Discovering Severe Adverse Reactions From Pharmacokinetic Drug-Drug Interactions Through Literature Analysis and Electronic Health Record Verification

Eugene Jeong; Yu Su; Lang Li; You Chen

doi:10.1002/cpt.3500

Discovering Severe Adverse Reactions From Pharmacokinetic Drug-Drug Interactions Through Literature Analysis and Electronic Health Record Verification

Clin Pharmacol Ther. 2024 Nov 25. doi: 10.1002/cpt.3500. Online ahead of print.

Authors

Eugene Jeong¹, Yu Su², Lang Li³, You Chen^{1

4}

Affiliations

¹ Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
² Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA.
³ Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA.
⁴ Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, Tennessee, USA.

PMID: 39585167
DOI: 10.1002/cpt.3500

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.