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Predictive Models and Computational Toxicology

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Book cover Teratogenicity Testing

Part of the book series: Methods in Molecular Biology ((MIMB,volume 947))

Abstract

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.

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Acknowledgements

This work was performed under EPA’s Chemical Safety for Sustainability Research Program (CSS). The authors wish to acknowledge the ToxCast and Virtual Embryo project teams for their helpful insight.

Disclaimer: The U.S. EPA, through its Office of Research and Development funded and managed the research described here. It has been subjected to agency review and approved for publication. The views expressed are the opinions of the authors and do not reflect Agency policy. The authors declare they have no competing financial interests.

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Authors and Affiliations

  1. U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA

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

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  1. Thomas Knudsen
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  2. Matthew Martin
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  3. Kelly Chandler
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  4. Nicole Kleinstreuer
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  5. Richard Judson
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  6. Nisha Sipes
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Correspondence to Thomas Knudsen .

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  1. CiToxLAB, BP 563, Evreux, 27005, France

    Paul C. Barrow

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Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

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Knudsen, T., Martin, M., Chandler, K., Kleinstreuer, N., Judson, R., Sipes, N. (2013). Predictive Models and Computational Toxicology. In: Barrow, P. (eds) Teratogenicity Testing. Methods in Molecular Biology, vol 947. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-131-8_26

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  • DOI: https://doi.org/10.1007/978-1-62703-131-8_26

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