ArticleClinical pharmacology and therapeutics2025
Discovering Severe Adverse Reactions From Pharmacokinetic Drug-Drug Interactions Through Literature Analysis and Electronic Health Record Verification.
Article in Clinical pharmacology and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection.Frontiers in drug safety and regulation · 2026Pooled it
- Hypericin and hyperforin from St. John's Wort are potent intestinal UGT inhibitors with distinct binding modes: mechanistic and quantitative prediction of clinical drug-drug interaction risk.Frontiers in pharmacology · 2026Article
- Association of Omeprazole-Related Myopathy With Drug-Drug and Drug-Gene Interactions Involving CYP2C19 and CYP3A4: A Nested Case-Control Study.Pharmacotherapy · 2025Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- G6PC1 expression as a prognostic biomarker associated with metabolic reprogramming and tumor microenvironment in hepatocellular carcinoma.Frontiers in immunology · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
While drug-drug interactions (DDIs) and their pharmacokinetic (PK) mechanisms are well-studied prior to drug approval, severe adverse drug reactions (SADRs) caused by DDIs often remain underrecognized due to limitations in pre-marketing clinical trials. To address this gap, our study utilized a literature database, applied natural language processing (NLP) techniques, and conducted multi-source electronic health record (EHR) validation to uncover underrecognized DDI-SADR signals that warrant further investigation. PubMed abstracts related to DDIs from January 1962 to December 2023 were retrieved. We utilized PubTator Central for Named Entity Recognition (NER) to identify drugs and SADRs and employed SciFive for Relation Extraction (RE) to extract DDI-SADR signals. The extracted signals were cross-referenced with the DrugBank database and validated using logistic regression, considering risk factors including patient demographics, drug usage, and comorbidities, based on EHRs from Vanderbilt University Medical Center (VUMC) and the All of Us research program. From 160,321 abstracts, we identified 111 DDI-SADR signals. Seventeen were statistically significant (13 by one EHR and 4 by both EHR databases), with 9 being previously not recorded in the DrugBank. These included methadone-ciprofloxacin-respiratory depression, oxycodone-fluvoxamine-clonus, tramadol-fluconazole-hallucination, simvastatin-fluconazole-rhabdomyolysis, ibrutinib-amiodarone-atrial fibrillation, fentanyl-diltiazem-delirium, clarithromycin-voriconazole-acute kidney injury, colchicine-cyclosporine-rhabdomyolysis, and methadone-voriconazole-arrhythmia (odds ratios (ORs) ranged from 1.9 to 35.83, with P-values ranging from < 0.001 to 0.017). Utilizing NLP to extract DDI-SADRs from Biomedical Literature and validating these findings through multiple-source EHRs represents a pioneering approach in pharmacovigilance. This method uncovers clinically relevant SADRs resulting from DDIs that were not evident in pre-marketing trials or the existing DDI knowledge base.
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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.