Machine learning-based structural analysis of OATP1B1 interactors/non-interactors: Discriminating toxic and non-toxic alerts for transporter-mediated toxicity

Abstract

This hepatic transporter, OATP1B1, plays a critical role in transporter-related toxic responses and drug-drug interactions (DDIs). Several drug-drug interactions associated with OATP1B1 are clinically reported during combination therapies of lipid-lowering statins with antihypertensive, antiviral, and antibiotic drugs.

In the present study, different molecular properties of OATP1B1-interactors and non-interactors were initially compared, and the results revealed a distinct pattern in molecular weight, hydrophobicity, and number of rotatable bonds between them. Further chemical space, scaffold content, and diversity analyses indicated that OATP1B1-interactors/non-interactors are structurally diverse. Recursive partitioning and Bayesian classification analyses, involving ECFP and FCFP fingerprints, highlighted critical structural features that may serve as alerts for toxic or non-toxic effects on OATP1B1-mediated toxicity. Other machine learning-based classification models were also constructed, where Support Vector Classifier (SVC) shows higher statistical significance and predictive ability (accuracy: 0.797; precision: 0.833, and recall: 0.758). Moreover, local and global SHAP analyses were also performed to explain the distinctive structural features of OATP1B1-interactors and non-interactors.

Overall, the study offers insights into structural determinants of OATP1B1 interactions and provides predictive models to distinguish interactors from non-interactors, which may aid in reducing transporter-related toxicity risks in drug development. The outcomes may assist in advancing the safety and performance of medicinal compounds.