| アブストラクト | OBJECTIVE: Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation. METHODS: We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals. RESULTS: Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals. CONCLUSION: The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs. |
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| ジャーナル名 | Journal of biomedical informatics |
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| Pubmed追加日 | 2024/4/8 |
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| 投稿者 | Jeong, Eugene; Su, Yu; Li, Lang; Chen, You |
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| 組織名 | Department of Biomedical Informatics, School of Medicine, Vanderbilt University;Medical Center, Nashville, TN, United States.;Department of Computer Science and Engineering, College of Engineering, The Ohio;State University, Columbus, OH, United States.;Department of Biomedical Informatics, College of Medicine, The Ohio State;University, Columbus, OH, United States.;Medical Center, Nashville, TN, United States; Department of Computer Science,;School of Engineering, Vanderbilt University, Nashville, TN, United States.;Electronic address: you.chen@vanderbilt.edu. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/38583580/ |
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