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Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

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Carolina Environmental Bioinformatics Research Center, Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Campus Box 7568, 327 Beard Hall, Chapel Hill, North Carolina 27599-7568, and Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio 45268
* To whom correspondence should be addressed. Tel: 919-966-2955. Fax: 919-966-0204. E-mail: [email protected]
†Carolina Environmental Bioinformatics Research Center.
‡University of North Carolina at Chapel Hill.
§U.S. Environmental Protection Agency.
Cite this: Chem. Res. Toxicol. 2009, 22, 12, 1913–1921
Publication Date (Web):October 21, 2009
https://doi.org/10.1021/tx900189p
Copyright © 2009 American Chemical Society
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    Abstract

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    Few quantitative structure−activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT’s training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

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