| アブストラクト | INTRODUCTION: Biochemical drug similarity-based methods demonstrate successes in predicting adverse drug events (ADEs) in preclinical settings and enhancing signals of ADEs in real-world data mining. Despite these successes, drug similarity-based ADE detection shall be expanded with false-positive control and evaluated under a time-to-detection setting. METHODS: We tested a drug similarity-based Bayesian method for early ADE detection with false-positive control. Under the tested method, prior distribution of ADE probability of a less frequent drug could be derived from frequent drugs with a high biochemical similarity, and posterior probability of null hypothesis could be used for signal detection and false-positive control. We evaluated the tested and reference methods by mining relatively newer drugs in real-world data (e.g., the US Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS) data) and conducting a simulation study. RESULTS: In FAERS analysis, the times to achieve a same probability of detection for drug-labeled ADEs following initial drug reporting were 5 years and >/= 7 years for the tested method and reference methods, respectively. Additionally, the tested method compared with reference methods had higher AUC values (0.57-0.79 vs. 0.32-0.71), especially within 3 years following initial drug reporting. In a simulation study, the tested method demonstrated proper false-positive control, and had higher probabilities of detection (0.31-0.60 vs. 0.11-0.41) and AUC values (0.88-0.95 vs. 0.69-0.86) compared with reference methods. Additionally, we identified different types of drug similarities had a comparable performance in high-throughput ADE mining. CONCLUSION: The drug similarity-based Bayesian ADE detection method might be able to accelerate ADE detection while controlling the false-positive rate. |
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| ジャーナル名 | Drug safety |
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| Pubmed追加日 | 2025/4/22 |
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| 投稿者 | Shi, Yi; Yang, Yuedi; Liu, Ruoqi; Sun, Anna; Peng, Xueqiao; Li, Lang; Zhang, Ping; Zhang, Pengyue |
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| 組織名 | Department of Biostatistics and Health Data Science, Indiana University,;Indianapolis, IN, USA.;Department of Computer Science and Engineering, The Ohio State University,;Columbus, OH, USA.;Department of Biomedical Informatics, The Ohio State University, Columbus, OH,;USA.;Columbus, OH, USA. ping.zhang@osumc.edu.;USA. ping.zhang@osumc.edu.;Indianapolis, IN, USA. zhangpe@iu.edu. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/40261506/ |
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