The network approaches of systems pharmacology and toxicology serve as early predictors of the most relevant screening approach to pursue both in drug discovery and development and ecotoxicological assessments. Computational approaches have the potential to improve toxicological experimental design, enable more rapid drug efficacy and safety testing and also reduce the number of animals used in experimentation. Rapid advances in availability of computing technology hold tremendous promise for advancing applied and basic science and increasing the efficiency of risk assessment.
This book provides an understanding of the basic principles of computational toxicology and the current methods of predictive toxicology using chemical structures, toxicity-related databases, in silico chemical-protein docking, and biological pathway tools. The book begins with an introduction to systems pharmacology and toxicology and computational tools followed by a section exploring modelling adverse outcomes and events. The second part of the book covers the discovery of protein targets and the characterisation of toxicant-protein interactions. Final chapters include case studies and additionally discuss interactions between phytochemicals and Western therapeutics.
This book will be useful for scientists involved in environmental research and risk assessment. It will be a valuable resource for postgraduate students and researchers wishing to learn about key methods used in studying biological targets both from a toxicity and pharmacological activity standpoint.
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Dr Dale E. Johnson is currently an Adjunct Professor in Molecular Toxicology at UC Berkeley and an Adjunct Professor in Environmental Health Sciences at the University of Michigan. He teaches computational toxicology, drug discovery & development, and lectures in pharmacogenomics. He is also President and CEO of Emiliem, Inc. and CEO of Elara Bioscience LLC, a software company. He also consults for biopharmaceutical companies and investment firms. Dr Johnson has over 30 years’ experience as a research and development scientist, manager, executive, and entrepreneur in the biopharmaceutical field working in large pharmaceutical and biotech companies and several start-ups. Prior to Emiliem, he served as VP Drug Assessment & Development and VP Preclinical Development at Chiron Corporation and previously worked at Hoechst-Roussel Pharmaceuticals, International Research and Development Corp., Medical Research Division of American Cyanamid, Eos Biotechnology and Ddplatform. Dr Rudy J. Richardson is currently the Dow Professor of Toxicology at the University of Michigan. He also holds a joint appointment in the Neurology Department. Sabbatical leaves at Warner-Lambert/Parke-Davis (now Pfizer) in Ann Arbor and the University of Padua in Italy. Dr Richardson has served several times as the Director of the Toxicology Program, and currently he serves as Graduate Chair of the EHS Department. His research interests include computational toxicology, biomarkers and biosensors of exposure and disease, mechanisms of neurodegenerative disease, chemistry and toxicology of organophosphorus and organic nitro compounds, and interactions of toxicants with target macromolecules.
The network approaches of systems pharmacology and toxicology serve as early predictors of the most relevant screening approach to pursue both in drug discovery and development and ecotoxicological assessments. Computational approaches have the potential to improve toxicological experimental design, enable more rapid drug efficacy and safety testing and also reduce the number of animals used in experimentation. Rapid advances in availability of computing technology hold tremendous promise for advancing applied and basic science and increasing the efficiency of risk assessment.
This book provides an understanding of the basic principles of computational toxicology and the current methods of predictive toxicology using chemical structures, toxicity-related databases, in silico chemical-protein docking, and biological pathway tools. The book begins with an introduction to systems pharmacology and toxicology and computational tools followed by a section exploring modelling adverse outcomes and events. The second part of the book covers the discovery of protein targets and the characterisation of toxicant-protein interactions. Final chapters include case studies and additionally discuss interactions between phytochemicals and Western therapeutics.
This book will be useful for scientists involved in environmental research and risk assessment. It will be a valuable resource for postgraduate students and researchers wishing to learn about key methods used in studying biological targets both from a toxicity and pharmacological activity standpoint.
The network approaches of systems pharmacology and toxicology serve as early predictors of the most relevant screening approach to pursue both in drug discovery and development and ecotoxicological assessments. Computational approaches have the potential to improve toxicological experimental design, enable more rapid drug efficacy and safety testing and also reduce the number of animals used in experimentation. Rapid advances in availability of computing technology hold tremendous promise for advancing applied and basic science and increasing the efficiency of risk assessment.
This book provides an understanding of the basic principles of computational toxicology and the current methods of predictive toxicology using chemical structures, toxicity-related databases, in silico chemical-protein docking, and biological pathway tools. The book begins with an introduction to systems pharmacology and toxicology and computational tools followed by a section exploring modelling adverse outcomes and events. The second part of the book covers the discovery of protein targets and the characterisation of toxicant-protein interactions. Final chapters include case studies and additionally discuss interactions between phytochemicals and Western therapeutics.
This book will be useful for scientists involved in environmental research and risk assessment. It will be a valuable resource for postgraduate students and researchers wishing to learn about key methods used in studying biological targets both from a toxicity and pharmacological activity standpoint.
