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

Abstract

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

A Review of CYP-Mediated Drug Interactions: Mechanisms and In Vitro Drug-Drug Interaction Assessment

“…It is challenging to determine in vivo clearance and f m and new models may help to bridge the gap. Recent work has used only the structure of the compound to predict the contribution ratio of a specific enzyme involved in metabolism [89]. This work has shown that in vivo values can be predicted in this way and are similar to in vitro values obtained in the lab [89].…”

“…Recent work has used only the structure of the compound to predict the contribution ratio of a specific enzyme involved in metabolism [89]. This work has shown that in vivo values can be predicted in this way and are similar to in vitro values obtained in the lab [89]. Other work in this space includes creating machine learning models to extrapolate in vivo parameters from in vitro inputs.…”

Enhancing Drug-Drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

“…HLM are a prevalent in vitro model for determining drug metabolism or f m , as they are affordable and cost-effective. In addition to traditional methods such as in vitro or in vivo testing, in silico methods can be employed to predict the fate of molecules in the body through computer simulations (Watanabe et al, 2023). These methods are generally more economical.…”