A PRIORI PREDICTIVE METHODS OF ASSESSING HEALTH EFFECTS OF CHEICALS IN THE ENVIRONMENT

Passage of the Toxic Substances Control Act (TSCA) last year emphasized the urgent need for the formulation of viable criteria and interim standards limiting the exposure of increasingly large segments of the U.S. population to environmental chemical toxicants. Unfortunately, current methods of developing these standards are both time-consuming and costly. The resulting need for a priori predictive techniques to assess the inherent potential of chemicals, such as the halocarbons found in chlorinated waters, for inducing adverse biological effects, has led to the use of a number of analytical methods designed primarily for screening large numbers of chemical compounds before they impose unacceptable environmental hazards, frequently of crisis proportions. Four of the techniques best adapted to dealing with the multifactorial environmental problems of chemical health effects will be briefly described: (1) quantitative structure/activity relationships (QSAR); (2) factor analysis (FA); (3) pattern recognition/artificial intelligence (PR/AI); and (4) molecular connectivity (MC). While it is clear that none provides easy answers, it would appear that the more recent areas of PR and MC both merit more intensive investigation as predictive tools. In particular, the relative simplicity of the MC approach and the possibility of substantially reducing the empirical component are attractive incentives for pursuing further work in this area

Toxic Substances Control Act: a chemist's view

The growing awareness of the hazards of chemicals in the environment has resulted in the passing of the Toxic Substances Control Act (TSCA) on Oct. 11, 1976. This paper attempts to cover some of the salient features of Public Law 94-469, EPA's approach to implementation of its provisions, and some of a chemist's perceptions of TSCA's prospects of success

TREATMENT OF MULTIVARIATE ENVIRONMENTAL AND HEALTH PROBLEMS ASSOCIATED WITH OIL SHALE TECHNOLOGY

A statistical matrix approach for solving complex environmental problems involving oil shale mining and processing is presented. Four methods are described: quantitative structure-activity and molecular connectivity relationships (QSAR,MC); factor analysis (FA); and pattern recognition (PR). QSAR and MC are useful in the prediction of toxic behavior for individual members of a class of compounds for which SAR data are already available. The methods of FA and PR have both been applied to data derived from structure-activity relationships (SAR), and PR techniques have also been used with connectivity functions. Each method is evaluated and the scope of their application is defined. (DMC