Dataset Information

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

ABSTRACT: Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate.

SUBMITTER: Vilar S

PROVIDER: S-EPMC4466327 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

Vilar Santiago S Lorberbaum Tal T Hripcsak George G Tatonetti Nicholas P NP

PloS one 20150612 6

Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor perfo ...[more]

PMID: 26068584

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Project description:IntroductionAdverse drug reactions related to drug-drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance.ObjectiveThe aim of this study was to evaluate the feasibility of signal detection of drug-drug interactions in collections of individual case reports of suspected adverse drug reactions.MethodsThis study was conducted in VigiBase, the WHO global database of individual case safety reports. The data lock point was 31 August 2016, which provided 13.6 million reports for analysis after deduplication. Statistical signal detection was performed using a previously developed predictive model for possible drug interactions. The model accounts for an interaction disproportionality measure, expressed suspicion of an interaction by the reporter, potential for interaction through cytochrome P450 activity of drugs, and reported information indicative of unexpected therapeutic response or altered therapeutic effect. Triage filters focused the preliminary signal assessment on combinations relating to serious adverse events with case series of no more than 30 reports from at least two countries, with at least one report during the previous 2 years. Additional filters sought to eliminate already known drug interactions through text mining of standard literature sources. Preliminary signal assessment was performed by a multidisciplinary group of pharmacovigilance professionals from Uppsala Monitoring Centre and collaborating organizations, whereas in-depth signal assessment was performed by experienced pharmacovigilance assessors.ResultsWe performed preliminary signal assessment for 407 unique drug pairs. Of these, 157 drug pairs were considered already known to interact, whereas 232 were closed after preliminary assessment for other reasons. Ten drug pairs were subjected to in-depth signal assessment and an additional eight were decided to be kept under review awaiting additional reports. The triage filters had a major impact in focusing our preliminary signal assessment on just 14% of the statistical signals generated by the predictive model for drug interactions. In-depth assessment led to three signals communicated with the broader pharmacovigilance community, six closed signals and one to be kept under review.ConclusionThis study shows that signals of adverse drug interactions can be detected through broad statistical screening of individual case reports. It further shows that signal assessment related to possible drug interactions requires more detailed information on the temporal relationship between different drugs and the adverse event. Future research may consider whether interaction signal detection should be performed not for individual adverse event terms but for pairs of drugs across a spectrum of adverse events.

Project description:Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods for drug-drug interaction prediction based on artificial neural networks and factor propagation over graph nodes: adjacency matrix factorization (AMF) and adjacency matrix factorization with propagation (AMFP). We conduct a retrospective analysis by training our models on a previous release of the DrugBank database with 1,141 drugs and 45,296 drug-drug interactions and evaluate the results on a later version of DrugBank with 1,440 drugs and 248,146 drug-drug interactions. Additionally, we perform a holdout analysis using DrugBank. We report an area under the receiver operating characteristic curve score of 0.807 and 0.990 for the retrospective and holdout analyses respectively. Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0.814 and 0.991 for the retrospective and the holdout analyses. We demonstrate that AMF and AMFP provide state of the art results compared to existing methods and that the ensemble-based classifier improves the performance by combining various predictors. Additionally, we compare our methods with multi-source data-based predictors using cross-validation. In the multi-source data comparison, our methods outperform various ensembles created using 29 different predictors based on several data sources. These results suggest that AMF, AMFP, and the proposed ensemble-based classifier can provide important information during drug development and regarding drug prescription given only partial or noisy data. Additionally, the results indicate that the interaction network (known DDIs) is the most useful data source for identifying potential DDIs and that our methods take advantage of it better than the other methods investigated. The methods we present can also be used to solve other link prediction problems. Drug embeddings (compressed representations) created when training our models using the interaction network have been made public.

Dataset Information

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

Publications

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

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