Predicting Chemical Toxicity and Fate - Section 1 ppsx

Predicting Chemical Toxicity and Fate © 2004 by CRC Press LLC CRC PRESS Boca Raton London New York Washington, D.C. Mark T.D. Cronin Liverpool John Moores University Liverpool, England David J. Livingstone University of Portsmouth Portsmouth, England Predicting Chemical Toxicity and Fate © 2004 by CRC Press LLC This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. 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(David). III. Title. RA1220.3.P74 2004 615.9—dc22 2004043999 © 2004 by CRC Press LLC Dedications From MC To AMC and CCFC, for the pleasure and the pain. © 2004 by CRC Press LLC Table of Contents Section 1 Introduction. Chapter 1 Predicting Chemical Toxicity and Fate in Humans and the Environment — An Introduction Mark T.D. Cronin Section 2 Methodology . Chapter 2 Toxicity Data Sources Klaus L.E. Kaiser Chapter 3 Calculation of Physicochemical Properties Mark T.D. Cronin and David J. Livingstone Chapter 4 Good Practice in Physicochemical Property Prediction Peter R. Fisk, Louise McLaughlin, Rosalind J. Wildey Chapter 5 Whole Molecule and Atom-Based Topological Descriptors Tatiana I. Netzeva Chapter 6 Quantum Chemical Descriptors in Structure-Activity Relationships — Calculation, Interpretation, and Comparison of Methods Gerrit Schüürmann Chapter 7 Building QSAR Models: A Practical Guide David J. Livingstone Section 3 QSARs for Human Health Endpoints. Chapter 8 Prediction of Human Health Endpoints: Mutagenicity and Carcinogenicity Romualdo Benigni Chapter 9 The Use of Expert Systems for Toxicity Prediction: Illustrated with Reference to the DEREK Program Robert D. Combes and Rosemary A. Rodford Chapter 10 Computer-Based Methods for the Prediction of Chemical Metabolism and Biotransformation within Biological Organisms Martin P. Payne Chapter 11 Prediction of Pharmacokinetic Parameters in Drug Design and Toxicology Judith C. Duffy © 2004 by CRC Press LLC Section 4 QSARs for Environmental Toxicity and Fate. Chapter 12 Development and Evaluation of QSARs for Ecotoxic Endpoints: The Benzene Response-Surface Model for Tetrahymena Toxicity T. Wayne Schultz and Tatiana I. Netzeva Chapter 13 Receptor-Mediated Toxicity: QSARs for Estrogen Receptor Binding and Priority Setting of Potential Estrogenic Endocrine Disruptors Weida Tong, Hong Fang, Huixiao Hong, Qian Xie, Roger Perkins, and Daniel M. Sheehan Chapter 14 Prediction of Persistence Monika Nendza Chapter 15 QSAR Modeling of Bioaccumulation John C. Dearden Chapter 16 QSAR Modeling of Soil Sorption John C. Dearden Chapter 17 Application of Catabolic-Based Biosensors to Develop QSARs for Degradation Graeme I. Paton, Jacob G. Bundy, Colin D. Campbell, and Helena Maciel Section 5 Application. Chapter 18 The Tiered Approach to Toxicity Assessment Based on the Integrated Use of Alternative (Non-animal) Tests Andrew P. Worth Chapter 19 The Use by Governmental Regulatory Agencies of Quantitative Structure-Activity Relationships and Expert Systems to Predict Toxicity Mark T.D. Cronin Chapter 20 A Framework for Promoting the Acceptance and Regulatory Use of (Quantitative) Structure-Activity Relationships Andrew P. Worth, Mark T.D. Cronin, and Cornelius J. Van Leeuwen © 2004 by CRC Press LLC Contributors Romualdo Benigni Istituto Superiore di Sanità Rome, Italy Jacob G. Bundy School of Biological Sciences University of Aberdeen Aberdeen, Scotland Colin D. Campbell Macaulay Institute Aberdeen, Scotland Robert D. Combes Fund for the Replacement of Animals in Medical Experiments Russell & Burch House Nottingham, England Mark T.D. Cronin School of Pharmacy and Chemistry Liverpool John Moores University Liverpool, England John C. Dearden School of Pharmacy and Chemistry Liverpool John Moores University Liverpool, England Judith C. Duffy School of Pharmacy and Chemistry Liverpool John Moores University Liverpool, England Hong Fang Logicon ROW Sciences Jefferson, AR, U.S.A. Huixiao Hong Logicon ROW Sciences Jefferson, AR, U.S.A. Peter R. Fisk Peter Fisk Associates Whitstable, England Klaus L.E. Kaiser TerraBase, Inc. Hamilton, Ontario, Canada David J. Livingstone ChemQuest, Isle of Wight, England Centre for Molecular Design, University of Portsmouth, Portsmouth, U.K. Helena Maciel School of Biological Sciences University of Aberdeen Aberdeen, U.K. Louise McLaughlin Peter Fisk Associates Whitstable, England Monika Nendza Analytisches Laboratorium Luhnstedt, Germany Tatiana I. Netzeva School of