Vilar Varela, SantiagoUriarte Villares, EugenioSantana Penín, María LourdesTatonetti, Nicholas P.Friedman, Carol2020-05-122020-05-122013Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C (2013) Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints. PLoS ONE 8(3): e58321. https://doi.org/10.1371/journal.pone.0058321http://hdl.handle.net/10347/22256Drug-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.eng© 2013 Vilar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.https://creativecommons.org/licenses/by/2.0/journal article10.1371/journal.pone.00583211932-6203open access