Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates

Inappropriate use of prescription drugs is potentially more harmful in fetuses/neonates than in adults. Cytochrome P450 (CYP) 3A subfamily undergoes developmental changes in expression, such as a transition from CYP3A7 to CYP3A4 shortly after birth, which provides a potential way to distinguish medication effects on fetuses/neonates and adults. The purpose of this study was to build first-in-class predictive models for both inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The performance varied for each CYP3A7/CYP3A4 inhibitor/ substrate model depending on the data set type, model type, rebalancing method, and specific feature set. For the active inhibitor/ substrate data set, the optimal models achieved AUC-ROC values ranging from 0.77 +/- 0.01 to 0.84 +/- 0.01. For the selective inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.72 +/- 0.02 to 0.79 +/- 0.04. The predictive power of the optimal models was validated by compounds with known potencies as CYP3A7/CYP3A4 inhibitors or substrates. In addition, we identified structural features significant for CYP3A7/CYP3A4 selective or common inhibitors and substrates. In summary, the top performing models can be further applied as a tool to rapidly evaluate the safety and efficacy of new drugs separately for fetuses/neonates and adults. The significant structural features could guide the design of new therapeutic drugs as well as aid in the optimization of existing medicine for fetuses/neonates.

Automated summary

This study developed predictive models for inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. The models' performance was evaluated using AUC-ROC, balanced accuracy, and Matthews correlation coefficient. The top performing models can be used to assess the safety and efficacy of new drugs separately for fetuses/neonates and adults, as well as guide the design and optimization of therapeutic drugs.