häring HU, Schleicher ED, Xu G, Lehmann R. Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at? Am. J. Physiol. Endocrinol. Metab. 2009, 296(2): E384–93. 35. Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, Carr SA, Thadhani R, Gerszten RE, Mootha VK. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 2008, 4: 214. 36. Goodacre R. Metabolomics of a superorganism. J. Nutr. 2007, 137(1 Suppl): 259S–266╃S. Review. 37. Hall WL, Vafeiadou K, Hallund J, Bugel S, Reimann M, Koebnick C, Zunft HJ, Ferrari M, Branca F, Dadd T, Talbot D, Powell J, Minihane AM, Cassidy A, Nilsson M, Dahlman-Wright K, Gustafsson JA, Williams CM. Soy-isoflavone-enriched foods and markers of lipid and glucose metabolism in postmenopausal women: interactions with genotype and equol production. Am. J. Clin. Nutr. 2006, 83(3): 592–600. 38. Fuchs D, Piller R, Linseisen J, Daniel H, Wenzel U. The human peripheral blood mononuclear cell proteome responds to a dietary flaxseed-intervention and proteins identified suggest a protective effect in atherosclerosis. Proteomics. 2007, 7(18): 3278–88. 39. Fuchs D, Vafeiadou K, Hall WL, Daniel H, Williams CM, Schroot JH, Wenzel U. Proteomic biomarkers of peripheral blood mononuclear cells obtained from postmenopausal women undergoing an intervention with soy isoflavones. Am. J. Clin. Nutr. 2007, 86(5): 1369–75. 40. Rimbach G, Boesch-Saadatmandi C, Frank J, Fuchs D, Wenzel U, Daniel H, Hall WL, Weinberg PD. Dietary isoflavones in the prevention of cardiovascular disease-a molecular perspective. Food Chem. Toxicol. 2008, 46(4): 1308–19. 41. Fuchs D, de Pascual-Teresa S, Rimbach G, Virgili F, Ambra R, Turner R, Daniel H, Wenzel U. Proteome analysis for identification of target proteins of genistein in primary human endothelial cells stressed with oxidized LDL or homocysteine. Eur. J. Nutr. 2005, 44(2): 95–104. 42. Reimann M, Dierkes J, Carlsohn A, Talbot D, Ferrari M, Hallund J, Hall WL, Vafeiadou K, Huebner U, Branca F, Bugel S, Williams CM, Zunft HJ, Koebnick C. Consumption of soy isoflavones does not affect plasma total homocysteine or asymmetric dimethylarginine concentrations in healthy postmenopausal women. J. Nutr. 2006, 136(1): 100–5.

211

212

Contributions

3.12 The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics Claudine Manach1, Jane Hubert1, Rafael Llorach2, and Augustin Scalbert3 This manuscript was originally published in Mol. Nutr. Food Res., 2009, 53: page 1303–1315.

Abstract A large variety of phytochemicals commonly consumed with the human diet influence health and may contribute to the prevention of diseases. However, it is still difficult to make nutritional recommendations for these bioactive compounds. Current studies of phytochemicals are generally focused on specific compounds and their effects on a limited number of markers. New approaches are needed to take into account both the diversity of phytochemicals found in the diet and the complexity of their biological effects. Recent progress in highthroughput analytical technologies and in bioinformatics now allow the simultaneous analysis of the hundreds or more metabolites constituting the metabolome in urine or plasma. These analyses give complex metabolic fingerprints characteristic of a given phenotype. The exploitation of the wealth of information it contains, in randomized controlled trials and cohort studies, should lead to the discovery of new markers of intake for phytochemicals and new markers of effects. In this paper, we briefly review the current methods used to evaluate intake of phytochemicals and their effects on health. We then describe the applications of metabolomics in this field. Recent metabolomics studies illustrate the potential of such a global approach to explore the complex relationships linking phytochemical intake to metabolism and health.

3.12.1 Introduction Epidemiological evidence suggests that a regular consumption of fruits, vegetables and whole grains is associated with reduced risks of developing chronic diseases such as cancer and cardiovascular diseases [1, 2]. This association has been partly ascribed to the presence of a variety of non-nutritive phytochemicals naturally occurring in plant-based foods [3–5]. These phytochemicals show

1

INRA, Clermont-Ferrand Research Centre Theix, UMR 1019, Human Nutrition Unit, F-63122 Saint-Genès-Champanelle, France.

2

University of Barcelona, Pharmacy School, XarTA, Nutrition and Food Science Department, Av. Joan XXIII s/n, E-08028 Barcelona, Spain.

3

Correspondence to: Augustin Scalbert, INRA, Clermont-Ferrand Research Centre, UMR 1019, Human Nutrition Unit, F-63122 Saint-Genès-Champanelle, France, Fax +╃33 473 624638, [email protected].

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

highly diverse chemical structures. More than 5000 individual phytochemicals have been identified in foods and beverages [6, 7]. They can be classified into

Figure 1: Major classes of phytochemicals in foods.

213

214

Contributions

several major groups: polyphenols, terpenoids, alkaloids and other nitrogen compounds, carbohydrates and lipids (Fig.╃1) [8]. A large variety of biological effects and mechanisms of action have been described depending on their chemical structures. Plant sterols, for instance, which exhibit structural similarities to cholesterol, reduce LDL-cholesterol levels in humans by interfering with cholesterol intestinal absorption [9]. Soy isoflavones are structurally related to 17-β-oestradiol and show some oestrogenic or anti-oestrogenic properties. They are referred to as phyto-oestrogens. Their consumption has been associated with a reduced risk of some hormone-dependent diseases [10]. A number of phytochemicals such as curcuminoids from curcuma, glucosinolates from cruciferous vegetables, isoflavones from soy or lycopene from tomatoes show anticarcinogenic properties [11–15]. They act through diverse cellular and molecular mechanisms including the stimulation of detoxifying systems, inhibition of cell cycle proliferation, induction of apoptosis, immuno-modulation or inhibition of angiogenesis, [6, 16]. Numerous reports have highlighted the free radical scavenging properties of phytochemical anti-oxidants such as polyphenols or carotenoids [17, 18]. It appears today that the biological effects of such anti-oxidants are more diverse and involve cell-mediated responses and the modulation of various cell-signalling pathways [19–22]. Hypotheses on the mechanisms of action of phytochemicals have regularly been derived from knowledge on their chemical structures and physicochemical properties. They have been tested in in vitro studies but they must still be proven in vivo and more particularly in human studies. With the notable exception of phytosterols as cholesterol-lowering agents, it is still difficult to make nutritional recommendations for phytochemicals. Beyond elucidation of their mechanisms of action, such recommendations should be largely based on randomized control trials and epidemiological studies. However, most often the number of intervention studies is still too limited to draw definitive conclusions on the effects of phytochemicals on health. Furthermore, these studies are generally short-term studies carried out using foods rich in phytochemicals, rather than pure phytochemicals [23]. A number of observational studies have suggested an inverse association between intake of some phytochemicals and disease occurrence, but the limited number of phytochemicals considered in these epidemiological studies, is far from representing the wide diversity of compounds consumed with the diet. New tools and approaches are needed to take into account the following two levels of complexity: (1) The diversity of phytochemicals in the human diet and (2) the complexity of their biological effects. Metabolomics is one such promising approach. High-throughput analytical methods such as NMR spectroscopy or mass spectrometry allow to simultaneously analyze the hundreds of metabolites constituting the urine or plasma metabolome [24]. The metabolome (the complete set of low-molecular weight metabolites in a biological sample) can be divided into several fractions: The endogenous metabolome which includes all metabolites produced by a cell, a tissue or an organism, the microbial metabolome produced by the microbiota and the xenometabolome which includes all foreign metabolites derived from drugs, pollutants and dietary compounds [25,

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

26] (Figure 2). The set of metabolites derived from the digestion of food is also called the food metabolome [27]. Variations of the endogenous metabolome upon an intervention with a phytochemical may reveal new mechanisms of action and lead to the discovery of new markers of effects. Ingested phytochemicals are absorbed through the gut barrier and metabolized. The resulting phytochemical metabolites are part of the food metabolome [27]. Their concentrations in plasma and urine generally increase proportionally to the amount ingested. For this reason, they have been used as markers of phytochemical intake [28]. Endogenous metabolites and exogenous phytochemical metabolites form a signature characteristic of the intake of a given phytochemical. This signature contains detailed information on phytochemical intake and on the effects of these phytochemicals on host metabolism. In this paper we describe the current methods used to evaluate dietary intake of phytochemicals in populations, either through the use of food composition tables and dietary records or biomarkers. Basic principles of metabolomics and its application to the discovery of new biomarkers of intake and effects are then presented. This high-throughput technique may be the most suitable one to characterize the health effects of phytochemicals, by tackling both the complexity of their chemical structures and their biological effects.

Figure 2: Phytochemical metabolites as part of the food metabolome. A large number of phytochemicals (Ph1,2,…n) are present in foods of plant origin. When ingested, they are transformed in the body into various metabolites (Ph1–1,1–2,…1-n), all part of the “food metabolome”. Some of these phytochemical metabolites modulate cell metabolism, resulting in changes of the endogenous metabolome profile. All connections linking the various phytochemicals in foods to metabolic changes in the body influence health and disease risk. See the glossary for definition of the different fractions of the metabolome.

215

216

Contributions

3.12.2 Measuring Dietary Intake of Phytochemicals: Current Â�Limitations Accurate measurements of intake of the various phytochemicals ingested with our diet or with dietary supplements are needed to identify those associated to health and diseases. Dietary records and food composition tables for phytochemicals are required to estimate intake. However, this approach suffers from a number of limitations, inherent to difficulties in assessing food intakes and to the lack of comprehensive food composition tables for phytochemicals. The most common methods employed to assess dietary intakes are based on food frequency questionnaires or multiple 24-h recalls, but the accuracy of such questionnaires and self-reports remains uncertain [29]. Several food composition tables for phytochemicals have been constructed in recent years [30]. They include for instance the different databases from the US Department of Agriculture related to the levels of isoflavones, flavonoids, procyanidins, or carotenoids in selected foods [31–34], the Phenol-Explorer database for all polyphenols including phenolic acids [35], the VENUS database related to the levels of phyto-oestrogens in plant foods [36], databases dedicated to the glucosinolates in cruciferous vegetables [37] or phytosterols in various foods [38, 39]. However, these databases are still incomplete with regard to the considerable diversity of phytochemicals in food plants. Various parameters such as genetic, environmental factors (including growing location or agricultural practices) and food processing and storage have a profound effect on the levels of phytochemicals in foods [40–42]. Some phytochemicals may be present in a few plant cultivars and absent in others (e.╃g. anthocyanin pigments in blood oranges) but these varieties are often not distinguished either in the food composition tables or in dietary records, making the estimation of the phytochemical intake less accurate. Phytochemical databases need to be further developed to better take into account these key factors that may affect their levels in commonly consumed plant-based foods. However, these classic methods currently used to estimate food consumption and the intake of phytochemicals will still have inherent limitations bound to food complexity and the variability of their composition. The direct estimation of phytochemicals in human urine or plasma allows to partly circumvent these limitations.

3.12.3 Biomarkers of Phytochemical Intake Phytochemicals, once absorbed, are found in the systemic circulation either unchanged or in the form of various metabolites and are eventually excreted in urine. Polyphenols are largely deglycosylated when absorbed through the gut barrier and conjugated to glucuronyl and sulphate groups in the gut barrier and the liver. Glucosinolates form isothiocyanates upon myrosinase-catalyzed reaction, and are conjugated to glutathione in the enterocytes and in the liver and finally excreted as mercapturates [43]. Carotenoids are absorbed through the

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

gut and some of them (provitamin A carotenoids) are cleaved in the intestine and in the liver to form vitamin A and other breakdown products [44]. Concentrations of these metabolites in urine or plasma usually reflect the amount of phytochemicals ingested. Isoflavones in urine or plasma were well correlated to their intake in cohort studies [45, 46]. The urinary excretion of various polyphenols such as phloretin, flavanones, gallic acid and chlorogenic acid were also found to be well correlated to the consumption of some of their major dietary sources, respectively apples, citrus fruits, as well as tea, wine and coffee [28]. All such compounds estimated in urine or plasma constitute potential markers of intake for phytochemicals. Plasmatic or urinary isoflavones, lignans and carotenoids have effectively been used as biomarkers of intake in various epidemiological studies to look for associations with disease or disease risk [5, 47, and 48]. The selection of candidate biomarkers of intake is usually based on previous knowledge on the metabolism and pharmacokinetics of the phytochemicals of interest and on the effects of the various factors which may influence their absorption, metabolism and excretion, as possible sources of uncontrolled variability [49]. The factors which affect the reliability of such candidate markers of intake have been discussed previously; they notably include pharmacokinetic parameters, interactions with the food matrix and inter-individual variations in absorption, metabolism and excretion [50, 51]. Pharmacokinetic parameters depend on the chemical structure of the phytochemical and determine the kind of information borne by a biomarker of intake. Depending on the phytochemical life-time in the body, they may reflect short or long-term intake. A phytochemical metabolite quickly eliminated may only reflect acute intake. Its use as a biomarker will require repeated biofluid sampling over time. On the contrary, some phytochemicals such as carotenoids or other lipophilic phytochemicals accumulate in fat tissues where they are in equilibrium with the plasma pool. Carotenoids in plasma can thus be used as biomarkers reflecting longer-term intake [52]. Other phytochemicals such as chlorogenic acid abundant in coffee or catechins abundant in tea, although quickly eliminated within less than a day, can still be used as biomarkers of intake due to the very regular consumption of their major food sources by some individuals [28]. This regular ingestion results in smoother variations of the phytochemical concentrations in biofluids. A second factor influencing the reliability of biomarkers of intake is the food matrix which may interact with phytochemicals. Several observations suggest that the composition of the diet and the food matrix may interfere with the absorption of some phytochemicals in the gut [53]. Plasma concentrations of lycopene are higher after consumption of a tomato puree rather than fresh raw tomatoes [54] and carotenoid bioavailability was shown to depend on the lipid content of the diet [55, 56]. Bioavailability of green tea catechins was also significantly enhanced when consumed under fasting conditions rather than with a meal [57]. For phytochemicals whose bioavailability is influenced by the food matrix, plasma concentrations may better reflect tissue exposure than intake. Lastly, absorption and metabolism of phytochemicals can also differ widely between individuals and this may also influence the reliability of a biomarker

217

218

Contributions

of intake [58, 59]. These inter-individual differences can be explained by genetic polymorphism, physiological state and gut microbiota [60]. The nature of intestinal microbiota contributes significantly to the inter-individual variations in the metabolism of dietary phytochemicals. For instance, different types of metabolite excreters have been identified on the basis of their ability to produce equol, an active end product resulting from the intestinal bacterial metabolism of the soy isoflavone daidzein [61]. The variability in the concentrations of phytochemicals in urine or plasma, induced by these various factors should be studied more extensively. The variability induced by these factors should be minimal as compared to that induced by the diet to use a given phytochemical as a marker of intake. The nature of the biofluid from which the phytochemical is estimated is also a key parameter. Due to their accessibility, urine and plasma are the main body fluids used to assess phytochemical intake in cohort studies. However, many phytochemicals with short half-lives show large variations of concentrations in the plasma over one day [49]. Lesser variations are expected in urine due to their accumulation over several hours. Therefore, for phytochemicals quickly excreted, urine should better reflect their intake during the previous day.╃24-h urine samples would be ideal to analyze such biomarkers but they are not easily collected in cohort studies. However, spot urine samples can also be used. Thirteen polyphenols have been estimated in both spot urine samples and 24-h urine samples collected by 154 subjects following their regular diets. Good correlations were observed between most polyphenols and the consumption of their major food sources in both types of urine samples [62]. The phytochemicals most quickly excreted might be under-represented in urine or plasma collected under fasting conditions. However, good correlations between intake of various polyphenols and concentrations in spot urine samples collected under fasting conditions have been observed [62]. Once candidate biomarkers have been identified and validated, analytical methods can be developed to quantify the set of selected compounds in urine or plasma in a targeted approach [63]. Alternatively, a metabolomics fingerprinting approach [24] could be used to identify new and unexpected candidate biomarkers of intake for phytochemicals.

3.12.4 Metabolomics and Biomarker Discovery With the recent development of post-genomic technologies, it has become possible to characterize in an integrative way the molecular regulation of cells and whole organisms. Metabolomics combines a high-throughput analysis measuring all small molecules present in a biological system with multivariate statistical treatments enabling the discrimination of specific metabolites which together characterize a particular physiological state or the response to a given intervention (Fig.╃3). Data capture is most often based on nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS). First metabolomics studies were carried out by NMR spectroscopy [64], a technique characterized by its

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

high reproducibility but also by its limited sensitivity. More sensitive MS techniques hyphenated to gas or liquid chromatography have more recently been applied to metabolomics studies [24, 65]. About one to several hundreds metabolites can be analyzed in a given biological sample. Gas chromatography coupled with mass spectrometry (GC-MS) is used to analyze the more volatile constituents [66]. Multidimensional gas chromatography (GC×GC-MS) is another recent option providing high peak resolution and spectral purity by using two successive columns of different polarity [67]. High-performance or ultra-performance liquid chromatography coupled with high-resolution timeof-flight mass spectrometers (LC-Tof-MS) are also increasingly used to analyze the metabolome with limited sample preparation [68]. All these analytical tools are in constant evolution for metabolomic studies; the choice of the analytical method will depend on the nature of the samples, the nature of the compounds under examination, i.╃e. polar or apolar, volatile or non-volatile, and on the targeted (compounds a priori known) or non-targeted approach (compounds a priori unknown) chosen [24]. The complex datasets obtained from various samples, once aligned, are analyzed by multivariate statistical tools such as Principal Component Analysis (PCA) or Hierarchical Clustering Analysis (HCA). These two unsupervised methods (no prior knowledge of sample classes) enable differentiation between samples based on their metabolite composition. Supervised methods such as Partial Least Square Discriminate Analysis (PLS-DA) can also be employed to visualize metabolic differences between pre-defined sample classes [69]. Models generated by supervised analysis must be carefully validated to avoid data overfitting [65, 70]. These different statistical treatments allow separating sample groups on the basis of their metabolic signatures. Biological interpretation requires identification of the sets of metabolites characterizing each sample group. Metabolite identification is carried out by comparing mass spectral data (exact mass and mass fragments) to those available in metabolite databases such as the KEGG Ligand Database (http://www.genome.jp/kegg), the PubChem Project (http://pubchem.ncbi.nlm.nih.gov), the Human Metabolome DataBase (http:// www.hmdb.ca), Metabolome Japan (http://www.metabolome.jp) or METLIN database (http://metlin.scripps.edu/) or to those of authentic standards when available [71]. Metabolomics appears promising to diagnose pathological states [72–75] or to identify key metabolic features characterizing different physiological states. Applications in the field of nutrition are relatively recent compared to pharmacology and toxicology. However, metabolomics appears particularly adapted to the study of the subtle and dynamic metabolic changes induced by the diet [76– 79]. In particular, metabolomics should allow to finely characterize the food metabolome to assess phytochemical intake. The characterization of the effects of phytochemicals on the endogenous metabolome should also help identify key mechanisms of action as described in section 3.12.6. Metabolomics appears promising but methods still evolve rapidly and need further improvements. Limits of the current methodologies have recently been reviewed and directions put forward to make this approach fully operation-

219

220

Contributions

Figure 3: Flow process for metabolomic analyses.

al [65]. Current limits include most notably the lack of well established and standardized methods or procedures, insufficient coverage of the human metabolome by current analytical procedures, insufficient data exploitation or data overfitting, incomplete identification of the metabolites, lack of bioinformatic tools to interpret changes in metabolomics fingerprints and lack of standards for absolute quantification. Despite such difficulties, some metabolomics studies on phytochemicals illustrate the potential power of such approaches.

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

3.12.5 Metabolomics and Phytochemical Intake As stressed above, a limited number of phytochemical metabolites have so far been studied as markers of phytochemical or plant food intake, always on the basis of a simple relation ”one metabolite – one food”. However, such a targeted approach cannot take into account the considerable variety of phytochemicals present in the human diet. Up to 100–300 phytochemicals are commonly described in any given plant foods as reported in Dr. Duke’s Phytochemical and Ethnobotanical Database (www.ars-grin.gov/duke/plants.html). Furthermore, each of these phytochemicals can be further transformed in the body into a variety of different metabolites [80], a number of which are still unidentified [81]. Metabolomics offers the possibility today to simultaneously analyze hundred(s) of these phytochemicals or their metabolites in biofluids and to characterize the food metabolome in a global way. A LC-ToF-MS metabolomic approach has recently been used to characterize the urinary metabolome of rats supplemented with different phenolic compounds, ferulic acid, sinapic acid or lignins [27]. Characteristic fingerprints were obtained for each diet. Each fingerprint comprised a large number of characteristic phenolic metabolites, providing new information of the metabolism of these compounds. GC-MS based metabolomics also allowed to identify several phenolic acids in urine or faeces, formed by the microbial degradation of flavanoids after ingestion by human subjects of tea or of an extract of wine and grape juice [82, 83]. In a controlled intervention study comparing a 2â•›dconsumption of a low-phytochemical diet and 2â•›d-consumption of the same diet supplemented with fruit and vegetable drinks, urinary metabolomic profiles analyzed with NMR and mass spectrometry were shown to be significantly changed [84]. Although the discriminating metabolites were not identified, the study suggested that phytochemicals in fruits and vegetables influence the food metabolome which comprises metabolites which could be used as markers of phytochemical intake. In a recent controlled study with cross-over design, we showed that MS-based urine fingerprints clearly discriminate a low- from a high-fruit and vegetable diet comprising cruciferous vegetable, citrus fruits and soy products; and that phytochemical metabolites were major discriminating features of the urine metabolome (Llorach, Manach, Lampe & Scalbert, manuscript in preparation). This approach applied to human body fluids collected after various defined phytochemical-rich diets should lead to the discovery of new phytochemical metabolites and new biomarkers of intake (Fig.╃4). Some poorly-studied phytochemicals may also emerge as key contributors of the food metabolome. Identification of phytochemical metabolites in urine or plasma fingerprints is necessary to determine the parent phytochemical from which they originate before eventually using them as biomarkers of intake of the corresponding phytochemical in cohort studies. However, this identification is still difficult due to the lack of comprehensive databases for phytochemical metabolites and the lack of appropriate standards. The PubChem database which offers a public access to more than 12 million compounds, covers a wide range of phytochemi-

221

222

Contributions

Figure 4: Metabolomics and the discovery of new markers of intake and new mechanisms of action of phytochemicals. Research on the role of phytochemicals in the prevention of diseases is largely hypothesis-driven. A hypothesis, based on current scientific knowledge is tested in an intervention study with a phytochemical or a phytochemical-rich food. The information collected is most often limited to a few markers and the significance of the results depends on the value of the hypothesis. In a metabolomics study, a large volume of data is collected, and the relevant information is sorted by multivariate statistics (data-driven approach). The metabolites corresponding to discriminating ions (mass spectrometry) or chemical shifts (NMR spectroscopy) are identified as putative biomarkers of intake or effect. Pathway analysis leads to the generation of new hypotheses on the mechanisms of action. Biomarkers are then validated and mechanisms of action confirmed in further targeted experiments.

cals found in plants, but few of their metabolites. For instance, among the 23 metabolites of quercetin detected in human plasma or urine after ingestion of onions [85], only 11 appear in the Pubchem database. Similarly, the KEGG Ligand database and the Human Metabolome DataBase (HMDB) contain a limited number of phytochemicals and virtually no conjugated metabolites such as glucuronide, sulphate or glycine conjugates. More comprehensive databases are needed to identify these phytochemical metabolites on the basis of their exact mass, a key spectral information generated in LC-Tof-MS analyses. A valuable strategy would be to combine knowledge on phytochemical composition of foods and on phytochemical metabolism to build a database which would include all phytochemical metabolites possibly present in biological fluids. For some widely consumed fruit and vegetables, a large amount of information is already available from Dr. Duke’s Phytochemical and ethnobotanical Database, KNApSacK database (http://kanaya.naist.jp/KNApSAcK/) and the Dictionary of Natural Products (http://dnp.chemnetbase.com/intro/index.jsp). Metabolomic profiling of foods should help build up such databases [86]. In a survey on 96 tomato cultivars, 43 phytochemicals could be detected using an open LC-MS metabolomic method with 14 of them described for the first time in this food [87]. In silico tools, especially rule-based expert systems such as Meteor or MetabolExpert designed to predict the metabolic fate of chemicals from their structure may also be of great value for the construction of a phytochemical

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

metabolite theoretical database [88, 89]. Such a theoretical database should provide a list of all expected phytochemical metabolite masses associated with the intake of a food or phytochemical under study, and facilitate the identification of characteristic markers in the food metabolome. Algorithms have been developed to help recognize from their predicted exact masses expected phase II metabolites conjugated with e.╃g. glucuronoyl, sulphate, or glutathionyl groups [90, 91]. The combined exploitation of such phytochemical databases and of phenotypic databases capturing metabolomic fingerprints and metadata from various controlled intervention and cohort studies [92] may allow searching for new associations between intake of phytochemicals or phytochemical-rich foods and metabolic and health outcomes.

3.12.6 Metabolomics and Biological Effects of Phytochemicals Due to their high diversity, phytochemicals can affect a wide array of physiological functions and metabolic pathways. Most phytochemicals present in the human diet are clearly different from a drug specifically designed to interact with a specific target. Each phytochemical molecule most likely interacts with more than one molecular target therefore influencing different signalling pathways and the expression of a large variety of genes and modulating various metabolic pathways [93–96]. The small number of markers that are generally used to evaluate the effects of phytochemicals in short-term clinical trials may fail to accurately describe or predict health effects of phytochemicals. Several long-term intervention studies have failed to show any protective health effects (total mortality) upon supplementation with some anti-oxidant vitamins [97, 98] in contrast to a large number of short-term studies which have shown an improvement of various surrogate markers. Metabolomics should allow to better characterize phenotypes in healthy subjects or subjects at an early disease stage rather than late stage disease, like most diagnostic techniques [99]. It opens new perspectives which should contribute to the elaboration of nutritional and dietary recommendations [92]. Global metabolic changes resulting from a phytochemical intervention are characterized with no restriction to an a priori selected metabolic pathway, as most commonly done up to now. Such an open metabolite profiling strategy should lead to the generation of novel hypotheses on unexpected modes of action of phytochemicals and to the discovery of new markers of effects able to characterize the subtle metabolic changes induced in nutritional interventions (Fig.╃4) [100]. The analysis of urinary profiles of healthy pre-menopausal women following a soy or miso controlled dietary intervention, revealed an influence of both free and conjugated isoflavones on kidney osmolyte activity and energy metabolism (Tab.╃1) [101]. Using a similar approach, the consumption of green tea by healthy volunteers was shown to result in a significant increase in the levels of several citric acid cycle intermediates such as citrate, pyruvate and oxaloacetate, also suggesting an effect of tea flavanols on oxidative energy metabolism [83]. The consumption of chamomile tea by healthy subjects was shown to decrease the urinary excretion of creati-

223

224

Contributions

nine and to increase that of hippurate and glycine indicating a possible effect of chamomile on gut microflora metabolism [102]. More recently, we used a metabolomic approach based on high-resolution mass spectrometry to characterize the metabolic effects of catechin, a flavonoid abundant in various fruits, cocoa and wine, in rats fed a hyperlipidic diet [96]. About 1000 metabolic variables were found to be affected by the hyperlipidic diet, among which 76 were fully reversed by catechin supplementation. Some of these variables could be identified by comparison of their exact mass with those stored in open metabolite databases, revealing unexpected effects on nicotinic acid and the tryptophan pathway. Here again, the identification of the ions detected by high-resolution mass spectrometry is essential to interpret the changes observed in the endogenous metabolome [65]. No more than 10â•›% of the ions observed by high-resolution mass spectrometry can be identified today by screening exact masses in open metabolite libraries such as KEGG or HMDB [103]. As a consequence, various authors have developed targeted methods rather than open fingerprinting approach in which a selection of about 100 to 200 metabolites a priori known as most abundant in the samples of interest are estimated in one or several analytical platforms [103, 104]. Such targeted approaches already improve our understanding of the effect of a diet or nutrient on the metabolism but still leaves unexplored the major fraction of the human metabolome. Another major problem commonly encountered in metabolomics studies is the interpretation of the metabolic changes. It is still difficult to interpret an increase or a decrease of the concentrations of a metabolite in a given context. New bioinformatic tools like NuGOwiki (http://www.nugowiki.org/index.php/ Main_Page) or HMDB (http://www.hmdb.ca/) will contribute to provide such information [65].

3.12.7 Conclusion Phytochemicals have been selected throughout the evolution and stored in tissues to defend plants against pathogens and predators or serve as signal compounds [105]. Often specific to one species or a larger group of species, they contribute to give the plant its own identity. Mammals have also evolved alongside plants, depending on some of them as staple food. Mammals have thus been exposed to a variety of phytochemicals for millions of years. Through the careful selection of plant species for food, they have avoided most phytochemicals that cause acute toxicity. However, a number of phytochemicals present in our foods still affect human health on a long-term basis positively or negatively. The challenge today is to interpret the complex relationships between phytochemicals present in the human diet and human health, taking into account both the diversity of their chemical structures and the complexity of their metabolic effects. In contrast to single markers or metabolites, commonly measured in clinical trials or cohort studies, the whole metabolome bears considerable information

Table 1: Reported endogenous metabolite modifications resulting from phytochemical intake. Intervention

Subjects (samples)

Fardet et al., 2008

Animal study: Normo-(5â•›%) or hyperlipidic (15 and 25â•›%) diets supplemented or not with (+)-catechin (0.2â•›% diet) for 6 weeks

6 groups of 8 LC-ToF male Wistar rats (urine)

Non-controlled human study (parallel design): Low-phytochemical diet for 2 days followed by a standard phytochemical diet for 2 days (apple, carrot, strawberry drinks)

21 healthy women (n=╃12) and men (n=╃9) (spot urine)

Walsh et al., 2007

Analytical technique

H-NMR LC-ToF

1

Bertram et Animal study: Comparison 6 female pigs 1H-NMR of a rye-based diet (whole (plasma and LC-MS al., 2006 grain) and a wheat-based urine) diet (non-whole grain), each diet for one week

Modified endogenous metabolites

Biological hypotheses

↑ Deoxycytidine, ↑ Nicotinic acid, ↑ Â�Dihydroxyquinoline, ↑ Pipecolinic acid

Possible increase in DNA breakdown, chronic liver dysfunction or peroxisomal disorders Possible inhibition of microbiota growth by Catechin

↑ Hippuric acid, ↑ Hydroxyhippuric acid, ↑ Catechin (glucuronide and aglycone), ↑€Methylcatechin (glucuronide and aglycone), ↑ Dihydroxyphenylvalerolactone glucuronide, ↑ Methoxy-hydroxyphenylvalerolactone (glucuronide, aglycone, and sulphate)

Catechin metabolites

↑ Creatinine, ↑ 3-Methylhistidine

Possible changes in energy metabolism and muscle proteolysis

↑ Hippurate

Intestinal bacterial metabolism of phytochemicals

↑ Betaine, ↑ Hippurate after the whole-grain diet (rye) ↑ Creatinine after the non-whole grain diet (wheat)

Further studies needed to elucidate the role of Betaine and its potential connection with Creatinine excretion in the health-promoting effect of wholegrain cereals

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

Reference

225

226

Intervention

Stella et al., 2006

Randomized crossover study: 12 healthy Comparison of vegetarmen ian (420â•›g/day), low-meat (24-h urine) (60â•›g/day), high-meat (420â•›g/day) diets for 15 days

Randomized crossover study: Van Dorsten et Consumption of black tea (6â•›g/d), green tea (6â•›g/d), al., 2006 or Caffeine (control) for 2 days

Subjects (samples)

17 healthy men (plasma and 24 hr urine)

Analytical technique 1

1

H-NMR

H-NMR

Modified endogenous metabolites

Biological hypotheses

↑ Creatinine, ↑ Taurine, ↑ TMAO, ↑ Methylhistidine after the high meat diet

Biomarkers of meat consumption

↑ Carnitine after the high meat diet

Changes in energy metabolism

↑ p-Hydroxyphenylacetate after vegetarian diet

Microbial metabolism of plant foods

↑ N-Acetyl-5-Hydroxytryptamine after the high meat diet

Changes in tryptophan metabolism

Urine: ↑ Hippurate ↑ 1,3-Dihydroxyphenyl-2- Intestinal bacterial metabolism of tea flavanols O-sulphate Urine: ↑ Citrate, ↑ Succinate, ↑ Oxaloacetate, ↑ 2-Oxoglutarate

Stimulation of oxidative energy metabolism

Urine€: ↑ β-Hydroxybutyrate (only after black tea)

Liver ketogenesis/ fatty acid oxidation

Plasma€: ↓ Lactate, ↓ Alanine (only after green Reduction in anaerobic glycolysis tea) Plasma€: ↓ Glucose

Enhanced insulin activity

Contributions

Reference

Reference

Intervention

Solanky et Controlled study: Miso (50â•›g/d) or soy protein al., 2005 (60â•›g/d) intervention for one month

Subjects (samples)

Analytical technique

9 healthy premenopausal women (24-hr urine)

1

H-NMR

Modified endogenous metabolites

Biological hypotheses

↑ TMAO, ↑ Choline, ↓ Creatinine, ↑ Creatine Changes in glomerular or kidney functions ↑ Methylamine, ↑ Dimethylamine

Changes in lipid and cholesterol metabolism

↓ Citrate, ↓ Lactate (only after miso intake)

Changes in carbohydrate metabolism

↓ Hippurate, ↓ Benzoate

Changes in phenyl/ benzoate metabolism

↑ Glutamine, ↑ Glutamate (only after soy intake)

Changes in tricarboxylic acid cycle Increase in protein breakdown

1

H-NMR

↑ Hippurate, ↑ Glycine, ↓ Creatinine (after chamomile intake) ↑ Citrate and ↑ Glycine in women ↑ Creatinine in men

Perturbation of the gut microflora activity High intrinsic physiological variations

Solanky et Controlled study: Soy protein intervention for 1 al., 2003 month (60â•›g/day containing 45 mg isoflavones)

5 healthy premenopausal women (plasma)

1

H-NMR

↑ 3-Hydroxybutyrate, ↑ N-acetyl glycoÂ� proteins, ↑ Lactate, ↓ Carbohydrates

Changes in carbohydrate/energy metabolism Increase in anaerobic metabolism

Solanky et Animal study: Single dose of Epicatechin (22€mg) al., 2003

10 SpragueDawley rats (spot urine)

1

H-NMR

↓ Citrate, ↓ 2-Oxoglutarate, ↓ DimethylÂ� amine, ↓ Creatinine, ↓ Taurine

Modification in carbohydrate metabolism Changes in liver and kidney functions

Controlled study: chamomile tea intervention (200€ml/day, 25mg/ml chamomile flowers) for 2 weeks

TMAO: Trimethylamine-N-oxide.

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

14 healthy women (n=╃7) and men (n=╃7) (spot urine)

Wang et al., 2005

227

228

Contributions

on phenotype and exposure to all chemicals present in our environment. We are just beginning to learn how to decipher this information. Expected progress in high-throughput analyses of metabolites and in bioinformatics should in the near future allow us to characterize the food metabolome and the endogenous metabolome in human biofluids or tissues in far more details than is possible today. This should help us unravel the complex links between diet and human health.

Acknowledgments Rafael Llorach is supported by the Spanish Ministries of Health and Education and Science (F.I.S.CD06/00161, AGL 2006–14228-C03–02, INGENIO-CONSOLIDER programme FUN-C-FOOD CSD2007).

Glossary4 Phytochemical: A plant chemical substance that is biologically active but not nutritive. Metabolome: All low molecular weight metabolites present in a biological sample. Endogenous metabolome: All metabolites formed under direct cell genome/ proteome control in a given cell, tissue or organism. Xenometabolome: All compounds foreign to the host and the products of their metabolism by the host. Food metabolome: All metabolites in cells, tissues or biofluids directly derived from the digestion of food by the host. Metabolomics: The quantitative measurement of the multivariate metabolic responses of a cell, tissue or organism to pathophysiological stimuli or genetic modification. Biomarker: A characteristic (often a substance detected in body fluids and/ or tissues) that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarker of intake: A characteristic (a metabolite or the product of an interaction between the substance and some molecular target) objectively measured and evaluated as an indicator of the intake of a xenobiotic (food constituent, drug, toxic).

4

In part adapted from [106, 107].

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

Conflict of Interest Statement None of the authors had a conflict of interest.

References â•⁄ 1. Hu, F.╃B., Plant-based foods and prevention of cardiovascular disease: an overview. Am. J. Clin. Nutr. 2003, 78, 544S–551╃S.╃ â•⁄ 2. Riboli, E., Norat, T., Epidemiologic evidence of the protective effect of fruit and vegetables on cancer risk. Am. J. Clin. Nutr. 2003, 78, 559S–569╃S.╃ â•⁄ 3. Liu, R.╃H., Health benefits of fruit and vegetables are from additive and synergistic combinations of phytochemicals. Am. J. Clin. Nutr. 2003, 78, 517S–520╃S.╃ â•⁄ 4. Johnson, I.╃T., Phytochemicals and cancer. Proc. Nutr. Soc.╃2007, 66, 207– 215. â•⁄ 5. Arts, I.╃C., Hollman, P.╃C., Polyphenols and disease risk in epidemiologic studies. Am. J. Clin. Nutr. 2005, 81, 317S–325. â•⁄ 6. Liu, R.╃H., Potential synergy of phytochemicals in cancer prevention: mechanism of action. J. Nutr. 2004, 134, 3479S–3485╃S.╃ â•⁄ 7. Crozier, A., Clifford, M.╃N., Ashihara, H. (Eds.), Plant Secondary Metabolites. Occurrence, Structure and Role in the Human Diet, Blackwell Publishing 2006. â•⁄ 8. Harborne, J.╃B., Baxter, H., Moss, G.╃P. (Eds.), Phytochemical dictionary – A handbook of bioactive compounds from plants, Taylor & Francis, London 1999. â•⁄ 9. Katan, M.╃B., Grundy, S.╃M., Jones, P., Law, M., et al., Efficacy and safety of plant stanols and sterols in the management of blood cholesterol levels. Mayo Clinic Proc. 2003, 78, 965–978. 10. Branca, F., Lorenzetti, S., Health effects of phytoestrogens. Diet Divers.Health Prom. 2005, pp.╃100–111. 11. Khan, N., Afaq, F., Mukhtar, H., Cancer chemoprevention through dietary antioxidants: Progress and promise. Antioxid. Redox Signal. 2008, 10, 475– 510. 12. Anto, R.╃J., George, J., Babu, K.╃V., Rajasekharan, K.╃N., et al., Antimutagenic and anticarcinogenic activity of natural and synthetic curcuminoids. Mut. Res. Gen. Toxicol. 1996, 370, 127–131. 13. Keum, Y.╃S., Jeong, W.╃S., Kong, A.╃N.╃T., Chemopreventive functions of isothiocyanates. Drug News Perspect. 2005, 18, 445–451. 14. Perabo, F.╃G.╃E., Von Low, E.╃C., Ellinger, J., Von Rucker, A., et al., Soy isoflavone genistein in prevention and treatment of prostate cancer. Prost. Cancer Prost. Dis.╃2008, 11, 6–12. 15. Seren, S., Lieberman, R., Bayraktar, U., Heath, E., et al., Lycopene in cancer prevention and treatment. Am. J. Ther. 2008, 15, 66–81. 16. Williams, R.╃J., Spencer, J.╃P.╃E., Rice-Evans, C., Flavonoids: antioxidants or signalling molecules? Free Rad. Biol. Med.╃2004, 36, 838–849.

229

230

Contributions

17. Salah, N., Miller, N.╃J., Paganga, G., Tijburg, L., et al., Polyphenolic flavanols as scavengers of aqueous-phase radicals and as chain-breaking antioxidants. Arch. Biochem. Biophys. 1995, 322, 339–346. 18. Loo, G., Redox-sensitive mechanisms of phytochemical-mediated inhibition of cancer cell proliferation (Review). J. Nutr. Biochem. 2003, 14, 64– 73. 19. Shen, G., Jeong, W.╃S., Hu, R., Kong, A.╃N., Regulation of Nrf2, NF-kappaB, and AP-1 Signaling pathways by chemopreventive agents. Antioxid. Redox Signal. 2005, 7, 1648–1663. 20. Scalbert, A., Manach, C., Morand, C., Rémésy, C., et al., Dietary polyphenols and the prevention of diseases. Crit. Rev. Food Sci. Nutr. 2005, 45, 287– 306. 21. Scalbert, A., Johnson, I.╃T., Saltmarsh, M., Polyphenols: antioxidants and beyond. Am. J. Clin. Nutr. 2005, 81, 215S–217╃S.╃ 22. Stevenson, D.╃E., Hurst, R.╃D., Polyphenolic phytochemicals – just antioxidants or much more? Cellular and Molecular Life Sci.╃2007, 64, 2900–2916. 23. Hooper, L., Kroon, P.╃A., Rimm, E.╃B., Cohn, J.╃S., et al., Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 2008, 88, 38–50. 24. Dettmer, K., Aronov, P.╃A., Hammock, B.╃D., Mass spectrometry-based metabolomics. Mass Spectrom. Rev.╃2007, 26, 51–78. 25. Nicholson, J.╃K., Holmes, E., Wilson, I.╃D., Gut microorganisms, mammalian metabolism and personalized health care. Nat. Rev. Microbiol. 2005, 3, 431–438. 26. Holmes, E., Loo, R.╃L., Cloarec, O., Coen, M., et al., Detection of urinary drug metabolite (Xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal. Chem. 2007, 79, 2629–2640. 27. Fardet, A., Llorach, R., Orsoni, A., Martin, J.╃F., et al., Metabolomics Provide New insights on the metabolism of dietary phytochemicals in rats. J. Nutr. 2008, 138, 1282–1287. 28. Mennen, L., Sapinho, D., Ito, H., Galan, P., et al., Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. Br. J. Nutr. 2006, 96, 191–198. 29. Tucker, K.╃L., Assessment of usual dietary intake in population studies of gene-diet interaction. Nutr. Metab. Carbiovasc. Dis.╃2007, 17, 74–81. 30. Ziegler, R.╃G., The future of phytochemical databases. Am. J. Clin. Nutr. 2001, 74, 4–5. 31. US Department of Agriculture, Nutrient Data Laboratory, Database on the Isoflavone Content of Foods (http://www.nal.usda.gov/fnic/foodcomp/ Data/isoflav/isoflav.html). 2002. 32. US Department of Agriculture, Nutrient Data Laboratory, Database for the flavonoid content of selected foods (http://www.nal.usda.gov/fnic/foodcomp/). 2003.

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

33. US Department of Agriculture, Nutrient Data Laboratory, Database for the Proanthocyanidin Content of Selected Foods (http://www.nal.usda.gov/ fnic/foodcomp/Data/PA/PA.html). 2004. 34. Holden, J.╃M., Eldridge, A.╃L., Beecher, G.╃R., Marilyn Buzzard, I., et al., Carotenoid content of US foods: an update of the database. J. Food Comp. Anal. 1999, 12, 169–196. 35. Neveu, V., Vos, F., du Chaffaut, L., Mennen, L., et al., 10th European Nutrition Conference, Paris 2007, p.╃122 (abstract). 36. Kiely, M., Faughnan, M., Wahala, K., Brants, H., et al., Phyto-oestrogen levels in foods: the design and construction of the VENUS database. Br. J. Nutr. 2003, 89 Suppl 1, S19–23. 37. McNaughton, S.╃A., Marks, G.╃C., Development of a food composition database for the estimation of dietary intakes of glucosinolates, the biologically active constituents of cruciferous vegetables. Br. J. Nutr. 2003, 90, 687–697. 38. Klingberg, S., Andersson, H., Mulligan, A., Bhaniani, A., et al., Food sources of plant sterols in the EPIC Norfolk population. Eur. J. Clin. Nutr. 2008, 62, 695–703. 39. Valsta, L.╃M., Lemstrom, A., Ovaskainen, M.╃L., Lampi, A.╃M., et al., Estimation of plant sterol and cholesterol intake in Finland: quality of new values and their effect on intake. Br. J. Nutr. 2004, 92, 671–678. 40. van der Sluis, A.╃A., Dekker, M., de Jager, A., Jongen, W.╃M., Activity and concentration of polyphenolic antioxidants in apple: effect of cultivar, harvest year, and storage conditions. J. Agric. Food Chem. 2001, 49, 3606– 3613. 41. Mithen, R.╃F., Dekker, M., Verkerk, R., Rabot, S., et al., The nutritional significance, biosynthesis and bioavailability of glucosinolates in human foods. J. Sci. Food Agric. 2000, 80, 967–984. 42. Setchell, K.╃D.╃R., Cole, S.╃J., Variations in isoflavone levels in soy foods and soy protein isolates and issues related to isoflavone databases and food labeling. J. Agric. Food Chem. 2003, 51, 4146–4155. 43. Holst, B., Williamson, G., A critical review of the bioavailability of glucosinolates and related compounds. Nat. Prod. Rep.╃2004, 21, 425–447. 44. Yeum, K.╃J., Russell, R.╃M., Carotenoid bioavailability and bioconversion. Annu. Rev. Nutr. 2002, 22, 483–504. 45. Ritchie, M.╃R., Morton, M.╃S., Thompson, A.╃M., Deighton, N., et al., Investigation of the reliability of 24â•›h urine excretion as a biomarker of isoflavone exposure over time and over a wide range of isoflavone intakes. Eur. J. Clin. Nutr. 2004, 58, 1286–1289. 46. Grace, P.╃B., Taylor, J.╃I., Low, Y.╃L., Luben, R.╃N., et al., Phytoestrogen concentrations in serum and spot urine as biomarkers for dietary phytoestrogen intake and their relation to breast cancer risk in European prospective Investigation of Cancer and Nutrition-Norfolk. Cancer Epidem. Biomark. Prev. 2004, 13, 698–708.

231

232

Contributions

47. Dai, Q., Franke, A.╃A., Jin, F., Shu, X.╃O., et al., Urinary excretion of phytoestrogens and risk of breast cancer among Chinese women in Shanghai. Cancer Epidem. Biomark. Prev. 2002, 11, 815–821. 48. Rissanen, T.╃H., Voutilainen, S., Nyyssonen, K., Salonen, R., et al., Serum lycopene concentrations and carotid atherosclerosis: the Kuopio Ischaemic Heart Disease Risk Factor Study. Am. J. Clin. Nutr. 2003, 77, 133–138. 49. Manach, C., Scalbert, A., Morand, C., Rémésy, C., et al., Polyphenols – Food sources and bioavailability. Am. J. Clin. Nutr. 2004, 79, 727–747. 50. Spencer, J.╃P.╃E., Abd El Mohsen, M.╃M., Minihane, A.╃M., Mathers, J.╃C., Biomarkers of the intake of dietary polyphenols: strengths, limitations and application in nutrition research. Br. J. Nutr. 2008, 99, 12–22. 51. Davis, C.╃D., Milner, J.╃A., Biomarkers for diet and cancer prevention research: potentials and challenges. Acta Pharm. Sin.╃2007, 28, 1262–1273. 52. Brevik, A., Rasmussen, S.╃E., Drevon, C.╃A., Andersen, L.╃F., Urinary excretion of flavonoids reflects even small changes in the dietary intake of fruits and vegetables. Cancer Epidem. Biomark. Prev. 2004, 13, 843–849. 53. Lila, M.╃A., Raskin, I., Health-related interactions of phytochemicals. J. Food Sci.╃2005, 70, R20–R27. 54. Porrini, M., Riso, P., Testolin, G., Absorption of lycopene from single or daily portions of raw and processed tomato. Br. J. Nutr. 1998, 80, 353–361. 55. Brown, M.╃J., Ferruzzi, M.╃G., Nguyen, M.╃L., Cooper, D.╃A., et al., Carotenoid bioavailability is higher from salads ingested with full-fat than with fat-reduced salad dressings as measured with electrochemical detection. Am. J. Clin. Nutr. 2004, 80, 396–403. 56. Yonekura, L., Nagao, A., Intestinal absorption of dietary carotenoids. Mol. Nutr. Food Res.╃2007, 51, 107–115. 57. Chow, H.╃H.╃S., Hakim, I.╃A., Vining, D.╃R., Crowel, J.╃A., et al., Effects of dosing condition on the oral bioavailability of green tea catechins after single-dose administration of Polyphenon E in healthy individuals. Clin. Cancer Res.╃2005, 11, 4627–4633. 58. Erlund, I., Silaste, M.╃L., Alfthan, G., Rantala, M., et al., Plasma concentrations of the flavonoids hesperetin, naringenin and quercetin in human subjects following their habitual diets, and diets high or low in fruit and vegetables. Eur. J. Clin. Nutr. 2002, 56, 891–898. 59. Cerda, B., Tomas-Barberan, F.╃A., Espin, J.╃C., Metabolism of antioxidant and chemopreventive ellagitannins from strawberries, raspberries, walnuts, and oak-aged wine in humans: Identification of biomarkers and individual variability. J. Agric. Food Chem. 2005, 53, 227–235. 60. Lampe, J.╃W., Chang, J.╃L., Interindividual differences in phytochemical metabolism and disposition. Semin. Cancer Biol. 2007, 17, 347–353. 61. Setchell, K.╃D.╃R., Brown, N.╃M., Lydeking-Olsen, E., The clinical importance of the metabolite Equol – a clue to the effectiveness of soy and its isoflavones. J. Nutr. 2002, 132, 3577–3584. 62. Mennen, L.╃I., Sapinho, D., Ito, H., Galan, P., et al., Urinary excretion of 13 dietary flavonoids and phenolic acids in free-living healthy subjects – vari-

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

63.

64. 65.

66.

67.

68.

69. 70. 71.

72.

73.

74.

75.

76. 77.

ability and possible use as biomarkers of polyphenol intake. Eur. J. Clin. Nutr. 2008, 62, 519–525. Ito, H., Gonthier, M.-P., Manach, C., Morand, C., et al., Polyphenol levels in human urine after intake of six different polyphenol-rich beverages. Br. J. Nutr. 2005, 94, 500–509. Jurs, P., Pattern recognition used to investigate multivariate data in analytical chemistry. Science 1986, 232, 1219–1224. Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., et al., Mass-spectrometry-based metabolomics in nutrition – current limitations and recommendations. 2009, submitted. Jiye, A., Huang, Q., Wang, G.╃J., Zha, W.╃B., et al., Global analysis of metabolites in rat and human urine based on gas chromatography/ time-of-flight mass spectrometry. Anal. Biochem. 2008, 379, 20–26. Shellie, R.╃A., Welthagen, W., Zrostlikova, J., Spranger, J., et al., Statistical methods for comparing comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry results: metabolomic analysis of mouse tissue extracts. J. Chromatogr. A 2005, 1086, 83–90. Nordström, A., O’Maille, G., Qin, C., Siuzdak, G., Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum. Anal. Chem. 2006, 78, 3289–3295. Trygg, J., Holmes, E., Lundstedt, T., Chemometrics in metabonomics. J. Proteome Res.╃2007, 6, 469–479. Ioannidis, J.╃P.╃A., Is molecular profiling ready for use in clinical decision making? Oncologist 2007, 12, 301–311. Werner, E., Heilier, J.-F., Ducruix, C., Ezan, E., et al., Mass spectrometry for the identification of the discriminating signals from metabolomics: current status and future trends. J. Chromatogr. B 2008, 871, 143–163. Brindle, J.╃T., Antti, H., Holmes, E., Tranter, G., et al., Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med.╃2002, 8, 1439–1444. Odunsi, K., Wollman, R.╃M., Ambrosone, C.╃B., Hutson, A., et al., Detection of epithelial ovarian cancer using H-1-NMR-based metabonomics. Int. J. Cancer 2005, 113, 782–788. Yang, J., Xu, G., Zheng, Y., Kong, H., et al., Diagnosis of liver cancer using HPLC-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases. J. Chromatogr. B 2004, 813, 59–65. Ippolito, J.╃E., Xu, J., Jain, S., Moulder, K., et al., An integrated functional genomics and metabolomics approach for defining poor prognosis in human neuroendocrine cancers. Proc. Natl. Acad. Sci. USA 2005, 102, 9901– 9906. Whitfield, P.╃D., German, A.╃J., Noble, P.╃J., Metabolomics: an emerging post-genomic tool for nutrition. Br. J. Nutr. 2004, 92, 549–555. Gibney, M.╃J., Walsh, M., Brennan, L., Roche, H.╃M., et al., Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 2005, 82, 497–503.

233

234

Contributions

78. Rezzi, S., Ramadan, Z., Fay, L.╃B., Kochhar, S., Nutritional Metabonomics: applications and perspectives. J. Proteome Res.╃2007, 6, 513–525. 79. Wishart, D.╃S., Metabolomics: applications to food science and nutrition research. Trends Food Sci. Technol. 2008, 19, 482–493. 80. Spencer, J.╃P.╃E., Abd El Mohsen, M., Minihane, A.╃M., Metabolism of dietary phytochemicals: a review of the metabolic forms identified in humans. Curr. Topics Nutraceut. Res.╃2006, 4, 187–203. 81. Felgines, C., Texier, O., Besson, C., Lyan, B., et al., Strawberry pelargonidin glycosides are excreted in urine as intact glycosides and glucuronidated pelargonidin derivatives in rats. Br. J. Nutr. 2007, 98, 1126–1131. 82. Grun, C.╃H., van Dorsten, F.╃A., Jacobs, D.╃M., Le Belleguic, M., et al., GCMS methods for metabolic profiling of microbial fermentation products of dietary polyphenols in human and in vitro intervention studies. J. Chromatogr. B 2008, 871, 212–219. 83. Van Dorsten, F.╃A., Daykin, C.╃A., Mulder, T.╃P.╃J., Van Duynhoven, J.╃P.╃M., Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J. Agric. Food Chem. 2006, 54, 6929–6938. 84. Walsh, M.╃C., Brennan, L., Pujos-Guyot, E., Sebedio, J.-L., et al., Influence of acute phytochemical intake on human urinary metabolomic profiles. Am. J. Clin. Nutr. 2007, 86, 1687–1693. 85. Mullen, W., Edwards, C.╃A., Crozier, A., Absorption, excretion and metabolite profiling of methyl-, glucuronyl-, glucosyl- and sulpho-conjugates of quercetin in human plasma and urine after ingestion of onions. Br. J. Nutr. 2006, 96, 107–116. 86. Stewart, D., McDougall, G.╃J., Sungurtas, J., Verrall, S., et al., Metabolomic approach to identifying bioactive compounds in berries: advances toward fruit nutritional enhancement. Mol. Nutr. Food Res.╃2007, 51, 645–651. 87. Moco, S., Bino, R.╃J., Vorst, O., Verhoeven, H.╃A., et al., A liquid chromatography-mass spectrometry-based metabolome database for tomato. Plant Physiol. 2006, 141, 1205–1218. 88. Langowski, J., Long, A., Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Deliv. Rev.╃2002, 54, 407–415. 89. Anari, M.╃R., Baillie, T.╃A., Bridging cheminformatic metabolite prediction and tandem mass spectrometry. Drug Discov. Today 2005, 10, 711–717. 90. Levsen, K., al., e., Structure elucidation of phase II metabolites by tandem mass spectrometry: An overview. J. Chromatogr. 2005, 1067, 55–72. 91. Overy, D.╃P., Enot, D.╃P., Tailliart, K., Jenkins, H., et al., Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints. Nature Protocols 2008, 3, 471–485. 92. German, J.╃B., Watkins, S.╃M., Fay, L.╃B., Metabolomics in practice: emerging knowledge to guide future dietetic advice toward individualized health. J.╃Am. Diet Assoc. 2005, 105, 1425–1432. 93. Scalbert, A., Knasmüller, S., Genomic effects of phytochemicals and their implication in the maintenance of health. Br. J. Nutr. 2008, 99, ES1–ES2.

The Complex Links between Dietary Phytochemicals and Human Health Deciphered by Metabolomics

â•⁄ 94. Na, H.╃K., Surh, Y.╃J., Intracellular signaling network as a prime chemopreventive target of (-)-epigallocatechin gallate. Mol. Nutr. Food Res.╃2006, 50, 152–159. â•⁄ 95. Spencer, J.╃P., Flavonoids: modulators of brain function? Br. J. Nutr. 2008, 99, ES60–ES77. â•⁄ 96. Fardet, A., Llorach, R., Martin, J.-F., Besson, C., et al., A liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomic approach reveals new metabolic effects of catechin in rats fed high-fat diets. J. Proteome Res.╃2008, 7, 2388–2398. â•⁄ 97. Bjelakovic, G., Nikolova, D., Gluud, L.╃L., Simonetti, R.╃G., et al., Mortality in randomized trials of antioxidant supplements for primary and secondary prevention: systematic review and meta-analysis. J.╃Am. Med. Assoc. 2007, 297, 842–857. â•⁄ 98. Miller, E.╃R., III, Pastor-Barriuso, R., Dalal, D., Riemersma, R.╃A., et al., Meta-analysis: high-dosage vitamin E supplementation may increase allcause mortality. Ann. Intern. Med.╃2004, 142, 37–46. â•⁄ 99. Oresic, M., Simell, S., Sysi-Aho, M., Nanto-Salonen, K., et al., Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J. Exp. Med.╃2008, 205, 2975–2984. 100. van der Greef, J., Stroobant, P., van der Heijden, R., The role of analytical sciences medical systems biology. Curr. Opin. Chem. Biol. 2004, 8, 559–565. 101. Solanky, K.╃S., Bailey, N.╃J., Beckwith-Hall, B.╃M., Bingham, S., et al., Biofluid 1H NMR-based metabonomic techniques in nutrition research – metabolic effects of dietary isoflavones in humans. J. Nutr. Biochem. 2005, 16, 236–244. 102. Wang, Y., Tang, H., Nicholson, J.╃K., Hylands, P.╃J., et al., A metabonomic strategy for the detection of the metabolic effects of chamomile (Matricaria recutita L.) ingestion. J. Agric. Food Chem. 2005, 53, 191–196. 103. Wishart, D.╃S., Lewis, M.╃J., Morrissey, J.╃A., Flegel, M.╃D., et al., The human cerebrospinal fluid metabolome. J. Chromatogr. B 2008, 871, 164–173. 104. Shaham, O., Ru Wei, T.╃J.╃W., Ricciardi, C., Lewis, G.╃D., Ramachandran S.╃V., Carr, S.╃A., Thadhani, R., Gerszten, R.╃E. , Mootha, V.╃K., Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 2008, 4, Article No.╃214. 105. Parr, A.╃J., Bolwell, G.╃P., Phenols in the plant and in man. The potential for possible nutritional enhancement of the diet by midfying the phenols content or profile. J. Sci. Food Agric. 2000, 80, 985–1012. 106. Nicholson, J.╃K., Wilson, I.╃D., Understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2003, 2, 668–676. 107. Atkinson, A.╃J., Colburn, W.╃A., DeGruttola, V.╃G., DeMets, D.╃L., et al., Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharm. Ther. 2001, 69, 89–95.

235

236

Contributions

3.13 Anti-Oxidative and Antigenotoxic Properties of Â�Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology Theo M.╃C.╃M. de Kok1, Pim de Waard2, Lonneke C. Wilms3, and Simone G.╃J. van Breda3 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 208–217.

Abstract There is considerable evidence that consumption of fruits and vegetables may contribute to the prevention of cancer. It is, however, remarkable, that evidence for such a preventive action arising from mechanistic studies is becoming stronger, whereas results of some recent prospective studies are less convincing. This apparent discrepancy may be overcome, or at least understood, by introducing molecular markers in future epidemiological studies, taking modulation of molecular processes as well as genetic variability in human populations into account. Both human and animal studies demonstrated that vegetable intake modulates gene expression in the gastro-intestinal tract of many genes involved in biological pathways in favour of cancer risk prevention. Gene sets identified in this type of studies can be further evaluated, linked to the biological effects of phytochemicals and developed into biomarkers for larger human studies. Human dietary intervention studies have demonstrated that, apart from target tissues, also peripheral lymphocytes can be used for biomonitoring of chemopreventive effects. Transcriptomic responses and metabolite profiling, may link phenotypic markers of preventive effects to specific molecular processes. The use of genomics techniques appears to be a promising approach to establish mechanistic pathways involved in chemoprevention by phytochemicals, particularly when genetic variability is taken into account.

1

Correspondence to: Theo M.╃C.╃M. de Kok, Maastricht University, Faculty of Health, Medicine and Molecular Life Sciences, Department of Health Risk Analysis and Toxicology, P.╃O. Box 616, NL-6200 MD Maastricht, The Netherlands, [email protected].

2

National Institute for Public Health and the Environment, P.╃O. Box 1, NL-3720 BA Bilthoven, The Netherlands.

3

Maastricht University, Faculty of Health, Medicine and Molecular Life Sciences, Department of Health Risk Analysis and Toxicology, P.╃O. Box 616, NL-6200 MD Maastricht, The Netherlands.

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

3.13.1 Introduction Epidemiological studies indicate an interactive association between dietary habits, life-style, genetics and the risk of many different chronic diseases, including cancer. The most consistent finding on diet as a determinant of cancer risk prevention is the association between consumption of vegetables and fruits and reduced risk of several types of cancers. In order to establish the strength of the evidence for a cancer-preventive effect of fruits and vegetables, the World Cancer Research Fund (WCRF) first reviewed the literature in 1997.╃At that time, the WCRF panel concluded that the evidence for a protective effect for cancers of the colon/ rectum, lung, stomach, oesophagus and mouth/ pharynx was “convincing”, whereas the evidence for such an effect in the larynx, pancreas, breast and bladder was considered “probable” [1]. More recently, a number of prospective studies failed to support such protective effects, and as a consequence, these initial judgments were adjusted accordingly, both in the IARC Handbook of Cancer Prevention on Fruits and Vegetables [2] and in the update of the initial WCRF report [3]. Although these null findings may indeed be correct, there are several potential reasons why the effect of vegetable consumption may be attenuated in epidemiological studies. In view of the multi-factorial complexities of dietary exposures, all dietary intake assessment methods applied in epidemiological studies are associated with measurement errors which affect dietary estimates and may obscure disease–risk associations [4–6]. Furthermore, genetic variation within study populations may influence dietary intake as well as kinetics and metabolism of nutrients and phytochemicals [7, 8]. However, when reviewing the literature on the effects of bioactive compounds in animal and in vitro mechanistic studies, there is ample evidence that specific anti-oxidants and other phytochemicals present in foods of plant origin protect against genotoxicity and other cancer initiating or promoting processes [9, 10]. The majority of potentially anticarcinogenic compounds in the diet are from plant origin [11], and several classes of compounds have been identified and their respective modes of action have been studied for many years [12, 13]. In many of the postulated mechanisms, the modulation of gene expression is found to play a role. Since only a limited number of genes have been investigated, mostly in pseudotarget cells such as lymphocytes instead of the ultimate target organ, molecular targets at the genome level are mostly unknown. Nowadays, high-throughput technologies such as microarrays and 2-D gel electrophoresis can be used to investigate the effect of a specific diet on the expression of thousands of genes and proteins in a single experiment. These technologies can help us to generate new hypotheses based on the identification of potentially relevant genes and to characterize the basic molecular pathways of gene regulation by vegetables. This may eventually lead to the development of validated biomarkers for the assessment of dietary effects in humans. Introducing such molecular markers in future epidemiological studies, thereby taking molecular processes as well as genetic variability in human populations into account, may help to eliminate the present controversy between different

237

238

Contributions

types of studies and establish the potential of phytochemicals from dietary fruits and vegetables in cancer risk reduction. In this short review, the potential value of introducing genomics markers in human studies on both risks and benefits of dietary phytochemicals is discussed, with particular emphasis on the chemopreventive effect on colon cancer risk.

3.13.2 Colorectal Cancer Risk Prevention by Vegetables The model of colorectal cancer as proposed by Fearon and Vogelstein, consists of successive genetic changes, in which a number of genes are involved, including APC (adenopolyposis coli), K-RAS, DCC (deleted in colorectal cancer) and p 53 [14]. During the last decade, additional genetic events and specific molecular pathways have been identified. It became clear that the intact or mutated key molecules of the Vogelstein model interact and form a network of molecular events affecting additional genes [15, 16]. These genetic pathways and the involved genes are obvious molecular targets for the protection against colorectal cancer. Vegetables and fruits contain many different compounds that may influence the risk of cancer development, including dietary fibre, nutritive compounds (e.╃g. selenium, folic acid, vitamin C and E, carotenoids) and non-nutritive phytochemicals which have no nutritional value but may exert specific biological activities (e.╃g. flavonoids, indoles and isothiocyanates) [11, 13, and 17]. These dietary constituents may interfere with the carcinogenic process through various modes of action at various stages of cancer development. Some of the involved mechanisms may be partially overlapping, complementary or even synergistic [18]. In Table 1, an overview of main mechanisms of anticarcinogenicity is presented with some examples for each category. In many of the presented mechanisms, the modulation of gene expression is likely to play a role, but the molecular targets at the genome level and the involved genetic pathways are to a large extent unknown. Recently, we reviewed the literature on studies investigating the mechanisms underlying the prevention of colorectal cancer and lung cancer by vegetables or vegetable constituents [12]. It was demonstrated that most of the data on modulation of gene and protein expression in the colon is provided by studies of a few pre-defined genes and/ or proteins, focusing predominantly on those involved in apoptosis, cell cycle, cell proliferation and enzymatic bioactivation/ detoxification. A limited number of microarray studies identified additional processes that might be relevant, including cell differentiation [33–35], cell–cell interaction [33, 34], signal transduction [33, 34] and immune related responses [33]. Updating the number of experimental studies investigating the effect of vegetables and phytochemicals on gene and/ or protein expression, as indicated in our previous review, shows that still only 3 out of 12 [33–35] in vitro studies and 2 out of 7 animal studies [35–36] made use of “OMICS” techniques. The animal studies investigating multiple genes and proteins, demonstrated that apart from the pre-selected genes involved in biotransformation, also apoptosis, DNA repair, cell cycle and polyamine metabolism were modulated by vegetables [35, 36]. To the best of

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology Table 1: Main anticarcinogenic mechanisms of bioactive compounds in fruits and vegetables. Mechanism of anticarcinogenicity

Constituent

Mode of action

Reference

Prevention of carcinogen uptake

Dietary fibre

Adsorption of carcinogens

[19]

Inhibition of enzymatic carcinogen activation

Flavonoids Modulation of phase I and II en- [20–23] Isothiocyanates zymatic bioactivation/ detoxificaOrganosulphur comp. tion pathways Indoles

Scavenging reactive � metabolites

Flavonoids Isothiocyanates Vitamin C and E Carotenoids

Direct scavenging of reactive oxygen species and free radicals

[23]

Induction of DNA repair

Flavonoids

Inhibition of DNA topoisomerase II

[24]

Inhibition of cell proliferation

Quercetin Indoles

Blocking of G1-S transition in cell cycle; Inhibition of cyclin-dependent kinase 6

[25–27]

Induction of apoptosis

Indoles Isothiocyanates Flavonoids Organosulfur comp.

Induction of caspases, P53, BAX; [25, 28, 29] Reduction/ inhibition of BCL-2 and NF-kB

Induction of cell differentiation

Flavonoids

Inhib. of abl oncogene tyrosine kinase;

[30, 31]

β-carotene Vitamin A

Up-regulation of retinoid receptor; Expression; induction of TGF-β

[32]

our knowledge only 1 human dietary intervention study with vegetables made use of microarray technologies [37], of which the main results are discussed below.

3.13.3 Gene Expression Modulation in the Colon by Vegetables and Phytochemicals The first human study that has focused on the effects of dietary vegetables on the expression of genes in the colon using microarray techniques was published in 2004 [37]. It was investigating the hypothesis that an important contribution of the anticarcinogenic effects of vegetables in the colorectum is mediated by modulation of the expression of genes involved in biological and genetic pathways that are relevant for chemical carcinogenesis. In this study, both healthy controls and patients with adenomatous polyps (a population regarded to be at increased risk for the development of colorectal cancer), received a diet with either a 50╛% decreased (=╃75╛g/day) or a doubled intake (=╃300╛g/day) of a mixture of vegetables (cauliflower, carrots, peas and onions) during a period of 2 weeks. A dedicated microarray, containing 597 genes representing pathways

239

240

Contributions

relevant for carcinogenesis, was used to analyze gene expression changes in normal colorectal mucosa, obtained by endoscopic biopsy. Comparison of preand post-intervention measurements revealed that in total 52 genes were differentially expressed, and according to literature review, 20 of these genes could be linked to the (colon) carcinogenic process. As is shown in Figure 1, in both patients and controls 7 genes were similarly up- or down-regulated; in the high vegetable group for instance the fos proto-oncogene and ornithine decarboxylase. On the other hand, 16 genes were modulated differently in patients as compared to controls. It appeared that in control subjects, genes that are likely to play a role in the relatively early phases of carcinogenesis were modulated (e.╃g. the down-regulation of cytochrome P450 enzymes involved in the metabolism of xenobiotic agents), whereas effects in patients were found on genes involved in the later stages, such as MDM2. An increased intake of vegetables resulted in the down-regulation of genes promoting cell proliferation, and bioactivation of procarcinogens, and in up-regulation of genes involved in cell growth arrest. In contrast, a decreased intake of vegetables resulted in down-regulation of genes that inhibit cell growth and up-regulation of genes promoting cellular dedifferentiation and bioactivation of procarcinogens. Table 2 summarizes the observed effect of the intervention according to the different study populations for the most relevant genes and provides an interpretation of the up- or downregulations in terms of likeliness to be preventive or risk enhancing, based on their theoretical effect described in the literature. The overall conclusion is that almost all the effects observed after increased vegetable intake may be mechanistically linked to cellular processes that may prevent or reduce colon cancer risk, whereas reduced intake of vegetables resulted in a greater number of affected genes, and is more likely linked to increased cancer risk. Although this study does not link gene expression modulation directly to cancer as the ultimate disease end point, it demonstrates the potential value of gene expression profiling in nutritional epidemiology. If this type of genomics markers is indeed going to be used in epidemiological studies, it should be validated that comparable gene expression changes can also be established in lymphocytes as target tissue that may be available from large cohort studies. Ideally, dietary induced gene expression changes would also reflect inter-individual differences as a result of genetic polymorphisms, thereby identifying subpopulations that may particularly benefit from specific dietary preventive factors.

3.13.4 Genetic Polymorphisms and Anti-Oxidative Response In order to establish whether or not lymphocytes can be used in human studies to establish gene expression changes, as well as the influence of genetic polymorphisms, a series of studies has been performed in our department [38–40]. These studies focused on the chemopreventive effect of dietary quercetin, a well studied flavonoid, present in many fruits and vegetables. Quercetin possesses good anti-oxidant properties and has been shown to protect against the induction of DNA damage [41–44]. A first pilot study demonstrated that it

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

Figure 1: Differentially expressed genes in patients with colorectal adenomas and controls, after a dietary intervention with decreased (75╛g/day) or increased (300╛g/day) intake of vegetables; �arrow up means up-regulation, arrow down means down-regulation. Table 2: Effect of increased or decreased dietary intake of vegetables on several genes involved in colon carcinogenesis and the theoretical effect on colorectal cancer risk. Gene

Decreased vegetable intake

Increased vegetable intake

Effect on expression

Theoretical effect on CRC

Effect on expression

Theoretical effect on CRC

AMACR

+

+

€

€

ODC1

€

€

–

–

PKCB1

+

+

€

€

CCNA2

–

–

€

€

CCNG1

€

€

+

–

MDM2

–

?

+

?

CHK1

–

+

€

€

C-FOS

€

€

–

–

COX-2

+

+

€

€

CYP2C9

+

+

€

€

CYP2C19

€

€

–

?

CYP2D6

€

€

–

?

CYP3A4

€

€

–

–

CYP27B1

–

+

+

–

Involved � pathway

Metabolism

Cell cycle/ growth

Oxidoreductase activity

Up-regulation of gene expression or stimulating colorectal cancer risk. + – Down-regulation of gene expression or preventive effect on colorectal cancer risk. ? Not possible to interpret. CRC: Colorectal cancer.

241

242

Contributions

was possible to increase plasma TEAC values and quercetin levels by a 4-weeks dietary intervention with a blueberry–apple juice mixture, and that after the intervention, the level of both oxidative damage and DNA adduct levels, induced after ex vivo exposure to hydrogen peroxide and benzo[a]pyrene (B[a]P) respectively, were (non-significantly; n=╃8) reduced [38]. In a second pilot study, the impact was established of genetic polymorphisms in GSTM1 and GSTT1, both encoding for enzymes involved in anti-oxidative defence, on the induction of oxidative DNA damage and on the effectiveness of quercetin and ascorbic acid to prevent this type of damage in human lymphocytes in vitro [39]. We found no differences between base line levels or ex vivo induced oxidative DNA damage between variants and wild-types. However, the level of protection against hydrogen peroxide induced oxidative DNA damage by a pre-incubation of lymphocytes with quercetin was significantly higher in GSTT1 wild-types than in GSTT1 variants. Also pre-incubation of lymphocytes with ascorbic acid resulted in a protection against the induction of oxidative DNA damage in GSTT1 wildtypes, whereas GSTT1 variants showed an increase of damage. Based on these promising data, a large scale human dietary intervention study was designed to test the hypothesis that individuals with genetic polymorphisms for genes related to quercetin metabolism, B[a]P metabolism, oxidative stress response and DNA repair, differ in their response to the DNA protective effects of increased fruit-borne anti-oxidant intake [40]. Before and after a 4-weeks intervention with a quercetin-rich blueberry–apple juice, freshly isolated lymphocytes of in total 168 participants were challenged ex vivo with hydrogen peroxide and B[a] P and analyzed for oxidative damage and the induction BPDE-DNA adducts using the comet assay and 32P postlabelling respectively. All participants were screened for 34 different single nucleotide polymorphisms, of which 6 were found to influence the outcome of the intervention. Table 3 summarizes the impact of these polymorphisms. Particularly individuals bearing the wild-type for GSTT1 benefit the most from the protection against the induction of oxidative DNA damage, whereas the variant alleles show the highest increase in plasma anti-oxidant capacity (TEAC). This indicates that increased anti-oxidant levels in blood plasma are not necessarily a good predictor of protection against oxidative DNA damage. In contrast to the overall preventive effect of the intervention against oxidative DNA damage, ex vivo induced BPDE-DNA adduct levels were higher after 4 weeks of intervention. Again, the effect was linked to genetic polymorphisms, showing relatively low levels of damage in CYP1B1*5 variants with low enzymatic activity. This may be the consequence of the reduced capacity to metabolize B[a]P and form reactive intermediates, and an additional inhibition of enzyme activity by dietary flavonoids present in the blueberry–apple juice [47, 48]. Furthermore, COMT1 variants showed a profound increase in adduct formation. This is probably due to low activity inherent to this genotype, resulting in a decreased elimination of reactive B[a]P metabolites [47]. Overall, out of the total research population, 139 GSTT1 wild-types and 30 CYP1B1*5 variants may benefit most from increased fruit-borne anti-oxidant intake with respect to reducing risks of DNA damage, while for 43 carriers of the COMT1 variants, risk of genetic damage may be increased. Ongoing metabolomics and

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

gene expression analysis are expected to provide further insight in the underlying mechanisms of the observed effects. Preliminary data show that also transcriptomic responses to the intervention are different between individuals with different genetic make-up. For instance, GSTT1 variants show a much stronger response as compared to the wild-types; in variants, 1091 genes differentially expressed before and after the intervention (656 up- and 435 down- regulated) versus wild-types, in which 259 genes were differentially expressed (1432 upand 1147 down-regulated, unpublished results). Overall, these findings demonstrate that the evaluation of the impact of genetic polymorphisms can provide a useful tool in assessing susceptible subpopulations and groups that benefit from specific dietary modifications or interventions, and that lymphocytes can be used to monitor gene expression responses to dietary factors. Whether or not these gene expression profiles are helpful to understand the molecular mechanisms behind potentially harmful or chemopreventive effects of phytochemicals remains to be established. It is therefore of crucial importance that all identified genomics responses and affected pathways are carefully evaluated in validation studies.

3.13.5 Risk-Benefit Analysis of Dietary Phytochemicals It is well documented that phytochemicals from fruits and vegetables are not under all circumstances beneficial, but in fact may also exert toxic effects, particularly when added to the diet as supplements [49]. An elaborate meta-analysis by Bjelakovic et al., of 68 randomized trials of anti-oxidant intake through supplements revealed that beta-carotene, vitamin A and vitamin E significantly increased all-cause mortality, whereas selenium and vitamin C supplementation had no significant effect on mortality, neither positive nor negative [50]. Like vitamins, flavonoids are thought to contribute to the protection by fruits and vegetables against cancer and other degenerative diseases, but excessive flavonoid intake should be avoided as anti-oxidant effects can turn into pro-oxidant effects depending on the concentration [51, 52]. This is the result of auto-oxidation of the flavonoids, or by its metabolism resulting in o-semiquinone and oquinone structures. It has been shown for instance that the oxidation products of quercetin display various toxic effects due to their ability to arylate protein thiols [52–55]. Excessive flavonoid intake will most likely occur by ingestion of commercially available food supplements of which recommended doses greatly exceed the dose that can be reached by normal or vegetarian diets [56]. With regard to the balance between risk and benefit, another interesting class of phytochemicals is presented by the Natural AhReceptor agonists (NAhRAs), found in cruciferous vegetables and citrus fruits. Because the activation of the aryl hydrocarbon receptor (AhR) is thought to be essential in the toxicity of dioxins, a well-known group of environmental pollutants of which 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is the most potent one, it is of great importance to investigate whether these naturally occurring agonists may also exert similar toxic effects as dioxins, or dioxin-like compounds [57–60]. The importance of

243

244

Genetic polymorphism

Effect on study outcome

Remarks

NQO1* 2 (Phase II detoxification)

Heterozygous subjects showed larger increase in plasma quercetin levels.

Wild-types have a higher enzyme activity and may have a higher metabolism of quercetin resulting in lower levels of free quercetin in plasma; quercetin may induce NQO1 gene expression, resulting in even more �enhanced metabolism of quercetin [45].

Heterozygous and homozygous variants show an CAT*1 (Oxidative stress response) increased plasma ascorbic acid concentration.

This finding suggests that the lack of enzymatic anti-oxidant activity is compensated by a more efficient uptake of dietary ascorbic c acid.

GSTT1 (Phase II detoxification)

The variants had a significantly higher total anti-oxidant potential (TEAC) after the intervention; wild-type subjects showed higher protection against ex vivo induced oxidative DNA damage.

GSTT1 deletion is associated with a reduced enzymatic anti-oxidant defence, and could have been expected to cause lower plasma TEAC values; this may be explained by effective compensatory mechanisms; the genotype with the optimal anti-oxidant effect also benefits most of the intervention; this confirms earlier findings [39]; the mechanism behind this protection is unclear.

XRCC1*4 (DNA repair)

Included in the same stepwise regression model as GSTT1 and gender, predicting protection against oxidative DNA damage in the COMET assay; variants show reduced DNA-repair capacity.

Wild-types show the highest reduction of DNA damage after the intervention, indicating that phytochemicals in blueberry juice further stimulate the most optimally functioning DNA repair system.

CYP1B1*5 (Phase I bioactivation)

Variants have relatively low BPDE-DNA adduct levels Variants have lower CYP activity resulting in reduced bioactivation of as compared to wild-types after the intervention. benzo[a]pyrene; flavonoids like quercetin are known to inhibit CYP activity [46].

COMT1 (Phase II detoxification)

Variants show a profound increase of ex vivo induced BPDE-DNA adducts.

NQO1: CAT: GSTTI: XRCC1: CYP: COMT:

NAD(P)H dehydrogenase, quinone 1. Catalase Glutathione S-transferase T1. X-ray repair cross-complementing group 1. Cytochrome P450. Catechol O-Methyltransferase.

COMT1 is involved in the elimination of benzo[a]pyrene metabolites [47]; the reduced enzyme activity may result in increase DNA damage.

Contributions

Table 3: Impact of 6 out of 34 analyzed genetic polymorphisms on the outcome of a blueberry–apple juice intervention study (based on [40]).

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

investigating such a potential health concern is even more evident in view of the fact that the intake of high doses of purified NAhRAs is being promoted as healthy food supplements. The interest in this type of food supplements is based on the observation that some identified NAhRAs from vegetables and fruits, like indole-3-carbinol (I3C) in cruciferous vegetables, furocoumarins in citrus fruits and flavonoids such as chrysin, baicalein and cantharidin in vegetables and herbs, can show health promoting effects, especially as tumour suppressors, and are thought to be an important cause for the observed beneficial effects of vegetable and fruit enriched diets [3, 61–63]. It is remarkable that the proposed mechanism of antitumour activity is also based on the AhR activation, and the subsequent induction of phase I and II biotransformation enzymes like CYP1A1, CYP1A2 and UDPGT1A6 [64, 65]. This seemingly contradiction of both toxic and health beneficial effects initiated by the same receptor may be caused by differences downstream the AhR-activation or alternative mechanisms besides the AhR-activation. Possibly, differences between AhR-mediated toxic effects induced by TCDD and beneficial health effects of NAhRAs may be caused by interactions with enzyme activity at the protein level rather than at the level of gene expression. It was reported that a NAhRA containing extract of grapefruit juice induced the AhR-related gene expression strongly, but inhibited the enzymatic activity of the gene product CYP1A1 and CYP1B1 [66, 67]. In order to establish whether or not activation of the AhR pathway by NAhRAs and dioxin-like substances results in similar cellular responses, gene expression profiles induced in Caco-2 cells were studied using microarray analysis [66]. Cells were exposed to indolo[3,2-b]carbazole (ICZ) and to extracts of citrus pulp and grapefruit juice. Gene expression profiles induced by these NAhRAs were compared to those of the xenobiotic AhR agonists TCDD and B[a]P. More than 20 genes were found more than 1.5 times up- or down-regulated by TCDD, and the expression of most of these genes was modulated in the same direction and to a similar extent by B[a]P and the NAhRAs, and many of these genes may be involved in dioxin-related toxic effects. The same phase I and II biotransformation enzymes like CYP1A1, CYP1B1 and UDPGT1A6, and a number of genes involved in cell proliferation were elevated to the same magnitude. Although not extensively investigated, no important difference in gene regulation could be detected between NAhRAs and TCDD. The TCDD-like gene expression profiles of the NAhRAs suggest that NAhRAs may indeed induce TCDD-like toxicity, and therefore we investigated whether the expression of the most important AhR-responsive genes could also be detected in ex vivo exposed human lymphocytes. Blood is a relatively easy obtainable human tissue, and finding genomics biomarkers for exposure or effects in human blood cells would enable toxicological evaluation of NAhRA exposure in human populations. For that purpose, it was investigated whether or not a same kind of gene expression profile as in Caco-2 cells was found in freshly isolated human lymphocytes after in vitro exposure to NAhRAs and TCDD [68]. Although the lymphocytes appeared to be less sensitive than the Caco-2 cells, a small number of AhR-specific genes were significantly up-regulated, including CYP1A1, CYP1B1 and NQO1. As the elevated expression of these three genes was

245

246

Contributions

considered to be a promising biomarker for human exposure to AhR-agonists, a pilot study was initiated to verify the up-regulation of these three genes in human blood cells after consumption of NAhRA-rich food items. Screening of foods using the DR CALUX®-assay had shown that most NAhRA-activity was found in grapefruit juice and cruciferous vegetables [69], so these were used for a three-day intervention study. Although the level of NAhRA intake was considerable, no up-regulation of gene expression of CYP1A1, CYP1B1 or NQO1 could be detected both 3 and 24 hours after the dietary intervention. Two alternative measurements were performed to establish AhR-related activity after the dietary intervention. Firstly, the effect of the intervention on CYP1A2 induction was determined by measuring caffeine-metabolite ratios in urine samples, and secondly, the DR CALUX®-activity was measured in plasma samples. No effect on CYP1A2 induction could be detected, although studies with comparable amounts of cruciferous vegetables showed CYP1A2 induction by way of altered caffeine metabolism [70, 71], but the DR CALUX®-assay showed a slight increase in activity after consumption of cruciferous vegetables. A newly developed gene reporter assay, the Dioxin Responsive Element-driven Chemical Activated FLUorescent protein eXpression (DRE-CAFLUX) assay confirmed the slight elevation in AhR-activity found in the human plasma samples from the intervention with cruciferous vegetables (unpublished results). Although the AhR-activity in grapefruit juice extract (GJE) was much higher than in the cruciferous vegetables, the NAhRAs in GJE do not appear to reach the bloodstream in high amounts. Therefore, it can be concluded that gene expression profiling in human lymphocytes is not sensitive enough for the detection for NAhRAexposure or AhR-related effects in humans.

3.13.6 Concluding Remarks The relationship between diet and cancer is extensively studied, and particularly the consumption of vegetables and fruits, containing a wide range of bioactive phytochemicals, has been suggested to reduce cancer risk. A rapidly increasing number of mechanistic studies provide insight in the different modes of action for all different classes of compounds, making a relationship between dietary intake of phytochemicals and beneficial health outcomes biologically plausible. With the introduction of new “OMICS” techniques, new types of intermediate biomarkers can be developed that may not only reflect intake or exposure, but can potentially also provide insight in molecular responses to dietary factors. Results from the relatively small number of studies that have applied such genomics approaches, demonstrate that gene expression changes can indeed be established in target tissue after dietary intervention with vegetables, even in a small research population. Also lymphocytes have been used as surrogate tissue for biomarker analysis, showing that individuals with different genetic polymorphisms in a number of relevant genes respond differently to dietary intake of phytochemicals, both with regard to phenotypic markers of genotoxic damage and at the level of modulated gene expression. The link between mo-

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

lecular pathways in which these genes are involved and the altered protection against induced DNA damage remains, however, to be established. It appears an attractive future perspective that the evaluation of the impact of genetic polymorphisms on gene expression profiles may identify susceptible subpopulations and groups that benefit from specific dietary interventions. However, there are still numerous scientific and ethical issues that hamper sound personalized nutritional recommendations. Coming from a “one diet fits all” approach to public health, a steadily growing body of literature demonstrates the full complexity of gene–environment interactions and has illustrated that also a “one gene needs one diet” translation does not exist. Particularly the selection of genes to be studied and the assessment of the overall interactive effects of different polymorphisms appear to be of crucial interest. In their recent review, Williams et al. [72] identify the challenges for molecular nutrition research in order to establish the contribution of genetics in variable responsiveness to dietary factors and provide directions for future research to come to personalized nutritional recommendations. It appears to be of particular interest to establish in future studies the potential use of genomics markers in order to discriminate between beneficial and detrimental effects of dietary supplements containing high levels of isolated phytochemicals. Classical exposure biomarkers, such as plasma concentrations of specific compounds may provide information on dietary intake, but cannot indicate toxic responses, whereas specific markers of for instance DNA damage or oxidative stress in general may overlook health effects that are mediated by other molecular mechanisms, such as activation of the Ah-Receptor. Finally, it has also been demonstrated that transcriptomics analysis has its limitations. As microarray analysis of gene expression responses evaluate the effects on thousands of genes simultaneously, it is inevitable that some differences in gene expression levels will be found. This implies that such approaches may be particularly useful in the identification of potentially relevant genes and pathways and may generate new hypothesis on molecular mechanisms of interest, but also that the findings generated by such techniques require extensive validation studies to ensure that the indicated responses are indeed reproducible and meaningful. The microarray technologies may not be sensitive enough to detect all relevant gene responses, and modulation of enzyme activity at the protein level may be more relevant than the level of gene expression. This implies that gene expression analysis is most likely to be of additional value in human studies when combined with conventional biomarkers of effect, a fundamental research concept also indicated as phenotypic anchoring [73]. Biomarkers of interest could be all sorts of clinical chemistry markers, histopathology or more specific markers of genotoxic damage, such as DNA strand breaks or oxidative DNA damage. Once such experiments have provided sufficient proof of principle, further integration of data coming from “OMICS” technologies, especially proteomics and metabolomics analysis, may contribute to the further understanding of phytochemically-induced human health effects.

247

248

Contributions

References â•⁄ 1. World Cancer Research Fund and American Institute for Cancer Research (1997) Food, nutrition and the prevention of cancer: a global perspective. World Cancer Research Fund; American Institute for Cancer Research, Washington, DC. â•⁄ 2. IARC Working Group on the Evaluation of Cancer Preventive Strategies (2003) Fruit and vegetables. In: Vaino H, Bianchini F (eds) IARC Handbooks of cancer prevention, Vol 8.╃IARC Press, Lyon. â•⁄ 3. World Cancer Research Fund (2007) Food, nutrition, physical activity and the prevention of cancer: a global perspective. World Cancer Research Fund; American Institute for Cancer Research, Washington, DC.╃ â•⁄ 4. Schatzkin, A., Subar, A. F , Moore, S., Park, Y., Potischman, N., Thompson, F.╃E., Leitzmann, M., Hollenbeck, A., Morrissey, K.╃G., Kipnis, V., Observational epidemiologic studies of nutrition and cancer: the next generation (with better observation). Cancer Epidemiol. Biomarkers Prev., 2009, 18, 1026–1032. â•⁄ 5. Nagel, G., Zoller, D., Ruf, T., Rohrmann, S., Linseisen, J., Long-term reproducibility of a food-frequency questionnaire and dietary changes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Heidelberg cohort. Br. J. Nutr., 2007, 98, 194–200. â•⁄ 6. Kelemen, L.╃E., GI Epidemiology: nutritional epidemiology. Aliment. Pharmacol. Ther., 2007, 25, 401–7. â•⁄ 7. Lampe, J.╃W., Interindividual differences in response to plant-based diets: implications for cancer risk. Am. J. Clin. Nutr.,. 2009, 89, 1553S–1557╃S.╃ â•⁄ 8. Reszka, E., Wasowicz, W., Gromadzinska, J., Genetic polymorphism of xenobiotic metabolising enzymes, diet and cancer susceptibility. Br. J. Nutr., 2006, 96, 609–619. â•⁄ 9. Akesson, B., Mercke, P., (Eds). Dietary vitamins, polyphenols, selenium and probiotics: biomarkers of exposure and mechanisms of anticarcinogenic action. ECNIS network of excellence, The Nofer Institute of Occupational Medicine, Lodz, Poland, 2007. 10. Akesson, B., Kyrtopoulos, S.╃A., Compounds in food. Eur. J. Nutr., 2008, 47, Suppl 2, 1–2. 11. Mercke, P., Overview of possible anticarcinogenic food components. In: Akesson, B., Mercke, P., (Eds.) Dietary vitamins, polyphenols, selenium and probiotics: biomarkers of exposure and mechanisms of anticarcinogenic action. ECNIS network of excellence, The Nofer Institute of Occupational Medicine, Lodz, Poland, 2007, pp 18–23. 12. van Breda, S.╃G., de Kok, T.╃M., and van Delft, J.╃H., Mechanisms of colorectal and lung cancer prevention by vegetables: A genomic approach J. Nutr. Biochem., 2008, 19, 139–157. 13. Amin, A.╃R., Kucuk, O., Khuri, F.╃R., Shin, D.╃M., Perspectives for cancer prevention with natural compounds. J. Clin. Oncol., 2009, 27, 2712–25. 14. Fearon, E.╃R., Vogelstein, B.╃A., A genetic model for colorectal tumorigenesis. Cell, 1990, 61, 759–767.

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

15. Arends, J.╃W., Molecular interactions in the Vogelstein model of colorectal carcinoma. J. Pathol., 2000,190, 412–416. 16. Chung, D.╃C., The genetic basis of colorectal cancer: insights into critical pathways of tumorigenesis. Gastroenterology, 2000, 119, 854–865. 17. Johnson, I.╃T., Phytochemicals and cancer. Proc. Nutr. Soc., 2007, 66, 207– 215. 18. De Kok, T.╃M., van Breda, S.╃G., Manson, M.╃M., Mechanisms of combined action of different chemopreventive dietary compounds: a review. Eur. J. Nutr., 2008, 47 Suppl 2: 51–59. 19. Jacobs, L.╃R., Fiber and colon cancer. Gastroenterol. Clin. North Am., 1988, 17, 747–760. 20. Lampe, J.╃W., Peterson, S., Brassica, biotransformation and cancer risk: genetic polymorphisms alter the preventive effects of cruciferous vegetables. J. Nutr., 2002, 132, 2991–2994. 21. van Poppel, G., Verhoeven, D.╃T., Verhagen, H., Goldbohm, R.╃A., Brassica vegetables and cancer prevention. Epidemiology and mechanisms. Adv. Exp. Med. Biol., 1999, 472, 159–168. 22. Smith, T.╃J., Yang, C.╃S., Effect of organosulfur compounds from garlic and cruciferous vegetables on drug metabolism enzymes. Drug Metabol. Drug Interact., 2000, 17, 23–49. 23. Nijveldt, R.╃J., van Nood, E., van Hoorn, D.╃E., Boelens, P.╃G., van Norren, K., van Leeuwen, P.╃A., Flavonoids: a review of probable mechanisms of action and potential applications. Am. J. Clin. Nutr., 2001, 74, 418–425. 24. Ross, J.╃A., Kasum, C.╃M., Dietary flavonoids: bioavailability, metabolic effects, and safety. Ann. Rev. Nutr., 2002, 22, 19–34. 25. Gamet-Payrastre, L., Li, P., Lumeau, S., Cassar, G., Dupont, M.╃A., Chevolleau, S., Gasc, N., Tulliez, J., Tercé, F., Sulforaphane, a naturally occurring isothiocyanate, induces cell cycle arrest and apoptosis in HT29 human colon cancer cells. Cancer Res.╃2000, 60, 1426–1433. 26. Deschner, E.╃E., Ruperto, J., Wong, G., Newmark, H.╃L., Quercetin and rutin as inhibitors of azoxymethanol-induced colonic neoplasia. Carcinogenesis, 1991, 12, 1193–1196. 27. Cover, C.╃M., Hsieh, S.╃J., Tran, S.╃H., Hallden, G., Kim, G.╃S., Bjeldanes, L.╃F., Firestone, G.╃L., Indole-3-carbinol inhibits the expression of cyclin-dependent kinase-6 and induces a G1 cell cycle arrest of human breast cancer cells independent of estrogen receptor signaling. J. Biol. Chem., 1998, 273, 3838–3847. 28. Wenzel, U., Kuntz, S., Brendel, M.╃D., Daniel H., Dietary flavone is a potent apoptosis inducer in human colon carcinoma cells. Cancer Res., 2000, 60, 3823–3831. 29. Bonnesen, C., Eggleston, I.╃M., Hayes, J.╃D., Dietary indoles and isothiocyanates that are generated from cruciferous vegetables can both stimulate apoptosis and confer protection against DNA damage in human colon cell lines. Cancer Res., 2001, 61, 6120–6130. 30. Ren, W., Qiao, Z., Wang, H., Zhu, L., Zhang, L., Flavonoids: promising anticancer agents. Med. Res. Rev., 2003, 23, 519–534.

249

250

Contributions

31. Honma, Y., Okabe-Kado, J., Kasukabe, T., Hozumi, M., Umezawa, K.., Inhibition of abl oncogene tyrosine kinase induces erythroid differentiation of human myelogenous leukemia K562 cells. Jpn. J. Cancer Res., 1990, 81, 1132–1136. 32. McBurney, M.╃W., Costa, S., Pratt, M.╃A., Retinoids and cancer: a basis for differentiation therapy. Cancer Invest., 1993, 11, 590–598. 33. Herzog, A., Kindermann, B., Doring, F., Daniel, H., Wenzel, U., Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mRNA expression profiling. Proteomics, 2004, 4, 2455–2464. 34. van Erk, M.╃J., Roepman, P., van der Lende, T.╃R., Stierum, R.╃H., Aarts, J.╃M., van Bladeren, P.╃J., van Ommen, B., Integrated assessment by multiple gene expression analysis of quercetin bioactivity on anticancer-related mechanisms in colon cancer cells in vitro. Eur. J. Nutr., 2005, 44, 143–156. 35. Breikers, G., van Breda, S.╃G., Bouwman, F.╃G., van Herwijnen, M.╃H., Renes, J., Mariman, E.╃C., Kleinjans, J.╃C., van Delft, J.╃H., Potential protein markers for nutritional health effects on colorectal cancer in the mouse as revealed by proteomics analysis. Proteomics, 2006, 6, 2844–2852. 36. van Breda, S.╃G., van Agen, E., van Sanden, S., Burzykowski, T., Kienhuis, A.╃S., Kleinjans, J.╃C., van Delft, J.╃H., Vegetables affect the expression of genes involved in anticarcinogenic processes in the colonic mucosa of C57BL/6 female mice. J. Nutr., 2005, 135, 1879–1888. 37. van Breda SG, van Agen E, Engels LG, Moonen EJ, Kleinjans JC, van Delft JH., Altered vegetable intake affects pivotal carcinogenesis pathways in colon mucosa from adenoma patients and controls. Carcinogenesis, 2004, 25, 2207–2216. 38. Wilms, L.╃C., Hollman, P.╃C., Boots, A.╃W., Kleinjans, J.╃C., Protection by quercetin and quercetin-rich fruit juice against induced oxidative DNA damage and formation of BPDE-DNA adducts in human lymphocytes. Mutation Res., 2005, 582, 155–162. 39. Wilms, L.╃C., Claughton, T.╃A., de Kok, T.╃M., Kleinjans, J.╃C.╃S., GSTM1 and GSTT1 polymorphism influences protection against induced oxidative DNA damage by quercetin and ascorbic acid in human lymphocytes in vitro. Food Chem. Tox., 2007, 45, 2592–2596. 40. Wilms, L.╃C., Boots, A.╃W., de Boer, V.╃C., Maas, L.╃M., Pachen, D.╃M., Gottschalk, R.╃W., Ketelslegers, H.╃B., Godschalk, R.╃W., Haenen, G.╃R., Schooten, F.╃J., Kleinjans, J.╃C., Impact of multiple genetic polymorphisms on effects of a 4-week blueberry juice intervention on ex vivo induced lymphocytic DNA damage in human volunteers. Carcinogenesis, 2007, 8, 1800–1806. 41. Noroozi, M., Angerson, W.╃J., Lean, M.╃E., Effects of flavonoids and vitamin C on oxidative DNA damage to human lymphocytes. Am. J. Clin. Nutr. 1998, 67, 1210–1218. 42. Duthie, S.╃J., Collins, A.╃R., Duthie, G.╃G., Dobson, V.╃L., Quercetin and myricetin protect against hydrogen peroxide-induced DNA damage (strand breaks and oxidised pyrimidines) in human lymphocytes. Mutat. Res., 1997, 393, 223–2331.

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

43. Moller, P., Loft, S., Interventions with antioxidants and nutrients in relation to oxidative DNA damage and repair. Mutat. Res., 2004, 551, 79–89. 44. Boots, A., Haenen, G.╃R., Bast, A., Health effects of quercetin: from antioxidant to nutraceutical. Eur. J. Pharmacol., 2008, 585, 325–337. 45. Valerio, L.╃G., Kepa, J.╃K., Pickwell, G.╃V., Quattrochi, L.╃C., Induction of human NAD(P)H:quinine oxidoreductase (NQO1) gene expression by the flavonol quercetin. Toxicol. Lett., 2001, 119, 49–57. 46. Kale, A., Gawande, S., Kotwal, S., Cancer phytotherapeutics: Role for flavonoids at the cellular level. Phytotherapy Res., 2008, 22, 567–577. 47. Lombardi, P.╃E., Mayhew, J.╃W., Goldin, B.╃R., Gregory, M.╃E., Lynch, M.╃A., Sullivan, C.╃E., Gorbach, S.╃L., Enzymatic methylation of microsomal metabolites of benzo[a]pyrene. Cancer Res., 1981, 41, 4415–4419. 48. Doyle, A E., Goodman, J.╃E., Silber, P.╃M., Yager, J.╃D., Catechol-o-methyltransferase low activity genotype (COMTLL) is associated with low levels of COMT protein in human hepatocytes. Cancer Lett., 2004, 214, 1890195. 49. Hodek, P., Krízková, J., Burdová, K., Sulc, M., Kizek, R., Hudecek, J., Stiborová, M., Chemopreventive compounds – view from the other side. Chem. Biol. Interact., 2009, 180, 1–9. 50. Bjelakovic, G., Nikolova, D., Gluud, L.╃L., Simonetti, R.╃G., Gluud, C., Mortality in randomized trials of antioxidant supplements for primary and secondary prevention: systematic review and meta-analysis. JAMA, 2007, 297, 842–857. 51. Galati, G., Sabzevari, O., Wilson, J.╃X., O’Brien, P.╃J., Prooxidant activity and cellular effects of the pheonxyl radicals of dietary flavonoids and other polyphenolics. Toxicology, 2002, 177, 91–104. 52. Metodiewa, D., Jaiswal, A.╃K., Cenas, N., Dickancaité, E., Segura-Aguilar, J., Quercetin may act as a cytotoxic prooxidant after its metabolic activation to semiquinone and quionoidal product. Free Radic. Biol. Med., 1999, 2, 107–116. 53. Kalyanaraman, B., Premovic, P.╃I., Sealy, R.╃C., Semiquinone anion radicals from addition of amino-acids, peptides, and proteins to quinones derived from oxidation of catechols and catecholamines, An ESR spin stabilization study. J. Biol. Chem., 1987, 262, 11080–11087. 54. Ito, S., Kato, T., Fujita, K., Covalent binding of catechols to proteins through the sulphydryl group. Biochem. Pharmacol., 1988, 37, 1707–1710. 55. Monks, T.╃J., Hanzlik, R.╃P., Cohen, G.╃M., Ross, D., Graham, D.╃G., Quinone chemistry and toxicity. Toxicol. Appl. Pharmacol., 1992, 112, 2–16. 56. Skibola, C.╃F., Smith, M.╃T., Potential health impacts of excessive flavonoid intake. Free Radic. Biol. Med., 2000, 29, 375–383. 57. Fernandez-Salguero, P.╃M., Hilbert, D.╃M., Rudikoff, S., Ward, J.╃M., Gonzalez, F.╃J., Aryl-hydrocarbon receptor-deficient mice are resistant to 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced toxicity. Toxicol. Appl. Pharmacol., 1996, 140, 173–179. 58. Safe, S., Molecular biology of the Ah receptor and its role in carcinogenesis. Toxicol. Lett., 2001, 120, 1–7.╃

251

252

Contributions

59. Mimura, J., Fujii-Kuriyama, Y., Functional role of AhR in the expression of toxic effects by TCDD. Biochim. Biophys. Acta, 2003, 1619, 263–268. 60. Steenland, K., Bertazzi, P., Baccarelli, A., Kogevinas, M., Dioxin revisited: developments since the 1997 IARC classification of dioxin as a human carcinogen. Environ. Health Perspect., 2004, 112, 1265–1268. 61. Zhang, S., Qin, C., Safe, S.╃H., Flavonoids as aryl hydrocarbon receptor agonists/ antagonists: effects of structure and cell context. Environ. Health Perspect., 2003, 111, 1877–1882. 62. Aggarwal, B.╃B., Ichikawa, H., Molecular targets and anticancer potential of indole-3-carbinol and its derivatives. Cell Cycle, 2005, 4, 1201–1215. 63. Chlouchi, A., Girard, C., Bonet, A., Viollon-Abadie, C., Heyd, B., Mantion, G., Martin, H., Richert, L., Effect of chrysin and natural coumarins on UGT1A1 and 1A6 activities in rat and human hepatocytes in primary culture. Planta Med., 2007, 73, 742–747. 64. Broadbent, T.╃A., Broadbent, H.╃S., The chemistry and pharmacology of indole-3-carbinol (indole-3-methanol) and 3-(methoxymethyl)indole. [Part I]. Curr. Med. Chem., 1998, 5, 337–352. 65. Broadbent, T.╃A., Broadbent, H.╃S., The chemistry and pharmacology of indole-3-carbinol (indole-3-methanol) and 3-(methoxymethyl)indole. [Part II]. Curr. Med. Chem., 1998, 5, 469–491. 66. de Waard, W.╃J., Aarts, J.╃M., Peijnenburg, A.╃C., Baykus, H., Talsma, E., Punt, A., de Kok, T.╃M., van Schooten, F.╃J. Hoogenboom, L.╃A., Gene expression profiling in Caco-2 human colon cells exposed to TCDD, benzo[a] pyrene, and natural Ah receptor agonists from cruciferous vegetables and citrus fruits. Toxicol. In Vitro, 2008, 22, 396–410. 67. Baumgart, A., Schmidt, M., Schmitz, H.╃J., Schrenk, D., Natural furocoumarins as inducers and inhibitors of cytochrome P450 1A1 in rat hepatocytes. Biochem. Pharmacol., 2005, 69, 657–667. 68. de Waard, W.╃F., Peijnenburg, A.╃C., Baykus H., Aarts, J.╃M., Hoogenboom, L. A, van Schooten, F.╃J., de Kok, T.╃M., A human intervention study with foods containing natural Ah-receptor agonists does not significantly show AhR-mediated effects measured in blood cells and urine. Chem. Biol. Interact., 2008, 176, 19–29. 69. de Waard, W.╃F., Aarts, J.╃M., Peijnenburg, A.╃C., de Kok, T.╃M., van Schooten, F.╃J. Hoogenboom, L.╃A., Ah receptor agonist activity in frequently consumed food items. Food Add. Contam., 2008, 25, 779–787. 70. Kall, M.╃A., Vang, O., Clausen, J., Effects of dietary broccoli on human in vivo drug metabolizing enzymes: evaluation of caffeine, oestrone and chlorzoxazone metabolism. Carcinogenesis, 1996, 17, 793–799. 71. Lampe, J.╃W., King, I.╃B., Li, S., Grate, M.╃T., Barale, K.╃V., Chen, C., Feng, Z., Potter, J.╃D., Brassica vegetables increase and apiaceous vegetables decrease cytochrome P450 1A2 activity in humans: changes in caffeine metabolite ratios in response to controlled vegetable diets. Carcinogenesis, 2000, 21, 1157–1162.

Anti-Oxidative and Antigenotoxic Properties of �Vegetables and Dietary Phytochemicals: The Value of Genomics Biomarkers in Molecular Epidemiology

72. Williams, C.╃M., Ordovas, J.╃M., Lairon, D., Hesketh, J., Lietz, G., Gibney, M., van Ommen, B., The challenges for molecular nutrition research 1: linking genotype to healthy nutrition. Genes Nutr., 2008, 3, 41–49. 73. Paules, R., Phenotypic anchoring: linking cause and effect. Environ. Health Persp. 2003, 111, A338–339.

253

254

Contributions

3.14 The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening Takeki Uehara1,2, Atsushi Ono2,3, Toshiyuki Maruyama4, Ikuo Kato4, Hiroshi Yamada2, Yasuo Ohno2,3, and Tetsuro Urushidani2,5 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 218–227.

Abstract Biotechnology advances have provided novel methods for the risk assessment of chemicals. The application of microarray technologies to toxicology, known as toxicogenomics, is becoming an accepted approach for identifying chemicals with potential safety problems. Gene expression profiling is expected to identify the mechanisms that underlie the potential toxicity of chemicals. This technology has also been applied to identify biomarkers of toxicity to predict potential hazardous chemicals. Ultimately, toxicogenomics is expected to aid in risk assessment. The following discussion explores potential applications and features of the Japanese toxicogenomics project.

3.14.1 Introduction Today, in the post-genomic era, there have been remarkable advances in the technology of drug development. Drug development in the previous century was usually based on screening the effects of chemicals in model animals with artificially created diseases; subsequently, it sometimes happened that an excellent drug was produced not for humans but for rats. In recent years, however, it has been possible to start the development process by targeting disease-related genes whose molecular functions are well elucidated, and indeed, human genes

1

Correspondence to: Takeki Uehara, D.╃V.╃M., Ph.D., Developmental Research Laboratories, Shionogi & Co., Ltd., 3–1-1 Futaba-cho, Toyonaka, JP-Osaka 561–0825, Japan, Tel: +╃81 6 6331 8241, Fax: +╃81 6 6332 6385, [email protected].

2

Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, 7–6-8 Asagi, Ibaraki, JP-Osaka 567–0085, Japan.

3

National Institute of Health Sciences, 1–18–1 Kamiyoga, Setagaya-ku, JP-Tokyo 158–8501, Japan.

4

1Developmental Research Laboratories, Shionogi & Co., Ltd., 3–1-1 Futaba-cho, Toyonaka, JP-Osaka 561–0825, Japan.

5

Department of Pathophysiology, Faculty of Pharmaceutical Sciences, Doshisha Women’s Â�College of Liberal Arts, Kodo, Kyotanabe, JP-Kyoto 610–0395, Japan.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

are always available. Therefore, it is now easy to select a chemical that is effective on the human molecule on at least the in vitro level. Even with this advantage, many candidate drugs have been eliminated because of toxicity that could not be found in pre-clinical tests in the early stage of drug development; rather, the toxicity became apparent at the late stage of drug development, such as during long-term toxicity studies for animal models and after several stages of clinical trials [1]. In extreme cases, serious adverse effects emerge even after the drugs are widely distributed on the world market. A top priority should be the solution of this paradox; i.╃e., how to predict “unpredictable” toxicity. The response of the organism to the toxicant that subsequently causes pathological changes in certain organs with a low dose should be detectable as changes in the expression of genes, protein synthesis, and metabolism. Of these changes, the expression of genes, or the amount of mRNA, is the most sensitive measure and one of the largest advantages in the technology of genomics. Therefore, toxicogenomics, which enables us to comprehensively analyze gene expression changes caused by an external stimulus in a specific organ, is considered to be one of the most powerful strategies. In particular, the identification of predictive biomarkers for drug-induced toxicity at or before the pre-clinical stages of drug development is of great importance to pharmaceutical companies.

3.14.2 Current Status of Worldwide Toxicogenomics Database �Creation To appropriately interpret the microarray data, it is desirable to perform comparative analyses with data obtained from prototypical toxicants. Moreover, toxicogenomics studies are built on standard toxicology studies, and one goal of toxicogenomics is to detect relationships between changes in gene expression and toxicological end point data, such as histopathology, clinical chemistry, and other toxicity data. Therefore, a large-scale, high quality, and well designed toxicogenomics database of gene expression information and standard toxicological data is essential. Several public toxicogenomic database efforts have been initiated, such as Gene Expression Omnibus [2, 3] (GEO; National Center for Biotechnology Information, National Institutes of Health; www.ncbi.nlm. nih.gov/geo), ArrayExpress [4, 5] (European Bioinformatics Institute; www. ebi.ac.uk/microarray-as/ae/), Center for Information Biology Gene Expression [6] (CIBEX; DNA Data Bank of Japan, National Institute of Genetics; http:// cibex.nig.ac.jp/), EDGE (McArdle Laboratory for Cancer Research [7], University of Wisconsin-Madison; http://edge.oncology.wisc.edu/edge3.php), Chemical Effects in Biological Systems [8] (CEBS; National Institute of Environmental Health Sciences; http://cebs.niehs.nih.gov/cebs-browser/), dbZach [9] (Department of Biochemistry and Molecular Biology, Michigan State University; http:// dbzach.fst.msu.edu), and Comparative Toxicogenomics Database [10, 11] (CTD; Mount Desert Island Biological Laboratory; http://ctd.mdibl.org). In addition to these public microarray databases, public consortia provide a forum to address questions requiring more resources than one organization

255

256

Contributions

alone could provide and to engage many sectors of the scientific community. InnoMed PredTox [12] is a joint industry and European Commission collaboration to improve drug safety. The consortium is a collaborative project of 15 research groups from 12 pharmaceutical companies, three academic institutions and two technology providers. The goal of this consortium is to assess the value of combining results from “OMICS” technologies (transcriptomics, proteomics, metabolomics) with results from more conventional toxicology methods for more informed decision making in pre-clinical safety evaluation. Genedata (http://www.genedata.com/), a company that offers expertise in research informatics combined with open and scalable computational solutions, has provided the computational infrastructure for InnoMed PredTox, in particular the software for data management and analysis. The Liver Toxicity Biomarker Study (LTBS) [13] is a collaborative pre-clinical research effort in molecular systems toxicology between the National Center for Toxicological Research and BG Medicine, Inc., and it is supported by seven pharmaceutical companies and three technology providers. The LTBS is an innovative approach to investigate drug-induced liver injury because it compares molecular events produced in vivo by compound pairs that (a) are similar in structure and mechanism of action, (b) are associated with few or no signs of liver toxicity in pre-clinical studies, and (c) show marked differences in hepatotoxic potential. In Japan, the Toxicogenomics Project (TGP) has established a large-scale toxicogenomics database known as TG-GATEs [Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan]. Several genomic candidate biomarkers to predict the toxicity of chemicals have been successfully identified by using our database. The work and results reviewed here focus on our efforts in toxicogenomics research and highlight recent progress in the application of toxicogenomics.

3.14.3 The Toxicogenomics Project in Japan 3.14.3.1 Features of the Project

The Ministry of Health, Labour and Welfare, National Institute of Health Sciences (NIHS), and the working group of Japan Pharmaceutical Manufacturers Association planned the Toxicogenomics Project (TGP), a collaborative project of the government and private companies (Urushidani and Nagao, 2004). The TGP was a 5-year project (2002 to 2007) performed by NIHS, 15 pharmaceutical companies (Astellas, Chugai, Daiichi, Dainippon-Sumitomo, Eisai, Kissei, Mitsubishi, Mochida, Ono, Otsuka, Sankyo, Sanwa, Shionogi, Takeda, Tanabe) and the National Institute Biomedical Innovation (NIBIO), which was the core institute where the actual work was performed. Half of the budget was provided by a grant from the Ministry of Health, Labour and Welfare, and the other half was provided by the pharmaceutical companies.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening Table 1: List of chemicals selected in TGP. Acetaminophen

Doxorubicin Hydrochloride

Nitrofurantoin

Acetazolamide

D-Penicillamine

Nitrofurazone

Ajmaline

Enalapril Maleate

N-Nitrosodiethylamine

Allopurinol

Erythromycin Ethylsuccinate

N-Phenylanthranilic Acid

Allyl Alcohol

Ethambutol Dihydrochloride

Omeprazole

alpha-Naphthyl Isothiocyanate

Ethanol

Papaverine Hydrochloride

Amiodarone Hydrochloride

Ethionamide

Pemoline

Amitriptyline Hydrochloride

Etoposide

Perhexiline Maleate

Aspirin

Famotidine

Phenacetin

Azathioprine

Fenofibrate

Phenobarbital Sodium

Bendazac

Fluphenazine Dihydrochloride

Phenylbutazone

Benzbromarone

Flutamide

Phenytoin

Benziodarone

Furosemide

Promethazine Hydrochloride

Bromobenzene

Gemfibrozil

Propylthiouracil

Bucetin

Gentamicin Sulfate

Puromycin Aminonucleoside

Caffeine

Glibenclamide

Quinidine Sulfate

Captopril

Griseofulvin

Ranitidine Hydrochloride

Carbamazepine

Haloperidol

Rifampicin

Carbon Tetrachloride

Hexachlorobenzene

Simvastatin

Carboplatin

Hydroxyzine Dihydrochloride

Sodium Valproate

Cephalothin Sodium

Ibuprofen

Sulfasalazine

Chloramphenicol

Imipramine Hydrochloride

Sulindac

Chlormadinone Acetate

Indomethacin

Tacrine Hydrochloride

Chlormezanone

Iproniazid Phosphate

Tamoxifen Citrate

Chlorpromazine Hydrochloride

Isoniazid

Tannic Acid

Chlorpropamide

Ketoconazole

Terbinafine Hydrochloride

Cimetidine

Labetalol Hydrochloride

Tetracycline Hydrochloride

Ciprofloxacin Hydrochloride

Lomustine

Theophylline

Cisplatin

Lornoxicam

Thioacetamide

Clofibrate

Mefenamic Acid

Thioridazine Hydrochloride

Clomipramine Hydrochloride

Meloxicam

Ticlopidine Hydrochloride

Colchicine

Metformin Hydrochloride

Tiopronin

Coumarin

Methapyrilene Hydrochloride

Tolbutamide

257

258

Contributions

Cyclophosphamide Mono� hydrate

Methimazole

Triamterene

Cyclosporine A

Methotrexate

Triazolam

Danazol

Methyldopa

Trimethadione

Dantrolene Sodium � Hemiheptahydrate

Methyltestosterone

Vancomycin Hydrochloride

Diazepam

Mexiletine Hydrochloride

Vitamin A

Diclofenac Sodium

Monocrotaline

WY-14,643

Diltiazem Hydrochloride

Moxisylyte Hydrochloride

(±)-Chlorpheniramine Maleate

Disopyramide

Naproxen

(±)-Sulpiride

Disulfiram

Nicotinic Acid

17-alpha-Ethinyl Estradiol

DL-Ethionine

Nifedipine

2-Acetamidofluorene

Doxepin Hydrochloride

Nimesulide

2-Bromoethylamine Hydrobromide

132 chemicals were selected from in total 150 compounds. Drug candidates supplied from the member companies, which were withdrawn in various stages of drug development, were �excluded.

The primary goal of the TGP was to create a gene expression database by using the Affymetrix GeneChip® of 150 chemicals, mainly medical drugs (Tab.╃1), and the main target organ was the liver. Most clinically serious adverse effects occur in the liver, and the cell-type composition of the liver is relatively homogenous; thus, the expected variation based on sampling differences would be minimal. For these reasons, the liver was selected as the target organ to accumulate know-how regarding the toxicogenomics technique. Nephrotoxicity was also considered to be important; therefore, the kidney, in addition to the liver, was sampled for microarray analysis and pathologically examined in all animals. The TGP was completed in 2007.╃The entire system consists of a database, an analysis system, and a prediction system and is named TG-GATEs. The database will be available to the public in the near future.

3.14.3.2 Contents of the Database

Our standard study protocol is summarized in Table 2. In Vivo Study: The rat was selected as the species for analysis. Rats are very frequently used in non-clinical examinations, and toxicological information for the rat has been accumulated. Both, a single-dose study, consisting of multiple time points with multiple dose levels, and a repeated-dose study, consisting of multiple dose periods with multiple dose levels, were performed. Data obtained from each ani-

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening Table 2: The standard study protocol in TGP. In vivo Animal Vehicle Dose Route Sacrifice Sampling Microarray analysis Items examined

Sprague-Dawley rat (6 weeks old, N=╃5 for each group) 0.5╛% methylcellulose or corn oil (oral dose) Saline or 5╛% glucose solution (intravenous dose) Low, middle, high (mainly 1:3:10) Oral (intravenous in a few cases) 3, 6, 9 and 24╛h after a single administration 24╛h after the last dose of repeated administration for 3, 7, 14 and 28€days Liver, kidney, plasma Affymetrix GeneChip (N=╃3 for each group) Histopathology: Liver and kidney Body weight, organ weight (liver and kidney), food consumption, hematology, blood biochemistry

In vitro: rat Animal Cell Vehicle Concentration Treatment Microarray analysis Items examined

Sprague-Dawley rat (6 weeks old) Hepatocyte isolated by collagenase digestion Culture medium or DMSO Low, middle, high (1:5:25) 2, 8, 24â•›h Duplicate Cell viability (LDH release and DNA contents)

In vitro: human Cell Vehicle Concentration Treatment Microarray analysis Items examined

Human frozen hepatocytes Culture medium or DMSO Low, middle, high (1:5:25, low is omitted in some cases) 2, 8, 24â•›h (2â•›h is omitted in some cases) Duplicate Cell viability (LDH release and DNA contents)

mal included body weight, general symptoms, haematology, blood biochemistry, organ weight, and a histopathological examination of the liver and kidney. Gene expression in the liver and kidney was comprehensively analyzed by using Affymetrix GeneChip® arrays. In Vitro Study: A modified two-step collagenase perfusion method was used to isolate liver cells from 6-week-old male Sprague-Dawley rats. A comprehensive gene expression analysis was performed on the primary cultured cells at multiple time points after treatment with various concentrations of each of the 150 compounds. The same gene expression analysis was also performed with human liver cultured cells obtained from Tissue Transformation Technologies, Inc.╃

3.14.3.3 Analysis and Prediction Systems

Analysis System: Since microarray data consist of large amounts of numerical data, statistical knowledge and computational skills are required to interpret the results. Multivariate analysis methods are utilized for both, data mining and pattern recogni-

259

260

Contributions

tion, such as hierarchical clustering, K-means clustering, self-organizing map (SOM) and principal component analysis (PCA). PCA is a convenient tool for the qualitative classification of compounds against a list of genes. As a prediction system, however, some quantitative data would be favourable for the final output. Therefore, in our system, when the user specifies a principal component with high contribution, the compounds are sorted by value, and the genes with large eigenvector values are easily obtained. This analysis provides the relative position of the test drug among the drugs in the database and supports to generate a candidate gene list for further investigation. Prediction System: Discriminant analysis is a powerful technique that can be used when a phenotype that can be judged as positive or negative is available [14]. Prediction analysis of microarray (PAM) [15] and support vector machine (SVM) [16] have been employed in our systems. The sample size and appropriate selection of the training dataset are crucial for establishing reliable classifiers. In our system, by a semi-automatic system of training and validation, the efficiency improves for the creation of classifiers. BaseView System: When an assessment or prediction of toxicity is made by a list of multiple measures, it is necessary to summarize or quantify these measurements. Ideally, the quantification process should be optimized for each marker gene list. However, because this approach is practically difficult, a uniform system has to be created. In our system, a new scoring system was developed in one trial. The TGP1 score is calculated based on the ratio to control value (log 2) for each gene in the marker list and expressed as a heat-map [17, 18]. This scoring system makes it easy to summarize the assessments of a target compound against many marker lists and to summarize the assessments of many compounds against a particular marker list. However, this system has some problems; the score is biased when the list contains a gene whose expression change is extremely large (e.╃g., CYP1A1), and changes are cancelled when up- and down-regulated genes coexists in the list. Therefore, another scoring system, the TGP2 score, is available in our system. The TGP2 score is based on the effect size and calculated as the absolute value of the difference between means divided by the covariance [19].

3.14.4 Application of Toxicogenomics Our strategy is to prepare a large set of genomic biomarkers that are related to toxicological phenotypes, pathways, or any other biologically meaningful factor. Until now, several potential genomic biomarkers to predict the toxicity of chemicals have been successfully identified. In this chapter, we provide several applications of toxicogenomics by using our database.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

3.14.4.1 Glutathione Depletion [20]

The hepatotoxicity of acetaminophen is caused by the excessive production of active metabolite that exceeds the detoxification capacity of intra-cellular glutathione [21]. Therefore, drugs that have the potential to deplete hepatocyte glutathione carry the risk of causing acetaminophen-type hepatotoxicity with overdosage. In a previous report, a list of marker genes for glutathione depletion was extracted by using BSO, a glutathione biosynthesis inhibitor [22]. However, phorone is considered to be superior to BSO as a model system, since its mechanism of glutathione depletion is similar to that of acetaminophen-type hepatotoxicants (i.╃e., it covalently binds to glutathione and is excreted from the cell). Phorone (40, 120, or 400 mg/kg) was administered according to the same protocol as the regular single dose experiments, and the glutathione content was measured. Phorone caused a marked but transient depletion of glutathione with maximal depletion occurring at 3╛h. Then, the glutathione level recovered, and it was increased at 24╛h as a rebound effect. A total of 161 probe sets was identified with signal levels that were inversely correlated with the hepatic glutathione content (Fig.╃1). PCA of the chemicals in the database with these probe sets revealed that chemicals with a risk of glutathione depletion, such as bromobenzene and coumarin, in addition to acetaminophen, were clearly separated from other chemicals or controls toward the direction of principal component 1, suggesting that the list was useful as a genomic biomarker for risk assessment of glutathione depletion.

Figure 1: Identification and application of genomic biomarkers for assessing glutathione depletion. A model case for identifying the candidate genomic biomarker associated with glutathione depletion-type liver injury is presented. Rats were treated with a glutathione depletor, phorone, and microarray analysis was performed on the liver tissue. (A) A total of 161 probe sets had signal levels that were inversely correlated with the hepatic glutathione content. (B) The validity of these probe sets as biomarkers for the evaluation of glutathione depletion risk was evaluated by PCA. This evaluation revealed that chemicals with a risk of glutathione depletion, such as bromobenzene and coumarin, in addition to acetaminophen, were clearly separated from other chemicals or controls toward the direction of PC1.

261

262

Contributions

3.14.4.2 Phospholipidosis [23]

In toxicity studies, phospholipidosis is often observed in various tissues including the liver. Despite efforts to establish methods to predict the phospholipidosis of drugs, sensitive diagnostic markers and effective prognostic markers were still desired. To identify a genomic biomarker for the prediction of hepatic phospholipidosis, we extracted 78 probe sets of rat hepatic genes based on data from five drugs (amiodarone, amitriptyline, clomipramine, imipramine, and ketoconazole) that induce this phenotype. A principal component analysis was performed, and the possible induction of phospholipidosis was predictable by the expression of these genes 24â•›h after a single administration.

3.14.4.3 Cholestasis [24]

Cholestatic hepatitis is the most common type of drug-induced cholestasis and is more frequent than cholestatic viral hepatitis. Cholestasis is caused by a functional defect in bile formation at the level of the hepatocyte or by impairment in bile secretion and flow at the level of the bile ductules or ducts. To identify a biomarker for the diagnosis of elevated total and direct bilirubin, we extracted 59 probe sets of rat hepatic genes based on data from seven drugs (gemfibrozil, phalloidin, colchicine, bendazac, rifampicin, cyclosporine A and chlorpromazine) that induce cholestatic hepatitis after 3 to 28 days of repeated administration. PCA with these probe sets clearly separated dose- and time-dependent clusters in the treated groups from the control groups. Although further work is required to improve and generalize the candidate for a marker suggested in this study, these identified probe sets should be useful to diagnose the cause of elevated total and direct bilirubin.

3.14.4.4 Non-Genotoxic Hepatocarcinogenicity [25]

Assessing carcinogenicity in animals is difficult and costly; therefore, an alternative strategy is desired. Genotoxic compounds are usually identified and removed early from compound pipelines. However, the discovery of unexpected, presumed non-genotoxic, carcinogenicity late in drug development may prevent potentially good medicines from reaching patients for years while the human risk is qualified. Microarrays and expression profiling have been used to make classifiers for the early prediction of non-genotoxic carcinogenicity in the liver [26–29]. The goal of these studies was to extract common gene sets coordinately deregulated by different classes of non-genotoxic hepatocarcinogenesis. These publications confirm that multiple genes are required for accurate classification due to the multiple mechanisms of action that must be included in the prediction model. Therefore, the effects of chemicals with similar mechanisms are likely to be reflected in similar gene expression profiles in the early stage of non-genotoxic carcinogenesis [28]. Arguably more important than the

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

identification of potential carcinogenicity of a compound is the identification of the mechanism of action [30]. Our strategy was to focus on one important mechanism, cytotoxic oxidative stress, responsible for non-genotoxic hepatocarcinogenesis. We selected thioacetamide and methapyrilene as prototypic oxidative stressmediated, non-genotoxic hepatocarcinogens and performed PAM discriminant analysis. A PAM classifier containing 112 probe sets that yielded an overall success rate of 95╛% was successfully obtained from the training procedure. Based on gene ontology, the content of genes related to cellular metabolism, including anti-oxidative metabolism, cell proliferation, cell cycle, and response to DNA damage stimulus, was significantly high. The validity of this classifier was checked for 30 chemicals. The classification results showed characteristic time-dependent increases by treatment with several non-genotoxic hepatocarcinogens, including thioacetamide, methapyrilene, coumarin, and ethionine (Fig.╃2). Although all of the carbon tetrachloride-treated groups were predicted as negative, the score tended to increase with repeated dosing. On the other hand, the enzyme inducers with carcinogenic activity, phenobarbital and

Figure 2: Time-course changes in prediction results for non-genotoxic hepatocarcinogenicity of chemicals. A model case for identifying the candidate genomic biomarkers associated with nongenotoxic hepatocarcinogenicity is presented. The PAM class probability was converted to a score to enable quantitative comparison. The PAM score showed characteristic time-course changes for several non-genotoxic hepatocarcinogens. For methapyrilene, thioacetamide and other carcinogens, such as the ethionine and coumarin, the scores transiently increased at an early time point after a single dosing. In the case of repeated dosing, the scores increased with the repeated doses. The following samples were classified as positive: methapyrilene (MP) 100 mg/kg (high dose, H); thioacetamide (TAA) 15 mg/kg (middle dose, M) and 45 mg/kg (H); coumarin (CMA) 150 mg/ kg; ethionine (ET) 250 mg/kg; Wy-14,643 (WY) 100 mg/kg; and bromobenzene (BBZ) 300 mg/kg. Each box indicates the PAM score. Black boxes indicate samples that are predicted as negative.

263

264

Contributions

hexachlorobenzene, and peroxisome proliferators other than Wy-14,643 (i.╃e., clofibrate and gemfibrozil) had negative scores for all time points. Of the noncarcinogenic samples, bromobenzene had a transient score increase at 24â•›h but returned to negative during repeated dosing. Almost all of the non-carcinogens were correctly predicted as negative, but it was not possible to completely eliminate false positives. This work suggested the possibility of lowering the days of repeated administration to less than 28, at least for a category of non-genotoxic hepatocarcinogens causing oxidative stress. The carcinogenicity working group of the C-Path Predictive Safety Testing Consortium (PSTC) has selected genes from published toxicogenomics research that were determined to be of high predictive value in the early recognition of non-genotoxic hepatocarcinogenicity. The group consolidated this list for refinement and qualification as a gene signature to predict a compound’s potential to be a non-genotoxic hepatocarcinogen. To ensure the independence and cost effectiveness of the platform, mRNA for these genes was assayed by real-time quantitative PCR, and a final signature was rederived from genes with confirmed expression. The robustness and potential utility of this new quantitative PCR-based signature will be discussed in future reports.

3.14.4.5 Bridging between In Vivo and In Vitro: PPARα-Mediated Response [31]

Data from three ligands of peroxisome proliferator-activated receptor alpha (PPARα) – i.╃e., clofibrate, WY-14,643 and gemfibrozil – in our database were analyzed. Many of the β-oxidation-related genes were commonly induced in vivo and in vitro, whereas expression changes in genes related to cell proliferation and apoptosis were detected in vivo but not in vitro (Fig.╃3). By using the genes commonly up-regulated both in vivo and in vitro, PCA was performed for 32 compounds, and principal component 1 was identified as a convenient pa-

Figure 3: An in vivo–in vitro bridge for genomic biomarkers to assess PPARα agonistic action. A model case for creating an in vivo–in vitro bridge for genomic biomarkers is presented. The data from three agonists of peroxisome proliferator activated receptor alpha (PPARα) in our database (clofibrate, WY-14,643 and gemfibrozil) were analyzed, and 41 commonly up-regulated probe sets between in vivo and in vitro were extracted. The validity of these probe sets as biomarkers for the evaluation of PPARα agonistic activity was evaluated by PCA. These plots show the principal separation of samples due to putative PPARα agonistic activity toward the negative direction on the x-axis, PC1.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

rameter to extract PPARα agonists from the database (Fig.╃3). This study is one of the first to create an in vivo–in vitro bridge for the validation of a genomic biomarker.

3.14.4.6 Bridging between the Rat and Human: Coumarin-Induced Hepato� toxicity19

A system that perfectly predicts hepatotoxicity in the rat would not necessarily improve the prediction of hepatotoxicity in humans. The final goal must be the prediction of hepatotoxicity in humans for drug development. The extrapolation of toxicity data from rodent to human is not sufficient. However, if general toxic mechanisms or toxicological pathways are conserved over species, they would be useful bridges between animal models and clinical events. One expected result from toxicogenomics technology is to overcome the barrier due to species difference in the prediction of clinical toxicity. We investigated the possibility of an informational bridge connecting transcript responses between rat and human hepatocytes and rat liver in vivo after the administration of coumarin. In this experiment, primary cultured rat hepatocytes were exposed to 12, 60, and 300╃µM coumarin for 24â•›h. No obvious cytotoxicity was detected by LDH release (100.5â•›%, 97.7â•›%, and 95.1â•›% of control, respectively). Then, we extracted the significant genes according to the gene

Figure 4: Heat-map of the expression profiles of probe sets in rat liver and rat hepatocytes treated with coumarin. A considerable number of the in vivo-selected probe sets show similar profiles between in vivo and in vitro assays. The selected genes, namely the in vivo–in vitro bridging probes, had clear dose-dependent changes in expression.

265

266

Contributions

list obtained from the in vivo study; the extracted genes showed significant upregulation (136 probe sets) or down-regulation (79 probe sets) in livers treated with 150 mg/kg coumarin. A similar trend was observed between in vivo and in vitro cell responses, although the extent of the response (the fold-change) was generally smaller, and fewer genes showed a measurable change in the in vitro cell assay (Fig.╃4). Probe sets showing changes of 1.5-fold or more or 0.6-fold or less than that of the control at the highest concentration (300╃µM) in rat hepatocytes were selected as in vivo–in vitro bridging probes that reflect the toxicological mechanism of coumarin in vivo. The selected genes (37 up-regulated and 29 down-regulated) had clear dose-dependent changes in expression that enabled us to assess the hepatotoxicity of coumarin by using the in vitro data (Fig.╃4). Next, cultured human hepatocytes were exposed to 12, 60, and 300╃µM coumarin for 24â•›h. No obvious cytotoxicity was detected by LDH release (100.6â•›%, 100.9â•›%, and 102.0â•›% of control, respectively). The in vivo–in vitro bridging probes were assigned to their human ortholog genes to form a set of rat–human bridging probes, and changes in their expression were compared in rat versus human hepatocytes. In total, 14 up-regulated probe sets and 11 down-regulated probe sets were identified; their relative expression levels are shown in Figure 5.╃The pattern of changes in gene expression was similar in rat and human cells,

Figure 5: Heat-map of the expression profile of probe sets in rat and human hepatocytes treated with coumarin. Among the in vivo–in vitro bridging probes for rats, 14 up-regulated and 11 downregulated probe sets were assigned to human ortholog (species bridging marker), and their expression is shown as a heat-map of the expression profile in rat and human hepatocytes treated with coumarin (12, 60 and 300╃µM). Each probe set dose-dependently responded to coumarin in both species, whereas the extent of the changes appears to be more prominent in rats than in humans.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

but the extent of the changes was more prominent in rat cells than in human cells, in accordance with the known species-specific difference in hepatotoxicity [32–38]. In the case of diclofenac, which is a hepatotoxicant without species difference, there was no evidence of a species-specific difference in gene expression between rat and human cells. The observation that the induction of stress-related genes was more robust in rat cells than in human cells could be a direct reflection of the extent of stress and subsequent damage caused by coumarin in each species. Although more data are needed to connect species and model systems with human risk assessment, this approach is an important step in bridging the differences between species.

3.14.5 Future Perspectives This review focuses on our efforts in toxicogenomics research and highlights recent progress in the application of toxicogenomics. In the early stage of drug development, genomic biomarkers are used to identify and optimize lead compounds among several candidates. As full-scale toxicity testing is quite costly, safety assessment of candidate drugs is usually performed just before the clinical trial. If serious toxicity emerges at this stage, it might be necessary to return to the screening of seed compounds, because toxicity is often inherent to the basic structure and is thus never eliminated by minor modification. If the potential phenotype (when repeatedly dosed) is predictable in the early stage of drug development by gene expression data from a small number of experimental animals, it would effectively cut the time and cost of drug development. The use of genomic biomarkers in the early stage of drug development will strengthen the safety screening of drug candidates before they are administered to humans. The use of genomic biomarkers will also reduce the number of animals sacrificed during drug development. However, the candidate biomarkers reviewed here have not necessarily been evaluated with large independent test sets and are rarely validated across laboratories. Further definitive validation studies are absolutely essential for judging the acceptability of candidate genomic biomarkers in pre-clinical safety assessments. Furthermore, regulatory agencies, the pharmaceutical industry and academia must establish guidelines for the integration of “OMICS” data, including toxicogenomics and genomic biomarkers, into drug safety assessment. We are currently in the project’s second stage, known as the Toxicogenomics Informatics Project (TGP2). Our goals are as follows: (1) Establishment of genomic biomarkers to predict the toxicity of drug candidates in the early stage of drug development, (2) bridging of species differences, and (3) application of toxicogenomic data for regulatory science. These efforts will contribute to the accelerated development of more effective and safer drugs. The Predictive Safety Testing Consortium (PSTC) also represents a next important step in the validation and regulatory use of new pre-clinical biomarker tests with the initiative of the C-Path Institute. The novel biomarkers internally developed and used by each individual pharmaceutical company and consortium are of limited value for regulatory use because the methods used have not

267

268

Contributions

been validated by an independent party. To resolve these issues, there is a growing need for a large and cross-institutional study on a global scale. The PSTC is a public–private partnership, led by the C-Path Institute, which brings together pharmaceutical companies to share and validate each other’s safety testing methods under advisement of the Food and Drug Administration (FDA) and its European counterpart, the European Medicines Evaluation Agency (EMEA). The aim of the PSTC is to identify and qualify safety biomarkers for regulatory use as part of the FDA’s Critical Path Initiative. The 17 corporate members of the consortium share internally developed pre-clinical safety biomarkers in five workgroups: Carcinogenicity, kidney, liver, muscle and vascular injury. Consortium members are sharing their new pre-clinical biomarker tests for examination and cross-validation by other members of the consortium. Candidate genomic biomarkers reviewed here will need a similar validation process through collaborative research like that of PSTC. These processes are expected to enable the regulatory agencies to write new guidelines for industry that identify more accurate methods to predict drug safety.

Acknowledgements These studies were supported by a grant from the Ministry of Health, Labour and Welfare of Japan (H14-Toxico-001 and H19-Toxico-001).

Conflict of Interest Statement The authors have declared no conflict of interest.

References â•⁄ 1. Ismail, K., Landis, J., Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 2003, 3, 711–715. â•⁄ 2. Edgar, R., Domrachev, M., Lash, A.╃E., Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res.╃2002, 30, 207–210. â•⁄ 3. Barrett, T., Troup, D.╃B., Wilhite, S.╃E., Ledoux, P., et al., NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res.╃2009, 37, D885–890. â•⁄ 4. Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., et al., ArrayExpress – a public repository for microarray gene expression data at the EBI. Nucleic Acids Res.╃2003, 31, 68–71. â•⁄ 5. Parkinson, H., Kapushesky, M., Kolesnikov, N., Rustici, G., et al., ArrayExpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res.╃2009, 37, D868–872.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

â•⁄ 6. Ikeo, K., Ishi-i, J., Tamura, T., Gojobori, T., et al., CIBEX: center for information biology gene expression database. C.╃R. Biol. 2003, 326, 1079–1082. â•⁄ 7. Hayes, K.╃R., Vollrath, A.╃L., Zastrow, G.╃M., McMillan, B.╃J., et al., EDGE: a centralized resource for the comparison, analysis, and distribution of toxicogenomic information. Mol. Pharmacol. 2005, 67, 1360–1368. â•⁄ 8. Waters, M., Stasiewicz, S., Merrick, B.╃A., Tomer, K., et al. CEBS – Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data. Nucleic Acids Res.╃2008, 36, D892–900. â•⁄ 9. Burgoon, L.╃D., Zacharewski, T.╃R., dbZach toxicogenomic information management system. Pharmacogenomics. 2007, 8, 287−291. 10. Mattingly, C.╃J., Colby, G.╃T., Forrest, J.╃N., Boyer, J.╃L. The Comparative Toxicogenomics Database (CTD). Environ. Health Perspect. 2003, 111, 793−795. 11. Mattingly, C.╃J., Rosenstein, M.╃C., Colby, G.╃T., Forrest, J.╃N. Jr., et al. The Comparative Toxicogenomics Database (CTD): a resource for comparative toxicological studies. J. Exp. Zoolog. A Comp. Exp. Biol. 2006, 305, 689−692. 12. Mulrane, L., Rexhepaj, E., Smart, V., Callanan, J.╃J., et al. Creation of a digital slide and tissue microarray resource from a multi-institutional predictive toxicology study in the rat: an initial report from the PredTox group. Exp. Toxicol. Pathol. 2008, 60, 235−245. 13. McBurney, R.╃N., Hines, W.╃M., Von Tungeln, L.╃S., Schnackenberg, L.╃K., et al. The liver toxicity biomarker study: phase I design and preliminary results. Toxicol. Pathol. 2009, 37, 52−64. 14. Porter, M.╃W., Castle, A.╃L., Orr, M.╃S., Mendrick, D.╃L., in: Burczynski, M.╃E. (Ed), Predictive Toxicogenomics, An Introduction to Toxicogenomics, CRC Press, Boca Raton 2003, pp.╃225−260. 15. Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G., Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA.╃2002, 99, 6567−6572. 16. Brown, M.╃P., Grundy, W.╃N., Lin, D., Cristianini, N., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines.　 Proc. Natl. Acad. Sci. USA.╃2000, 97, 262−267. 17. Kiyosawa, N., Shiwaku, K., Hirode, M., Omura, K., et al., Utilization of a one-dimensional score for surveying chemical-induced changes in expression levels of multiple biomarker gene sets using a large-scale toxicogenomics database. J. Toxicol. Sci.╃2006, 31, 433–448. 18. Uehara, T., Kiyosawa, N., Hirode, M., Omura, K., et al., Gene expression profiling of methapyrilene-induced hepatotoxicity in rat. J. Toxicol. Sci.╃2008, 33, 37–50. 19. Uehara, T., Kiyosawa, N., Shimizu, T., Omura, K., et al., Species-specific differences in coumarin-induced hepatotoxicity as an example toxicogenomics-based approach to assessing risk of toxicity to humans. Hum. Exp. Toxicol. 2008, 27, 23–35. 20. Kiyosawa, N., Uehara, T., Gao, W., Omura, K., et al., Identification of glutathione depletion-responsive genes using phorone-treated rat liver. J. Toxicol. Sci.╃2007, 32, 469–486.

269

270

Contributions

21. James, L.╃P., Mayeux, P.╃R., Hinson, J.╃A. Acetaminophen-induced hepatotoxicity. Drug Metab. Dispos. 2003, 31, 1499−1506. 22. Kiyosawa, N., Ito, K., Sakuma, K., Niino, N., et al., Evaluation of glutathione deficiency in rat livers by microarray analysis. Biochem. Pharmacol. 2004, 68, 1465−1475. 23. Hirode, M., Ono, A., Miyagishima, T., Nagao, T., et al., Gene expression profiling in rat liver treated with compounds inducing phospholipidosis. Toxicol. Appl. Pharmacol. 2008, 229, 290–299. 24. Hirode, M., Horinouchi, A., Uehara, T., Ono, A. et al., Gene expression profiling in rat liver treated with compounds inducing elevation of bilirubin. Hum. Exp. Toxicol. in press. 25. Uehara, T., Hirode, M., Ono, A., Kiyosawa, N., et al., A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats. Toxicology 2008, 250, 15–26. 26. Ellinger-Ziegelbauer, H., Gmuender, H., Bandenburg, A., Ahr, H.╃J., Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat. Res., 2008, 637, 23–39. 27. Ellinger-Ziegelbauer, H., Stuart, B., Wahle, B., Bomann, W., Ahr, H.╃J., Comparison of the expression profiles induced by genotoxic and nongenotoxic carcinogens in rat liver. Mutat. Res.╃2005, 575, 61–84. 28. Fielden, M.╃R., Brennan, R., Gollub, J., A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals. Toxicol. Sci.╃2007, 99, 90−100. 29. Nie, A.╃Y., McMillian, M., Parker, J.╃B., Leone, A., et al., Predictive toxicogenomics approaches reveal underlying molecular mechanisms of nongenotoxic carcinogenicity. Mol. Carcinog. 2006, 45, 914−933. 30. Fielden, M.╃R., Nie, A., McMillian, M., Elangbam, C.╃S., Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci.╃2008, 103, 28−34 31. Tamura, K., Ono, A., Miyagishima, T., Nagao, T., et al., Profiling of gene expression in rat liver and rat primary cultured hepatocytes treated with peroxisome proliferators. J. Toxicol. Sci.╃2006, 31, 471–490. 32. Vassallo, J.╃D., Hicks, S.╃M., Daston, G.╃P., Lehman-McKeeman, L.╃D., Metabolic detoxification determines species differences in coumarin-induced hepatotoxicity. Toxicol. Sci.╃2004, 80, 249–257. 33. Felter, S.╃P., Vassallo, J.╃D., Carlton, B.╃D., Daston, G.╃P., A safety assessment of coumarin taking into account species-specificity of toxicokinetics. Food Chem. Toxicol. 2006, 44, 462–475. 34. Vassallo, J.╃D., Hicks, S.╃M., Daston, G.╃P., Lehman-McKeeman, L.╃D., Metabolic detoxification determines species differences in coumarin-induced hepatotoxicity. Toxicol. Sci.╃2004, 80, 249–257. 35. Felter, S.╃P., Vassallo, J.╃D., Carlton, B.╃D., Daston, G.╃P., A safety assessment of coumarin taking into account species-specificity of toxicokinetics. Food Chem. Toxicol. 2006, 44, 462–475.

The Japanese Toxicogenomics Project: Application of Toxicogenomics – Utilizing Toxicogenomics into Drug Safety Screening

36. Born, S.╃L., Hu, J.╃K., Lehman-McKeeman, L.╃D., O-hydroxyphenylacetaldehyde is a hepatotoxic metabolite of coumarin. Drug Metab. Dispos. 2000, 28, 218–223. 37. Born, S.╃L., Caudill, D., Smith, B.╃J., Lehman-McKeeman, L.╃D., In vitro kinetics of coumarin 3,4-epoxidation: application to species differences in toxicity and carcinogenicity. Toxicol. Sci.╃2000, 58, 23–31. 38. Lake, B.╃G., Investigations into the mechanism of coumarin-induced hepatotoxicity in the rat. Arch. Toxicol. 1984, Suppl. 7, 16–29.

271

272

Contributions

3.15 Toxicology and Risk Assessment of Coumarin: Focus on Human Data Klaus Abraham1, Friederike Wöhrlin2, Oliver Lindtner2, Gerhard Â�Heinemeyer2, and Alfonso Lampen2 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 228–239.

Abstract Coumarin is a secondary phytochemical with hepatotoxic and carcinogenic properties. For the carcinogenic effect, a genotoxic mechanism was considered possible, but was discounted by the European Food Safety Authority (EFSA) in 2004 based on new evidence. This allowed the derivation of a Tolerable Daily Intake (TDI) for the first time, and a value of 0.1 mg/kg body weight was arrived at based on animal hepatotoxicity data. However, clinical data on hepatotoxicity from patients treated with coumarin as medicinal drug are also available. These data revealed a subgroup of the human population being more susceptible for the hepatotoxic effect than the animal species investigated. The cause of the high susceptibility is currently unknown; possible mechanisms are discussed. Using the human data, a TDI of 0.1 mg/kg body weight was derived, confirming that of the EFSA. Nutritional exposure may be considerable, and is mainly due to use of cassia cinnamon which is a popular spice especially used for cookies and sweet dishes. To estimate exposure to coumarin during the Christmas season in Germany, a telephone survey was performed with more than 1000 randomly selected persons. Heavy consumers of cassia cinnamon may reach a daily coumarin intake corresponding to the TDI.╃

3.15.1 Introduction Coumarin (1,2-benzopyrone, CAS No.╃91–64–5) consists of an aromatic ring fused to a condensed lactone ring (Fig.╃1). It is a naturally occurring constituent of many plants with a pleasant spicy odor of fresh hay, woodruff or vanilla. Along with safrole and estragole, it belongs to the group of ingredients in spices and herbs that are listed by the Council of Europe as “active principles”. In foods they may have a strong flavour but they are also toxicologically relevant.

1

Correspondence to: PD Dr. Klaus Abraham, Federal Institute for Risk Assessment (BfR), Department of Food Safety, Thielallee 88–92, D-14195 Berlin, Germany, Fax: +╃49 30 8412 3763, [email protected] .

2

Federal Institute for Risk Assessment, Germany.

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

Figure 1: Major metabolic pathways (phase I) of coumarin.

Coumarin has a long and interesting history of use and regulation. In 1822, the substance was isolated and purified for the first time from tonka beans (seed of Dipteryx odorata, also called Coumarouna odorata). After chemical synthesis in 1868, coumarin was marketed and used as food flavouring for a long time [1]. In the middle of the last century, coumarin was discovered to cause hepatic damage in laboratory animals [2], and the addition of synthetic coumarin to foods was banned, first in the USA in 1954.╃Furthermore, the formation of tumours was observed in long-term animal experiments [3], and for a long time, a genotoxic mechanism of action could not be ruled out. According to the ALARA principle (as low as reasonably achievable), in 1988 the European Union set a strong coumarin limit of 2€mg/kg for food in general resulting from the use of natural spices and herbs, with exceptions for special foods (chewing gum, caramel confectionery, alcoholic beverages). As for other “active principles” regulated in the council directive 88/388/EEC [4], the value of 2€mg/kg represented the limit of detection at that time. During the following years, however, compliance with this limit was evidently not monitored closely by food regulatory authorities in Europe. In 2005, the CVUA in Münster, Germany, by chance discovered a coumarin content of 22€mg/kg in a sample of cinnamon star cookies (“Zimtsterne”, a typical German Christmas cookie) [5]. This prompted a great increase in coumarin measurements in food from the German market [6], and a political debate on product recalls and the level of compliance with regulatory limits by the food industry in Europe [7]. As a result of these investigations and discussions, new coumarin limits for cinnamon-containing foods were laid down in the European Regulation EC 1334/2008, which replaced the earlier Directive 88/388/EEC. These changes in the regulation of coumarin also reflect changes in the scientific understanding of coumarin toxicology. On the European level, comprehensive opinions of the former Scientific Committee on Food (SCF) of the Euro-

273

274

Contributions

pean Commission are available from 1994 and 1999.╃The Committee could not rule out a genotoxic mode of action for tumour formation and recommended a maximum level of coumarin of 0.5€mg/kg in foods. These opinions were revised by the Panel of EFSA (the successor of the SCF) in 2004 [4] on the basis of new evidence for a non-genotoxic mode of action. This made it possible to derive a TDI for the first time. In the past, reports and opinions dealing with the question of risk assessment for coumarin focused primarily on animal data and its extrapolation to humans [3, 4, 8]. However, considerable human data, indicating hepatotoxicity, is available from use of coumarin as a medicinal drug. In this review, we take a closer look at this clinical data which in our view has not been sufficiently evaluated before, and use this information to derive a TDI. We also survey the possible causes of coumarin hepatotoxicity in a subgroup of the human population. Coumarin exposure is discussed in the light of data on coumarin content in cinnamon-containing foods from Germany, and on new consumption data from a telephone survey during the Christmas season. This information allows us to make a risk assessment for coumarin.

