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THE TOXCAST PROGRAM FOR PRIORITIZING TOXICITY TESTING OF ENVIRONMENTAL CHEMICALS
Citation:
DIX, D. J., K. A. HOUCK, M. MARTIN, A. M. RICHARD, R. W. SETZER, AND R. J. KAVLOCK. THE TOXCAST PROGRAM FOR PRIORITIZING TOXICITY TESTING OF ENVIRONMENTAL CHEMICALS . TOXICOLOGICAL SCIENCES. Oxford University Press, Cary, NC, 95(1):5-12, (2007).
Impact/Purpose:
This chemical prioritization research program, entitled "ToxCast," is being intiated with the purpose of developing the ability to forecast toxicity based on bioactivity profiling.
Description:
The United States Environmental Protection Agency (EPA) is developing methods for utilizing computational chemistry, high-throughput screening (HTS) and various toxicogenomic technologies to predict potential for toxicity and prioritize limited testing resources towards chemicals that likely represent the greatest hazard to human health and the environment. This chemical prioritization research program, entitled ToxCast, is being initiated with the purpose of developing the ability to forecast toxicity based on bioactivity profiling. The proof-of-concept phase of ToxCast will focus upon chemicals with an existing, rich toxicological database in order to provide an interpretive context for the ToxCast data. This set of several hundred reference chemicals will represent numerous structural classes and phenotypic outcomes, including tumorigens, developmental and reproductive toxicants, neurotoxicants and immunotoxicants. The ToxCast program will evaluate chemical properties and bioactivity profiles across a broad spectrum of data domains: physical-chemical, predicted biological activities based on existing structure-activity models, biochemical properties based on HTS assays, cell-based phenotypic assays, and genomic and metabolomic analyses of cells. These data will be generated through a series of external contracts, along with collaborations across EPA, with the National Toxicology Program, and the National Institutes of Health Chemical Genomics Center. The resulting multidimensional dataset provides an informatics challenge requiring appropriate computational methods for integrating various chemical, biological and toxicological data into profiles and models predicting toxicity.