Pharmacy and Chemistry Liverpool John Moores University Liverpool, U.K. Graeme I. Paton School of Biological Sciences University of Aberdeen Aberdeen, Scotland Martin P. Payne LHASA Ltd. Department of Chemistry University of Leeds Leeds, England Roger Perkins Logicon ROW Sciences Jefferson, AR, U.S.A. Rosemary A. Rodford SoloSTAR Ltd. Bedford, England © 2004 by CRC Press LLC T. Wayne Schultz College of Veterinary Medicine University of Tennessee Knoxville, TN, U.S.A. Gerrit Schüürmann Department of Chemical Ecotoxicology UFZ Centre for Environmental Research Leipzig, Germany Daniel M. Sheehan Food and Drug Administration’s National Center for Toxicological Research, Jefferson, AR, U.S.A. Daniel M. Sheehan and Associates, Little Rock, AR, U.S.A. Weida Tong Food and Drug Administration’s National Center for Toxicological Research Jefferson, AR, U.S.A. Cornelius J. Van Leeuwen Institute for Health and Consumer Protection, Joint Research Centre European Commission Ispra, Italy Gilman D. Veith International QSAR Foundation to Reduce Animal Testing Duluth, MN, U.S.A. Rosalind J. Wildey Peter Fisk Associates Whitstable, England Andrew P. Worth European Center for the Validation of Alternative Methods Institute for Health and Consumer Protection, Joint Research Center European Commission Ispra, Italy Qian Xie Logicon ROW Sciences Jefferson, AR, U.S.A. © 2004 by CRC Press LLC Foreword When Corwin Hansch and Al Leo encouraged me in applying quantitative structure-activity relationships (QSARs) to the screening of environmental hazards, the U.S. Toxic Substances Control Act was still only a concept, and most QSAR calculations were still being made with a pencil. Their encouragement included two principles for QSAR along with a word of caution. The principles were that QSAR ought to be based on well-defined endpoints of intrinsic chemical activities as well as on molecular descriptors that could be interpreted mechanistically. The word of caution was that bureaucracies founded on laboratory testing, whether private or a regulatory agency, will only begrudgingly accept QSAR as a strategic tool in designing chemicals and managing chemical risks. Looking back over the last three decades, the Hansch/Leo principles for QSAR development have been largely ignored, if not disputed, by the growing QSAR community, with the possible exception in Europe where QSAR acceptance criteria will require transparency and a mechanistic foundation. Only the skepticism toward QSAR itself by our testing-oriented society seems to have been steadfast over three decades. The increasing costs of testing have produced renewed interest in more strategic in silico methods at a time when QSAR has been freed from many early computational barriers. Now more than ever, the scientific community needs an expert summary of QSAR methods like this book. The guiding principles for QSAR development were intended to aid in the discovery of useful and robust models. The literature is replete with more than 10,000 QSAR correlations and models, yet few of them are useful enough to sway the skeptics. Still, progress in QSAR research can be measured by its own critics and the changing nature of their skepticism. The “yes-but” skeptics are particularly instructive to me. In 1974, our research plans faced the criticism, “yes, QSAR may be able to predict some chemical properties, but it will never be able to predict bioaccumulation of chemical residues.” In 1981, we faced, “okay, QSAR may be able to predict bioconcentration potential, but it will never be able to predict toxicity.” When the acute toxicity models appeared, we were confronted by “yes, QSAR may be able to predict some ecotoxicity endpoints, but it will never predict chronic toxicity in mammals.” Today, as the first mechanistic QSAR models are emerging for chronic reproductive effects and mutagenicity, this historical perspective on the QSAR skeptics serves as benchmarks for progress, if not encouragement. Chemical reactivity in biological systems is far more complex than 20th century computational capabilities could have allowed one to address in quantitative terms. The rapid progress in computing power over the last decade enabled a steady stream of new computational methods in QSAR to emerge. Unfortunately, these new capabilities were not matched with the generation of high-quality biological databases needed to reveal systematic variation within heterogeneous chemical invento- ries. While many