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SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS
Citation:
Richard, A M. AND C. R. Williams. SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS. Presented at Society for Risk Analysis: Symposium on Applying Quantitative Structure Activity Relationship Models in the Risk Estimation Process, Seattle, Washington, Dec 2-5, 2001.
Description:
Abstract
SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS
Risk assessment typically incorporates some relevant toxicity information upon which to base a sound estimation for a chemical of concern. However, there are many circumstances in which estimation of risk or relative risk must be made in the absence of definitive toxicological results. These include prioritizing chemicals for testing ( e.g. Hazardous Air Pollutants, Disinfection By- Products), estimating relative toxicities among closely related chemical analogues to refine risk estimations within regulated classes of chemicals (e.g. PCBs, trihalomethanes), and extrapolating across species, routes of exposure, target tissues, etc. Commercial structure-based toxicity prediction models provide some limited capability of estimating the potential toxicity of a query chemical across a variety of endpoints. Their appropriate use, however, must involve access to relevant data and the ability to provide independent support for a prediction. This folds into a larger, more generalized strategy of data mining and data exploration across and within toxicity databases that can both enhance and extend beyond currently available SAR toxicity prediction models. This presentation will consider current approaches for structure-based toxicity prediction, anlong with new database and data exploration tools being developed to provide broader access to toxicity information linked with property and structure information, both within and across health and ecological toxicity endpoints.
This abstract does not necessarily reflect EPA policy or endorsement.