Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.

The contribution ratio of metabolic enzymes such as cytochrome P450 to clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental fm values is challenging, those derived from experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting fm using chemical structure information alone. We collected fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that fm values can be obtained from structural information alone with a performance comparable to that based on experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using values. Our new approach to predicting fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.