combinatorial problems in QSAR are likely to challenge computer sciences for years, present computer capabilities are sufficient to make future QSAR progress limited mostly by the databases for relevant, well-defined endpoints. Our QSAR program at the Duluth, MN, U.S.A., laboratory focused on well-defined ecotoxi- cological endpoints that could be used directly in regulatory decisions. Our proof-of-concept paper in 1979 for estimating the bioconcentration potential required only a minimal database. Since then, many researchers have contributed to the evolution of bioaccumulation models and to extend them from simple screening-level methods for new chemicals to more exact estimates of tissue residues for risk assessments. In contrast to the bioconcentration database, the creation of the Duluth ecotoxicity database involved a multimillion dollar investment and dozens of scientists over most of a decade. Finding chemicals with common toxicity pathways to build mechanistic structure- toxicity relationships required better diagnostic bioassays, including behavioral symptomology (fish acute toxicity syndromes) and joint-toxicity studies. Our first paper on acute toxicity in 1983 was delayed almost 3 years due to rejections from toxicological journals based on our use of the term “narcosis” in describing reversible, baseline lethality — a criticism that lingers today in the health © 2004 by CRC Press LLC research community. The dozens of more recent supporting papers on baseline toxicity and the even larger toxicity database created by Terry Schultz at the University of Tennessee (Knoxville) should be sufficient to overcome the skeptics of acute toxicity predictions so that the full attention of effects research can focus on important chronic toxicity endpoints. The European Chemical Industry Council-led analysis of the state of QSAR in Setubal, Portugal (March 2002), concluded that QSARs for biodegradability were still the largest research gap in exposure research. Developing QSARs for important chemical properties progressed rapidly in the 1980s, but developing structure-biodegradability models has been paralyzed by a lack of systematic databases. Fortunately, in 1985 Hiroshi Tadakoro at the Hita laboratory in Japan recognized the need for a biodegradation database, and his team devoted more than a decade to systematically testing chemicals using activated sludge. Almost immediately after the Hita database was made available, the first QSAR screening models for biodegradability began to appear at scientific meetings. Again, these advances illustrate the importance of generating systematic data on crucial endpoints in the overall progress of predictive methods. Finding such endpoints and understanding how they can be reliably used in risk management is the central research challenge for QSAR. Once identified, QSAR progress seems to depend only on government funding to generate the systematic data needed to build acceptable QSARs for the respective endpoints. The estimation of lethality and biodegradability directly from chemical structure has been one of the important first steps in applying QSAR to risk management. Shifting our focus to chronic effects and persistence of chemicals will require us to cross some exciting new frontiers, not the least of which will be the merger of metabolism and effects models as QSAR is incorporated into systems biology. To meet these challenges, scores of chronic toxicity pathways will have to be described, and “-omics” technology promises to open new doors in clustering chemicals by common toxicity pathways for QSAR modeling. With metabolic activation a critical step in many pathways, metabonomics offers unprecedented capability for identifying the key molecular initiating events for chronic effects, many being the new well-defined endpoints QSAR needs for chronic hazard identification. It is hoped that this book will play an important role in advancing QSAR in the face of healthy skepticism, and will bring greater attention to the need for high-quality data in strategic testing. Dr. Gilman Veith Duluth, MN © 2004 by CRC Press LLC [...]