Applied with success in a number of areas, QSAR studies have become particularly popular in the rational design of drugs and pesticides. Much has been published on the principles of QSAR in this area, but not on their application s to toxic chemicals. This book provides the first comprehensive, interdisciplinary presentation of QSAR studies on

This book presents a comprehensive review of the interaction between cancer research and mathematical methods for both investigators in the field and newcomers just entering it. The book's primary focus is on the use of computer-assisted mathematical modeling in carcinogenesis and cancer prevention. The first two chapters include a general presentation of the carcinogenesis and anticarcinogenesis molecular mechanisms, followed by a discussion of mathematical models of triggers for gene regulation. A description of the prediction of both carcinogenicity and mutagenicity using quantum mechanical, topological or physico-chemical indices is presented, as well as a discussion of the QSAR analysis of carcinogenesis-inhibiting compounds (known as blocking or suppressive agents).

Already in 1978, Elisabeth C. Miller and James A. Miller came with a presumption that electrophilic molecules are predicted to be carcinogens. It is because DNA molecule is reached in nucleophilic centres that may covalently bind to such substances. Rules deduced by Millers are even nowadays irrefutable, and they are used as the basis of testing of the substance for its carcinogenicity potential. Toxicological discipline that emerged from Millers' research is based on dependence of chemical structure of the substance and their biological activity. Even further, there are strict regularities between molecular structures and activities. The tool used in assessment of biological activity of a substance is known as SAR, an abbreviation from structure-activity relationship. Besides electrophilic centres, in assessment of carcinogenic potential of a substance, the SAR also encounters chemical surrounding (neighbouring functional groups), size of the substance, its lipophilicity, number and position of aryl rings, substitutions of hydrogens, epoxides in aliphatic moieties or rings, resonance stabilisation, et cetera To these days, SAR has been upgraded to quantitative SAR (QSAR) which applies multivariate statistical methods quantitatively comparing detected characteristics of "alerts" with biological activity of known carcinogens. Nowadays, chemical industry developing novel active substances is unthinkable without application of QSAR.

Quantitative Structure-Activity Relationships (QSARs) are increasingly used to predict the harmful effects of chemicals to humans and the environment. The increased use of these methods in a variety of areas (academic, industrial, regulatory) results from a realization that very little toxicological or fate data is available on the vast amount of chemicals to which humans and the environment are exposed. Predicting Chemical Toxicity and Fate provides a comprehensive explanation of the state-of-the-art methods that are available to predict the effects of chemicals on humans and the environment. It describes the use of predictive methods to estimate the physiochemical properties, biological activities, and fate of chemicals. The methods described may be used to predict the properties of drugs before their development, and to predict the environmental effects of chemicals. These methods also reduce the cost of product development and the need for animal testing. This book fills an obvious need by providing a comprehensive explanation of these prediction methods. It is a practical book that illustrates the use of these techniques in real life scenarios. This book will demystify QSARs for those students unsure of them, and professionals in environmental toxicology and chemistry will find this a useful reference in their everyday working lives.

This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.

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.

This unique new reference contains the Carcinogenic Potency Database (CPDB), which analyzes results of decades of animal cancer tests, including all Technical Reports of the National Toxicology Program (NTP) and the general published literature. A guide to the literature of animal cancer tests, the CPDB includes references to each published experiment and never-before published analyses. For each of 5,000 long-term experiments on 1,300 chemicals, the user-friendly format includes data on the species, strain, and sex of the test animal; features of experimental protocol such as the route of administration, duration of dosing, dose levels, and duration of the experiment; histopathology and tumor incidence; the shape of the dose-response curve; published author's opinion about the carcinogenicity at each site; and reference to the original publication of the test results. In addition, a measure of carcinogenic potency, the TD50, its statistical significance and confidence limits, are given for each tumor site. An overview is provided of earlier publication updates, such as positivity rates, reproducibility, interspecies extrapolation, and ranking possible carcinogenic hazards. The book also includes a summary of the NTP genetic toxicity test results on 1,500 chemicals, which are referenced to the original publications, including the Salmonella (Ames) test, L5178Y mouse lymphoma cell mutation test, chromosome aberration and sister chromatid exchange tests in cultured Chinese hamster ovary cells, and the sex-linked recessive lethal mutation test in Drosophila melanogaster. An index with chemicals listed by CAS number allows cross referencing between the carcinogenicity and genotoxicity databases, making data easy to find.