4.5 Article

The Next Generation Blueprint of Computational Toxicology at the US Environmental Protection Agency

Journal

TOXICOLOGICAL SCIENCES
Volume 169, Issue 2, Pages 317-332

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/toxsci/kfz058

Keywords

computational toxicology; predictive toxicology; high-throughput assays; toxicokinetics; exposure; risk assessment; ToxCast; ExpoCast; Tox21; cheminformatics

Categories

Funding

  1. United States Environmental Protection Agency through its Office of Research and Development

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The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

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