Structure‐activity relationships (SARs) among mutagens and carcinogens: A review

Publisher Website
Google Scholar
Abstract
This review is an introduction to methods for evaluating structure‐activity relationships (SARs), and, in particular, to those methods that have been applied to study mutagenicity and carcinogenicity. A brief history and some background material on the earliest attempts to correlate molecular structure and biological activity are included. Most of the discussion focuses on modern methods utilizing extrather‐modynamic and physical property variables such as the Hansch method and SIMCA, and approaches based on molecular connectivity such as the ADAPT, CASE, and Enslein methods. In general, the latter class is potentially the most useful in the study of the large and structurally diverse databases so often encountered in the study of mutagenicity and carcinogenicity. They also are not very sensitive to lab‐to‐lab variances in reported activities and outright misclassifications in activities of some compounds. This is chiefly because the statistical treatments used in these methods tend to dilute the importance of outliers. The methods using physicochemical and extrathermodynamic variables are especially important in relatively small, congeneric databases and can help fine‐tune the role of physicochemical properties in mechanistic hypotheses. All of the above methods have been used to look at mutagenicity and carcinogenicity and some of the results reported in the literature are reviewed here. As far as specific methods go, ADAPT, CASE, SIMCA and the Enslein approach all seem to have similar classification powers (in the range of 75–95%), depending very much on the database studied. The emphasis in this review is on showing that the use of these computer‐aided storage, retrieval and analysis techniques is a timely approach to predicting and even understanding the toxicity of environmental substances. However, each of the methods discussed is still under development, and their potential usefulness for predictive purposes is still being explored.

This publication has 108 references indexed in Scilit: