INDI: a computational framework for inferring drug interactions and their associated recommendations.
Inferring drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve) ≥0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/~bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.
The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. assafgot@tau.ac.il
Assaf
A
Gottlieb
Gideon Y
GY
Stein
Yoram
Y
Oron
Eytan
E
Ruppin
Roded
R
Sharan
D000465:Algorithms; D019540:Area Under Curve; D003198:Computer Simulation; D051544:Cytochrome P-450 CYP3A / Q000378:metabolism; D016208:Databases, Factual; D004347:Drug Interactions; D004359:Drug Therapy, Combination; D006801:Humans; D008954:Models, Biological; D010599:Pharmacokinetics
D051544:Cytochrome P-450 CYP3A