Detection of drug-drug interactions by modeling interaction profile fingerprints.
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 th...
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doaj-d6ebfb1a5a1d4bcbb7c9827bb2af23a02020-11-25T01:46:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0183e5832110.1371/journal.pone.0058321Detection of drug-drug interactions by modeling interaction profile fingerprints.Santiago VilarEugenio UriarteLourdes SantanaNicholas P TatonettiCarol FriedmanDrug-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.http://europepmc.org/articles/PMC3592896?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Santiago Vilar Eugenio Uriarte Lourdes Santana Nicholas P Tatonetti Carol Friedman |
spellingShingle |
Santiago Vilar Eugenio Uriarte Lourdes Santana Nicholas P Tatonetti Carol Friedman Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS ONE |
author_facet |
Santiago Vilar Eugenio Uriarte Lourdes Santana Nicholas P Tatonetti Carol Friedman |
author_sort |
Santiago Vilar |
title |
Detection of drug-drug interactions by modeling interaction profile fingerprints. |
title_short |
Detection of drug-drug interactions by modeling interaction profile fingerprints. |
title_full |
Detection of drug-drug interactions by modeling interaction profile fingerprints. |
title_fullStr |
Detection of drug-drug interactions by modeling interaction profile fingerprints. |
title_full_unstemmed |
Detection of drug-drug interactions by modeling interaction profile fingerprints. |
title_sort |
detection of drug-drug interactions by modeling interaction profile fingerprints. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3592896?pdf=render |
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