Chapter 1 Systems Biology Approaches in Pharmacology and Toxicology Dale E. Johnson,
Chapter 2 Databases Facilitating Systems Biology Approaches in Toxicology Dale E. Johnson and Ann M. H. Heslin,
Chapter 3 Tools for Green Molecular Design to Reduce Toxicological Risk David Faulkner, Leah K. Rubin Shen, Vanessa Y. De La Rosa, Dale E. Johnson, Rachel Hemingway, Richard V. Williams, Philip N. Judson, John Arnold and Chris D. Vulpe,
Chapter 4 Linking Environmental Exposure to Toxicity Noffisat Oki, Jeremy Leonard, Mark Nelms, Shannon Bell, Yu-Mei Tan, Lyle Burgoon and Stephen Edwards,
Chapter 5 Linking Drug or Phytochemical Exposure to Toxicity C. A. Rodríguez, N. S. Teuscher and J. A. Uchizono,
Chapter 6 Chemical Similarity, Shape Matching and QSAR E. V. Radchenko, G. F. Makhaeva, V. A. Palyulin and N. S. Zefirov,
Chapter 7 In silico Chemical-Protein Docking and Molecular Dynamics Sanjeeva J. Wijeyesakere and Rudy J. Richardson,
Chapter 8 Computational Tools for Chemical Toxicity Testing and Risk Assessment Under the Framework of Adverse Outcome Pathways M. Mumtaz, P. Ruiz and Q. Zhang,
Chapter 9 In silico Toxicology: An Overview of Toxicity Databases, Prediction Methodologies, and Expert Review D. Bower, K. P. Cross, S. Escher, G. J. Myatt and D. P. Quigley,
Chapter 10 Data Sources for Herbal and Traditional Medicines Hsueh-Fen Juan,
Chapter 11 Network Pharmacology Research Approaches for Chinese Herbal Medicines Dale E. Johnson,
Chapter 12 Chemical-Disease Category Linkage (CDCL): Computational Methods Linking Traditional Chinese Medicines and Western Therapeutics Dale E. Johnson and Kit Wun Kathy Cheung,
Chapter 13 Educational Programs for Computational Toxicology and Pharmacology Dale E. Johnson and Rudy J. Richardson,
Subject Index,
Systems Biology Approaches in Pharmacology and Toxicology
DALE E. JOHNSON
1.1 Introduction
The science and practical field of toxicology has been changing dramatically over the last 15–20 years, transitioning into a more systems biology and network-based approach. Several factors have been involved, including the developing genomics era where the understanding of genetic changes has enhanced the ability to understand diseases and chemically-induced toxicities at the molecular level. The genomics era has also ushered in "omics" technologies and approaches such as transcriptomics, metabolomics, proteomics, and epigenomics, which have changed the way we view mechanisms of toxicity and the perturbation of biological systems that lead to adverse outcomes. These advances have been coupled with the public availability of large datasets of information and new modeling approaches that have enhanced the ability to understand toxicological events and effects at multiple biological levels. Since our scientific approaches, inquiries, and visions aimed at understanding toxicological events and outcomes have been broadened tremendously, this reinforces our need for new and better ways to assess toxicity and risk. The large numbers of uncharacterized chemicals already present in the environment and new chemicals that continue to enter it has required hazard and risk assessments to be made with very few data. These factors have had a major influence on the need to accelerate new approaches and move away from an overdependence on in vivo animal testing and make better use of computational, molecular, and in vitro tools. The identification of the majority of toxic events in in vivo animal toxicology studies rely on high-dose exposure to the animals and default linear extrapolation procedures, with the incorporation of newer technologies absent in the vast majority of animal studies. This has been considered a shortcoming in risk assessment and several weaknesses in this process include the comparative shape of the dose–response relationship after relevant levels of human exposure, whether biological and/or toxicological thresholds do in fact exist and for what toxicological endpoints, and potential population variability in response.
1.2 Systems Toxicology
Accordingly, research in toxicology has moved into a new systems-oriented phase called systems toxicology, which involves the study of complex molecular response networks initiated by exposure (both intentional and unintentional) to chemical substances. At the heart of systems toxicology approaches are the development and usage of quantitative mechanistic models that create a predictive toxicology aspect relevant to all toxicology fields, including drug research and development and environmental research. The overall approach involves the integration of classical toxicology with the quantitative analysis of large networks of chemically-induced molecular and functional changes, which occur across multiple levels of biological organization. Examples of key influential events in this transition since the year 2000 include the release of human genome sequencing data including specific signal transduction domains, the development and issuance of the report Toxicity Testing in the Twenty-first Century by the National Research Council (NRC),which has influenced all sectors of the toxicology field, and the development and publication of the adverse outcome pathway (AOP) approach, which has highlighted the realities that exist as the science moves away from an overdependence on in vivo testing and makes greater use of computational, molecular, and focused in vitro tools. Additional drivers of change include the European Union (EU) report from the Scientific Committee on Health and Environmental Risks, the EU's Registration, Evaluation, Authorisation and Restriction of Chemical Substances (REACH) program, and the International Programme on Chemical Safety (IPCS). The paradigm shift can also be seen in the drug research and development sector, but rather than focusing on drugs during late stages of development or on marketed drugs, the systems-related efforts are positioned at the front end of research, both on safer chemical design and extensive target research. While the drug industry is required to conduct animal toxicology studies by regulatory agencies and international guidelines, the major effort underway is to determine chemical liabilities early in the drug discovery pipeline, both to reduce the time and cost of failures later in the process, but also to avoid costly failures once a drug reaches the market. Currently, there is an International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), where several pharmaceutical and biotechnology companies have created a Nonclinical to Clinical Translational Database (WG1) to allow analysis of the reliability and potential limitations of nonclinical data in predicting clinical outcomes, including the evaluation of conventional biomarkers of toxicity. Current screening approaches applied to the front end of drug research are described below.
1.3 Chemical Toxicities
1.3.1 Single-Target Toxicity Concepts
The science and practice of toxicology over the past several decades have consistently used classic toxicological approaches, such as in vivo and in vitro toxicology studies, combined with predictive toxicological methodologies. The desired endpoints of the in vivo...
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