The Superfund Research Program (SRP), in collaboration with the US Environmental Protection Agency's Office of Superfund Remediation and Technology Innovation (OSRTI), presents the final session in the Spring/Summer 2009 edition of Risk eLearning: "Computational Toxicology: New Approaches for the 21st Century." This session, "Computational Toxicology: ToxCast and the Comparative Toxicogenomics Database (CTD)," will indicate the utility of the computational approaches to achieving a better understanding of the potential risks of contaminants.
Dr. David J. Dix, Acting Deputy Director of EPA/ORD's National Center for Computational Toxicology will present "ToxCast - Screening and Prioritization of Environmental Chemicals Based on Bioactivity Profiling and Predictions of Toxicity." The objective of EPA's ToxCast research program is to develop a cost-effective and rapid approach for screening and prioritizing a large number of chemicals for further toxicological testing. Using data from high throughput screening (HTS) bioassays, ToxCast is generating data, constructing databases, building computational models and prioritization tools based on the potential human toxicity of chemicals. These hazard predictions will provide EPA regulatory programs with science-based information helpful in prioritizing chemicals for more detailed toxicological evaluations, ultimately leading to more intelligent targeted testing.
NIEHS grantee and Mount Desert Island Biological Laboratory investigator, Dr. Carolyn Mattingly, will present "The Comparative Toxicogenomics Database: A resource for predicting chemical-gene-disease networks." The etiology of many chronic diseases involves interactions between the environment and genes that modulate biological processes. The Comparative Toxicogenomics Database (CTD) promotes understanding about the underlying mechanisms of environmental diseases by providing curated data describing relationships between chemicals, genes/proteins, and human diseases. Coupled with custom analysis tools, these data provide the foundation for predicting novel chemical-gene-disease networks.