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COMPUTATIONAL TOXICOLOGY
Impact/Purpose:
The overall goal of this research is to use emerging technologies to improve quantitative risk assessment and reduce the uncertainties that lie between the source of the chemical in the environment and the series of biological events that lead to the manifestation of an adverse outcome. The three strategic objectives are to develop improved linkages across the source-to-outcome continuum, develop approaches for prioritizing chemicals for subsequent screening and testing, and develop better methods and predictive models for quantitative risk assessment. To bring this emerging area of computational toxicology into the service of EPA, ORD will work with other agencies and academic scientists to incorporate the power of molecular profiling and simulations into the current approaches for understanding toxic effects and assessing risks through non-animal models and increasingly refined animal testing that eliminates redundancy in current protocols.
Description:
Over the last several years, there has been increased pressure to utilize novel technologies derived from computational chemistry, molecular biology and systems biology in toxicological risk assessment. This new area has been referred to as "Computational Toxicology". Our research utilizes approaches derived from modern computational methods, molecular biology, and systems biology to address the question of "when and how" to test specific chemicals for hazard identification and to improve quantitative dose-response assessment. The research uses many computational and biological approaches associated with the general area of computational toxicology, including diagnostic/prognostic molecular markers, improved dose metrics, characterization of toxicity pathways, metabonomics, system biological approaches, and modeling frameworks and sensitivity analysis. Our computational toxicological approaches will be used to address a number of research needs associated with dose-response assessment, cross-species extrapolation, and chemical mixtures.