Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions
Open Access
- 19 April 2013
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 8 (4), e61468
- https://doi.org/10.1371/journal.pone.0061468
Abstract
Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.Keywords
This publication has 36 references indexed in Scilit:
- Large-scale prediction and testing of drug activity on side-effect targetsNature, 2012
- A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reportsJournal of the American Medical Informatics Association, 2012
- Detecting Drug Interactions From Adverse-Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose LevelsClinical Pharmacology & Therapeutics, 2011
- The NCGC Pharmaceutical Collection: A Comprehensive Resource of Clinically Approved Drugs Enabling Repurposing and Chemical GenomicsScience Translational Medicine, 2011
- Interactome Networks and Human DiseaseCell, 2011
- Ability of pharmacy clinical decision-support software to alert users about clinically important drug—drug interactionsJournal of the American Medical Informatics Association, 2011
- Network medicine: a network-based approach to human diseaseNature Reviews Genetics, 2010
- Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network ModelPLoS Computational Biology, 2010
- Black Box Warning Contraindicated Comedications: Concordance Among Three Major Drug Interaction Screening ProgramsAnnals of Pharmacotherapy, 2010
- Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post‐marketing settingPharmacoepidemiology and Drug Safety, 2003