title: Detection of drug-drug interactions by modeling interaction profile fingerprints.
abstract: Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4-0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America. sav7003@dbmi.columbia.edu
authors.forename: Santiago
authors.initials: S
authors.lastname: Vilar
authors.forename: Eugenio
authors.initials: E
authors.lastname: Uriarte
authors.forename: Lourdes
authors.initials: L
authors.lastname: Santana
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Tatonetti
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D054316:Biomarkers, Pharmacological / Q000378:metabolism; D016208:Databases, Factual; D004347:Drug Interactions; D006801:Humans; D008954:Models, Biological; D011237:Predictive Value of Tests
chemical_list: D054316:Biomarkers, Pharmacological