Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug-like Molecules.

Abstract

The human cytochrome P450 (CYP) enzyme family is involved in the biotransformation of many xenobiotics. As part of the U.S. Tox21 Phase I effort, we profiled the CYP activity of approximately three thousand compounds, primarily those of environmental concern, against human CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 isoforms in a quantitative high throughput screening (qHTS) format. In order to evaluate the extent to which computational models built from a drug-like library screened in these five CYP assays under the same conditions can accurately predict the outcome of an environmental compound library, five support vector machines (SVM) models built from over 17,000 drug-like compounds were challenged to predict the CYP activities of the Tox21 compound collection. Although a large fraction of the test compounds fall outside of the applicability domain (AD) of the models, as measured by k-nearest neighbor (k-NN) similarities, the predictions were largely accurate for CYP1A2, CYP2C9, and CYP3A4 ioszymes with area under the receiver operator characteristic curves (AUC-ROC) ranging between 0.82 and 0.84. The lower predictive power of the CYP2C19 model (AUC-ROC = 0.76) is caused by experimental errors and that of the CYP2D6 model (AUC-ROC = 0.76) can be rescued by rebalancing the training data. Our results demonstrate that decomposing molecules into atom types enhanced the coverage of the AD and that computational models built from drug-like molecules can be used to predict the ability of non-drug like compounds to interact with these CYPs.