3.15.2 Hazard of Coumarin 3.15.2.1 Data from Laboratory Animals

Extensive data is available on the toxicity of coumarin in laboratory animals, and these have been evaluated in various overview articles and expert opinions of scientific bodies. Of the effects observed in vivo, the carcinogenic and hepatotoxic properties are of major importance. Hepatotoxicity was observed not only in rodents, but also in many other mammal species. Tumour formation was observed in long-term experiments with rodents: adenomas and carcinomas of the liver and bile ducts and adenomas of the kidney in rats, as well as adenomas and carcinomas of the lung and liver adenomas in mice. Carcinomas were found only at doses higher than 100 mg/kg body weight per day. Since the findings on the carcinogenic properties of coumarin in the 1960╛s, there had been discussions about their importance for humans and the underlying mechanism of action [3, 4]. After evaluation of new data on DNA adduct formation in 2004 [9, 10, 11], EFSA concluded that in vivo, coumarin does not bind in a covalent manner to the DNA of target organs and therefore that its carcinogenic effect does not have a genotoxic mechanism. Instead, coumarin induces tumours by a mechanism which is preceded by toxicity in the same target organ, and this allows a threshold-based approach and the establishment of No Observed Adverse Effect Level (NOAEL) [4]. After evaluation of the available oral animal studies for subacute and chronic toxicity, hepatotoxicity in Beagle dogs [12] was identified as the most sensitive effect. In this study, hepatotoxic effects were evident in the animals given 25€mg/kg body weight daily, but not in the animals given 10€mg/kg body weight daily (autopsies between day 297 and day 350). These results were used to establish a NOAEL of 10€mg/kg body weight daily;

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

by using a safety factor of 100 (10 for inter-species variation and 10 for interhuman variability), a TDI of 0.1€mg/kg body weight daily was derived [4]. As to the mechanism of hepatotoxicity, many in vivo and in vitro studies have been performed in laboratory animals to elucidate the metabolism of coumarin [3, 4]. Briefly, the two most important pathways of coumarin metabolism are 7-hydroxylation leading to detoxification, which is predominant in primates [13], and metabolism of the lactone ring to form a coumarin 3,4-epoxide intermediate (Fig.╃1). This can be conjugated with glutathione or may spontaneously degrade with the loss of carbon dioxide to form o-hydroxyphenylacetaldehyde (o-HPA). The latter compound was found to be a hepatotoxic metabolite and is detoxified by oxidation to o-hydroxyphenylacetic acid (o-HPAA). Much less o-HPAA is formed in rats than mice, explaining the higher susceptibility of rats to coumarin-induced hepatotoxicity. Therefore, differences in detoxification of o-HPA are assumed to be a determining factor for species differences in sensitivity to coumarin hepatotoxicity [14].

3.15.2.2 Human Experience from Coumarin Use as Medicinal Drug

Coumarin was approved from the 1970╛s onwards in various countries as a medicinal product to treat oedemas caused by venous (chronic venous insufficiency) and lymphatic (lymphatic oedemas) drainage disorders, possibly through stimulation of proteolysis by tissue macrophages. In addition, direct antitumour activity was reported, and the substance was used to treat renal cell carcinomas and other tumours, with doses of up to 7000 mg daily [15]. Some of the patients developed severe hepatotoxicity (toxic hepatitis, liver failure in a few cases) from a few weeks to six months after commencement of treatment [16, 17, 18]. These observations led to these products being withdrawn from the market in several countries in the 1990╛s (Australia, Belgium, France, and Canada) [18]. Varying frequencies of the hepatotoxic response were reported, with dependence on the method used to detect hepatotoxicity (clinical observation only or blood sampling to detect elevated liver enzyme levels), as might be expected. In one report, 3 of 48 patients with metastatic prostatic carcinoma (6.3╛%) treated with 3000€mg coumarin daily responded with elevated liver enzymes [19]. Cox et al. observed a study group of 2173 patients with cancer or chronic infections; they were treated with 25 to 2000 mg coumarin daily (the majority received 100 mg daily for one month, followed by 50€mg daily for two years; blood samples were taken every 3 months). Seventeen patients developed elevated liver enzymes levels of sufficient magnitude (i.╃e. at least double the normal maximum level3). Four of them were diagnosed as probably having other causes than coumarin treatment. Of the remaining 13 patients (0.60╛%), elevated liver

3

Alanine aminotransferase levels were between 115 and 960 units per liter (normal maximum level 35 to 50 U/l)

275

276

Contributions

enzymes levels returned to normal while still on coumarin in five, and only in the remaining eight (0.37â•›%) was the hepatotoxicity attributed to coumarin treatment. Several of the patients were re-exposed to coumarin showing the same hepatotoxic response, but with a faster onset (see section 3.15.2.4) [16]. A more reliable estimate of the percentage of patients sensitive to the hepatotoxic effect of coumarin can be expected from systematic placebo-controlled studies with recurrent blood sampling. Loprinzi et al. studied 140 women with chronic lymphoedema of the ipsilateral arm after treatment for breast cancer [20]. They received 200€ mg coumarin or placebo twice daily for six months and then the other medication for the following six months (cross-over design). Blood samples were taken for the first time after three to four months and after one month during the first and second treatment period, respectively. The incidence of hepatotoxic effects was substantially higher with coumarin than with placebo. In none of the women did serum aminotransferase levels reach 2.5 times the upper limit of normal during the placebo period, whereas in nine women (6â•›%) the levels became high during treatment with coumarin (p=╃0.006). The most prominent instance of hepatotoxicity occurred in a woman who developed jaundice with a serum bilirubin concentration of 19.3 mg per decilitre while she was receiving coumarin. In these nine women, the enzyme levels returned to normal after coumarin treatment was stopped. The authors were unable to identify any pre-disposing factors, excluding, among others, therapy with tamoxifen and high body weight. In a clinical study from Germany [21, 22, 23], 231 patients with chronic venous insufficiency were randomly assigned verum (90 mg coumarin and 540 mg troxerutin per day, n=╃114) or placebo (n=╃117) for 16 weeks. Blood liver enzymes were monitored at baseline and at five time points during the treatment. Four weeks after the beginning of the therapy, nine out of the 114 patients treated with verum (7.9â•›%) manifested elevated transaminase levels above 2.5-fold the upper limit of the normal range. They were analyzed with respect to causality: 3 were assessed as “unrelated”, two as “unlikely”, three as “possible” and one as “probable”. Elevated liver enzymes were also observed in the placebo group (number not reported). The authors tried to identify “risk factors” (see section 3.15.2.4); “risk factor adjusted” logistic regression was performed and the basic risk of elevated liver enzyme levels was estimated to be 4.9 and 2.2â•›% in the verum and placebo group, respectively. The results were not easy to interpret, because some of the patients had a history of hepatitis and elevated liver enzymes before the start of the treatment. In addition, it has to be taken into account that coumarin was administered as cotreatment with troxerutin, which may have a hepatoprotective effect (study findings from isolated perfused rat liver [24]). This would be a reason to expect a stronger hepatotoxic effect when coumarin is administered alone. The published data are consistent with the existence of a subgroup of the human population that reacts sensitively to coumarin with hepatotoxic effects. If recurrent blood sampling is used to monitor liver enzyme levels, this group may amount to a single-digit percentage of the population, much higher than in drug responses often classified as “idiosyncratic”. No clear-cut dose-dependent

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

increase in severity of the effect was observed within the subgroup, although liver failure was observed only in patients treated with high doses (more than 100 mg daily). On the other hand, people not sensitive to the effect evidently tolerate daily coumarin doses in the gram range. Drug-dependent moderate elevation of liver enzyme levels is often tolerated in clinical practice; if positive effects for the patient are expected, treatment is often continued with monitoring of the laboratory parameters. However, such risk–benefit considerations are not permissible for foods. These have to be safe in any case, and if dietary exposure to coumarin caused elevated blood liver enzyme levels, even if they were reversible, it would not be acceptable.

3.15.2.3 Hazard Assessment Using Human Data

As outlined above, the frequency as well as the severity of coumarin hepatotoxicity in the human subpopulation is relevant, and the effect should be considered in hazard assessment. Due to underreporting, the cases known to the authorities cannot be used to estimate the frequency of these reactions, but they can be used to identify the lowest daily dose of coumarin able to cause hepatotoxicity. This dose can be used as a staring point for deriving a TDI. In 1999, an expert opinion “on the assessment of coumarin in medicinal products with regard to a hepatotoxic effect in humans” was commissioned by the Federal Institute for Medicinal Products and Medical Devices (BfArM) in Germany [25]. The 82€case reports (international notifications) of possible coumarin-associated liver damage available to the institute at that time were evaluated from a pharmacological perspective. These reports included cases of liver failure (survival of the patients) and seven fatalities. A dose classification was possible for 51 cases from France, Ireland and Germany. The most frequent daily dose was 90€mg coumarin prescribed for the main indication “lymphatic disorders and varicose veins”. Five cases (10â•›%) occurred at the lowest doses (25 and 30€mg daily); of the three cases from Germany documented in more detail, two had developed hepatitis. According to the expert report, for part of the population liver damage cannot be ruled out at a daily dose of 25€mg coumarin. In order to extrapolate from this effect level to a human NOAEL, a factor of 5 is considered justified in the case of a severe effect at the Lowest Observed Adverse Effect Level. This results in a level of 5€mg coumarin per day which is expected to cause no adverse effects even in sensitive subjects. When choosing this factor, it was borne in mind that knowledge on the mechanism of action in sensitive individuals is not available. As this group of persons must already be viewed as the most sensitive subgroup in the population, no additional intraspecies factor was applied. Using the established safe daily dose of 5€mg coumarin for an adult weighing 60 kg, a (rounded) TDI of 0.1 mg/kg body weight was derived by the BfR [5, 26]. This value based on human data agrees with, and lends support to the EFSA value based on animal data [4]. Case reports evaluated by Bergmann also allow an estimation of the time period critical for the onset of hepatitis in sensitive subjects [25]. The shortest

277

278

Contributions

periods documented was 5€days (dose: 90€mg daily), 16€days and 18€days (dose: 30€mg daily each). Therefore, it is not acceptable to significantly exceed the TDI over several weeks. The European Commission asked EFSA for an opinion on the relatively high cinnamon exposure during the Christmas season (see section 3.15.3.3), and EFSA concluded in 2008 that exposure to coumarin resulting in an intake 3 times higher than the TDI for one to two weeks is not a cause for concern [27].

3.15.2.4 What is the Cause of Coumarin Hepatotoxicity in the Human Subgroup?

As outlined above, the varying susceptibility to hepatotoxicity of coumarin observed in animal species was attributed to the varying ability to detoxify the substance via the 7-hydroxylation pathway. In the human population, 7-hydroxylation of coumarin is catalyzed by a high-affinity CYP2A6 liver enzyme which is the only enzyme metabolizing this reaction in the human liver [28, 29]. Due to this predominant pathway, coumarin usually has a large first-pass effect in the human liver after oral administration (94╖to 98╛%) [30], leading to rapid urinary excretion of 7-hydroxycoumarin and its glucuronide. Using 5€mg coumarin as test substance to investigate the CYP2A6 phenotype, a mean total 7-hydroxycoumarin formation of 64╛% (range 20 to 100╛%) of the dose was observed in a European population of 110 volunteers; more than 95╛% of the metabolite formed was excreted within 4€hours [31]. In subjects with high excretion rates of 7-hydroxycoumarin measured after oral coumarin administration, 0.13╛% of the dose or less was found as 3-hydroxycoumarin [32, 33]. In the same investigation, mean urinary excretion of o-HPAA (via 3,4-epoxidation) was found to be 3.5╛% (range 1.8 to 7.0╛%) of a dose of 1000€mg coumarin [34], comparable to the number of 4╛% (range 1 to 6╛%) of the dose of 200€mg coumarin reported earlier for eight volunteers [35]. The human data on rapid detoxification of coumarin is consistent with data of patients (see section 3.15.2.2) treated for weeks with high doses up to 7000€mg coumarin daily, corresponding to maximal doses of more than 100 mg/kg body weight daily (assumed weight 60€kg) tolerated without signs of hepatotoxicity [15]. Such a dose was lethal to two Beagle dogs within 9 and 16 days, respectively [12]. The apparently low susceptibility to the hepatotoxic effect of coumarin in the majority of the human population, as compared to many animal species, was attributed to detoxification via the 7-hydroxylation pathway predominant in humans. The ability to catalyze the 7-hydroxylation of coumarin was tested using liver microsomes from nine mammalian species. 7-hydroxycoumarin was the major metabolite (greater than 70╛%) in humans and monkeys, but only a minor metabolite in rats (less than 1╛%), mice (3╛%) and dogs (18╛%) [13]. According to these and comparable results of other authors [3], it was discussed whether the inter-species factor used in risk assessment for extrapolation from animals to humans should be reduced [4, 8, 27]. However, as outlined above, a relevant subgroup of the human population is much more susceptible, as is most impressively demonstrated by the study

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

of Loprinzi et al. [20] and with some limitations by the German study [22]. From the daily doses applied in these studies (about 6.7 and 1.5 mg/kg body weight, respectively, assuming a body weight of 60 kg) and from even lower doses (down to about 0.5 mg/kg body weight) in single cases reported to the authorities [25] it can be concluded that individuals of the human subgroup are more susceptible than various animal species investigated (NOAEL of 10€ mg coumarin per kg body weight daily in Beagle dogs identified as most sensitive species [4]). The cause of this higher susceptibility is unknown. Unfortunately, no phenotype data of coumarin metabolism is available from any patient affected by a hepatotoxic response following the treatment with coumarin. A genetic polymorphism of CYP2A6 as underlying mechanism has been discussed for a long time, possibly leading to an increased formation of 3,4-coumarin epoxide and o-HPA. This has been observed in a homozygous individual with an inactivating CYP2A6*2 allele: Following the oral administration of 2€mg coumarin, about 50â•›% of the dose was excreted in the 8-hour urine as o-HPAA, whereas 7-hydroxycoumarin could not be detected; the urinary metabolite excretion of the heterozygous parents was not found to differ from that of controls [36]. However, this is the only in vivo observation with documentation of alternative metabolism via the 3,4-epoxidation pathway in a CYP2A6â•‚deficient subject. A very low or missing urinary excretion of 7-hydroxycoumarin after oral administration of coumarin was found in a significant proportion of Asians who lack the CYP2A6 protein completely due to the relatively high incidence of CYP2A6 gene deletion alleles in Japanese and Chinese populations. The frequency of poor metabolizers in Asian populations (up to 20â•›%) is much higher than in Caucasian populations [37, 38, 39]. Unfortunately, the possible alternative routes of metabolism were not investigated in any of the studies of individuals identified as deficient in the 7-hydroxycoumarin pathway. Therefore, it is currently unknown what CYP2A6 polymorphism with deficient 7-hydroxylation of coumarin means with respect to the 3,4-coumarin epoxide pathway and the possible generation of toxic metabolites. In addition to this lack of evidence, further observations do not support the assumption of a CYP2A6 polymorphism as cause of the higher susceptibility in the human subgroup: ►⌺ The vast majority of patients with hepatotoxic response following the treatment with coumarin is Caucasian in race; in this population, the frequency of poor metabolizers is close to zero [37]. In contrast, the two systematic placebo-controlled studies with recurrent blood sampling revealed a proportion of sensitive subjects in the single-digit range [20, 22]. In addition, CYP2A6 genotyping of 216 patients of the German study [22] revealed 7.4â•›% subjects with defective genotype (CYP2A6*2 or CYP2A6*3 allele) all found to be heterozygous for the variant alleles; of the nine patients with elevated liver enzyme levels in the verum group, only one carrier of a variant allele was identified who exhibited an isolated γ-GT elevation without concomitant increase in transaminases. Additional genotyping of affected patients for the deletion of the CYP2A6 gene (CYP2A6*4 allele) revealed no further poly-

279

280

Contributions

morphism. Therefore, no evidence was obtained that the polymorphism in CYP2A6 is a determinant of the coumarin-associated elevation of blood liver enzymes [21]. ►⌺ Rietjens et al. used a physiologically based toxicokinetic model to predict liver levels of the toxic o-HPA metabolite in rats and in human subjects with normal or deficient CYP2A6â•‚catalyzed coumarin 7-hydroxylation phenotype; data from the literature as well as data obtained from in vitro investigations of microsomes were used to determine kinetic parameters for coumarin metabolism of animals and humans [40]. Modelling allowed the prediction of maximum tissue concentration of o-HPA in the liver of wild-type human subjects and of subjects deficient in 7-hydroxylation which were three and one order of magnitude, respectively, lower than the values predicted for rat liver. The authors concluded that even when 7-hydroxylation activity is missing, the formation of the hepatotoxic o-HPA metabolite will be significantly lower in the liver of humans than those expected in the liver of rats when exposed to a similar (low) dose per body weight. As the rat is a relatively coumarin-sensitive species [41] comparable to dogs (NOAEL about 10 mg/kg body weight) [12], it can be concluded that o-HPA is unlikely to be the toxic agent causing hepatotoxicity in the human subgroup which is more susceptible than rats and dogs, as outlined above. From these observations it can be reasoned that CYP2A6 polymorphism with deficient 7-hydroxylation of coumarin is not the cause of the high susceptibility to hepatotoxicity in the human subgroup. Regarding other possible causes, previous hepatitis and elevated baseline γ-GT were identified as risk factors in the German coumarin study [22]. At least two disease states seem to reduce coumarin 7-hydroxylation. In patients with alcohol-induced liver disease, mean urinary 7-hydroxycoumarin excretion over 2 hours was decreased in severe (18.0â•›% of dose) and moderate (34.2â•›% of dose), but not in mild (49.7â•›% of dose) disease relative to controls (56.2â•›% of dose) [42]. In previously healthy adult patients with acute jaundice from hepatitis virus A infection, mean total urinary 7-hydroxycoumarin excretion (0 to 8 hours) was decreased by 37â•›% compared with the values obtained from healthy volunteers [43]. However, the patients of the German coumarin study did not suffer from severe or acute liver disease, and moderately decreased CYP2A6 activity levels are not expected to result in a strong increase of o-HPA liver concentrations (modelling data [40]). Therefore, the risk factors of previous hepatitis and elevated baseline γ-GT are probably not related to impaired coumarin detoxification. Since evidence for a metabolic cause of the high susceptibility to hepatotoxicity in a human subgroup is missing, other possible causes have to be considered as well. Of the patients with hepatotoxic response described by Cox et al., about half were re-treated with coumarin (after cessation of medication and return of elevated liver enzymes levels to normal) [16]. The authors observed that the time to onset of a rise in the liver enzymes was much shorter than in the first treatment period. In addition, they observed a favourable response (lower liver enzyme levels) to immunosuppressive therapy in three patients while on cou-

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

marin. These observations as well as the absence of any clear dose-dependency of the severity of response within the subgroup suggest an immune mechanism may be involved in the coumarin-induced hepatotoxicity in humans. To test for a drug-mediated allergic response, lymphocytes from the patients of the German coumarin study with elevated liver parameters were incubated with coumarin and 7-hydroxycoumarin; however, none of them showed a positive lymphocyte proliferation test response with a stimulation index of more than€three [22]. In general, hepatotoxic responses observed during treatment are classified as “adverse drug reactions” which do not occur in most patients at any readily achieved dose of a drug and do not involve the known pharmacological effects of the drug [44].

3.15.3 Human Exposure 3.15.3.1 Coumarin in Cinnamon Species

There has been no large-scale analysis of coumarin in foods. The substance is contained in various plants (for example sweet clover, tonka beans, lavender); however, given the general eating habits in most countries, these are probably not relevant for nutritional coumarin exposure. Recently, coumarin levels in specific plants or their essential oils were analyzed using a very sensitive method. Significant concentrations in plants relevant for food consumption were only found in cassia cinnamon (see below) and in woodruff (Asperula odorata, 203€mg/kg) [45]. The latter is used in Germany to flavour May punch (“Maibowle”) and has a long history of regulations due to its coumarin content. However, the alcoholic beverage is consumed by very few people and for only a short period each year. Cinnamon bark is the dried inner bark of the shoots grown on cut stock of Cinnamomum verum J.╃S. Presl (Syn. Cinnamomum zeylanicum Nees, true cinnamon, Ceylon cinnamon) or of the trunk bark, freed of cork, of Cinnamomum cassia Blume (Syn. Cinnamomum aromaticum Nees, Chinese cinnamon, Saigon cinnamon, Vietnam cinnamon, cassia cinnamon) [46]. The chemical composition of the two cinnamon species is different, particularly with respect to their coumarin levels. Coumarin concentrations were detected from below the detection limit to 190€mg/kg in Ceylon cinnamon (n=╃12) and from 700 to 12230€mg/ kg in cassia cinnamon (n=╃12) [47]. Due to these high concentrations in cassia cinnamon (compared to other foods), it seems obvious that – despite the relatively low amounts of the consumption of spices – coumarin exposure from food consumption is mainly due to cassia cinnamon. This applies to both the direct addition of cinnamon to foods but also the use of cinnamon oils by the food industry. Cinnamon is available in the grocery store in dried form as cinnamon powder or as sticks; for the latter only, a visual differentiation between cassia and Ceylon cinnamon is possible. On the German retail market, mostly cassia cinnamon is available [48]; in the majority of cases, however, the botanical species is not

281

282

Contributions

indicated on the packaging. Analyses of cinnamon samples and of the most important cinnamon-containing foods done by the Federal States in Germany in 2006 and reported to the Federal Office of Consumer Protection and Food Safety (BVL) up to March 2007 revealed the coumarin levels listed in Table 1.╃Some analyses done by the CVUA Karlsruhe were published in more detail [6]. With a median coumarin level of 2920 mg/kg (maximum 8790 mg/kg) the analyses confirmed the predominance of cassia cinnamon in the retail market. Coumarin levels in cinnamon-containing foods were found in the range expected from the recipes. For example, cinnamon star cookies (“Zimtsterne”) with the highest cinnamon content (about 1â•›%, see also section 3.15.3.3) were found to have the highest coumarin content (median 39.4€mg/kg, maximum 113.3 mg/kg). Most of these coumarin levels considerably exceeded the maximum permitted level (2€mg/kg) of the EU Flavourings Directive 88/388/EEC. Following the public discussion in Germany, most of the manufacturers took measures to reduce the coumarin content in 2007 and 2008.

3.15.3.2 Estimate of Oral Coumarin Exposure

In its opinion of 2004, the EFSA calculated a TAMDI (Theoretical Added Maximum Daily Intake) of 1.5 mg coumarin for an adult with a default body weight of 60€kg (0.025 mg/kg body weight per day) [4]. The calculation was performed considering the concomitant consumption of 324â•›g general beverages, 133.4â•›g general solid food, 27â•›g sweets, 2â•›g chewing gum and 20â•›g alcoholic beverages – each with the maximum level of coumarin permitted by the Flavourings Directive 88/388/EEC (2€ mg/kg for foodstuffs and beverages in general). Lake calculated a TAMDI of 4.1€mg coumarin from general consumption data which he rated as unrealistically high. A value of 1.2 mg coumarin (0.02 mg/kg body weight for 60 kg weight) seemed to be more realistic to him (assumption: maximum 5â•›% of solid food flavoured with cinnamon) [3]. These two worst-case estimates were based on high amounts of flavoured foods consumed daily and on the maximum level of coumarin permitted by the Flavourings Directive 88/388/EEC. However, due to the predominant use of cassia, the majority of the cinnamon-containing foods in Germany were found to be far above this level, as shown by the data in Table 1.╃Therefore, the TAMDI approaches mentioned [3, 4] cannot be considered as reliable estimates at least for Germany. Even with the improvements in knowledge on the coumarin levels in food (Tab.╃1), it is still not easy to estimate the maximum daily intake of coumarin from cinnamon and cinnamon-containing foods. Epidemiological data on consumption of spices are inadequate. This is not only due to missing consumption data on particular spiced foods, but also due to a lack of estimates of the amount of spices used by the consumer at home (for example the use of cinnamon and sugar to spice rice pudding). In addition, a simultaneous dermal coumarin exposure may also be needed to be considered (see section 3.15.3.4). As an approach to estimate the seasonally higher consumption of cinnamon-containing

Toxicology and Risk Assessment of Coumarin: Focus on Human Data Table 1: Coumarin levels (mean, median and maximum) in cinnamon and cinnamon-containing foods from the German market measured in 2006. n

mean (mg/kg)

median (mg/kg)

maximum (mg/kg)

170

2680

2920

8790

16

231.3

105.0

918.0

218

37.7

39.4

113.3

Cereals with cinnamon

28

25.5

23.9

60.0

Almond cookies (“Spekulatius”)

40

16.2

17.0

30.2

Gingerbread cake (“Lebkuchen“)

80

10.3

7.8

46.0

Desserts with cinnamon

29

10.2

10.8

19.0

Chocolate with cinnamon

25

9.4

5.6

32.9

Mulled wine

48

0.2

0.1

4.3

Cinnamon1 Tea with cinnamon Cinnamon star cookies (“Zimtsterne”)

1 Ground to powder in most cases (no differentiation between cassia and Ceylon cinnamon). Analyses were done by the Federal states in Germany in 2006 and reported to the Federal Office of Consumer Protection and Food Safety (BVL) until March 2007.╃For all foods, minimum values were below the limit of detection.

foods during Christmas time, BfR has directed a telephone survey of adults (see section 3.15.3.3). When estimating the consumption of cinnamon, young children require a separate consideration as a group with possibly high exposure on a body weight basis (higher food consumption due to their higher energy requirements and special eating habits). In this case, exposure data from the German VELS4 study is helpful. For two non-consecutive three-day periods, parents kept food records [49]. The evaluation of toddlers aged between 2 and 5 years (n=╃475) showed that 140 children ate cinnamon or cinnamon-containing products at least on one of the six days recorded; 47â•›% of the consumption days were between September and December. For these consumers the 97.5 percentile showed consumption of 0.22â•›g cinnamon per kg body weight (exposure on a single day, normally by eating rice pudding with cinnamon and sugar). Exposure lasting for longer periods was estimated by a worst case approach assuming two of these meals per week. This results in a daily exposure of 0.063â•›g cinnamon per kg body weight per day. Based on a coumarin level of 3000€mg/kg cinnamon this would indicate a coumarin exposure of 0.19€ mg/kg body weight as the worst case for oral exposure of the 2- to 5-year-old children [5]. A simple estimate of coumarin exposure in young children is possible using the levels measured in Christmas cookies (Tab.╃1). For a 4-year-old child weighing 15 kg, a daily consumption of three cinnamon star cookies (“Zimtsterne” weighing about 6â•›g each) with the maximum level measured already results in a

4

Food consumption study to determine the dietary intake of infants and toddlers in order to estimate the acute toxicity risk from pesticide residues.

283

284

Contributions

coumarin exposure of 0.13 mg/kg body weight daily higher than the TDI (0.046 mg/kg body weight daily for the cookies with the median level). This calculation allows an estimation of the coumarin intake from single foods; however, other coumarin-containing foods may be consumed simultaneously. In order to get an estimation of the total coumarin intake during Christmas time (in adults), a telephone survey has been performed.

3.15.3.3 Telephone Survey on Cinnamon in Christmas-Related Foods

Christmas time is the period of highest consumption of cinnamon-containing foods. Typical Christmas treats like almond cookies (“Spekulatius”), gingerbread cakes (“Lebkuchen”) and cinnamon star cookies (“Zimtsterne”) have a long tradition in Germany as well as in other European countries. In addition, a variety of teas, chocolates and desserts containing cinnamon have been launched in recent years to have a typical taste and flavour of winter and Christmas. In order to estimate the total amount of cinnamon consumed with Christmasrelated foods by German adults, the BfR performed a questionnaire survey by telephone. The survey was carried out by USUMA GmbH, an independent research institute for questionnaire surveys in Berlin. During three days of the third week of December in 2006, 1012 persons aged 14 years or older living in Germanspeaking private households were interviewed using a standardized questionnaire. The interviews lasted 12 minutes on average. The amount of selected foods was assessed by asking for “the frequency of consumption within a typical Christmas week” and typical portion sizes. To validate the estimates given by consumers for one week, a 24-h-recall question was included for each of the selected food items. A plausibility check excluded two interviews because of implausibly high consumption of cinnamon-containing foods. The questionnaire also included socio-demographic parameters like age, gender and education. The sample was randomly generated and population-proportionally weighted to adjust for unavoidable biases. Hence, the results are representative for the German population aged 14 years and older. Interview partners were asked about their average consumption of ten different cinnamon-containing foods presented in Table€2 (which also shows the estimated average cinnamon content and portion size of each food item). To assess the individual coumarin intake, consumption data were multiplied by the mean coumarin levels of the foods in Table€1 (food monitoring data of 2006). In case of “Dominosteine” and “home-baked Christmas-cookies”, only data on their cinnamon content were available; therefore, the coumarin content was estimated assuming an average coumarin content of 3000 mg/kg cinnamon as a rough estimate. In case of winter/Christmas specialty teas, the use of 2â•›g tea for a cup containing 200€ml and a coumarin extraction rate of 50â•›% was assumed. Possible co-exposure to other cinnamon-containing meals eaten throughout the year (for example: rice pudding with home-used cinnamon or breakfast cereals with cinnamon) was not considered in the survey.

Toxicology and Risk Assessment of Coumarin: Focus on Human Data Table 2: Christmas-related foods in Germany asked for in the telephone interviews: Estimated average cinnamon content, size of one portion, and resulting cinnamon in one portion. In the right column, the relative contribution of each food to the total cinnamon consumption of the interview sample is given. Average cinnamon content (g/kg)

Size of one portion (g)

Cinnamon content in one Portion (g)

Proportion of the total cinnamon consumption (%)

Tea1

77.12

200

0.105

22.2

Mulled wine

0.072

200

0.02

0.9

Dessert a)

3.42

150

0.52

4.4

Chocolate a)

2.8

100

0.28

10.2

Almond cookies (“Spekulatius”)

2

5.8

7.9

0.05

19.3

Domino cookies (“Dominosteine”)

0.53

12.4

0.01

1.1

Cinnamon star cookies (“Zimtsterne”)

12.62

5.8

0.07

5.2

Small gingerbread cake

3.22

10.0

0.03

7.5

Large gingerbread cake

3.2

50.0

0.16

9.8

Home-baked cookies

5.0

17.0

0.09

19.4

2

2

4

1 Marketed as a winter/Christmas specialty. 2 Estimated from mean content of coumarin in Table 1, assuming a coumarin content in cinnamon of 3000 mg/kg. 3 “Dominostein” is a cubical-shaped sweet primarily sold during Christmas season in Germany. On average one piece weighs 12.4â•›g. The base (15â•›%) consists of soft gingerbread (manufacturer information). A layer of jelly follows and a layer of either marzipan or persipan is on top. It is Â�covered with a thin icing of dark chocolate. Since there was no coumarin data available, cinnamon contents had to be estimated based on measurements of gingerbread cakes. 4 Based on a list of cinnamon containing Christmas cookie recipes (n=╃30), on average one cookie weighs about 17â•›g. Regarding this recipes cinnamon containing homemade cookie dough consists on average of 0.5â•›% cinnamon. The first question was about the number of consumed homemade cookies, then the interviewee had to estimate how many of these cookies did contain cinnamon (34â•›% of homemade cookies did reputedly contain cinnamon). If cinnamon proportion was not specified, this value was taken as computation base. 5 Assuming the use of 2â•›g tea for a cup containing 200 ml and a coumarin transfer rate of 50â•›% (average rate of transfer experiments performed by the BfR).

The total coumarin intake was estimated for each of the interview partners by adding up the intake data from the 10 different Christmas-related foods. Figure 2 shows the distribution of all of the estimated individual consumptions per week, ordered by total amounts. As expected for spice-related consumptions, a high variation was observed: 42 persons (4.2╛%) reported that they did not consume any of the 10€foods, whereas 154 persons (15.2╛%) consumed half of the total coumarin amount. Mean coumarin intake was estimated to be 5.0€mg coumarin per week; median, 95th and 97.5th percentile were estimated to be 3.2€mg, 15.9€mg and 21.6€mg coumarin per week, respectively. The coumarin intake of the heaviest consumers (6€subjects) was estimated to be higher than

285

286

Contributions

35€mg per week (maximally 47.5€mg per week). These consumers more or less reach an estimated daily coumarin intake of 0.1€mg/kg body weight (assumed weight 60 kg). These results confirm the often used worst-case rule of thumb that the heaviest consumer may eat up to 10€times more than the average consumer. Regarding subgroups of the population, average coumarin intake was lower in women (5.0€mg per week) than in men (5.8€mg per week); this may be due to their lower general food and calorie intake. The age distribution showed a distinct trend towards a lower consumption at higher ages. Whereas the average intake of the group of 14- to 24-year-old persons was 8.4€mg coumarin per week, it was 5.0€mg per week in the group of 35- to 54-year-old persons, and 3.1€mg coumarin per week in the group of the more than 75-year-old persons. As expected, the contribution of the ten different foods to the total weekly intake of coumarin in the interview sample was different (Tab.╃2). The main contributions were from winter/ Christmas specialty teas (22.2â•›%), almond cookies (“Spekulatius”, 19.3â•›%), home-baked cookies with cinnamon (19.4â•›%) and gingerbread cakes (small and large size, 17.3â•›%). Despite their relatively high coumarin content, cinnamon star cookies (“Zimtsterne”) accounted only for 5.2â•›% of the total coumarin intake, as they were only eaten by a few people: Nearly 80â•›% of the interview partners reported not eating any of these cookies. This may have been influenced by the public discussion on coumarin in cinnamon and especially on cinnamon star cookies which took place in Germany in late 2006. 67â•›% of the interview partners said they had noticed warnings in

Figure 2: Individual weekly coumarin intake from Christmas-related foods in Germany in 2006, estimated from the telephone survey of 1012 consumers. The estimated intake of each individual subject is presented; subjects were sorted by their intake value.

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

the media against a high consumption of cinnamon and cinnamon-containing foods. The average consumption of cinnamon obtained from the telephone interviews was checked against market data from Germany. A total consumption of 2887 tonnes (amount imported minus amount exported) corresponding to an average consumption of 35.2╛g cinnamon per year in Germany (population: 82 million) and a weekly average of 0.68╛g cinnamon has been reported [48]. This number is lower than that of 1.7╛g per week estimated from our study (assuming a coumarin content of 3000€mg/kg cinnamon). However, an important proportion of the yearly cinnamon consumption is expected to take place during Christmas time. If we assume that Christmas-related foods are offered intensively over a 10-week period, this would correspond to an average consumption of 17╛g cinnamon or roughly half of the total annual cinnamon consumption during the Christmas period. This is considered plausible.

3.15.3.4 Dermal Exposure

In addition to oral exposure, a simultaneous dermal exposure has to be taken into account as well. This is an uncommon case in risk assessment of food ingredients. In contrast to food production, coumarin is used without constraints as a fragrance in cosmetics, leading to a dermal exposure of consumers which is by no means insignificant. The annual world production for the use as a fragrance in cosmetics today amounts to about 2000 tonnes [50]. Based on production data from the USA, an average daily coumarin amount of 1.2€mg per US American was calculated [51]. As the formulations of cosmetics are not normally made public, only insufficient data are available on coumarin levels. Lake referred to a compilation of the International Fragrance Association in Geneva, which indicates a coumarin level of 6.4╛% in a few thousand fragrance mixtures as the 97.5th percentile [3]. Based on this value he estimated a daily dermal exposure of 9.8€mg coumarin for adults (0.16 mg/kg body weight for a person weighing 60 kg) as a worst case. However, this amount appeared to be unrealistically high for him and he re-estimated a daily intake of 2.3€ mg coumarin (0.04€mg/kg body weight for an adult weighing 60€kg) as reasonable worst case scenario. This calculation reflected the fact that coumarin is absorbed quickly and efficiently via the skin (absorption rate approximately 60╛%: [3, 51]). Currently it is under discussion whether the risk of a hepatotoxic effect of coumarin from dermal uptake is comparable to that from oral intake. Due to slower absorption and the fact that the first-pass phenomenon does not apply, hepatic peak concentrations of coumarin are expected to be much lower after dermal than after oral exposure to the same dose. Accordingly, coumarin would be much less hepatotoxic after dermal than after oral exposure, if hepatotoxicity is a threshold effect depending on the peak concentration, but not if hepatotoxicity is related to the area under the curve (AUC). It is currently an open question which dose metric is relevant, especially for the sensitive human subpopulation.

287

288

Contributions

3.15.4 Risk Assessment As is evident from the estimation of exposure, heavy consumers may reach a daily coumarin intake of 0.1€mg/kg body weight corresponding to the TDI just from the consumption of Christmas-related foods during autumn and winter. With an additional consumption of cinnamon, these consumers may exceed the TDI. Similarly, a heavy home use of cinnamon as spice itself (for example rice pudding with sugar and cinnamon) may lead to an exposure in excess of the TDI during a relevant period of time, especially if the particular product is at the upper end of the range of coumarin content (possibly higher than 8000€Â�mg/Â�kg). Therefore, no risk of hepatotoxicity of coumarin is expected for the vast majority of the population consuming low or moderate amounts of (cassia) cinnamon. However, consumers who really eat a lot may have a risk if they belong to the sensitive subgroup. During the public discussions on coumarin and cinnamon in autumn 2006 in Germany, the question was raised whether coumarin in the food matrix of cinnamon has the same bioavailability as the pure compound used in animal experiments and human medicine. This issue was addressed by the German “Senatskommission zur Beurteilung der gesundheitlichen Unbedenklichkeit von Lebensmitteln“ (SKLM) [52]. It was concluded that in the absence of data on the influence of the respective constituent and the respective matrix in a foodstuff, toxicological data on the pure compound should be used as a basis for risk assessments. Clarification of the issue can only be achieved by further investigations on a case-by-case basis. Currently, the BfR is performing a human study to investigate the bioavailability of pure coumarin compared to coumarin in different foods containing cassia cinnamon. Evidence for the possibility of a hepatotoxic response during high consumption of cassia cinnamon arises from a case reported to the BfR. In summer 2006, a 23-year-old woman was hospitalized with acute hepatitis. Laboratory tests produced no evidence of a viral infection. Liver biopsy showed distinct signs of inflammation, acute cholangitis and canalicular cholestasis. A toxic hepatitis was presumed, but anamnesis did not turn up any hints as to its cause. A slow but continuous recovery was observed in the following weeks. In autumn 2006, the patient became aware of the public discussions on cinnamon and hepatotoxicity. She remembered that as a big fan of cinnamon, during the one to two months before the onset of her hepatitis, she had consumed even higher amounts than before (about 1 to 2â•›g cinnamon every day, used to spice different foods). It is therefore suspected that this case was caused by high consumption of cassia cinnamon.

3.15.5 Summary and Conclusion Clinical data from patients treated with coumarin (case reports and controlled studies) revealed the existence of a relevant subgroup making up a single-digit

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

percentage of the human population which is sensitive for the hepatotoxic effect. In contrast to coumarin-treated patients, even slightly elevated liver enzyme levels, as a sign of liver damage, are not acceptable (even if reversible) in case of consumers eating food with a high coumarin content. The underlying mechanism of coumarin-related hepatotoxicity in a human subgroup has not yet been elucidated. Considering all the data available, evidence for a genetic polymorphism of CYP2A6 with deficient 7-hydroxylation of coumarin as the cause of high sensitivity is missing. Especially investigations of coumarin metabolism pathways in patients with hepatotoxic response following the treatment with coumarin and in subjects with deficient 7-hydroxylation would be helpful to clarify the toxic mechanism. As long as these data are not available the question of a possibly metabolic cause for the hepatotoxicity in the human subgroup remains open, and other possible mechanisms have to be considered as well. Evidence of coumarin hepatotoxicity in a subgroup of the human population is striking and has to be considered for risk assessment of coumarin, whatever the underlying mechanism may be. Considering the lowest dose (25 mg€coumarin daily) with documentation of a hepatotoxic response in patients, a TDI of 0.1€mg/kg body weight daily was derived identical to the TDI derived by EFSA in 2004 using animal data [4]. For the latter derivation, an inter-species factor of 10 was used, as confirmed by EFSA in 2008 [27]. A reduction of this factor [53] from 10 to 2.5 (no kinetic subfactor) was suggested by Felter et al. [8], based on the fact that – in contrast to humans – the CYP2A6 mediated detoxification to 7-hydroxycoumarin is only a minor pathway in many animal species including rodents and dogs. Their TDI of 0.64€mg/kg body weight daily, starting from a NOAEL in rats of 0.16€mg/kg body weight daily [41], would be equivalent to an absolute dose of about 38€mg coumarin (assuming a body weight of 60€kg); for these doses, however, elevated liver enzyme levels and/ or hepatitis is documented in sensitive people. Therefore, a reduction of the inter-species factor, based on kinetic data only, may be misleading in risk assessment, because a high susceptibility in a relevant human subgroup may be due to dynamic causes not covered by an inter-species factor of 2.5 and an intra-species factor of 10. Comparison of the toxicological data on coumarin available from animals and humans on the other hand demonstrates that toxicological mechanisms may not be identical, and that in humans an important subgroup may be much more susceptible than the majority in the population. If available, data on toxicological effects in humans should be considered in risk assessment with high preference. Often such data are not available and risk assessment has to use animal data. In this case, a reduction of safety factors during extrapolation from animals to humans should only be made if this is securely justified.

289

290

Contributions

Abbreviations BfArM: Federal Institute for Medicinal Products and Medical Devices BfR: Bundesinstitut für Risikobewertung BVL: Federal Office of Consumer Protection and Food Safety CVUA: Chemisches und Veterinäruntersuchungsamt CYP: cytochrome P450 EFSA: European Food Safety Authority o-HPA: o-hydroxyphenylacetaldehyde o-HPAA: o-hydroxyphenylacetic acid NOAEL: No Observed Adverse Effect Level TAMDI: Theoretical Added Maximum Daily Intake TDI: Tolerable Daily Intake

Conflict of Interest Statement The authors have declared no conflict of interest.

References â•⁄ 1. Egan, D., O’Kennedy, R., Moran, E., Cox, D., et al., The pharmacology, metabolism, analysis, and applications of coumarin and coumarin-related compounds. Drug Metab. Rev.╃1990, 22, 503–529. â•⁄ 2. Hazleton, L.╃W., Tusing T.╃W., Zeitlin B.╃R., Thiessen R., Murer H.╃K., Toxicity of coumarin. J.€Pharmacol. Exp. Ther. 1956, 118, 348–358. â•⁄ 3. Lake, B.╃G., Coumarin metabolism, toxicity and carcinogenicity: relevance for human risk assessment. Food Chem. Toxicol. 1999, 37, 423–453. â•⁄ 4. EFSA, Opinion of the Scientific Panel on Food Additives, Flavourings, Processing Aids and Materials in Contacts with Food (AFC) on a request from the Commission related to Coumarin; adopted on 6 October 2004, EFSA J.╃2004, 104, 1–36.╃Includes in the Appendix also the previous opinions of the Scientific Committee on Food (SCF) of 1994 and 1999. â•⁄ 5. BfR, High daily intakes of cinnamon: Health risk cannot be ruled out. Health Assessment No.╃044/2006, 2006, www.bfr.bund.de/cm/245/high_daily_intakes_of_cinnamon_health_risk_cannot_be_ruled_out.pdf. â•⁄ 6. Sproll, C., Ruge, W., Andlauer, C., Godelmann, R., Lachenmeier, D.╃W., HPLC analysis and safety assessment of coumarin in foods. Food Chem. 2008, 109, 462–469. â•⁄ 7. EU Food Law, Cinnamon flavoured cereal recalled over coumarin levels. EU Food Law 2006, November 10: 10–11. â•⁄ 8. Felter, S.╃P., Vassallo, J.╃D., Carlton, B.╃D., Daston, G.╃P., A safety assessment of coumarin Â�taking into account species-specificity of toxicokinetics. Food Chem. Toxicol. 2006, 44, 462–75.

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

â•⁄ 9. Edwards, A.╃J., Price, R.╃J., Renwick, A.╃B., Lake, B.╃G., Lack of effect of coumarin on unscheduled DNA synthesis in the in vivo rat hepatocyte DNA Repair Assay. Food Chem. Toxicol. 2000, 38, 403–409. 10. Api, A.╃M., Lack of effect of coumarin on the formation of micronuclei in an in vivo mouse micronucleus assay. Food Chem. Toxicol. 2001, 39, 837–841. 11. Swenberg, J.╃A., Covalent binding index study on coumarin, Report of Laboratory of Molecular Carcinogenesis and Mutagenesis, University of North Carolina, Chapel Hull, NC 27599, USA. April 2003, Submitted by European Flavour and Flagrance Association (EFFA), Square Marie-Louise, 49, B–1000, Brussels. 12. Hagan, E.╃C., Hansen, W.╃H., Fitzhugh, O.╃G., Jenner, P.╃M., et al., Food flavourings and compounds of related structure. II. Subacute and chronic toxicity. Food Cosmet. Toxicol. 1967, 5,141–157. 13. Pearce, R., Greenway, D., Parkinson, A., Species differences and interindividual variation in liver microsomal cytochrome P450 2A enzymes: effects on coumarin, dicumarol, and testosterone oxidation. Arch. Biochem. Biophys. 1992, 298, 211–225. 14. Vassallo, J.╃D., Hicks, S.╃M., Daston, G.╃P., Lehman-McKeeman, L.╃D., Metabolic detoxification determines species differences in coumarin-induced hepatotoxicity. Toxicol. Sci.╃2004, 80, 249–257. 15. Marshall, M.╃E., Mohler, J.╃L., Edmonds, K., Williams, B., et al., An updated review of the clinical development of coumarin (1,2-benzopyrone) and 7-hydroxycoumarin. J. Cancer Res. Clin. Oncol. 1994, 120 Suppl, S39–42. 16. Cox, D., O’Kennedy, R., Thornes, R.╃D., The rarity of liver toxicity in patients treated with coumarin (1,2-benzopyrone). Hum. Toxicol. 1989, 8, 501–506. 17. WHO, Coumarin: a strong association with hepatotoxicity. WHO Drug Information, 1995, 9, 159. 18. Andrejak, M., Gersberg, M., Sgro, C., Decocq, G., et al., French pharmacovigilance survey evaluating the hepatic toxicity of coumarin. Pharmacoepidemiol. Drug. Saf.╃1998, 7 Suppl 1, S45–50. 19. Mohler, J.╃L., Gomella, L.╃G., Crawford, E.╃D., Glode, L.╃M., et al., Phase II evaluation of coumarin (1,2-benzopyrone) in metastatic prostatic carcinoma. Prostate. 1992, 20, 123–131. 20. Loprinzi, C.╃L., Kugler, J.╃W., Sloan, J.╃A., Rooke, T.╃W., et al., Lack of effect of coumarin in women with lymphedema after treatment for breast cancer. N. Engl. J. Med.╃1999, 340, 346–50. 21. Burian, M., Freudenstein, J., Tegtmeier, M., Naser-Hijazi, B., et al., Single copy of variant CYP2A6 alleles does not confer susceptibility to liver dysfunction in patients treated with coumarin. Int. J. Clin. Pharmacol. Ther. 2003, 41, 141–147. 22. Schmeck-Lindenau, H.╃J., Naser-Hijazi, B., Becker, E.╃W., Henneicke-von Zepelin, H.╃H., Schnitker, J. Safety aspects of a coumarin-troxerutin combination regarding liver function in a double-blind placebo-controlled study. Int. J. Clin. Pharmacol. Ther. 2003, 41, 193–199. 23. Vanscheidt, W., Rabe, E., Naser-Hijazi, B., Ramelet, A.╃A., et al., The efficacy and safety of a coumarin-/troxerutin-combination (SB-LOT) in patients

291

292

Contributions

with chronic venous insufficiency: a double blind placebo-controlled randomised study. Vasa. 2002, 31, 185–190. 24. Adam, B.╃S., Pentz, R., Siegers, C.╃P., Strubelt, O., Tegtmeier, M., Troxerutin protects the isolated perfused rat liver from a possible lipid peroxidation by coumarin. Phytomedicine. 2005, 12, 52–61. 25. Bergmann, K., Sachverständigengutachten zur Beurteilung von Cumarin in Arzneimitteln in Bezug auf lebertoxische Wirkung beim Menschen, Rheinische Friedrich-Wilhelms-Universität Bonn. 1999, Original report written in German available from the BfArM, Bonn. 26. Abraham, K., Zimt und Cumarin: eine Klarstellung aus wissenschaftlichbehördlicher Sicht. Dtsch. Lebensmittel-Rundschau. 2007, 103, 480–487. 27. EFSA, Scientific Opinion of the Panel on Food Additives, Flavourings, Processing Aids and Materials in Contact with Food on a request from the European Commission on Coumarin in flavourings and other food ingredients with flavouring properties. The EFSA Journal. 2008, 793, 1–15. 28. Pelkonen, O., Rautio, A., Raunio, H., Pasanen, M., CYP2A6: a human coumarin 7-hydroxylase. Toxicology. 2000, 144, 139–147. 29. Zhang, Q.╃Y., Gu, J., Su, T., Cui, H., et al., Generation and characterization of a transgenic mouse model with hepatic expression of human CYP2A6. Biochem. Biophys. Res. Commun. 2005, 338, 318–324. 30. Ritschel, W.╃A., Brady, M.╃E., Tan, H.╃S., First-pass effect of coumarin in man. Int. J. Clin. Pharmacol. Biopharm. 1979, 17, 99–103. 31. Rautio, A., Kraul, H., Kojo, A., Salmela, E., Pelkonen, O., Interindividual variability of coumarin 7-hydroxylation in healthy volunteers. Pharmacogenetics. 1992, 2, 227–233. 32. Sharifi, S. Pharmakokinetische Untersuchungen von Cumarin und seinen Metaboliten nach oraler und intravenöser Applikation bei gesunden Probanden. PhD thesis 1990, University of Göttingen. 33. Sharifi, S., Lotterer, E., Michaelis, H.╃C., Bircher, J., Pharmacokinetics of coumarin and its metabolites. Preliminary results in three volunteers. J.╃Ir. Coll. Physicians Surg. 1993, 22, 29–32. 34. Meineke, I., Desel, H., Kahl, R., Kahl, G.╃F., Gundert-Remy, U., Determination of 2-hydroxyphenylacetic acid (2HPAA) in urine after oral and parenteral administration of coumarin by gas-liquid chromatography with flameionization detection. J. Pharm. Biomed. Anal. 1998, 17, 487–492. 35. Shilling, W.╃H., Crampton, R.╃F., Longland, R.╃C., Metabolism of coumarin in man. Nature. 1969, 221, 664–665. 36. Hadidi, H., Zahlsen, K., Idlem J.╃R., Cholerton, S.╃A., Single amino acid substitution (Leu160His) in cytochrome P450 CYP2A6 causes switching from 7-hydroxylation to 3-hydroxylation of coumarin. Food Chem. Toxicol. 1997, 35, 903–907. 37. Mizutani, T., PM frequencies of major CYPs in Asians and Caucasians. Drug Metab. Rev.╃2003, 35, 99–106. 38. Rautio, A., Polymorphic CYP2A6 and its clinical and toxicological significance. Pharmacogenomics J.╃2003, 3, 5–7.

Toxicology and Risk Assessment of Coumarin: Focus on Human Data

39. Peamkrasatam, S., Sriwatanakul, K., Kiyotani, K., Fujieda, M., et al., In vivo evaluation of coumarin and nicotine as probe drugs to predict the metabolic capacity of CYP2A6 due to genetic polymorphism in Thais. Drug Metab. Pharmacokinet. 2006, 21, 475–484. 40. Rietjens, I.╃M., Boersma, M.╃G., Zaleska, M., Punt, A., Differences in simulated liver concentrations of toxic coumarin metabolites in rats and different human populations evaluated through physiologically based biokinetic (PBBK) modeling. Toxicol. In Vitro. 2008, 22, 1890–1901. 41. Carlton, B.╃D., Aubrun, J.-C., Simon, G.╃S., Effects of coumarin following perinatal and chronic exposure in Sprague–Dawley rats and CD-1 rats. Fundam. Appl. Toxicol. 1996, 30, 145–151. 42. Sotaniemi, E.╃A., Rautio, A., Bäckstrom, M., Arvela, P., Pelkonen, O., CYP3A4 and CYP2A6 activities marked by the metabolism of lignocaine and coumarin in patients with liver and kidney diseases and epileptic patients. Br. J. Clin. Pharmacol. 1995, 39, 71–76. 43. Pasanen, M., Rannala, Z., Tooming, A., Sotaniemi, E.╃A., et al., Hepatitis A impairs the function of human hepatic CYP2A6 in vivo. Toxicology. 1997, 123, 177–184. 44. Uetrecht, J., Idiosyncratic Drug Reactions: Past, Present, and Future. Chem. Res. Toxicol. 2008, 21, 84–92. 45. Rychlik, M., Quantification of free coumarin and its liberation from glucosylated precursors by stable isotope dilution assays based on liquid chromatography-tandem mass spectrometric detection. J. Agric. Food. Chem. 2008, 56, 796–801. 46. WHO (World Health Organization) 1999, Cortex Cinnamomi. WHO monographs on selected medicinal plants, Volume 1, Geneva, 1999, pp.╃96–104. 47. Miller, K.╃G., Poole, C.╃F., Chichila, T.╃M.╃P., Solvent-assisted supercritical fluid extraction for the isolation of semivolatile flavor compounds from cinnamons of commerce and their separation by series-coupled column gas chromatography. J. High. Resol. Chromatogr. 1995, 18, 461–471. 48. Weber, G., Zimt – Warenkunde und Markt. Dtsch. Lebensmittel-Rundschau. 2007, 103, 492–493. 49. Banasiak, U., Heseker, H., Sieke, C., Sommerfeld, C., Vohmann, C., Abschätzung der Aufnahme von Pflanzenschutzmittel-Rückständen in der Nahrung mit neuen Verzehrsmengen für Kinder. Bundesgesundheitsbl Gesundheitsforsch Gesundheitsschutz. 2005, 1, 84–98. 50. Floc’h, F., Mauger, F., Desmus, J.╃R., Gard, et al., Coumarin in plants and fruits: implication in perfumery. Perfumer Flavorist. 2002, 27, 332–336. 51. Yourick, J.╃J., Bronaugh, R.╃L., Percutaneous absorption and metabolism of Coumarin in human and rat skin. J. Appl. Toxicol. 1997, 17, 153–8. 52. SKLM (Senatskommission zur Beurteilung der gesundheitlichen Unbedenklichkeit von Lebensmitteln), Natural Constituents of Foods: Assessing the toxicity of substances administered as pure compounds compared to their ingestion as inherent food components. Statement of the DFG Senate Commission on Food Safety. 2006,

293

294

Contributions

http://www.dfg.de/aktuelles_presse/reden_stellungnahmen/2006/download/sklm_natinh_en_05092006.pdf. 53. Renwick, A.╃G., Data-derived safety factors for the evaluation of food additives and environmental contaminants. Food Addit. Contam. 1993, 10, 275– 305.

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

3.16 Risk from Furocoumarins in Food? An€�Exposure€�Assessment Dieter Schrenk1, Sabine Guth2, Nicole Raquet1, Christiane Lohr1, and Eva Gorgus1

Abstract Furocoumarins occurring in food plants including citrus species, parsnip, parsley, celery, and figs are phototoxic and photogenotoxic natural constituents. They exhibit dose- and time-related phototoxic and photogenotoxic properties in combination with UV radiation, though less is known about the phototoxicity of the coumarin derivative limettin mainly found in limes and lemons. Cases of combined consumption of furocoumarin-rich food and UV exposure have been associated with phototoxic skin reactions, while long-term oral exposure to high doses of certain pure furocoumarins in PUVA therapy may lead to some types of skin tumours in humans and experimental animals. Risk assessment of natural furocoumarins in the diet is currently based on a threshold approach and on estimates of 1.2–1.45 mg for the average daily exposure to furocoumarins for adults via the diet in several countries. In these estimates, the major contribution to overall daily exposure has been attributed to citrusflavoured non-alcoholic beverages, in spite of a lack of analytical data for those products. Therefore, we analyzed a number of furocoumarins in a variety of citrus-containing beverages and included limettin in the pattern of analyzed constituents. In summary, our findings provide strong evidence that grapefruit juice and not citrus-flavoured non-alcoholic beverages is the major source of furocoumarin exposure in a Western diet. Based on these findings it can be assumed that the average dietary exposure to furocoumarins is probably about three-fold lower than previously estimated, i.╃e. in the range of 548 and 2237╃µg/day for the average and high consumer, respectively. The coumarin derivative limettin was mainly found in lime products. The question of phototoxic properties of limettin and related coumarins warrants further investigation.

3.16.1 Introduction Furocoumarins are natural plant constituents, bearing a coumarin basic structure attached to a furan ring, while limettin is a natural coumarin derivative

1

University of Kaiserslautern, Food Chemistry and Toxicology, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany.

2

Senate Commission on Food Safety (SKLM), Scientific Secretariat, University of Kaisers� lautern, Food Chemistry and Toxicology, Erwin-Schroedinger-Str.╃52, D-67663 Kaisers� lautern, Germany.

295

296

Contributions

(Fig.╃1). Furocoumarins are usually divided into linear (psoralen type) and angular (angelicin type) compounds. They occur in various plant families such as Apicaceae and Umbelliferae, most notably in species such as Ammi, Pimpinella, Angelica and Heracleum, in Fabaceae, and in citrus plants belonging to the Rutaceae [1, 2]. Most probably, these plants produce furocoumarins as protectants (phytoalexins) against microorganisms and/ or other external factors. In humans and laboratory animals, systemic and dermal application of furocoumarins can lead to phototoxic effects in combination with UV light, i.╃e. furocoumarins strongly enhance the basic skin toxicity of UV light. Furthermore, photoactivation of furocoumarins can result in DNA damage, mutations, and skin cancer [3–5]. In food, furocoumarins occur, e.╃g., in celery (Apium graveolens L.), parsnip (Pastinaca sativa), parsley (Petrosilenum crispum), carrot (Daucus carota L.), orange (Citrus sinensis L.), lemon (Citrus limon), lime (Citrus aurantifolia) [2], and grapefruit [6]. In particular citrus oils can contain very high levels of furocoumarins, if the oils are produced by cold-pressing of citrus fruits or peels without subsequent distillation. Furthermore, furocoumarin levels can vary tremendously depending on the conditions of crop cultivation and storage. Likewise, microbial infections of stored parsnips can result in an enormous increase in furocoumarin levels of up to 570 mg/kg [7] or 2500 mg/kg [8], respectively. In citrus fruits, furocoumarins are mainly found in the peel. In lime peel, levels of 334 mg/kg (Westindian origin) and 502 mg/kg (Iranian origin) were found. Major coumarin and furocoumarin components are limettin and 5-MOP. In lime flesh, levels were much lower (5–6 mg/kg) isopimpinellin being the major furocoumarin [9]. Few data are available on furocoumarin levels in citrus flavourings, extracts etc. It can be expected, however, that marked differences exist between cold pressed and distilled citrus oils because of the very low volatility of furocoumarins under standard conditions. The worldwide production of cold-pressed lime oil was estimated as 100–150 metric tons in 1967, the furocoumarin content being in the range of 3–6â•›%. No reliable data are available for more recent production figures. According to these estimates, addition of 50 mg lime oil to one litre of flavoured non-alcoholic beverages would result in concentrations of up to 3 mg/l in the consumer product. No data were available, however, on the actual furocoumarin levels in such products [10]. The uptake of furocoumarins via food is probably subject to pronounced inter-individual and daily variation. High exposure may occur from celery or parsnips infected with microorganisms. Likewise, consumption of 200â•›g of infected parsnips could lead to an uptake of up to 100 mg total furocoumarins per adult, assuming a total furocoumarin content of 500 mg/kg with microbial infections [10]. Estimates for the average daily intake of furocoumarins via food in adults were published being in the range of 1.3 mg for the US [2], and 1.45 mg for Germany [10]. For the United Kingdom, the extreme daily dietary intake of furocoumarins has been estimated to be 1.2 mg [11]. According to these estimates, citrus oils used as flavourings in caffeine-containing and caffeine-free non-alcoholic beverages (soft drinks) represent the major source for

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

furocoumarins in food. In contrast, fruits and vegetables are considered being of minor importance. In this study, we analyzed the actual furocoumarin levels as bergamottin, isopimpinellin, and 5-methoxypsoralen (5-MOP) in a variety of citrus-flavoured non-alcoholic beverages, citrus juices, concentrate (lime syrup), and home-style caipirinha to improve the database for a better estimate of average furocoumarin intake. We also analyzed the levels of the coumarin derivative limettin also occurring in citrus juices and oils because of the possible photo(geno-)toxicity of this compound.

3.16.2 Materials and Methods 3.16.2.1 Materials

Limettin (citropten, 5,7-dimethoxycoumarin), 8-MOP, and 5-MOP (bergapten) were from Sigma Aldrich (Steinheim, Germany), bergamottin (geranyloxypsoralen) Rotichrom® and isopimpinellin Rotichrom® from Roth (Karlsruhe, Germany). Diethylether p.╃a. was obtained from Hüls (Marl, Germany), ethanol and methanol for spectroscopy, and acetonitril from Baker (Deventer, The Netherlands), chloroform for spectroscopy from Fisher Scientific (Schwerte, Germany), and ethyl acetate for spectroscopy Uvasol® from Merck (Darmstadt, Germany). Food products analyzed were “Limette Pur lime juice”, “Brasilian Lime”, a caffeine-free an a caffeine-containing soft drink, “Frucht-Sirup Limette”, and

Figure 1: Structures of selected furocoumarins and limettin.

297

298

Contributions

“grapefruit juice”. Limes of Brazilian origin were purchased from a local retailer. For the preparation of home-style caipirinha, the upper and lower end of a lime were cut off, and the fruit was cut into 12 pieces. These were mixed with 15â•›g of brown cane sugar and squeezed for 1 min with a wooden pestle. Then, 50â•›g of crushed ice and 40 ml of 40â•›% aqueous ethanol were added, kept at room temperature for 15 min and decanted. The supernatants from six preparations were compound for extraction.

3.16.2.2 Extraction and Sample Preparation

Sample extraction was carried out following the methods of Nigg et al. [9] with a few modifications. Briefly, 10–25 ml of sample were mixed with 15 ml diethyl ether, agitated for 2 min, and centrifuged at 3000â•›g and 5╃°C for 5╃min. The organic layer was removed. The aqueous layer was extracted three more times in the same way. The organic phases were combined in a 100 ml vessel and the solvent was evaporated to dryness under reduced pressure and room temperature. The solid residue was dissolved in 2 ml acetonitril/ 1 ml water under gentle heating in an ultrasonic bath. The solution was then applied to a C18 Solid Phase Extraction (SPE) column (3ml/ 500mg Bakerbond SPE octadecyl-modified silica; J.╃T. Baker, Deventer, The Netherlands), pre-conditioned with 10 ml ethanol/ 10 ml water. The eluate was re-applied to the column four times. Then, the column was rinsed under slightly reduced after-column pressure with 15 ml 60â•›% aqueous acetonitril and the eluate was collected in a vessel containing 10 ml ethanol. The solvents were evaporated under reduced pressure in a water bath at 50–70╃°C. The dry residue was dissolved in 4╃×╃1 ml chloroform and applied to a SiOH SPE column (3ml/ 500mg Bakerbond SPE unmodified silica; J.╃T. Baker, Deventer, The Netherlands), pre-conditioned with 10 ml chloroform. Elution was carried out with 15 ml 7.5â•›% ethyl acetate in chloroform. The solvents were removed under reduced pressure; the dry residue was dissolved in a defined amount of a 1:1 methanol/ water mixture and filtered through a Nylon®/ PVDF membrane filter (0.45╃µm). All steps were carried out under red light any other light being strictly excluded. For the determination of the recovery rate a defined amount of 8-MOP was added in certain experiments.

3.16.2.3 Chromatographic Analysis

HPLC analysis of furocoumarins was carried out according to Frérot and Decorzant [12]. The chromatographic system comprised a Beckman System Gold HPLC system equipped with diode array detection (System Gold 168 Detector), and an Interchim column filled with Uptisphere ODB MS 3 150╃×╃2 mm, 3╃µm with pre-column (UP3ODB 10╃×╃2 mm) both from Laubscher (Labs, Switzerland).

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

The furocoumarin content was analyzed based on peak areas of absorbance at a wave length of 310 nm using external standards. For data analysis the HPLC data analysis program 32 Karat 5.0 was used. Calibration curves were established with standard compounds in a concentration ranging from 2.5 to 50╃µg/ ml. Constituents were identified based on their characteristic retention times and UV spectra. Separation and elution were carried out using solvent A (wate r:acetonitril:tetrahydrofuran; 85:10:5) and solvent B (acetonitril:methanol:tetr ahydrofuran; 65:30:5) with the following gradient: 0–5 min, 0â•›% B; 5–20 min, 0–32â•›% B (linear); 20–24 min, 32â•›% B; 24–38 min, 32–55â•›% B (linear); 38–40 min, 55–90â•›% B (linear); 40–90min, 90â•›% B; 90–116 min, 0â•›% B (conditioning) at a constant flow rate of 0.3 ml/min and an injection volume of 50╃µl.╃

3.16.3 Results and Discussion The lowest reference furocoumarin/ limettin concentration which could be quantified for all reference compounds (LOQ; limit of quantification) was 2.5╃µg/ml. All reference compounds exhibited characteristic UV absorbance spectra between 200 and 300 nm and a broad absorbance maximum above 300╃nm. Mean retention times were 38.6 min (8-MOP), 44.0 min (limettin), 46.8 min (isopimpinellin), 48.2 min (5-MOP) and 74.4 min (bergamottin). The separation of all identified furocoumarins and limettin was very good allowing an identification of all targeted compounds based on their retention time and UV absorbance spectrum. In all samples (Tab.╃1) limettin was among the most abundant target compounds and could be quantified. It showed particularly high levels in lime peel, while commercial lime juice, lime syrup and home-style caipirinha contained much lower levels. Low or very low concentrations of limettin were found in a commercial grapefruit juice, and in soft drinks. Among the furocoumarins, bergamottin, isopimpinellin, and 5-MOP could be detected at very high levels in lime peel, at much lower levels in commercial lime juice and lime syrup, and in home-style caipirinha, their sum being in the same range as limettin. A commercial grapefruit juice contained no detectable amounts of isopimpinellin and 5-MOP while bergamottin was more abundant than in the commercial lime products. Brazilian Lime contained isopimpinellin and 5-MOP but did not show any detectable levels of bergamottin. No furocoumarins were found in soft drinks. In the literature, few data are available on the furocoumarin levels in citrus fruits. According to a qualitative survey by Ramaswamy [1], lime is a furocoumarin-rich fruit. In lime oil, Cieri [13] found bergamottin (2.2–2.5â•›%), 5-MOP (0.17–0.33â•›%), and traces of 5-OH-psoralen and isopimpinellin. Similar levels of 5-MOP were reported by Zaynoun et al. [14] for bergamot oil. In Iranian lime Nigg et al. [9] found traces of psoralen and the following levels in the peel (means): 5.9╃µg/g 8-MOP, 127.7╃µg/g 5-MOP, 53.7╃µg/g isopimpinellin, and 310.1╃µg/g limettin, and in the flesh: 0.1╃µg/g 8-MOP, 1.1╃µg/g 5-MOP, 2.9╃µg/g isopimpinellin, and 1.7╃µg/g limettin. The levels found in lime peel in this study

299

300

Contributions

are very similar except for bergamottin which was not described by Nigg et al. The authors estimated the sum of furocoumarins in freshly prepared lemonade from limes as about 3╃µg/g. In fact, we found about 800╃µg/100 ml i.╃e.╃8╃µg/ml in a commercial lime juice as the sum of isopimpinellin, 5-MOP, and bergamottin. Considering a typical dilution of juices for the preparation of home-made lemonades, the estimate by Nigg et al. appears to be realistic. Furocoumarins exhibit phototoxic and photo-genotoxic properties in combination with UVA irradiation [15–17]. It is thought that they can intercalate between strands of the DNA double helix, and upon UVA irradiation, form covalent bindings with DNA bases. While angular furocoumarins form monoadducts, both mono- and cross-linking di-adducts can be formed from linear furocoumarins [18–21]. Furthermore, UV-activation of furocoumarins can lead to the formation of cytotoxic/ genotoxic singlet oxygen [20, 22], to lysosomemediated cell damage [23], and to the formation of covalent protein adducts with possible antigen properties [24]. In experimental animals, furocoumarins (5-MOP, 8-MOP and others) show a low acute and subchronic toxicity in the absence of relevant doses of UV light [25–30]. In a number of chronic toxicity/ carcinogenicity studies in mice, however, repeated dermal application of 5-MOP, 8-MOP or psoralen together

Table 1: Furocoumarin and limettin contents1 in citrus or citrus-flavoured food items and lime peel (in µg/100 ml or 100â•›g, respectively). Sample

Number of samples

Limettin

Iso� pimpinellin

5-MOP

Bergamottin

Sum of means (furocoumarins)

Lime juice pure

15

1,443╃±â•ƒ301

218╃±â•ƒ52

418╃±â•ƒ84

107╃±â•ƒ31

743

Lime syrup

9

734╃±â•ƒ85

102╃±â•ƒ11

177╃±â•ƒ23

246╃±â•ƒ70

525

Brazilian Lime

10

150╃±â•ƒ22

96╃±â•ƒ13

17╃±â•ƒ4

< LOQ

113

Caipirinha2

12

562╃±â•ƒ51

226╃±â•ƒ13

244╃±â•ƒ20

61╃±â•ƒ30

531

Lime peel3

7

52,615╃±â•ƒ4766

6,862╃±â•ƒ679

Grapefruit juice

9

37╃±â•ƒ2

< LOQ

< LOQ

1,650╃±â•ƒ106

1,650

Caffeinefree soft drink

4

10╃±â•ƒ1

< LOQ

< LOQ

< LOQ

< LOQ

Caffeinecontaining soft drink

4

1╃±â•ƒ0,2

< LOQ

< LOQ

< LOQ

< LOQ

19,006╃±â•ƒ1572 29,723╃±â•ƒ3809

1 Means and standard deviations from n samples. 2 Prepared as described under “Materials and Methods”. 3 Isolated as described under “Materials and Methods”. LOQ: limit of quantification.

55,591

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

with UVA or simulated sunlight led to an enhanced formation of papilloma or carcinoma of the epidermis [31–33]. In humans, PUVA (psoralen/UVA) therapy of psoriasis and other dermal diseases can lead to skin irritations and erythema. Long-term observations have indicated that certain types of PUVA therapy may be related to an increased incidence of skin tumours such as squamous cell carcinoma, basalioma and melanoma [5, 34–36]. Brickl et al. [37] described 14 mg 8-MOP per adult as the lowest phototoxic single oral dose corresponding to about 0.23 mg/kg bw for a 60 kg adult. Schlatter et al. [38] tested a combination of 8-MOP and 5-MOP in healthy volunteers and found a phototoxic single oral “threshold dose” of 10 mg 8-MOP plus 10 mg 5-MOP (according to the authors, corresponding to 15 mg “8-MOP equivalents”, i.╃e. about 0.25 mg equivalents/kg bw for a 60 kg adult). Much more frequently than after oral exposure to pure furocoumarins, direct contact of the skin to furocoumarin-containing plants including celery, parsnip, carrot, parsley, citrus fruits, and figs has been reported to lead to photodermatitis in humans [39–42]. Severe dermal damage has been reported in particular after contact with limes [43, 44]. However, very few information is available about the risk of consumption of furocoumarin-containing food combined with UV light. In one case, high consumption of celery corresponding to an estimated total furocoumarin dose of 45 mg was described to have resulted in severe sunburn after subsequent UV exposure of the skin [45], while exposure to a parsnip meal containing 45╃µg 8-MOP had no phototoxic effect [46]. In order to estimate the amounts of beverages/ food items to be consumed to achieve a daily intake of 20 mg furocoumarins which is in the range of the phototoxic single oral threshold dose we made calculations based on the furocoumarin levels shown in Table 1.╃It was found (Tab.╃2) that none of the food items analyzed contained enough furocoumarins to exceed the phototoxic threshold level assuming realistic consumption levels. Even for grapefruit juice a daily consumption of >1.2â•›l appears unrealistic. However, we can not exclude that other types or brands of grapefruit juice may contain higher levels of furocoumarins. A cumulative situation where several food items contribute to a composite exposure exceeding the 20 mg value, also appears unlikely since grapefruit juice, lime syrup and lime juice are competing food items unlikely to be consumed in parallel by the same individual. In the case of home-style caipirinha, it was evident that the preparation failed to extract a high percentage of the furocoumarins present in the lime peel used. Reasons for this fact may be that the used aqueous ethanol is not lipophilic enough for efficient extraction, in particular at approximately 0╃°â•–C. From these data, even extensive consumption of caipirinha is unlikely to lead to phototoxic effects. Consumption of the tested non-alcoholic soft drinks does not cause any risk of phototoxicity due to furocoumarins. For the purpose of risk assessment, realistic exposure scenarios are of outstanding importance. Estimates for the average daily furocoumarin intake via food in adults have been published being in the range of 1.3 mg for the US [1], and 1.45 mg for Germany [10]. For the United Kingdom the extreme daily di-

301

302

Contributions

etary intake of furocoumarins has been estimated to be 1.2 mg [11]. According to these estimates citrus oils as flavouring ingredients in caffeine-containing and caffeine-free non-alcoholic beverages are considered to be the major source of furocoumarin exposure. In general, fruits and vegetables are considered as minor contributors, whereas much higher exposure may occur when microbially infected vegetables are consumed. According to these estimates, the average daily intake, e.╃g. in Germany, is about 1000-fold lower than the photo-/ genotoxic and carcinogenic dose range in animals, and about 10-fold lower than the phototoxic “threshold dose” in humans. We have taken the German average and maximum consumption estimates for grapefruit juice and orange juice from SKLM [10] as a basis for exposure assessment. Furthermore, we have assumed that lime juice accounts for 1â•›% of the total juice consumption, while established data on lime juice consumption were not available. Furthermore, we also used consumption data for caffeine-free and caffeine-containing soft drinks assuming that the furocoumarin contents in the tested soft drinks are not different from those of major competitors on the market. This assumption could be confirmed by exploratory analysis of such products (data not shown). Among the caffeine-free soft drinks we estimated that 1â•›% were lime juice-based beverages such as Fanta® World Limette. Data from the National Nutrition Survey (NNS) are now available for Germany [47, 48]. We replaced the consumption data for fruit juices and lemonades from 1995 used by SKLM [49] by these recent data. The consumption of fruit juices has increased substantially, whereas the consumption of soft drinks has remained within the same range. The NNS data do not distinguish between caffeine-containing and caffeine-free soft drinks. However, this fact was considered of minor importance since no relevant furocoumarin levels were found in both. According to the NNS, vegetable juices are only consumed in minor amounts and, therefore, are not included in the estimation.

Table 2: Amount of various food items or lime peel containing 20 mg furocoumarins based on average contents (Tab.╃1). Food item

Amount (ml or g)

Lime juice pure

â•⁄ 2,691

Lime syrup

â•⁄ 3,809

Brazilian Lime

17,699

Caipirinha

â•⁄ 3,766

1

Lime peel2

â•⁄â•⁄â•‹â•⁄ 36

Grapefruit juice

â•⁄ 1,212

Caffeine-free soft drink

No furocoumarins determined

Caffeine-containing soft drink

No furocoumarins determined

1 Prepared as described under “Materials and Methods”. 2 Isolated as described under “Materials and Methods”.

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

Non-flavoured food and bakery products/ pastries were not investigated in our study. Since the information from the NNS II is not as detailed as in the former survey [49] which further subdivided into several vegetable/ fruit varieties or different bakeries/ pastries the estimates for composite furocoumarin intake via other non-flavoured foods and bakery products/ pastries were taken from SKLM [10]. As shown in Table 3, a major contributor of dietary furocoumarin exposure is grapefruit juice, whereas the soft drinks analyzed in our study did not contribute to furocoumarin exposure with the exception of a lime-based product (Fanta® World Limette). However, the levels in this beverage were low enough to exclude it as a relevant source of exposure even in case of extensive consumption. Because of lack of consumption data we did not include alcoholic beverages in Table 3.╃According to the NNS II survey the consumption of flavoured alcoholic beverages including cocktails is 3â•›g per day [48]. The maximum contribution from limoncello (Italian lemon liquor) is therefore estimated to be 0.06 mg indicating a very low contribution from this type of drink. In a study by Andrea et al. [50] bergamottin was found in limoncello in a range of 0.9–21.5 mg/l, besides small amounts of imperatorin. The levels found in home-style caipirinha, also support the assumption of a very low contribution to the overall furocoumarin exposure. The mean limettin (citropten) level in home-style caipirinha was found at 562╃µg/100 ml being slightly higher than the 30–400╃µg/100 ml reported for limoncellos [50]. Although lime peel used for the preparation of caipirinha contained very high limettin levels, the about 50–100-fold lower levels found in caipirinha (and lime juice) indicate a limited transfer, possibly due to the weak water solubility of limettin, together with some dilution during preparation of the beverages. Others found limettin in lime peel [9] at about 300╃µg/g, in the flesh at 1–3╃µg/g, and in lime oil [51] at more than 4000╃µg/g. Application of a 1â•›% limettin solution to human skin did not result in phototoxic reactions [52]. Likewise, no phototoxcicity was reported in Guinea pigs but limettin was phototoxic in rabbits [53]. Unpublished findings in our laboratory indicate that limettin is photogenotoxic in vitro exhibiting potency comparable to that of the furocoumarin angelicin. Thus, limettin and other coumarins present in lime and other citrus products warrant further attention.

3.16.4 Conclusions In summary, our findings provide strong evidence that grapefruit juice and not citrus-flavoured non-alcoholic beverages is the major source of potentially phototoxic furocoumarin exposure in a Western diet. Based on these findings it can be assumed that the average dietary exposure to furocoumarins is probably about three-fold lower than previously estimated, i.╃e. in the range of 548 and 2237╃µg/day for the average and high consumer, respectively. This conclusion is based on the complete lack of quantifiable furocoumarins in widely consumed

303

304

Contributions Table 3: Estimated average and maximum daily intake of furocoumarins in Germany. Foodstuff

Average consumption (g per day)2 (48)

Maximum consumption (g per day)3 (48)

Fruit/ vegetable juices estimated to contain: 10â•›% grapefruit juice 1â•›% lime juice 60â•›% orange juice with 0.25â•›% orange oil

251

1100

25.1 2.51 150.6 0.38

110 11 660 1.65

Average furocoumarin concentration (from Table 1) (µg/kg)

Average/ maximum furocoumarin intake (µg/person per day)

16,500 7,430

414/1815 â•⁄ 19/82

0.5

0.0002/0.0008

Sum

433/1897

Other non-flavoured foods1

â•⁄ 39/91

A. Sum of dietary furocoumarins from non-flavoured food

472/1988

Soft drinks including 156 caffeine-containing ones [48]

915

< LOQ

–

Bakery products/ pastries1

â•⁄ 76/248

B. Sum of dietary furocoumarins from flavoured food

â•⁄ 76/248

Total (A + B)

548/2236

According to [10]. 1 2 Mean for women and men. 3 95th percentile for women and men. LOQ: Limit of quantification.

soft drinks. The coumarin derivative limettin was mainly found in lime products. The question of phototoxic properties of limettin and related coumarins will be addressed in future studies.

Acknowledgements The authors wish to thank Dr. Klaus Reif, Phytolab, Vestenbergsgreuth, for his advice with respect to analytical methods. This work was supported by a �research grant from Steigerwald Corp., Darmstadt, Germany.

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

References â•⁄ 1. Ramaswamy S.╃1975.╃Psoralens in foods. Ind Food Packer 8, 37–46. â•⁄ 2. Wagstaff DJ.╃1991.╃Dietary exposure to furocoumarins. Reg Toxicol Pharmacol 14, 261–272. â•⁄ 3. IARC. 1986â•›a.╃5-methxypsoralen. International Agency for Research on Cancer. Monogr Eval Carcinog Risk Chem Man 40, 327. â•⁄ 4. IARC. 1986â•›b. Angelicin and some synthetic derivatives. International Agency for Research on Cancer. Monogr Eval Carcinog Risk Chem Man 40, 291. â•⁄ 5. IARC. 1987. 8-methxypsoralen (methoxsalen) plus ultraviolet radiation. International Agency for Research on Cancer. Monogr Eval Carcinog Risk Chem Man Suppl. 7, 261. â•⁄ 6. Tassaneeyakul W, Guo L-Q, Fukuda K, Ohta T, Yamazoe Y.╃2000.╃Inhibition selectivity of grapefruit juice components on human cytochrome P450. Arch Biochem Biophys 378, 356–363. â•⁄ 7. Ostertag E, Becker T, Ammon J, Bauer-Aymanns H, Schrenk D.╃2002.╃Effects of storage conditions on furocoumarin levels in intact, chopped or homogenized parsnips (Pastinaca sativa L.). J Agr Food Chem 50, 2565–2570. â•⁄ 8. Ceska O, Chaudhary SK, Warrington PJ, Poulton GA, Ashwood-Smith MJ.╃1986.╃Naturally occurring crystals of photocarcinogenic furocoumarins on the surface of parsnip roots sold as food. Experientia 42, 1302–1304. â•⁄ 9. Nigg HN, Nordby HE, Beier RC, Dillman A, Macias C, Hansen RC.╃1993.╃Phototoxic coumarins in limes. Fd Chem Toxicol 31, 331–335. 10. SKLM. 2006.╃Toxicological assessment of furocoumarins in foodstuffs. DFG Senate Commission on Food Safety. Kaiserslautern, Germany. 11. COT.╃1996.╃Toxicity, Mutagenicity and Carcinogenicity Report 1996.╃Committee on Toxicity of Chemicals in Food, Consumer Products and the Environment. http://www.archive.official-documents.co.uk/document/doh/ toxicity/chap-1â•›c.htm.╃ 12. Frérot E, Decorzant E.╃2004.╃Quantification of total furocumarins in citrus oil by HPLC couples with UV, fluorescence, and mass detection. J Agric Food Chem 52, 6879–6886. 13. Cieri UR.╃1969.╃Characterisation of the steam non-volatile residue of bergamot oil and some other essential oils. J Assoc Off Anal Chem 52, 719–728. 14. Zaynoun ST, Johnson BE, Frain-Bell W.╃1977.╃A study of oil of bergamot and its importance as a phototoxic agent. Br J Dermatol 96, 475–482. 15. Ashwood-Smith MJ, Poulton GA, Barker M, Mildenberger M.╃1980. 5-Methxypsoralen, an ingredient in several suntan preparations, has lethal, mutagenic and clastogenic properties. Nature 285, 407–409. 16. Berkley SF, Hightower AW, Beier RC, Fleming DW, Brokopp CD, Ivie GW, Broome CV.╃1986.╃Dermatitis in grocery workers associated with high natural concentrations of furocoumarins in celery. Ann Intern Med 105, 351– 355. 17. Schlatter J.╃1988.╃Die toxikologische Bedeutung von Furocoumarinen in pflanzlichen Lebensmitteln. Mitt Geb Lebensm Hyg 79, 130–143.

305

306

Contributions

18. Musajo L, Rodighiero G.╃1972.╃Mode of photosensitizing action of furocoumarins. Photophysiol 7, 115–147. 19. Dall’Acqua F.╃1977.╃New chemical aspects of the photoreaction between psoralen and DNA. In: Research in Photobiology (Castallani A, ed.), Plenum, New York, pp 245–255. 20. Grossweiner LI.╃1984.╃Mechanisms of photosensitization by furocoumarins. Natl Cancer Inst Monogr 66, 47–54. 21. Dall’Acqua F, Vedaldi D, Caffieri S, Guiotto A, Bordin F, Rodighiero G.╃1984.╃Chemical basis of the photosensitizing activity of angelicins. Natl Cancer Inst Monogr 66, 55–60. 22. Joshi PC, Pathak MA . 1983.╃Production of singlet oxygen and superoxide radicals by psoralens and their biological significance. Biochem Biophys Res Commun 112, 638–646. 23. Fredericksen S, Nilesen PE, Hoyer PE.╃1989.╃Lysosomes: A possible target for psoralen photodamage. J Photochem Photobiol B3, 437–447. 24. Gasparro FP, Liao B, Foly PJ, Wang XM, Madison McNiff JM.╃1990.╃Psoralen chemotherapy, clinical efficacy, and photomutagenicity: The role of molecular epidemiology in minimizing risks. Environ Mol Mutagen 31, 105–112. 25. Apostolou A, Williams RE, Comereski CR.╃1979.╃Acute toxicity of micronized 8-methoxy-psoralen in rodents. Drug Chem Toxicol 2, 309–313. 26. Herold H, Berbey B, Angignard D, Le Duc R.╃1981.╃Toxicological study of the compound 5-methoxypsoralen (5-MOP). In: Psoralens in Cosmetics and Dermatology (Cahn J, Forlot P, Grupper C, Maybeck A, Urbach F, eds.), Pergamon Press, New York, NY, pp.╃303–309. 27. Booer M.╃1970.╃Pharmacological activity of coumarins isolated from Afraegle paniculata. Ghana J Sci 10, 82. 28. Chandhoke N, Ghatak BJRA. 1975.╃Pharmacological investigations of angelicin. Tranquillosedative and anticonvulsant agent. Indian J Med Res 63, 833. 29. Dunnick JK, Davis WE, Jorgenson TA, Rosen VJ, McConnell EE.╃1984.╃Subchronic toxicity in rats administered oral 8-methoxypsoralen. Natl Cancer Inst Monogr 66, 91–95. 30. NTP.╃1989.╃Technical Report on the Toxicology and Carcinogenesis Studies of 8-Methoxy-psoralen (CAS No.╃298–81–7) in F344/N rats (gavage studies). National Toxicology Program. NIH Publication No.╃89–2814.╃US Department of Health and Human Services, National Institutes of Health, Bethesda Md, USA. 31. Zajdela F, Bisagni E.╃1981. 5-Methoxypsoralen, the melanogenic additive in sun tan preparations, is tumorigenic in mice exposed to 365 nm UV irradiation. Carcinogenesis 2, 121–127. 32. Cartwright LE, Walter JF.╃1983.╃Psoralen-containing sunscreen is tumorigenic in hairless mice. J Am Acad Dermatol 8, 830–836. 33. Young AR, Magnus IA, Davies AC, Smith NP.╃1983.╃A comparison of the phototumorigenic potential of 8-MOP and 5-MOP in hairless albino mice exposed to solar simulated radiation. Br J Dermatol 108, 507–518.

Risk from Furocoumarins in Food? An€�Exposure€�Assessment

34. Stern RS, Liebmann EJ, Vakeva L.╃1998.╃Oral psoralen and ultraviolet-A light (PUVA) treatment of psoriasis and persistent risk of nonmelanoma skin cancer. PUVA follow-up study. J Natl Cancer Inst 90, 1278–1284. 35. Stern RS et al.╃2001.╃The risk of melanoma in association with long-term exposure to PUVA. J Am Acad Dermatol 44, 755–761. 36. Katz KA, Marcil I, Stern RS.╃2002.╃Incidence and risk factors associated with a second squamous cell carcinoma or basal cell carcinoma in psoralen + ultraviolet A light-treated psoriasis patients. J Investig Dermatol 118, 1038– 1043. 37. Brickl R, Schmid J, Koss FW.╃1984.╃Pharmacokinetics and pharmacodynamics of psoralens after oral administration: Considerations and conclusions. J Natl Cancer Inst Monogr 66, 63–67. 38. Schlatter J, Zimmerli B, Dick R, Panizzon R, Schlatter C.╃1991.╃Dietary intake and risk assessment of phototoxic furocoumarins in humans. Food Chem Toxicol 29, 523–530. 39. Finkelstein E, Afek U, Gross E, Aharoni N, Rosenberg L, Halevy S.╃1994.╃An outbreak of phytophotodermatitis due to celery. Int J Dermatol 33, 116– 118. 40. Lutchman L, Inyang V, Hodgkinson D.╃1999.╃Phytophotodermatitis associated with parsnip picking. J Accid Emerg Med 16, 453–454. 41. Smith DM.╃1985.╃Occupational photodermatitis from parsley. 1985.╃Practitioner 229, 673–675. 42. Watemberg N, Urkin Y, Witztum A.╃1991.╃Phytophotodermatitis due to figs. 48,151–152. 43. Weber IC, Davis CP, Greeson DM.╃1999.╃Phytophotodermatitis: the other “lime” disease. J Emerg Med 17, 235–237. 44. Wagner AM, Wu JJ, Hansen RC, Nigg HN, Beiere RC.╃2002.╃Bullous phytophotodermatitis associated with high natural concentrations of furanocoumarins in limes. Am J Contact Dermat 13, 10–14. 45. Ljunggren B.╃1990.╃Severe phototoxic burn following celery ingestion. Arch Dermatol 126, 1334–1336. 46. Beattie PE, Wilkie MJ, Smith G, Ferguson J, Ibbotson SH.╃2007.╃Can dietary furanocoumarin ingestion enhance the erythemal response during highdose UVA1 therapy? J Am Acad Dermatol 56, 84–87. 47. National Nutrition Survey II (Nationale Verzehrsstudie II). Report part 1 –Ergebnisbericht Teil 1. 2008.╃Editor: Max Rubner-Institut, Federal Research Institute of Nutrition and Food. http://www.was-esse-ich.de/uploads/media/NVS_II_Ergebnisbericht_Teil_1.pdf 48. National Nutrition Survey II (Nationale Verzehrsstudie II), Report Part 2 –Ergebnisbericht Teil 2. 2008.╃Editor: Max Rubner-Institute, Federal Research Institute of Nutrition and Food. http://www.was-esse-ich.de/uploads/media/NVSII_Ergebnisbericht_Teil2.pdf. 49. Ausschuss für Umwelthygiene (AUH). 1995.╃Standards zur Expositionsabschätzung. Bericht des Ausschusses für Umwelthygiene. Behörde für Arbeit, Gesundheit und Soziales, Hamburg (ed.), Hamburg.

307

308

Contributions

50. Andrea V, Nadia N, Teresa RM, Andrea A.╃2003.╃Analysis of some Italian lemon liquors (Â�limoncello). J Agric Food Chem 51, 4978–4983. 51. Stanley WL, Vannier SH.╃1967.╃Psoralens and substituted coumarins from expressed oil of lime. Phytochemistry 6, 585–596. 52. Marzulli FN, Maibach HI.╃1970.╃Perfume phototoxicity. J Soc Cosmet Chem 21, 695–715. 53. Naganuma M, Hirose S, Nakayama Y, Nakajima K, Someya T.╃1985.╃A study of the phototoxicity of lemon oil. Arch Dermatol Res 278, 31–36.

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

3.17 Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components Jaap Keijer1,2, Yvonne G.╃J. van Helden1,3,4, Annelies Bunschoten1, and Evert M. van Schothorst1. This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 218–227.

Abstract The establishment of functional effects due to variation in concentrations of micronutrients in our diet is hard, since they are often not immediately recognized as being healthy or unhealthy. Indeed, effects induced by micronutrients are complicated to identify and therefore the establishment of the recommended daily intake, the optimal intake and the upper limit pose a challenge. For bioactive food components this is even more difficult. Whole genome transcriptome analysis is highly suitable to obtain unbiased information on potential affected biological processes on a whole genome level. Here, we will describe and discuss several aspects of transcriptome analysis in benefit–risk assessment, including effect size, sensitivity and statistical power, that have to be taken into account to faithfully identify functional effects of micronutrients and bioactive food components.

3.17.1 Introduction It is well established that different intakes of micronutrients affect our health. How they do this over a range of concentrations is less clear. This information is important for determining optimal intakes and for the application of micronutrients in dietary strategies to increase a healthy life-span. Clearly, deficient as well as excessive intakes of micronutrients can result in physiological changes and ultimately in pathological effects. Between these extremes, many physiological effects over a range of concentrations can occur, with a certain optimal beneficial intake. Benefit–risk assessment means to establish the borders where shortage changes into “enough”, defined as the recommended daily intake (RDI), and where “enough” changes into “too much”, defined as the

1

Wageningen University, Human and Animal Physiology, P.╃O. Box 338, NL-6700 AH Wageningen, The Netherlands.

2

Correspondence to: [email protected].

3

Maastricht University, Research Institute NUTRIM, Department of Health Risk Analysis and Toxicology, P.╃O. Box 616, NL-6200 MD Maastricht, The Netherlands.

4

RIKILT-Institute of Food Safety, P.╃O. Box 230, NL-6700 EA Wageningen, The Netherlands.

309

310

Contributions

upper limit (UL), as well as the establishment of the optimal beneficial intake (Fig.╃1, [1]). The RDI is established based on the minimal intake that is needed to avoid deficiency symptoms in 97╛% of the population. While in some cases this is already difficult to establish, it is even more difficult to determine the optimal beneficial concentration, supporting maximal health over a life-time. Indeed, for most micronutrients this is not known. To be able to assess this properly, an overview of all possible effects is needed and functional genomic technologies are potentially suitable to obtain such an overview. What is true for micronutrients is also true for bioactive food components with claimed beneficial health effects as a consequence of habitual intake, such as polyphenols and carotenoids. Of course, for bioactive food components functionality has to be established first. The question can be raised whether a slightly higher intake than the RDI can be of additional functional importance. That this may be true has been shown for the micronutrient selenium, which, to our knowledge, is the only example available at present. It is well known that (severe) selenium deficiency results in Keshan disease, Kashin-Beck disease, and even symptoms of hypothyroidism due to its function in thyroid metabolism (reviewed by [2]), while an excess intake results in toxicity known as selenosis [3]. Although epidemiological studies showed positive health effects of high selenium intake, the underlying mechanisms were not clear, making it difficult to causally link intake to effect. Gene expression analysis of human lymphocytes showed an increase in the protein translation machinery when the selenium status was increased from just below to a level just above the RDI [4]. The same result, an increase in the protein translation machinery, was also found when gene expression in the colon of wild-type C57BL/6J mice on marginal deficient and adequate selenium diets were compared [5], thereby validating the observations made in humans. At present it is not clear whether the effect on the protein translation machinery is related to the main physiological effects associated with deficiency (e.╃g. Keshan disease), but its identification allows to establish not only this causality, but also

Figure 1: The recommended daily intake (RDI) and the upper limit (UL) border the window of benefit [35]. Deficiency occurs below the RDI and toxicity above the UL. Between these borders the amount of a micronutrient can be adequate to support physiological performance, but additive benefit may occur at certain concentrations or under certain conditions (adapted from [1]).

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

the minimal, and possibly optimal, required selenium intake for optimal protein translation capacity, making it a potential biomarker. This example indicates that understanding the mechanisms of nutrient-induced functional effects is important, since they may affect fitness, wellbeing and healthy aging. Moreover, understanding the mode of action will help to define susceptible population groups. By definition, no overt pathology is involved at micronutrient intakes between the RDI and UL and we are thus faced with the task to identify small functional and molecular effects. This poses constraints on the experimental setup and on the analysis techniques. Furthermore, we do not know which effects will occur and the use of an initial unbiased approach is essential. Whole genome transcriptome analysis is highly suited for this. In this review we describe this technique and several technical as well as experimental considerations with regard to its application in nutritional studies in general and in benefit–risk assessment in more detail.

3.17.2 Whole Genome Transcriptome Analysis as a Tool for Benefit– Risk Analysis A highly suitable tool towards the identification of mechanisms underlying nutrient-induced functional effects is whole genome expression profiling (also known as whole genome DNA microarray analysis). This is a highly comprehensive profiling technique, able to screen for differences in gene expression between (groups of) individuals. This technique has become robust over the past years, and is able to detect small differences between individuals and is thus suitable as a first phase screen in benefit–risk analysis. In transcriptome profiling, global gene expression in one condition is compared to another condition. There are several different microarray platforms, of which those of Affymetrix and Agilent are mostly used. To determine global gene mRNA expression, both systems use a linear amplification and labelling procedure to obtain cRNA which is hybridized to the microarray. Affymetrix uses statistical ranking of the binding of cRNA to multiple sense versus missense probes per probe set, with each probe being 16 to 25 bases in length. In contrast Agilent probes are 60 bases in length, but only one or a limited number of probes are used to represent an expression product. A second difference is that the Affymetrix platform uses a single dye to label an experimental sample, and normalization is fully statistical, while Agilent arrays can hybridize and detect two or even more dyes, for example one for the experimental sample and one for a reference sample. Both types of arrays have become highly robust and allow generation of high-quality data [6]. More details are given below. The Agilent platform is used as an example in this manuscript. Agilent whole genome microarrays contain roughly 44,000 probes, including positive and negative controls, which represent over 40,000 different annotations. Since some annotations represent the same gene, the arrays allow screening for differences in expression of over at least 25,000 unique genes, plus around 1,400 annotations of unassigned genes. Since generally two dyes

311

312

Contributions

are used, direct comparison of two different experimental samples is possible, although this approach has two major disadvantages. The first disadvantage is that a dye swap is necessary, since the two dyes give rise to different results, due to chemical differences in the labelling procedure. The second disadvantage is that only pre-defined comparisons can be analyzed and experiments comparing more experimental groups require highly intricate designs. To circumvent these disadvantages a reference pool design can be used. A reference pool design allows for post hoc selection of the experimental groups to be compared and a dye swap is not necessary. In a reference pool design, each experimental sample is individually labelled with a fluorescent dye (e.╃g. Cy5-cRNA) and hybridized to a microarray together with the same amount of a mixed sample, ideally containing an equal molar mixture of all experimental samples, labelled with a second dye (e.╃g. Cy3-cRNA) [7, 8]. 500 ng of total input RNA suffices in the amplification and labelling reaction [8]. After hybridization, the microarrays are washed and scanned. The fluorescent intensity for each probe on the array is then integrated and represents a quantitative measure for the amount of the corresponding mRNA in the sample. The microarrays first pass a quality control pipeline that controls labelling and hybridization. Since values close to the background may give aberrant fold-changes between groups, these are generally discarded for further analysis. We normally discard all signal values that are on average below two times the local background values for both the samples and the reference pool, although with present day microarray reproducibility lower cut-offs are possible. In general, at least 50â•›%–60â•›% of the probes are being expressed two times above background in any tissue analyzed. In the next step, expressed probe intensities are normalized by correcting the signals relative to the reference sample probe signals [7]. The use of the reference sample facilitates experimental normalization and, compared to single dye experiments, is less sensitive to technical variation such as hybridization conditions and microarray quality. The use of a method that is low in technical variation is crucial for benefit–risk assessment, since changes in gene expression are small and the used method has to be sensitive enough to separate these changes from the technical noise. In the end, a fluorescent normalized log intensity value is obtained for each of the expressed probes on the array, for each experimental sample. A datasheet results that is used for downstream applications, such as statistical analyses, pathway overrepresentation analysis, cluster analysis, and other methods that facilitate data interpretation (see below).

3.17.3 Data Confirmation by qRT-PCR Changes in gene expression identified using whole genome microarrays are usually confirmed by quantitative real-time PCR (qRT-PCR). In qRT-PCR, specific mRNA are amplified using intron-spanning primers and amplification is monitored in real-time allowing mRNA quantification. To normalize for possible variation in the amount and quality of RNA between different samples, the level of a target gene is compared and expressed relative to that of one or

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

more reference genes. Therefore qRT-PCR has a semi-quantitative character and the choice of reference genes is particularly important for the validity of the results. The reference genes should be stably expressed over all the conditions that are analyzed. For example, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), historically used as a reference gene in mRNA Northern blot quantification, shows 1.5- to 3-fold changes in many dietary interventions (Tab.╃1). It is therefore generally not suitable as a reference gene, especially since changes of around 1.3-fold are often encountered as being physiologically relevant in nutritional studies. Other reference gene candidates like 18S rRNA, general transcription factor II (GTFII) and microarray identified stable expressed genes are better suited as reference candidates. Nevertheless, testing the stability of the expression of different reference genes for the experimental conditions examined remains a sometimes difficult prerequisite and it has therefore been proposed to use the geometrical mean of multiple reference genes instead of using a single reference gene [9]. As for microarray analysis, qRT-PCR analysis needs to meet up to several qualitatively important thresholds. We and others use normally a standard curve for each primer pair which should result by linear regression analysis in a R2 of >0.99 (preferably >0.995) and a PCR efficiency of 100â•›%±â•ƒ10â•›%. More qRT-PCR details and thresholds are recently put forward to be included in analysis and publications (MIQE) similar to the MIAME compliant strategy for microarray analysis [10]. Initially, qRT-PCR validation of microarray results was an absolute necessity, because microarray analysis was insufficiently robust. Present day microarray platforms in the hands of experienced groups are highly robust and technical validation is more a matter of making sure, than of necessity. Indeed, recent experiments in our lab and others have given identical results in a quantitative and qualitative manner, provided that microarray selected reference genes were used in qRT-PCR. It can be argued that data obtained using microarrays are more dependable, rather than less, compared to qRT-PCR, which is particularly important when small differences in gene expression are considered. The microarray whole genome transcriptome profiling technique is a powerful tool to measure differences in mRNA quantity. Differences in mRNA expression are of interest, but are only of relevance if they match to functional biology, and thus are linked to changes in either protein or metabolite levels, or Table 1: Differential gene expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in adipose tissue of wild-type C57BL/6J male adult mice following nutritional intervention (n=╃12 per group) as analyzed by DNA microarray analysis (for details see text and legend of Fig.╃2). Gene Name

GAPDH C: CR: LF: E:

CR versus C

LF versus C

E versus C

ratio

p-value

ratio

p-value

Ratio

p-value

3.32

5.4·10–9

1.46

0.001

-1.02

0.69

Control, a purified 30enâ•–% high fat diet. 30â•›% caloric restriction of C. A purified low fat diet (10en% fat, fat substituted by starch). C plus 0.5â•›% EGCg.

313

314

Contributions

to the predicted physiological changes. Differences at the protein level can be confirmed using Western blotting [11] or flowmeter based multiplex analysis [12], which both provide quantitative information. Moreover, immunohistochemistry [13] can be used to provide positional information. However, these methods can be difficult or even impossible to perform when a specific and well characterized antibody is lacking or when protein concentrations are low. Another technique that can be used is 2D gel electrophoresis coupled to mass spectrometry [14]. Using this technique, proteins are separated by their isoelectric point in the first dimension and by the size of protein in the second dimension. Thereafter, proteins are stained and quantified using image analysis software. Differentially expressed proteins can be cut from the gel to be identified by mass spectrometry. This allows for simultaneous analysis of many proteins in one sample, which is referred to as proteome analysis. Proteomics has the advantage over transcriptome analysis that it directly assesses at a functional biochemical level. The diverse characteristics of proteins, including variation in size, hydrophobicity, abundancy and secondary, tertiary, or quaternary modifications imply a smaller window of targets that can be analyzed simultaneously. This precludes replacement of transcriptome analysis for genome wide identification of potential functional effects of micronutrients. This will hold true even if robustness and the window of analysis will be improved by implementing other proteomic approaches that are being developed, which include antibody arrays [15] and chromatographic separation [16]. In many ways proteome analysis is complementary to transcriptome analysis, each targeting a different functional level.

3.17.4 Magnitude of Micronutrient Effects As indicated, benefit–risk assessment will inherently deal with small functional effects, because studies will try to capture stable differences in homeostatic conditions in a relatively short time frame, rather than waiting for a life-time outcome of these changes. To exemplify the magnitude of the effects that can be expected, the number (and magnitude) of genes that changed expression was assessed for a mouse dietary intervention with three different bioactive food components and compared to a strong nutritional intervention (calorie restriction) and a widely examined nutritional intervention (high fat versus low fat). The 30â•›% calorie restriction (CR) intervention resulted in 15,041 genes that were significantly (p<0.05) differentially expressed compared to the control high fat diet. The gene expression response of a low fat diet was approximately three times less (5,603 regulated genes, p<0.05), while a diet supplemented with one of three polyphenols again showed a more than 3- fold lower differential expression using the same cut-off (p<0.05). 1,650 genes changed expression by chronic supplementation with 0.5â•›% epigallocatechingallate (EGCg; E), 1,683 by supplementation with an equimolar amount of resveratrol (R) and 977 by supplementation with an equimolar amount of quercetin (Q). This shows that effects of bioactive food components are relatively small (Fig.╃2),

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

Figure 2: Differential gene regulation by nutritional interventions as analyzed by microarrays. Wild-type C57BL/6J male adult mice (n=╃72) received a control purified high fat diet (30en╖% fat) during a 3 week run-in period, were stratified on body weight, followed by 13 weeks dietary intervention with control high fat diet (n=╃12), 30╛% caloric restriction (CR, n=╃12), a purified low fat diet (10en╖% fat, fat substituted by starch; LF, n=╃12), a 0.5╛% EGCg supplemented high fat diet (E, n=╃12) or equimolar resveratrol (R, n=╃12) or quercetin (Q, n=╃12) supplemented high fat diets with dietary groups considered to be independent. The CR diet was adjusted to provide mice with equal micronutrients intake. After the intervention, animals were killed and tissues isolated. All 72 murine epididymal white adipose tissue total RNA samples were analyzed using Agilent 44K whole genome microarrays as described in the text. Microarray data were normalized and mean gene expression was calculated per dietary group and analyzed versus the control high fat diet group. First, the number of significantly up-regulated (white bar) and down-regulated (black bar) genes was analyzed (p<0.05, panel 2A), and the maximal observed fold-change range per treatment (treated/ control) is shown as double headed arrows in panel 2B (positive numbers: up-regulated, negative: down-regulated). The animal experiment (DEC2007086) was performed at Wageningen University in The Netherlands fully complying with National and European regulations on animal experimentation. All personnel involved in animal handling have the required experience and certificates.

both in terms of the number of differentially regulated genes (Fig.╃2╛a) and in terms of the fold-changes observed (Fig.╃2╛b) when compared to a high versus low fat diet and especially when compared to calorie restriction. It should be realized that a high versus low fat diet intervention already has a mild effect on gene expression when compared to a pharmacological intervention or gene knock-out strategy [17].

315

316

Contributions

3.17.5 Data Interpretation To determine statistical differences between average quantitative (log) values per expressed probe, different statistical tests can be used depending on the experimental setup and question to be answered. If a pre-defined gene is statistically differentially expressed between two conditions, a Student’s t-test might be perfectly suitable. In this manner the expression values of a few pre-defined genes can be examined. The data are directly interpreted by the investigator, more or less as an alternative to qRT-PCR with an additional advantage that for the normalization thousands of genes are used and signals are therefore more reliable. However, when testing differences in expression levels of thousands of probe sets, a correction for multiple comparisons is needed. For this, the Benjamini-Hochberg false discovery rate (FDR) is a frequently used statistical method [18]. But, as stated above, benefit–risk assessment deals mostly with small effects and the use of this very strict statistical method will result in a very rapid decrease in sensitivity, ultimately rendering insufficient information for functional interpretation. This decrease in sensitivity can be circumvented in two ways. First, by increasing the number of individuals or samples. This has practical disadvantages, including costs, time and space limitations. In addition, there may be ethical hesitation when laboratory animals are used. The second approach to increase sensitivity in data analysis is by considering the genes at the biological process or pathway level rather than at the individual gene level. In practice this is done by pre-selecting the genes by a conventional statistical method, the Student’s t-test or a transcriptome focused modification of this test, such as the Limma t-test. Thereafter, these genes are grouped using pathway, network, or gene ontology (GO) overrepresentation. To consider genes in the context of a process rather than on an individual basis makes biological sense since an orchestrated regulation of the majority of genes within one pathway seems to have more biological relevance than a single gene within the same pathway. As an example, analysis of rat intestinal infection by Salmonella showed consistent up-regulation of chemokines in three repetitions of an oral infection experiment. Although a similar number of chemokines were up-regulated in these 3 consecutive experiments, only a minority of chemokines appeared in all three experiments. These results suggest that a response at the process level is more robust than on individual genes ([19]; unpublished results). Identified pathways have to be investigated subsequently to see whether gene expression changes make biological sense, which is unlikely with false positive genes. Another method to group genes is by the use of correlation analysis which is performed using statistical programmes that examine the correlation in variation between samples [20]. Correlation analysis takes all genes (above threshold) into account and can thus provide information on non-annotated genes. If their behaviour is identical to that of annotated genes, in other words when they group together, they are likely to be functionally correlated [21]. Statistical correlation generally provides limited clues on gene and/ or protein function, because this depends on functional knowledge of the genes present within each group.

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

Although a selection of significantly regulated genes is often used to group genes and to investigate regulated pathways, functional knowledge can also be gained with pathway analysis programmes on a selection of, or even the whole dataset. These programmes are bioinformatic software programmes that examine the overrepresentation of certain pathways/ networks/ GO terms in the dataset (public available tools like GSEA, Ermine J, DAVID/EASE, Pathvisio or commercial tools from Ingenuity or MetaCore, among others, [22, 23]). The pathway analysis programmes rapidly improve, but still are far from optimal for two main reasons. First, a large number of gene annotations (most) are not recognized or included in the pathways, nor properly translated (e.╃g. murine gene annotation loaded into a human pathway analysis programme) [21]. Second, the pathways are built based on existing knowledge, leading to a bias for well studied pathways and a bias for “classic” views and questions and against new parts, connections or perspectives. For example, although gastro-intestinal hormones can be considered as a common, established denominator of gastrointestinal endocrinology [24], they are not present as an entity in the most frequently used pathway analysis programmes. Despite these current drawbacks, pathway analysis programmes are an indispensable tool in data interpretation. However, manual inspection of the data and literature mining by the scientist is still required and forms an essential, but time-consuming step in the data analysis. It can be envisaged that future analysis will make use of integrated statistical correlation analysis and pathway analysis in a highly structured manner, and a framework of this has been proposed [21].

3.17.6 In Vivo and In Vitro Approaches Transcriptomics is a potential powerful tool for unbiased identification of functional effects of nutrients. However, human studies are difficult to control, making it more difficult to identify small effects. Differences in behaviour and nonnutritional environmental variation add on to difficulties to faithfully monitor consumption patterns as well as consumption of specific food components. The inability to properly monitor the intake of specific food components is in part being tackled by plasma or urine analysis for specific metabolites that can be considered as biomarkers for intake [25]. Another approach is to perform a fully controlled study, but this has its limitations both in terms of duration as well as in terms of number of subjects that can be analyzed. Another disadvantage is that the human population has a high genetic heterogeneity, thus requiring large numbers of study subjects and higher costs. Another important disadvantage in human studies is that only a few tissues can be easily assessed. These are mainly blood, blood cells, urine, saliva and, with more difficulty, muscle, fat and intestinal biopsies. Indeed, peripheral blood mononuclear cells (PBMC) are increasingly used in nutritional transcription profiling, both as a target tissue as well as a surrogate tissue [4, 26, 27]. In vitro approaches, using human immortal cells in culture can also be used for effect analysis. The main advantages are high throughput, a well control-

317

318

Contributions

led environment and relatively low costs, as compared to in vivo analysis. One major disadvantage is that most cell models are tumour-derived or transformed cells that are physiologically compromised. A second disadvantage is that they do not occur in their natural local environment. Furthermore, nutrient metabolism occurring upon ingestion has to be taken into account. For example, most dietary polyphenols are in vivo metabolized in the intestinal mucosa, with functional consequences [28]. As a result of metabolism in intestinal mucosa and liver, peripheral tissues are hardly, or not at all, in contact with the unmetabolized polyphenols. Nevertheless, many in vitro studies using cell cultures from peripheral origin were performed exposing the cells to these unmetabolized polyphenols, which results in physiologically irrelevant studies. Another, more practical disadvantage is that many physiological and molecular parameters are strongly dependent on the exact cell culture conditions, such as cell density. Transcriptome profiling of in vitro dose–response analysis therefore requires multiple replications and even then the results have to be interpreted with extreme caution. Moreover, an in vitro approach is unable to detect effects due to interactions between different organs, as occurs in vivo. Our own experience trying to confirm new leads that were obtained using in vitro models was highly disappointing, making it more logical to first start in vivo and then, knowing the validity of the target and the response, proceed using in vitro or ex vivo systems that show the relevant response.

3.17.7 Animal Models and Diets Micronutritional benefit–risk studies can also be performed in model animals, with rodents being mostly used. Rodent nutritional studies have the advantage that the environment (temperature, stress), as well as diet and food intake can be highly controlled. Moreover, genetic background can, dependent on the strain, largely be controlled and effects on individual tissues can be assessed in relation to each other. Of importance for benefit–risk assessment is that physiology is largely conserved between rodents and humans [29], although the necessity remains to validate observed effects in humans. An additional advantage of mice is the availability of a large number of strains that lack a specific functional gene (knock-out), either completely, or in a tissue-specific or temporal manner. In addition, many transgenic strains do exist that express a specific gene at higher level. Both types of strains allow for analysis of the functionality of the gene of interest. For both mice and rats a large number of experimental tools are available, including species-specific gene expression microarrays and many characterized antibodies. Rat and murine genomes are both sequenced [30, 31] and are moderately (rat) to well (mouse) annotated, and many pathways have been investigated, described and are available in pathway analysis tools. Together, this makes these rodents the preferred species of choice for mechanistic studies. In case of a question that focuses on one specific physiological aspect, other species may have a preference over rodents. For example, ferrets display a carotenoid metabolism that is more similar to humans as compared to rodents.

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

In rodents, beta-carotene, also known as provitamin A, is almost completely cleaved by carotenoid-monooxygenase (BCMO1) in the intestine, while less enzyme activity is observed in the ferret and, like in humans, where intact betacarotene is detected in plasma and peripheral tissues. We have successfully used ferrets to examine effects of beta-carotene in the lung ([32]), but our molecular analyses were frustrating because of a limited availability of annotated ferret sequences (at present, only 311 nucleotide sequences are available in the NCBI database, of which only 111 are annotated as gene (mRNA) which is only a tiny fraction compared to the 265,635 murine mRNA sequences). Rats clearly have the advantage of the larger amount of tissue that is available for analysis and the higher availability of physiological knowledge compared to the mouse. For this and other reasons, rats have become the species of choice in toxicological and physiological studies. Mice were, and still are, the favourite species of molecular geneticists, because knock-out models were earlier and easier available. Also, mice can be maintained at lower costs. Since many nutritional studies focus on disease prevention, nutritionists adopted the mouse as their model of choice following the medical field. As a consequence, mechanistic benefit–risk studies are being performed in rats as well as in mice, often depending on the history of the laboratory involved. Many different mouse strains are being used. The inbred strain that is most widely used in nutrigenomics is C57BL/6J (“black 6”). These mice are obesity prone and are able to develop insulin resistance upon a high fat feeding. Its counterpart is the A/J strain, which is resistant to diet-induced obesity. It should be kept in mind that there are more differences between these strains, for example A/J mice are also sensitive to lung disease. Of course many other strains are being used, dependent on the phenotype that has the interest of the scientist. SV129 is generally encountered, because it provides the usual genetic background in which specific gene knock-outs are generated. Most nutritional toxicology and nutritional studies in rats are being performed using the outbred Wistar strain or the inbred Sprague-Dawley strain, and to a lesser extent the Fisher or Brown Norway strains. In nutritional studies using rodents, attention should be paid to the following two aspects. First, the use of purified diets for all animals, including controls. In many studies chow is used as control and high fat diets are either from different origin or are generated by adding fat. In both cases this will result in many differences apart from the amount of fat and carbohydrate. Chow based diets should be strongly discouraged, since protein and lipid sources may differ from batch to batch and may result in 1000-fold concentration differences of specific nutrients [33], thus masking specific effects of micronutrients or bioactive food components. Therefore, despite higher costs, purified or semi-purified diets of precisely known composition should always be used and described in the materials and methods section. Second, it should be realized that many diets, even purified diets, may contain excessive amounts of certain vitamins and bioactive food components. Generally, vitamin E and beta-carotene are present at high levels, mainly to prevent lipid oxidation, which precludes proper analysis of these and related compounds and may strongly affect the results obtained with,

319

320

Contributions

for example, other anti-oxidant compounds. For example, the vitamin E content in AIN-93G-based purified diets is 300 mg/kg, providing mice on average a daily intake of 25 mg/kg body weight, while for humans the RDI is only 0.131 mg/kg body weight. It is therefore important to carefully appraise the diet when designing an experiment.

3.17.8 Sensitivity and Power As shown above, functional effects of micronutritional compounds are small and, although well controlled, in vivo experiments using rodents have variations close to the nutritional effects. In order to obtain as many statistically significantly regulated genes studies need to be sufficiently powered. To get some insight in the effect of the number of animals used (n) on the number of significantly regulated genes in a micronutritional microarray experiment, we determined the number of significantly regulated genes using a smaller number of animals from the dataset described above. This was done by random assignment of the desired number of animals (n=╃4 up to n=╃11) out of the group of 12 animals for both the intervention group supplemented with EGCg and the intervention group showing the largest effects (CR) as well as the control group. Since different results can be expected depending on the specific animals that were randomly assigned to the group, analysis of the number of significantly regulated genes (with a more stringent level of p<0.01) for a certain n was performed 1000 times with randomly drawn groups. As shown in Figure 3â•›a, the average number of significantly regulated genes of CR versus control group dropped from 12,179 (n=╃12) to just below 6000 regulated genes for n=╃4.╃Examining the effect of the supplementation with 0.5â•›% of the food bioactive compound EGCg, the average number of significantly regulated genes (p<0.01) was much lower than upon CR and dropped from 490 (n=╃11) to 258 (n=╃4) regulated genes on average with decreasing group size (Fig.╃3â•›b). Most striking in these datasets is the large variation in the number of regulated genes when a small number of animals are used (Fig.╃3â•›b). This means, for example, that by using n=╃4 animals 45 regulated genes but also 1500 regulated genes could be identified upon EGCg supplementation, depending on randomly drawn animals in the respective groups. This large variation can result, on the one hand, in a large number of false positives, making it hard to identify “real” results, or on the other hand, this can result in an underestimation of the actual number of regulated genes, making it hard to perform pathway analysis to interpret the regulated genes in a systems biology manner. In benefit–risk assessment, often an absolute fold-change is also used besides the p-value as a cut-off value. We therefore re-analyzed the treatments using an absolute fold-change >1.5 in addition to a p<0.╃01.╃As a result, the average number of regulated genes upon CR reaches a plateau at n>7 (Fig.╃3â•›c). The average number of regulated genes upon a dietary supplementation with 0.5â•›% EGCg did hardly change for n=╃4–11 animals, but the degree of variation between each randomly taken sample dropped dramatically (Fig.╃3â•›d). Finally, analyzing the resveratrol and quercetin supple-

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

mented groups using the same procedure showed results similar to those of the EGCg supplemented group (data not shown). Altogether these results imply that a high number of false positives or false negatives may be obtained when a low number of animals are being used. The number of animals that should be used depends on the criteria that are chosen, such as significance, fold-change, minimal number of genes to perform pathway analysis, but also on the effect that is expected. While in the comparison of calorie restriction versus control no genes are outside the average number of differentially expressed genes plus or minus 25╛% when 9 or more animals (in fact: good quality arrays) are used, in the comparison of EGCg supplementation versus control still 60╛% of the genes are outside this range when n=╃9 animals per group are sampled.

3.17.9 Conclusion In conclusion, micronutrients and bioactive food components may have functional effects that only become apparent in later life, that is, after chronic differences in intake, or under conditions of stress, affecting fitness and wellbeing. While for intake between RDI and UL no overt differences in health can be

Figure 3: Average number of regulated genes during intervention for different group sizes. Number of regulated genes in a microarray gene expression experiment of mice fed the control diet (C) compared to mice fed the calorie restriction (CR) or EGCg (E) diet for different group sizes. To obtain a different group size, the group size number of animals was randomly drawn from a group of 12 animals on the control diet and compared to a similar randomly drawn intervention group. This was performed 1000 times per group size and the number of significant genes per randomly drawn group comparison was calculated and plotted in whiskers box plots (1â•›%–99â•›%). A gene was considered significantly different when 3a, p<0.01 for C vs CR; 3b p<0.01 for C vs E; 3c, an absolute FC >1.5 and p<0.01 for C vs CR and 3d, an absolute FC >1.5 and p<0.01 for C€vs€E.

321

322

Contributions

expected, these differences may be very important for healthy aging and resistance to disease. They may have particular beneficial effects on the overarching processes; metabolic stress, oxidative stress, inflammatory stress and psychological stress [34]. To be able to establish functional health effects of micronutrients and bioactive food components, an overview of mechanistic effects is needed and transcriptome analysis is highly suited as a first step. When applied correctly, it has the power to detect the relatively small differences in gene expression that are induced by micronutrients and food bioactive compounds. It is, however, not a miracle technique. It takes experience and hard work to use it properly and to generate true and useful data. But when used sensibly, it is highly powerful and will generate data that will contribute to the establishment of functional effects and mechanisms of action of micronutrients and bioactive food components, a prerequisite for their application in health promoting functional foods.

Acknowledgements We thank all group members for their efforts over the past years in development of experimental aspects of the application of transcriptome analysis in nutritional intervention studies. We are members of Mitofood (FA0602) and of NuGO. Yvonne van Helden is funded by the graduate school VLAG.

Conflict of Interest Statement All authors declare no financial or commercial conflict of interest.

References â•⁄ 1. Palou, A., Pico, C., Keijer, J., From risk assessment to risk-benefit evaluation: the need for guidelines, concepts and mechanisms, Crit. Rev. Food Sci. Nutr. 2009, 49, 670–680. â•⁄ 2. Chan, S., Gerson, B., Subramaniam, S., The role of copper, molybdenum, selenium, and zinc in nutrition and health, Clin. Lab. Med.╃1998, 18, 673– 685. â•⁄ 3. Koller, L.╃D., Exon, J.╃H., The two faces of selenium-deficiency and toxicity– are similar in animals and man, Can. J. Vet. Res.╃1986, 50, 297–306. â•⁄ 4. Pagmantidis, V., Meplan, C., van Schothorst, E.╃M., Keijer, J., Hesketh, J.╃E., Supplementation of healthy volunteers with nutritionally relevant amounts of selenium increases the expression of lymphocyte protein biosynthesis genes, Am. J. Clin. Nutr. 2008, 87, 181–189. â•⁄ 5. Kipp, A., Banning, A., Van Schothorst, E.╃M., Meplan, C., et al., Four selenoproteins, protein biosynthesis, and Wnt signalling are particularly sensitive to selenium intake in mice colon, Mol. Nutr. Food Res.╃2009, 53, 1561–1572.

Transcriptome Analysis in Benefit–Risk Assessment of Micronutrients and Bioactive Food Components

â•⁄ 6. de Reynies, A., Geromin, D., Cayuela, J.╃M., Petel, F., et al., Comparison of the latest commercial short and long oligonucleotide microarray technologies, BMC Genomics 2006, 7, 51. â•⁄ 7. Pellis, L., Franssen-van Hal, N.╃L., Burema, J., Keijer, J., The intraclass correlation coefficient applied for evaluation of data correction, labeling methods, and rectal biopsy sampling in DNA microarray experiments, Physiol. Genomics 2003, 16, 99–106. â•⁄ 8. van Schothorst, E.╃M., Pagmantidis, V., de Boer, V.╃C., Hesketh, J., Keijer, J., Assessment of reducing RNA input for Agilent oligo microarrays, Anal. Biochem. 2007, 363, 315–317. â•⁄ 9. Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., et al., Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes, Genome Biol. 2002, 3, RESEARCH0034. 10. Bustin, S.╃A., Benes, V., Garson, J.╃A., Hellemans, J., et al., The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments, Clin. Chem. 2009, 55, 611–622. 11. Mathews, S.╃T., Plaisance, E.╃P., Kim, T., Imaging systems for westerns: chemiluminescence vs. infrared detection, Methods Mol. Biol. 2009, 536, 499–513. 12. Djoba Siawaya, J.╃F., Roberts, T., Babb, C., Black, G., et al., An evaluation of commercial fluorescent bead-based luminex cytokine assays, PLoS One 2008, 3, e2535. 13. Sullivan, C.╃A., Chung, G.╃G., Biomarker validation: in situ analysis of protein expression using semiquantitative immunohistochemistry-based techniques, Clin. Colorectal Cancer 2008, 7, 172–177. 14. Bonk, T., Humeny, A., MALDI-TOF-MS analysis of protein and DNA, Neurosci. 2001, 7, 6–12. 15. Zichi, D., Eaton, B., Singer, B., Gold, L., Proteomics and diagnostics: let’s get specific, again, Curr. Opin. Chem. Biol. 2008, 12, 78–85. 16. Conrotto, P., Souchelnytskyi, S., Proteomic approaches in biological and medical sciences: principles and applications, Exp. Oncol. 2008, 30, 171– 180. 17. Patsouris, D., Reddy, J.╃K., Muller, M., Kersten, S., Peroxisome proliferatoractivated receptor alpha mediates the effects of high-fat diet on hepatic gene expression, Endocrinology 2006, 147, 1508–1516. 18. Hochberg, Y., Benjamini, Y., More powerful procedures for multiple significance testing, Stat. Med.╃1990, 9, 811–818. 19. Rodenburg, W., Bovee-Oudenhoven, I.╃M., Kramer, E., van der Meer, R., Keijer, J., Gene expression response of the rat small intestine following oral Salmonella infection, Physiol Genomics 2007, 30, 123–133. 20. Mansson, R., Tsapogas, P., Akerlund, M., Lagergren, A., et al., Pearson correlation analysis of microarray data allows for the identification of genetic targets for early B-cell factor, J. Biol. Chem. 2004, 279, 17905–17913. 21. Rodenburg, W., Heidema, A.╃G., Boer, J.╃M., Bovee-Oudenhoven, I.╃M., et al., A framework to identify physiological responses in microarray-based

323

324

Contributions

gene expression studies: selection and interpretation of biologically relevant genes, Physiol Genomics 2008, 33, 78–90. 22. Werner, T., Bioinformatics applications for pathway analysis of microarray data, Curr. Opin. Biotech. 2008, 19, 50–54. 23. Verducci, J.╃S., Melfi, V.╃F., Lin, S., Wang, Z., et al., Microarray analysis of gene expression: considerations in data mining and statistical treatment, Physiol. Genomics 2006, 25, 355–363. 24. Nauck, M.╃A., Unraveling the science of incretin biology, Am. J. Med.╃2009, 122, S3–S10. 25. Walsh, M.╃C., Brennan, L., Pujos-Guillot, E., Sebedio, J.╃L., et al., Influence of acute phytochemical intake on human urinary metabolomic profiles, Am. J. Clin. Nutr. 2007, 86, 1687–1693. 26. Afman, L., Muller, M., Nutrigenomics: from molecular nutrition to prevention of disease, J.╃Am. Diet. Assoc. 2006, 106, 569–576. 27. Eady, J.╃J., Wortley, G.╃M., Wormstone, Y.╃M., Hughes, J.╃C., et al., Variation in gene expression profiles of peripheral blood mononuclear cells from healthy volunteers, Physiol. Genomics 2005, 22, 402–411. 28. de Boer, V.╃C., de Goffau, M.╃C., Arts, I.╃C., Hollman, P.╃C., Keijer, J., SIRT1 stimulation by polyphenols is affected by their stability and metabolism, Mech. Ageing Dev.╃2006, 127, 618–627. 29. Moreno, C., Lazar, J., Jacob, H.╃J., Kwitek, A.╃E., Comparative genomics for detecting human disease genes, Adv. Genet. 2008, 60, 655–697. 30. Waterston, R.╃H., Lindblad-Toh, K., Birney, E., Rogers, J., et al., Initial sequencing and comparative analysis of the mouse genome, Nature 2002, 420, 520–562. 31. Gibbs, R.╃A., Weinstock, G.╃M., Metzker, M.╃L., Muzny, D.╃M., et al., Genome sequence of the Brown Norway rat yields insights into mammalian evolution, Nature 2004, 428, 493–521. 32. van Helden, Y.╃G., Keijer, J., Heil, S.╃G., Pico, C., et al., Beta-carotene affects oxidative stress related DNA damage in lung epithelial cells and in ferret lung, Carcinogenesis 2009, 30, 2070-2076. 33. Wang, H., Tranguch, S., Xie, H., Hanley, G., et al., Variation in commercial rodent diets induces disparate molecular and physiological changes in the mouse uterus, Proc. Natl. Acad. Sci. USA 2005, 102, 9960–9965. 34. van Ommen, B., Keijer, J., Heil, S.╃G., Kaput, J., Challenging homeostasis to define biomarkers for nutrition related health, Mol. Nutr. Food Res.╃2009, 53, 795–804. 35. Elliott, R., Pico, C., Dommels, Y., Wybranska, I., et al., Nutrigenomic approaches for benefit-risk analysis of foods and food components: defining markers of health, Br. J. Nutr. 2007, 98, 1095–1100.

Colorectal and Prostate Cancer: The Role of �Candidate Genes in Nutritional Pathways

3.18 Colorectal and Prostate Cancer: The Role of �Candidate Genes in Nutritional Pathways Ulrike Peters1

Abstract Colorectal and prostate cancer remain a major public health problem with around 1 million and close to 700,000 cases recorded worldwide in 2002, respectively. Peto and Doll estimated that about one third of cancer deaths can be attributed to dietary factors. Furthermore, twin studies showed that inherited factors attributed up to 35â•›% to 40â•›% of these common cancers [1]. These data indicate that both diet and genetic factors are important contributors to these severe diseases and it is likely that these factors interact with each other. An increasing number of studies investigate the impact of genetic variation in candidate genes of nutritional pathways on cancer risk. This review will describe work for selenium as an important nutritional pathway for colorectal and prostate cancer and will end with a discussion on the overall evidence from candidate gene association studies for cancer.

3.18.1 Selenium: Biologic Mechanisms of the Chemopreventive Â�Effects of Selenium The essential trace element selenium can be found in a variety of foods, including grains, dairy products, eggs, meat, and fish. A chemopreventive effect of selenium against various cancers, including colorectal and prostate cancer has been shown in numerous experimental studies. Direct effects of selenium have been demonstrated for different molecular targets, including nuclear factor-κ B [2], p53 [3], caspase-8 [4], and protein kinase C [5] and indirect effects are mediated through the activity of selenoenzymes, which incorporate selenium as selenocysteine and have important anti-oxidative properties [6]. These selenoenzymes [such as glutathione peroxidases (GPXs) or selenoprotein P] are expressed in the gastro-intestinal tract and prostate tissue. Selenoenzyme activity may also decrease inflammation by reducing hydroperoxides, thus limiting the activation of cyclooxygenases, including COX-2 [7, 8]. Results from mouse studies with targeted disruption of GPX genes provide intriguing support for protective effects of selenium against inflammation and colorectal carcinogenesis: Disruption of both GPX1 and GPX2 genes in mice results in a high susceptibility to bacteria-induced inflammation and colon cancer [9, 10].

1

Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA, Tel. +╃1 206 667 2450, [email protected].

325

326

Contributions

3.18.2 Evidence for a Cancerpreventive Effect of Selenium from Epidemiologic Studies Because questionnaire data are much less accurate given that the selenium content of foods varies substantially depending on the selenium content of the soil where the crop was grown [11, 12] most epidemiological studies relied on biological samples, such as blood, urine, and nails [13] to measure selenium intake. As summarized previously [14], 18 out of 23 studies provide at least some support for a preventive effect of selenium against colorectal cancer and adenoma, although the majority of studies were small with 15 studies reporting fewer than 100 cases. The two largest studies, which were based on about 1╖000 recurrent adenoma cases [15] and 750 adenoma cases [16] both observed a statistically significant inverse association with colorectal cancer, with risk reductions of about 25╛% to 35╛%. For prostate cancer we summarized that results from population-based casecontrol studies provide limited support for a beneficial effect of selenium while, most although not all case-control studies nested within prospective cohorts showed a protective association between selenium levels and prostate cancer risk [14]. More recent analyses of cohort studies included sufficient numbers of prostate cancer cases to investigate advanced cases separately showing consistently slightly stronger protective associations between selenium and risk of advanced prostate cancer; however, risk estimates for advanced diseases are less precise due to smaller numbers [14]. This finding for advanced diseases underlines a potential importance of selenium in prostate cancer prevention given the known higher mortality from advanced diseases, while the relevance of local stage and low grade diseases is less clear. Asides from observational studies two randomized clinical trials have been conducted. First results came from a small trial with 1╖312 patients with a history of skin cancer (randomized to either 200 μg selenium per day or placebo) which investigated cancers other than skin cancers as secondary outcomes [17]. This trial reported a statistically significantly reduced risk for incident cancers of the colon, rectum, lung, and prostate, as well as overall cancer death after a follow-up period of up to 10 years (4.5 years average). These findings spurred substantial interest in selenium as a chemopreventive agent against prostate cancer, although little in the possibility of a similar role against colorectal cancer, despite the fact that two additional years of follow-up provided further and comparable support for both outcomes [18]. Based on the promising observational studies and trial findings the Selenium and Vitamin E Cancer Prevention Trial (SELECT), one of the largest cancer prevention trials conducted to date, was launched. This trial randomized more than 35,000 men to one of the four groups: selenium (200 μg), vitamin E, selenium + vitamin E, or placebo [19]. The primary outcome of the trial was prostate cancer and one of the secondary outcomes was colorectal cancer. The trial was closed early after it was determined that selenium showed no preventive effect against prostate cancer or any other cancers, including colorectal cancer. One of the arguments for the

Colorectal and Prostate Cancer: The Role of �Candidate Genes in Nutritional Pathways

null finding in SELECT that has been made is that the selenium concentrations of the participating men were already relatively high at baseline, beyond which selenium supplementation may have no further beneficial effect on the activity of selenoenzymes [20], as supported by our finding in prostate tissue [21]. So far data on genetic variation in selenoenzymes and risk of cancer are limited [22]. Stratifying participants based on their genetic background may provide further information about the underlying pathways. We recently resequenced several selenoenzymes relevant for colorectal and prostate cancer (GPX1–4, SEPP1, and TXNRD1) and identified numerous genetic variants [23], which we genotyped in a nested case-control study for colorectal adenoma [22]. While this study supports an impact of some of the genetic variants in these selenoenzymes on colorectal carcinogenesis, these findings need to be replicated in large independent study populations, ideally those with available data on serum selenium concentrations to further explore potential interactions. Further information about the genetic susceptibility may be provided by currently ongoing genome-wide association studies. Although initial genomewide scans have not pointed towards the involvement of selenoenzymes in prostate and colorectal cancer [24–31] it is possible that further analyses in larger scans, powered to identify weaker associations, will do so. Incorporating information on the genetic background into the ongoing selenium prevention trials may allow identifying subgroups that are particularly susceptible to the potential chemopreventive effect of selenium supplementation.

3.18.3 Overall Evidence from Candidate-Gene Association Studies for Cancer Given the available technology at the time, candidate-gene association studies became the workhorse to examine low-penetrance loci in common complex diseases. However, they have been criticized for limited reproducibility [32, 33] [34–36]. As an attempt to identify loci genuinely associated with cancer, we systematically examined results of meta-analyses and pooled analyses [37], showing that among 344 gene-variant cancer associations 100 were significant at p<0.╃05.╃However, to avoid an inflated type I error rate due to multiple testing we applied the false positive report probability (FPRP) [38]. At a prior probability suggested for candidate-gene association studies (1000:1) [39] only 13 of the 100 associations remained noteworthy [40–48] and may reflect cancer susceptibility loci, one of which is for CRC (Tab.╃1). These 13 associations were also statistically highly significant (p-value 0.0002 to 9·10–15). Four of these ten associations remained noteworthy even at a much lower probability of 1,000,000:1, similar to a probability assumed for a randomly selected SNP in a genome-wide scan, adding further evidence for a true association. Overall, our analysis demonstrates success of candidate-gene association studies, although the number of identified susceptibility loci is modest in light of the large number of conducted studies. However, at the same time the number of tested variants in candidategene association studies has been very small compared with several hundred

327

328

Gene

Variant

MAF

OR

95╃%╖CI

p-value

GSTM1

Null

0.5

1.5

1.3–1.6

1.9·10

28

NAT2

Slow acetyl.

0.56

1.46

1.26–1.68

2.5·10–7

D302H

0.13

0.88

0.84–0.92

CHEK2

*1100delC

0.007

2.34

Colorectal

GSTT1

Null

0.21

Leukemia

GSTM1

Null

GSTT1 XPD

Type Bladder

Breast

Lung

Prostate 1

Studies

Cases

N

FPRP1 at Prior Prob.

Ref

0.001

0.000001

5,072

<0.001

<0.001

[40]

36

5,747

<0.001

<0.163

[41]

1.1·10

14

16,423 0.028

0.967

[42]

1.72–3.20

–8

8.0·10

10

10,860 0.037

0.974

[43]

1.37

1.17–1.60

8.1·10–5

11

1,490

0.074

0.988

[44]

0.28–0.57

1.20

1.14–1.25

8.6·10

19

3,532

<0.001

<0.001

[45]

Null

0.08–0.32

1.19

1.14–1.29

3.5·10–8

17

3,484

0.023

1.000

[45]

Lys751Gln

0.30

1.30

1.13–1.49

0.0002

15

5,004

0.143

0.994

[46]

XRCC1

Arg399Gln

0.27–0.46

1.34

1.16–1.54

5.2·10

6

1,702

0.038

0.975

[47]

RNASEL

Asp541Glu

0.42–0.57

1.27

1.13–1.44

0.0001

6

3,038

0.162

0.995

[48]

CASP8

–14

–7

–15

–5

Shading indicates noteworthiness of association at suggested ≤╃0.2 level. None of the listed findings showed evidence of publication bias or Â�heterogeneity.

Contributions

Cancer

thousand markers in genome-wide scans. These results confirm that minimum sample sizes of 1000â•›s are required to obtain robust evidence [35, 49–51].

Table 1. Meta-analysis and pooled analysis of candidate-gene studies for cancer. Limited to significant associations that are noteworthy [False Positive Report Probabilities (FPRP) ≤╃0.2] at a prior probability of 1 000:1.

Colorectal and Prostate Cancer: The Role of �Candidate Genes in Nutritional Pathways

References â•⁄ 1. P. Lichtenstein, et al., Environmental and heritable factors in the causation of cancer – analyses of cohorts of twins from Sweden, Denmark, and Finland, N. Engl. J. Med.╃343 (2000) 78–85. â•⁄ 2. M.╃J. Christensen, E.╃T. Nartey, A.╃L. Hada, R.╃L. Legg, B.╃R. Barzee, High selenium reduces NF-κB-regulated gene expression in uninduced human prostate cancer cells, Nutr. Cancer. 58 (2007) 197–204. â•⁄ 3. Y.╃R. Seo, M.╃R. Kelley, M.╃L. Smith, Selenomethionine regulation of p53 by a ref1-dependent redox mechanism, Proc. Natl. Acad. Sci. USA.╃99 (2002) 14548–14553. â•⁄ 4. C. Jiang, Z. Wang, H. Ganther, J. Lu, Caspases as key executors of methyl selenium-induced apoptosis (anoikis) of DU-145 prostate cancer cells, Cancer Res.╃61 (2001) 3062–3070. â•⁄ 5. M.╃P. Rayman, Selenium in cancer prevention: a review of the evidence and mechanism of action, Proc. Nutr. Soc.╃64 (2005) 527–542. â•⁄ 6. W. Leinfelder, E. Zehelein, M.╃A. Mandrand-Berthelot, A. Bock, Gene for a novel tRNA species that accepts L-serine and cotranslationally inserts selenocysteine, Nature 331 (1988) 723–725. â•⁄ 7. H. Imai, Y. Nakagawa, Biological significance of phospholipid hydroperoxide glutathione peroxidase (PHGPx, GPx4) in mammalian cells, Free Radic. Biol. Med.╃34 (2003) 145–169. â•⁄ 8. M.╃P. Rayman, The importance of selenium to human health, Lancet. 356 (2000) 233–241. â•⁄ 9. F.╃F. Chu, R.╃S. Esworthy, J.╃H. Doroshow, Role of Se-dependent glutathione peroxidases in gastrointestinal inflammation and cancer, Free Radic. Biol. Med.╃36 (2004) 1481–1495. 10. F.╃F. Chu, et al., Bacteria-induced intestinal cancer in mice with disrupted Gpx1 and Gpx2 genes, Cancer Res.╃64 (2004) 962–968. 11. O.╃A. Levander, Considerations on the assessment of selenium status, Fed. Proc. 44 (1985) 2579–2583. 12. J. Holden, R. Gebhardt, C. Davis, D. Lurie, A nationwide study of the selenium content and variability in white bread, Journal Food Composition Analysis. 4 (1991) 183–195. 13. M.╃P. Longnecker, et al., Use of selenium concentration in whole blood, serum, toenails, or urine as a surrogate measure of selenium intake, Epidemiology. 7 (1996) 384–390. 14. U. Peters, Y. Takata, Selenium and the prevention of prostate and colorectal cancer, Mol. Nutr. Food Res.╃52 (2008). 15. E.╃T. Jacobs, et al., Selenium and colorectal adenoma: results of a pooled analysis, J. Natl. Cancer Inst. 96 (2004) 1669–1675. 16. U. Peters, et al., High serum selenium and reduced risk of advanced colorectal adenoma in a colorectal cancer early detection program, Cancer Epidemiol. Biomarkers Prev. 15 (2006) 315–320.

329

330

Contributions

17. L.╃C. Clark, et al., Effects of selenium supplementation for cancer prevention in patients with carcinoma of the skin. A randomized controlled trial. Nutritional Prevention of Cancer Study Group, JAMA 276 (1996) 1957–1963. 18. A.╃J. Duffield-Lillico, et al., Baseline characteristics and the effect of selenium supplementation on cancer incidence in a randomized clinical trial: a summary report of the Nutritional Prevention of Cancer Trial, Cancer Epidemiol. Biomarkers Prev. 11 (2002) 630–639. 19. S.╃M. Lippman, et al., Designing the Selenium and Vitamin E Cancer Prevention Trial (SELECT), J. Natl. Cancer Inst. 97 (2005) 94–102. 20. S.╃M. Lippman, et al., Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the Selenium and Vitamin E Cancer Prevention Trial (SELECT), JAMA 301 (2009) 39–51. 21. Y. Takata, et al., Correlation between selenium concentrations and glutathione peroxidase activity in serum and human prostate tissue, Prostate. (2009). 22. U. Peters, et al., Variation in the selenoenzyme genes and risk of advanced distal colorectal adenoma, Cancer Epidemiol. Biomarkers Prev. in press (2008). 23. C.╃B. Foster, K. Aswath, S.╃J. Chanock, H.╃F. McKay, U. Peters, Polymorphism analysis of six selenoprotein genes: support for a selective sweep at the glutathione peroxidase 1 locus (3p21) in Asian populations, BMC. Genet. 7 (2006) 56. 24. P. Broderick, et al., A genome-wide association study shows that common alleles of SMAD7 influence colorectal cancer risk, Nat. Genet. 39 (2007) 1315–1317. 25. R.╃A. Eeles, et al., Multiple newly identified loci associated with prostate cancer susceptibility, Nat. Genet. 40 (2008) 316–321. 26. J. Gudmundsson, et al., Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24, Nat. Genet. 39 (2007) 631– 637. 27. J. Gudmundsson, et al., Common sequence variants on 2p15 and Xp11.22 confer susceptibility to prostate cancer, Nat. Genet. 40 (2008) 281–283. 28. G. Thomas, et al., Multiple loci identified in a genome-wide association study of prostate cancer, Nat. Genet. 40 (2008) 310–315. 29. I. Tomlinson, et al., A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21, Nat. Genet. 39 (2007) 984–988. 30. M. Yeager, et al., Genome-wide association study of prostate cancer identifies a second risk locus at 8q24, Nat. Genet. 39 (2007) 645–649. 31. B.╃W. Zanke, et al., Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24, Nat. Genet. 39 (2007) 989– 994. 32. A.╃M. Glazier, J.╃H. Nadeau, T.╃J. Aitman, Finding genes that underlie complex traits, Science. 298 (2002) 2345–2349. 33. L.╃R. Cardon, J.╃I. Bell, Association study designs for complex diseases, Nat. Rev. Genet. 2 (2001) 91–99.

Colorectal and Prostate Cancer: The Role of �Candidate Genes in Nutritional Pathways

34. S.╃J. Chanock, et al., Replicating genotype-phenotype associations, Nature. 447 (2007) 655–660. 35. J.╃P. Ioannidis, Why most published research findings are false, PLoS Med.╃2 (2005) e124. 36. J.╃P. Ioannidis, T.╃A. Trikalinos, E.╃E. Ntzani, D.╃G. Contopoulos-Ioannidis, Genetic associations in large versus small studies: an empirical assessment, Lancet. 361 (2003) 567–571. 37. L.╃M. Dong, et al., Genetic susceptibility to cancer: the role of polymorphisms in candidate genes, JAMA. 299 (2008) 2423–2436. 38. S. Wacholder, S. Chanock, M. Garcia-Closas, G.╃L. El, N. Rothman, Assessing the probability that a positive report is false: an approach for molecular epidemiology studies, J. Natl. Cancer Inst. 96 (2004) 434–442. 39. D.╃C. Thomas, D.╃G. Clayton, Betting odds and genetic associations, J. Natl. Cancer Inst. 96 (2004) 421–423. 40. M. Garcia-Closas, et al., NAT2 slow acetylation, GSTM1 null genotype, and risk of bladder cancer: results from the Spanish Bladder Cancer Study and meta-analyses, Lancet. 366 (2005) 649–659. 41. S. Sanderson, G. Salanti, J. Higgins, Joint effects of the N-acetyltransferase 1 and 2 (NAT1 and NAT2) genes and smoking on bladder carcinogenesis: a literature-based systematic HuGE review and evidence synthesis, Am. J. Epidemiol. 166 (2007) 741–751. 42. A. Cox, et al., A common coding variant in CASP8 is associated with breast cancer risk, Nat. Genet. 39 (2007) 352–358. 43. Breast Cancer Case-Control Consortium, CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies, Am. J. Hum. Genet. 74 (2004) 1175–1182. 44. M.╃M. de Jong, et al., Low-penetrance genes and their involvement in colorectal cancer susceptibility, Cancer Epidemiol. Biomarkers Prev. 11 (2002) 1332–1352. 45. Z. Ye, H. Song, Glutathione s-transferase polymorphisms (GSTM1, GSTP1 and GSTT1) and the risk of acute leukaemia: a systematic review and metaanalysis, Eur. J. Cancer 41 (2005) 980–989. 46. C. Kiyohara, K. Yoshimasu, Genetic polymorphisms in the nucleotide excision repair pathway and lung cancer risk: a meta-analysis, Int. J. Med. Sci.╃4 (2007) 59–71. 47. C. Kiyohara, K. Takayama, Y. Nakanishi, Association of genetic polymorphisms in the base excision repair pathway with lung cancer risk: a metaanalysis, Lung Cancer. 54 (2006) 267–283. 48. H. Li, B.╃C. Tai, RNASEL gene polymorphisms and the risk of prostate cancer: a meta-analysis, Clin. Cancer Res.╃12 (2006) 5713–5719. 49. K.╃E. Lohmueller, C.╃L. Pearce, M. Pike, E.╃S. Lander, J.╃N. Hirschhorn, Metaanalysis of genetic association studies supports a contribution of common variants to susceptibility to common disease, Nat. Genet. 33 (2003) 177– 182.

331

332

Contributions

50. D. Altshuler, et al., The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes, Nat. Genet. 26 (2000) 76– 80. 51. L.╃R. Cardon, Genetics. Delivering new disease genes, Science. 314 (2006) 1403–1405.

Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro

3.19 Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro Chimgee Baasanjav Gerber1, Wolfram Engst1, Simone Florian1, Bernhard Monien1, Jessica Barillari2, Renato Iori2, Heinz Frank3, Albrecht Seidel3, Angelika Krumbein4, Monika Schreiner4, and Hansruedi Glatt1,5

Abstract P-postlabelling analysis indicated the time-dependent formation of five characteristic spots of adducts with the endogenous DNA in broccoli homogenates. One of these adduct spots was also consistently detected in tissues of rats and mice fed raw or steamed broccoli. Its structure was identified by mass spectro� metry and direct chemical synthesis. It was formed by a specific glucosinolate (GLS-A); administration of purified GLS-A or a degradation product formed from GLS-A by myrosinase (DEG-A) to mice led to the formation of the same DNA adduct as observed after feeding broccoli. However, when purified GLS-A was used, adduct formation was largely restricted to the large bowel, whereas adducts were detected in many additional tissues after feeding broccoli or administering DEG-A. This difference may result from the enzymes mediating the first step of bioactivation: glycosidases from intestinal bacteria with purified GLS-A, plant myrosinase in the case of broccoli; this activation step is not required with the DEG-A. Unlike other GLS, GLS-A was highly mutagenic to Salmonella typhimurium strains in the presence of myrosinase. It remains to be studied whether broccoli-induced mutagenic DNA adducts can induce tumourigenesis. 32

3.19.1 Introduction Plants are at the beginning of the food chain and have to defend themselves against premature consumption by microorganisms and animals. To this end they use physical and chemical means. Some natural pesticides target specific

1

German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany.

2

Research Centre for Industrial Crops, Agricultural Research Council, Via di Corticella 133, I-40128 Bologna, Italy.

3

Biochemical Institute for Environmental Carcinogens, Prof. Dr. Gernot Grimmer-Foundation, Lurup 4, D-22927 Großhansdorf.

4

Leibniz-Institute of Vegetable and Ornamental Crops, Theodor-Echtermeyer-Weg 1, D-14979 Großbeeren, Germany.

5

Correspondence to: Hansruedi Glatt, German Institute of Human Nutrition (DIfE) PotsdamRehbrücke, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany, [email protected].

333

334

Contributions

structures of enemies. For example, various alkaloids block or activate receptors for neurotransmitters or inhibit enzymes and transporter proteins that regulate the levels of these neurotransmitters in animals. Obviously these defence systems are only effective against enemies containing the corresponding target structures. A much broader protection could be provided by chemically reactive compounds, which usually can modify numerous different biomolecules, sometimes ranging from lipids and proteins to nucleic acids. Of course, reactive molecules must not damage the healthy plant. Thus, they have to be stored, for example, in a protected compartment or in an inactive chemical form to be activated in a defence situation by appropriate factors, such as enzymes of the plant or the enemy. Examples of inactive precursor molecules are the glucosinolates (GLS), typical secondary metabolites of plants of the order of Brassicales, which contains many food and feed plants, such as Brassica oleraceae varieties (e.╃g. cabagge, broccoli, brussels sprouts), Brassica napus (rape), Brassica juncea (mustard), Brassica rapa sp. chinensis (pak choi) and Eruca sativa (rock salad) [1–3]. GLS are chemically non-reactive, but are converted to various electrophilic molecules (such as isothiocyanates, thiocyanates, nitriles and epithionitriles) upon hydrolytic cleavage of a β-thioglycosidic bond. This hydrolysis can be mediated by the plant’s own myrosinase, which is stored in separate cells, neighbouring those containing the GLS. Thus, activation of the GLS only occurs in the damaged parts of the plant. As an alternative to myrosinase, other glycosidases – e.╃g. formed by intestinal bacteria – may activate GLS. Most reactive molecules released from GLS are soft electrophiles, and therefore have a preference for soft nucleophiles, such as thiol groups in proteins. Nevertheless, extracts from Brassica plants have induced gene mutations in Salmonella typhimurium and chromosomal aberrations in mammalian cells in culture, although the nucleophilic sites of nucleobases have hard character [4]. Similar effects were also observed with isothiocyanates, products of GLS, tested as individual chemicals [4]. DNA damage may be useful in the defence of the plant against rapidly proliferating bacteria, but not sufficiently effective against animal enemies due to the delayed action. Thus, in animals RNA, proteins and lipids may be the primary targets in the defence of plants. Although DNA damage in animals may be unimportant for the defence of the plant, it may have serious consequences. This damage may occur even at low exposure levels and can accumulate, e.╃g. in the form of mutations, and eventually lead to cancer in consumers, for example. While humans have quickly learned to avoid consumption of acutely toxic plants or to inactivate their toxic components by appropriate food preparation, it is much more difficult to recognize delayed adverse effects of food constituents, such as the induction of cancer. We are striving to detect possible DNAdamaging compounds released from food plants in defence situations and to elucidate resulting health effects in mammalian organisms.

Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro

3.19.2 Formation of DNA-Reactive Molecules in Plant Homogenates We hypothesized that some reactive metabolites formed in damaged plants as a defence against pathogenic microorganisms and herbivorous animals may react with DNA. To test this hypothesis we devised a novel test system using the endogenous DNA of the plant as a surrogate target. Plants were homogenized (an extreme form of physical assault simulating a pathogen or herbivore attack) and then incubated at 37╃°C for a certain period, usually 2â•›h. Subsequently, the plant DNA was analyzed for the presence of adducts by 32P-postlabelling and multidirectional thin-layer chromatography followed by electronic autoradiography [5, 6]. This technique detects many different, yet not all, types of bulky DNA adducts with high sensitivity. We got positive results for homogenates from a number of food and herbal plants. Broccoli provided a characteristic pattern of five adduct spots (Fig.╃1â•›b). Similar patterns of adduct spots were detected in homogenates of various other Brassica plants, such as white cabbage, cauliflower,

Figure 1: 32P-postlabelling analysis of DNA adducts. (A) DNA immediately isolated from shockfrozen broccoli. (B) DNA from freshly prepared broccoli homogenate incubated at 37╃°C for 2â•›h. (C) Colon DNA from a control mouse (receiving standard laboratory chow only). (D) Colon DNA from a mouse fed with steamed broccoli (the mouse had free access to steamed broccoli as well as standard laboratory chow during the last 2â•›d before sacrifice; broccoli was steamed for 15╃min. (E) Salmon sperm DNA incubated with glucoraphanin and myrosinase [500╃µg/ml DNA, 20 mU/ ml myrosinase and 10╃µM glucoraphanin in sodium phosphate buffer (20 mM, pH 6.5) at 37╃°C for 2â•›h]. (F) Salmon sperm DNA incubated with GLS-A and myrosinase (conditions as in E, but glucoraphanin replaced by 10╃µM GLS-A). (G) Colon mucosa of a mouse after administration of GLS-A (85 mg per kg body mass by gavage; the animal was killed 18â•›h after dosing). (H) Colon mucosa of a mouse after administration of the DEG-A, a degradation product of GLS-A formed by myrosinase (treatment as in G, but GLS-A replaced by an equimolar dose of DEG-A). All mice were young adult males of the FVB/N strain (4 to 9 weeks old). DNA was isolated by phenol/ chloroform extraction. Adducts were analyzed using standard procedures with a nuclease-P1enrichment step [6]. 0, site of sample application; 1–5, adduct spots using broccoli homogenate as a reference sample. The following solvents were used for the chromatography: D1, 2.3 M sodium phosphate buffer, pH 5.7; D3, 2.8 M lithium formate, 6.6 M urea, pH 3.35; D4, 0.8 M lithium formate, 0.5 M Tris-HCl, 8.5 M urea, pH 8.╃0.

335

336

Contributions

rape and pak choi. Adducts were absent in DNA immediately isolated from shock-frozen broccoli (Fig.╃1╛a). This finding implies that reactive metabolites were generated in the homogenate and then formed adducts with the DNA.

3.19.3 Formation of DNA Adducts in Animals Fed with Broccoli In the experiments described here young animals were used shortly after weaning. All animals had free access to standard laboratory feed. Some animals had additional free access to raw or steamed broccoli florets (steaming duration was 15 min, retaining detectable myrosinase activity). The amount of broccoli consumed varied between individual animals and during the feeding period. The mean daily intake per animal was approximately 6 and 45â•›g for mice and rats, respectively. In the initial experiments, we used rats. Later, when we also tested individual compounds isolated from broccoli or prepared chemically, we used mice to save test compound. No distinct spots were detected in the autoradiograms of 32P-labelled digests of DNA from any tissue of control animals (receiving standard laboratory feed only), apart from the site of sample application (“0”) and unresolved smears at the edges of the thin-layer chromatogram after extensive development (Fig.╃1â•›c). These signals may represent reagents and labelling side-products rather than DNA adducts. However, a clear adduct spot was found in many tissues of animals fed raw or steamed broccoli (Fig.╃1â•›d). This spot showed the same chromatographic properties as spot 3 found in DNA of broccoli homogenate (Fig.╃1â•›b). Sporadically, a second, faint adduct spot was detected in tissues of broccoli-fed animals. Its chromatographic properties were similar to those of spot 5 in DNA from broccoli homogenate. However, no adduct spots matching spots 1, 2 and 4 of broccoli homogenate were detected in any case in the animals investigated.

3.19.4 Elucidation of the Structure of Broccoli-Associated DNA �Adducts and Identification of the Substances Involved As the adduct patterns detected in broccoli homogenate were also detected with various other Brassicales, but not with genetically remote plants, GLS were primary candidates for the compounds involved in this effect. Therefore, we purified a series of GLS to homogeneity and incubated them individually with DNA in the presence of myrosinase. Most GLS did not form any detectable DNA adducts or they produced faint signals only after extended autoradiography. The chromatographic properties of these adducts usually resembled those of the minor adduct spot 5 of broccoli homogenate, as shown for glucoraphanin-treated DNA in Fig.╃1╛e. However, we eventually found compounds that formed the major adduct spots detected in broccoli homogenate. One GLS generated high levels of adducts that matched spots 1, 2 and 4; another GLS primarily produced an adduct that matched spot 3 (i.╃e. the adduct spot seen in animals fed broccoli) and additionally gave minor signals at the sites of spots 4 and 5 (Fig.╃1╛f). The

Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro

latter GLS is termed GLS-A (structure to be disclosed shortly in original publications). Subsequently, we treated mice with GLS-A, which we purified from broccoli and pak choi, or its degradation product (DEG-A), for which a new synthesis had to be developed. Both compounds led to the appearance of adduct spot 3 and, faintly, spot 5 in tissue DNA (Figs. 1â•›g–h). DEG-A formed these adducts in many tissues, including small and large bowel, liver and kidney. On the contrary, the effect of GLS-A was largely restricted to large bowel (caecum and colon), where, however, the effect was very strong – stronger than observed with DEG-A, and much stronger than seen with the heterocyclic amine 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), a colon carcinogen used as a positive control. In order to further elucidate the structure of the adduct(s) contained in spot 3, we incubated GLS-A with the individual 2’-deoxynucleosides (for mass-spectrometric analysis) or their 3’-phosphonucleotides (for 32P-postlabelling analysis), rather than DNA, in the presence of myrosinase. Clearly the strongest signals in the 32P-postlabelling analysis were obtained with dG 3’-phosphate. The major product chromatographically matched spot 3 of broccoli homogenate. A minor product corresponded to spot 4.╃Adducts were also formed with dA and dC, but their levels were lower and they chromatographically differed from spot 3.╃Since adducts were only formed with nucleotides containing an exocyclic amino group (but not with dT), we postulated that the active metabolite(s) of GLS-A binds to these positions. We chemically synthesized the corresponding 2’-deoxyguanosine adduct and verified its structure by 1H-NMR. Its mass spectrum in the positive ionization mode showed two main fragments, representing the loss of deoxyribose and dG, respectively. These fragmentations were used in analyses by ultraperformance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Using these characteristic transitions and the ratio of their signals, it was verified that rats and mice fed with broccoli, but not control animals, contain DNA adducts that correspond to the chemically synthesized standard.

3.19.5 Mutagenicity of GLS-A GLS-A and various other GLS were investigated for mutagenicity in Salmonella typhimurium strains. In the absence of myrosinase, none of the congeners showed any mutagenic or cytotoxic effects up to the highest dose tested (1 to 10╃µmol in a total incubation volume of 610╃µl). When myrosinase (isolated from Sinapis alba) was added, all GLS turned cytotoxic, as indicated by a dilution of the his- background lawn and a decrease in the number of revertant colonies per plate below the spontaneous level (with an ED50 near a dose of 1╃µmol). Some GLS showed a small increase in the number of revertants (1.2–1.5-fold background level) in a narrow dose range before toxicity predominated. Others, like glucoraphanin did not demonstrate any sign of mutagenicity. Yet, GLS-A strongly differed from the other GLS tested, as clear mutagenic effects started at low doses (0.001╃µmol) and increased over a wide dose range. Like the positive

337

338

Contributions

control compound glycidamide (chosen due to its high water solubility, shared with the GLS), GLS-A was mutagenic in strains TA100 and TA104 (Fig.╃2), but not in TA1538 (data not shown). However, GLS-A was at least 1000-fold more potent than glycidamide, as indicated by the left-shift of the dose–response curves.

3.19.6 Conclusions and Perspectives We have devised a new test system for the detection of some potential genotoxicants produced by plants. What is detected are ultimate DNA-reactive com-

Figure 2: Mutagenicity of GLS-A and glucoraphanin to Salmonella typhimurium strains TA100 and TA104. Glycidamide was used as a positive control compound. Mutagenicity was determined using a protocol previously described [15]. However, 10 mU myrosinase was added to each incubation. Omission of myrosinase abolished the cytotoxicity of the GLS and the mutagenicity of GLS-A, but did not affect the effects of glycidamide (data not shown). Values are means ± SE of 3€incubations (some SE bars being within the symbol).

Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro

pounds formed by plant enzymes. The plant’s own DNA served as a surrogate target. We found a number of food and herbal plants that gave a positive result in this test system. This led to the question whether DNA damage and its sequelae (e.╃g. mutations and tumourigenesis) are also induced in consumers of these plants. In our current studies this question is being addressed for broccoli and individual GLS contained in this vegetable. We found that the DNA adducts observed in broccoli homogenate were produced by at least two different GLS. One GLS formed spots 1, 2 and 3.╃These adduct spots were not detected in tissues of animals fed broccoli. Data not presented here indicate that the reactive metabolites formed by myrosinase from this GLS are not able to penetrate cell membranes. Another GLS, GLS-A, was involved in the formation of adduct spot 3 in broccoli homogenate. The same adduct spot was detected in vivo. It was further analyzed. Using a chemically synthesized standard and mass spectrometric analyses, we clearly demonstrated the formation of GLS-A dG adducts in rats and mice fed with broccoli. The same adduct was formed in animals after gavage of GLS-A or its decomposition product, DEG-A. However, the distribution of the adducts among tissues strongly depended on the treatment regimen. When raw broccoli was fed or the DEG-A was administered, DNA adducts were detected in many different tissues. Partial inactivation of myrosinase in steamed broccoli led to an increased adduct formation in large bowel. When purified GLS-A was administered, DNA adduct formation was strongly concentrated to large bowel. Of course, in this situation no plant myrosinase was present to activate the GLS. We suspect that glycosidases from intestinal bacteria mediated the activation of GLS-A in this situation. The next question concerns the mutagenic and carcinogenic potential of the DNA adducts associated with the consumption of broccoli and other Brassicales. We have already shown that GLS-A is a potent inducer of gene mutations in bacteria (Fig.╃2) and mammalian (Chinese hamster V79) cells in culture (data not shown). Initial small carcinogenicity studies are in progress, but will be insufficient to clarify the complex situation. A first treatment scheme in carcinogenicity studies may involve feeding of broccoli to animals. It might best reflect the human exposure scenario. However, a negative result in such studies would be of low value, as the exposure level remains low: As small animals require higher food uptake per kg body mass than humans, GLS-A exposure may exceed 20-fold that of humans consuming much broccoli, cabbage and other Brassicales. The minimal carcinogenic effect to be detected in an animal study with statistical significance amounts to an approximately 10â•›% increase in the incidence of tumours at a given site under favourable conditions (at least 50 animals per treatment group, no spontaneous tumours at this site). Linear extrapolation to 20 times lower exposure levels, as may be the case in some human populations, would still result in excess 0.5â•›% tumour risk at any given site. As nearly the entire world population consumes food prepared from Brassicales, this could add up to a high number of cancer cases. A second approach would involve the direct administration of GLS-A. A chemical synthesis of this compound has not yet been described. It certainly

339

340

Contributions

would be demanding and – most likely – ineffective to provide the large amounts required in carcinogenicity studies. Therefore, we are trying to increase the GLS-A level in plant material used for purification of the compound by an appropriate choice of the Brassica species combined with targeted elicitor applications. In any case, the preparation of GLS-A at an appropriate scale requires big efforts. As outlined in section 3.19.4 purified GLS-A forms DNA adducts with high tissue specificity in large bowel. It does not mimic the formation of adducts in many tissues as observed with steamed or raw broccoli. Thus, the direct usage of GLS-A may be very useful to test its colorectal carcinogenic activity, but other potential target tissues require alternative experimental conditions. Therefore, a third approach, the testing of degradation products of GLS-A, should be taken into consideration. These compounds are commercially not available. We have devised a novel route of their synthesis. As shown in Figure 1â•›h, DEG-A generated the same DNA adducts in mouse tissues as observed after feeding of broccoli. Thus, this approach appears feasible. However, this relatively difficult synthesis has to be scaled up to produce the quantities required for long-term animal studies. Our findings indicate that GLS-A, after bioactivation, forms high levels of DNA adducts in vivo and is strongly mutagenic in vitro; thus, the likelihood that it is carcinogenic under some conditions is high. This contrasts to common claims that GLS in general and broccoli as a source of many GLS in particular exert anticarcinogenic effects. Indeed, such anticarcinogenic effects have clearly been demonstrated in some model systems. They appear to involve several different mechanisms. For example, sulforaphane, the isothiocyanate formed from glucoraphanin, is a potent activator of Nrf2, a transcription factor that enhances the expression of various enzymes that detoxify electrophiles or prevent their formation [7]. Activation of this system can lead to very efficient protection against the carcinogenicity of aflatoxin B1, for example [8]. Moreover, consumption of Brussels sprout by human volunteers led to the down-regulation of sulphotransferases involved in the bioactivation of the food carcinogen PhIP and the up-regulation of defence against oxidants [9]. Likewise, indole-3-carbinol, formed by myrosinase from glucobrassicin, can form di- and oligomers, such as indolo[3,2-b]carbazol, in the acidic milieu of the stomach, that are extremely potent activators of the arylhydrocarbon receptor [10]. On the one hand, this activated receptor enhances the expression of many phase-1 and phase-2 xenobiotic-metabolizing enzymes. On the other hand, it also enhances degradation of β-catenin, a key factor involved in the Wnt pathway, playing an important role in the genesis of colorectal and other cancers [11]. We suspect that these potentially adverse and beneficial effects of GLS may represent two sides of the same coin: The plant produces reactive metabolites in order to damage any enemy. However, some organisms have evolved counteracting systems, allowing consumption of the plant without, or with tolerable, damage. For example, some insects feeding on Brassicales, such as Plutella xylostella [12] and Schistocerca gregaria [13], express high levels of sulphatases that sequester GLS before they are activated by myrosinase. Mammals have inducible defence systems that prepare for elevated burden by electrophiles.

Glucosinolates: DNA Adduct Formation In Vivo and Mutagenicity In Vitro

Some electrophiles formed from GLS are potent inducing stimuli. On the one hand, our data indicate that this defence system is not fully effective, at least not on the biotransformation level, as substantial concentrations of GLS-derived DNA adducts are formed in mice and rats fed broccoli. On the other hand, these defence systems are not only directed against GLS, but also against other compounds that are electrophilic or are metabolized to electrophiles, such as aflatoxin B1, that are not recognized by the organism as dangerous, i.╃e. do not alert the organism. Moreover, the potentially dangerous GLS and those alerting the organism may not coincide. For example, glucoraphanin is a potent inducer of detoxifying enzymes after its conversion to sulforaphane [14], but does not appear to form significant levels of DNA adducts or to induce mutations, or at least is by many orders of magnitude less active than GLS-A in this regard, as indicated by results of the present study. Thus, the net health effect of the consumption of Brassicales vegetable could be variable depending on many factors, such as the profile of GLS contained, the frequency of consumption of these vegetables (determining the induction state), the individual responsiveness of the adaptive systems, and co-exposure to other potential carcinogens/ genotoxicants. In view of the exceptionally high level of DNA adduct formation as well as the strong induction of defence systems by constituents of Brassicales, the individual effects and their interactions represent a challenge urgently requiring further investigation.

Acknowledgements We thank Christine Gumz, Andrea Katschak and Martina Scholtyssek for excellent technical assistance. This work was financially supported by the German Federal Ministry of Education and Research (grant BIO/0313053A) and Danone Institute for Nutrition.

References â•⁄ 1. A.╃M. Bones, J.╃T. Rossiter, The myrosinase-glucosinolate system, its organisation and biochemistry, Plant. Physiol. 97 (1996) 194–208. â•⁄ 2. J.╃W. Fahey, A.╃T. Zalcmann, P. Talalay, The chemical diversity and distribution of glucosinolates and isothiocyanates among plants, Phytochem. Rev.╃56 (2001) 5–51. â•⁄ 3. R. Verkerk, M. Schreiner, A. Krumbein, E. Ciska, B. Holst, I. Rowland, R. de Schrijver, M. Hansen, C. Gerhäuser, R. Mithen, M. Dekker, Glucosinolates in Brassica vegetables: The influence of the food supply chain on intake, bioavailability and human health, Mol. Nutr. Food Res. 53 (2009) Sâ•›219–Sâ•›265. â•⁄ 4. F. Kassie, W. Parzefall, S. Musk, I. Johnson, G. Lamprecht, G. Sontag, S. Knasmüller, Genotoxic effects of crude juices from Brassica vegetables and juices and extracts from phytopharmaceutical preparations and spices of

341

342

Contributions

cruciferous plants origin in bacterial and mammalian cells, Chem.-Biol. Interact. 102 (1996) 1–16. â•⁄ 5. D.╃H. Phillips, Detection of DNA modifications by the 32P-postlabelling assay, Mutation Res.╃378 (1997) 1–12. â•⁄ 6. D.╃H. Phillips, V.╃M. Arlt, The 32P-postlabeling assay for DNA adducts, Nat. Protoc. 2 (2007) 2772–2781. â•⁄ 7. Y. Zhang, C.╃G. Cho, G.╃H. Posner, P. Talalay, Spectroscopic quantitation of organic isothiocyanates by cyclocondensation with vicinal dithiols, Anal. Biochem. 205 (1992) 100–107. â•⁄ 8. M.╃S. Yates, M.╃K. Kwak, P.╃A. Egner, J.╃D. Groopman, S. Bodreddigari, T.╃R. Sutter, K.╃J. Baumgartner, B.╃D. Roebuck, K.╃T. Liby, M.╃M. Yore, T. Honda, G.╃W. Gribble, M.╃B. Sporn, T.╃W. Kensler, Potent protection against aflatoxin-induced tumorigenesis through induction of Nrf2-regulated pathways by the triterpenoid 1-[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole, Cancer Res.╃66 (2006) 2488–2494. â•⁄ 9. C. Hoelzl, H.╃R. Glatt, W. Meinl, G. Sontag, G. Haidinger, M. Kundi, T. Simic, A. Chakraborty, J. Bichler, F. Ferk, K. Angelis, A. Nersesyan, S. Knasmüller, Consumption of Brussels sprouts protects peripheral human lymphocytes against 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) and oxidative DNA-damage: results of a controlled human intervention trial, Mol. Nutr. Food Res.╃52 (2008) 330–341. 10. L.╃F. Bjeldanes, J.╃Y. Kim, K.╃R. Grose, J.╃C. Bartholomew, C.╃A. Bradfield, Aromatic hydrocarbon responsiveness-receptor agonists generated from indole-3-carbinol in vitro and in vivo: comparisons with 2,3,7,8-tetrachlorodibenzo-p-dioxin, Proc. Natl. Acad. Sci. USA 88 (1991) 9543–9547. 11. K. Kawajiri, Y. Kobayashi, F. Ohtake, T. Ikuta, Y. Matsushima, J. Mimura, S. Pettersson, R.╃S. Pollenz, T. Sakaki, T. Hirokawa, T. Akiyama, M. Kurosumi, L. Poellinger, S. Kato, Y. Fujii-Kuriyama, Aryl hydrocarbon receptor suppresses intestinal carcinogenesis in ApcMin/+ mice with natural ligands, Proc. Natl. Acad. Sci. USA 106 (2009) 13481–13486. 12. A. Ratzka, H. Vogel, D.╃J. Kliebenstein, T. Mitchell-Olds, J. Kroymann, Disarming the mustard oil bomb, Proc. Natl. Acad. Sci. USA 99 (2002) 11223– 11228. 13. K.╃L. Falk, J. Gershenzon, The desert locust, Schistocerca gregaria, detoxifies the glucosinolates of Schouwia purpurea by desulfation, J. Chem. Ecol. 33 (2007) 1542–1555. 14. Y. Zhang, P. Talalay, C.╃G. Cho, G.╃H. Posner, A major inducer of anticarcinogenic protective enzymes from broccoli: isolation and elucidation of structure, Proc. Natl. Acad. Sci. USA 89 (1992) 2399–2403. 15. H.╃R. Glatt, W. Meinl, Use of genetically manipulated Salmonella typhimurium strains to evaluate the role of sulfotransferases and acetyltransferases in nitrofen mutagenicity, Carcinogenesis 25 (2004) 779–786.

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

3.20 Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals Johanna Fink-Gremmels1 This manuscript was originally published in Mol. Nutr. Food Res., 2010, 54(2): page 249–258.

Abstract Plant secondary metabolites (PSMs) are non-nutritional components that occur in numerous feed materials and are able to exert toxic effects in animals. The current article aims to summarize innate defence strategies developed by different animal species to avoid excessive exposure to PSMs. These mechanisms include pre-systemic degradation of PSMs by rumen microbiota, the intestinal barrier including efflux transporters of monogastric species, as well as pre-hepatic and intra-hepatic biotransformation processes. These physiological barriers determine systemic exposure and ultimately the dose-dependent adverse effects in the target animal species. Considering the large number of potentially toxic PSMs, which makes an evaluation of all individual PSMs virtually impossible, such a mechanism-oriented approach could improve the predictability of adverse effects and support the interpretation of clinical field observations. Moreover, mechanistic data related to tissue disposition and excretion pathways of PSMs for example into milk, could substantially support the assessment of the risks for consumers of foods derived from PSM-exposed animals.

3.20.1 Introduction Phytochemicals are defined as non-nutritive plant metabolites, which are produced by plants in response to environmental stress conditions and in response to injury following the invasion by phytopathogenic agents such as viruses, bacteria, fungi or herbivorous insects [1–4]. The defence response is coordinated by a complex network of plant hormones, which activate specific transcription factors in the plant genome. This innate immune response is linked to biosynthetic pathways that are involved in the production of individual classes of plant secondary metabolites (PSMs) [5, 6]. Major biosynthetic pathways are associated with the availability of acetyl-CoA, which is involved in the formation of anthraquinones, or shikimic acid serving as building block for alkaloids and phenylpropanoids such as lignans, aromatic essential oils and coumarins. Mevalonate and deoxycellulose pathways lead to the synthesis of terpenoids

1

Utrecht University, Faculty of Veterinary Medicine, Division of Veterinary Pharmacology, Pharmacy and Toxicology, Yalelaan 104, NL-3584 CM Utrecht, The Netherlands.

343

344

Contributions

and steroid metabolites including many saponins, terpenoid essential oils and carotenoids [7]. The biological activity of PSMs is diverse and includes attractant and deterrent activity to insects and herbivorous mammals, as well as antiviral, antibacterial, and antifungal effects. Grasses and sedges tolerate herbivorous animals as their basal meristem facilitates rapid re-growth. Subsequently these monocotyledons form only a small number of secondary metabolites. Well-known examples are the synthesis of avenacins (antibacterial triterpene glycosides, belonging to the group of saponins) by Avena species, including oats [8, 9] and tannins [10]. In addition, various grasses are protected by symbiotic endophytes producing essential insect repellents such as pyramine. Pyramine is a loline derivative, and the same biosynthetic pathway results in more complex chemical structures, as for example the group of lolitrems, which exert neurotoxic effects in grazing animals (rye grass staggers) or ergot alkaloids, affecting animal health and reproduction (tall fescue toxicosis) [11, 12]. Pasture grasses can also be invaded by numerous other toxinogenic moulds, particularly by Fusarium species [12, 13]. Dicotyledons use PSMs in the form of colorants and volatile compounds to attract insects but also as protective agents against deterioration by grazing mammals [14]. In a natural environment, animals recognize these toxic plants and avoid their consumption. However, when plants (or animals) are transferred to non-endemic regions or plants are harvested and dried, this recognition is often lost. Moreover, the use of by-products from plant oil production or distilling processes as feed materials can be associated with undesirable high exposure of farm animals to PSMs and hence with antinutritive or toxic effects [15]. Table 1: Major classes of plant secondary metabolites (modified [7]). Chemical class Alkaloids

Estimated number of products 12,000

Non-protein amino acids

600

Amines

100

Cyanogenic glycosides

100

Glucosinolates

600

Monoterpenes

1,000

Sesquiterpenes

3,000

Diterpenes

2,000

Triterpenes, Saponines, Steroids

4,000

Flavonoids Polyacetylenes Polyketides Phenylpropanes

>2,000 1,000 750 1,000

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

PSMs have been studied for many centuries, both as toxic substances as well as therapeutic agents [7]. The number of potentially toxic plant metabolites is large, exceeding 100,000 individual substances (Tab.╃1), and taken together they can affect virtually any function of a living organism. Prominent examples for chemical classes of plant secondary metabolites that occur in animal feeds and are related to clinical signs of intoxications are the alkaloids (tropane alkaloids as well as pyrrolizidine alkaloids), glucosinolates, terpenes and saponines, as well as flavonoids. It is beyond the scope of a single review to describe all chemical properties and the biological activity of all currently known PSMs that may affect the health and productivity of farm animals [16, 17]. Instead, the current review will focus on the physiological defence mechanisms that mammals have developed during their evolution to protect themselves against toxic plant metabolites.

3.20.2 Polygastric Herbivory: Pre-Systemic Detoxification of �Phytochemicals by the Rumen Microbiota The most effective system to utilize diverse, even low quality plant materials is found in ruminants. The rumen flora consists of a complex community of micro-organisms, of which bacteria and protozoa are the major classes. Their physiological function is the degradation of cellulose, hemicelluloses, pectin and other complex fibres, which in turn are used for the production of volatile fatty acids and microbial proteins. These products are utilized by the mammalian host to fulfil its nutritive demands. The rumen microbiota shows a speciesspecific composition, which is unique (finger-print) for the individual animal. Individual populations of micro-organisms can be affected temporarily by dietary components [18]. It is generally assumed that the rumen micro-organisms are able to hydrolyze and deactivate virtually all toxic plant metabolites, thus protecting the animal. This assumption is based on clinical observations indicating that ruminants are indeed less susceptible to many PSMs when compared to (monogastric) horses, which often consume comparable roughages. However, details about the actual bio�transformation pathways used by rumen microbiota to convert potentially toxic PSMs are often lacking and may involve biochemical reactions, which can be performed only by some micro-organisms. One of the most prominent examples that demonstrate this high specificity is the degradation of mimosine, a metabolite of the tropical shrub leucaena (Leucaena leucocephala) [19]. Leucaena is a persistent legume originating from Central America now being spread to many other continents. Clinical reports described intoxications in animals in non-endemic areas, whereas in endemic areas the plant was well tolerated by ruminants. Investigations into the rumen stability of mimosine, the major PSM, revealed that some rumen bacteria convert this compound into 3-hydroxy-4(1H) pyridone (3,4 DHP), a strong goitrogen. When affected animals in nonendemic areas were inoculated with rumen fluid from endemic areas, tolerance to leucaena (and mimosine) could be achieved [20]. Further investigations

345

346

Contributions

showed that the critical step in the bacterial biotransformation is conducted by an individual DHP-degrading organism (Synergistes jonesii), which commonly occurs in the rumen flora in endemic regions. When Synergistes was inoculated in a few animals of a herd, it rapidly spread to all animals and conferred tolerance to leucaena PSMs. The insight in this mechanism allowed the use of leucaena, a high-quality legume, as forage plant in tropical and subtropical regions [21]. Glucosides of 3-nitro-1 propanol (nitropropanol) and glucose esters of 3-nitro-1 propanoic acid, which occur in many Astragalus species, comprise a group of nitrotoxins that, following hepatic activation, act as potent enzyme inhibitors inactivating cellular succinate dehydrogenase and subsequently ATP formation, resulting in clinical signs of intoxication such as dyspnoea, muscular incoordination, and depression and weight loss [22]. Tolerance of grazing animals depends on the capacity of the rumen flora to degrade the nitrotoxins prior to absorption, a process which is mainly catalyzed by the rumen bacterium Denitrobacterium detoxificans. Feeding strategies, such as addition of soybean proteins to the animal’s diet, increase the density of D. detoxificans in the rumen and hence the detoxicification capacity, thereby increasing the tolerance of animals to nitrotoxins present in forages [22]. Saponins are found in many plant species, comprising a group of glycosides with a triterpene or steroidal aglycone-moiety that is linked to one or more sugar chains [10]. Saponins have been intensively studied in ruminants due to their potential antiprotozoal activity, which might increase animal performance, particularly in ruminants fed a low protein diet. Prominent examples for saponin producing plants are alfalfa [23], Yucca schidegera [24], Acacia auriculoformis [25] and fenugreek seeds [26]. Their utilization depends on the ingested amount (dose) but also on the composition of the diet, and hence both, beneficial and toxic effects have been reported. Glucosinolates, a large group of compounds characterized by a β-D-thioglucose group linked to variable side chains, consisting of amino acids such as methionine, tryptophan or phenylalanine are the major metabolites of Brassica species [27, 28]. Following the rupture of plant structures, for example by harvesting or chewing, plant-derived β-thioglucosidase (myrosinase, which in the plant is sequestered in vesicles) converts the non-toxic glucosinolates into instable aglucons, which are further metabolized by different pathways into isothiocyanates, oxazolidinethiones (5-vinyl-2-oxazolidinethione and 5-vinyl-1,3 oxazolidine2-thione), thiocyanates, nitriles, epithionitriles and other toxic indol-3-ylmethyl derivatives [29]. The toxicity of glucosinolates depends predominantly on the formation of thiocyanates, oxazolidines and nitriles. These compounds interfere with iodine uptake (thiocyanates) and the synthesis of the thyroid hormones T3 and T4, (oxazolidinethiones) leading eventually to hypothyroidism and enlargement of the thyroid gland (goitre) [30]. In ruminants the initial hydrolysis of the glucosinolates can also be performed by bacterial β-glucosidases. However, as the same micro-organisms degrade the toxic oxazolidines and (epithio) nitriles ruminants are still more tolerant to Brassica toxins than monogastric herbivores [31].

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

Like other complex groups of PSMs, polyphenolic tannins can exert beneficial effects as well as adverse reactions, depending on the animal species and the chemical nature and concentrations in feeds [32, 33]. Tannins occur in two distinct forms, denoted hydrolysable tannins, which are derivatives of gallic acid, which can be partly esterified to a polyol such as glucose, glucitol, quercitol or shikimic acid, and condensed tannins (CTs), comprising pro-anthocyanidines that are either di-, tri- or polymers of anthocyanidins and/ or catechin-flavan3-ol, or leucoanthocyanides that are dimers of flavones-3,4-di-ol flavonoids. As the monomers can assemble to a virtually infinite number of oligomers, the biological properties vary according to the chemical structure [34, 35]. In general, tannins have been regarded as inhibitory to the rumen microbiota (36). However, different tannins have also different effects on individual microbial species, apparently related to their molecular weight [37]. This applies also to their antiprotozoal activity, as tannins from certain plants reduce the total number of rumen protozoa [38], while others seem to have no effect [39] or even increase protozoal numbers, such as tannins from Acacia leaves, sulla (Hedysarum coronarium L.) or birdsfoot trefoil (Lotus corniculatus) [40]. Moderate amounts of condensed tannins have shown beneficial effects in stabilizing rumen pH followed by an increased milk yield in ewes [41]. Recently, plant derived essential oils, generally consisting of diverse phenylpropenes and terpenes, have gained increasing interest in animal nutrition due to their antibiotic activity. These effects have been known for centuries and are used in traditional food preservation in the form of herbs and spices [42}. These compounds are successfully used in monogastric species, to stabilize their gut flora [43]. In ruminants, their antibiotic activity can be detrimental to the rumen flora. Indeed, Busquet et al. [44], when testing plant extracts and isolated PSMs, identified anethol, anise oil, carvane and tea-tree oil as compounds that decrease rumen acetate and propionate production, making them nutritionally non-beneficial to dairy cattle. Diverging effects of PSMs on rumen flora fatty acid production were also measured at different pH conditions [45]. These few examples should demonstrate that the capacity of the rumen flora to detoxify secondary plant metabolites is not a universal trait, but is often related to individual rumen micro-organisms or the overall, concentration-dependent, detoxification capacity. The rumen flora represents also a target for various PSMs, as demonstrated for many essential oils. At present, various lines of research focus on the modulation of the rumen flora to increase its degradation capacity as this would allow a broader use of legumes and other potentially toxic plants as feed materials and probably reduce an undesirable overproduction of methane in large ruminants [36, 45].

3.20.3 Monogastric Herbivory: Are Acquired Feeding Strategies �Sufficiently Protective? In monogastric herbivores fermentation of plant cellulose and fibres occurs in the large intestines, for example in the caecum of equidae and avian species.

347

348

Contributions

As small sufficiently polar plant metabolites may pass the stomach unaffectedly and are rapidly absorbed in the small intestine, these animals need to select their diets more carefully. Insight into specific defence mechanisms of monogastric animals originate form studies with wild herbivores, of which approximately 1â•›% are dietary specialists [46], consuming plants with a very high concentration of PSMs. The most commonly known example is the Koala (Phasolarctos cinereus) which feeds exclusively on eucalyptus trees (Eucalyptus punctata). The best studied herbivore specialist, however, is the common woodrat, which occurs in North America in two varieties: Neotoma albigula, a dietary generalist, and Neotoma stephensi, a dietary specialist, which feeds more or less exclusively on juniper plants [46, 47]. These plants contain high amounts of α-pinene, a terpenoid with known toxicity in many herbivorous species. Observational studies revealed that Neotoma stephensi has developed a specific feeding strategy in that the animals eat only very small quantities of plant material per individual meal, hence avoiding peak plasma and toxic tissue levels of α-pinene. The abundance of plant material and the absence of aggressive predators make it possible that woodrats can rely on this adaptive feeding behaviour and easily survive in certain geographic regions [47]. Another important result in the experiments with woodrats was the observation that Neotoma stephensi had a high expression of Table 2: Intestinal efflux transporters expressed at the apical side of enterocytes, their physiological ligands, and examples of indentified PSMs substrates (48, 49, 52, 59). Transporter

Tissues (intestines and others)

Physiological �substrates

Examples of PSMs known to be �substrates

P-gp (MDR1, ABCB1)

Intestines, Blood tissue barriers, brain, choroid plexus, placenta, prostate, ovary, kidneys, liver, lung, skeletal muscle, spleen

Neutral and cationic hydrophobic compounds, amphipathic substances and drugs

Flavonoids Diterpenes Piperidine alkaloids

MRP 2 (ABCC2)

Intestines, brain, Amphipathic organic Flavonoids �placenta, kidneys, liver, anions, organic anions, Isothiocyanates lung anionic conjugates (glucuronides, sulphates), GSSG, GSH, leukotriene C4)

MRP 4 (ABCC4)

Small intestines, Conjugated steroids, brain, testis, kidney, bile acids, folic acid, liver, gall bladder, lung, cGMP, cAMP spleen, tonsils, thymus, �ovaries, prostate, pancreas, skeletal muscle

BCRP (ABCG2)

Intestine, breast, ovary, placenta, kidney, liver, lung, heart, spleen, thymus

Flavonoids Isothiocyanates Rotenoids

Amphipathic comFlavonoids pounds, organic anions, conjugated organic anions, conjugates (glucuronides, sulphates, glutathione), weak bases

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

enteric P-gp as compared to the feeding generalist Neotoma albigula (Tab.╃2) [48, 49]. This finding underlined the importance of a functional intestinal barrier as the 1st line of defence against toxic PSM in monogastric species.

3.20.4 Efflux Transporters: Functional Elements of the Intestinal Barrier For many decades, the epithelial cell layer, closely connected by tight junction proteins, has been described as the barrier system of the intestines, preventing the absorption of many PSMs, as well as biocides and drugs. This assumption was supported by the identification of specific transporters that facilitate the absorption of polar nutrients, such as amino acids and sugars (soluble ligand carriers (SLC) previously denoted as organic anion transporters, glucose transporters and others) [50]. Among the many functional proteins and enzymes in the gut wall, a glycoprotein (P-gp) was identified already in 1976, occurring in a distinct pattern in enterocytes of the intestinal wall [51]. As no specific function could be attributed to this protein, it did not get significant attention for more than two decades. This situation changed, when the same protein was detected on tumour cells, and particularly on tumour cells of patients who became resistant to various commonly used cytostatic agents. Hence P-gp was described as multi-drug resistance protein (MDR-1). MDR-1 confers resistance to cytostatic agents by pumping these lipophilic compounds out of the tumour cell, thus preventing their interaction with intracellular target sites. Years later and by way of an accident it was found that P-gp (MDR-1) recognizes not only cytostatic drugs, but also a large variety of medicinal products, and even more importantly numerous PSMs [52, 53, and 54]. This makes P-gp and other efflux transporters that pump their substrates back into the luminal compartment potent functional elements of the intestinal barrier and other biological barriers, such as the blood brain barrier and the placental barrier. It is now well established that MDR-1 is a member of a large family of ATP-dependent transporters, denoted ABC transporters as they share an ATP-binding cassette (http://nutrigne.4â•›t.com/humanabc.htm). ABC transporters play an indispensable role in limiting drug and xenobiotic absorption from the gastro-intestinal tract, preventing their distribution into vulnerable tissues [55]. Numerous reports describe the inhibition of P-gp (MDR1, ABCB1) by PSMs. Nabekura et al. investigated the effect of selected flavonoids (quercetin and (-)-epigallocatechin, sesamine (from sesame seeds), matairesinol (from soybeans), glycerrhetinic acid and glabridin from liquorice in in vitro assays with P-pg over-expressing cells [54]. All selected dietary phytochemicals increased the accumulation of daunorubicin, which was used as a functional marker to demonstrate Pg-P activity. Interestingly, glycerrhetinic acid was found to reverse multi-drug resistance in some cells by modulating ATPase activity. A selective inhibition of P-gp activity has been also described for steroidal saponins from Paris polyphylla, of which the rhizome is used widely in traditional medi-

349

350

Contributions

cine for its analgesic, antipyretic, anti-inflammatory and antitumour properties [56]. Both reports had been driven by the intention to identify compounds that might be useful additives to common anticancer therapies with cytostatic agents. However, as herbal remedies are increasingly used as food supplements or health foods in industrialized countries, the P-gp inhibiting effect of PSMs might comprise an unexpected risk, leading to drug–drug interactions. This became obvious when Hypericum perforatum (St. John’s Wort) was widely propagated as natural antidepressant. The crude extract of Hypericum contains a complex mixture of hypericin (the major antidepressant), hyperforin, quercitin, isoquercitin, biflavonoids, naphthoandrones, catechins and tannins and many other minor constituents. Long-term use of Hypericum formulations is known to interfere with dynamic properties of other antidepressants of the serotonin re-uptake inhibitor group, and kinetic interactions have been described resulting in reduced plasma concentrations of structurally diverse drugs such as for example cyclosporine, digoxin, theophylline, midazolam, indanavir and saquinavir [57]. Other common herbal remedies, known to cause interactions with therapeutic agents are Allium sativa (garlic) when used in higher concentrations, Gingko biloba, Panax ginseng and Silybum marianum [58, 59]. Undesirable changes in the absorption rate are particularly related to the expression of MDR-1 and CYP3A4, the major sensors of PSMs [60]. In farm animal species, the effects of PSMs on ABC transporters have been studied only to a very limited extent in pigs, poultry and clinical studies in sheep, despite the fact that exposure to PSMs occurs daily over the entire lifespan [61–63]. Probably the most prominent example of a compound that is regularly present in animal’s diets is pheophorbide A, a breakdown product of chlorophyll. Pheophorbide A has been shown to be a selective BCRP (ABCG2) substrate in all tested mammalian species and as it is expressed in the intestines, it can modulate the oral bioavailability of other xenobiotics [64]. BCPR is also present in the mammary gland where it facilitates the excretion of xenobiotics with milk, contributing to body clearance, but also the contamination of milk with drugs and plant metabolites [65]. The latter finding is of importance for the prediction and assessment of undesirable substances in milk for human consumption. PSMs that are substrates for efflux transporters are often also substrates for drug metabolizing enzymes, particularly CYP450 enzymes. The risk for undesirable effects or intoxications is increased when both, the efflux transporters as well as the predominant CYP450, are inhibited [66].

3.20.5 Pre-Systemic Elimination by Biotransformation The important role of hepatic and pre-hepatic biotransformation enzymes limiting systemic exposure to potentially hazardous substances was recognized even before the identification of ABC efflux transporters. The most prominent enzyme system facilitating biotransformation processes is the superfamily of cytochrome P450 enzymes, an ancient enzyme family which can be found in all

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

living organisms [67]. The mammalian enzyme families CYP1 to CYP4 are the most competent in oxidizing and hydrolyzing xenobiotics, including PSMs. The resulting oxidation products are generally more polar, less biologically active, and readily excreted via the kidneys. Certain CYP450 isozymes and other Phase I enzymes can also activate PSMs, converting them into electrophilic compounds that may interact with DNA and cause mutagenicity or carcinogenicity [68]. This metabolic activation has first been recognized for the procarcinogens benzo(a)pyrene and aflatoxin B1, as their oxidation results in highly reactive epoxides [69, 70]. Within the group of PSMs, the pyrrolizidine alkaloids are a prominent class of multiple compounds that require metabolic activation to form toxic imines [71, 72]. CYP450 enzymes are expressed in all tissues that are potentially exposed to xenobiotics. Recent reviews [73–75] have summarized already the current knowledge regarding the interaction of PSMs with efflux transporters and selected CYP450 enzymes. Typical examples are the induction of CYP3A4 by Hyperforin from St. John’s Wort [76], the induction of CYP1A1 and UGT 1A1 by curcumine or the differential induction of CYP1A1 by quercetin, whereas CYP1A2 and CYP3A4 are inhibited [73, 77]. While the liver remains the organ with the highest catalytic activity of CYP450 enzymes, significant (and often underestimated) enzyme levels can be found in all cells lining the gastro-intestinal tract [66, 78]. Polymorphisms in individual enzymes can modulate the susceptibility of individuals to certain (classes of) chemicals, a phenomenon that has been intensively studied in relation to drug dosing regimens, and the prevalence of undesirable side effects of common drugs in certain human subpopulations [75]. In animals, specific polymorphisms are less well investigated, but comparative studies in cattle and horses, confirm age- and gender-specific expression levels, as well as inter-species and inter-breed differences, which makes it difficult to extrapolate data related to the biotransformation capacity between individual animals or animal populations [79–83].

3.20.6 Transcriptional Regulation of Efflux Transporters and �Biotransformation Enzymes The transcriptional regulation of biotransformation enzymes remained a matter of controversial debate for many decades, despite the many observations on enzyme induction in response to certain chemicals and the modulation of enzyme activities in the course of systemic infectious diseases. Only within the last decade a specific group of orphan nuclear receptors involved in the regulation of the expression of both biotransformation enzymes and ABC transporters, has been elucidated. Major nuclear receptors facilitating constitutive and induced gene expression are the pregnane X receptor (PXR), constitutive androstane receptor (CAR), aromatic hydrocarbon receptor (AhR), glucocorticoid receptors (GR), peroxisome proliferator activated receptor (PPAR), activator protein-1 (AP-1), nuclear factor-erythroid 2 p45- related factor 2 (Nrf2), liver X receptor

351

352

Contributions

(LXR) and farnesoid X receptor (FXR) [84, 85]. PXR, CAR, FXR, and LXR bind as heterodimers to the retinoid X receptor (RXR) and then to DNA responsive elements. Upon activation by xenobiotics the ligand–receptor complex is translocated to the nucleus, except for PXR, which is already located in the nucleus. Within the nucleus the complex binds to responsive elements located upstream of the relevant gene in the promoter region and regulates gene expression. Recent studies have shown that these transcription factors do not only regulate the expression of CYP450 enzymes and some other Phase I drug metabolizing enzymes, but also phase II enzymes and ABC transporters [86]. Typical examples are PXR and CAR, which have a broad and overlapping xenobiotic specificity. Being physiologically involved in the homeostasis of cholesterol and bile acids, they play a pivotal role also in regulating the expression of CYP3A, CYP2B, CYP2C and the phase II enzymes UDP-glucuronosyltransferase (UGT), sulphotransferases (ST), and glutathione-S-transferase (GST) [87]. In particular, PXR is recognized as a predominant regulator of CYP3A4, an important enzyme, which is involved in the metabolism of many drugs in humans, both in the intestines as well as in the liver. PXR facilitates also the transcription of MDR-1 (ABCB1), one of the most effective efflux transporters in the gastrointestinal tract as described above. CYP3A4 and P-gp have overlapping substrate specificities and similarities in their induction patterns, emphasizing their synergistic activity in contributing to a functional intestinal barrier [88, 89]. Many phytochemicals are able to activate PXR [90] and other transcription factors and hence may alter the levels of expression of biotransformation enzymes and efflux transporters [91]. An alternative pathway is the activation of the transcription factor Nrf2, which is activated by cellular oxidative stress. Nrf2 dimerizes with ARE (anti-oxidant responsive element), a specific DNA-promoter-binding region that activates the transcription of a wide array of Phase II enzymes [92]. The direct transcriptional activation of ARE is induced by various PSMs, including diphenols, quinolones, isothiocyanates and dithiothioles [93]. These detoxification pathways are now considered as a major target for chemoprevention, i.╃e. the blockade of DNA damage by carcinogenic insults.

3.20.7 Carnivorous Species: When Plant Metabolites Become Lethal Glucuronidation is the most prominent pathway for many plant-derived phenols, coumarins, flavonoids, anthraquinones and steroid(-like) compounds possessing a hydroxyl, amino, carboxyl of sulfhydryl group. Significant glucuronidation activity is found in the small intestines and in hepatocytes, contributing to pre-systemic inactivation and elimination of drugs and toxins. Plant-derived substances such as cruciferous isothiocyanates or piperine can inhibit UGT (UDP-glucuronosyl transferases) impairing not only the elimination of these molecules but also affecting the rate of elimination of other endogenous and exogenous compounds [94]. In animals, the extremely low expression of UGT1A in cats is well-known to all feline practitioners. In felidae UGT1A6 is a pseudo-gene that does not al-

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

low the transcription of an active enzyme. Subsequently, cats are sensitive to all compounds for which glucuronidation is a major excretion pathway, which is reduced to less than 10â•›% as compared to other animal species. Repetitive exposures (or treatments) result easily in accumulation of the toxin or drug. The most prominent example of this decreased tolerance to drugs in cats is the antiphlogistic acetaminophen, which can induce lethal hepatotoxicity upon repetitive application [94]. Less well defined is the high sensitivity of cats to plant phenols. Common textbooks contain long lists of plants that are considered to be toxic for cats. In all cases these plants contain various phenols and terpenes, which require conjugation to be excreted. In cats these compounds accumulate in the liver, causing mild or progressive liver damage and subsequently loss of appetite, fatty liver syndrome and in severe cases hepatic encephalopathy. These molecular mechanisms determining the sensitivity of felidae is of particular relevance to veterinarians and animal poison centres, as it may explain clinical symptoms, following exposure PSMs and intact plants and point to intervention strategies. At the same time the UGT1A6 pseudo-gene in felidae is the only gene defect identified in animals that is directly correlated to a high sensitivity towards PSMs.

3.20.8 Risk Assessment of Phytochemicals in Animal Feeds The risk assessment of phytochemicals in animal feeds needs to cover two different aspects: Firstly, the risk for the target animal for which the feed is intended or which may consume toxic plant materials as part of their normal diet, and secondly, the risk for the consumer of foods from animal origin, including milk, meat and eggs. The approach towards this assessment is often entirely different. In the assessment of animal health risks, major toxicological end points are organ-specific toxic effects, but also antinutritional effects resulting in a reduced feed intake and feed conversion and productivity. As yet, risk assessment of toxic phytochemicals was mainly based on feeding experiments, in which plant materials were mixed in one or more concentrations into an animal diet, and the effects on feed intake and utilization and clinical signs of intoxication were measured. Many of these experiments are of limited value for the risk assessment, as the actual pattern and the concentration of toxic plant metabolites have not been measured. Moreover, the rate of absorption of toxic compounds can vary depending on the diet, the actual concentration of one or more plant metabolites in the diet, and the age and breed of the target animal. This emphasizes the demand for mechanism-based studies that provide insights into the absorption, biotransformation, tissue distribution and routes of excretion of phytochemicals. In contrast to the assessment of adverse effects of PSMs on animal health and productivity, the evaluation of animal-derived products is driven by the quantities of residues in animal tissues and critical end points such as genotoxicity. In many cases, the animal functions as a filter, converting and excreting most of the ingested toxic compounds prior to the use of animal products by the

353

354

Contributions

human consumer. This implies that for many phytochemicals the direct exposure via the ingestion of plant-derived foods is much higher than that of foods from animal origin. This assumption was confirmed in the recently conducted re-evaluation of undesirable substances in animal feeds, including toxic plant metabolites, as provided by EFSA in the frame of the amendment of Council Directive 2002/32/EC (http://www.efsa.europa.eu). These opinions provide an actual overview about the effects of prominent PSMs that occur in animal feed materials such as cyanogenic compounds, glucosinolates, saponines, tropane alkaloids, pyrrolizidin alkaloids and individual substances such as ricin, theobromine or gossypol and animal health. This directive defines maximum permissible levels for toxic plants and their metabolites in animal feeds, with the objective to protect animal health and productivity. Moreover, an assessment of the potential risks of residues of phytochemicals in animal-derived products is addressed. In this assessment, the excretion of toxic compounds in (dairy) milk, which may result in an undesirable exposure of children, was identified. Children are not only a vulnerable group within the human population, but are also high consumers of milk and dairy products. The recent insights (and testing protocols) into the role of efflux transporters such as BCRP (ABCG2) might serve as an example that mechanistic approaches may contribute significantly to the prediction of undesirable residues of PSMs in animal-derived products. In conclusion, PSMs are natural ingredients in the diets of herbivorous and omnivorous species. Their occurrence in commercial feed materials cause significant economic losses and limits the use of many protein-rich crops. PSMs impair palatability, weight gain and performance, and can adversely affect the reproductive capacity and limit the use of otherwise nutritionally valuable plants as animal feeds. Considering the large number of identified plant secondary metabolites, a systematic analysis of their oral bioavailability and the defence mechanisms that animals have acquired to prevent systemic exposure is essential for the veterinary profession as well as for animal poison centres. The most prominent field for a mechanistic approach in the evaluation of toxic plant metabolites is the risk assessment of feed materials for food producing animals that has to address animal health and productivity as well as the safety of consumers of products from animal origin.

References â•⁄ 1. Feeny P., Plant appearance and chemical defense. Recent Adv. Phytochem. 1976, 10, 1–40. â•⁄ 2. Bennet, R.╃N., Wallsgrove, W., Secondary metabolites in plant defensemechanisms. New Phytol. 1994, 127, 617–633. â•⁄ 3. Bednarek, P., Osbourn, A., Plant-microbe interactions: chemical diversity in plant defense. Science. 2009, 324, 746–748. â•⁄ 4. Shennan, C., Biotic interactions, ecological knowledge and agriculture. Phil. Trans. R. Soc.╃2008, 363, 717–739.

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

â•⁄ 5. Acamovic, T., Brooker, J.╃D., Biochemistry of plant secondary metabolites and their effects in animals. Proc. Nutrition Soc.╃2005, 64, 402–412. â•⁄ 6. Allwood, J.╃W., Ellis, D.╃I., Goodacre, R., Metabolomic technologies and their application to the study of plants and plant-host interactions. Physiol. Plant. 2008, 132, 117–135. â•⁄ 7. Wink, M.╃2004.╃Evolution of toxins and antinutritional factors in plants with special emphasis on Leguminosae. In: Poisonous Plants and Related Toxins (eds T. Acamovic, S.╃C. Stewart, T.╃W. Pennyscott). Wallingford, Oxon. CABI publishing. â•⁄ 8. Haralampidis, K., Trojanowska, M., Osbourn, A.╃E., Biosynthesis of triterpenoid saponins in plants. Adv. Biochem. Eng. Biotechnol. 2002, 75, 31–49. â•⁄ 9. Qi, X., Bakht, S., Leggett, M., Hemmings, A., Mellon, F., Eagles, J., WerkcReichert, D., Schaller, H., Lesot, A., Melton, R., Osbourn, A., A different function of a member of an ancient and highly conserved cytochrome P450 family: from essential sterols to plant defense. PNAS. 2006, 103, 18848– 18853. 10. Reed, J.╃D., Nutritional toxicology of tannins and related polyphenols in forage legumes. J. Anim. Sci.╃1995, 73, 1516–28. 11. Schardl, C.╃L., Epichloe festuca and related mutualistic symbionts of grasses. Fungal Genet Biol. 2001, 33, 69–82. 12. Fink-Gremmels, J., 2005. Mycotoxins in forages. In: The Mycotoxin Blue Book (ed, Diaz, D.╃E.). Nottingham University Press, pp.╃249–268. 13. O’Brien, M., O’Kiely, P., Forristal, P.╃D., Fuller, H.╃T., Fungi isolated from contaminated baled grass silage on farms in the Irish Midlands. FEMS Microbiol Lett. 2005, 247, 131–5. 14. Iason, G.╃R., The role of plant secondary metabolites in mammalian herbivory: ecological perspectives. Proc. Nutrition Soc.╃2005, 64, 123–131. 15. Iason G.╃R., Villalba, J.╃J., Behavioral strategies of mammalian herbivores against plant secondary metabolites: the avoidance-tolerance continuum. J. Chem. Ecol. 2006, 32, 115–1132. 16. Guitart, R., Croubels, S., Caloni, F., Sachana, M., et al., Animal poisoning in Europe. Part 1: Farm livestock and poultry. Vet. J.╃2009 Apr 7.╃doi:10â•›1016/j. tvjl.2009.03â•›002 17. Berny, P., Caloni, F., Croubels, S., Sachana, M., et al., Animal poisoning in Europe. Part 2: Companion animals. Vet. J.╃2009 Jun 22.╃doi:10â•›1016/j. tvjl.2009.03â•›034 18. Annison, E.╃F., Bryden, W.╃L., Perspectives on ruminant nutrition and metabolism I. Metabolism in the Rumen. Nutr. Res. Rev.╃1998, 11, 173–98. 19. Hammond, A.╃C., Leucaena toxicosis and its control in ruminants. J. Anim. Sci.╃1995, 73, 1478–1492. 20. Allison, M.╃J., Hammond, A.╃C., Jones, R.╃J., Detection of ruminal bacteria that degrade toxic dihydroxypyridine compounds produced from mimosine. Appl. Environ. Microbiol. 1990, 56, 590–594. 21. Akingbade, A.╃A., Nsahlai, I.╃V., Bonsi, M.╃L., Morris, C.╃D., du Toit, L.╃P., Reproductive performance of South African indigenous goats inoculated with

355

356

Contributions

DHP-degrading rumen bacteria and maintained on Leucaena leucocephala/ grass mixture and natural pasture. Small Rumin. Res.╃2001, 39, 73–85. 22. Anderson R.╃C., Majak, W., Rassmussen, M.╃A., Callawat, T.╃R., et al., Toxicity and metabolism of the conjugates of 3-nitropropanol and 3-nitroproprionic acid in forages poisonous to livestock. J. Agric. Food Chem. 2005, 53, 2344–2350. 23. Lu, C.╃D., Jorensen, N.╃A., Alfalfa saponins affect site and extent of nutrient digestion in ruminants. J. Nutr. 1987, 117, 919–927. 24. Valdez, F.╃R., Bush, L.╃J., Goetsch, A.╃L., Owens, F.╃N., Effect of steroidal sapogenins on rumen fermentation and on production of lactating dairy cows. Anim. Feed Sci. Technol. 1999, 78, 11–20. 25. Makkar, H.╃P.╃S., Sen, S., Blummel, M., Becker, K., Effects of fractions containing saponins from Yucca schidigera, Quillaja saponaria and Acacia auriculoformis on rumen fermentation, J. Agric. Food Chem. 1998, 46, 4324–4328. 26. Goel, G., Makkar, H.╃P.╃S., Becker, K., Changes in microbial community structure, methanogenesis and rumen fermentation in response to saponinrich fractions from different plant materials. J. Appl. Microbiol. 2008, 105, 770–777. 27. Fahey, J.╃W., Zalcman, A.╃T., Talalay, P., The chemical diversity and distribution of glucosinolates and isothiocyanates among plants. Phytochem. 2001, 56, 5–51. 28. Halkier, B.╃A. and Gershenzon, J., Biology and biochemistry of glucosinolates. Ann. Rev. Plant. Biol. 2006, 57, 303–333. 29. Kliebenstein, D.╃J., Kroymann, J. and Mitchell-Olds, T., The glucosinolatemyrosinase system in an ecological and evolutionary context. Curr. Opin. Plant Biol. 2005, 8, 264–71. 30. Griffiths, D.╃W., Birch, A.╃N.╃A., Hillmann, J.╃R., Antinutritional compounds in the Brassicaceae – Analysis, biosynthesis, chemistry and dietary effects. J. Hort. Sci. Biotech. 1998, 73, 1–18. 31. Tripathi, M.╃K. and Mishra, A.╃S., Glucosinolates in animal nutrition. Anim. Feed. Sci. Technol. 2007, 132, 1–27. 32. Waghorn, G.╃C., McNabb, W.╃C., Consequences of plant phenolic compounds for productivity and health of ruminants. Proc. Nutr. Soc.╃2003, 62, 383–92. 33. McSweeney, C.╃S., Palmer, B., McNeill, D.╃M., Krause, D.╃O., Microbial interactions with tannins: nutritional consequences for ruminants. Anim. Feed. Sci. Technol. 2001, 91, 83–93. 34. Aerts, R.╃J., Barry, T.╃N., McNabb WC, Polyphenols and agriculture: beneficial effects of proanthocyanidins in forages. Agric. Ecosys. Environm. 1999, 75, 1–12. 35. Serrano, J., Puupponen-Piia, R., Dauer, A., Aura, A-M., Saura-Calixto, F., Tannins, current knowledge of food sources, intake, bioavailability and biological effects. Mol. Nutr. Food Res.╃2009, 53, DOI 10â•›1002. 36. Patra, A.╃P., Saxena, J., Dietary phytochemicals as rumen modifiers: a review on the effect on microbial populations. Antonie van Leeuwenhoek, 2009, DOI 10â•›1007/s10482–009–9346–1.

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

37. Smith, A.╃H., Zoetendal, E.╃G., Mackie, R.╃I., Bacterial mechanisms to overcome inhibitory effects of dietary tannins. Microb. Ecol. 2005, 50, 197–205. 38. Makkar H.╃P.╃S., Blummel, M., Becker, K., In vitro effects and interactions between tannins and saponins and fate of tannins in the rumen. L. Sci. Food Agric. 1995, 69, 841–893. 39. Newbold, C.╃J., Hassan, S.╃M.╃E., Wang, J., Ortega, M.╃E., Wallace, R.╃J., Influence of foliage from African multipurpose tree on activity of rumen protozoa and bacteria. Br. J. Nutr. 1997, 78, 237–249. 40. Chiquette, K., Cheng, K.╃J., Rode, L.╃M., Millgan, L.╃P., Effect of tannin content on two isosynthetic strains of birdsfoot trefoil (Lotus cornuculatus) of feed digestibility and rumen fluid composition in sheep, Can. J. Anim. Sci.╃1989, 69, 1031–1039. 41. Barry, T.╃N., McNabb, W.╃C., The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 1999, 81, 263–272. 42. Wallace, R.╃J., Antimicrobial properties of plant secondary metabolites. Proc. Nutr. Soc., 2003, 63, 621–629. 43. Greathead H., Plants and plant extracts for improving animal productivity. Proc. Nutr. Soc, 2003, 62, 279–290. 44. Busquet, M., Calsamiglia, S., Ferret, A., Kamel, C., Plant extracts affect in vitro rumen microbial fermentation. J. Dairy Sci, 2006, 89, 761–771. 45. Rochfort, S., Parker, A.╃J., Dunshea, F.╃R., Plant bioactives for ruminant health and productivity. Phytochemistry 2008, 69, 299–322. 46. Freeland, W.╃J., Plant secondary metabolites: biochemical evolution with herbivores. In: Palo R., Robbins CT, (eds.) Plant defences against mammalian herbivory. 1991, CRC, Boca Raton, USA, pp.╃61–82. 47. Dearing, M.╃D., Mangione, A.╃M., Karasov, W.╃H., Diet breath of mammalian herbivores: nutrient versus detoxification constrains. Oecologica 2000, 123, 397–405. 48. Green, A.╃K., Haley, S.╃L., Dearing, M.╃D., Barnes, D.╃M., et al., Intestinal capacity of P-glycoprotein is higher in the juniper specialist, Neotoma stephensi, than the sympatric generalist, Neotoma albigula. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 2004, 139, 325–333. 49. McLean, S. Duncan, A.╃J., Pharmacological perspectives on the detoxification of secondary plant metabolites: implications for ingestive behaviour of herbivores. J. Chem. Ecol. 2006, 32, 1213–1228. 50. Palacín, M., Estévez, R., Bertran, J., Zorzano, A., Molecular biology of mammalian plasma membrane amino acid transporters. Physiol. Rev.╃1998, 78, 969–1054. 51. Juliano, R.╃L., Ling V., A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. Biochim. Biophys. Acta. 1976, 455, 152–62. 52. Dietrich, C.╃G., Geier, A., Oude Elferink, R.╃P., ABC of oral bioavailability: transporters as gatekeepers in the gut. Gut 2003, 52, 1788–95. 53. Takano, M., Yumoto, R., Murakami, T., Expression and function of efflux drug transporters in the intestine. Pharmacol. Ther. 2006, 109, 137–61.

357

358

Contributions

54. Nabekura, T., Yamaki, T., Ueno, K., Kitagawa, S., Inhibition of P-glycoprotein and multidrug resistance protein 1 by dietary phytochemicals. Cancer Chemother. Pharmacol. 2008, 62, 867–873. 55. Oostendorp, R.╃L., Beijnen, J.╃H., Schellens, J.╃H.╃M., 2008.╃The biological and clinical role of drug transporters at the intestinal barrier. Cancer Treat. Rev.╃2009, 35, 137–47. 56. Nguyen, V.╃T., Darbour, N., Bayet, C., Doreau, A., et al., Selective modulation of P-glycoprotein activity by steroidal saponines from Paris polyphylla. Fitoterapia. 2009, 80, 39–42. 57. Zhou, S., Chan, E., Pan S.╃Q., Huang, M., et al., Pharmacokinetic interactions of drugs with St John’s wort. Psychopharmacol. 2004, 18, 262–76. 58. Wu, C.╃P., Calcagno, A.╃M., Hladky, S.╃B., Ambudkar, S.╃V., Barrand, M.╃A., Modulatory effects of plant phenols on human multidrug-resistance proteins 1, 4 and 5 (ABCC1, 4 and 5). FEBS J.╃2005, 272, 4725–4740. 59. Telang, U., Ji, Y., Morris, M.╃E., ABC transporters and isothiocyanates: potential for pharmacokinetic diet-drug interactions. Biopharm. Drug Dispos. 2009 Jul 21 DOI: 10â•›1002/bdd.╃668 60. Pal, D., Mitra A.╃K., MDR- and CYP3A4-mediated drug-herbal interactions. Life Sci.╃2006, 78, 2131–45. 61. Brand, W., Schutte, M.╃E., Williamson, G., van Zanden, J.╃J., et al., Flavonoid-mediated inhibition of intestinal ABC transporters may affect the oral bioavailability of drugs, food-borne toxic compounds and bioactive ingredients. Biomed. Pharmacother. 2006, 60, 508–519. 62. Schrickx, J.╃A., Fink-Gremmels, J., Implications of ABC transporters on the disposition of typical veterinary medicinal products. Eur. J. Pharmacol. 2008, 585, 510–519. 63. Haritova, A.╃M., Schrickx, J.╃A., Lashev, L.╃D., Fink-Gremmels, J., Expression of MDR1, MRP2 and BCRP mRNA in tissues of turkeys. J. Vet. Pharmacol. Ther. 2008, 31, 378–85. 64. Robey, R.╃W., Steadman, K., Polgar, O., Morisaki, K., Blayney, M., Mistry, P., Bates, S.╃E., Pheophorbide is a specific probe for ABCG2 function and inhibition. Cancer Res.╃2004, 64, 1242–6.╃65.╃Alvarez, A.╃I., Real, R., Pérez, M., Mendoza, G., Prieto, J.╃G., Merino G., Modulation of the activity of ABC transporters (P-glycoprotein, MRP2, BCRP) by flavonoids and drug response. J. Pharm. Sci.╃2009 Jun 18.╃DOI 10â•›1002/jps.╃21851. 66. Sergent, T., Ribonnet, J., Kolosova, A., Garsou, S., Schaut, A., De Saeger, S., Van Peteghem, C., Larondelle, Y., Pussemier, L., Schneider, Y-J., Molecular and cellular effects of food contaminants and plant components and their plausible interactions at the intestinal level. Food. Chem. Toxicol, 2008, 46, 813–841. 67. Nebert, D.╃W., Russell, D.╃W., Clinical importance of the cytochromes P450, Lancet, 2002, 360, 1155–62. 68. Wogan, G.╃N., Hecht, S.╃S., Felton, J.╃S., Conney, A.╃H., et al., Environmental and chemical carcinogenesis. Semin. Cancer Biol. 2004, 14, 473–86.

Defence Mechanisms against Toxic Phytochemicals in the Diet of Domestic Animals

69. Gelboin,H.╃V., Benzo[alpha]pyrene metabolism, activation and carcinogenesis: role and regulation of mixed-function oxidases and related enzymes. Physiol. Rev.╃1980, 60, 1107–1166. 70. Guengerich, F.╃P., Johnson, W.╃W., Ueng, Y.╃F., Yamazaki, H., Shimada, T., Involvement of cytochrome P450, glutathione S-transferase, and epoxide hydrolase in the metabolism of aflatoxin B1 and relevance to risk of human liver cancer. Environ. Health Perspect. 1996, 104 S3, 557–62. 71. Huan, J.╃Y., Miranda, C.╃L., Buhler, D.╃R. and Cheeke, P.╃R., Species differences in the hepatic microsomal enzyme metabolism of the pyrrolizidine alkaloids. Toxicol. Lett. 1998, 99, 127–137. 72. Fu, P.╃P., Xia, Q., Lin, G., Chou, M.╃W., Pyrrolizidine alkaloids – genotoxicity, metabolism, enzymes, metabolic activation and mechanisms. Drug Met. Rev.╃2004, 36, 1–55. 73. Zhou S., Gao Y., Jiang W., Huang M., Xu A., Paxton J.╃W., Interactions of herbs with cytochrome P450. Drug Metab. Rev.╃2003, 35, 35–98. 74. Moon, Y.╃J., Wang, X., Morris, M.╃E., Dietary flavonoids: effects on xenobiotic and carcinogen metabolism. Toxicol. in Vitro 2006, 20, 187–210. 75. Bosch, T.╃M., Meijerman, I., Beijnen, J.╃H., Schellens, J.╃H., Genetic polymorphisms of drug-metabolising enzymes and drug transporters in the chemotherapeutic treatment of cancer. Clin. Pharmacokinet. 2006, 45, 253–285. 76. Komoroski, B.╃J., Zhang, S., Cai, H., Hutzler, J.╃M., et al., Induction and inhibition of cytochromes P450 by the St. John’s wort constituent hyperforin in human hepatocyte cultures. Drug Metab. Dispos. 2004, 32, 512–518. 77. Gross-Steinmeyer, K., Spepleton, P.╃L., Lis, F., Tracy, J.╃H., et al., Phytochemical-induced changes in gene expression of carcinogen-metabolizing enzymes in cultures human primary hepatocytes. Xenobiotica. 2004, 34, 619– 623. 78. Pavek, P., Dvorak, Z., Xenobiotic-induced transcriptional regulation of xenobiotic metabolizing enzymes of the cytochrome P450 superfamily in human extrahepatic tissues. Curr. Drug Metab. 2008, 9, 129–43. 79. Dacasto, M., Eeckhoutte, C., Francesca, C., Dupuy, J., et al., Effect of breed and gender on bovine liver cytochrome P450 3A (CYP3A) expression and inter-species comparison with other domestic ruminants. Vet. Res.╃2005, 36, 179–190. 80. Gusson, F., Carletti, M., Albo, A.╃G., Dacasto, M., Nebbia, C., Comparison of hydrolytic and conjugative biotransformation pathways in horse, cattle, pig, broiler chick, rabbit and rat liver subcellullar fractions. Vet. Res. Commun. 2006, 30, 271–283. 81. Nebbia, C., Biotransformation enzymes as determinants of xenobiotic toxicity in domestic animals. Vet. J.╃2001, 161, 238–252. 82. Ioannides, C., Cytochrome p450 expression in the liver of food-producing animals. Curr. Drug. Metab. 2006, 7, 335–348. 83. Fink-Gremmels J., Implications of hepatic cytochrome P450-related biotransformation processes in veterinary sciences. Eur. J. Pharmacol. 2008, 585, 502–509.

359

360

Contributions

84. Wang, H., LeCluyse, E.╃L., Role of orphan nuclear receptors in the regulation of drug-metabolising enzymes. Clin. Pharmacokinet. 2003, 42, 1331–1357. 85. Kliewer, S.╃A., B. Goodwin, et al. The nuclear pregnane X receptor: a key regulator of xenobiotic metabolism. Endocr. Rev., 2002, 23, 687–702. 86. Xu, C., Li, C.╃Y., Kong, A.╃N., Induction of phase I, II and III drug metabolism/transport by xenobiotics. Arc. Pharm. Res.╃2005, 28, 249–68. 87. Handschin, C., Meyer, U.╃A., Regulatory network of lipid-sensing nuclear receptors: roles for CAR, PXR, LXR, and FXR. Arch. Biochem. Biophys. 2005, 433, 387–396. 88. Lin, J.╃H., Yamazaki, M., Role of P-glycoprotein in pharmacokinetics: clinical implications. Clin. Pharmacokinet. 2003, 42, 59–98. 89. Sorensen, J.╃S., Dearing, M.╃D., Efflux transporters as a novel herbivore countermechanism to plant chemical defenses. J. Chem. Ecol. 2006, 32, 1181–1196. 90. Satsu, H., Hiura, Y., Mochizuki, K., Hamada, M., Shimizu, M., Activation of pregnane X receptor and induction of MDR1 by dietary phytochemicals. J. Agric. Food Chem. 2008, 56, 5366–5373. 91. Sergent, T., Ribonnet, L., Kolosova, A., Garsou, S., et al., Molecular and cellular effects of food contaminants and secondary plant components and their plausible interactions at the intestinal level. Food Chem. Toxicol. 2008, 46, 813–841. 92. Lee, J.╃S., Surh, Y.╃J., Nrf2 as a novel molecular target for chemoprevention. Cancer Lett. 2005, 224, 171–84. 93. Hayes, J.╃D., Kelleher, M.╃O., Eggleston, I.╃M., The cancer chemopreventive actions of phytochemicals derived from glucosinolates. Eur. J. Nutr. 2008, 47 S2, 73–88. 94. Court, M.╃H., Greenblatt, D.╃J., Molecular genetic basis for deficient acetaminophen glucuronidation by cats: UGT1A6 is a pseudogene, and evidence for reduced diversity of expressed hepatic UGT1A isoforms. Pharmacogenetics, 2000, 10, 355–69.

361

4

Posters

4.1 Coumarin Risk Assessment: Lessons from Human Data Klaus Abraham1, Klaus-Erich Appel, and Alfonso Lampen Coumarin is a secondary phytochemical with carcinogenic and hepatotoxic properties. It is found in relatively high concentrations in bark from Cassia cinnamon. In 2004, EFSA derived a Tolerable Daily Intake (TDI) of 0.1 mg/kg bw based on animal data [1]. A safety factor of 100 (10 for inter-species variation and 10 for inter-human variability) was applied to the No Observed Adverse Effect Level (NOAEL) of 10 mg/kg bw observed for hepatotoxicity in dogs (most sensitive species) [2]. EFSA discussed a possible reduction of the inter-species factor from 10 to 2.5 (no kinetic subfactor). The reason for this consideration is based on the fact that – in contrast to humans – the CYP2A6-mediated detoxification towards 7-hydroxycoumarin is only a minor pathway in many animal species including rodents and dogs. However, clinical data on hepatotoxicity in humans is available from the use of coumarin as medicinal drug to treat patients with different diseases. Results from two placebo-controlled studies [3, 4] show that a subgroup of the population in the one-digit range is sensitive for the effect. From the doses applied in these studies (about 6.7 and 1.5 mg/kg bw respectively) and from even lower doses (minimally about 0.4 mg/kg) in single cases reported to the authorities [5] it can be concluded that individuals of the human subgroup are more susceptible than various animal species investigated. The cause of the higher susceptibility is unknown. Unfortunately, no phenotype data on coumarin metabolism of affected patients is available. It is discussed that a CYP2A6 polymorphism may be underlying; however, the following observations do not support this assumption: ►⌺ CYP2A6 genotyping [6] of the patients of Schmeck-Lindenau et al. [4] did not reveal an association of subtypes with hepatotoxic responses. In addition, the frequency of a defect CYP2A6 (resulting in a missing detoxification towards 7-hydroxy-coumarin) in the population is much lower than the frequency of the sensitive individuals.

1

Correspondence to: Klaus Abraham, Federal Institute for Risk Assessment, Thielallee 88–92, D-14195 Berlin, Germany, [email protected].

362

Posters

In vitro experiments with human liver microsomes of slow metabolizers (7-hydroxylation phenotype) and following PBBK modelling with a low dose [7] revealed much lower concentrations of the toxic metabolite o-hydroxyphenylacetaldehyde (o-HPA) in these humans compared to rats, suggesting a different mechanism of hepatotoxicity in human individuals being more susceptible than rats. ►⌺ Clinical observations (re-exposure, immunosuppressive cotreatment) by Cox et al. [8] may suggest an immunotoxic mechanism probably involved in the coumarin induced hepatotoxicity in a subgroup of humans. ►⌺

In conclusion, a reduction of the inter-species factor (e.╃g. to 2.5, Felter et al. [9]) based on considerations of metabolic differences (humans detoxify more effectively than rodents) may be misleading in risk assessment, because a high susceptibility in humans may be due to dynamic causes not covered by an interspecies factor of 2.5. If human data are available it should be considered in risk assessment with high priority.

