State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity.

Abstract

Co-administration of drugs is a primary cause of Adverse Drug Reactions (ADRs) and a drain on the health care industry costing billions of dollars and reducing quality of life. Drug-Drug Interactions (DDIs) account for as much as 30% of all ADRs. Unfortunately, DDIs are not systematically explored pre-clinically and are difficult to detect in post-marketing drug surveillance. For this reason, the detection and prediction of DDIs is an important problem in both drug development and pharmacovigilance. The comparison of the 3D drug structures provides a powerful tool for DDI prediction. In this article, we present the first large scale model for predicting DDIs using the drug's 3D molecular structure. In addition to identifying putative drug interactions we can also isolate the pharmacological or clinical effect associated with the predicted interactions. The model has good performance in two different hold-out validations and in external test sets. We found that the top scored drug pairs were significantly enriched for known clinically relevant interactions and that 3D structure data is providing significantly independent information from other approaches, including 2D structure (p=0.003). We demonstrated the usefulness of the proposed methodology to systematically identify pharmacokinetic and pharmacodynamic interactions, provided an exploratory tool that can be used for patient safety and pre-clinical toxicity screening, and reviewed the state of the art methods used to detect DDIs.