Health-effects related structure–toxicity relationships: a paradigm for the first decade of the new millennium

Abstract

Prediction of the effects of industrial chemicals to humans will be an area of increasing concern in the next century. The role of quantitative structure–activity relationships (QSARs) is to aid in the prediction of effects by determination of the limits of variation in structure that are consistent with the production of a specific toxic effect and define the ways in which alterations in structure influences toxicity. The paradigm followed in the development of QSARs for ecotoxic-endpoints has been successful as a direct result of the availability of in vivo toxicity data in which to build initial QSARs and validate surrogate test systems. However, the lack of quantitative in vivo toxicity data means this paradigm cannot be used in the prediction of human health-effect endpoints. Therefore, a new paradigm, which provides guidance in the use of predictive QSARs for health-effects that serves to circumvent the problems associated with the lack of whole-mammal toxicity data must be established. A scenario is given that provides for the development, standardization, and validation of health-effects related QSARs in the first decade of the 21st century. Due to the structural diversity and sheer number of industrial organic chemicals, assays used to garner health-effects data must be based on quantifiable, rapid, reliable, and inexpensive surrogate endpoints. New ‘biotools’ developed using modern molecular techniques will aid in the circumvention of in vivo health-effects data and provide measurable endpoints, which can be used as surrogates for health-effects endpoints. This has particular application in receptor-mediated toxicity and gene expression. The lack of whole-mammal health-effects data means that the standardization and validation, of these endpoints will be accomplished in novel ways. Databases garnered using modern biotools will allow the derivation of QSARs developed for validated surrogate health-effect endpoints. If QSARs are to be useful in bridging gaps in health-effects data, they must be based on accurate, reproducible data for a robust series of non-congeneric chemicals. The latter applies to both toxicity and descriptor values. Because health-effects are often the result of metabolic activation, if these new QSARs are to be meaningful, validated software for predicting metabolite formation must be incorporated. Lastly, once QSARs and software are developed and validated, they will need to be linked into some type of expert system.