Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions

Abstract

Drug–drug interaction (DDI) is a major public health problem contributing to 30% of the unexpected clinical adverse drug events. Informatics-based studies for DDI signal detection have been evolving in the last decade. We aim at providing a boosted machine learning (ML) framework to predict novel DDI safety signals with high precision. We propose a similarity-based machine learning framework called “SMDIP” using DrugBank as one of the most reliable pharmaceutical knowledge bases. For this study, DrugBank provides the latest drug information in terms of DDIs, targets, enzymes, transporters, and carriers. We computed drug–drug similarities using a Russell–Rao measure for the available biological and structural information on DrugBank for representing the sparse feature space. Logistic regression is adopted to conduct DDI classification with a focus on searching for key similarity predictors. Six types of ML models are deployed on the selected DDI key features. Our study reveals that SMDIP has yielded favourable predictive performance compared to relevant studies with results as follows: AUC 76%, precision 82%, accuracy 79%, recall 62%, specificity 90%, and F-measure 78%. To further confirm the reliability and reproducibility of SMDIP, we investigate SMDIP on an unseen subset of direct-acting-antiviral (DAA) drugs for treating hepatitis C infections. Forty novel DAA DDIs are predicted that show consistency with the pharmacokinetic and pharmacodynamic profiles of these drugs. Furthermore, several reports from the pharmacovigilance literature corroborate our framework results. Those evaluations show that SMDIP is a promising framework for uncovering DDIs, which can be multifariously feasible in drug development, postmarketing surveillance, and public health fields.