Similarity-based modeling in large-scale prediction of drug-drug interactions

Abstract

The authors of this protocol describe a similarity-based, large-scale approach to predicting novel drug-drug interactions (DDIs) integrating a reference standard database of known DDIs with drug similarity information from a variety of sources. Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5–7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.