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Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening
MARTIN, M. T., T. B. KNUDSEN, D. REIF, K. A. HOUCK, R. JUDSON, R. J. KAVLOCK, AND D. J. DIX. Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening. BIOLOGY OF REPRODUCTION. Society for the Study of Reproduction, 85(2):327-339, (2011).
The EPA ToxCast research program uses high throughput screening for bioactivity profiling and predicting the toxicity of large numbers of chemicals. ToxCast Phase‐I tested 309 well‐characterized chemicals in over 500 assays for a wide range of molecular targets and cellular responses. Of the 309 environmental chemicals in Phase I of ToxCast, 256 were linked to high quality rat multigeneration reproductive toxicity studies in the relational Toxicity Reference Database. Reproductive toxicants were defined here as having achieved a reproductive lowest observed adverse effect level less than 500 milligram per kilogram of body weight per day. 86 chemicals were identified as reproductive toxicants in rat; 68 of those with sufficient in vitro bioactivity to model. Each assay was assessed for univariate association with the identified reproductive toxicants. Significantly associated assays were linked to gene sets and used for the subsequent predictive modeling. Using linear discriminant analysis and fivefold cross‐validation, a robust and stable predictive model was produced capable of identifying rodent reproductive toxicants with 77±2% and 74±5% training and test cross‐validation balanced accuracies, respectively. With a 21 chemical external validation set the model was 76% accurate, further indicating the model’s potential for prioritizing the many thousands of environmental chemicals with little to no hazard information. The biological features of the model include steroidal and non‐steroidal nuclear receptors, cytochrome P450 enzyme inhibition, G protein‐coupled receptors, and cell signaling pathway readouts‐ mechanistic information suggesting additional targeted, integrated testing strategies and potential applications of in vitro HTS to risk assessment.
The ability of this predictive reproductive toxicity model to externally predict numerous chemicals with biological and structural diversity demonstrates suitability for chemical testing prioritization. Although the model does not provide quantitative dose response information, it does provide accurate predictions of a chemical’s reproductive toxicity potential. Since the model is based on HTS data, it is amenable to screening and prioritizing thousands of chemicals. Additionally, the biological features of the model provide mechanistic insights into modes of action useful in developing an integrated testing strategy for reproductive toxicity.
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Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY