{"doi":"10.1007/s40264-025-01545-6","title":"A Drug Similarity-Based Bayesian Method for Early Adverse Drug Event Detection","abstract":"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.","journal":"Drug Safety","year":2025,"id":543875,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9556,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1150921,"name":"Yuedi Yang","orcid":"0000-0003-3355-4723","position":1,"is_corresponding":false},{"id":551857,"name":"Ruoqi Liu","orcid":"0000-0002-2770-0651","position":2,"is_corresponding":false},{"id":1150920,"name":"Anna Sun","orcid":"0009-0007-6467-0563","position":3,"is_corresponding":false},{"id":1186938,"name":"Xueqiao Peng","orcid":"0000-0001-8004-393X","position":4,"is_corresponding":false},{"id":12406,"name":"Lang Li","orcid":"0000-0002-0746-1809","position":5,"is_corresponding":false},{"id":551859,"name":"Ping Zhang","orcid":"0000-0002-4601-0779","position":6,"is_corresponding":false},{"id":1150922,"name":"Pengyue Zhang","orcid":"0000-0002-9148-5589","position":7,"is_corresponding":false},{"id":1150919,"name":"Yi Shi","orcid":"0000-0003-3575-4948","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:53:08.338070Z","pmid":"40261506","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}