New predictors for several ADME/Tox properties: Aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors

Abstract

In silico predictive models for aqueous solubility, human intestinal absorption (HIA), and Ames genotoxicity were developed principally using artificial neural net (ANN) analysis and topological descriptors. Approximately 10,000 compounds spread across three data sets were used in the construction of these quantitative-structure-activity/property-relationship (QSAR/QSPR) models. For aqueous solubility, 5,037 chemically diverse compounds were used to construct ANN-QSPRs for intrinsic aqueous solubility. When these robust models were applied to 938 compounds in external validation, they gave an r2 = 0.78 with 84% predicted within 1 log unit for these new chemical entities (NCEs). 417 therapeutic drugs were used in the development of an ANN-QSPR to predict for percent oral absorption (%OA). For validation testing on 195 new drugs, 92% of the compounds were predicted to within 25% of their reported %OA values, which ranged from 0% to 100%. Polar surface area and logP, the octanol-water partition coefficient, were found to be important descriptors in our QSPR model. Development of an ANN-QSAR as a genotoxicity predictor for S. typhimurium employed 2963 compounds including 290 therapeutic drugs. Validation results on 400 NCEs with the ANN-QSAR gave a concordance of 83% which rose to 91% when a confidence indicator was applied. With new drugs a concordance of 92% was reached, which increased to 97% when the reliably indicator was invoked.