A Predictive Model for Drug-Drug Interaction Using a Similarity Measure
Abirami Ariyur Mahadevan, Anagha Vishnuvajjala, Naman Dosi, Shrisha Rao · 2019
Drug-drug interaction causes potential impact on patients when a second drug is administered during the duration of action of the first. It may result in the delay or decrease in the absorption of rate of drugs or enhance their absorption. This also in turn may affect the action of drugs or induce adverse effects on patients. There exists a need to study the drug-drug interactions, and their potential effects on the human system, including for drugs not yet approved. This paper proposes using eight features (substructure, targets, transporters, enzymes, pathways, indications, side-effect and off-side-effect, obtained from five different databases: PubChem, Drugbank, KEGG, SIDER, Offsides, and a similarity-based ensemble prediction model to identify the potential drug-drug interactions. The ensemble model uses the Jaccards coefficient method for identifying similarity measures between drugs. This similarity indices are given to a neighbor recommender method and random walk method for the base prediction of drug-drug interaction. This predictive model is improved by an ensemble modrisk and may be of little clinical significance. It can take years to clinically check DDIs for every pair of drugs. Sometimes DDIs may not get detected in clinical trials. Moreover, it may take many years to check DDIs of all known drugs with a newly discovered one before it is introduced to the market. Hence, there exists the need for efficient DDI-checking with fewer time-consuming, expensive, and risky clinical trials.