Predictive models and computational toxicology.

Thomas Knudsen, Matthew Martin, Kelly Chandler, Nicole Kleinstreuer, Richard Judson, Nisha Sipes

U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA. Knudsen.Thomas@epamail.epa.gov

Methods in molecular biology (Clifton, N.J.) 2013

filter terms:

Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The ToxCast computational toxicology research program was launched by EPA in 2007 and is part of the federal Tox21 consortium to develop a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals and the application of this information to assessing human toxicology. ToxCast addresses this problem through an integrated workflow using high-throughput screening (HTS) of chemical libraries across more than 650 in vitro assays including biochemical assays, human cells and cell lines, and alternative models such as mouse embryonic stem cells and zebrafish embryo development. The initial phase of ToxCast profiled a library of 309 environmental chemicals, mostly pesticidal actives having rich in vivo data from guideline studies that include chronic/cancer bioassays in mice and rats, multigenerational reproductive studies in rats, and prenatal developmental toxicity endpoints in rats and rabbits. The first phase of ToxCast was used to build models that aim to determine how well in vivo animal effects can be predicted solely from the in vitro data. Phase I is now complete and both the in vitro data (ToxCast) and anchoring in vivo database (ToxRefDB) have been made available to the public (http://actor.epa.gov/). As Phase II of ToxCast is now underway, the purpose of this chapter is to review progress to date with ToxCast predictive modeling, using specific examples on developmental and reproductive effects in rats and rabbits with lessons learned during Phase I.

Citation

Thomas Knudsen, Matthew Martin, Kelly Chandler, Nicole Kleinstreuer, Richard Judson, Nisha Sipes. Predictive models and computational toxicology. Methods in molecular biology (Clifton, N.J.). 2013;947:343-74