Abstract

Animal bioassays by themselves can yield ambiguous results, especially in relation to human risk assessment. Three major alternates are proposed for integration in toxicology evaluations. The first is mathematical modeling and expression, viz. , statistical and quantitative structure/activity relationship (QSAR). Mathematical modeling can run indefinitely after animal experiments have terminated, and can serve as a powerful adjunct to animal experiments. Quantum mechanical analysis (QMA) can help researchers to study, on the molecular level, the interactions of chemicals and the body and to understand and predict side effects. QSAR assumes that a functional dependence exists between the observed biological response and certain physiological properties of molecules. With QMA implementation in QSAR, one can obtain reactivity characteristics in order to relate molecular structure to the observed biological activity. Stereology (morphometry) can be used to attach quantitative values to complex biological structures identified in light and electron micrographs. By integrating structural/functional data one expects to pinpoint the specific cellular responses to one or several chemicals, and even to predict genetic events from cytoplasmic changes. The second alternate is a battery of in vitro toxicity tests. For example, plasticity of synapse formation with cultured nerve cells should yield rich dividends when coupled with monoclonal antibody and recombinant DNA technology. Monoclonal antibody technology, when applied to studying nerve cell biochemistry, should be extremely useful in identifying the structure of various surface components on nerve cells. Recombinant DNA technology is likely to yield important information with regard to specific gene expression. An application of in vitro systems to teratogenecity testing has been actively pursued. The third alternate is the use of plants. Plant systems show promise for immediate use in laboratory short-term bioassays for toxicity evaluation of specific chemicals or chemical mixtures. For testing under field conditions, few test organisms offer the advantages provided by plants.

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