Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Abstract

Adverse drug reactions (ADRs) associated with drug-drug interactions (DDIs) represent a significant threat to public health. Unfortunately, most conventional methods for prediction of DDI-associated ADRs suffer from limited applicability and/or provide no mechanistic insight into DDIs. In this study, a hierarchical machine learning model was created to predict DDI-associated ADRs and pharmacological insight thereof for any drug pair. Briefly, the model takes drugs' chemical structures as inputs to predict their target, enzyme, and transporter (TET) profiles, which are subsequently utilized to assess occurrences of ADRs, with an overall accuracy of ~91%. The robustness of the model for ADR classification was validated with DDIs involving three widely prescribed drugs. The model was then applied for interstitial lung disease (ILD) associated with DDIs involving atorvastatin, identifying the involvement of multiple targets, enzymes, and transporters in ILD. The model presented here is anticipated to serve as a versatile tool for enhancing drug safety.