In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State, Connectivity, and Molecular Property Descriptors.

ABSTRACT Genetic toxicity testing is a critical parameter in the safety assessment of pharmaceuticals, food constituents, and environmental and industrial chemicals. Quantitative structure-activity relationship (QSAR) software offers a rapid, cost-effective means of prioritizing the genotoxic potential of chemicals. Our goal is to develop and validate a complete battery of complementary QSAR models for genetic toxicity. We previously reported the development of MDL-QSAR models for the prediction of mutations in Salmonella typhimurium and Escherichia coli ( Contrera et al. 2005b ); this report describes the development of eight additional models for mutagenicity, clastogenicity, and DNA damage. The models were created using MDL-QSAR atom-type E-state, simple connectivity and molecular property descriptor categories, and nonparametric discriminant analysis. In 10% leave-group-out internal validation studies, the specificity of the models ranged from 63% for the mouse lymphoma (L5178Y-tk) model to 88% for chromosome aberrations in vivo. Sensitivity ranged from a high of 74% for the mouse lymphoma model to a low of 39% for the unscheduled DNA synthesis model. The receiver operator characteristic (ROC) was >/=2.00, a value indicative of good predictive performance. The predictive performance of MDL-QSAR models was also shown to compare favorably to the results of MultiCase MC4PC ( Matthews et al. 2006b ) genotoxicity models prepared with the same training data sets. MDL-QSAR software models exhibit good specificity, sensitivity, and coverage and they can provide rapid and cost-effective large-scale screening of compounds for genotoxic potential by the chemical and pharmaceutical industry and for regulatory decision support applications.