References 1. EFSA, Opinion of the Scientific Panel on Food Additives, Flavourings, Processing Aids and Materials in Contacts with Food (AFC) on a request from the Commission related to Coumarin; adopted on 6 October 2004, EFSA J.╃2004, 104, 1–36. 2. Hagan, E.╃C., Hansen, W.╃H., Fitzhugh, O.╃G., Jenner, P.╃M., et al., Food flavourings and compounds of related structure. II. Subacute and chronic toxicity. Food Cosmet. Toxicol. 1967, 5, 141–157. 3. Loprinzi, C.╃L., Kugler, J.╃W., Sloan, J.╃A., Rooke, T.╃W., et al., Lack of effect of coumarin in women with lymphedema after treatment for breast cancer. N. Engl. J. Med.╃1999, 340, 346–350. 4. Schmeck-Lindenau, H.╃J., Naser-Hijazi, B., Becker, E.╃W., Henneicke-von Zepelin, H.╃H., Schnitker, J. Safety aspects of a coumarin-troxerutin combination regarding liver function in a double-blind placebo-controlled study. Int. J. Clin. Pharmacol. Ther. 2003, 41, 193–199. 5. Bergmann, K., Sachverständigengutachten zur Beurteilung von Cumarin in Arzneimitteln in Bezug auf lebertoxische Wirkung beim Menschen, Rheinische Friedrich-Wilhelms-Universität Bonn. 1999, Original report written in German available from the BfArM, Bonn. 6. Burian, M., Freudenstein, J., Tegtmeier, M., Naser-Hijazi, B., et al., Single copy of variant CYP2A6 alleles does not confer susceptibility to liver dysfunction in patients treated with coumarin. Int. J. Clin. Pharmacol. Ther. 2003, 41, 141–147. 7. Rietjens, I.╃M., Boersma, M.╃G., Zaleska, M., Punt, A., Differences in simulated liver concentrations of toxic coumarin metabolites in rats and different human populations evaluated through physiologically based biokinetic (PBBK) modeling. Toxicol. In Vitro 2008, 22, 1890–1901.

Coumarin Risk Assessment: Lessons from Human Data

8. Cox, D., O’Kennedy, R., Thornes, R.╃D., The rarity of liver toxicity in patients treated with coumarin (1,2-benzopyrone). Hum. Toxicol. 1989, 8, 501–506. 9. Felter, S.╃P., Vassallo, J.╃D., Carlton, B.╃D., Daston, G.╃P., A safety assessment of coumarin taking into account species-specificity of toxicokinetics. Food Chem. Toxicol. 2006, 44, 462–475.

363

364

Posters

4.2 Coffee and Coffee Compounds are Effective �Antioxidants in Human Cells and In Vivo Tamara Bakuradze, Matthias Baum, Gerhard Eisenbrand, and Christine Janzowski1

Abstract Epidemiological studies suggest that coffee can reduce the risk of degenerative diseases such as diabetes type 2, cardiovascular disease and cancer. These beneficial effects have in part been attributed to the antioxidant activity of coffee brew, a complex mixture of bioactive compounds. We studied the direct antioxidant capacity (TEAC) of coffee constituents (polyphenols, hydroxybenzenes, N-methylpyridinium compounds)/ extracts and their potential to reduce DNA damage (comet assay) in Caco-2 cells. Additionally, modulation of DNA damage was investigated in a human intervention study with coffee, either rich in chlorogenic acids or in N-methylpyridinium ion. Chlorogenic and caffeic acid exhibited highest TEAC values (1.3 mM Trolox, both), phenolic degradation products, alkylpyridines and coffee extracts were less effective. Menadione-induced DNA damage in Caco-2 cells was reduced by N-methyl-(2-methyl)-pyridinium ion (1–30╃µM; p<0.05) and slightly diminished by N-methylpyridinium ion (3╃µM) and caffeic acid (10 and 30╃µM). In healthy probands, oxidative DNA damage in blood was reduced by both coffee brews. The results demonstrate that polyphenols and N-methylpyridinium compounds contribute to the antioxidative activity of coffee in human cells and in vivo.

4.2.1 Introduction Coffee is one of the most popular and widely consumed beverages worldwide. Recent data suggest that coffee consumption has beneficial effects on human health, with emphasis on type 2 diabetes, cardiovascular disease and colon cancer [1–3]. Increased formation of reactive oxygen species (ROS) and an imbalance between pro- and antioxidative processes has been implicated with the pathogenesis of these diseases. Coffee brew, a complex mixture of more than a thousand bioactive compounds such as chlorogenic acid (ChA), trigonelline (TRIG), N-methylpyridinium ion (NMP) and caffeine, exhibits distinct antioxidant activity. It is, however, not clear yet which constituents are most efficient in protecting cells against oxidative damage. Results from in vitro and in vivo studies suggest that phenolic compounds (ChA, phenolic degradation products) and alkylpyridines substantially contribute to the antioxidative effectiveness

1

University of Kaiserslautern, Department of Chemistry, Division of Food Chemistry and Toxicology, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany.

Coffee and Coffee Compounds are Effective �Antioxidants in Human Cells and In Vivo

of coffee [4, 5]. In the present study, the antioxidant potential of coffee constituents and extracts was investigated in a cell free system (trolox equivalent antioxidant capacity, TEAC) and in the human colon cancer cell line Caco-2 (modulation of DNA damage, induced with the redox-cycler menadione, Md). Additionally, modulation of DNA damage was determined in a human intervention study with coffee brews.

4.2.2 Materials and Methods Coffee constituents and extracts were obtained from Prof. T. Hofmann, Nutrition and Food Research Centre, TU Munich.

4.2.2.1 Antioxidant Capacity (TEAC)

TEAC of coffee compounds/ extracts was determined with the ABTS [2.2-azinobis-(3-ethyl-benzothiazoline-6-sulfonic acid)] radical cation decolourization assay, according to Schaefer et al. [6]. Briefly, coffee constituents/ extracts, dissolved in DMSO/ double distilled water, were added to pre-activated ABTS solution and absorbance at 734 nm was measured. TEAC values are expressed in mM Trolox, equivalent to the antioxidant capacity of 1mM solution of the constituents and of 1 mg/ml of the extracts, respectively. 4.2.2.2 Modulation of DNA Damage (Comet Assay) in Caco-2 Cells

Caco-2 cells were maintained in DMEM/ Nutrient Mix F12 (1:1), supplemented with 20â•›% FCS and 100 U/ml penicillin/ streptomycin at 37╃°C, 5â•›% CO2 and 95â•›% saturated atmospheric humidity. Caco-2 cells (2.5·105), cultivated for 24â•›h, were incubated with coffee constituents (1–100╃µM; 24â•›h), washed, treated with 6╃µM Md (1â•›h), isolated by trypsin (0.5â•›% w/v) and used for determination of viability (trypan blue exclusion) and DNA damage as described [6]. Alkaline single-cell gel electrophoresis was performed according to Collins et al. [7], with slight modifications [8, 9]. Cells (4╃×╃50,000) were mixed with low melting agarose, distributed onto an agarose-coated microscope slide (two gels per slide), submitted to lysis (1â•›h at 4╃°C), and covered with 50╃µl of either enzyme buffer or formamidopyrimidine-DNA-glycosylase (FPG) enzyme to differentiate between DNA strand breaks and total damage (strand breaks + DNA oxidation damage). After DNA unwinding (pH 13.5, 20 min, 4╃°C) and horizontal gel electrophoresis at 4╃°C for 20 min, slides were washed, stained with ethidium bromide, examined microscopically with a Zeiss Axioskop and analyzed by computerized image analysis (Perceptive Instruments), scoring 2╃×╃50 images per slide. DNA damage (with/ without FPG treatment) was expressed as relative tail intensity in % of Md-treated control (rel. TIâ•–%). Results are reported as mean ± SD of three independent experiments. Data were analyzed for significant difference (p<0.05) by unpaired one-sided t-test.

365

366

Posters

4.2.2.3 Determination of DNA Damage in an Intervention Study with Coffee Brews

An intervention study with 30 healthy probands was performed at the German Research Centre for Food Chemistry (DFA, Prof. V. Somoza), Garching. The total study period (12 weeks) was subdivided into a 2-week wash-out phase, followed by 4-week intervention with coffee 1 (rich in ChA), 2-week wash-out phase, and 4-week intervention with coffee 2 (rich in NMP-ion). In the coffee uptake phases, the probands consumed 500 ml of study coffee daily in one portion (morning). Probands were instructed to keep their usual dietary habits for the duration of the study, except for avoiding intake of supplements and polyphenol rich foods. Venous blood (6╃µl) from sampling at the beginning of the study and at the end of each study phase was submitted to the comet assay and treated as described above. DNA damage in whole blood (with/ without FPG treatment) was expressed as tail intensity in % (TIâ•–%). Data were analyzed for normal distribution and differences between study phases by Anderson Darling test and one-sided, paired t-test, respectively.

4.2.3 Results and Discussion Within the coffee compounds tested, the highest cell free antioxidant capacity (1.3–0.9 mM Trolox) was observed with ChA, CA and phenolic degradation products (catechol, CAT; 1, 2, 4-trihydroxybenzene, THB); alkylpyridines TRIG and NMP were clearly less effective (Fig.╃1). All tested coffee extracts showed distinct antioxidant capacity with TEAC values ranging between 1.5 and 0.8 mM Trolox (in mg /ml).

Figure 1: Trolox equivalent antioxidant capacity (TEAC) of coffee compounds (a) and extracts (b), the mmolar concentration of a Trolox solution having an antioxidant capacity equivalent to (a) 1.0 mM solution (compound) or (b) 1.0 mg/ml solution (coffee extract).

Coffee and Coffee Compounds are Effective �Antioxidants in Human Cells and In Vivo

In Caco-2 cells, Md-induced total DNA damage was distinctly diminished by N-methyl-(2-methyl)-pyridinium ion (1–30╃µM; p<0.05; cell viability >85â•›%); other coffee compounds were less potent (NMP, 3╃µM; caffeic acid, 10 and 30╃µM) or ineffective. DNA strand breaks (without FPG treatment) were not modulated by coffee compounds under these conditions. The time course of DNA strand breaks and total DNA damage in the intervention study with coffee brews (coffee 1: rich in ChA and coffee 2: rich in NMP-ion) is shown in Figure 2.╃Since DNA strand breaks were only detected in small amounts, FPG sensitive oxidation damage predominantly accounts for total DNA damage. Four week intervention with the coffee 1 resulted in decrease of total DNA damage by 22â•›%, compared to the preceding wash-out phase. During uptake of NMP-rich coffee 2 total DNA damage was reduced by 15â•›%. DNA strand breaks remained almost unchanged in the complete time course of the study.

Figure 2: DNA strand breaks and total DNA damage in lymphocytes during wash-out (WO) and coffee phases in mean ± SD; significance (one-sided, paired t-test): ***p < 0.001.

Taken together, the results of our intervention study confirm the potential of coffee brew to reduce DNA oxidation damage in healthy probands [10]. The distinct effectiveness of both (ChA- and NMP-rich) coffee brews, together with the antioxidant potential of phenolics and alkylpyridinium ions in vitro, supports the relevance of these constituents for the preventive activity of coffee in humans.

Acknowledgments We thank Prof. T. Hofmann, TU Munich and Prof. V. Somoza, DFA Garching for providing coffee constituents/ extracts and the blood samples, respectively. Support: Federal Ministry of Education and Research (BMBF), Grant No.╃0313843.

367

368

Posters

References â•⁄ 1. Van Dam, R.╃M., Manson, J.╃E., Willett, W.╃C., Hu, F.╃B., Coffee, Coffeine and Risk of Type 2 Diabetes. Diabetes Care. 2006; 29: 398–403. â•⁄ 2. Bonita, J.╃S., Mandarano, M., Shuta, D., Vinson, J., Coffee and cardiovascular disease: in vitro, cellular, animal, and human studies. Pharmacol. Res.╃2007; 55: 187–98. â•⁄ 3. Larsson, S.╃C., Bergkvist, L., Giovannucci, E., Wolk, A., Coffee consumption and incidence of colorectal cancer in two prospective cohort studies of Swedish women and men. Am. J. Epidemiol. 2006; 1: 638–44. â•⁄ 4. Somoza, V., Lindenmeier, M., Wenzel, E., Frank, O., Erbersdobler, H.╃F., Hofmann, T., Activity-guided identification of a chemopreventive compound in coffee beverage using in vitro and in vivo techniques. J. Agric. Food Chem. 2003; 51: 6861–9. â•⁄ 5. Daglia, M., Racchi, M., Papetti, A., Lanni, C., Govoni, S., Gazzani, G., In vitro and ex vivo antihydroxyl radical activity of green and roasted coffee. J. Agric. Food Chem. 2004; 52: 1700–4. â•⁄ 6. Schaefer, S., Baum, M., Eisenbrand, G., Dietrich, H., Will, F., Janzowski, C., Polyphenolic apple juice extracts and their major constituents reduce oxidative damage in human colon cell lines. Mol. Nutr. Food Res.╃2006; 50: 24–33. â•⁄ 7. Collins, A.╃R., Dusinska, M., Gedik, C.╃M., Stetina, R., Oxidative damage of DNA: do we have a reliable biomarker? Environ. Health Perspect. 1996; 104: 465–469. â•⁄ 8. Weisel, T., Baum, M., Eisenbrand, G., Dietrich, H., Will, F., Stockis, J.╃P., Kulling, S., Rüfer, C., Johannes, C., Janzowski, C., An anthocyanin/ polyphenolic-rich fruit juice reduces oxidative DNA damage and increases glutathione level in healthy probands. Biotechnol. J.╃2006; 1: 388–97. â•⁄ 9. Spormann, T.╃M., Albert, F.╃W., Rath, T., Dietrich, H., Will, F., Stockis, J.╃P., Eisenbrand, G., Janzowski, C., Antocyanin/ Polyphenolic rich fruit juice reduces oxidative cell damage in an intervention study with patients on hemodialysis. Cancer Epidemiol. Biomarkers Prev. 2008; 17: 3372–80. 10. Bichler, J., Cavin, C., Simic, T., Chakraborty, A., Ferk, F., Hoelzl, C., SchulteHermann, R., Kundi, M., Haidinger, G., Angelis, K., Knasmüller, S., Coffee consumption protects human lymphocytes against oxidative and 3-amino1-methyl-5H-pyrido[4,3-b]indole acetate (Trp-P-2) induced DNA-damage: results of an experimental study with human volunteers. Food Chem. Toxicol. 2007; 45: 1428–36.

Studying Absorption, Distribution, Metabolism, and Excretion of a Complex Extract

4.3 Studying Absorption, Distribution, Metabolism, and Excretion of a Complex Extract Mareike Beck1, Martine Bruchlen, Volker Elste, Peter Mair, and Robert Rümbeli Oregano, one of the most popular and common culinary herbs, is known to possess high antiÂ�-oxidant as well as antimicrobial activity. Like most herbal extracts or essential oils, oregano extract (OE) is a complex mixture of numerous different compounds. Main constituents of OE include carvacrol (>60â•›%), thymoquinone (>4â•›%), and thymol. Furthermore, it may contain a variety of monoterpene hydrocarbons and monoterpene alcohols. High doses as well as highly sophisticated analytical methods would be necessary to obtain data on kinetic properties or metabolism of the major component carvacrol. Even then it would not be possible to achieve a complete balance. Alternatively, studies with the single compound(s) could be performed. To avoid unphysiologically high doses and – at the same time – maintain the natural composition of OE and the mutual interaction between its components, we have chosen a different approach. OE was spiked with radioactively labelled single compounds (14C-carvacrol or 14C-thymoquiÂ�none). The spiked extract was used in a number of studies addressing absorption, distribution, metabolism, and excretion (ADME) of these two major extract components after oral administration to rats at relevant doses. After an oral dose of 14C-carvacrol spiked OE, radioactivity, representing carvacrol and all carvacrol metabolites carrying the 14C-label, was determined in plasma, bile, urine, faeces, organs and tissues. Quantitative whole-body autoradiogaphy was used to investigate tissue distribution. All tissues and organs showed their cmax at the first sampling point 0.5â•›h after oral administration. The highest concentration was found in the kidneys, followed by liver, lungs, and blood. The concentration in all tissues and organs depleted rapidly with halflifes of 2–3â•›h. In an excretion balance study the majority of the dose (>85â•›% of radioactivity) is excreted in the urine 24â•›h after dosing, and only around 10â•›% is found in faeces. In plasma, an early first peak (0.5â•›h) indicating fast absorption, is followed by a rapid decline. A second increase in the 2–8â•›h time period is most probably caused by re-uptake of carvacrol through enterohepatic recirculation. First-pass elimination was confirmed in a study using bile duct cannulated rats. Around 20â•›% of the radioactive dose is excreted in the bile, most of it within 4â•›h and exclusively in form of carvacrol-O-glucuronide. The same pattern of four carvacrol metabolites was detected in plasma and urine. The two main metabolites were tentatively identified by LC/MS as the sulfo-conjugates of carvacrol and of a hydroxylated carvacrol, the minor metabolites as carvacrol-glucuronide and a sulfate of dehydro-carvacrol.

1

DSM Nutritional Products Ltd, R&D Human Nutrition and Health, Safety, P.╃O. Box 2676, CH4202 Basel, Switzerland, [email protected].

369

370

Posters

Taken together, the use of 14C-labelled compounds spiked to oregano extract allows gaining complete insight into kinetics, distribution, excretion pathways, tissue distribution, and metabolism of extract ingredients in their natural �matrix.

Polyphenolic Apple Extracts and their Constituents Modulate DNA Strand Breaks and Oxidation �Damage in Human Colon Carcinoma Cells

4.4 Polyphenolic Apple Extracts and their Constituents Modulate DNA Strand Breaks and Oxidation �Damage in Human Colon Carcinoma Cells Phillip Bellion1, Frank Will2, Helmut Dietrich2, Matthias Baum1, Gerhard Eisenbrand1, and Christine Janzowski1

Abstract Diets rich in fruits and vegetables are associated with a lower risk of tumour incidence in the intestine and other sites. This is mainly attributed to secondary plant constituents (e.╃g. polyphenols), displaying bioactive effects, such as prevention from DNA oxidation. We studied the potential of polyphenolic extracts from apple juices (AE), pomace extraction juice (APE), and apple peel (PE) to reduce menadione-induced DNA damage in the human colon cell line Caco-2, in comparison to antioxidant capacity and polyphenol composition. DNA strand breaks/ oxidation damage were assessed by the alkaline Comet Assay with/ without the repair enzyme FPG; antioxidant capacity was measured by ORAC and TEAC assays. All extracts reduced DNA strand breaks at low concentrations (1–3╃µg/ml) whereas higher AE concentrations (≥╃10╃µg/ml) differentially increased DNA damage. Reduction of DNA damage by the extracts could partially be explained by their polyphenol pattern, whereas no influence of direct antioxidant capacity was observed. In conclusion, apple phenolics at low, nutritional relevant concentrations may prevent intestinal cells from ROSinduced DNA damage.

4.4.1 Introduction The aetiology of cancer and other diseases (e.╃g. inflammation or coronary heart disease) is associated with an imbalance in the cellular redox system leading to an increased level of reactive oxygen species (ROS). Since the initiation progress of carcinogenesis involves mutation of the DNA, oxidation of DNA bases by ROS is supposed to be a crucial factor. Epidemiological studies indicate an inverse correlation between fruit and vegetable consumption and the prevalence for colorectal cancer, the third most common cancer in Western countries [1]. This is mainly attributed to the amount of secondary plant metabolites such as polyphenols, which act as antioxidants by scavenging radicals

1

University of Kaiserslautern, Department of Chemistry, Division of Food Chemistry and Toxicology, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany.

2

Geisenheim Research Institute, Section of Wine Analysis and Beverage Research, Von-LadeStr.╃1, D-65366 Geisenheim, Germany.

371

372

Posters

and chelation of redox active metal ions. Additionally, they can induce cellular antioxidant defence and repair. Apples and apple juices contain large amounts of such polyphenols, known to protect colon cells against oxidative cell damage in€ vitro, including prevention of ROS-mediated DNA damage, mutations and cell death [2–4]. In this study, the potential of polyphenolic apple extracts to protect against DNA oxidation damage was assessed in Caco-2 colon carcinoma cells. The extracts were obtained from two apple juices (AE05, AE06), from apple pomace extraction juice (APE), and from apple peel (PE). To estimate the influence of polyphenol pattern and radical scavenging activity on the preventive efficacy of the extracts, their composition and antioxidant capacity were measured by HPLC-DAD and TEAC/ ORAC assay, respectively.

4.4.2 Methods Apple extracts were produced mainly from cider apple juices (AE05, AE06, and APE; [5]) except for the PE, originating from table apple peels after hot water extraction. Determination of polyphenols was performed by HPLC-DAD [5] and antioxidant capacity of the extracts was assessed by TEAC and ORAC assays [6, 7]. Caco-2 cells were grown in DMEM/F12 (1:1) mix, supplemented with 20â•›% FCS and penicillin/streptomycin. After 24â•›h incubation with polyphenolic extracts (dissolved in DMSO) in serum reduced medium (10â•›% FCS) cells were treated with menadione (Md, 6╃µmol/l) for 1â•›h to induce DNA oxidation damage and isolated by trypsin-treatment [8]. Artifactual H2O2 generation in the medium during extract incubation was observed at best in trace amounts (<1╃µM, FOX assay) [5]. Viability of cells was assured to be >80â•›% by trypan blue exclusion assay. To detect DNA damage, the alkaline single-cell gel electrophoresis (Comet Assay) was performed as described [8, 9]. Additional FPGenzyme treatment was used to recognize specific DNA oxidation damage [10]. DNA migration was calculated as mean tail intensity (TI%: DNA in the comet tail in percent of total DNA). Results were expressed as relative TI in % of Mdtreated control. Data were obtained from n≥╃3 independent experiments, each performed at least in duplicate. Results were analyzed for significant difference (p<0.05) to the respective Md-treated control by Student’s t-test. Coefficient of correlation R between antioxidant capacity values and extract composition was calculated by linear regression analysis.

4.4.3 Results and Discussion After 24â•›h incubation, all tested extracts significantly reduced Md-induced DNA strand breaks (Fig.╃1). Among the extracts tested, PE was the most effective showing a preventive potential over the full concentration range tested (1–

Polyphenolic Apple Extracts and their Constituents Modulate DNA Strand Breaks and Oxidation �Damage in Human Colon Carcinoma Cells

100╃µg/ml; Fig.╃1â•›d), with a maximum reduction at 3╃µg/ml. The apple juice extracts (AE05, AE06; Figs. 1â•›a and b) and APE (Fig.╃1â•›c) were also effective reducers of DNA strand breaks, but only at lower concentrations (1–10╃µg/ml). When exceeding 3╃µg/ml (APE: 10╃µg/ml), the protective effect of AEs/ APE was no longer detectable and, for AE06, even converted into pro-oxidative damage, significantly increasing DNA strand breaks. Similar concentration-dependent effects were also observed for total DNA damage (including FPG-specific oxidation damage), with approx. twofold increased tail intensities compared to strand breaks alone. The protective efficacy of the extracts against DNA damage can be attributed to their amount of polyphenols [2]. Comparing polyphenol pattern of the extracts with DNA damage reduction gives slight indications for a contribution of quercetin glycosides, predominant in PE (Fig.╃2) and known to efficiently reduce DNA damage in Caco-2 cells [2, 4]. The observed pro-oxidant effects of AEs at higher concentrations could be ascribed to their high amount of hydroxycinnamic acids, which can easily generate ROS at physiological pH [5]. All extracts exhibited distinct antioxidant capacity, with TEAC and ORAC values of 4.8–6.4 and 3.4–6.5 mM Trolox, respectively. Since the TEAC/ ORAC values of the extracts did not correspond to their Comet Assay results, it can be assumed that induction of cellular antioxidant defence is the predominant mechanism for the protection against ROS-induced DNA damage.

Figure 1: Modulation of Md-induced DNA (oxidation) damage in Caco-2 cells after 24â•›h incubation with the apple extracts (a) AE05, (b) AE06, (c) APE, and (d) PE. —— DNA strand breaks (-FPG); —●— total DNA damage (+FPG). Mean and SD of n=╃3–5 independent experiments. Significant change to menadione (Md) treated control: *p<0.05, **p<0.01, ***p<0.001.

373

374

Posters

Figure 2: Composition of (a) study extracts, (b) polyphenol subgroup. All values are given in % of extract mass. *) Taken from [5].

Comparing the total polyphenol and procyanidin amount of the extracts (Fig.╃2) with their antioxidant capacity underlines the relevance of phenolic substances (ORAC: R=╃0.995 ORAC; TEAC: R=╃0.728). Within the group of phenolic substances, the most distinct correlation with antioxidant capacity was observed for procyanidins (ORAC: R=╃0.839) and polyphenols (TEAC: R=╃0.687).

4.4.4 Conclusion Apple phenolics at low, nutritionally relevant concentrations may prevent intestinal cells from ROS-induced DNA damage. Although the extracts exhibit distinct direct antioxidant capacity, induction of cellular defence seems to be substantial for DNA protection.

Acknowledgements The authors gratefully acknowledge the gift of FPG enzyme by Prof. A.╃R. Collins (University of Oslo, Norway). They also thank S. Schmidt for competent assistance and the German Federal Ministry of Education and Research (Grant No.╃01EA0501) for financial support!

Polyphenolic Apple Extracts and their Constituents Modulate DNA Strand Breaks and Oxidation �Damage in Human Colon Carcinoma Cells

References â•⁄ 1. WCRF and AICR, Food, nutrition, physical activity, and the prevention of cancer: A global perspective. 2007, Washington DC: American Institute for Cancer Research. â•⁄ 2. Schaefer, S., Baum, M., Eisenbrand, G., Dietrich, H., Will, F., Janzowski, C., Polyphenolic apple juice extracts and their major constituents reduce oxidative damage in human colon cell lines. Mol Nutr Food Res.╃2006, 50 (1): 24–33. â•⁄ 3. Schaefer, S., Baum, M., Eisenbrand, G., Janzowski, C., Modulation of oxidative cell damage by reconstituted mixtures of phenolic apple juice extracts in human colon cell lines. Mol Nutr Food Res.╃2006, 50 (4–5): 413–417. â•⁄ 4. Aherne, S.╃A. and O’Brien, N.╃M., Mechanism of protection by the flavonoids, quercetin and rutin, against tert-butylhydroperoxide- and menadione-induced DNA single strand breaks in Caco-2 cells. Free Radic Biol Med.╃2000, 29 (6): 507–514. â•⁄ 5. Bellion, P., Olk, M., Will, F., Dietrich, H., Baum, M., Eisenbrand, G., Janzowski, C., Formation of hydrogen peroxide in cell culture media by apple polyphenols and its effect on antioxidant biomarkers in the colon cell line HT-29.╃Mol Nutr Food Res.╃2009, in press. â•⁄ 6. Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M.,Rice-Evans, C., Antioxidant activity applying an improved ABTS radical cation decolorization assay. Free Radic Biol Med.╃1999, 26 (9–10): 1231–1237. â•⁄ 7. Ou, B., Hampsch-Woodill, M., Prior, R.╃L., Development and validation of an improved oxygen radical absorbance capacity assay using fluorescein as the fluorescent probe. J Agric Food Chem. 2001, 49 (10): 4619–4626. â•⁄ 8. Bellion, P., Hofmann, T., Pool-Zobel, B.╃L., Will, F., Dietrich, H., Knaup, B., Richling, E., Baum, M., Eisenbrand, G., Janzowski, C., Antioxidant effectiveness of phenolic apple juice extracts and their gut fermentation products in the human colon carcinoma cell line Caco-2.╃J Agric Food Chem. 2008, 56 (15): 6310–6317. â•⁄ 9. Singh, N.╃P., McCoy, M.╃T., Tice, R.╃R., Schneider, E.╃L., A simple technique for quantitation of low levels of DNA damage in individual cells. Exp Cell Res.╃1988, 175 (1): 184–191. 10. Laval, J., Role of DNA repair enzymes in the cellular resistance to oxidative stress. Pathol Biol (Paris). 1996, 44 (1): 14–24.

375

376

Posters

4.5 Comparative Evaluation of Experimental Data on α-Amylase Inhibition by Flavonoids Using Molecular Modelling Lisa M. Bode1, Thomas Homann2, Harshadrai M. Rawel1, and Sabine E. Kulling1,3 The antihyperglycemic potential of the flavonoids is a very important property with regard to non-insulin-dependent diabetes mellitus, one of the most serious life-style-related disorders. The effect on post-meal blood glucose may be due to inhibition of the starch digesting enzymes α-glucosidase as well as α-amylase within the gastrointestinal tract. The inhibiting and binding properties of various flavonoids against αâ•‚amylase from porcine pancreas were determined. To identify structure–activity relationships representative compounds of all six subclasses differing in their hydroxylation and/ or glycosylation patterns were considered. The influence of flavonoids on the α-amylase activity was determined by the release of 4-nitrophenol from p-nitrophenol-α-maltoside. The binding of flavonoids to α-amylase was studied by the intrinsic protein tryptophan fluorescence. Binding constants (KA) and the Gibbs energy (∆G) were calculated. The experimental inhibition data were applied for theoretical evaluation by molecular binding. α-Amylase structures were obtained from diverse protein data banks and the 3D hydrated models were optimized using MOE. Molecular docking was investigated by MOE and Molegro software. Flavonoids show diverse inhibitory effects on α-amylase activity independent of their specific subclass. Selected flavanols, e.╃g. (-)-epicatechin and procyanidin B1, show hardly any inhibition. Flavonoids of all other subclasses inhibit α-amylase depending on structural requirements present: e.╃g. position of the sugar, sugar residue in C3-position, planarity of the molecule, and substitution pattern of the C-ring. The hydroxylation pattern of the B-ring plays only a minor role in inhibiting α-amylase. The highest inhibitory effect among all flavonoids was caused by the flavonol aglycons. The free enthalpy (∆G) calculated from KA ranges between -14 and -18 kJ/ mol. The exergonic nature of ∆G indicates the spontaneity of the interactions between flavonoids and α-amylase. The values of ∆G also indicate that hydrophobic interactions may play a substantial role. Molecular docking showed that the aglycons bind preferably to the active site as documented for acabose, known to bind exclusively to the active site. In case of the aglycons, both hy-

1

University of Potsdam, Institute of Nutritional Science, Department of Food Chemistry, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany.

2

Steenbok Biopharmaceuticals, Am Waldpark 28, D-13467, Germany, Dr.Homann@steenbok. de.

3

[email protected].

Comparative Evaluation of Experimental Data on α-Amylase Inhibition by Flavonoids Using Molecular Modelling

drophobic and hydrogen bonds are possible, depending on the structure of the flavonoid involved. The following participating amino acid residues of the active site were identified exemplary for kaempferol: trp 59, asp 197, his 299 and tyr 62.╃Selected 3D docking results will be illustrated in the poster.

377

378

Posters

4.6 Potential Risk of Furan in Foods J. Brück1, Dieter Schrenk1, U. Schauer,2 A. Mally2, W. Dekant2, R. Stadler3, and L.╃Gall3 Furan in foods has been detected at concentrations of up to 80╃µg/kg after heat treatment (brewed coffee). Previous studies in rats and mice (US National Toxicology Program NTP) showed hepatotoxic and carcinogenic effects. Treatment of Fischer (F344) rats with doses of 2–8 mg/kg body weight per day over 2 years resulted in a tumour rate of 86–100â•›% in the livers. Another study with higher dosed rats showed the formation of adenomas, as a precursor of cancer, after shorter periods of treatment. Major effects were seen in the epithelial cells of the bile ducts. The US Food and Drug Authority (FDA) classified furan in foods as a potential mutagenic substance. The knowledge about the actual carcinogenic risk of dietary furan exposure is incomplete and the different formation pathways are mainly undiscovered. The Furan-RA Project within the 6th Frame Project of the European Union is aimed at elucidating the links between furan exposure and liver damage. Especially the risk of low doses which may occur by food consumption is of particular interest. Analogous to the previous rodent studies, rats were treated with 4 doses of furan (0, 0.1, 0.5, 2.0 mg/kg bw) for up to 4 weeks, partly with an additional recovery period of 2 weeks. The prepared liver slices were stained with H&E and with antibodies against the proliferation marker PCNA. Both showed no furan related changes in the liver. In a second part the influence of furan on different types of liver cells is measured. Primary hepatocytes as well as the cell lines H4IIE, FAO, HepG2 and their transfected descendants C34 and E47 (expressing CYP2E1) were incubated with concentrations of furan between 0.5 and 30╃mM. The resulting surprisingly low cytotoxicity with an EC50 of >10 mM must be seen critically since medium samples showed a loss of at least 90â•›% of furan after 1 hour due to the volatility of furan (Fp.╃31╃°C). The concentration during incubation was re-calculated resulting in an estimated EC50 of about 0.4 mM in rat hepatocytes. This value will be verified by incubations under accompanied control of the furan content in the media using headspace-GC. To enable a steady concentration over a longer period a closed system was developed with a gas chamber allowing an equilibration between gas and liquid phase.

1

TU Kaiserslautern, Food Chemistry and Toxicology, Erwin-Schrödinger-Str.╃52, D-67663 Kaiserslautern, Germany.

2

University of Würzburg, Department of Toxicology, Versbacher Str.╃9, D-97078 Würzburg, Germany.

3

Nestlé Product Technology Centre, Orbe Nestec Ltd., 1, rue de Chavornay, CH-1350 Orbe, Switzerland.

Comparative Study on the Toxicity of Alternariol and Alternariol Monomethyl Ether in Human Tumour Cells of Different Origin

4.7 Comparative Study on the Toxicity of Alternariol and Alternariol Monomethyl Ether in Human Tumour Cells of Different Origin Julia Burkart1, Markus Fehr, Gudrun Pahlke, and Doris Marko Mycotoxins of the genus Alternaria are widespread contaminants of food and feed comprising a potential risk for health. Exposure to extracts of Alternaria spp. as well as single mycotoxins of Alternaria have been associated with enhanced incidence of oesophageal cancer [1]. Genotoxic and mutagenic properties of Alternaria toxins such as alternariol (AOH) and AOH monomethyl ether (AME) have been described in vitro. However, the mechanism of action is not known. Recently, we have identified AOH, a main metabolite of Alternaria, as a topoisomerase poison, with preference to the II alpha isoform. Topoisomerase poisoning and DNA strand breakage occurred within the same concentration range, thus topoisomerase poisoning might at least contribute to the genotoxic properties of AOH [2]. The aim of the present study was to compare the toxic properties of AOH and AME in cells of different origin: Colon (HT29), oesophagus (KYSE510) and liver (HepG2) to investigate potential organ specificity. In focus was the impact of both mycotoxins on cell viability, DNA integrity and on potentially cellular oxidative stress. Cell viability was determined after 72â•›h of incubation using the sulforhodamin B assay. AOH and AME significantly inhibited the growth of all tested cell lines. HepG2 cells seem less potently influenced in proliferation (IC50 AOH=╃84╃±â•ƒ12╃µM; IC50 AME=╃92╃±â•ƒ10╃µM) by AOH and AME than cells of colon/ oesophageal origin. Colon carcinoma cells (HT29) are preferentially affected in their growth by AOH (IC50 AOH=╃42╃±â•ƒ10╃µM; IC50 AME>100╃µM). Notably, oesophageal carcinoma cells (KYSE510) are most sensitive (IC50 AOH=╃41╃±â•ƒ3╃µM; IC50 AME=╃48╃±â•ƒ3╃µM) to AOH and AME. Both Alternaria toxins significantly increased the rate of DNA damage after 1â•›h of incubation at concentrations ≥╃1╃µM in all tested cell lines, as measured by alkaline comet assay, indicating substantial genotoxic potential independent of organ origin. However, the rate of DNA double strand breaks, detected with the neutral comet assay, was only increased in colon and oesophageal tumour cells at concentrations ≥╃10╃µM. Furthermore, AOH and AME at concentrations ≥╃25╃µM induced the generation of intracellular ROS in cells of colon/ oesophageal origin as measured by dichlorofluorescein (DCF) assay, potentially contributing to their genotoxic properties. ROS generation in HepG2 cells was observed only by AOH incubation. In summary, AOH and AME affect cell viability and DNA integrity and induce the generation of ROS in all tested cell lines. Tumour cells of colon and

1

University of Karlsruhe (TH), Institute of Applied Biosciences, Section of Food Toxicology, Adenauerring 20â•›a, D-76131 Karlsruhe, Germany.

379

380

Posters

oesophageal origin appear to respond slightly more sensitive to the Alternaria toxins than liver carcinoma cells.

References 1. Liu GT, Qian YZ, Zhang P, Dong WH, Qi YM, Guo HT (1992) Etiological role of Alternaria alternata in human esophageal cancer. Chin. Med. J. (Engl), 105: 394–400. 2. Fehr M, Pahlke G, Fritz J, Christensen MO, Boege F, Altemöller M, Podlech J, Marko D (2009) Alternariol acts as a topoisomerase poison, preferentially affecting the II alpha isoform. Mol. Nutr. Food Res., 53: 441–451.

A Role for Resveratrol and Curcumine in Sensitization of Glioblastoma Cells to Genotoxic Stress Induced by Alkylating Chemotherapeutics

4.8 A Role for Resveratrol and Curcumine in Sensitization of Glioblastoma Cells to Genotoxic Stress Induced by Alkylating Chemotherapeutics Markus Christmann1, N. Berdelle, G. Nagel, and B. Kaina Curcumine and resveratrol are phytochemicals for which anticancer, anti-oxidant and anti-inflammatory activity have been reported. Resveratrol is a phytoalexin which is found in the skin of red grapes, curcumine is a polyphenol, responsible for the yellow colour of turmeric. Both agents are currently discussed as potential drug modifiers, either protecting or sensitizing cells against genotoxic stress. However, the impact of these compounds on chemotherapy has not been elucidated in great detail. To clarify this, we analyzed the influence of curcumine and resveratrol on DNA repair and sensitivity to cytostatic drugs. To this end we utilized glioblastoma cell lines differing in their p53 and MGMT status. All these cell lines were similarly sensitive to curcumine and resveratrol. Whereas no influence of curcumine and resveratrol on base excision repair was detected, we could show an effect on the DNA repair enzyme MGMT. Treatment with curcumine and resveratrol induced a down-regulation of MGMT on the level of mRNA, protein and activity. In addition, curcumine and resveratrol were able to sensitize glioblastoma cells to the chemotherapeutical drugs ACNU and temozolomide. Since this sensitization is observed also in MGMT deficient cells, the inhibitory effect of curcumine and resveratrol on MGMT activity cannot be the only reason for the observed sensitization. In MGMT deficient cells, additional mechanisms appear to be activated by curcumine and resveratrol that sensitize cells to anticancer drugs. This issue is in the focus of current investigations.

1

University of Mainz, Department of Toxicology, Obere Zahlbacher Str.╃67, D-55131 Mainz, Germany, [email protected].

381

382

Posters

4.9 BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids Birgit Dusemund1, Klaus-Erich Appel, and Alfonso Lampen

Abstract Three examples for risk assessment approaches concerning the occurrence of alkaloids in food are presented: Quinine, the principal alkaloid of cinchona bark (Cinchonae cortex), the dried bark of Cinchona pubescens, Vahl (synonym: Cinchona succirubra, Pavon), was evaluated as component of bitter soft drinks. The opium alkaloids morphine, codeine, papaverine, noscapine and thebaine have been evaluated as contaminants of poppy seeds from Papaver somniferum L. Leaves and flowers of Senecio vulgaris L. containing the unsaturated pyrrolizidine alkaloids senecionine, seneciphylline, retrorsine and riddeliine have been assessed as contaminants of a mixed salad. Regarding quinine consumption of larger amounts can constitute a health problem for certain susceptible consumer groups. BfR sees risks in particular when quinine is consumed during pregnancy. In respect of morphine BfR recommends that a “provisional daily upper intake level” of 6.3 microgram morphine per kilogram body weight per day should not be exceeded. With regard to S. vulgaris L. its major components are cyclic diesters of pyrrolizidine alkaloids unsaturated in the 1,2 bond with a high liver toxic and carcinogenic potential for which no threshold can be assumed. Contamination of food with these compounds should be as low as reasonably achievable.

4.9.1 Quinine in Bitter Soft Drinks – Are there Special Risk Groups? 4.9.1.1 Agent

Besides quinidine, quinine (6-methoxycinchonan-9-ol, CAS No.╃130–95–0) is the most important alkaloid of cinchona bark (Cinchonae cortex), the dried bark of Cinchona pubescens, Vahl (synonyms: Cinchona succirubra, Pavon; family: Â�Rubiaceae).

1

Federal Institute for Risk Assessment, Thielallee 88–92, D-14195 Berlin, Germany.

BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids

4.9.1.2 Regulations for Food

According to Directive 2002/67/EC of the Commission of 18 July 2002 quinine and its salts, which are used as flavourings in the production or preparation of a foodstuff, must be mentioned by name in the list of ingredients immediately after the term “Flavouring”. The latest version of the German Flavourings Ordinance of 02 May 2006 (Annexes 4 and 5) gives for quinine, quinine hydrochloride and quinine sulphate the following maximum levels in ready-to-eat foods, calculated as quinine: total 300 mg/kg in spirits and 85 mg/kg in non-alcoholic beverages.

4.9.1.3 Adverse Effects and Interactions after Consumption of QuinineContaining Soft Drinks

Evans et al. [4] report about a neonate, which was described as jittery 24 hours after birth. These symptoms were still diagnosed on days 7 and 15, but not 2€months after birth. From the 24th week of pregnancy until birth in the 41st week of pregnancy, the mother had drunk 1136 ml of tonic water daily. This is equivalent to a daily quinine intake of 60╃mg. During this time she had suffered twice from tinnitus. On the 7th day after birth maternal blood was tested negative for quinine, however, quinine could be detected in the infant’s urine. The authors believe that quinine withdrawal caused the infant’s symptoms. They explain that quinine increases the refractory period of skeletal muscles and that the withdrawal of quinine had probably led to disinhibition of the skeletal Â�muscles. Furthermore, cases of visual and auditory disorders and severe hypersensitivity reactions e.╃g. thrombocytopenic purpura indicated as “bitter lemon purpura” or haemolytic anaemia (symptom: petechiae), in some cases complicated by kidney failure or disseminated intravasal coagulations are known. Hypersensitivity may be triggered by small amounts, e.╃g. one glass of bitter lemon: Quinine intake from larger amounts of tonic water (1–2â•›l) may lead to the need to readjust anticoagulant treatment [1].

4.9.1.4 Information from Medical Use

Besides for treatment of malaria (0.8–1â•›g free quinine base orally per day for 1½–2 weeks) quinine is used as peripheral muscle relaxant in the treatment of nocturnal leg cramps: Single oral daily doses of 200 mg quinine sulphate dihydrate, equivalent to 166 mg free quinine base, are applied. Because of known side effects the following contraindications, precautions and interactions have to be considered for this indication [5]. Contraindications are pregnancy (easy placenta accessibility; oxytocic action and embryotoxicity at high doses (eye defects and deafness)), hypersensitivity to cinchona alkaloids, bradycardia and other cardiac dysrhythmias of clinical relevance, tinnitus, prior damage to the

383

384

Posters

optic nerve, glucose-6-phosphate dehydrogenase deficiency (symptom: haemolytic anaemia), myasthenia gravis. In patients suffering from cardiac arrhythmia a precautious dosage is recommended. Quinine may enhance the effects of cardiac glycosides, muscle relaxants and anticoagulants [1, 5].

4.9.1.5 Summarized Risk Assessment

4.9.1.5.1╇ Pregnancy In view of various reprotoxic effects having been described for humans and animals, BfR focuses on whether quinine intake from soft drinks during pregnancy could result in adverse reactions for the maternal organism and/ or offspring. As far as embryotoxic effects in animals are concerned, a no observed adverse effect level (NOAEL) of 100 mg quinine hydrochloride/kg bw daily (equivalent to 82 mg quinine base/kg bw daily) was derived from a study in rats [6]. This NOAEL only has a margin of safety (MOS) of 47 to the highest maximum daily exposure mentioned by JECFA of 104.4 mg quinine/person daily (equivalent to 1.74 mg quinine/kg bw per day) from quinine-containing soft drinks [7]. In humans various teratogenic effects were linked to the ingestion of quinine as an abortifacient normally in amounts of 1–4â•›g (effective dose) [8, 9] leading only to a margin of factor 10–40 to the above mentioned maximum daily quinine exposure from soft drinks. The case of withdrawal symptoms and related major health impairments in a newborn baby whose mother had regularly consumed amounts of tonic water during pregnancy that corresponded to 60 mg quinine per day is of particular relevance [5]. Since in the past doses of between 300 mg and 500 mg of quinine were administered twice daily (orally) to induce labour, a possible oxytocic action of quinine has additionally to be taken into account. There are no sufficient data on dose–response relationships or threshold doses concerning the effects recorded in humans mentioned before. Pregnancy is a contraindication for the treatment of nocturnal leg cramps with quinine, the dose used here corresponds to 166 mg free quinine base and is, therefore, only 1.6 times higher than the above-mentioned maximum daily intake of 104.4 mg quinine from bitter soft drinks [1]. Based on the data currently available and for the purposes of preventive health protection, BfR advises against consuming quinine-containing beverages during pregnancy [1]. 4.9.1.5.2╇ Consumption by Other Risk Groups BfR recommends that people who are advised against taking quinine based on existing contraindications in the medicinal product sector should refrain from consuming quinine-containing soft drinks as well. This applies, e.╃g. to people who suffer from bradycardia, tinnitus, pre-existing damage to the optic nerve, glucose-6-phosphate dehydrogenase deficiency, myasthenia gravis or who are hypersensitive to cinchona alkaloids. Patients with cardiac arrhythmia and persons, who take medicine that interacts with quinine, should only drink quinine-

BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids

containing soft drinks after consulting their doctors. This applies in particular to medications which inhibit blood coagulation [1].

4.9.2 Opium Alkaloids as Contaminants of Poppy Seeds 4.9.2.1 Subject Matter

Up to 330 μg morphine/g had been detected in edible poppy seeds in Germany in 2005.╃Drug addicts were, therefore, using them to extract opium alkaloids. Furthermore, a case of intoxication happened: After eating a dish, which had been sprinkled with ground poppy seed, a consumer observed an “uneasy feeling” in the head, had to vomit and felt like having a hangover the next day. The person concerned had ingested approximately 75â•›g blue poppy seeds, containing 210 μg morphine/g and 39 μg codeine/g. This corresponds to intake doses of 16 mg morphine and 3 mg codeine.

4.9.2.2 Agent

Poppy seeds are the mature seeds of Papaver somniferum L. (opium poppy, family: Papaveraceae) which are the only parts of the plant being regarded as “free of opium alkaloids”. Opium alkaloid impurities found in the seeds are considered to be a consequence of contamination with the milky sap (latex) of the capsules during harvesting. Morphine is the main alkaloid detected in poppy seeds besides codeine, papaverine, noscapine and thebaine. Only data regarding morphine are presented in the following. Data on the remaining opium alkaloids are presented in the BfR Health Assessment No.╃012/2006 [10].

4.9.2.3 Pharmacological Effects of Morphine

Morphine is mainly used to treat severe pain. Adverse reactions include nausea, vomiting, light headedness, respiratory depression and cardiovascular effects. Long-term use can lead to tolerance development as well as psychological and physical dependence. Individual sensitivity varies markedly. This applies both to the desired effects and adverse reactions in medicinal usage. In animal experiments morphine had a negative impact on development and reproduction. Mutagenic effects were also observed [10, 11].

4.9.2.4 Summarized Risk Assessment and Recommendation of a Guidance Value

Concerning exposure estimates intake of 50â•›g poppy seeds (a 200â•›g piece of cake with 25â•›% poppy seed) per adult once a day during a meal is considered to be a moderate consumption, intakes of >50â•›g up to 100â•›g per day are regarded as

385

386

Posters

high consumption, and intakes of 150â•›g poppy seeds as worst case. In the case of highly morphine contaminated samples (up to 330 μg morphine/g) the worst case would lead to intakes of up to 49.5 mg morphine. As this amount already exceeds the recommended highest oral single therapeutic dose of 45.6 mg, serious central nervous and peripheral effects are to be expected which include impaired consciousness, respiratory depression and cardiovascular effects [10]. By comparing to the lowest effective oral therapeutic single dose of morphine being 1.9 mg (equivalent to 31.7 μg/kg bw at a body weight of 60 kg) and applying a 5-fold uncertainty factor, BfR derived a “provisional daily upper intake level” for morphine of 6.3 μg/kg bw per day. It indicates the morphine intake which should not be exceeded during one meal or several meals spread over one day. This uncertainty factor takes into account the existing uncertainty concerning (1) the threshold doses of health-relevant effects, in particular psychomotoric effects, (2) possible interactions e.╃g. with other opium alkaloids in poppy seeds, central nervous pharmaceutics and alcohol, and (3) the expected inter-individual variations in sensitivity [10]. Referring to the intake estimates for high consumption (up to 100â•›g poppy seeds/day) for an adult weighing 60 kg, this leads to a provisional guidance value for poppy seeds of a maximum of 4 μg morphine/g [10].

4.9.2.5 Recent Developments

Following specific BfR recommendation efforts have been made to reduce the contamination of commercially available poppy seeds with opium alkaloids. Thus e.╃g. import of highly contaminated Australian poppy seeds have been

Figure 1: Processing of poppy seeds by washing and drying (Figure according to the results presented by General et al., 2007 [12]): One part of poppy seeds has been washed with two parts of water (100╃°C) and dried for 120 minutes at 90╃°C. Baking: 45 minutes for 180╃°C.╃

BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids

stopped, the cultivation of poppy varieties with low alkaloid contents have been promoted and methods for effective purification of poppy seeds have been �developed [12, 13].

4.9.3 Senecio vulgaris L. as Contaminant of Mixed Salad 4.9.3.1 Subject Matter

Leaves and stipes of Senecio vulgaris L. were detected in a commercially available salad mixture of frisee lettuce, radicchio and field salad. The contents of the parts of Senecio vulgaris L. amounted to 1.7â•›% of the salad mixture [14].

4.9.3.2 Agent

Senecio vulgaris L. contains unsaturated pyrrolizidine alkaloids (PA) such as seneciphylline, senecionine, retrorsine, riddelliine, senecivernine, integerrimine (isomer of senecionine), spartioidine (isomer of seneciphylline) and usaramine (isomer of retrorsine) which partly exist in the form of the N-oxids in the plant

4.9.3.3 Structure–Toxicity Relationships

The following structural features of PA are linked to liver toxicity, carcinogenicity and genotoxicity: 1. A double bond on the necine ring between C-1 and C-2 2. An esterified hydroxyl-methyl group at least in C-1 position of the necine ring 3. A branched-C5 -chain in at least one of the esterifying acids. The toxic effects are amplified in compounds bearing a second OH-group in C-7 position of the necine ring and are furthermore enhanced if this group is esterified. The strongest toxic and carcinogenic effects are associated with PAs having the structure of a cyclic diester in addition. These characteristics are found in the PAs of Senecio vulgaris L.╃

4.9.3.4 Summarized Risk Assessment

4.9.3.4.1╇ Exposure Acute and exposure estimates are performed on the basis of national food consumption data (Banasiak et al., 2005; VERA, 1995). Analytical estimates regarding the contents of unsaturated PA in Senecio vulgaris L. are taken from Borstel et al. (1989) [15].

387

388

Posters

Children (2 to < 5 years; bw: 16 kg): Acute consumption of 41.2â•›g salad results in an intake of unsaturated PA of 441–700 μg, corresponding to 28–44 μg/kg bw; chronic consumption of 5.5â•›g salad per day results in an intake of unsaturated PA of 58.9–93.5 μg per d, corresponding to 3.7–5.8 μg/kg bw per day [16]. Adults (25–50 years; bw: 60 kg): Acute consumption of 150â•›g salad results in an intake of unsaturated PA of 1.61–2.55 mg, corresponding to 26.8–42.5 μg/kg bw; chronic consumption of 20.5â•›g salad results in an intake of unsaturated PA of 220–349 μg per day, corresponding to 3.7–5.8 μg/kg bw per day [17]. 4.9.3.4.2╇Liver Toxicity (Veno-Occlusive Disease) after Short- and Medium- Term Exposure Cases of human poisonings are described in consequence of ingestion of parts or extracts of Senecio vulgaris L. or other Senecio species containing riddelliine and retrorsine-N-oxide. As is typical for PA toxicity poisonings manifested as venoocclusive disease (VOD), characterized by obliterative lesions of the centrilobular and sublobular hepatic veins giving rise to hepatic congestion, hepatomegaly and ascites. Limited lesions were followed by recovery, while extensive lesions resulted in liver cirrhosis and in some cases in death [14]. The estimated acute exposure with unsaturated PA of 28–44 μg/kg bw resulting from consumption of the salad lies only a factor of 68–152 below the dose that caused death in a boy and a factor of 18–61 below the dose that caused liver cirrhosis in a girl. Thus, in view of the severity of effects it is concluded that liver damage as a consequence of the consumption of the contaminated salad cannot be ruled out [14]. 4.9.3.4.3╇Carcinogenicity, Genotoxicity Retrorsine and retrorsine-N-oxide exhibited carcinogenic effects in experimental animals [18]. Riddelliine is classified by IARC [19] as “possibly carcinogenic to humans”. According to the IARC evaluation [19] there is sufficient evidence for the carcinogenicity of riddelliine from animal experiments. Dehydroretronecine as the main metabolite of seneciphylline, retrorsine and riddelliine showed a carcinogenic effect on rat skin. The components of Senecio vulgaris L. and their metabolites gave positive results in various in vitro genotoxicity tests for gene mutations and chromosomal aberrations. In vivo genotoxicity testing resulted in equivocal outcome. The estimated chronic exposure for adults resulting from consumption of a salad contaminated as indicated would be 220–349 μg unsaturated PA per day which is a factor of 2200–3490 above the tolerated intake of unsaturated PA with internal medicines (0.1 μg/d) [14]. In view of the carcinogenicity data, salads contaminated with parts of Senecio vulgaris L. as indicated are considered to be dangerous to health. Furthermore, BfR advises that contamination of food with PAs from Senecio vulgaris L. should be as low as reasonably achievable (ALARA principle) [14].

BfR Risk Assessment of Alkaloids as Ingredients and Contaminants of Food: Quinine, Opium Alkaloids, and Senecio Pyrrolizidine Alkaloids

References â•⁄ 1. Updated BfR Health Assessment No 020/2008, 17 February 2005, Quininecontaining beverages may cause health problems, http://www.bfr.bund.de/ cm/245/quinine_containing_beverages_may_cause_health_problems.pdf.╃ â•⁄ 2. BfR Health Assessment No.╃012/2006, 27 December 2005, BfR recommends provisional daily upper intake level and a guidance value for morphine in poppy seeds, http://www.bfr.bund.de/cm/245/bfr_recommends_provisional_daily_upper_intake_level_and_a_guidance_value_for_morphine_in_ poppy_seeds.pdf.╃ â•⁄ 3. Stellungnahme Nr.╃028/2007 des BfR vom 10.╃Januar 2007, Salatmischung mit Pyrrolizidinalkaloid-haltigem Greiskraut verunreinigt http://www.bfr. bund.de/cm/208/salatmischung_mit_pyrrolizidinalkaloid_haltigem_geiskraut_verunreinigt.pdf.╃ â•⁄ 4. Evans, A.╃N.╃W.; Brooke, O.╃G.; West, R.╃J. (1980) The ingestion by pregnant women of substances toxic to the foetus. The Practitioner 224, 315–319. â•⁄ 5. Fachinformation Limptar® N 200 mg, Oktober 2007, Rote Liste Service GmbH, Berlin. â•⁄ 6. Colley, J.╃C.; Edwards, J.╃A.; Heywood, R.; Purser, D. (1989) Toxicity studies with quinine hydrochloride. Toxicology 54, 219–226. â•⁄ 7. Joint FAO/WHO Expert Committee on Food Additives (JECFA) (1993) Toxicological evaluation of certain food additives and contaminants, WHO Food Additives Series: 30, 81–85, Quinine, World Health Organization, Genf. â•⁄ 8. Dannenberg, A.╃L.; Dorfman, S.╃F.; Johnson, J. (1983) Use of quinine for self-induced abortion. Southern Medical Journal 76, No.╃7: 846–849. â•⁄ 9. Nishimura, H.; Tanimura, T. (1976) Clinical aspects of the teratogenicity of drugs. Excerpta Medica, p.╃141–143. 10. BfR recommends provisional daily upper intake level and a guidance value for morphine in poppy seeds, BfR Health Assessment No.╃012/2006, www.bfr. bund.de.╃ 11. BfArM; 11.╃11. 2004.╃www.bfarm.de, Fachinformationen, Mustertexte zu: Morphin, Lösung, Filmtablette, Brausetablette. 12. General, J.; Unbehend, G.; Lindhauer, M.╃G.; Kniel, B.; Moser, M. (2007) Untersuchungen zur Reduzierung von Morphin in Mohnsamen und Mohngebäcken mit praktikablen technologischen Maßnahmen. Getreidetechnologie 61, 1: 36–42. 13. Dusemund B, Morphin in Mohnsamen, 4.╃BfR-Forum Verbraucherschutz, 5 and 6 July 2007, www.bfr.bund.de.╃ 14. Salatmischung mit Pyrrolizidinalkaloid-haltigem Greiskraut verunreinigt. Stellungnahme Nr.╃028/2007 des BfR vom 10.╃Januar 2007, www.bfr.bund. de.╃ 15. v. Borstel, K.; Witte, L.; Hartmann, T. (1989) Pyrrolizidine alkaloid patterns in populations of Senecio vulgaris, S. vernalis and their hybrids. Phytochemistry 28, 6, 1635–1638. 16. Banasiak, U.; Heseker, H.; Sieke, C.; Sommerfeld, C.; Vohmann, C. (2005) Abschätzung der Aufnahme von Pflanzenschutzmittel-Rückständen in der

389

390

Posters

Nahrung mit neuen Verzehrsmengen für Kinder. Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz 48, 84–98. 17. VERA-Schriftenreihe (1995) Ergebnisse der Nationalen Verzehrsstudie (1985–1988). Band XI, Wissenschaftlicher Fachverlag. 18. WHO (1987) IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Volumes 1 to 42, Supplement 7.╃ 19. WHO (2002) IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Volume 82.

Elucidation of the Genotoxic Activity of the Alkaloid Ellipticine in Human Cell Lines

4.10 Elucidation of the Genotoxic Activity of the Alkaloid Ellipticine in Human Cell Lines Eva Frei1, Jitka Poljaková2, Lucie Borˇ ek-Dohalská, Christian Bieler, and Marie Stiborová Ellipticine (5,11-dimethyl-6H-pyrol[4,3-b]carbazole) an alkaloid from Apocyanaceae is active against a variety of tumours. Amongst its many biological effects it intercalates into DNA and inhibits topoisomerase II, but it also binds covalently to DNA after metabolic activation. Activating enzymes are cytochromes P450 (CYP) and peroxidases (myeloperoxidase [MPO], cyclooxygenases [COX] and lactoperoxidase). The oxidative metabolites 13-hydroxy-ellipticine and 12-hydroxy-ellipticine form adducts with guanosine in DNA, while 9-hydroxy-ellipticine (9-OH-elli) and 7-hydroxy-ellipticine (7-OH-elli) are excreted. Distinct CYP species are responsible for the generation of activating and detoxicating metabolites. The balance between these metabolites determines the genotoxic activity of the compound in different species but also to different organs in a single species. In addition to in vivo data obtained with rats and mice on DNA adduct formation and persistence we have investigated several human tumour cell lines and V79 cells transfected with the relevant CYP for their potential to form DNA adducts, the correlation of DNA adduct levels with cytotoxicity and the enzyme patterns in these cells. Table 1: Biological effects of ellipticine in different cell lines. Cell line

Expressed enzymes

IC50 [µM]

Leukaemia: HL-60 CCRF-CEM

CYP1A1, MPO COX-1 CYP1A1

0.64 4.27

Mamma: MCF-7

CYP3A4, CYP1A1

Elli: 1.25 9-OH-elli: 3.25 7-OH-elli: >20

Neuroblastoma: IMR-32 UKF-NB-4 UKF-NB-3

All express: CYP1A, 1B1, 3A4

Hamster V79 transfected with:

Wild-type CYP1A1 CYP1A2 CYP3A4

0.27 0.44 0.44 0.39 0.31 0.25 0.37

Correlation Cytotoxicity/ DNA adducts Correlation: High DNA adduct levels Low DNA adduct levels Correlation with DNA adduct levels Correlation with DNA adduct levels under diff. culture conditions No correlation Low DNA adduct levels Low DNA adduct levels Low DNA adduct levels 10-fold higher DNA adducts, no correlation

1

German Centre for Cancer Research, Molecular Toxicology, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.

2

Charles University, Department of Biochemistry, Albertov 2030, CZ-12840 Prague 2, Czech Republic.

391

392

Posters

Conclusions In cell lines derived from human tumours, expressing CYP or peroxidases cytotoxicity correlates with DNA adducts levels, except in one of three neuroblastoma cell lines where cytotoxicity did not correlate with DNA adduct levels or CYP expression. Expression of human CYP3A4 in V79 leads to high DNAadduct levels, but not to increased sensitivity of the cells to ellipticine. Although 9-hydroxy-ellipticine is excreted in vivo and considered a detoxication metabolite it is cytotoxic to MCF-7 cells. Therefore, effects in a closed cell culture system might be more pronounced than in an organism, enzyme expression levels might not always reflect their activity and other properties of ellipticine than DNA damage might become more important in different cell types.

References 1. 2. 3. 4.

Frei E. et al. Biochem. Pharmacol. 64, 289 (2002). Borˇek-Dohalska J. et al. Collect Czech. Chem. Commu.n 69, 603 (2004). Poljakova J. et al. Cancer Lett. 252, 270 (2007). Poljakova J. et al. Biochem. Pharmacol (accepted).

Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€ of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA

4.11 Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA Frauke Gaedcke, Bernd Eberwein, Olaf Kelber, Karin Kraft, Manuela StaussGrabo, Martin Tegtmeier, Volker Schulz, Hilke Winterhoff, and Fritz Kemper1,2

4.11.1 Introduction Herbal medicinal products are trusted by the public. Their therapeutic indications are validated in established authorization procedures by regulatory authorities. Regulation (EC) 1924/2006, proposed by the EU commission and in effect since 19 January 2007, is dedicated to meet the expectations of the consumers in correctness and scientific validity of health claims for dietary supplements, as claims will have to be authorized by the European Food Safety Authority EFSA in the future. The EC “Consolidated List of Article 13 Health claims“ [1] recently published by the EFSA seems to contradict public expectations, that the process of scientific validation of the claims by the EFSA will meet the aims of this EC regulation, and has led to vehement discussions [1, 2]. In this list, provided by the EFSA, the claim proposals of manufacturers of dietary supplements from all over Europe are presented. The list contains, besides many other substances, almost all herbal drugs in “well established use“ or “traditional use” as herbal medicinal products with therapeutic indications authorized by the German drug regulatory authority BfArM. This fact has raised doubts, whether it will be possible also in future to distinguish dietary supplements with possibly scientifically unfounded claims from herbal medicinal products authorized for the treatment of patients and therefore meeting high quality standards. It can be assumed, that such doubts were not intended by the EU commission. On the background of these doubts, the German Society for Phytotherapy makes the following statement: ►⌺

“A clear distinction of dietary supplements from herbal Â�medicinal products is necessary and has to be possible also in the future.”

Herbal medicinal products differ principally from herbal food and nutraceuticals by their general purpose. Herbal medicinal products are intended to be used for the curing, relief, prevention or diagnosis of diseases. Their suitability to that purpose is proven by pharmacological, toxicological and clinical studies as well as other scientific data (herbal medicinal products in well-established use) or

1

On behalf of Gesellschaft für Phytotherapie/Society for Phytotherapy, Köln, Germany·

2

Correspondence to: Gesellschaft für Phytotherapie, Uferstr.╃4, D-51063 Köln, [email protected], www.phytotherapy.org.

393

394

Posters

has been shown by long-standing successful use (traditional herbal medicinal products). Nutraceuticals, on the other hand, are intended to be used in nutrition or are provided for use as food due to their physiological effects. They are dedicated to maintain health and can therefore also help to prevent diseases. Despite this principally different general purpose, the “Consolidated List of Article 13 Health claims” (Tab.╃1) contains a large number of plants, for which scientific data only give evidence for their suitability for the therapy of diseases, but not for their possible use as nutraceuticals in healthy consumers. But it is necessary to be aware of the fact, that this list of “botanicals” is only an unfiltered and unreviewed collection of proposals of companies from the different European countries, and in no way allows conclusions on their possible suitability for use as food. Decisive for the result of the pending review of the listed preparations by the EFSA will be, whether the individual preparation making a relevant contribution to the maintenance of health is sufficiently proven by scientific evidence and whether the recommended dose or amount is in accordance with that in use as food. According to the settings of the EU commission, it will be decisive for the result of the review, whether the scientifically proven effect for the maintenance of health is on one hand significant and representative for the respective target population, but on the other is not to be seen as a proof of a pharmacological effect or a clinical efficacy in the patient.

4.11.2 Conclusion In the future, stricter standards will be set for health claims of nutraceuticals. This means that a scientific review of the information regarding their intended use and their safety is now going on. A clear cut differentiation of herbal nutraceuticals and herbal medicinal products will be necessary, for enabling physicians, pharmacists and patients to differentiate preparations indicated for the therapy of diseases from those, which may be used for nutrition due to their health related claims. It is of special importance that the therapeutic indication of herbal medicinal products for use in patients and the health claims for use in healthy populations are clearly separated, so that the consumer will be easily aware of the differences. The pre condition of a successful assignment to one group, pharmaceuticals or nutraceuticals, is a close cooperation of the Herbal Medicinal Product Committee (HMPC) of the EMEA and the EFSA and a differentiated legal regulation, fulfilling the expectations of patients in pharmaceuticals and of consumers in food. Doing this, it will not be possible to avoid some minimum overlap, but this may not harm the successful regulation of herbal medicinal products. Health claims for nutraceuticals, according to the preconditions of the EU, will only be possible in cases where a physiological effect, having a positive impact on health, is substantiated scientifically, and where safety is guaranteed.

Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€ of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA

Herbal preparations in well-established use or traditional use should also in future be available exclusively as pharmaceuticals, if doses used in medicinal preparations have pharmacological effects. Only in few, well-founded cases, in which at the same time a physiological effect can be substantiated, the use as nutraceutical may be admissible. With tremendous efforts during the past 30 years, the actual high standards of quality, efficacy and safety of herbal medicinal products has been established. It is therefore to be expected that EFSA will evaluate nutraceuticals on the base of an adequate differentiation from herbal medicinal products, in collaboration with EMEA. Only under these preconditions safety and efficacy in patients and consumers will be assured also in future.

Dedication in memoriam This contribution is dedicated to Prof. Dr. Hilke Winterhoff, who passed away on May 9, 2010, in Münster, Germany, in honour of her important services to the Society for Phytotherapy (Gesellschaft für Phytotherapie), Köln, Germany, and of her exemplary contributions to the study of the pharmacology and toxicology of herbal medicines.

References 1. Alban, S., Dingermann, T., Blume, H., Schubert-Zsilavecz, M.: Stellungnahme der DPhG zur geplanten Health Claims-Verordnung. DPhG-Verlautbarung: URL http://www.dphg.de/includes/dphg_document.php?id=╃89, Date: 16.╃01. 2009. 2. Scheffler, M., Büttner, T.: Werbeaussagen müssen gesichert sein. Stellungnahme zur Health-Claims-Verordnung. DAZ 148, Nr.╃51/52, 5799–5800. 3. Kommission E des ehemaligen BGA, Liste der Monographien. URL: http:// www.bfarm.de/cln_030/nn_1199002/SharedDocs€ /Publikationen/DE/Arzneimâ•‚tel/2__zulassung/zulArten/besâ•‚therap/amâ•‚pflanzl/monâ•‚kome,templa teId=raw,property=publicationFile.pdf/mon-kome.pdf, Date: 16.╃01. 2009. 4. Committee on Herbal Medicinal Products (HMPC): Overview of status of HMPC assessment work – September 2008: Priority list (Doc. Ref. EMEA/ HMPC/278067/2006):€ URL€ http://www.emea.europa.eu/pdfs/human/ hmpc/27806706en.pdf, Date: 16.╃01. 2009. 5. EMEA-HMPC, Community Monographs und Monograph drafts: URL http://www.emea. europa.eu/htms/human/hmpc/hmpcmonographs.htm, Date: 16.╃01. 2009. 6. BfArM,€ Traditionsliste€ nach€ AMG€ §â•ƒ109â•›a:€ URL€ http://www.bfarm.de/ cln_030/nn_1199044/SharedDocs€ /Publikationen/DE/Arzneimittel/2__zulassung/zulArten/besâ•‚thâ•‚rap/amâ•‚trad/indikatlistepara109€ 040824,template Id=raw,property=publicationFile.pdf/indikatlistepara109–040824.pdf, Date: 16.╃01. 2009. 7. Eâ•‚SA,€Consolidated€list€of€Article€13€Health€claims,€URL:€http://www.efsa. europa.eu/EFSA/General/art13claims,0.zip, Date: 16.╃01. 2009.

395

396

Herbal Drugs

Hippocastani semen (horse chestnut seed)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC (draft): Herbal medicinal product for treatment of chronic venous insufficiency characterized by swollen legs, varicose veins, a feeling of heaviness, pain, tiredness, itching, tension and cramps in the calves. Commission E: Treatment of complaints found in pathological conditions of the veins of the legs (chronic venous insufficiency), for example, pains and a sensation of heaviness in the legs, nocturnal systremma (cramps in the calves), pruritus and swelling of the legs. Trophic changes, e.╃g. ulcus cruris. Posttraumatic and postoperative oedema of soft tissue.

HMPC (draft): (A) Traditional herbal medicinal product to relieve symptoms of discomfort and heaviness of legs related to minor venous circulatory disturbances. (B) Traditional herbal medicinal product for relief of symptoms of bruises, such as oedema and haematoma. The product is a traditional herbal medicinal product for use in the specified indication exclusively based upon long-standing use. AMG 109â•›a: For improving the well-being in bruise/ heavy legs. This indication is exclusively based on tradition and long-standing use.

NEM-Claims (Consolidated list of Art.╃13) [7]

Horse chestnut extracts help maintain proper circulation. Horse chestnut extracts help support healthy veins and capillaries.

Posters

Table 1: Comparison of the indications of herbal medicinal products and the claims of food supplements from the Consolidated List of Article 13 Health claims (examples).

Althaeae officinalis folium/radix (marshmallow leaf/ -root)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

Commission E:

HMPC (draft): Traditional herbal medicinal product for use as a demulcent preparation: (A) For the symptomatic treatment of oral or pharyngeal irritation and associated dry cough. (B) For the symptomatic relief of mild gastrointestinal discomfort. The product is a traditional herbal medicinal product for use in specified indications exclusively based upon long-standing use.

Folium: Irritation of the oral and pharyngeal mucosa and associated dry cough. Radix, in addition: Mild inflammation of the gastric mucosa.

AMG 109╛a: E.╃g. in combination with essential oil from thyme as instant tea powder: For supporting loosening mucus in the respiratory tract. This indication is exclusively based on tradition and long-standing use. Crataegi HMPC: folium cum flore Rapporteur assigned. (hawthorn leaf and with flowers) Commission E: Decreasing cardiac output as described in functional Stage II of NYHA.

HMPC: Rapporteur assigned. AMG 109â•›a: For supporting cardiovascular function. This indication is exclusively based on tradition and long-standing use.

NEM-Claims (Consolidated list of Art.╃13) [7]

Soothing for mouth and throat. Reliefs in case of tickle in the throat and pharynx. Soothing and pleasant effect on throat, pharynx and vocal cords. Support gastrointestinal health. Helps to support the digestion. Maintenance of the intestinal functions. Contributes to physical well-being. Supports better bowel performance. Supports regular bowel movements. For a regular bowel motion. Supports bowel transit. Maintains a regular bowel function. Helps to maintain bowel function. Helps to maintain optimum digestive comfort. Hawthorn extract supports heart functions, increases oxygen inflow and improves peripheral blood circulation. Used to help to find a better sleep.

Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€ of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA

Herbal Drugs

397

398

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC: Rapporteur assigned.

HMPC: Rapporteur assigned.

Commission E: Dyspeptic problems.

AMG 109â•›a: For supporting digestive function. This indication is exclusively based on tradition and long-standing use.

Echinaceae pallidae/ purpureae radix/herba (echinacea pallidae root, purple coneflower herb)

HMPC (draft): Herbal medicinal product for the short-term prevention and treatment of common cold.

HMPC (draft): Traditional herbal medicinal product for treatment of small superficial wounds.

Commission E: E. pallidae radix: Supportive therapy for influenza-like infection. E. purpureae herba: Supportive therapy for colds and chronic infections of the respiratory tract and lower urinary tract. Poorly healing wounds and chronic ulcerations.

AMG 109â•›a: As mildly effective medicinal product for supporting wound healing. This indication is exclusively based on tradition and long-standing use.

Ephedrae herba (ephedra)

Not any. Commission E: Diseases of the respiratory tract with mild bronchospasms in adults and children over the age of six. (Remark: Pharmaceuticals containing ephedrine are part of the doping list of IOC and German sport union)

Cynarae folium (artichoke leaf)

NEM-Claims (Consolidated list of Art.╃13) [7]

Helps to support digestion, contributes to the normal function of the intestinal tract, contributes to intestinal comfort, and contributes to the normal function of intestinal tract. Contributes to normal blood lipid levels. Contains antioxidant/s. Useful to protect from free radicals which cause cells and tissues damage. Anti-oxidant and anti-ageing activity. Contributes to the oral well-being. Valid and efficient help during the cool season/ relief for the throat – helps the upper respiratory tract. Supports the immune system and the body’s defence (anti-oxidant).

Ephedrine alkaloids may help support weight loss.

Posters

Herbal Drugs

Ginkgo bilobae folium (ginkgo biloba leaf)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC: Rapporteur assigned.

HMPC: Rapporteur assigned.

Commission€E: (Monograph for the dry extract (35–67: 1) from Ginkgo biloba L. leaf, extracted with acetone/ water). For symptomatic treatment of disturbed performance in organic brain syndrome within the regime of a therapeutic concept in cases of Â�dementia syndromes with the following principal syndromes: Memory deficits, disturbances of concentration, depressive emotional condition, dizziness, tinnitus, and headache. The primary target indications are dementia syndromes, including primary degenerative dementia, vascular dementia, and mixed forms of both.

NEM-Claims (Consolidated list of Art.╃13) [7]

Helps maintaining good cognitive function. Helps maintaining memory with age decline and to preserve cognitive function/ enhancement of cognitive performance. For symptomatic treatment of mild to moderate cerebro-vascular insufficiency. Contains naturally occurring anti-oxidants. Helps maintaining mental well-being (e.╃g. focus on the work memory in the short-term and during moments of increased stress). Helps maintaining memory with age decline and preserves cognitive function. Helps maintaining good cognitive functions, contributes to a normal blood circulation associated with brain performance (retained reactivity and recall facts), helps maintaining clear thinking€and day-to-day focus. Contribution to mental and cognitive activities.

Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€ of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA

Herbal Drugs

399

400

Hyperici herba (St. John´s Wort)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC (draft): Herbal medicinal product for the symptomatic treatment of mild depressive episodes.

