Tens of thousands of chemicals are released into the environment every day. High-throughput screening (HTS) has offered a more efficient and cost-effective alternative to traditional toxicity tests that can profile these chemicals for potential adverse effects with the aim to prioritize a manageable number for more in depth testing and to provide clues to mechanism of toxicity. The Tox21 program, a collaboration between the National Institute of Environmental Health Sciences (NIEHS)/National Toxicology Program (NTP), the U.S. Environmental Protection Agency’s (EPA) National Center for Computational Toxicology (NCCT), the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS), and the U.S. Food and Drug Administration (FDA), has generated quantitative high-throughput screening (qHTS) data on a library of 10K compounds, including environmental chemicals and drugs, against a panel of nuclear receptor and stress response pathway assays during its production phase (phase II). The Tox21 Challenge, a worldwide modeling competition, was launched that asks a “crowd” of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. In the Challenge participants were asked to model twelve assays related to nuclear receptor and stress response pathways using the data generated against the Tox21 10K compound library as the training set. The computational models built within this Challenge are expected to improve the community’s ability to prioritize novel chemicals with respect to potential concern to human health. This research topic presents the resulting computational models with good predictive performance from this Challenge.

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.

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alternative testing initiatives. Predictive Analytics for Toxicology: Applications in Discovery Science provides a comprehensive overview of the application of predictive analytics in the field of toxicology, highlighting its role and applications in discovery science. This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology. It underscores the importance of predictive toxicology in proposing and explaining adverse outcomes resulting from human exposures to specific toxicants, especially when experimental and observational data on the toxicant are incomplete or unavailable. Key features: Includes a plain language description of predictive analytics in toxicology adding an overview of the wide range of applications Examines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine learning Opens the hood on AI and its applications in toxicology Features coverage on how in silico toxicity predictions are translational science tools The book integrates strategies and practices of predictive toxicology and offers practical information that students and professionals of the toxicology, chemical, and pharmaceutical industries will find essential. It fulfills the expectations of student researchers seeking to learn predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine learning, and inspire students and seasoned scientists interested in automated science to pick up new research using predictive in silico models to evaluate chemical-induced toxicity. With its focus on practical applications and real-world examples, this book serves as a guide for navigating the complex issues and practices of discovery toxicology. It is an essential resource for those interested in computer-based methods in toxicology, providing valuable insights into the use of predictive analytics.

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 History of Alternative Test Methods in Toxicology uses a chronological approach to demonstrate how the use of alternative methods has evolved from their conception as adjuncts to traditional animal toxicity tests to replacements for them. This volume in the History of Toxicology and Environmental Health series explores the history of alternative test development, validation, and use, with an emphasis on humanity and good science, in line with the Three Rs (Replacement,Reduction, Refinement) concept expounded by William Russell and Rex Burch in 1959 in their now classic volume, The Principles of Humane Experimental Technique. The book describes the historical development of technologies that have influenced the application of alternatives in toxicology and safety testing. These range from single cell monocultures to sophisticated, miniaturised and microfluidic organism-on-a-chip devices, and also include molecular modelling, chemoinformatics and QSAR analysis, and the use of stem cells, tissue engineering and hollow fibre bioreactors. This has been facilitated by the wider availability of human tissues, advances in tissue culture, analytical and diagnostic methods, increases in computational processing, capabilities, and a greater understanding of cell biology and molecular mechanisms of toxicity. These technological developments have enhanced the range and information content of the toxicity endpoints detected, and therefore the relevance of test systems and data interpretation, while new techniques for non-invasive diagnostic imaging and high resolution detection methods have permitted an increased role for human studies. Several key examples of how these technologies are being harnessed to meet 21st century safety assessment challenges are provided, including their deployment in integrated testing schemes in conjunction with kinetic modelling, and in specialized areas, such as inhalation toxicity studies. The History of Alternative Test Methods in Toxicology uses a chronological approach to demonstrate how the use of alternative methods has evolved from their conception as adjuncts to traditional animal toxicity tests to replacements for them. This volume in the History of Toxicology and Environmental Health series explores the history of alternative test development, validation, and use, with an emphasis on humanity and good science, in line with the Three Rs (Replacement, Reduction, Refinement) concept expounded by William Russell and Rex Burch in 1959 in their now-classic volume, The Principles of Humane Experimental Technique. The book describes the historical development of technologies that have influenced the application of alternatives in toxicology and safety testing. These range from single cell monocultures to sophisticated miniaturised and microfluidic organism-on-a-chip devices, and also include molecular modelling, chemoinformatics and QSAR analysis, and the use of stem cells, tissue engineering and hollow fibre bioreactors. This has been facilitated by the wider availability of human tissues, advances in tissue culture, analytical and diagnostic methods, increases in computational processing capabilities, and a greater understanding of cell biology and molecular mechanisms of toxicity. These technological developments have enhanced the range and information content of the toxicity endpoints detected, and therefore the relevance of test systems and data interpretation, while new techniques for non-invasive diagnostic imaging and high resolution detection methods have permitted an increased role for human studies. Several key examples of how these technologies are being harnessed to meet 21st century safety assessment challenges are provided, including their deployment in integrated testing schemes in conjunction with kinetic modelling, and in specialised areas, such as inhalation toxicity studies.

This handbook of the series Biomarkers in Disease informs comprehensively about all aspects of monitoring and detecting toxicity in the human body and model organisms. Biomarkers for assessing toxicity in diverse organs are presented and different assays and methods are explained. Single compounds and drugs and their toxicity for humans are shown and the methods for detection described. Similar to all the volumes of the Biomarkers in Disease series, the chapters are written by experts in their field, each chapter features key facts summarizing the most important aspects of its respective topic and the definitions of words and terms facilitate the reading and understanding. This handbook is a must-have for researchers in toxicology and biomedicine who analyze the effects of drugs and various other substances in the human body and in model organisms. It also serves as a thorough guide for clinicians and pharmacologists.