Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects
Open Access
- 21 July 2015
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 5 (1), 12339
- https://doi.org/10.1038/srep12339
Abstract
Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html.This publication has 33 references indexed in Scilit:
- Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based ApproachPLOS ONE, 2013
- Pharmacointeraction Network Models Predict Unknown Drug-Drug InteractionsPLOS ONE, 2013
- Non-steroidal anti-inflammatory drugs (NSAIDs) and hypertension treatment intensification: a population-based cohort studyEuropean Journal of Clinical Pharmacology, 2012
- Data-Driven Prediction of Drug Effects and InteractionsScience Translational Medicine, 2012
- INDI: a computational framework for inferring drug interactions and their associated recommendationsMolecular Systems Biology, 2012
- Detecting Drug Interactions From Adverse-Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose LevelsClinical Pharmacology & Therapeutics, 2011
- DrugBank 3.0: a comprehensive resource for 'Omics' research on drugsNucleic Acids Research, 2010
- STITCH 2: an interaction network database for small molecules and proteinsNucleic Acids Research, 2009
- PubChem: a public information system for analyzing bioactivities of small moleculesNucleic Acids Research, 2009
- Drug Target Identification Using Side-Effect SimilarityScience, 2008