HMPC (draft): Indication 1: Herbal substance, herbal preparations A, B, C, E, F, G, H, I, J, K: Traditional herbal medicinal product for the relief of temporary mental exhaustion (neurasthenia). Indication 2: Herbal preparations D, F, G, K: Traditional herbal medicinal product for the symptomatic treatment of minor inflammations of the skin (such as sunburn) and as an aid in healing of minor wounds. The product is a traditional herbal medicinal product for use in specified indications exclusively based upon long-standing use.

Commission E: Psychovegetative disturbances, depressive moods, anxiety and/ or nervous unrest. Oily hypericum preparations for dyspeptic complaints. Oily hypericum preparations for treatment and post-therapy of acute and contused injuries, myalgia and first-degree burns.

NEM-Claims (Consolidated list of Art.╃13) [7]

Contributes to emotional balance and general well-being. Contributes to optimal relaxation. Helps to support the relaxation and mental and physical well-being. Helps to maintain a healthy sleep/ and a positive mood.

AMG 109â•›a: For improvement of well-being in case of nerval stress. For tonicising the stomach. This indication is exclusively based on tradition and long-standing use. For suppporting dermal function. This indication is exclusively based on tradition and longstanding use. Kava Kava rhizoma (kava kava root)

Commission E: Conditions of nervous anxiety, stress, and restlessness. Germany: Withdrawal of marketing �authorization due to suspected risks.

Not any.

Kava extracts help to promote a calm state of mind.

Posters

Herbal Drugs

Passiflorae herba (passion flower herb)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC: Final opinion adopted.

HMPC: Traditional herbal medicinal product for relief of mild symptoms of mental stress and to aid sleep. The product is a traditional herbal medicinal product for use in the specified indications exclusively based upon long-standing use.

Commission E: Nervous restlessness.

NEM-Claims (Consolidated list of Art.╃13) [7]

Contributes to optimum relaxation. Contributes to normal sleep. Helps maintaining a healthy sleep. Helps to become calm after embarrassment, recommended in feeling of weakness and tiredness, helps relaxing.

AMG 109â•›a: For improvement of well-being in case of stress. This indication is exclusively based on tradition and long-standing use. Rhei radix (rhubarb root)

HMPC: Herbal medicinal product for short-term use in cases of occasional constipation. Commission E: Constipation. Germany: Not authorized for issuing outside the pharmacy.

Not any.

Contributes to intestinal transit and intestinal function Helps to have a good intestinal functioning/ intestinal well-being.

Dietary Supplements and Herbal Medicinal Â�Products€– for a Clear Differentiation. Â�Statement€ of€the Â�Society for Phytotherapy (GPT) to€the “Article€13 Health Claim List” of the EFSA

Herbal Drugs

401

402

Salicis cortex (willow bark)

Medicinal products – Indications (Commission E - & HMPC-Monographs, German AMG §â•ƒ109â•›a-list) [3, 4, 5, 6] Well established medicinal use

Traditional use

HMPC: Herbal medicinal product used for the short symptomatic treatment of low back pain.

HMPC: Traditional herbal medicinal product used for the symptomatic relief of: (A) Minor articular pain (B) Fever associated with common cold (C) Headache. The product is a traditional herbal medicinal product for use in specified indications exclusively based upon long-standing use.

Commission E: Diseases accompanied by fever, rheumatic ailments, headaches.

NEM-Claims (Consolidated list of Art.╃13) [7]

For healthy joints/ contributes to healthy muscles and joints. Contains herbs with body strengthening and restorative properties.

AMG 109â•›a:´ In combinations: For improvement of well-being in rheumatic complaints. This indication is exclusively based on tradition and long-standing use. Valerianae radix (valerian root)

HMPC: Herbal medicinal product for the relief of mild nervous tension and sleep disorders. Commission E: Restlessness, sleeping disorders based on nervous conditions.

HMPC: Traditional herbal medicinal product for relief of mild symptoms of mental stress and to aid sleep. The product is a traditional herbal medicinal product for use in specified indications exclusively based on long-standing use. 109â•›a: For improvement of well-being in nervous stress. This indication is exclusively based on tradition and long-standing use.

To help sleep onset, Clinically/ scientifically proven to help normalize/ promote sleep (onset), Valerian helps to maintain a natural sleep, to support calmness and in case of irritability. Helps you to cope calmly with the stress of a busy life-style. Support of mental well-being in cases of tension and stress. Contributes to optimal relaxation. Contributes to recover physically and maintain mental well-being.

Posters

Herbal Drugs

Assessment of Genotoxicity of Herbal Medicinal Preparations According to the Guideline EMEA/HMPC/107079/2007 – A Model Project of Â�Kooperation Phytopharmaka, Bonn, Germany

4.12 Assessment of Genotoxicity of Herbal Medicinal Preparations According to the Guideline EMEA/HMPC/107079/2007 – A Model Project of Â�Kooperation Phytopharmaka, Bonn, Germany Frauke Gaedcke, Olaf Kelber, Karin Kraft, Barbara Steinhoff, and Hilke Winterhoff1,2

4.12.1 The Genotoxicity Guideline of HMPC The “Guideline on the Assessment of Genotoxicity of Herbal Substances/ Preparations“ was published by HMPC, the Herbal Medicinal Product Committee of the European regulatory agency EMEA, on 1 December 2008, giving guidance on the assessment of genotoxicity of herbal medicinal substances/ preparations [1]. In this guideline it is stated, that for many herbal substances/ preparations, contained in well established or traditional herbal medicinal products (HMPs), an adequate safety profile may be confirmed by their documented history of medicinal use. However, the complete lack of some specific non-clinical studies (e.╃g. genotoxicity studies) may present a safety concern, because important questions relating to product safety would remain unanswered. The guideline describes a pragmatic framework on how to assess the potential genotoxicity of HMPs and how to interpret the results, describing a stepwise test strategy, beginning with the AMES test, followed, in case of positive results, by a mammalian cell assay and, in case of another positive result, in vivo genotoxicity tests. If the respective step gives negative results, progressing to the next test step is not required. A new guideline of HMPC [2] has meanwhile been published, supporting a bracketing and matrixing approach for avoiding unnecessary duplication of effort.

4.12.2 Scope of the Project Already in advance to the adoption of this guideline, Kooperation Phytopharmaka, a German scientific organisation in the field of HMPs, has started a model project for the screening of herbal medicinal preparations in accordance with this guideline. The scope of this project is to offer an alternative to testing each individual preparation of a certain herb by a joint conduction of tests.

1

On behalf of the Working Group “Efficacy and Safety” of Kooperation Phytopharmaka, Bonn, Germany.

2

Correspondence to: Kooperation Phytopharmaka GbR, Plittersdorfer Str.╃218, D-53173 Bonn, Germany, [email protected], www.koop-phyto.org.

403

404

Posters

Figure 1: Decision tree on the assessment of genotoxicity of herbal preparations (from [1]). A disadvantage of the AMES test is false positive results for herbal compounds like quercetin [3].

Assessment of Genotoxicity of Herbal Medicinal Preparations According to the Guideline EMEA/HMPC/107079/2007 – A Model Project of Â�Kooperation Phytopharmaka, Bonn, Germany

4.12.3 Material and Methods The project is conducted following a bracketing and matrixing approach, covering extracts from the whole range of polarity of extraction solvents from unpolar to polar solvents. This even allows the detection of possible toxicity of medicinal drug powder preparations otherwise not accessible to in vitro methods. Extracts, characterized according to their individual specifications, were provided by pharmaceutical companies. Tests were conducted in cooperation with GLP-conform laboratories, according to all actual guidelines, including those of OECD, ICH and EMEA, and included even validation of test results by independent testing in two laboratories. Tests were started with the first step of the test strategy, the AMES test, in five bacterial strains.

4.12.4 Results The project has until now produced data on several of the most important herbal drugs in Europe (Table1).

Figure 2: Backeting and matrixing. Example: Preparations from hops.

Table 1: Data produced by the “Kooperation Phytopharmaka“ on important herbal drug plants in Europe. Artichoke

Garlic

Primrose

Bittersweet

Caraway

Rosemary

Stinging nettle

Pumpkin oil

Horse chestnut

Marshmallow

Pine oil

Liquorices

Gingko

Milk thistle

Thyme

Ginseng

Melissa

Whitethorn leaves and flowers

Hops

Mistle

Whitethorn fruits

St. John’s Wort

Passion flower

Devil’s claw

405

406

Posters

Figure 3: Negative results from the AMES Test, showing data from two S. typhimurium strains (TA€98 and TA 100) and controls (P1╃=quercetin). These negative results have been validated by independent testing in a second laboratory.

Assessment of Genotoxicity of Herbal Medicinal Preparations According to the Guideline EMEA/HMPC/107079/2007 – A Model Project of Â�Kooperation Phytopharmaka, Bonn, Germany

Conclusions The project has not only broadened the knowledge on the safety of important herbs used in Europe and allowed to meet actual regulatory requirements, but unexpectedly also shown, that the safety profile of some of them, previously under discussion [4], has to be re-rated when tested with modern validated methods. So, this project has turned out to be an important step in the continuous process of actualization of the safety profile of modern phytotherapy, which is already now well documented. For expanding the project to further drugs, cooperation partners are still welcome.

Dedication in memoriam This contribution is dedicated to Prof. Dr. Hilke Winterhoff, who passed away on May 9, 2010, in Münster, Germany, in honour of her important services to the Kooperation Phytopharmaka, as the head of its scientific working group on efficacy and safety, and of her exemplary contributions to the study of the pharmacology and toxicology of herbal medicines.

References 1. Guideline on the assessment of genotoxicity of herbal medicinal substances/ preparations, Doc. Ref. EMEA/HMPC/107079/2007. 2. Guideline on selection of test materials for genotoxicity testing for traditional herbal medicinal products/herbal medicinal products, Doc. Ref. EMEA/ HMPC/651266/2009.╃ 3. Okpanyi S.╃N. et al., Genotoxizität eines standardisierten Hypericum-Extraktes. Arzneim.-Forsch./ Drug. Res.╃1990, 40 (II): 851–855. 4. Podginsky B. et al., Johanniskraut (Hypericum perforatum L.). Genotoxizität bedingt durch den Quercetingehalt. Deutsche Apotheker Zeitung 128, 1988, 1364–1366.

407

408

Posters

4.13 Implications for an Adverse Effect of Vitamin C in Photodynamic Therapy Stefanie Grimm1, Nicolle Breusing, and Tilman Grune Photodynamic therapy (PDT) for cancer patients has developed into an important new clinical treatment procedure. During PDT, 5-aminolevulinic acid (ALA) as a photosensitizer, light (wavelength 420–800nm) and oxygen are used to cause photochemically induced cell death. Hereby, heme oxygenase-1 (Â�Â�HO-1) being induced in response to a variety of chemical and physical stress conditions, plays an important role as a cytoprotective molecule. ALA-PDT enhances the expression and activity of HO-1. Cells contain a large number of anti-oxidants to prevent or repair the damage caused by reactive oxygen species. In a cancer treatment anti-oxidants might protect cancer cells against PDT modality, thereby reducing the oncologic effectiveness of cytotoxic therapy. We investigated the effects of antioxidants during PDT alone or in combination with HO-1 inhibitor zinc protoporphyrin IX (ZnPPIX). In a dose depended manner, substitution of ZnPPIX (1–10╃µM) before radiation causes an enhancement in ALA-PDT induced cytotoxicity. Vitamin C (250╃µM) administered to the cells prior to light exposure is found to cause cytoprotective effects on PDT. It caused a significant inhibition (about 15â•›%) in the rate of ALA-photosensitized injury of melanoma cells. In conclusion, vitamin C acts as a potent antioxidant, by protecting cells of oxidative injury induced by ALA-PDT and may therefore be contraindicated in patients undergoing tumour treatment. The influence of other micronutrients should be shown in further experiments.

1

Institute for Biological Chemistry and Nutrition, Biofunctionality and Food Safety, University Hohenheim, Garbenstr.╃28, D-70593 Stuttgart, Germany, [email protected].

Using the Nematode Caenorhabditis elegans to Identify Mode of Action of the Flavonoid �Myricetin

4.14 Using the Nematode Caenorhabditis elegans to Identify Mode of Action of the Flavonoid Â�Myricetin Gregor Grünz1, B. Spanier, and Hannelore Daniel Flavonoids, representing a major group of secondary plant compounds possess a variety of biological effects and are considered as compounds promoting human health. However, in most cases the underlying molecular mechanisms by which flavonoids may affect the health status remain unknown. We employ the well-established model organism C. elegans to obtain insights into actions of the flavonol myricetin (3, 3’, 4’, 5, 5’, 7-Hexahydroxyflavone). Even though various effects (e.╃g. anti- and pro-oxidative properties, effects on cell signal cascades and enzymatic systems) have been described for this compound in a variety of in vitro systems, only little information is available for its actions in complex organisms such as C. elegans. We could demonstrate that myricetin (100╃µM) increases heat stress tolerance in the genetically modified C. elegans strain TK22 (mev-1) but not in wildtype animals. This mutant strain is carrying a genetic defect in the respiratory chain (complex II) that increases endogenous ROS-levels and reduces life-span markedly. Surprisingly, co-exposure of both worm strains to myricetin (100╃µM or 50╃µM) in the presence of the ROS-generating compound paraquat (1.6 mM) resulted in a drastically reduced survival rate compared to worms treated with paraquat alone. It therefore appears that myricetin alone might induce a mild stress in the animals that results in activation of stress defence mechanisms which further increase the overall stress resistance (hormesis effect) – at least against heat stress. However, in the presence of the strong ROS-generator paraquat the system appears overloaded finally resulting in a drastically diminished overall stress resistance. We also used GFP-reporter strains (GFP=green fluorescent protein) expressing fusion proteins of GFP coupled to certain stress defence enzymes or signal molecules to determine their expression when animals are exposed to myricetin. The expression of the heat shock protein hsp16.2 that acts as chaperone was moderately reduced after myricetin treatment following a heat shock, suggesting that myricetin reduces its expression. The transcription factor DAF16 (homologous to mammalian FOXO) is known to control the transcription of several enzymatic ROS defence systems in C. elegans such as SOD and catalase as well as enzymes required for GSH-synthesis. By using a GFP-reporter strain expressing a DAF16::GFP fusion protein we did not observe any alteration of DAF16-location by myricetin treatment whereas quercetin exposure of animals caused a nuclear translocation of DAF-16 that may explain the protective effects of this flavonoid.

1

Molecular Nutrition Unit, Technical University of Munich, Am Forum 5, D-85350 Freising, Germany, [email protected].

409

410

Posters

4.15 Low-Temperature Plasma – Mild Preservation Â�Technology for Minimal Processed Fresh Food? Franziska Grzegorzewski1,3, O.╃Schlüter1, J.╃Ehlbeck2, L.â•›W.╃Kroh3, and S.╃Rohn3 Due to the sensitivity of food products the use of conventional thermal processes for food preservation purposes is largely limited. Fresh fruit and vegetables are physiologically active food systems and temperatures above 45╃°C can result in unwanted alteration of valuable nutrients [1]. Furthermore the steadily increasing use of heat-sensitive packaging materials demands sterilization methods that operate at moderate temperatures. In this context, non-thermal atmospheric pressure plasma is an innovative and emerging technology that seems to be a promising alternative, since an efficient inactivation of microorganisms comes along with a moderate heating of the treated surface [2]. The reactive components emanating from a plasma are mainly radicals and other reactive oxygen species whose formation depend on the feeding gas composition and several other operative parameters [3]. Yet their respective roles and their influence on valuable bioactive compounds are still not clearly understood. The aim of this study was to elucidate the influence of low-temperature plasmas on the stability of secondary metabolites. To this end selected flavonoids deposited on inert substrates were exposed to various plasma sources [4] (Ar, N2, O2) and thereafter characterized by HPLC-DAD, spectroscopic methods (Xray photoelectron spectroscopy and FTIR) and contact angle measurements. Results show that all polyphenolics degrade upon plasmo-chemical reactions. The degradation rates strongly depend on the polyphenolic structure. Independently of the plasma source used a significant decrease in the contact angle occurs, indicating a strong surface oxidation. The observed increase in oxygen content can be attributed to newly formed carbonyl- and carboxyl-functions. The concomitant decrease of C–C bondings suggests an oxidative degradation of the flavonoids, which is in agreement to a thermally induced degradation mechanism [1, 5, and 6]. These results should lead to the development of simple kinetic models. In addition, the control of the plasma composition should be optimized to ensure its application as a secure food preservation technology.

References 1. 2. 3. 4. 5. 6.

Rohn et al., J. Agric. Food Chem. 2007, 55, 1568. Moisan et al., Int. J. Pharm. 2001, 226, 1. Jeong et al., J. Phys. Chem. A 2000, 104, 8027. Brandenburg et al., Contrib. Plasma Phys. 2007, 47, 72. Makris and Rossiter, J. Food Comp. Anal. 2002, 15, 103. Buchner et al., Rapid Commun. Mass Spectrom. 2006, 20, 3229.

1

Leibniz Institute for Agricultural Engineering Potsdam-Bornim, Department of Horticultural Engineering, D-Potsdam, Germany.

2

Leibniz Institute for Plasma Science and Technology, D-Greifswald, Germany.

3

Technical University Berlin, Chair of Food Analysis, Department of Food Technology and Food Chemistry, D-Berlin, Germany.

Influence of Fumonisin B1 on Gene Expression and Cytokine Production

4.16 Influence of Fumonisin B1 on Gene Expression and Cytokine Production Dorothee C. Hecker1,2, Christian Salzig3, and Dieter Schrenk1 Fumonisins are a group of structurally related mycotoxins produced as secondary metabolites by species of the genus Fusarium, fumonisin B1 (FB1) being a relevant food contaminant. This fungal metabolite causes several diseases in animals and chronic feeding to rats leads to both cancer initiation and promotion in the liver. One of the important toxic effects of FB1 is the disruption of sphingolipid metabolism by inhibition of the key enzyme ceramide synthase due to the structural similarity to sphingosine. It was the aim of the study to investigate the influence of FB1 on gene expression and TNF-α production in primary rat hepatocytes as well as in the human monocytic cell line Monomac6 (MM6) to further clarify the mechanism of FB1 carcinogenicity. Primary rat hepatocytes were incubated with different concentrations of FB1 for 24â•›h in order to perform microarray experiments and to investigate alterations in gene expression patterns. Total RNA was extracted and converted into cDNA. After labelling, the targets were hybridized to rat whole genome oligomicroarrays. Furthermore, MM6 cells were incubated with different concentrations of FB1 for 24â•›h to investigate whether the cytokine TNF-α is up-regulated either at the protein or the mRNA level. Data analysis of the microarrays shows a total of 96 significantly up-regulated as well as down-regulated genes of which 15 are annotated. With real-time RT-PCR the regulation of six genes was validated. In MM6 the incubation with FB1 for 24â•›h leads to an increase in TNF-α mRNA (determined with real-time RT-PCR) but has no effect on the TNF-α protein levels (measured with EIA). The majority of the affected genes is involved either in cell–cell-adhesion or in sphingosine related signalling. Both topics are in conceivable relationship with mechanisms of tumour promotion in rodent liver. Because of the hypothesis that peroxisome proliferators increase the cytokine production in rodent liver through activating hepatic macrophages – the Kupffer cells – we investigated whether the incubation with FB1 leads to an increase in TNF-α production in the human monocytic cell line MM6. The increase in TNF-α mRNA in MM6 cells without increasing protein levels indicates that FB1 carcinogenicity may be distinct from peroxisome proliferators like activity.

1

Food Chemistry and Toxicology, University of Kaiserslautern, Germany.

2

Correspondence to: [email protected]

3

Fraunhofer Institute for Mathematics on Technology and Economy, D-Kaiserslautern, Germany.

411

412

Posters

4.17 Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model Stefanie Hessel1, Andrea John2, Albrecht Seidel2, Hansruedi Glatt3, and Alfonso Lampen1

Abstract Quercetin, a flavonoid present in many vegetables, fruits, and beverages, has been extensively studied as a chemopreventive agent in several cancer models due to its anti-oxidant, anti-tumourigenic, and anti-inflammatory activity. In this context, we investigated the effects of quercetin on the metabolism and excretion of the food contaminant benzo[a]pyrene (B[a]P) in the human intestinal cell line Caco-2. This study demonstrates that B[a]P phenols are excreted apically as B[a]Psulfates. Inhibition experiments revealed that active excretion was mediated by the ATP-binding cassette transport protein breast cancer resistance protein (BCRP). In contrast, the detoxification of the ultimate carcinogenic phase I �B[a]�P metabolite anti-B[a]P-7,8-diol-9,10-epoxide (BPDE) primarily occurred as glutathione conjugate formed by glutathione-S-transferases. Transport experiments indicated that these glutathione conjugates of BPDE were excreted overall to the basolateral side of the polarized Caco-2 monolayer. The transport rates of the detected phase II metabolites of B[a]P were increased by pre-treatment with quercetin. On the one hand quercetin increased the apical export of the B[a]P-sulfates by inducing the BCRP protein expression, on the other hand the basolateral excretion of BPDE glutathione conjugates is enhanced. This resulted in an effective removal of procarcinogenic B[a]P from intestinal cells.

4.17.1 Introduction Quercetin, one of the most abundant flavonoids in human diet, is present in many vegetables, fruits, and beverages. Due to its anti-oxidant, anti-tumourigenic [1] and anti-inflammatory [2] activity, quercetin has been studied extensively as a chemopreventive agent in several cancer models. Quercetin mod-

1

Federal Institute for Risk Assessment, Department Food Safety, Thielallee 88–92, D-14195 Berlin, Germany.

2

Biochemical Institute for Environmental Carcinogens, Prof. Dr. Gernot Grimmer Foundation, Lurup 4, D-22927 Grosshansdorf, Germany.

3

German Institute of Human Nutrition (DIfE), Department of Nutritional Toxicology, ArthurScheunert-Allee 114–116, D-14558 Nuthetal, Germany.

Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model

ulates various enzyme activities and intracellular signalling pathways. As an example, it induces the ARE (Nrf2)-mediated gene expression [3]. The epithelium of the small intestine is the first physical and biochemical barrier for drug absorption from the gastro-intestinal tract. This epithelium plays an important role as a defence mechanism against xenobiotics, which are ingested via food. The epithelial cells of the small intestine express a variety of xenobiotic-metabolizing enzymes (XMEs), that are able to metabolize the food contaminant benzo[a]pyrene (B[a]P) [4, 5]. This polycyclic aromatic hydrocarbon is a procarcinogen. Genotoxicity of B[a]P depends on the formation of the ultimate carcinogenic phase I B[a]P metabolite anti-B[a]P-7,8-diol-9,10-epoxide (BPDE) [6, 7]. This epoxide is highly reactive and modifies DNA as well as proteins [8]. In the phase I metabolism B[a]P is converted by cytochrome P450 mono-oxygenases (CYP) 1A1/1B1 and microsomal epoxide hydrolases into phenols and dihydrodiols. The B[a]P-7,8-diol can be further metabolized by CYP 1A1/1B1 into the BPDE, which can be detoxified by glutathione-S-transferases. In previous studies it has been shown that after resorption in intestinal cells B[a]P is metabolized to phase I and further to phase II metabolites. Phase II metabolites such as B[a]P-sulfates and B[a]P-glucuronides are transported mainly by the ATP-binding cassette transport protein breast cancer resistance protein (BCRP) to the luminal side [9, 10]. In this study, we investigated the effects of quercetin on the metabolism and excretion of the food contaminant B[a]P in human Caco-2 cells, as a model for the human small intestine.

4.17.2 Methods 4.17.2.1 Cell culture

The human colon adenocarcinoma cell line Caco-2 was obtained from the European Collection of Cell Cultures (ECACC, Porton Down, UK) and maintained in Dulbecco’s modified Eagle’s medium (DMEM, GIBCO–Invitrogen, Karlsruhe, Germany) supplemented with 10â•›% fetal calf serum, 100 IU/ml penicillin, and 100╃µg/ml streptomycin in a humidified atmosphere of 5â•›% CO2 in air at 37╃°C.╃ 4.17.2.2 Analysis of B[a]P-3-sulfate and (+)-anti-BPDE and (-)-anti-BPDE Glutathione Conjugates

For transport analysis Caco-2 cells were grown on Transwell® inserts (4.71 cm² growth area, 0.4╃µm pore size, polycarbonate membranes; Corning Costar Co., Cambridge, MA) and cultured for 16 days after seeding and subsequently pretreated for 48â•›h with quercetin (Merck, Darmstadt, Germany). Subsequently, the incubation experiment was started by replacing the medium with medium containing 10╃µM of the substrate B[a]P-7,8-diol or 5╃µM B[a]P-3-OH.

413

414

Posters

B[a]P-3-sulfate was analyzed as previously reported [9, 11]. For analysis of the (+)-10R-(S-glutathionyl)-7S,8R,9R-trihydroxy-7,8,9,10-tetrahydrobenzo[a] pyrene [(+)-anti-BPD-SG] and (-)-10S-(S-glutathionyl)-7R,8S,9S-trihydroxy7,8,9,10-tetrahydrobenzo[a]pyrene [(-)-anti-BPD-SG] the thawed medium samples (2 ml) were spiked with S-(1-pyrenylmethyl)glutathione (1-MP-SG) as an internal standard. The medium samples were then purified by solid phase extraction using a SPE C-18 cartridge (J.╃T. Baker, Deventer, The Netherlands). This cartridge was washed with 5 ml water and the elution of the analytes was achieved with 10 ml methanol. The eluate was evaporated under vacuum at 200 mbar and reduced to a total volume of about 0.5 ml using a vacuum controller. After addition of methanol, the sample was further concentrated to a volume of approximately 100╃µl under a gentle stream of nitrogen at room temperature. Aliquots of 20╃µl were injected into the LC-MS/MS system and gradient chromatography was performed under the following conditions: starting with a mixture of methanol and water (40:60, v/v; each containing 0.1â•›% formic acid) for 2 min isocratically, then changing to 50â•›% methanol at 8 min, keeping these terms for 7 min, further toâ•–100â•›% methanol at 20 min and maintaining these conditions for 10 min at a flow rate of 0.2 ml/min. The analytical column (Reprosil-Pur C18-AQ, 150 mmx2 mm, 5╃µm; Maisch GmbH, Ammerbuch, Germany) was operated at 30╃°C and the UV-detection was performed at 254╃nm. The MS/MS system was processed in the negative electrospray ionisation (ESI) selected reaction monitoring mode (SRM) using the following ion mass transitions for detection: (+)-anti-BPD-SG and (-)-anti-BPD-SG: m/z 608  272 (22 V) and 1-MP-SG m/z 520  272 (25 V). Spray voltage was set to 6.0 kV and heated capillary temperature was held at 350╃°C. Nitrogen as sheath and auxiliary gas was adjusted to 80 psi and 40 arbitrary units, respectively. The collision gas (argon) for the MS/MS mode at quadrupole Q2 was set to 3╃mT.╃

4.17.2.3 Analysis of DNA Adducts by 32P-Postlabelling

For 32P-postlabelling analysis, differentiated Caco-2 cells were exposed to different concentrations of quercetin (5 to 100╃µM) for 48â•›h, the medium was replaced by medium containing 0.1╃µM BPDE for 1â•›h. Subsequently, the DNA was isolated using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). DNA adducts were analyzed as previously reported [12].

4.17.3 Results and Discussion 4.17.3.1 Excretion of Phase II Metabolites of B[a]P

To investigate the influence of the phytochemical quercetin on the excretion of phase€II metabolites in Caco-2 cells, cells were pre-treated with quercetin for 2 or 3 days to induce xenobiotic-metabolizing enzymes, such as CYP 1A1, as

Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model

Figure 1: Influence of quercetin on the excretion of B[a]P-3-sulfate. After pre-treatment for 72â•›h with 25╃µM quercetin, 5╃µM B[a]P-3-OH was supplied to both chambers of the Transwell system (n=╃3). Subsequently, the medium was collected at various time points and analyzed for the presence of B[a]P-3S by HPLC (* p<0.05, One-way-ANOVA, Tukey-Kramer post-test).

Figure 2: Influence of quercetin on the transport rate of glutathione conjugates. Differentiated Caco-2 cells were incubated with 50╃µM quercetin for 48â•›h. After pre-treatment, cells were exposed to 10╃µM B[a]P-7,8-diol for 24â•›h (n=╃3). Subsequently, medium from both Transwell compartments was collected and analyzed by HPLC-MS (* p<0.05, Student’s t-test).

well as transport proteins, such as BCRP [13]. The analysis of the metabolites revealed that quercetin enhances the export of B[a]P-3-sulfate (Figure 1), a metabolite resulting from the sulfonation by sulfotransferases, as well as the excretion of (+)â•‚anti-BPD-SG and (-)-anti-BPD-SG (Figure 2), the detoxified conjugates of the ultimate carcinogen (±)-anti-BPDE. As shown in Figure 2, the (+)-anti-BPD-SG and (-)-anti-BPD-SG conjugates were mainly excreted to the basolateral side of the polarized Caco-2 cells. This result indicates that the glutathione conjugates of the BPDE are not transported via BCRP, which is located at the apical side of Caco-2 cells [14].

415

416

Posters

Enhanced B[a]P detoxification as observed in the study may be a side effect of quercetin metabolism. It is already known that quercetin enhances the expression of phase I, phase II, and transport proteins. The flavonoid induces expression of CYP1A1 and CYP1B1 in human breast epithelial MCF10F cells [15, of glutathione-S-transferases in mice [16] and of BCRP in Caco-2 cells [11]. Hong et al. (2006) [17] reported the detection of glutathione conjugates of quercetin in human urine after consumption of 200â•›g cooked onions (74 mg quercetin). Furthermore, 22 metabolites of quercetin, including glucuronide, sulfate and glutathione conjugates were detected in murine hepatic suspensions [17]. Thus, quercetin may induce its own metabolism and excretion in the human Caco-2 cells. Consequently, other xenobiotics were increasingly metabolized and excreted under the influence of quercetin.

4.17.3.2 Influence of Quercetin on DNA Adduct Formation by the Ultimate �Carcinogen BPDE

It is known, that BPDE is a genotoxic phase I metabolite of B[a]P that forms protein and DNA adducts. We discovered that quercetin enhances the excretion of B[a]P-3-sulfate as well as the apical and basolateral transport of the detoxified glutathione conjugates of BPDE. To investigate the influence of quercetin on DNA adduct formation, differentiated Caco-2 cells were pre-treated with quercetin in different concentrations, and subsequently incubated with 0.1╃µM BPDE for 1â•›h.╃32P-postlabelling analysis of the extracted DNA revealed that quercetin reduces the amount of DNA adducts induced by BPDE already at low concentrations (5╃µM) of quercetin. Increasing quercetin concentrations (up to 100╃µM) reduced DNA adduct levels to 17â•›% compared to the control without quercetin (Figure 3). It has been reported that quercetin and quercetin-rich fruits reduce the formation of DNA adducts caused by BPDE in lymphocytes [18]. Furthermore, quercetin also decreased the amount of DNA adducts caused by the heterocyclic aromatic amine 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) in HepG2 cells and hepatocytes [19]. These data correlate to our finding that quercetin reduces the DNA adduct formation by BPDE strongly suggesting that the XME-inducing potential of quercetin enhances the detoxification of BPDE. Although the clear molecular mechanism for such preventive effect remains to be elucidated, increased excretion of phase II metabolites and decreased DNA adduct formation under the influence of quercetin indicate the preventive potential of quercetin by reducing the accumulation of toxic compounds.

4.17.4 Conclusions In summary, quercetin enhances the detoxification of B[a]P by increasing the transport rates of phase II metabolites of the food contaminant B[a]P. It induces the expression of detoxifying enzymes and transport proteins. This detoxifica-

Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model

Figure 3: Influence of quercetin on DNA adduct formation by BPDE. Caco-2 cells were cultured for 18 days to allow differentiation in 25 cm² cell culture flasks. The cells were pre-treated 48â•›h with different concentrations of quercetin. Afterwards, the cells were exposed to 0.1╃µM BPDE for 1â•›h, with 0.1â•›% DMSO or 100╃µM quercetin as negative control or 20╃µM 1-sulfooxymethylpyrene (1-SMP) as positive control. DNA adduct formation was detected by 32P-postlabelling as described in “Material and Methods”. (A) The arrow marks the DNA adduct resulting from the reaction of BPDE with DNA in the chromatograms. (B) Values in diagram B are means and range of two incubations.

tion machinery is probably mainly set up for the conjugation and removal of quercetin. However, other xenobiotics such as B[a]P are – maybe as a side effect – also effectively removed by this system. As a result, quercetin obviously circumvents increased DNA adduct levels caused by the ultimate carcinogen BPDE in intestinal cells and therefore preventing BPDE-induced DNA damages in these cells.

417

418

Posters

Acknowledgments We thank Mrs. Christine Gumz for excellent technical assistance in 32P-postlabelling experiments.

References â•⁄ 1. Huang, Y.╃T., Hwang, J.╃J., Lee, P.╃P., Ke, F.╃C., Huang, J.╃H., Huang, C.╃J., Kandaswami, C., Middleton E Jr, and Lee, M.╃T. (1999). Effects of luteolin and quercetin, inhibitors of tyrosine kinase, on cell growth and metastasisassociated properties in A431 cells overexpressing epidermal growth factor receptor. Br. J. Pharmacol. 128 (5): 999–1010. â•⁄ 2. Hamalainen, M., Nieminen, R., Vuorela, P., Heinonen, M., and Moilanen, E. (2007). Anti-inflammatory effects of flavonoids: genistein, kaempferol, quercetin, and daidzein inhibit STAT-1 and NF-kappaB activations, whereas flavone, isorhamnetin, naringenin, and pelargonidin inhibit only NF-kappaB activation along with their inhibitory effect on iNOS expression and NO production in activated macrophages. Mediators. Inflamm. 2007, 45673. â•⁄ 3. Tanigawa, S., Fujii, M., and Hou, D.╃X. (2007). Action of Nrf2 and Keap1 in ARE-mediated NQO1 expression by quercetin. Free Radic. Biol. Med.╃42 (11): 1690–1703. â•⁄ 4. Munzel, P.╃A., Schmohl, S., Heel, H., Kalberer, K., Bock-Hennig, B.╃S., and Bock, K.╃W. (1999). Induction of human UDP glucuronosyltransferases (UGT1A6, UGT1A9, and UGT2B7) by t-butylhydroquinone and 2,3,7,8-tetrachlorodibenzo-p-dioxin in Caco-2 cells. Drug Metab. Dispos. 27 (5): 569– 573. â•⁄ 5. Zhang, Q.╃Y., He, W., Dunbar, D., and Kaminsky, L. (1997). Induction of CYP1A1 by beta-naphthoflavone in IEC-18 rat intestinal epithelial cells and potentiation of induction by dibutyryl cAMP. Biochem. Biophys. Res. Commun. 233 (3): 623–626. â•⁄ 6. Buening, M.╃K., Wislocki, P.╃G., Levin, W., Yagi, H., Thakker, D.╃R., Akagi, H., Koreeda, M., Jerina, D.╃M., and Conney, A.╃H. (1978). Tumorigenicity of the optical enantiomers of the diastereomeric benzo[a]pyrene 7,8-diol9,10-epoxides in newborn mice: exceptional activity of (+)-7beta,8alphadihydroxy-9alpha,10alpha-epoxy-7,8,9,10-tetrahydrobenzo[a ]pyrene. Proc. Natl. Acad. Sci. USA 75 (11): 5358–5361. â•⁄ 7. Thakker, D.╃R., Yagi, H., Lu, A.╃Y., Levin, W., and Conney, A.╃H. (1976). Metabolism of benzo[a]pyrene: conversion of (+/-)-trans-7,8-dihydroxy-7,8dihydrobenzo[a]pyrene to highly mutagenic 7,8-diol-9,10-epoxides. Proc. Natl. Acad. Sci. USA 73 (10): 3381–3385. â•⁄ 8. Koreeda, M., Moore, P.╃D., Wislocki, P.╃G., Levin, W., Yagi, H., and Jerina, D.╃M. (1978). Binding of benzo[a]pyrene 7,8-diol-9,10-epoxides to DNA, RNA, and protein of mouse skin occurs with high stereoselectivity. Science 199 (4330): 778–781.

Effects of Quercetin on the Detoxification of the Food Contaminant Benzo[a]pyrene in the Human Intestinal Caco-2 Cell Model

â•⁄ 9. Buesen, R., Mock, M., Seidel, A., Jacob, J., and Lampen, A. (2002). Interaction between metabolism and transport of benzo[a]pyrene and its metabolites in enterocytes. Toxicol. Appl. Pharmacol. 183 (3): 168–178. 10. Ebert, B., Seidel, A., and Lampen, A. (2005â•›a). Identification of BCRP as transporter of benzo[a]pyrene conjugates metabolically formed in Caco-2 cells and its induction by Ah-receptor agonists. Carcinogenesis 26 (10): 1754– 1763. 11. Ebert, B., Seidel, A., and Lampen, A. (2007). Phytochemicals induce breast cancer resistance protein in Caco-2 cells and enhance the transport of benzo[a]pyrene-3-sulfate. Toxicol. Sci.╃96 (2): 227–236. 12. Monien, B.╃H., Muller, C., Engst, W., Frank, H., Seidel, A., and Glatt, H. (2008). Time course of hepatic 1-methylpyrene DNA adducts in rats determined by isotope dilution LC-MS/MS and 32P-postlabeling. Chem. Res. Toxicol. 21 (10): 2017–2025. 13. Ebert, B., Seidel, A., and Lampen, A. (2005â•›b). Induction of phase-1 metabolizing enzymes by oltipraz, flavone and indole-3-carbinol enhance the formation and transport of benzo[a]pyrene sulfate conjugates in intestinal Caco-2 cells. Toxicol. Lett. 158 (2): 140–151. 14. Xia, C.╃Q., Liu, N., Yang, D., Miwa, G., and Gan, L.╃S. (2005). Expression, localization, and functional characteristics of breast cancer resistance protein in Caco-2 cells. Drug Metab. Dispos. 33 (5): 637–643. 15. Mense, S.╃M., Chhabra, J., and Bhat, H.╃K. (2008). Preferential induction of cytochrome P450 1A1 over cytochrome P450 1B1 in human breast epithelial cells following exposure to quercetin. J. Steroid Biochem. Mol. Biol. 110 (1–2): 157–162. 16. Mitchell, A.╃E., Burns, S.╃A., and Rudolf, J.╃L. (2007). Isozyme- and genderspecific induction of glutathione S-transferases by flavonoids. Arch. Toxicol. 81 (11): 777–784. 17. Hong, Y.╃J., and Mitchell, A.╃E. (2006). Identification of glutathione-related quercetin metabolites in humans. Chem. Res. Toxicol. 19 (11): 1525–1532. 18. Wilms, L.╃C., Hollman, P.╃C., Boots, A.╃W., and Kleinjans, J.╃C. (2005). Protection by quercetin and quercetin-rich fruit juice against induction of oxidative DNA damage and formation of BPDE-DNA adducts in human lymphocytes. Mutat. Res.╃582(1–2): 155–162. 19. Bacon, J.╃R., Williamson, G., Garner, R.╃C., Lappin, G., Langouet, S., and Bao, Y. (2003). Sulforaphane and quercetin modulate PhIP-DNA adduct formation in human HepG2 cells and hepatocytes. Carcinogenesis 24 (12): 1903–1911.

419

420

Posters

4.18 Risk Assessment of T-2 and HT-2 Toxin Using Human Cells in Primary Culture Dennis Mulac1, Maika Königs1, Gerald Schwerdt2, Michael Gekle2, and Hans-Ulrich Humpf1,3 T-2 toxin belongs to the large group of trichothecene mycotoxins synthesized by various Fusarium molds which can infect raw agriculture materials. Among the trichothecenes, T-2 toxin is one of the most potent mycotoxins and poses a potential health risk in human nutrition. Several acute and chronic toxic effects were observed in humans after consumption of contaminated food. The aim of this work was to determine the effects of Tâ•‚2€toxin and of several of its metabolites, namely HTâ•‚2€toxin, neosolaniol, Tâ•‚2€triol and Tâ•‚2€tetraol, on two human cells in primary culture: Human renal proximal tubule epithelial cells (RPTEC) and normal human lung fibroblasts (NHLF). Concerning the cytotoxicity of T-2 toxin and its metabolites, different studies were performed with animal cells and cell lines but there are only limited data about cytotoxic effects in human cells available. The use of human cells in primary culture gives a good completion of the already known data because these might be limited due to the disadvantages of cell lines (e.╃g. immortalization, tumour derivation, long-time cultivation). In order to study the cytotoxicity and mode of cell death, the following parameters were measured after exposure to T-2 toxin and several of its metabolites: cell viability, caspase-3-activity and LDH-release. With IC50 values of 0.2 and 0.5╃µM T-2 toxin showed the strongest cytotoxic effect in both cells with triggering apoptosis as kind of cell death starting at a concentration of 100╃nm. The metabolites HT-2 toxin and neosolaniol revealed weaker cytotoxic effects (IC50: 0.7–3.0╃µM) and induced apoptosis at higher concentrations (>1╃µM). The other metabolites were less cytotoxic (IC50: 8.3–25.1╃µM) and did not activate caspase-3.╃In addition to the analysis of cytotoxic effects, we also studied the metabolism of T-2 toxin in these cells in primary culture. Using LC-ESI-MS/ MS we could demonstrate that both cells are able to transform T-2 toxin into HT-2 toxin. Further metabolic activity could only be observed in renal proximal tubule cells (RPTEC) by forming neosolaniol as a second metabolite.

1

Institute for Food Chemistry, Westphalian Wilhelm’s University Münster, Corrensstr. 45, D-48149 Münster, Germany.

2

Julius Bernstein Institute for Physiology, Martin Luther University, D-Halle-Wittenberg, Germany.

3

Correspondence to: [email protected].

Pyrrolizidine Alkaloids in Honey Bee Products

4.19 Pyrrolizidine Alkaloids in Honey Bee Products Michael Kempf1, Till Beuerle2, Annika Reinhard1, and Peter Schreier1

Abstract Recently, contamination of honey with pyrrolizidine alkaloids (PA) has been reported as potential health risk. Therefore, it was of interest to develop a reliable tool for selective and quantitative determination of PA in honey. Sample preparation of the novel method comprises strong cation exchange solid-phase extraction (SCX-SPE), followed by two reduction steps using zinc and LiAlH4 and subsequent trimethyl silylation. During this procedure the major toxic PA are converted into a single necin backbone which is analyzed by gas chromatography-mass spectrometry (GC-MS) in single ion monitoring mode (SIM). The method was applied to 216 commercially available floral honey samples and, after some modifications, to 55 pollen products available as food supplements. Among the honeys 19 samples (9â•›%) contained PA in the range from 0.02 to 0.12╃µg g-1 (calculated as retronecine equivalents). Pollen products showed a higher degree of contamination (17 out of 55; 31â•›%) and revealed much higher concentrations compared to honey (amounts from 1.08 to 16.35╃µg g-1). For the first time, the present method allows the exact and selective determination of toxic PA by simply detecting a single sum parameter which contains the toxic principle of the PA (1,2-double bond). The method can be applied independently from secondary information such as botanical origin or the oxidation state of the PA (tertiary PA, PA-N-oxides). The values observed – especially in pollen products – provoke the discussion of an international regulation of PA in food.

4.19.1 Introduction Pyrrolizidine alkaloids (PA) comprise about 370 different structures. Their occurrence is limited to only five plant families: the Asteraceae (Senecioneae and Eupatorieae), the Boraginaceae, the Apocynaceae, the genus Crotalaria within the Fabaceae and certain genera of the Orchidaceae [1]. PA are consisting of two building blocks, a basic structure (necine base), mostly retronecine, which is esterified by acids (necine acids), resulting in five main PA types [1] (Figure 1). Most PA occur in two major forms, the tertiary form and the corresponding N-oxide; tertiary PA containing a 1,2-double bond and an allylic ester functionality are pre-toxins [2]. However, ingested PA-N-oxides are unspecifically

1

Chair for Food Chemistry, Würzburg University, Am Hubland, D-97074 Würzburg, Germany.

2

Institute for Pharmaceutical Biology, Technical University of Braunschweig, Mendelssohn� str.╃1, D-38106 Braunschweig, Germany.

421

422

Posters

reduced to the pro-toxic tertiary PA in the presence of weak reducing agents which happens generally in the gut system. The bioactivation of 1,2-unsaturated PA toxification is well studied in vertebrates and occurs by the action of hepatic cytochrome-P450 enzymes. Acute, chronic and genotoxic effects have been documented [3–5]. The bioactivation is initiated by hydroxylation of the necine base followed by a spontaneous dehydratization to form pyrrolic ester structures that readily react with cellular nucleophiles [5]. The structural prerequisite is the concurrent presence of the 1,2-double bond together with an allylic ester functionality. Humans are directly exposed to these toxins by consumption of herbal medicine, herbal teas, dietary supplements or food containing PA-plant material. Secondary exposure was reported for food, where the upstream food chain was contaminated with PA, such as milk [6], eggs [7] or honey [8, 9]. There are several examples worldwide where PA containing plants caused severe intoxications to humans and livestock [5]. Besides the acute toxic effects, the genotoxic and tumourigenic potential of PA was demonstrated in some eukaryotic model systems [5]. In the International Programme on Chemical Safety (IPCS) the WHO has evaluated PA and concluded that their presence as contaminants in food is a threat to human health and safety [10]. In pharmaceuticals, the use of these plants is regulated by the German Federal Health Bureau to a total PA intake of 1╃µg per day for a six-week period per year, or, if six weeks are exceeded, the limit is reduced to 0.1╃µg total PA content per day. For pregnant or lactating women zero exposure is recommended [11]. Other countries have established similar policies or are in process of establishing regulations. Regarding foodstuff, the DFG Senate Commission on Food Safety (SKLM) passed an opinion that

Figure 1: Classification of PA into five groups.

Pyrrolizidine Alkaloids in Honey Bee Products

►⌺

[…] The existing data base dealing with the content of PA in honey collected from PA-containing plants […] as well as the data base dealing with the exposure of consumers to PA are judged to be inadequate […]. Furthermore they reminded that […] The main goal of future research should be the careful analytical determination of the PA content of honey and pollen […] [12].

Initiated by this opinion, the already proven PA transfer from plants to honey has found particular attention, and reliable tools for selective and quantitative determination of PA in bee products are necessary.

4.19.2 Method To analyze PA, we elaborated a new method consisting of strong cation exchange solid-phase extraction (SCX-SPE), two reduction steps followed by silylation and subsequent capillary gas chromatography–mass spectrometry (HRGC-MS) using SIM mode [13]. This procedure transfers the PA to their common skeletal structure, i.╃e. retronecine, and all different PA were therefore detected in the form of a single sum parameter (di-TMS-retronecine). Heliotrine, a rather rare naturally occurring PA, was used as internal standard. During the workup heliotrine is converted to heliotridine – a diastereomer to retronecine (Figure 2). Both can be separated via HRGC (Figure 3). The procedure was validated using extracts of Senecio vernalis as well as authentic reference PA and their N-oxides.

Figure 2: Chemical reactions (scheme) occurring in the course of our sample preparation, illustrated by senecionine (left) and the internal standard, heliotrine (right), leading to the respective silylated diastereomeric necin backbone structures retronecine and heliotridine.

423

424

Posters

Figure 3: SIM mode chromatogram (m/z 93, 183 and 299) of (a) PA positive honey sample (di-TMS-retronecine; RI [DB1]=╃1600; Sample No.╃21) without internal standard heliotrine and (b) after addition of the internal standard heliotrine (additional peak for di-TMS-heliotridine; RI€[DB1]=╃1632; Sample No.╃21).

4.19.3 Results and Discussion Our purpose was to generate a dataset on PA contamination for retail honeys and pollen products available on the German/ European market. We did not apply any selection criteria to the samples. No additional information, such as apiarist interrogations about the habitats of the bee colonies or pollen analyses was available beforehand. The samples were purchased from various supermarkets in Germany and other European countries, as well as from internet stores. The new developed method was applied to 216 commercially available floral honey samples [13] and, after some modification, to 55 pollen products available as food supplements [14]. Among the honeys 94 were from Europe, 34 from Central- and South-America, 6 from USA/ Canada, and 22 from Australia and New Zealand. Another 60 samples had no regional identification or were of dubious origin. These 60 samples were mostly mixtures of different proveniences labelled as “mixture

Pyrrolizidine Alkaloids in Honey Bee Products

from honeys of Non-EC-countries” or “mixture from honeys of EC-/ Non-ECcountries”. Within these 216 honeys under study 19 samples (9â•›%) contained PA in the range from 0.019 to 0.120╃µg g-1 (Figure 4). The average PA contamination was 0.056╃µg g-1.╃Seven out of the 19 PA-positive honeys found in our study were labelled as “Non-EC-countries”, five were labelled as “EC-/ NonECcountries”, three from New Zealand, three from Central-/ South America and one sample from Canada, respectively. Two honeys, including the sample with the highest amount of PA in our study (No.╃145), were labelled as borage honey (New Zealand). The other 17 PA containing samples were without any conspicuous declaration. From the 94 samples which were labelled as European origin no honey was tested PA-positive [13]. In retrospect, some PA positive honeys were analyzed microscopically for suspect PA plant pollen. In all cases PA plant pollen were detected. However, pollen of Echium spp. played the major role in this context [13]. Among the 55 pollen products 33 were from Europe, four from USA and Mexico, one from New Zealand, one from Asia, as well as 16 samples of not specified origin. Within these 55 pollen products 17 samples (31â•›%) revealed a PA contamination in the range from 1.08 to 16.35╃µg g-1 (Figure 5). Nine out of the 17 PA-positive pollen products were from European (Spain: 5, Rumania: 2, Italy: 1 and France: 1) and the remaining eight were of not specified origin. As shown by additional pollen analysis, all PA-positive pollen products exhibited a significant amount of PA-plant pollen (mostly Echium spp.) [14]. The per capita consumption of honey in Europe is regarded to be 1.3â•›g/day [15] (the worldwide highest amount). Neglecting the part of the “non-honey eaters” this level increases to 3.9â•›g/day [15, 16]. According to the suppliers, the recommended intake for pollen products is about 10â•›g (1–2 table spoons per day). Taking into account the genotoxic potential of the PA, it would be rea-

Figure 4: Total pyrrolizidine alkaloids (given as retronecine equivalents) in commercial honeys (n=╃216). Only the honeys containing PA are listed [13]. Standard deviations are given (n=╃3).

425

426

Posters

Figure 5: Total pyrrolizidine alkaloids (given as retronecine equivalents) in pollen products (n=╃55). Only the samples containing PA are listed [14]. Standard deviations are given (n=╃4).

sonable to reconsider also the honey results with respect to the usual amount consumed by a single person per day (2 table spoons equal 20â•›g). If we further consider that this calculation is based on retronecine equivalents and does not include otonecine type PA it represents the lowest calculable value. To compare the data with the current regulations of the German Federal Health Bureau for herbal pharmaceutical products one can assume at least a factor of 2 to calculate the concentration of the original mono- and diester PA in the sample. In doing so, one can calculate a total PA intake for a single person by assuming a “normal” daily consumption dose of 20â•›g for honey and 10â•›g for pollen. In this case all but one sample tested positive for PA will exceed the limit of the current regulations of the German Federal Health Bureau for herbal pharmaceutical products (1.0╃µg PA/day, restricted to a maximum of six weeks per year [11]). Especially pollen where almost one third of the samples showed PA contamination the levels of a single daily dose are in average 100 times higher as the limit for phytopharmaceuticals. Although there are no existing regulations for food so far, the Dutch authorities for food safety have discussed the complex toxicological problem in detail [17]. The mentioned virtual safe dose for carcinogenicity (VSD, based on riddelline) and a tolerable daily intake for acute liver toxicity (TDI) were 0.43 ng and 100 ng per kg body weight per day, respectively. Based on these calculations pollen consumers are well above the calculated limits. A 70 kg person exceeds the TDI in average (considering that 31â•›% of the samples contain PA) by a factor of 4, a 20 kg child by a factor of 15.╃In 2001 the FDA advised, that pyrrolizidine alkaloids should not be used as an ingredient in dietary supplements [18]. According to a recent position paper proposed by the German Federal Institute for Risk Assessment (Bundesinstitut für Risikobewertung) [19] there should be a zero tolerance for PA in foodstuff and animal feed.

Pyrrolizidine Alkaloids in Honey Bee Products

4.19.4 Conclusion For the first time, the present method allows the sensitive and selective determination of toxic PA by measuring a sum parameter which reveals the toxic principle of the PA (1,2-double bond). The method is non-targeted and does not depend on additional information such as botanical origin, marker PA or the oxidation state of the PA (tertiary PA, N-oxides). PA pollen contamination is alarming with regard to two aspects. First, it occurs more frequently (31â•›%) as compared to honey (9â•›%) and second, the degree of contamination of pollen is higher (in average 5.17╃µg g-1╃vs.╃0.056╃µg g-1 retronecine equivalents) [13, 14]. The PA concentration found in pollen products raise the question whether these products can be sold as food supplements without monitoring the PA content. A general regulation of PA in foodstuff should be discussed.

Acknowledgments This work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), Bonn, Project No. SCHR 211/22–1/23–1 and BE 3200/1–1/3–1.

References â•⁄ 1. Hartmann, T., Witte, L., Chemistry, biology and chemoecology of the pyrrolizidine alkaloids. In: Pelletier, S.╃W. (Ed.), Alkaloids: Chemical and Biological Perspectives, Vol.╃9.╃Pergamon Press, Oxford 1995, pp.╃155–233. â•⁄ 2. Williams, L., Chou, M.╃W., Yan, J., Young, J.╃F., Chan, P.╃C., Doerge, D.╃R., Toxicokinetics of riddelliine, a carcinogenic pyrrolizidine alkaloid, and metabolites in rats an mice. Toxicology and Applied Pharmacology 2002, 182, 98– 104. â•⁄ 3. Culvenor, C.╃C.╃J., Edgar, J.╃A., Jago, M.╃V., Outteridge, A., Peterson, J.╃E., Smith, L.╃W., Hepato- and pneumotoxicity of pyrrolizidine alkaloids and derivatives in relation to molecular structure. Chem. Biol. Interaction 1976, 12, 299–324. â•⁄ 4. Stegelmeier, B.╃L., Edgar, J.╃A., Steven, M., Gardner, D.╃R., Schoch, T.╃K., Coulombe, R.╃A., Molyneux, R.╃J., Pyrrolizidine alkaloid plants, metabolism and toxicity. Journal of Natural Toxins 1999, 8, 95–116. â•⁄ 5. Fu, P.╃P., Xia, Q., Lin, G., Chou, M.╃W., Pyrrolizidine alkaloids – genotoxicity, metabolism, enzymes, metabolic activation and mechanisms. Drug Metabolism Reviews 2004, 36, 1–55. â•⁄ 6. Panter, K.╃E., James, L.╃F., Natural plant toxicants in milk: a review. Journal of Animal Science 1990, 68, 892–904. â•⁄ 7. Edgar, J.╃A., Smith, L.╃W., in: Tu, A.╃T., Gaffield, W. (Eds.), Natural and Selected Synthetic Toxins: Biological Implications, ACS Symposium Series, Vol.╃745, American Chemical Society, Washington D.╃C.╃2000, pp.╃118–128.

427

428

Posters

â•⁄ 8. Deinzer, M.╃L., Thomson, P.╃H., Burgett, D.╃M., Isaacson, D.╃L., Pyrrolizidine alkaloids: their occurance in honey from tansy ragwort (Senecio jacobaea L.). Science 1977, 195, 497–499. â•⁄ 9. Culvenor, C.╃C., Edgar, J.╃A., John, A., Smith, L.╃W., Pyrrolizidine alkaloids in honey from Echium plantagineum L. Journal of Agricultural and Food Chemistry 1981, 29, 958–960. 10. WHO, ICPS Enviromental health criteria 80 Pyrrolizidine alkaloids. WHO, Geneva 1988, http://www.inchem.org/documents/ehc/ehc/ehc080.htm (12.╃March 2009). 11. German Federal Health Bureau. Bundesanzeiger, 1992, June 17, 4805; Dtsch. Apoth. Ztg.╃1992, 132, 1406–1408. 12. SKLM, DFG – Senate Commission on Food Safety, Pyrrolizidin alkaloids in honey, bee-keeper products and pollen products, 2002, Opinion on 8.╃11. 2002. (Shortened version, 10/11.╃April 2003) 13. Kempf, M., Beuerle, T., Bühringer, M., Denner, M., Trost, D., von der Ohe, K., Bhavanam V.╃B.╃R., Schreier, P., Pyrrolizidine alkaloids in honey: Risk analysis by gas-chromatography–mass spectrometry. Mol. Nutr. Food Res., 2008, 52, 1193–1200. 14. Kempf, M., Heil, S., Haßlauer, I., Schmidt, L., von der Ohe, K., Theuring, C., Reinhard, A., Schreier, P., Beuerle, T., Pyrrolizidine alkaloids in pollen and pollen products. Mol. Nutr. Food Res.╃2009, 53, submitted. 15. World Health Organisation, Food Safety Programme, GEMS/Food regional per capita consumption of raw and semi-processed agricultural commodities, WHO: Geneva, 1998 (WHO/FSF/FOS98.3). 16. MAFF U.╃K., Surveillance for pyrrolizidine alkaloids in honey, Joint Food Safety and Standards Group Food Surveillance Information Sheet 52. 1995. 17. RIVM Rijksinstituut voor Volksgezondheid en Milieu, RIKILT Institute of Food Safety, Risicobeoordeling inzake de Aanwezigheid van Pyrrolzidine Alkaloiden in Honing, http://www.vwa.nl/cdlpub/servlet/CDLServlet?p_ file_id=╃22703 (20.╃May 2009), Wageningen, Netherlands, 2007. 18. FDA, US Food and Drug Administration, Center for Food Safety and Applied Nutrition, FDA Advises Dietary Supplement Manufacturers to Remove Comfrey Products From the Market, Opinion from 06.╃07. 2001, http://www.cfsan.fda.gov/~dms/dspltr06.html (14.╃May). 19. Bundesinstitut für Risikobewertung, Nulltoleranzen in Lebens- und Futtermitteln – Positionspapier des BfR vom 12.╃März 2007, Berlin, Germany, 2007.╃http://www.bfr.bund.de/cm/208/nulltoleranzen_in_lebens_und_futtermitteln.pdf (14.╃May ).

Identification of Molecular Determinants for Cytotoxicity of Isoliquiritigenin from Liquorice (Glycyrrhiza glabra) towards Leukemia Cell Lines

4.20 Identification of Molecular Determinants for Cytotoxicity of Isoliquiritigenin from Liquorice (Glycyrrhiza glabra) towards Leukemia Cell Lines V. Badireenath Konkimalla1, Anne Kramer, Yujie Fu, Yuangang Zu, Bernhard Radlwimmer, Holger Sültmann, and Thomas Efferth Resistance to a broad range of therapeutic agents is the major reason for treatment failure in leukaemia patients. Phytoconstituents present in our daily diet possess a high preventive and therapeutic value, but lack scientific evidences on their mechanistic activity. About 70â•›% of anti-cancer drugs available in the market are obtained from natural sources or are produced semi-synthetically. Glycyrrhiza glabra (liquorice) is one such example that is well-known as a sweetener in food. In traditional Chinese medicine, G. glabra root has been used to regulate gastro-intestinal motility for ages. Isolation and further characterization of the root extract showed that isoliquiritigenin (ISL) is responsible for the activity [1]. ISL is a flavanoid chalcone that has also shown positive results in different cancer cell models and can serve as a potential anti-cancer drug. In the present work, to understand the spectrum of sensitivity or resistance of ISL across different leukaemia cell lines, 8 different human leukaemia cell lines were selected. An attempt was made to understand the molecular players involved in cell activity of ISL. Firstly, the cytotoxicity of ISL was determined using XTT assay. The 50â•›% inhibition concentrations (IC50) were compared with established chemotherapeutic agents such as methotrexate and doxorubicin. Matrix comparative genomic hybridization (M-CGH) and mRNA expression profiling by cDNA microarrays were performed on all 8 leukaemia cell lines. Both, hierarchical clustering and chi-square tests were performed by correlating data from the IC50 values MCGH and microarray data for ISL in 8 different leukaemia cell lines. Genetic loci and genes identified by this approach were further evaluated as candidate determinants for response of leukaemia cell lines towards ISL.

References 1. Chen et al., Phytother Res 2008

1

Presenting author: Badireenath Konkimalla, German Cancer Research Center, Pharmaceutical Biology (C015), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany, [email protected].

429

430

Posters

4.21 Functional Effects of Polyphenol Metabolites Produced by Colonic Microbiota in Colon Cells In Vitro Claudia Miene1 and Michael Glei

4.21.1 Introduction Polyphenols can act pro- or anti-oxidatively, affect signal transduction pathways and thus may play an important role in colon cancer development. Studies with healthy ileostomy patients have pointed out that after apple juice intake most of the ingested polyphenols reached the end of the small intestine and hence also the colon. After entering the colon, polyphenols are subject to metabolism by the human gut microbiota. The interest in these fermented gut products and their role in colon carcinogenesis is rapidly growing due to simulating the physiological conditions more than working with pure apple polyphenols in vitro. The modulation of biotransformation is one mechanism to modulate toxicity of endogenous compounds and xenobiotics. The aim of this study was to determine the effect of polyphenol metabolites formed in the human gut on parameters important for chemoprevention: Modulation of genes involved in detoxification and inflammation processes, cell number and DNA damage.

4.21.2 Methods Human colon adenoma cells (LT97) were incubated with 3,4-dihydroxyphenylacetic acid (ES) and 3,4-dihydroxyphenylpropionic acid (PS), metabolites of quercetin and chlorogenic acid/ caffeic acid, respectively. The effect on LT97 cell number was analyzed by staining with fluorescence dye 4′,6-diamidino2-phenylindol (DAPI). The influence on expression of selected genes (GSTT2, COX2) was measured by real-time PCR. Furthermore, alkaline Comet assay was performed to assess the impact of pre-incubation with these metabolites on DNA damage caused by the GSTT2 specific, genotoxic substrate cumene hydroperoxide (CumOOH).

4.21.3 Results At applied concentrations (ES: 0–18╃µM€ and PS: 0–90╃µM), none of the metabolites affected LT97 cell number. But both test compounds significantly

1

Department of Nutritional Toxicology, Institute of Nutrition, Friedrich-Schiller-University Jena, Dornburger Str.╃24, D-07743 Jena, Germany.

Functional Effects of Polyphenol Metabolites Produced by Colonic Microbiota in Colon Cells In Vitro

up-regulated GSTT2 mRNA expression after 12â•›h incubation (each 1.7-fold, p<0.05) and degreased COX2 mRNA after 12â•›h, 24â•›h and 48â•›h incubation (up to 0.5-fold, p<0.05; p<0.01; p<0.001). 12â•›h pre-treatment of LT97 cells with the polyphenol metabolites significantly reduced CumOOH-induced DNA damage compared to medium treated controls (about 16â•›% for ES, p<0.01, and 27â•›% for PS, p<0.05).

4.21.4 Conclusion Even at non-physiological concentrations, the metabolites ES and PS are not cytotoxic. An up-regulation of GSTT2 enzymes in the cell model LT97 could be associated with an enhanced cell defence against physiological peroxides generated during lipid acid metabolism and arachidonic peroxidation, which are involved in DNA adduct formation. Elevated levels of COX2 in the colon are associated with colon cancer. Thus a down-regulation of this gene could contribute to the possible chemopreventive potential of polyphenol degradation products. All in all, the evaluated chemopreventive properties of physiological metabolites formed in the human gut are comparable with already published data generated with apple polyphenols.

431

432

Posters

4.22 Lifelong Exposure to Isoflavones Results in a Â�Reduced Responsivity of the Mammary Gland in Female Rats towards Oestradiol Almut Molzberger1, Torsten Hertrampf1, Frank Möller2, Günther Vollmer2, Martina Velders1, and Patrick Diel1 The effects of phyto-oestrogens on oestrogen sensitive tissue are discussed controversially. Some evidence suggests that soy intake must be high during certain windows of development to exert anticancerogenic action. The aim of this study was to investigate the effects of isoflavone (ISO) exposure in different periods of life on mammary gland proliferation in female rats. Animals were exposed to three different diets: An ISO-free diet (IDD), an ISO-rich diet (IRD) and an IDD supplemented with genistein (GRD). The animals were mated and the dams received the appropriate diet during pregnancy and nursing. After weaning the female offspring had free access to the same diets. Two groups of intact animals were sacrificed after 50 (intact 50) or 80 days (intact 80). Three groups of rats were ovariectomized and subcutaneously (s.c) treated either with vehicle (OVX), oestradiol (E2) or genistein (GEN) for three days at the age of 80 days. After sacrifice, uterine wet weight was determined and proliferation of the mammary gland was analyzed using the proliferating cell nuclear antigen (PCNA). In the intact 50 group, PCNA expression showed an increase in both IRD and GRD compared to the IDD. This could be due to an earlier onset of puberty in rats fed the ISO and especially the GEN-enriched diet. At day 80, no differences in the expression of PCNA could be detected in intact, OVX and in rats treated with GEN. Treatment of 80 day old OVX rats with E2 resulted in an increase in PCNA expression in the IDD-fed group, but surprisingly not in IRD and GRD-fed diet. In summary, our data provide evidence that the lifelong exposure to ISO modulates the sensitivity of the mammary gland to treatment with E2. Obviously the presence of ISO results in an earlier onset of puberty. Whereas in the intact 80 group the diets do not seem to affect mammary gland proliferation patterns, the responsitivity of the mammary gland of OVX animals towards E2 is significantly reduced in animals exposed to ISO. This observation is conform with epidemiological evidence for a reduced breast cancer risk in countries where the population has a lifelong exposure to phyto-oestrogens.

1

Department of Molecular and Cellular Sports Medicine, German Sports University, D-Cologne, Germany.

2

Institute of Zoology, Department of Molecular Cell Physiology and Endocrinology, TU Dresden, D-Dresden, Germany.

Derivation of Maximum Amounts for the Addition of Functional Ingredients to Foods

4.23 Derivation of Maximum Amounts for the Addition of Functional Ingredients to Foods Sina Tischer1, Oliver Lindter, Almut Bauch2, and Birgit Niemann3 Functional ingredients are a diverse group of biologically active constituents that are intended to have a positive effect on the health of the consumer. The adding of functional ingredients to common foods will be declared with claims describing the asserted health effect. This kind of health claim driven extension of functional ingredients may raise the potential for an unacceptable high intake of bioactive ingredients among consumers inducing the need to limit their use in foods. In all known models for adding safe amounts of vitamins and minerals to foods the difference amount between a tolerable upper intake level (UL) and one of the highest intake percentiles of the substance in a defined population will be allocated to a variety of foods. This principle has been adopted to functional ingredients based on a novel food request applying the adding of lycopene to approximately 40 different foods. The adopted model is based on following consideration: The individual and the habitual intake of a food ingredient will remain a steady state at a defined number of ingested fortified foods containing the same amount of the ingredient in one average actual daily consumption amount. Therefore the model includes the parameter (1) the “actual daily consumption (ADC)” amount of the carrier foods as reference amount for fortification and (2) the “number of fortified foods that contribute to the intake progression“ as a factor and (3) the “highest observed habitual intake (HOHI)” of lycopene in Europe chosen as the intake limitation. The consideration has been re-assessed using the method of intake simulation in the model population of the German National Food Consumption Study 1985–89.╃The food choice for the intake simulation followed the intention of the novel food request. The ADC of the foods has been calculated as the average consumption amount related to the user and their days of consumption. The same amount of lycopene has been added to one ADC of all chosen foods to simulate the intake distribution depending on the number of ingested fortified foods. We determined a log function by regression analysis and the intake progression stopped around ten ingested fortified foods. Using a HOHI of 5 mg lycopene, calculated from the consumption of tomatoes and tomato products in 14 European local populations we derived a maximum amount of 0.5 mg lycopene.

1

German Institute of Human Nutrition (DIfE), Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany.

2

Robert Koch Institute, General-Pape-Str.╃62, D-12101 Berlin, Germany.

3

Institute for Risk Assessment, Thielallee 88–92, D-14192 Berlin, [email protected].

433

434

Posters

4.24 Constituents of Ginger Induce Micronuclei in Two Mammalian Cell Systems In Vitro Erika Pfeiffer1, Julia S. Dempe1, Marina J. Gary1, Suzanna M. Zick2, and Manfred Metzler1, 3 Rhizomes of the ginger plant Zingiber officinale and extracts of these rhizomes are used in various Asian countries as spices or for medical purposes. The pungent principle of ginger comprises a series of homologous gingerols and shogaols. In the present study, an extract prepared from ginger rhizomes and intended for clinical investigation has been analyzed for their content of [6]-, [8]- and [10]-gingerol as well as [6]-, [8]- and [10]-shogaol by HPLC and GCMS analysis. [6]-gingerol and [6]-shogaol were found to represent the major components. In order to study their genotoxic potential, pure [6]-gingerol and [6]-shogaol were assayed for the induction of micronuclei in cultured Chinese hamster V79 fibroblasts and human hepatoma HepG2 cells. Both compounds induced micronuclei in both cell lines in a concentration-dependent manner. [6]-shogaol was more genotoxic than [6]-gingerol in both cell systems, and V79 cells exhibited a higher susceptibility than HepG2 cells to both compounds. In contrast, both cell types were equally susceptible to the micronuclei-inducing effects of 4-nitroquinoline-N-oxide, which was used as a positive control. When the incubation media of the micronucleus assays were extracted with ethyl acetate and the extracts analyzed by HPLC, only parent [6]-gingerol and [6]-shogaol were found with V79 cells. In contrast, extracts of HepG2 cells contained significant amounts of two metabolites each of [6]-gingerol and [6]-shogaol. GC-MS analysis suggested that the metabolites of [6]-gingerol were two stereoisomers of gingerdiol, whereas the two metabolites of [6]-shogaol contained a reduced keto group and a reduced olefinic double bond, respectively. The lower susceptibility of HepG2 cells may be due to their ability to partly metabolize [6]-gingerol and [6]-shogaol to inactive products. A pronounced induction of micronuclei was also observed for the ginger extract in both V79 and HepG2 cells. The number of induced micronuclei exceeded that expected from the amount of [6]-gingerol and [6]-shogaol present in the extract, suggesting that the other gingerols and shogaols present as minor components contribute to the genotoxic activity of the extract. In summary, our study has demonstrated that gingerols and shogaols, which are common constituents of ginger products, exhibit genotoxic potential in vitro.

1

Karlsruhe Institute of Technology KIT, Institute of Applied Biosciences, Chair of Food �Chemistry, Adenauerring 20╛a, D-76131 Karlsruhe, Germany.

2

University of Michigan Medical Center, Michigan Integrative Medicine, Department of �Family Medicine, 715╃E. Huron St., MI-48104 Ann Arbor, USA.

3

[email protected].

Relative Photomutagenic Potency of Furocoumarins and Limettin

4.25 Relative Photomutagenic Potency of Furocoumarins and Limettin Christiane Lohr, Dieter Schrenk, and Nicole Raquet1 Furocoumarins occur in plants used as food (e.╃g. grapefruit, lime, parsley, parsnip), cosmetics (e.╃g. citrus oil in perfumes and lotions) or in phytomedicines (Ammi majus, Angelica archangelica). In combination with UV light, in vitro studies have shown phototoxic and photomutagenic effects. These are caused, e.╃g., by UV-induced covalent binding of furocoumarins to pyrimidine bases of the DNA. In contrast, a photomutagenic potency of limettin, a coumarin occurring in citrus products, has not been established so far. We performed the hPRT mutagenicity assay in order to detect possible DNA damage resulting in hereditary mutations of the hPRT (hypoxanthine-P-ribosyl-transferase) locus. Therefore, V79 cells were incubated with furocoumarins (5-MOP, 8-MOP, angelicin, isopimpinellin, bergamottin, 6´,7´-dihydroxybergamottin, imperatorin) or limettin (5,7-dimethoxycoumarin), and irradiated with various doses of UVA-light (0–200 mJ/cm2). In the absence of light, no significant genotoxic effects were detectable with any of the compounds. At various levels of 5-MOP and angelicin, an increase in mutagenicity with increasing UVA dose was observed. All furocoumarins tested and limettin led to a linear increase in mutagenicity with increasing concentrations at a given UVA-dose of 125 mJ/cm2. The slopes of these linear relationships allowed the calculation of in vitro photomutagenicity equivalency factors (PMEF) setting the PMEF of 5-MOP at 1.╃0.╃The in vitro PMEFs were so far calculated as 0.3 for 8-MOP, 0.02 for angelicin, and 0.02 for limettin, clearly demonstrating the photomutagenic potency of this compound.

1

Department of Food Chemistry and Toxicology, University of Kaiserslautern, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany, [email protected], schrenk@rhrk. uni-kl.de.

435

436

Posters

4.26 Degradation of Green Tea Catechins Markus Schantz1, Thomas Erk1, and Elke Richling1 Green tea (Camellia sinensis, Theaceae) and its major polyphenolic constituents, the catechins, have been reported to show chemopreventive activities [Review]. There is an increasing interest in their bioavailability and bioactivity in vivo. Data on the degradation and absorption of catechins in the gastro-intestinal tract are of importance. There is a great knowledge about metabolites occurring in urine and plasma and colonic degradation products after green tea consumption. However, little information is available on the degradation of green tea catechins in the human small intestine. We used an ex vivo model to simulate the gastro-intestinal tract to provide data on the microbial metabolism and chemical stability of catechins in the small intestine. (+)-catechin (C), (-)-epicatechin (EC), (-)-epicatechin 3-O-gallate (ECG), (-)-epigalloÂ�catechin (EGC), (-)-epigallocatchin-3-O-gallate (EGCG), and gallic acid (GA) were incubated under anaerobic conditions for 24â•›h using ileal content. Catechins and their metabolites were determined by HPLC-DAD and HPLC-DAD-MS/MS.╃3‘,4‘,5‘-trihydroxyphenyl- and 3‘,4‘,-dihydroxyphenyl-γvalerolactone were identified as metabolites of EC and C whereas EGC was transformed into 3‘,4‘,5‘-trihydroxyphenyl-γ-valerolactone. As degradation product of GA pyrogallol could be identified after 24â•›h of incubation. Gallic acid esters, EGCG and ECG, were hydrolized into gallic acid and their corresponding catechins, namely EGC and EC. The data achieved with our well established ex vivo model demonstrate that green tea catechins will undergo degradation into potentially bioactive forms in the small intestine of humans prior absorption. This aspect has to be considered when compounds are used for in vitro studies.

1

Department of Food Chemistry and Toxicology, Molecular Nutrition, University of Kaiserslautern, Erwin-Schroedinger-Str.╃52, D-67663, Germany.

Evaluation of the Cytotoxic Effects of Herbal �Homeopathic Extracts in Primary Human �Hepatocytes In Vitro

4.27 Evaluation of the Cytotoxic Effects of Herbal Â�Homeopathic Extracts in Primary Human Â�Hepatocytes In Vitro Ulrike Sobeck1, B. Rüdinger, F. Stintzing, and P. Vögele

4.27.1 Introduction In Europe, Petasites hybridus L. (butterbur) and Chelidonium majus L. (greater celandine) are being used for centuries in traditional and folk medicine. Even today, preparations from these plants represent valuable products in homeopathic and anthroposophic medicine, e.╃g. for the treatment of liver dysfunction (Chelidonium) and asthma or migraine (Petasites). Although both plants have been associated with a potential to cause hepatotoxic effects in vitro and in vivo, experimental data are insufficient to draw final conclusions and to determine toxicological threshold levels, especially for Petasites. In order to elucidate the hepatotoxic potential of mother tinctures from C. majus (HAB, Vs.╃34╛b) and P. hybridus (HAB Vs.╃33╛c), their toxicity was studied in primary human hepatocytes under GLP conditions. Due to the fact that primary human hepatocytes include almost the complete set of metabolic enzymes of the human organism preserving also the inducibility of the enzymes, they are recommended by various test guidelines [1]. In this sense they are suitable to evaluate hepatotoxic and cytotoxic effects in vitro and to investigate fundamental aspects of drug metabolism-linked toxicity.

4.27.2 Materials and Methods Chelidonium majus e radice ferm (HAB, Vs.╃34â•›b) and Petasites hybridus e radice ferm (HAB Vs.╃33â•›c) mother tinctures were prepared according to the official German Homeopathic Pharmacopoeia (HAB). The alkaloid concentration of the mother tinctures was determined by UV-VIS spectroscopy [2]. Chelidonium mother tincture contained 0.14â•›% (m/m) (1.4 mg/ml) alkaloids calculated as chelidonine equivalents and Petasites tincture contained 9.24 ppm (9.24·10–3 mg/ml) pyrrolizidine alkaloid equivalents. A previous screening experiment showed that P. hybridus mother tincture did not produce any detectable cytotoxicity. For C. majus only the highest concentration showed a cytotoxic effect. In order to determine EC50-values, 10 ml of the mother tinctures were concentrated in a vacuum centrifuge and resuspended in the cell culture medium HIMDex,PS to a final volume of 5╃ml. The final concentration was calculated on the basis of the extract mass (mother tincture) before

1

WALA Heilmittel GmbH, Dorfstr. 1, D-73087 Bad Boll/Eckwälden, Germany.

437

438

Posters

the concentration step. For Chelidonium, the test sample concentrations ranged from 2.8·10–3–2.8 mg/ml chelidonine equivalents corresponding to 2.019–2019 mg/ml mother tincture. Petasites was tested at concentration levels ranging from 1.84·10–4–1.84·10–2 mg/ml calculated as pyrrolizidine alkaloid equivalents corresponding to 19.92–1992 mg/ml mother tincture. Ascorbic acid known to be non-toxic was chosen as negative control and at concentration levels ranging from 0.2╃µg/ml to 20 mg/ml. Sodium fluoride (NaF) was used as positive control and dimethyl sulfoxide (DMSO) as solvent control. Freshly isolated primary human hepatocytes cultivated in 96-well-plates on collagen were used to model metabolism-dependent and independent hepatotoxicity in vitro. For detection and measurement of the cytotoxic potential, the MTT (mitochondrial activity/ early cytotoxicity) assay was applied. It takes advantage of the ability of viable cells to convert a tetrazolium salt (MTT) into formazan by intact mitochondria. The amount of formazan production can be measured photometrically and is proportional to the number of living cells. The test substances, positive and negative controls were incubated on the same plate with the hepatocytes for 24╃±â•ƒ4â•›h at 37╃°C in a 5â•›% CO2-atmosphere. The cells were washed one time with PBS. Afterwards MTT-solution (1 mg/ml in HIMDex,PS) was added to each well and incubated for 1.5 hours. Thereafter, the MTT medium was removed and the remaining dye was extracted for approx. 20 min with lysis buffer. At the end of the incubation period, morphology of the vital cells was examined and possible contaminations were excluded by optical check. The supernatant was checked for signs of precipitation before measuring the optical density at 570 nm (OD570). The OD570 values were included in the calculation of dose–effect curves to determine EC50-values. Each sample was tested in 8-fold replicates at each concentration level; the negative control and blank in 4-fold replicates.

4.27.3 Data Analysis and Acceptance Criteria The mean of n replicates was calculated using the Excel-function “mean”. The blank was subtracted from the means of the samples before further calculation. The SEM (standard error of mean), % compared to negative control, and SE (standard error) were calculated using the respective Excel-functions. A cytotoxic potential of a test item in the tested range is assumed if (a) a decrease of vital cells over the spread is observed, i.╃e. at least 20â•›% decrease of OD570 compared to negative control and (b) a dose–effect relationship is observed, i.╃e. if one concentration shows a 20â•›% decrease of OD570, all higher concentration levels should show a stronger decrease of OD570. The test is considered valid if the positive control produces a decrease of vital cells relative to the negative control (≤╃80â•›%) and the mean OD570 of the negative control minus blank is at least 0.╃1.

Evaluation of the Cytotoxic Effects of Herbal �Homeopathic Extracts in Primary Human �Hepatocytes In Vitro

4.27.4 Results After 24â•›h incubation with the positive control, sodium fluoride (NaF), the expected decrease of cell viability, as compared to the negative control, was observed. The test items did not produce any signs of vital cell morphology changes. Contaminants and precipitations were not observed. A cytotoxic effect of ascorbic acid was observed at 2 mg/ml and higher. The EC50-value was calculated to be 2.10 mg/ml. Chelidonium majus e radice ferm 34â•›b caused cytotoxic effects at concentrations of 0.283 mg/ml chelidonine corresponding to at least 201.9 mg/ml mother tincture. The EC50-value was calculated as 0.0336 mg/ml chelidonine equivalent to 23.69 mg/ml mother tincture. Petasites hybridus e radice ferm 33â•›c showed cytotoxic effects only at the highest concentration of 1.84·10–2 mg/ml pyrrolizidine alkaloids or 1992 mg/ml mother tincture. The EC50-value was calculated to be 0.0134 mg/ml alkaloids equivalent to 1468 mg/ml mother tincture (Fig.╃1).

4.27.5 Discussion Overall, the effects observed in human primary hepatocytes are concentration-dependent. The EC50-value of ascorbic acid, in the present experiment, was in a similar range as published earlier (0.8–3.5€mg/ml) [3]. In comparison with the non-toxic ascorbic acid, the aqueous homeopathic extracts of C. majus (EC50╃=╃23.69 mg/ml mother tincture) and P. hybridus (EC50╃=╃1468 mg/ ml mother tincture) even exhibited a lower hepato- and cytotoxic potential. Comparing the EC50-values of these extracts with literature data of ethanolic phytotherapeutic extracts ranging between 0.41 and 0.96 mg/ml [3] an even lower hepato- and cytotoxic potential was demonstrated in the present study. Moreover, the mother tinctures serve as stocks and are used for the preparation of diluted homeopathic potencies used in anthroposophic medicinal products. Therefore, the alkaloid levels which can be expected in vivo in the liver after application of the anthroposophic medicinal products are several orders of magnitude lower than the EC50-values determined in this experiment [4].

4.27.6 Conclusion Literature data raised concerns about a hepatotoxic potential of C. majus and P. hybridus. In contrast, the experimental data presented do not show any evidence for special hepato- and cytotoxicity of these homeopathic mother tinctures. These in vitro data are in accordance with clinical and pharmacovigilance information for medicinal products used in anthroposophic medicine containing these mother tinctures. Therefore, such products can be considered safe for adults and sensitive patient groups such as children, elderly and pregnant women.

439

440

Posters

Figure 1: Cytotoxic effects of (A) ascorbic acid in comparison with (B) C. majus and (C) P. hybridus mother tinctures on primary human hepatocytes.

Evaluation of the Cytotoxic Effects of Herbal �Homeopathic Extracts in Primary Human �Hepatocytes In Vitro

Acknowledgments We thank Dr. M. Schulz and H. Muschick of the BioProof AG, Munich for their scientific support and for performing the MTT assay.

References 1. 2. 3. 4.

EMEA/CHMP/SWP/150115/2006. Mosmann, T. J, Immunol. Methods (1983), 65, 55–63. Adler, M. et al., Poster, Planta Med. (2006), 72, 961–1089. Kosina, P. et al., Food Chem. Toxicol. (2004), 42, 85–91.

441

442

Posters

4.28 Modulation of Antioxidant Gene Expression by Â�Apple Juice in Rats Bülent Soyalan1, J. Minn1, Hans-Joachim Schmitz1, Dieter Schrenk1, Frank Will2, Helmut Dietrich2, Matthias Baum1, Gerhard Eisenbrand1, and Christine Janzowski1

Abstract The risk of cancer and other degenerative diseases is inversely correlated with consumption of fruits and vegetables. This beneficial effect is mainly attributed to secondary plant constituents such as polyphenols and redoxsensitive genes are supposed to play a major role in protection against ROS- (reactive oxygen species) associated toxicity. We studied the potential of apple juice (clear/ cloudy/ pulp-enriched) to modulate hepatic and colonic expression of ARE-dependent genes in male SD rats. Gene expression, studied with classic RT-PCR, varied with tissue type and composition of the apple juices: In colon, most target genes were slightly but significantly induced (e.╃g. GPX2, GSR, CAT, Nrf2; p<0.001), whereas in the liver only transcription of GPX1 and NQO1 mRNA was up-regulated, predominantly after consumption of cloudy AJ. Taken together, the results support the potential of polyphenol rich apple juice to increase antioxidant defence.

4.28.1 Introduction Apple juice (AJ) is the most widely consumed fruit juice in Germany, with an average annual total consumption of 11–13â•›l [1]. Epidemiological studies have linked the consumption of apples with reduced risk of cancer, cardiovascular disease, diabetes and other degenerative diseases [2]. Apple polyphenols which mainly occur in peel, core and pulp [3] are known as potent antioxidants in vitro [4]. They can protect against reactive oxygen species (ROS) by direct scavenging of free radicals and/ or modulation of redoxsensitive gene expression [5]. Induction of antioxidant enzymes, such as superoxide dismutase, glutathione peroxidase and NADP(H) quinone oxidoreductase is mediated by the transcription factor (erythroid-derived 2)-like 2 (Nrf2), a key factor regulating the expression of genes encoding antioxidant and detoxifying enzymes [6]. Results obtained from Nrf2 and Keap1 deficient mice support the emerging role of Nrf2 in protecting the liver and the gastrointestinal tract against ROS-related dis-

1

University of Kaiserslautern, Department of Chemistry, Division of Food Chemistry and �Toxicology, Erwin-Schroedinger Str.╃52, D-67663 Kaiserslautern, Germany.

2

Geisenheim Research Centre, Section of Wine Analysis and Beverage Technology, von-Lade-Str.╃1, D-65366 Geisenheim, Germany.

Modulation of Antioxidant Gene Expression by �Apple Juice in Rats

eases by regulating multifaceted cellular antioxidant defence [7]. In the present study, the potential of AJ to modulate the expression of selected Nrf2-dependent genes was investigated in rat liver and colon by reverse transcription (RT)PCR: Superoxide dismutase (SOD1, SOD2), glutathione peroxidase (GPX1, GPX2), γ-glutamylcysteine ligase (GCLC, GCLM), glutathione reductase (GSR), catalase (CAT), and NADP(H) quinone oxidoreductase (NQO1). Transcription of Nrf2 was included, since Nrf2 is postulated to auto-regulate its own expression through an ARE-like promoter element, resulting in elevated nuclear accumulation of Nrf2 protein [8].

4.28.2 Methods 4.28.2.1 Juices

Clear and cloudy AJ were produced from cider apple varieties (Bohnapfel, 10╛%; Winterrambour, 10╛%; Maunzen, 45╛%) and table-apple varieties (35╛%) at the Geisenheim Research Centre. Pulp-enriched apple juice (PEJ) was manufactured from a cloudy juice (Boskoop variety), blended with 40╛% apple puree [9]. Polyphenol-free control juice was obtained from clear AJ by SP70 adsorber resin treatment. After membrane filtration (0.45╃µm), clear AJ, cloudy AJ and control juice were directly analyzed for basic parameters (e.╃g. ascorbic acid), total phenolics (folin reaction; expressed as (+)-catechin equivalents), trolox equivalent anti-oxidant capacity (TEAC) and polyphenolic constituents (HPLC/ photodiode array detector) as described [10]. PEJ was centrifuged and the supernatant was used for determination of the basic juice parameters. HPLC of polyphenolic constituents was performed with methanolic extracts of freeze dried samples.

4.28.2.2 Animal Treatment and Tissue Sampling

Male SD rats (125–150â•›g bw, standard diet: Altromin 1314) received AJ (clear/ cloudy/ pulp-enriched) or polyphenol free control juice for ten consecutive days. After a subsequent 4-day period in which the juice was replaced by water (eight rats per group; juice uptake ad libitum, 4 treatment cycles) animals were sacrificed and caudate liver lobe and colon were excised. Liver specimens (20– 25 mg) were immediately transferred in 5 ml RNAlater and distal colon sections were flushed with PBS prior to snap-freezing in liquid nitrogen and storage at -80╃°C until use. The experimental protocol was performed in accordance with the guidelines of the ethics committee responsible for the administrative district of Kaiserslautern.

443

444

Posters

4.28.2.3 RNA Extraction and RT-PCR

Liver tissue specimens were homogenized (Precellys 24 homogenizer) and total RNA was isolated (peqGOLD Total RNA Kit) and checked for purity/ integrity (Nanodrop 1000 and Agilent 2100 Bioanalyzer). Total RNA from colon tissue was isolated with RNA Isolation Reagent (TRIR, ABgene®), according to the manufacturers instructions and checked for integrity by denaturating formaldehyde gel electrophoresis. All RNA samples were snap-frozen and stored at -80╃°C until use. RT and PCR were performed with 2╃µg of total RNA in a thermocycler (PTC100, MJ-Research) using TaqMan reverse transcription kit (Applied Biosystems). Primers (rat mRNA sequences: NCBI UNIGENE database) were designed with Primer Express v.╃2.0 Software (Applied Biosystems) and glyceraldehyde3-phosphate dehydrogenase (GAPDH) was used as endogenous reference gene [11]. Duplex RT-PCR (target/ reference gene) was performed using AmpliTaq Gold™ DNA polymerase (Applied Biosystems). All amplicons of each gene were simultaneously electrophorezed (1.3â•›% agarose gel, TAE buffer, 85 Volt DC, 90 min). Ethidium bromide stained DNA bands were visualized under UVtransillumination and quantified (Eagle Eye II, Stratagene). Net band intensities of target gene and GAPDH were used to calculate relative integrated optical density (IOD: target gene/GAPDH ratio). For all genes primer efficiency >90â•›% was assured. Results are expressed as means ±â•–SD from eight rats. Data were analyzed for normal distribution (Anderson-Darling test) and for differences between AJ and control juice groups (unpaired, one-sided Mann-Whitney Utest: * p<0.05; ** p<0.01; *** p<0.001; # p=╃0.052).

4.28.3 Results and Discussion 4.28.3.1 Juice Compounds and Anti-Oxidant Capacity

In the control juice polyphenols were detectable neither by HPLC-PDA nor by folin reaction. The cloudy AJ exhibited higher concentrations of total phenols (1343 mg/l), compared to the clear AJ (1027 mg/l). In both AJs, the high concentration of total phenols reflects the prevalent proportion of cider apples used. The total phenolics of PEJ amounted to 1339 mg/l. The value was determined from the supernatant after centrifugation and it is surely underestimated, because particle-bound polyphenols were not covered. The pattern of individual polyphenols in clear and cloudy AJ were almost similar, with chlorogenic acid as predominant compound, followed by procyanidins, coumaroyl quinic acids and epicatechin (Fig.╃1). The PEJ, however, exhibited distinctly higher concentrations of chlorogenic acid, phloretin-2´-xyloglucoside, phloridzin and quercetin glucosides, whereas procyanidins (B2,C1) and coumaroyl quinic acids occurred to a lesser extent. All AJs exhibited rather similar anti-oxidant capacities (mmol/l Trolox: 8.5, clear AJ; 9.8, cloudy AJ; 10.8, PEJ), which distinctly exceeded those of conven-

Modulation of Antioxidant Gene Expression by �Apple Juice in Rats

tional apple juices [10]. Due to the absence of polyphenols in the control juice there was no TEAC-activity.

4.28.3.2 Modulation of ARE-Dependent Gene Expression

Rats consuming polyphenol-free control juice showed rather similar basal expressions of most genes in colon and liver, with relative GAPDH ratios ranging from 2/ 2.5 (SOD1) to 0.7 (Nrf2) and 0.3 (NQO1), respectively (Fig.╃2). Tissue specific differences were observed particularly with GPX2 (distinctly expressed in colon, but not in liver [12]) and to a minor extent also with other genes e.╃g. GCLC and GSR (both: colon>liver). At intervention with polyphenol rich AJs, tissue and juice specific modulation of gene expression was observed: Differences between juice and control groups were small, but still significant due to the high expression homogeneity of the animals within each group. In colon, most target genes were up-regulated by apple juice intervention: Highly significant induction (p<0.001) was observed for CAT (all AJs), GPX2 (clear AJ, cloudy AJ), GSR (clear AJ, cloudy AJ) and the transcription factor Nrf2 (cloudy AJ, PEJ). SOD1, GCLC/M and NQO1 were less elevated and some genes were unchanged or down-regulated. In liver, induction of anti-oxidant genes was less prominent than in colon, only GPX1 and NQO1 were found significantly elevated by cloudy AJ. The other

Figure 1: Major phenolic constituents (mg/l) of clear AJ, cloudy AJ and PEJ* (analyzed by HPLCPDA). *Data taken from [9].

445

446

Posters

Figure 2: Redoxsensitive gene expression in rat colon and liver after intervention with clear, cloudy and pulp-enriched AJ, compared to polyphenol free control juice: Data are expressed as relative integrated optical density (IOD), normalized to active reference gene GAPDH (IOD=╃1). Means and SD from eight specimens; significant difference to control juice group: * p<0.05, ** p<0.01, *** p<0.001 (one-sided Mann-Whitney U-test, unpaired); # p=╃0.052.

anti-oxidant genes were down-regulated (SOD1, SOD2, GCLC/M, GSR) or not affected by apple juice intervention, concomitant with the absence of Nrf2induction. Differences in cellular availability/ metabolism of apple polyphenols in colon and liver are supposed to contribute to the observed tissue specific gene expression. Comparing the juices, cloudy AJ was a more potent inducer of redoxsensitive genes than clear AJ, even though the amounts of known phenolic constituents were almost identical. This suggests that other not yet identified constituents in the cloudy AJ contribute to its antioxidant potential. A higher preventive effectiveness of cloudy vs. clear AJ was also reported by Barth et al., who studied aberrant crypt foci reduction in colon of rats after apple juice intervention [13].

Modulation of Antioxidant Gene Expression by �Apple Juice in Rats

With regard to PEJ, it is not clear yet, whether differences in the polyphenol composition or non-polyphenolic compounds are responsible for the distinct tissue specific modulation.

4.28.4 Conclusion The results support the potential of polyphenol rich apple juice to increase antioxidant defence in rats. The modulation of gene expression varied with target tissue and polyphenol composition of the juices.

Acknowledgments Support: German Federal Ministry of Education and Research (BMBF), Grant No.╃01EA0501 and the State of Rhineland-Palatinate, “Schwerpunktförderung A4 – Darmgesundheit und Ernährung“.

References â•⁄ 1. Entwicklung des Pro-Kopf-Verbrauchs von alkoholfreien Getränken nach Getränkearten 2003–2007, in: Der AFG-Markt 2007, Wirtschaftsvereinigung Alkoholfreie Getränke e.╃V. â•⁄ 2. Lata, B.; Tomala, K. (2007) Apple peel as a contributor to whole fruit quantity of Â�potentially healthful bioactive compounds. Cultivar and year implication. J Agric Food Chem. 55, 10795–802. â•⁄ 3. Thielen, C.; Will, F., Zacharlas, J.; Dietrich, H.; Jacob, H. (2004) Polyphenols in apples. Distribution of polyphenols in apple tissue and comparison of fruit and juice. Dtsch Lebensmitt Rundsch. 100, 389–98. â•⁄ 4. Schaefer, S.; Baum, M.; Eisenbrand, G.; Dietrich, H.; Will, F.; Janzowski, C. (2006) Polyphenolic apple juice extracts and their major constituents reduce oxidative damage in human colon cell lines. Mol Nutr Food Res.╃50, 24–33. â•⁄ 5. Rahman, I.; Biswas, S., K.; Kirkham, P., A. (2006) Regulation of inflammation and redox signaling by dietary polyphenols. Biochem Pharmacol. 72, 1439–52. â•⁄ 6. Kobayashi, M.; Yamamoto, M. (2005) Molecular mechanisms activating the Nrf2-Keap1 pathway of antioxidant gene regulation. Antioxid Redox Signal. 7, 385–94. â•⁄ 7. Aleksunes, L., M.; Manautou, J., E. (2007) Emerging role of Nrf2 in protecting against Â�hepatic and gastrointestinal disease. Toxicologic Pathology. 35, 459–73. â•⁄ 8. Kwak, M., K.; Itoh, K.; Yamamoto, M.; Kensler, T., W. (2002) Enhanced expression of the transcription factor Nrf2 by cancer chemopreventive agents:

447

448

Posters

role of antioxidant response element-like sequences in the nrf2 promoter. Mol Cell Biol. 22, 2883–92. â•⁄ 9. Will, F.; Roth, M.; Olk, M.; Ludwig, M.; Dietrich, H. (2008) Processing and analytical characterisation of pulp-enriched cloudy apple juices. LWT – Food Science and Technology. 41, 2057–63. 10. Netzel, M.; Rossberg, A.; Straß, G.; Thielen, C.; Dietrich, H.; Bitsch, R.; Bitsch, I. (2005) Charakterisierung der antioxidativen Kapazität von naturtrüben Apfelsäften mit vier unterschiedlichen Testsystemen. Lebensmittelchemie. 59, 33. 11. Chen, J.; Rider, D., A.; Ruan, R. (2006) Identification of valid housekeeping genes and antioxidant enzyme gene expression change in the aging rat liver. J Gerontol A Biol Sci Med Sci.╃61, 20–7. 12. Chu, F., F.; Doroshow, J., H.; Esworthy, R., S. (1993) Expression, characterization, and tissue distribution of a new cellular selenium-dependent glutathione peroxidase, GSHPx-GI. J Biol Chem. 268, 2571–6. 13. Barth, S., W.; Faehndrich, C.; Bub, A.; Watzl, B.; Will, F.; Dietrich, H.; Rechkemmer, G.; Briviba, K. (2007) Cloudy apple juice is more effective than apple polyphenols and an apple juice derived cloud fraction in a rat model of colon carcinogenesis. J Agric Food Chem. 55, 1181–7.

Predictivity Comparison between Screening Assays for Bacterial Mutagenicity for Natural Compounds: Micro-Ames vs. Ames Fluctuation Method

4.29 Predictivity Comparison between Screening Assays for Bacterial Mutagenicity for Natural Compounds: Micro-Ames vs. Ames Fluctuation Method Gerlinde Pappa, Tina Wöhrle, Anette Thiel, and Michael Török1 Aim of this study was to compare two bacterial mutagenicity screening assays for their predictivity with respect to natural compounds for the OECD 471 guideline assay of bacterial reverse mutation. The micro-Ames uses 5 strains of S. typhimurium (TA98, TA100, TA102, TA1535 and TA1537) which are preincubated with the test compound in 96-well plates before being plated on agar dishes. Plating and subsequent colony counting is performed like described in the OECD 471 assay. On the other hand the Ames fluctuation method uses 2€strains (TA98 and TA100) which are pre-incubated with the test compound before reversion indicator medium containing the dye bromocresol purple is added. Subsequently, the solutions are distributed to 384-well plates and the mutagenic activity of the test item is detected by counting the number of wells shifted from purple to yellow. Both assays are run with and without metabolic activation. The big advantage of these screening assays is the low amount of compound needed, which is an eminent factor in the early stage of product development. Several natural compounds, as well as known mutagens were run in both above mentioned assays and the outcome was compared to the OECD 471 compliant assay. Overall, the micro-Ames showed a better predictivity compared to the Ames fluctuation method. One reason could be the fact that the Ames fluctuation method uses only two strains, which do not include a strain specific for detecting cross-links like the TA102. Moreover, evaluation and quantification of mutants is performed indirectly by colorimetric measurements, whereas in the micro-Ames actual colonies are counted on agar plates. However, testing further compounds of different structural classes is needed to consolidate the results. Taken together, the Ames fluctuation method is a suitable tool for highthroughput screening of mutagenicity. However, to predict the outcome of the full OECD 471 assay with low amounts of compound in early stage of product development, the micro-Ames is the preferred alternative.

1

DSM Nutritional Products Ltd, R&D Human Nutrition and Health, Safety, P.╃O. Box 2676, CH-4202 Basel, Switzerland, [email protected].

449

450

Posters

4.30 Automated In Vitro Micronucleus Testing of Natural Compounds in Correlation with Hydrogen Peroxide Gerlinde Pappa, Tina Wöhrle, Anette Thiel, and Michael Török1 The in vitro micronucleus test (MNT) is a well-accepted technique for assessing the clastogenic/ aneugenic potential of compounds. In a high content screening (HCS) approach we measure micronucleus induction in CHO cells. The fully automated evaluation of micronuclei is based on fluorescent image analysis. Similar to other mammalian genotoxicity tests, the in vitro MNT is characterized by a low specificity, meaning that this assay is prone to false-positive results. Some natural, anti-oxidative compounds like polyphenols have been shown to become pro-oxidative under certain culture conditions leading to the formation of peroxides, which may result in a positive in vitro MNT. This effect is an in vitro artefact and does not occur in vivo. The aim of the present study was to evaluate a set of natural compounds with regard to their genotoxicity and to correlate this potential with the formation of hydrogen peroxide in cell culture medium. A sensitive colorimetric assay was used for the detection of hydrogen peroxide with epigallocatechin gallate (EGCG) as a positive control. The micronucleus frequency induced by different concentrations of EGCG correlated well with the amount of hydrogen peroxide found in the medium. Only at cytotoxic concentrations micronucleus formation decreased while H2O2 production was increasing dose-dependently. Interestingly, other natural compounds that were also positive in the MNT were negative for peroxide formation. Further, a third group was identified of being negative in the MNT, although chemical structure and anti-oxidative potential would suggest positive results due to pro-oxidative action. Thus, distinct structural properties seem to be responsible for the potential of natural anti-oxidative compounds to yield a positive in vitro MNT, which are likely to be adjacent hydroxyl groups. Apart from the formation of hydrogen peroxide in the cell culture medium, other mechanisms play a role, e.╃g. topoisomerase II inhibition or energy depletion within the cell. Along with the growing interest in the properties of bioactive phytochemicals, it is essential to further investigate mechanisms of direct or indirect interaction with the DNA in order to interpret positive in vitro genotoxicity tests.

1

DSM Nutritional Products Ltd, R&D Human Nutrition and Health, Safety, P.╃O. Box 2676, CH-4002 Basel, Switzerland, [email protected].

Permeability of Apple Polyphenols in T84 Cell Model and their Influence on Tight Junctions

4.31 Permeability of Apple Polyphenols in T84 Cell Model and their Influence on Tight Junctions Hannah Bergmann1, Dorothee Rogoll2, Wolfgang Scheppach2,3, Ralph Melcher2,4, Sven Triebel1,5, and Elke Richling1,6 Apple polyphenols show positive health effects that strongly depend on their bioavailability. In order to assess the absorption and metabolism of these substances across the intestinal epithelium, monolayers of colon carcinoma cells (T84 cell line) mounted in Ussing-type chambers were incubated in the presence of physiological concentrations of various hydroxycinnamic acids (including ferulic, isoferulic, cinnamic, and hydrocinnamic acids) and flavonoids. Polyphenol concentrations on the apical, basolateral side and cell-associated were determined by HPLC-DAD. Our studies demonstrated that depending on their polarity the compounds passed from the apical to the basolateral side of the monolayers. Metabolites, such as glucuronides and sulphates were detectable in the model system at supra-physiological concentrations. Additionally, the status of tight junctions (TJ) – bonds between epithelial cells consisting of proteins providing a barrier for molecules and ions – was studied by measuring transepithelial resistance (TER) and expression of TJ proteins using real-time PCR. During incubation experiments with polyphenols TER of the monolayers was increasing whereas exposure to sodium caprate (C10), a tight TJ modulator, decreased TER, but was reversible by incubation with polyphenols. C10 did not promote fluxes of hydroxycinnamic acids across the monolayers. In parallel, a significant increase in expression of the TJ components ZO-1 and claudin-4, but reductions in occludin expression were observed. Our results provide confirmation that T84 cells can be used as model system to simulate the intestinal mucosa, and that polyphenols are able to increase the integrity of intact and modulated T84 monolayers. The study was performed as a part of NutritionNet, supported by the German Federal Ministry of Education and Research (BMBF), Project No.╃01EA0501.

1

Department of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany, hannah.bergmann@ rhrk.uni-kl.de.

2

Department of Medicine II, Division of Gastroenterology, University of Wuerzburg, Grombuehlstr. 16, D-97080 Wuerzburg, Germany, email for D. Rogoll: [email protected].

3

Juliusspital Wuerzburg, Juliuspromenade 16, D-97070 Wuerzburg, Germany, w.scheppach@ juliusspital.de.

4

[email protected].

5

Presenting author: [email protected].

6

[email protected].

451

452

Posters

4.32 Influence of Apple Polyphenols on Inflammatory Gene Expression Sven Triebel1, Ralph Melcher2, Gerhard Erkel3, and Elke Richling1,4 Apples (Malus spp., Rosaceae) and products thereof contain high amounts of polyphenols which show diverse biological activities. They may contribute to beneficial health effects, like protecting from chronic inflammatory bowel diseases (IBD). IBDs are characterized by an excessive release of several pro-inflammatory cytokines and chemokines by different cell types resulting in an increased pathologic inflammatory response. In the present study we investigated the preventive effectiveness of polyphenolic juice extracts and single major constituents on inflammatory gene expression in human colonic epithelial cells (T84). Furthermore, the protein levels of multiple released cytokines and chemokines were studied. DNA microarray analyses of several genes known to be strongly regulated during gastro-intestinal inflammation revealed that apple juice extract (AE) significantly inhibited the expression of NF-κB regulated pro-inflammatory genes (TNF-α, IL-1â•›β, CXCL9, CXCL10), inflammatory relevant enzymes (COX-2, CYP3A4), and transcription factors (STAT1, IRF1) in LPS/IFN-γ stimulated MonoMac6 cells. To identify active structures a screening of some major compounds of AE was performed. The flavan-3-ol dimers procyanidin B1 and B2 and the dihydrochalcones phloretin and 3-Hydroxyphloretin significantly inhibited pro-inflammatory gene expression (TNF-α, IL-8, CXCL10) in a dose-dependent manner. The influence on proinflammatory gene expression thereby strongly correlated with the corresponding protein levels investigated by human cytokine array studies. In summary, we identified apple derived compounds being responsible for the anti-inflammatory activity of AE. In particular, procyanidin B1, procyanidin B2, phloretin and 3-Hydroxyphloretin showed anti-inflammatory activities in vitro and therefore may act as transcription-based inhibitors of pro-inflammatory gene expression. The study was performed as a part of NutritionNet, supported by the German Federal Ministry of Education and Research (BMBF), Project No.╃01EA0501.

1

Department of Chemistry, Division of Food Chemistry and Toxicology, University of KaisersÂ� lautern, Erwin-Schroedinger-Str.╃52, D-67663 Kaiserslautern, Germany, presenting author: [email protected].

2

Department of Medicine II, Division of Gastroenterology, University of Wuerzburg, Grombuehlstr. 16, D-97080 Wuerzburg, Germany, [email protected].

3

Department of Biotechnology, University of Kaiserslautern, Kaiserslautern, Germany, erkel@ ibwf.uni-kl.de.

4

[email protected].

Diethylstilbestrol-Like Effects of Genistein on€Gene€Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line

4.33 Diethylstilbestrol-Like Effects of Genistein on€Gene€Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line Jörg Wagner1 and Leane Lehmann1

Abstract Diethylstilbestrol and genistein have been shown to influence the development of the reproductive tract. A possible target could be the Wnt-signalling cascade. Therefore, the influence of diethylstilbestrol and genistein on Wnt-signalling components was investigated in the endometrial Ishikawa cell line. The present study demonstrates that genistein as well as diethylstilbestrol influence Wnt-signalling in a manner that implies activation of canonical Wnt-signalling provoked or accompanied by reduction of non-canonical Wnt-signalling. Because of the similar molecular mechanisms of genistein and diethylstilbestrol in endometrial cells, the safety of genistein containing soy-based infant formulas should be evaluated.

4.33.1 Introduction The use of soy-based infant formulas containing isoflavones has raised concerns regarding the unclear bioactivity of the phytoestrogen genistein (GEN). GEN has been shown to disrupt the development of the female reproductive tract in animal studies in the same way as the synthetic estrogen and transplacental carcinogen diethylstilbestrol (DES) [1]. At the molecular level, DES possibly deregulates the tumour suppressor gene Wnt5â•›a which might result in aberrant canonical Wnt-signalling, which in turn is associated with tumour formation in various tissues [2]. Previous studies revealed that both GEN and DES down-regulated Wnt5â•›a gene expression in the endometrial Ishikawa cell line. The aim of the present study was to elucidate the impact of reduced Wnt5â•›a mRNA levels on canonical Wnt-signalling in Ishikawa cells. Due to the wellestablished estrogenic potential of DES and GEN, expression of genes involved in estrogen-signalling such as estrogen receptor α and β, progesterone receptor and the reporter gene for estrogen reaptor activation in Ishikawa cells, alkaline phosphatase (AlP) [3] was determined. Furthermore, mRNA level of genes which have important functions in the development of an organism and have been linked with estrogen- and Wnt-signalling, such as hoxa 10 and 11 was determined. To elucidate the role of Wnt5â•›a within the Wnt-signalling pathways,

1

University of Karlsruhe, Institute of Applied Biosciences, Dept. of Food Chemistry, Adenauerring 20â•›a, 76131 Karlsruhe, Germany.

453

454

Posters

expression of genes exerting a function in canonical and non-canonical Wntsignalling was investigated (Fig.╃1 and Tab.╃1). One criterion for the selection of genes was the fact that they code for proteins that exert their function in various compartments of the cell such as the surface, cytoplasm or in the nucleus of the cell (Fig.╃2 and Tab.╃1). Another criterion for the inclusion of genes in the present study was their response to Wnt-signalling at the mRNA rather than protein activity levels.

4.33.2 Methods 4.33.2.1 Cell Culture, RNA Isolation and Reverse Transcription

Ishikawa cells were seeded in 6-well plates (Nunc, Wiesbaden, Germany, 3·105 cells per well, respectively) using phenol red-free medium containing 5â•›% charcoal/ dextrane-treated FCS (Hyclone, South Logan, UT) 24â•›h prior to treatment with equi-estrogenic concentrations of DES (10 nM) and GEN (1000 nM). DES and GEN were dissolved in DMSO and added to the medium to yield a final DMSO concentration of 0.1â•›% (v/v). Control experiments were carried out with medium containing 0.1â•›% of DMSO without test compounds. After a treatment period of 48â•›h, total RNA was isolated using the GenElute total RNA isolation kit (Sigma, Taufkirchen, Germany). Contaminating traces of DNA were digested with DNase (Sigma), and 1╃µg total RNA was reversely transcribed using High Capacity cDNA kit (Applied Biosystems, Darmstadt, Germany).

Figure 1: Simplified schema of canonical and non-canonical Wnt-signalling pathways and components. Solid line represents the cell membrane; dashed line represents the nuclear membrane; for abbreviations see Table 1.

Diethylstilbestrol-Like Effects of Genistein on€Gene€Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line Table 1: List of abbreviations, full names of genes, and function in Wnt-signalling of gene products used in the present study. Abbreviation Gene name

Function

APC

Adenomatous polyposis coli

Component of β-catenin destruction complex.

Axin

Axin2

Component of beta catenin destruction complex.

CamK2β

Calcium/calmodulin-dependent protein kinase 2 beta

Component of non-canonical Wntsignalling

βCat

Beta catenin

Transducer of canonical Wnt-signalling

DKK2

Dickkopf 2

Inhibition of Wnt-signalling by binding to LRP co-receptor

Dvl

Dishevelled

Mediator of Wnt-signalling

Fz

Frizzled receptor

Receptor for Wnt proteins

GSK3

Glycogen synthase kinase 3

Component of β-catenin destruction complex

LRP

Low-density lipoprotein receptor related protein

Co-receptor for Wnt-signalling

MMP1

Matrix metalloproteinase

Expression regulated by non-canonical Wnt-signalling

NF-AT

Nuclear factor of activated T-cells

Transcription factor within the Wnt/�Ca-pathway

PKCβ

Protein kinase C-beta

Component of Wnt/Ca-pathway

PLCβ

Phospholipase C-beta

Component of Wnt/Ca-pathway

SFRP

Secreted frizzled-related protein

Inhibitors of Wnt-signalling

TCF

T-cell factor

Transcription factors within the canonical Wnt-signalling pathway

βTrCP

Beta-transducin repeat containing protein

Component of β-catenin destruction complex

Wnt

Wnt

Activators of Wnt-signalling

HPRT

Hypoxanthine guanine phosphoribosyltransferase

Housekeeping gene

β-actin

Beta-actin

Housekeeping gene

AlP

Alkaline phosphatase

Reporter gene for estrogens in Ishikawa cells

ESR1

Estrogen receptor α

Component of estrogen signalling

ESR2

Estrogen receptor β

Component of estrogen signalling

Hoxa 10/11

Homeobox A 10/11

Involved in uterine Wnt-signalling

MSX 1

Homeobox gene (formerly HOX 7.1) Involved in uterine Wnt-signalling

PGR

Progesterone receptor

Involved in uterine Wnt-signalling

455

456

Posters

4.33.2.2 PCR

TaqMan® Low Density Arrays (Applied Biosystems) in a customized format including 32 genes (Tab.╃1) were applied for gene expression analysis. 250 ng of total RNA converted to cDNA was used per port of the TaqMan® Low Density Arrays. Each 100╃µl reaction mixture per treatment were filled in 4 ports of the Micro Fluidic Card to obtain six reactions per treatment and gene. Ct values were obtained by means of SDS software (SDS 2.1, Applied Biosystems) and relative expression was calculated using the REST software [4], using both HPRT and β-actin as housekeeping genes.

4.33.3 Results and Discussion Progesterone receptor (PGR) and alkaline phosphatase (AlP) mRNA was induced more than 10-fold after treatment of Ishikawa cells with DES and GEN for 48â•›h. The increase of AlP mRNA levels after treatment with 10 nM DES and 1000 nM GEN was within the same range, indicating a similar estrogenic potency [3]. The expression of homeobox genes (Hoxa10 and 11) was not influenced by GEN and DES at this time point. Hoxa 10 and 11 are important for the development of the female reproductive tract [5], and have been reported to be part of a cross talk between estrogen- and Wnt-signalling. Treatment of cells with both, DES and GEN resulted in a decrease of Wnt5â•›a mRNA levels by 80â•›% confirming former experiments [6]. Moreover, mRNA levels of low density lipoprotein receptor-related protein-5 and -6 (LRP5, 6), which are part of the Wnt receptor complex at the cell membrane as well as TCF3 (DES) and TCF4 (GEN), transcription factors of the TCF/LEF family were decreased to 50â•›% after treatment with DES. This down-regulation of activators of Wnt-signalling has been reported to be due to a negative feedback loop, indicating previous activation of canonical Wnt-signalling [7–9]. Another indicator for activation of canonical Wnt-signalling could be the observed decrease of mRNA levels of secreted Frizzled-related protein-4 (SFRP4) to 25â•›% by both, DES and GEN. SFRP4 is an inhibitor of general Wnt-signalling, and is thought to antagonize Wnt action by competing with Frizzled-receptors for the binding of Wnt proteins [reviewed in 10]. Inverse correlation of expression of SFRP4 and beta-catenin (β-cat) has been reported in endometrial sarcomas, indicating an inhibitory role of SFRP4 in canonical Wnt-signalling [11]. Decreased SFRP4 expression is associated with increased cancer incidence in various tissues and is therefore a possible tumour suppressor candidate [12, 13]. Furthermore, GEN as well as DES reduced mRNA levels of beta-transducing repeat-containing protein (β-TRCP), which is part of the β-cat destruction complex (Tab.╃1). Moreover, mRNA levels of axin2, another protein of the β-cat destruction complex were reduced only by GEN to 50â•›%. These reductions support the idea of increased activity of canonical signalling due to a higher intra-cellular content of β-cat. Interestingly, β-TRCP and axin2 have shown to be feedback target genes of canonical Wnt-signalling, whereby expression has

Diethylstilbestrol-Like Effects of Genistein on€Gene€Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line

Figure 2: Influence of DES and GEN on the gene expression of various genes of Wnt- and estrogen-signalling components in Ishikawa cells. Cells were treated with 10 nM DES or 1000€nM GEN for 48╛h.╃

457

458

Posters

shown to be increased after stimulation of canonical Wnt-signalling [14–16]. To clarify if the reduction of mRNA levels of β-TRCP and axin2 was not a secondary effect due to a preceding induction as a canonical feedback regulatory loop, gene expression has to be investigated at additional time points. In order to determine the influence of GEN and DES on Wnt/Ca2╃+-pathway, protein kinase C(PKC)-β and Ca2╃+/calmodulin-dependent protein kinase (CaMK)-2â•›β known to inhibit canonical Wnt-signalling upstream (PKC-β) and downstream (CaMK-2â•›β) of β-catenin were examined. PKC-β (reduction to 25â•›% by both GEN and DES) and CaMK-2â•›β (reduction to 50â•›% only by GEN) were de-regulated in Ishikawa cells. The reduction of components of the Wnt/ Ca2╃+-pathway, which is reported to antagonize canonical Wnt-signalling, support the idea that canonical Wnt-signalling would be favoured after treatment of cells with both, DES and GEN.

4.33.4 Conclusion The present study demonstrates that GEN as well as DES influence Wnt-signalling in a manner that implies activation of canonical signalling provoked or accompanied by reduction of non-canonical signalling. Since this conclusion is based upon gene expression experiments, post-transcriptional and post-translational modifications are not taken into account. Nevertheless, due to similar molecular mechanisms of GEN and DES in endometrial cells, the safety of GEN containing soy-based infant formulas should be evaluated.

Acknowledgements Supported by DFG Le1329/8–1.

References â•⁄ 1. Jefferson, W.╃N., E. Padilla-Banks, and R.╃R. Newbold, Disruption of the female reproductive system by the phytoestrogen genistein Reprod Toxicol, 2007. 23 (3): 308–16. â•⁄ 2. Reya, T. and H. Clevers, Wnt signalling in stem cells and cancer Nature, 2005. 434 (7035): 843–50. â•⁄ 3. Holinka, C.╃F., et al., Effects of steroid hormones and antisteroids on alkaline phosphatase Â�activity in human endometrial cancer cells (Ishikawa line) Cancer Res, 1986. 46 (6): 2771–4.╃ â•⁄ 4. Pfaffl, M.╃W., G.╃W. Horgan, and L. Dempfle, Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR Nucleic Acids Res, 2002. 30 (9): e36. â•⁄ 5. Daftary, G.╃S. and H.╃S. Taylor, Endocrine regulation of HOX genes Endocr Rev, 2006. 27 (4): 331–55.

Diethylstilbestrol-Like Effects of Genistein on€Gene€Expression of Wnt-Signalling Components in the Endometrial Ishikawa Cell Line

â•⁄ 6. Wagner J, S.╃A., Rittmann P, Lehmann L, Role of the estrogen receptor in the diethylstilbestrol-induced disruption of the expression of WNT5A and WNT7A in human endometrial Ishikawa cells. Naunyn-Schmiedeberg’s Arch Pharmacol, 2007. 375 (Suppl.1): 451. â•⁄ 7. Wehrli, M., et al., Arrow encodes an LDL-receptor-related protein essential for Wingless Â�signalling Nature, 2000. 407 (6803): 527–30. â•⁄ 8. Khan, Z., et al., Analysis of endogenous LRP6 function reveals a novel feedback mechanism by which Wnt negatively regulates its receptor Mol Cell Biol, 2007. 27 (20): 7291–301. â•⁄ 9. Hovanes, K., et al., Beta-catenin-sensitive isoforms of lymphoid enhancer factor-1 are selectively expressed in colon cancer Nat Genet, 2001. 28 (1): 53–7. 10. Cadigan, K.╃M. and Y.╃I. Liu, Wnt signaling: complexity at the surface J Cell Sci, 2006. 119 (3): 395–402. 11. Hrzenjak, A., et al., Inverse correlation of secreted frizzled-related protein 4 and beta-catenin expression in endometrial stromal sarcomas J Pathol, 2004. 204 (1): 19–27. 12. Takagi, H., et al., Frequent epigenetic inactivation of SFRP genes in hepatocellular carcinoma J Gastroenterol, 2008. 43 (5): 378–89. 13. Horvath, L.╃G., et al., Secreted frizzled-related protein 4 inhibits proliferation and metastatic potential in prostate cancer Prostate, 2007. 67 (10): 1081– 90. 14. Spiegelman, V.╃S., et al., Wnt/beta-catenin signaling induces the expression and activity of betaTrCP ubiquitin ligase receptor Mol Cell, 2000. 5 (5): 877– 82. 15. Lustig, B., et al., Negative feedback loop of Wnt signaling through upregulation of conductin/axin2 in colorectal and liver tumors Mol Cell Biol, 2002. 22 (4): 1184–93. 16. Yan, D., et al., Elevated expression of axin2 and hnkd mRNA provides evidence that Wnt/beta -catenin signaling is activated in human colon tumors Proc Natl Acad Sci USA, 2001. 98€(26): 14973–8.

459

460

Posters

4.34 Effect of Dietary Flavonoids in Different Cell Lines: Comparison of Uptake, Modulation of Oxidative Stress and Cytotoxic Effects Wim Wätjen1, Sven Ruhl1,2, Ricarda Rohrig1, Yvonni Chovolou1, Andreas Kampkötter1, Peter Proksch2, and Regine Kahl1 Flavonoids possess a remarkable spectrum of biochemical and pharmacological activities; they affect cell functions such as growth, differentiation and apoptosis. Epidemiological studies have suggested that naturally occurring plant compounds such as flavonoids protect against various stages of the cancer process and are associated with a reduced incidence of coronary heart disease. As possible mechanisms by which flavonoids may affect tumourigenesis, anti-oxidative activities and alterations in gene expression are discussed. We used rat Hct116 colon carcinoma, H4IIE and HepG2 hepatoma as well as C6 glioma cells to investigate uptake and biotransformation of naturally occurring flavonoids as well as effects of flavonoids on oxidative stress (ARE-signalling pathway) and toxicity (induction of apoptosis/ necrosis). The flavonol quercetin is taken up rapidly in several cellular systems. In H4IIE cells the formation of glucuronic acid derivates was detected, while in HepG2-cells the most prominent metabolite isorhamnetin, a methyl derivative of quercetin, accumulated in growth media up to 15â•›% of the given flavonoid. This methylated derivative showed a higher toxicity than quercetin in the MTTassay. We furthermore investigated the effects of flavonoids on oxidative stress: Although flavonoids act as powerful anti-oxidants it was also shown that in high concentrations, they can also generate reactive oxygen species. We investigated the activation of the anti-oxidant responsive element ARE (luciferase reporter gene assay) by structurally related flavonoids in comparison to the well-known ARE-inductor tBHQ. We detected a strong induction of ARE after incubation with quercetin, this effect was even higher than the effect of tBHQ. To confirm that the activation of the ARE is dependent on Nrf2, we performed western blot analyses and assessed the induction of the ARE downstream target HO-1.╃In a last part we investigated the effects of flavonoids on cell death (apoptosis/ necrosis) by determination of oligonucleosomal DNA cleavage (“DNA ladder”), nuclear fragmentation and caspase activation.

1

Heinrich-Heine-University, Institute of Toxicology, P.╃O. Box 101007, D-40001 Düsseldorf, Germany, [email protected].

2

Heinrich-Heine-University, Institute of Pharmaceutical Biology, Universitätsstraße 1, D-40225 Düsseldorf, Germany.

Risk–Benefit Considerations of Isoflavone Supplements in the Treatment of Menopausal Vasomotor Symptoms

4.35 Risk–Benefit Considerations of Isoflavone Supplements in the Treatment of Menopausal Vasomotor Symptoms Uta Wegewitz1, Klaus Richter, A. Jacobs, Rolf Großklaus, and Alfonso Lampen Isoflavones are oestrogen-like substances that occur in large amounts in soybeans. These phytochemicals bind to oestrogen receptors and exert various oestrogenic or anti-oestroÂ�genic effects. Soybeans contain three primary isoflavones in their glycoside form: genistin, daidzin and glycitin. Because consumption of these compounds has been associated with several health benefits, they are becoming increasingly popular as food supplements and are frequently advertised as a natural and safe alternative to menopausal hormone therapy. Currently, numerous isoflavone preparations derived from soy are available on the market and are used by many women to relieve their menopausal hot flushes and night sweats. These supplements are hardly comparable because of variations in isoflavone source, manufacturing process, the inadequate information on composition and major deviations between the declared contents and actual composition, as well as varying consumption recommendations, complicating the assessment of their risk and efficacy in humans. Possible favourable effects of soy isoflavones in relieving menopausal vasomotor symptoms are still discussed controversially. A systematic review of randomized, placebo-controlled trials, recently performed at the BfR, revealed no conclusive evidence either. This finding was largely due to inconsistent study results and deficiencies in study quality. The current data on risk assessment of isoflavones are heterogeneous and insufficient. While isoflavone intake of a normal quantity of soy foods can be considered safe, the risk assessment of the consumption of preparations and products containing isolated/ fortified isoflavones is fundamentally different and these products cannot be deemed a priori to be safe. From the available human data no reliable conclusions can be drawn regarding the safety of isolated isoflavones. Experimental data from several animal studies suggest adverse effects of genistein by provoking an accelerated growth of oestrogen-sensitive tumour cells and changes in the reproductive system when administered to neonatal mice. Other concerns include the possible goitrogenic effects of soy isoflavones, especially in the case of iodine deficiency. The necessary long-term studies in humans, proving the safety of isoflavone-containing products, are not available. Until now, the claimed positive effects of soy isoflavones on vasomotor menopausal symptoms are not adequately substantiated scientifically. Furthermore, the safety of products containing isolated soy isoflavones has not been suffi-

1

Federal Institute for Risk Assessment (BfR), Department of Food Safety, Thielallee 88–92, D-14195 Berlin, Germany, [email protected].

461

462

Posters

ciently proven. For this reason, the long-term use of supplements with high contents of isolated isoflavones is not recommended.

Effect of Different Catechins on the Growth of HT-29 Cells

4.36 Effect of Different Catechins on the Growth of HT-29 Cells Stefanie Wiese1, Tuba Esatbeyoglu2, Peter Winterhalter2, and Sabine E. Kulling1

4.36.1 Background Catechins – widely distributed dietary polyphenols – are associated with different health promoting effects, like protection against oxidative stress [1], antiinflammatory [2] as well as anticarcinogenic properties [3–5]. Their proposed protective role in cancer chemoprevention may prevail especially in the intestinal tract due to direct exposure of the intestinal epithelial cells to potentially high concentrations of catechins after ingestion of certain food items.

4.36.2 Aim Our aim was to assess the structure-dependent antiproliferative and cytotoxic effects of various catechins, which differ in their hydroxylation pattern and degree of polymerisation, on the human colon cancer cell line HT-29.

4.36.3 Methods Cytotoxicity was studied using the Neutral Red as well as the Alamar Blue® assay. The antiproliferate activity was examined by a plating efficiency test system and the effect on cell cycle of HT-29 cells was determined by flow cytometry.

4.36.4 Results None of the catechins studied exert any effect on the cell morphology of HT-29 cells with the exception of an oligomeric procyanidin fraction, which causes an extensive development of cytoplasmatic vacuolation and an increase in HT-29 cell size. The tested catechins differ in their cytotoxic effect on HT-29 cells. While monomeric and dimeric catechins bearing a catechol moiety in the ring B of the flavonoid skeleton as well as 3’- and 4’-O-methyl epicatechin, in vivo me-

1

University of Potsdam, Institute of Nutritional Science, Department of Food Chemistry, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany.

2

University of Braunschweig, Institute of Food Chemistry, Schleinitzstr. 20, D-38106 Braunschweig, Germany.

463

464

Posters

tabolites of epicatechin, do not show any effect, all other tested catechins with a pyrogallol moiety as well as the oligomeric catechins cause significant growth retardation. However, the effects seen in the case of epigallocatechin and epigallocatechingallate might be induced by the generation of H2O2 during incubation, since the addition of the enzyme catalase to the cell culture medium suppresses the detected cytotoxicity. On the other hand, all studied catechins exhibit an antiproliferative activity on HT-29 cells which is dependent upon the number of hydroxyl groups. Flow cytometry analysis shows a slight but non-significant increase in the percentage of cells in the G1/G0 phase when treated with oligomeric procyanidins, 3’- and 4’-O-methyl epicatechin. All other studied catechins show no change in cell cycle, but since H2O2 leads to an increase in cells in the G2/M phase and a decrease in cells in the G1/G0 phase, it is possible that there is an overlap of opposite effects.

References 1. 2. 3. 4. 5.

Spencer, J.╃P., et al., Biochem J, 2001, 354, 493–500. Zessner, H., et al., Mol Nutr Food Res, 2008, 52, S28–44. Ahmad, N., et al., J Natl Cancer Inst, 1997, 89, 1881–6. Gosse, F., et al., Carcinogenesis, 2005, 26, 1291–5. Yang, G.╃Y., et al., Carcinogenesis, 1998, 19, 611–6.

Determination of the Isoflavone Content of Soy-Based Infant Formula of the German Market Using a Box-Behnken Experimental Design for Optimizing the Analytical Conditions

4.37 Determination of the Isoflavone Content of SoyBased Infant Formula of the German Market Using a Box-Behnken Experimental Design for Optimizing the Analytical Conditions Stefanie Witte1, Hans-Peter Kruse, and Sabine E. Kulling

4.37.1 Background The use of soy protein based baby formula leads to an intake of isoflavones (IFs). With reference to the current discussion on the possible health consequences of an IF exposition at baby age, the daily intake should be documented for the assessment of the accompanying risks.

4.37.2 Aim Due to conflicting data available from the literature and the variation of the IF pattern (aglycones, glycosides), a rapid and robust method for determining the total IF content of the soy infant formula available on the German market was to be developed. Two products, “Soja Instant Plus” (GranoVita) and “Humana SL” (Humana), were used to validate the method.

4.37.3 Method A three-level Box-Behnken factorial design was used to investigate the effect of three independent variables (sample-to-solvent ratio, solvent composition, extraction time) on the response – the yield of total IF. Thus, the optimal extraction conditions to obtain the maximum yield of IF were determined. The results of varying extraction parameters were determined and controlled by HPLCDAD/MS. Data were analyzed using StatGraphics Centurion software.

4.37.4 Results Depending on the product investigated different conditions for an exhaustive extraction were obtained. For maximizing the yield of IFs, the ratio between the solvent volume and the sample amount, as well as the proportion of the organic phase applied was important. Analyzing Soja Instant Plus (one batch) un-

1

University of Potsdam, Institute of Nutritional Science, Department of Food Chemistry, Arthur-Scheunert-Allee 114–116, D-14558 Nuthetal, Germany, [email protected].

465

466

Posters

der optimal extraction conditions (solid-solvent ratio=╃1:160, methanol=╃65â•›%, time=╃60 min) a total IF content (sum of daidzein, genistein and corresponding derivatives) of 249╃µg/g was recorded. To determine the batch-to-batch variation five different batches of Humana SL were analyzed. As a result, a total of 345–381╃µg/g (optimal conditions: solid-solvent ratio=╃1:160, methanol=╃74â•›%, time=╃74 min) was determined.

4.37.5 Discussion Converting the data (under consideration of the number of meals and the body weight of babies aged from two weeks to six months) to the corresponding dose information, a daily IF exposition of 1–9 mg/kg bw for Humana SL and 1–6 mg/ kg bw for Soja Instant Plus results.

4.37.6 Summary (1) A rapid and robust method for the determination of IFs in soy infant formulas was developed. (2) The results document that the IF exposition of babies exclusively fed on a soy based infant formula is still very high.



5

Appendix

Participants of the Symposium Risk Assessment of Phytochemicals in Food – Novel Approaches Surname

First name

Title

Address

Abraham

Klaus

PD Dr.

Federal Institute for Risk Assessment, Â� Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Ahr

Hans Jürgen

Dr. Dr.

Bayer Schering Pharma AG, Special Toxicology, Aprather Weg 18╛a, 42096€�Wuppertal, Germany

Appel

Klaus

Dr.

Federal Institute for Risk Assessment, Â�Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Bächler

Simone

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Bakuradze

Tamara

Food Chemistry & Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Bani Estivals

Marie-Hélène

Danone Research, CSA, RD 128–91767€Â�Palaiseau, France

Baum

Matthias

Dr.

Food Chemistry & Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Bausch

Jochen

Dr.

DSM Nutritional Products Ltd, P.╃O. Box 2676, Bldg 205/302, CH-4002 Basel, �Switzerland

Beck

Mareike

Dr.

DSM Nutritional Products Ltd, P.╃O. Box 2676, Bldg 205/325, CH-4002 Basel, �Switzerland

Bellion

Phillip

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Berger

Franz

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Beuerle

Till

Bode

Lisa Maria

Dr.

Technische Universität Braunschweig, Institut für Pharmazeutische Biologie, Mendelssohnstr. 1, 38106€Â�Braunschweig, Germany University of Potsdam, Institute of Nutritional Science, Department of Food Chemistry, ArthurScheunert-Allee 114–116, 14558€Â�Nuthetal, Â�Germany

467

468

Appendix

Surname

First name

Title

Address

Böhm

Nadine

Bohnenberger

Susanne

Brück

Jens

Food Chemistry & Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Burkart

Julia

Institute of Applied Biosciences, Section of Food Toxicology, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe

Cheng

Xinlai

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Christmann

Markus

Dr.

Department of Toxicology, University of Mainz, Obere Zahlbacher Str.╃67, 55131€�Mainz, Germany

Daniel

Hannelore

Prof. Dr.

Technische Universität München, Lehrstuhl für Ernährungsphysiologe, Molecular Nutrition Unit, Am Forum 5, 85350€Â�Freising-Weihenstephan, Germany

de Kok

Theo

Prof. Dr.

Department of Health Risk Analysis and Toxicology, University of Maastricht, P.╃O. Box 616, 6200 MD Maastricht, The Netherlands

Diel

Patrick

PD Dr.

Institut für Kreislaufforschung und Sportmedizin, Abt. Molekulare und Zelluläre Sportmedizin, Deutsche Sporthochschule Köln, 50927€Â�Köln, Germany

Dusemund

Birgit

Dr.

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Efferth

Thomas

Prof. Dr.

German Cancer Research Center, Pharmaceutical Biology (C015), Im Neuenheimer Feld 280, 69120€�Heidelberg, Germany

Eisenbrand

Gerhard

Prof. Dr.

DFG Senate Commission on Food Safety, Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Engel

Karl-Heinz

Prof. Dr.

Technische Universität München, Lehrstuhl für Lebensmitteltechnologie, Am Forum 2, 85350€Â�Freising-Weihenstephan, Germany

Erk

Thomas

Esselen

Melanie

Dr.

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Fabian

Eric

Dr.

Experimentelle Toxikologie und Ökologie, BASF SE, Carl-Bosch-Str.╃38, GV/TB – Z470, 67056€Â�Ludwigshafen, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany Dr.

Harlan Cytotest Cell Research GmbH, In den Leppsteinwiesen 19, 64380€�Rossdorf, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Participants of the Symposium Risk Assessment of Phytochemicals in Food – Novel Approaches

Surname

First name

Title

Address

Fehr

Markus

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Feld

Julia

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Fink-Gremmels Johanna

Prof. Dr.

Utrecht University, Faculty of Veterinary Medicine, Div. Pharmacology, Pharmacy and Toxicology, Yalelaan 104, 3505 TD Utrecht, The Netherlands

Frei

Eva

Dr.

Molekulare Toxikologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 581, 69120€�Heidelberg, Germany

Galli

Corrado

Prof. Dr.

University of Milan, Via Balzaretti 9, 20133€�Milan, Italy

Gil-Lostes

Javier

Glatt

Hans-Rudolf

Prof. Dr.

Deutsches Institut für Ernährungsforschung, Arthur-Scheunert-Allee 114–116, 14558€Â�Nuthetal, Germany

Glei

Michael

Prof. Dr.

Friedrich-Schiller-Universität Jena, Lehrstuhl für Ernährungstoxikologie, Dornburger Str.╃24, 07743€Â�Jena, Germany

Grimm

Stefanie

Gronewold

Claas

Dr.

KPSS – KAO Professional Salon Services GmbH, Pfungstädter Str.╃92–100, 64297€Â�Darmstadt, Â�Germany

Grunow

Werner

Prof. Dr.

Südwestkorso 15A, 12161€Â�Berlin, Germany

Grünz

Gregor

Technische Universität München, Lehrstuhl für Ernährungsphysiologe, Molecular Nutrition Unit, Am Forum 5, 85350€Â�Freising-Weihenstephan, Germany

Grzegorzewski

Franziska

Leibnitz-Institute for Agricultural Engineering Potsdam-Bornim (ATB) & Technical University �Berlin, Dept. Of Food Technology and Food Chemistry, Max-Eyth-Allee 100, 14469€�Potsdam, Germany

Guth

Sabine

Dr.

DFG Senate Commission on Food Safety, Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Habermeyer

Michael

Dr.

DFG Senate Commission on Food Safety, Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Hecker

Dorothee

Mars UK Petcare, Freeby Lane, Waltham on the worlds, England

Institute of Biological Chemistry and Nutrition, University of Hohenheim, Garbenstr. 28, 70593€�Stuttgart, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

469

470

Appendix

Surname

First name

Title

Address

Hengstler

Jan G.

Prof. Dr.

IfADo, Ardeystr. 67, 44139€�Dortmund, Germany

Hessel

Stefanie

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Höhn

Annika

Universität Hohenheim, Garbenstr. 28, 70599€Â�Stuttgart, Germany

Humpf

Hans-Ulrich

Hutter

Melanie

Janzowski

Christine

Dr.

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Joost

Hans-Georg

Prof. Dr. Dr.

Deutsches Institut für Ernährungsforschung, Arthur-Scheunert-Allee 114–116, 14558€Â�Nuthetal, Germany

Kamp

Hennicke

Dr.

BASF SE, GV/TA – Z470, 67056€Â�Ludwigshafen, Germany

Keijer

Jaap

Prof. Dr.

Human and Animal Physiology, Wageningen University, Marijkeweg 40, PO box 338, 6700 AH Wageningen, The Netherlands

Kelber

Olaf

Dr.

Steigerwald Arzneimittelwerk GmbH, Havelstr. 5, 64295€�Darmstadt, Germany

Kemper

Fritz

Prof. Dr.

Umweltprobenbank des Bundes, Universität Â�Münster, Domagkstr. 11, 48149€Â�Münster, Germany

Kempf

Michael

Knudsen

Ib

Dr.

Chief Adviser in Food Safety and Toxicology, Â�Tibberup Allé 11, DK-3500 Vaerloese, Denmark

Koch

Egon

Dr.

Dr. Wilmar Schwabe GmbH & Co. KG, WilmarSchwabe-Str.╃4, 76227€�Karlsruhe, Germany

Koch

Jaqueline

Dr.

Bundesinstitut für Arzneimittel und Medizinprodukte, K.-G.-Kiesinger-Allee 3, 53175€Â�Bonn, Germany

Kochte-Clemens

Barbara

Dr.

DFG Senate Commission on Food Safety, Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Konkimalla

Badireenath

Dr.

German Cancer Research Center, Pharmaceutical Biology (C015), Im Neuenheimer Feld 280, 69120€�Heidelberg, Germany

Prof. Dr.

Institut für Lebensmittelchemie, Westfälische Wilhelms-Universität Münster, Correnstr. 45, 48161€Â�Münster, Germany Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Lehrstuhl für Lebensmittelchemie, Universität Würzburg, Am Hubland, 97074€Â�Würzburg, Germany

Participants of the Symposium Risk Assessment of Phytochemicals in Food – Novel Approaches

Surname

First name

Title

Address

Kropat

Christopher

Kulling

Sabine

Prof. Dr.

Chair of Food Chemistry, Institute of Nutrition, University of Potsdam, Arthur-Scheunert-Allee 114–116, 14558€Â�Nuthetal, Germany

Lampen

Alfonso

Prof. Dr. Dr.

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Langenkämper

Georg

Dr.

Max Rubner-Institut, Institut für Sicherheit und Qualität bei Getreide, Schützenberg 12, 32756€Â�Detmold, Germany

Laskowski

Stéphanie

Lehmann

Leane

Dr.

University of Karlsruhe, Institute of Applied Biosciences, Dept. of Food Chemistry, Adenauerring 20╛a, 76131€�Karlsruhe, Germany

Leist

Marcel

Prof. Dr.

Doerenkamp-Zbinden Chair of Alternative in-vitro Methods, Faculty of Sciences / Department of Biology, University of Konstanz, Postfach M657, 78457€�Konstanz, Germany

Marko

Doris

Prof. Dr.

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Matissek

Reinhard

Prof. Dr.

Lebensmittelchemisches Institut (LCI), AdamsÂ�str.╃52–54, 51063€Â�Köln, Germany

Meisner

Anke

Dr.

Bundesamt für Verbraucherschutz und Lebensmittelsicherheit, Mauerstr. 39–42, 10117€Â�Berlin, Germany

Metzler

Manfred

Prof. Dr.

Lehrstuhl für Lebensmittelchemie, Karlsruhe Institut für Technologie (KIT), Adenauerring 20â•›a (Geb.╃50.41), 76131€Â�Karlsruhe, Germany

Miene

Claudia

Friedrich-Schiller-Universität Jena, Lehrstuhl für Ernährungstoxikologie, Dornburger Str.╃24, 07743€Â�Jena, Germany

Molzberger

Almut

Institut für Kreislaufforschung und Sportmedizin, Abt. Molekulare und Zelluläre Sportmedizin, Â�Deutsche Sporthochschule Köln, 50927€Â�Köln, Germany

Montoya

Gina

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Mulac

Dennis

Institut für Lebensmittelchemie, Westfälische Wilhelms-Universität Münster, Correnstr. 45, 48161€Â�Münster, Germany

Niemann

Birgit

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Danone Research, CSA, RD 128╃–╃91767€Â�Palaiseau, France

Dr.

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

471

472

Appendix

Surname

First name

Title

Address

Okpanyi

Samuel N.

Dr.

Philip-Holl-Str.╃16, 65195€�Wiesbaden, Germany

Ono

Atsushi

Dr.

National Institute of Health Sciences, Division of Risk Assessment, 1–18–1 Kamiyoga, Setagaya-ku, Tokyo 158–8501, Japan

Pahlke

Gudrun

Dr.

Institut für Angewandte Biowissenschaften, Universität Karlsruhe (TH), Adenauerring 20â•›a, 76131€Â�Karlsruhe, Germany

Peters

Ulrike

Dr.

Fred Hutchinson Cancer Research Center, 1100 Fairview Ave NW, M4-B402, Seattle WA 98109, USA

Petracco

Marino

Dr.

c/o illycaffé. Via Flavia, 110, 34147€Â�Trieste, Italy

Pfeiffer

Erika

Pfundstein

Beate

Raquet

Nicole

Rechkemmer

Gerhard

Prof. Dr. Dr.

Richling

Elke

Jun-Prof. Dept. of Chemistry, Division of Food ChemisDr. try and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Rietjens

Ivonne

Prof. Dr.

Division of Toxicology, Wageningen University, Tuinlaan 5, 6703 HE Wageningen, The Netherlands

Scalbert

Augustin

Dr.

INRA, Unité de Nutrition Humaine, Centre de Recherche de Clermont-Ferrand/Theix, 63122€Â�Saint-Genes-Champanelle, France

Schantz

Markus

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Scheuermann

Rainer

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Schilter

Benoit

Dr.

Quality and Safety Department, Nestlé Research Center, 1026 Lausanne Switzerland

Schlatter

Josef

Dr.

Federal Office of Public Health, Nutritional and Toxicological Risks Section, Stauffacherstr. 101, 8004 Zürich, Switzerland

Schmitz

Hans-Joachim Dr.

Lehrstuhl für Lebensmittelchemie, Karlsruhe Institut für Technologie (KIT), Adenauerring 20â•›a (Geb.╃50.41), 76131€Â�Karlsruhe, Germany Dr.

BDIH, Bundesverband Deutscher Industrie und Handelsunternehmen, L11,20–22, 68161€Â�Mannheim, Germany Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€Â�Kaiserslautern, Germany Max Rubner-Institut, Haid-und-Neu-Str.╃9, 76131€Â�Karlsruhe, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Participants of the Symposium Risk Assessment of Phytochemicals in Food – Novel Approaches

Surname

First name

Title

Address

Schreier

Peter

Prof. Dr.

Lehrstuhl für Lebensmittelchemie, Universität Würzburg, Am Hubland, 97074€Â�Würzburg, Â�Germany

Schrenk

Dieter

Prof. Dr. Dr.

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€��Kaiserslautern, Germany

Seidel

Albrecht

Dr.

Biochemisches Institut für Umweltcarcinogene, Prof. Dr. Gernot Grimmer-Stiftung, Lurup 4, 22927€Â�Grosshansdorf, Germany

Sobeck

Ulrike

Dr.

WALA Heilmittel GmbH, Dorfstr. 1, 73087€Â�Bad Boll/ Eckwälden, Germany

Soyalan

Bülent

Speijers

Gerrit

Dr.

GETS, General-Health Effects Toxicity Safety Food, Winterkoning 7, 3435 RN Nieuwegein, The �Netherlands

Spiteller

Michael

Prof. Dr.

Dortmund University of Technology, Institute of Environmental Research, Otto-Hahn-Str.╃6, 44227€�Dortmund, Germany

Steffen

Christian

Prof. Dr.

Bundesinstitut für Arzneimittel und Medizinprodukte, K.-G.-Kiesinger-Allee 3, 53175€Â�Bonn, Germany

Steinberg

Pablo

Prof. Dr.

Institute for Food Toxicology and Analytical Chemistry, University of Veterinary Medicine �Hannover, Bischofsholer Damm 15, 30173€�Hannover, Germany

Strelen

Heike

Dr.

Deutsche Forschungsgemeinschaft (DFG), Kennedyallee 40, D-53175€�Bonn, Germany

Thiel

Anette

Dr.

DSM Nutritional Products, Wurmisweg 576, 4303€Kaiseraugst, Switzerland

Török

Michael

Dr.

DSM Nutritional Products Ltd, P.╃O. Box 2676, Bldg 205/314, CH-4002 Basel, Switzerland

Triebel

Sven

Turowski

Angelika

Dr.

Fa. Symrise, Taunushöhe 27, 65779€Â�Kelkheim, Germany

Uehara

Takeki

Dr.

Shionogi & Co., Ltd., Developmental Research Laboratories, 3–1–1, Futaba-cho, Toyonaka, Osaka 561–0825, Japan

Uffelmann

Helena

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€��Kaiserslautern, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

Dept. of Chemistry, Division of Food Chemistry and Toxicology, University of Kaiserslautern, 67663€�Kaiserslautern, Germany

473

474

Appendix

Surname

First name

Title

Address

Valerio

Luis G.

Dr.

U.S. Food and Drug Administration, Center for Drug Evaluation and Research/OPS/SRS/ICSAS, WO51 Rm4128, 10903€�New Hampshire Avenue, Silver Spring, MD 20993€�USA

Vonmoos

Florence

Wagner

Jörg

Dr.

University of Karlsruhe, Institute of Applied Biosciences, Dept. of Food Chemistry, Adenauerring 20╛a, 76131€�Karlsruhe, Germany

Wätjen

Wim

PD Dr.

Heinrich Heine University, Institute of Toxicology, P.O.Box 101007, 4001 Düsseldorf, Germany

Wegewitz

Uta

Dr.

Federal Institute for Risk Assessment, Department of Food Safety, Thielallee 88–92, 14195€Â�Berlin, Germany

Weinert

Christoph

Institute of Nutrition, University of Potsdam, Arthur-Scheunert-Allee 114–116, 14558€Â�Nuthetal, Germany

Wiese

Stefanie

Institute of Nutrition, University of Potsdam, Arthur-Scheunert-Allee 114–116, 14558€Â�Nuthetal, Germany

Winterhoff

Hilke

Philip Morris, R&D, Neuchatel-CH, Quai Jeanrenaud 56, 2000 Neuchatel, Switzerland

Prof. Dr.

Institut für Pharmakologie und Toxikologie, Domagkstr. 12, 48149€Â�Münster, Germany

Members of the DFG Senate Commission on Food Safety: Mandate 2007–2010

Members of the DFG Senate Commission on Food Safety: Mandate 2007–2010

Prof. Dr. Gerhard Eisenbrand – Chairperson/ Vorsitzender

Lebensmittelchemie und Toxikologie, Technische Universität Kaiserslautern

Prof. Dr. Karl-Heinz Engel

Lehrstuhl für allgemeine Lebensmitteltechnologie, Technische Universität München

Prof. Dr. Johanna Fink-Gremmels

Division of Veterinary Pharmacology, Pharmacy and Toxicology, University of Utrecht/Niederlande

Prof. Dr. Jan Hengstler

Institut für Arbeitsphysiologie, Universität Dortmund

Prof. Dr. Thomas Hofmann

Lehrstuhl für Lebensmittelchemie und Molekulare Sensorik, Technische Universität München

Prof. Dr. Hans-Georg Joost

Deutsches Institut für Ernährungsforschung, Â�Potsdam

Prof. Dr. Dipl.-Ing. Dietrich Knorr

Institut für Lebensmitteltechnologie und Prozesstechnik, Technische Universität Berlin

Prof. Dr. Ib Knudsen

Chief Adviser in Food Safety and Toxicology, Â� Vaerloese/Dänemark

Prof. Dr. Sabine Kulling

Institut für Ernährungswissenschaft, Universität Potsdam (inzwischen Max Rubner Â�Institut, Karlsruhe)

Prof. Dr. Doris Marko

Institut für Analytische Chemie und Lebensmittel­ chemie, Universität Wien/Â�Österreich

Prof. Dr. Reinhard Matissek

LCI – Lebensmittelchemisches Institut des Bundesverbandes der Deutschen Süßwaren­ industrie e.╃V., Köln

Prof. Dr. ir. I.╃M.╃C.╃M. Ivonne Rietjens

Department of Toxicology, Wageningen University/ Niederlande

Dr. Josef Schlatter

Bundesamt für Gesundheit, Sektion Ernährungsund Toxikologische Risiken, Zürich/Schweiz

Prof. Dr. Peter Schreier

Lehrstuhl für Lebensmittelchemie, Universität Würzburg, Emeritus

Prof. Dr. Dr. Dieter Schrenk

Lebensmittelchemie und Toxikologie, Technische Universität Kaiserslautern

475

476

Appendix

Prof. Dr. Pablo Steinberg

Institut für Lebensmitteltoxikologie und Â�Chemische Analytik, Tierärztliche Hochschule Hannover

Prof. Dr. Rudi F. Vogel

Lehrstuhl für Technische Mikrobiologie, Technische Universität München

Ständige Gäste: Prof. Dr. Manfred Edelhäuser

Ministerium für Ernährung und Ländlichen Raum Baden-Württemberg, Stuttgart

Prof. Dr. Dr. Alfonso Lampen

Bundesinstitut für Risikobewertung, Berlin

Prof. Dr. Hans-Jürgen Altmann (retired 2008)

Bundesinstitut für Risikobewertung, Berlin

Prof. Dr. Christian Steffen

Bundesinstitut für Arzneimittel und MedizinÂ�produkte, Bonn

Prof. Dr. Gerhard Rechkemmer

Max-Rubner-Institut, Bundesforschungsanstalt für Ernährung und Lebensmittel, Karlsruhe

Prof. Dr. Stefan Vieths

Paul-Ehrlich-Institut, Bundesamt für Sera und Impfstoffe, Abteilung Allergologie, Langen