Among the FDA-approved small molecule drugs (2005-2016) that are primarily metabolized by cytochrome P450 (CYP), 64% are primarily metabolized by CYP3A4. As the proportion of an individual drug's fraction metabolized through CYP3A4 increases, the risk for the drug to be a victim of an interaction with CYP3A4 inhibitors or inducers increases. Therefore, it is important to assess the extent of involvement of individual CYP enzymes in the overall clearance for a scaffold early in discovery and mitigate the CYP3A4-mediated victim-drug-drug interaction (DDI) risk, if warranted by the desired clinical profile of the drug. To mitigate the CYP3A4-mediated victim DDI risk in discovery, we analyzed the physicochemical properties of the CYP3A4 substrates and found that molecular weight was the property that provided the best separation of the CYP3A4 substrates from other CYP substrates. In addition, neutral and basic compounds with MW ≥ 360 g/mol tend to be primarily metabolized by CYP3A4, whereas acidic compounds with MW < 360 g/mol are most likely to be primarily metabolized by other CYP enzymes. We then developed Support Vector Machine based on fingerprints (SVM-FP) and Deep-Learning (DL) models to predict if a molecule will be primarily metabolized by CYP3A4. Our models were trained on 2306 compounds, which is the largest training set among published models for this endpoint. Both models showed positive predictive values (PPV) > 80% in predicting a CYP3A4 substrate on a prospective testing set. Given the high PPV of the models, project teams can confidently deprioritize compounds predicted to be CYP3A4 substrates to avoid the potential liability of CYP3A4 victim DDI. Teams can then focus time and resources on synthesizing compounds that are predicted to have a lower dependency on CYP3A4 metabolism and confirm that experimentally. Through such iterative in silico-in vitro learning circles, drug discovery teams can decide if metabolism through non-CYP3A4 pathways could be achieved in the SAR of a chemical series to mitigate the CYP3A4 victim DDI risk.
Keywords: CYP3A4 substrates; drug−drug interaction; in silico models; machine learning models; structure−property relationships; victim DDI.