ArticlePLoS computational biology2012
Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it, 129 citations in OpenAlex.
- The efficacy and safety of common non-drug interventions on stress urinary incontinence: a systematic review and network meta-analysis.BMC women's health · 2026Pooled it
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- Discovering Severe Adverse Reactions From Pharmacokinetic Drug-Drug Interactions Through Literature Analysis and Electronic Health Record Verification.Clinical pharmacology and therapeutics · 2025Article
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Real-World Evidence Application in Translational Medicine: Making Use of Prescription Claims to Inform Drug-Drug Interactions of a New Psoriasis Treatment.Journal of clinical pharmacology · 2024Article
- Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population.Clinical and translational science · 2024Article
- Article
- TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.Chemical science · 2022Article
- Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature.Journal of biomedical informatics · 2022Article
- Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.BMC bioinformatics · 2022Article
- Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.Molecules (Basel, Switzerland) · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- On the robustness of generalization of drug-drug interaction models.BMC bioinformatics · 2021Article
- A machine learning framework for predicting drug-drug interactions.Scientific reports · 2021Article
- In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms.Pharmaceutics · 2021Article
- DPDDI: a deep predictor for drug-drug interactions.BMC bioinformatics · 2020Article
- A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy.Statistics in medicine · 2020Article
- Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics.Clinical pharmacology and therapeutics · 2020Review
- Improvement in the Analysis of Vaccine Adverse Event Reporting System Database.Statistics in biopharmaceutical research · 2020Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
Abstract
Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.