The new field of toxicogenomics presents a potentially powerful set of tools to better understand the health effects of exposures to toxicants in the environment. At the request of the National Institute of Environmental Health Sciences, the National Research Council assembled a committee to identify the benefits of toxicogenomics, the challenges to achieving them, and potential approaches to overcoming such challenges. The report concludes that realizing the potential of toxicogenomics to improve public health decisions will require a concerted effort to generate data, make use of existing data, and study data in new waysâ€"an effort requiring funding, interagency coordination, and data management strategies.

A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies, algorithms, and prediction methods to select, calculate, and represent the features and properties of chemical structures in biological systems. It provides sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry, biology, statistics, and data mining. Predictive Toxicology explores applications that go beyond classical structure-activity relationships and discusses programs such as OncoLogic, META, MC4PC, PASS, and lazar.

According to the Institute of Medicine (IOM) and U.S. Food and Drug Administration (FDA), 'developing new scientific approaches to detecting, understanding, predicting and preventing adverse events' was a critical path to the future of drug safety. This book brings together a collection of state-of-the-art chapters, written by experts in the drug safety field. It provides information on the present knowledge of drug side effects and their mitigation strategy during drug discovery, gives guidance for risk assessment and promotes evidence-based toxicology. Each specific area of toxicology relevant for drug discovery is discussed in detail, including theory, experimental approaches and data interpretation supported by comprehensive up-to-date references. Many chapters provide fascinating case studies, which are of general interest for those who have basic science training and are interested in how chemicals interact with the human body.

The sophistication of modelling and simulation technologies have improved dramatically over the past decade and their applications in toxicity prediction and risk assessment are of critical importance. The integration of predictive toxicology approaches will become increasingly necessary as industrial chemicals advance and as new pharmaceuticals enter the market. In this comprehensive discussion of predictive toxicology and its applications, leading experts express their views on the technologies currently available and the potential for future developments. The book covers a wide range of topics including in silico, in vitro and in vivo approaches that are being used in the safety assessment of chemical substances. It reflects the growing and urgent need to strengthen and improve our ability to predict the safety and risks posed by industrial and pharmaceutical chemicals in humans. The reader will find extensive information on the use of current animal models used for various toxicities and target mediated toxicities. Also discussed are the recent regulatory initiatives to improve the safety assessment of chemicals. The book provides an expert and comprehensive discussion on the current status and future directions of predictive toxicology and its application. The various chapters in the book also reflect the growing need for improvements in our technologies and abilities to predict toxicities of pharmaceutical and industrial chemicals to ensure product safety and protect public health.

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.

As a guide for pharmaceutical professionals to the issues and practices of drug discovery toxicology, this book integrates and reviews the strategy and application of tools and methods at each step of the drug discovery process. • Guides researchers as to what drug safety experiments are both practical and useful • Covers a variety of key topics – safety lead optimization, in vitro-in vivo translation, organ toxicology, ADME, animal models, biomarkers, and –omics tools • Describes what experiments are possible and useful and offers a view into the future, indicating key areas to watch for new predictive methods • Features contributions from firsthand industry experience, giving readers insight into the strategy and execution of predictive toxicology practices

Quantitative structure-activity relationships (QSARs) represent predictive models derived from the application of statistical tools correlating biological activity or other properties of chemicals with descriptors representative of molecular structure and/or property. Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment discusses recent advancements in the field of QSARs with special reference to their application in drug development, predictive toxicology, and chemical risk analysis. Focusing on emerging research in the field, this book is an ideal reference source for industry professionals, students, and academicians in the fields of medicinal chemistry and toxicology.