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.

This detailed volume explores key state-of-the-art computational applications that are crucial in Systems Toxicology. The recent technological developments in experimental biology and multi-omics measurements that enable Systems Biology and Systems Toxicology can only be fully leveraged by the application of a broad range of computational approaches ranging from data management to mathematical modeling. Taking this into account, chapters in this book cover data management and processing, data analysis, biological network building and analysis, as well as the application of computational methods to toxicological assessment. Written for the Methods in Pharmacology and Toxicology series, Computational Systems Toxicology includes the kind of key practical advice that will aid readers in furthering our knowledge of toxic substances and reactions to them.

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.

A comprehensive analysis of state-of-the-art molecular modeling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals This unique volume describes how the interaction of molecules with toxicologically relevant targets can be predicted using computer-based tools utilizing X-ray crystal structures or homology, receptor, pharmacophore, and quantitative structure activity relationship (QSAR) models of human proteins. It covers the in vitro models used, newer technologies, and regulatory aspects. The book offers a complete systems perspective to risk assessment prediction, discussing experimental and computational approaches in detail, with: * An introduction to toxicology methods and an explanation of computational methods * In-depth reviews of QSAR methods applied to enzymes, transporters, nuclear receptors, and ion channels * Sections on applying computers to toxicology assessment in the pharmaceutical industry and in the environmental arena * Chapters written by leading international experts * Figures that illustrate computational models and references for further information This is a key resource for toxicologists and scientists in the pharmaceutical industry and environmental sciences as well as researchers involved in ADMET, drug discovery, and technology and software development.

The costs of developing new drugs continue to rise while public and governmental expectations demand safer drugs. This demand for safer drugs can also significantly delay the time it takes to reach the market, as screening for drug safety can be a time-consuming process. In order to reduce costs and timelines while increasing safety, pharmaceutical firms have incorporated predictive methodologies to identify potential safety issues before a new drug is given to patients or even before a compound is tested in animals. Initial predictive efforts have focused on using compound structure to predict toxicity. This work has led to a number of successes in the ability to predict genotoxicity and carcinogenicity. In this chapter, we describe some of the methods used in pharmaceuticals to predict these adverse effects as well as methodologies used to predict organ-specific toxicity such as drug-induced liver injury, a major risk for new drugs. More recently, pharmaceuticals have begun to consider gene information when predicting toxicities. Activity against major risk genes such as the hERG is now routinely used to identify some risks. Finally, we discuss some approaches being developed to predict and/or categorize risk using gene, protein, or metabolite changes.

Computational Toxicology: Methods and Applications for Risk Assessment is an essential reference on the translation of computational toxicology data into information that can be used for more informed risk assessment decision-making. This book is authored by leading international investigators who have real-world experience in relating computational toxicology methods to risk assessment. Key topics of interest include QSAR modeling, chemical mixtures, applications to metabolomic and metabonomic data sets, toxicogenomic analyses, applications to REACH informational strategies and much more. The examples provided in this book are based on cutting-edge technologies and set out to stimulate the further development of this promising field to offer rapid, better and more cost-effective answers to major public health concerns. Authored by leading international researchers engaged in cutting-edge applications of computational methods for translating complex toxicological data sets into useful risk assessment information Incorporates real-world examples of how computational toxicological methods have been applied to advance the science of risk assessment Provides the framework necessary for new technologies and fosters common vocabularies and principles upon which the effects of new chemical entities should be compared

This book is intended to be an introduction to various applications of computational toxicology and to show how these approaches are currently being used effectively for risk assessment purposes in the near term. It is important to note that the field of computational toxicology is rapidly evolving and that subsequent editions of this book will take up new methods that are currently under development, such as high-throughput screening, and others that are still in a conceptual stage. There are many advantages for including computational toxicology approaches in the risk assessment process. Among these are reducing costs, minimizing use of animals in toxicology testing, improving speed in providing answers regarding chemicals in emergency situations such as the Gulf Oil spill, and dealing with the common problem of decision making for chemical mixtures. In addition, computational methods may be used for extrapolating or translating data from both in vitro and in vivo experimental animal test systems for human risk assessments of chemicals and drugs. In addition, computational methods may be used for focusing laboratory studies into productive areas by data mining the published literature and developing testable hypotheses by application of systems biology approaches to identify chemical interactions with functional molecular pathways to generate a more comprehensive picture of likely primary and secondary modes of chemical or drug activity. In summary, there is much that computational toxicology is now contributing to helping make better societal risk assessment decisions about chemicals and drugs. The future for these approaches is optimistic and limited only by human ingenuity and availability of resources.