Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates

Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism
of ∼7% of marketed drugs. The in vitro drug interaction studies
guidance for industry issued by the FDA stipulates that drug sponsors
need to evaluate whether the investigated drugs interact with the
major drug-metabolizing P450s including CYP2B6. Therefore, there has
been greater attention to the development of predictive models for
CYP2B6 inhibitors and substrates. In this study, conventional machine
learning and deep learning models were developed to predict CYP2B6
inhibitors and substrates. Our results showed that the best CYP2B6
inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold
cross-validation and the test set, respectively, and the best CYP2B6
substrate model produced the AUC values of 0.93 and 0.90 with the
10-fold cross-validation and the test set, respectively. The generalization
ability of the CYP2B6 inhibitor and substrate models was assessed
by using the external validation sets. Several significant substructural
fragments relevant to CYP2B6 inhibitors and substrates were detected
via frequency substructure analysis and information gain. In addition,
the applicability domain of the models was defined by employing a
nonparametric method based on the probability density distribution.
We anticipate that our results would be useful for the prediction
of potential CYP2B6 inhibitors and substrates in the early stage of
drug discovery.