... Events (Scientific and Sociological) That Have Given Rise to the Modern Science of Predictive Toxicology Year/Era c 5000 B.C and earlier 3000 B.C 15 50 B.C c 300 B.C Early 15 00s Middle Ages Early 18 00s 18 60’s 18 63 18 68 18 93 18 99 19 01 1939 19 40 19 64 19 70s 19 76 19 81 1980s 19 80s 19 80s 19 80s 19 80s Late 19 80s 19 90s 19 90s Early 19 90s Mid 19 90s Mid 19 90s Late 19 90s 2000 2000 2000 2000 20 01 20 01 Event Knowledge... prediction of toxicity (Chapters 8, 9, 12 , and 13 ), environmental fate (Chapters 14 to 16 ), and the effects of chemicals in humans (Chapters 8, 10 , 11 , 13 , and 17 ) In addition to those models reviewed in these chapters there are many more available in the open literature (see the next section) Despite these predictive models, animal tests are still being performed to assess toxicity and fate The question... number of commercially available expert system approaches to toxicity prediction (see Chapter 9) and algorithms for the prediction of absorption, distribution, metabolism, and excretion (ADME; see Chapters 10 and 11 ) B Motivation for Predicting Toxicity and Fate There is no single motivation for wishing to predict the toxicity and fate of chemicals — the desire to do so varies from user to user The... one-day meeting, “Modelling Environmental Fate and Toxicity, ” organized by the BioActive Sciences Group of the Society of Chemical Industry The meeting was chaired by Drs Mark Cronin and Dave Livingstone and held in London on March 27, 20 01 The speakers at the meeting were drawn from industry and academia and described how computational methods could be applied to predict the toxicity and fate of chemicals... (Chapters 8 to 11 ); their application to environmental toxicity and fate (Chapters 12 to 17 ); and the use of predictive models (Chapter 19 ), adoption by the regulatory authorities (Chapter 19 ), and validation (Chapter 20) A History of Predictive Methods for Toxicology and Fate It would be wrong to consider the history of predictive toxicology in complete isolation from other scientific and sociological... and Toxicology, Karcher, W and Devillers, J., Eds., Kluwer, Dordrecht, 19 90, pp 25–59 Hansch, C and Leo, A., Exploring QSAR: Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington D.C., 19 95 Karelson, M., Lobanov, V.S., and Katritzky, A.R., Quantum -chemical descriptors in QSAR/QSPR studies, Chem.Rev., 96, 10 27 10 43, 19 96 Karcher, W and Devillers, J., Eds., Practical... Structure-Activity Relationships (QSARs) in Environmental Chemistry and Toxicology, Kluwer, Dordrecht, 19 90 Könemann, H., Quantitative structure-activity-relationships in fish toxicity studies 1 Relationship for 50 industrial pollutants, Toxicology, 19 , 209–2 21, 19 81 Kubinyi, H., From narcosis to hyperspace: the history of QSAR, Quant Struct.-Act Relat., 21, 348–356, 2002 Lipnick, R.L., Correlative and mechanistic... program, J Comput.-Aided Mol Design, 4, 1 45, 19 90 Todeschini, R and Consonni, V., Eds., Handbook of Molecular Descriptors (Methods and Principles in Medicinal Chemistry), Wiley-VCH, Weinheim, 2000 van de Waterbeemd, H., The history of drug research — from Hansch to the present, Quant Struct.-Act Relat., 11 , 200–204, 19 92 Worth, A.P and Balls, M., Alternative (non-animal) methods for chemical testing:... and Fate B Motivation for Predicting Toxicity and Fate 1 Computer Models Provide a Prediction of Toxicity and Fate 2 Public Pressure to Reduce Animal Testing 3 Legislation 4 Filling Data Gaps 5 Cost of Testing — Finance and Time 6 Reaction to New Toxicological Problems 7 Designing New Compounds 8 Increased Understanding of the Biology and Chemistry C The Cornerstones of Predictive Methods for Toxicity. .. 10 , 239–248, 19 99 Livingstone, D.J., Data Analysis for Chemists: Applications to QSAR and Chemical Product Design, Oxford University Press, Oxford, 19 95 Livingstone, D.J., The characterization of chemical structures using molecular properties: a survey, J Chem Info Comput Sci., 40, 19 5–209, 2000 Rekker, R.F., The history of drug research — from Overton to Hansch, Quant Struct.-Act Relat., 11 , 19 5 19 9, . Cataloging-in-Publication Data Predicting chemical toxicity and fate / edited by Mark T.D. Cronin and David J. Livingstone. p. cm. Includes bibliographical references and index. ISBN 0-4 1 5-2 718 0-0 (alk Dedications From MC To AMC and CCFC, for the pleasure and the pain. © 2004 by CRC Press LLC Table of Contents Section 1 Introduction. Chapter 1 Predicting Chemical Toxicity and Fate in Humans and the Environment. Cronin CONTENTS I.Introduction A.History of Predictive Methods for Toxicology and Fate B.Motivation for Predicting Toxicity and Fate 1. Computer Models Provide a Prediction of Toxicity and Fate 2.Public Pressure to Reduce Animal Testing 3.Legislation 4.Filling