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Use of in Vitro HTS-Derived Concentration-Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity
Citation:
SEDYHK, A., H. ZHU, H. TANG, L. ZHANG, A. M. RICHARD, I. RUSYN, AND A. TROPSHA. Use of in Vitro HTS-Derived Concentration-Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 119(3):364-370, (2011).
Impact/Purpose:
Concentration-response qHTS data may serve as informative biological descriptors of molecules which, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity endpoints.
Description:
Background: Quantitative high-throughput screening (qHTS) assays are increasingly being employed to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the NIH Chemical Genomics Center. Objectives: To test a hypothesis that dose-response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, may improve the accuracy of Quantitative Structure-Activity Relationship (QSAR) models applied to prediction of in vivo toxicity endpoints. Methods and Results: The cell viability qHTS concentration-response data for 1,408 substances assayed in 13 cell lines were obtained from PubChem; for a subset of these compounds rodent acute toxicity LD50 data were also available. The classification k Nearest Neighbor and Random Forest QSAR methods were employed for modeling LD50 data using either chemical descriptors alone (conventional models) or in combination with biological descriptors derived from the concentration-response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. We show that both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models was superior to conventional models. Conclusions: Concentration-response qHTS data may serve as informative biological descriptors of molecules which, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity endpoints.
URLs/Downloads:
Use of in Vitro HTS-Derived Concentration-Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity