文献类型：期刊文献

英文题名：Sex-specific pharmacovigilance signals and time-to-onset patterns of drug-related myoclonus and epileptic seizures: a real-world pharmacovigilance analysis

作者：Zhang, Fu[1];Yuan, Wenjun[1];Yao, Jingxi[1];Wei, Yushu[1];Wei, Xing[1];Sun, Kexin[1];Shi, Zhenggang[1];Shang, Jing[1]

第一作者：张帆;张芳

通信作者：Shi, ZG[1];Shang, J[1]

机构：[1]Gansu Univ Chinese Med, Sch Clin Chinese Med, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Clin Chinese Med, Lanzhou, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：17

外文期刊名：FRONTIERS IN PHARMACOLOGY

收录：;Scopus(收录号：2-s2.0-105049764234);WOS:【SCI-EXPANDED(收录号:WOS:001869628900001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the National Natural Science Foundation of China under the Regional Science Fund Program (grant No. 82460951).

语种：英文

外文关键词：epileptic seizure; FAERS; myoclonus; pharmacovigilance; reporting odds ratio; time-to-onset

摘要：Background Drug-related myoclonus and epileptic seizures are neurological adverse events that may cause trauma, impaired consciousness, hospitalization, treatment interruption, and life-threatening outcomes. In practice, these events may be difficult to distinguish from neurological diseases, metabolic abnormalities, disease progression, or complications of comorbidities and polypharmacy. Research has focused on individual drugs, case reports, or specific populations, leaving the risk-drug spectrum, sex-related reporting patterns, and onset characteristics insufficiently characterized. Systematic evaluation of these features is important for early identification, optimized monitoring, and individualized risk management. Methods FAERS reports from 2004Q1 to 2025Q4 were analyzed. Drug names were standardized using RxNorm, and adverse events were identified using MedDRA 27.1 preferred terms. Primary suspect drugs were evaluated by reporting odds ratio (ROR)-based disproportionality analysis. Sex-stratified analysis, ATC classification, time-to-onset (TTO) analysis, Weibull modeling, and cross-database validation using CVARD were performed. Results Reports were concentrated in North America, Europe, and East Asia, with female predominance and most patients aged 18-65 years. A total of 165 myoclonus-related and 235 epileptic seizure-related risk drugs were identified. Nervous system agents predominated, while antiinfectives, antineoplastic and immunomodulating agents, metabolic drugs, and cardiovascular agents also contributed. Bupropion had the highest report count (1,412; 265 myoclonus and 1,147 epileptic seizure reports), followed by tramadol (829). For myoclonus, gabapentin showed strong signals in males (ROR = 23.31, 95% CI: 20.95-25.95) and females (ROR = 18.06, 95% CI: 16.24-20.08), whereas tranexamic acid showed the strongest male signal (ROR = 128.41, 95% CI: 98.29-167.75). For epileptic seizures, bupropion showed signals in males (ROR = 18.08, 95% CI: 16.21-20.17) and females (ROR = 20.21, 95% CI: 18.70-21.85), with tramadol signals in both sexes. Drugs exhibited early-failure Weibull patterns (beta < 1). Median TTOs were 16.5 days for pregabalin-related myoclonus and 31.0 days for tramadol-related seizures, but 214.0 days for lamotrigine and 387.0 days for interferon beta-1a. CVARD identified 498 myoclonus-related and 940 seizure-related drugs and reproduced core signals, including clozapine, gabapentin, baclofen, pregabalin, sertraline, bupropion, quetiapine, and olanzapine. Conclusion Integrating signal detection, sex-stratified analysis, TTO modeling, and cross-database validation identified core risk drugs and monitoring windows, supporting earlier recognition and individualized management of drug-related neurological adverse events.