This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making. The authors from various government agencies such as FDA, NCATS and NIEHS industry, and academic institutes share their real-world experience and discuss most current practices in computational toxicology and potential applications in regulatory science. Among the topics covered are molecular modeling and molecular dynamics simulations, machine learning methods for toxicity analysis, network-based approaches for the assessment of drug toxicity and toxicogenomic analyses. Offering a valuable reference guide to computational toxicology and potential applications in regulatory science, this book will appeal to chemists, toxicologists, drug discovery and development researchers as well as to regulatory scientists, government reviewers and graduate students interested in this field.

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

Humans are exposed to chemicals through voluntary and involuntary actions; to natural and synthetic chemicals all day, every day. Single chemical risk assessments are complex in and of themselves, and the assessment of chemical mixtures exponentially increases the complexity for toxicologists, regulators, and the public. Chemicals produce effects in biological systems which may or may not be related to their toxicity; some effects may be adaptive or may not be a direct part of their mode or mechanism of toxic action. These terms are commonly used and may be distinguished based on the level of detail implied. Mode of action usually describes the effect of a toxicant at the cellular or organ level, while mechanism of action implies an understanding of the interaction of the toxicant at the molecular level. Chemicals can have the same mode of action, but act via different mechanisms. Components in a chemical mixture are characterized by mode and/or mechanism for the purpose of grouping, described later.

Computational Toxicology for Drug Safety and a Sustainable Environment is a primer on computational techniques in environmental toxicology for scholars. The book presents 9 in-depth chapters authored by expert academicians and scientists aimed to give readers an understanding of how computational models, software and algorithms are being used to predict toxicological profiles of chemical compounds. The book also aims to help academics view toxicological assessment from the lens of sustainability by providing an overview of the recent developments in environmentally-friendly practices. The chapters review the strengths and weaknesses of the existing methodologies, and cover new developments in computational tools to explain how researchers aim to get accurate results. Each chapter features a simple introduction and list of references to benefit a broad range of academic readers. List of topics: 1. Applications of computational toxicology in pharmaceuticals, environmental and industrial practices 2. Verification, validation and sensitivity studies of computational models used in toxicology assessment 3. Computational toxicological approaches for drug profiling and development of online clinical repositories 4. How to neutralize chemicals that kill environment and humans: an application of computational toxicology 5. Adverse environmental impact of pharmaceutical waste and its computational assessment 6. Computational aspects of organochlorine compounds: DFT study and molecular docking calculations 7. In-silico studies of anisole and glyoxylic acid derivatives 8. Computational toxicology studies of chemical compounds released from firecrackers 9. Computational nanotoxicology and its applications Readership Graduate and postgraduate students, academics and researchers in pharmacology, computational biology, toxicology and environmental science programs.

New and emerging high-throughput toxicology and human exposure data sets offer great promise to expand or reshape the process of chemical risk assessment. While the methods to mine new computational toxicology data and assess its predictive power and compatibility with more traditional toxicology data are still under development, this chapter presents publicly available data sets that can be mined to support risk assessment. Risk assessors are faced with adapting to toxicity testing in the 21st century and soon may be required to consider new types of data for use in risk assessment. In the meantime, further work is required to quantify the uncertainty associated with high-throughput screening (HTS) data, collect and analyze further exposure data, and organize and evaluate new data from different sources across the existing risk assessment paradigm or as components of a new risk assessment paradigm.

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.

This first comprehensive treatment of the subject for more than a decade includes the latest research on nanoparticle toxicology. The practical handbook addresses all areas where toxic mixtures are encountered, from environmental via occupational to medical settings, giving special consideration to air and water, and to the specific requirements for study design in mixture toxicology. While no extensive prior knowledge or toxicological experience is required, the practice-oriented case studies and examples in the second part make this the ideal companion for the professional toxicologist in industry or healthcare institutions with little time for academic study.

The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment. This title is of particular relevance to researchers and postgraduates working and studying in the fields of computational methods, applied and physical chemistry, cheminformatics, biological sciences, predictive toxicology and safety and hazard assessment.

The focus of the chapter is on the development and application of computational toxicology methods to human risk assessment. The various CompTox methods are defined, and a brief history of their development and applications in risk assessment is provided. The technological, economic, and public health concerns driving the development of the methods are described, along with how the specific forces shaped the historical and current use of CompTox methods in risk assessment. Translation research programs in the United States and OECD are briefly reviewed, and current applications of CompTox to risk assessment in the areas of screening, priority setting for testing and regulation, and the elucidation of adverse outcome pathways are discussed. Potential future directions for the application of computational toxicology in a wider range of risk applications are identified, along with ongoing research needs.