基于FAERS数据库的培塞利珠单抗不良事件信号挖掘与分析  

Signal mining and analysis of adverse events of certolizumab based on FAERS database

作　　者：章婉春 陈心怡 蔡莹 洪红梅 李美霏 谢志军[1] ZHANG Wan-chun;CHEN Xin-yi;CAI Ying;HONG Hong-mei;LI Mei-fei;XIE Zhi-jun(School of Basic Medical Sciences,Zhejiang Chinese Medicine University,Hangzhou ZHEJIANG 310053,China)

机构地区：[1]浙江中医药大学基础医学院,浙江杭州310053

出　　处：《中国新药与临床杂志》2025年第1期63-67,共5页Chinese Journal of New Drugs and Clinical Remedies

基　　金：浙江省自然科学基金(Y21H270014);国家中医药管理局科技司-浙江省中医药管理局共建科技计划项目(GZY-ZJ-KJ-23009)。

摘　　要：目的为临床安全使用培塞利珠单抗(CZP)提供参考。方法基于美国食品药品管理局(FDA)不良事件报告系统(FAERS)数据库,收集2013年1月1日至2023年3月1日以CZP为首要怀疑药物的药品不良事件(ADE)报告,采用报告比值比(ROR)法和贝叶斯可信区间递进神经网络(BCPNN)法对ADE信号进行挖掘分析。结果共收集到ADE报告15675份,以女性(78.25%)和18~65岁者(45.09%)居多。共得到ADE信号298个,累及19个系统器官分类(SOC),报告数较多的SOC为免疫系统疾病,感染及侵染类疾病,呼吸系统、胸及纵隔疾病。共挖掘到19个CZP药品说明书中未记录的可疑信号,如过期妊娠、子宫内感染、耳部葡萄球菌感染等。结论临床使用CZP时,须重视对妊娠期及备孕期妇女的安全性评估,做好相关筛查与监测工作,以确保临床用药的安全性。AIM To provide a reference for the safe use of peselizumab(CZP).METHODS Based on the FDA Adverse Event Reporting System,adverse drug event(ADE)reports concerning CZP as the primary suspect drug from January 1,2013,to March 1,2023,were collected.ADE signal was mined by reporting odds ratio(ROR)method and Bayesian confidence propagation neural network(BCPNN)methods.RESULTS A total of 15675 ADE reports were collected,with a predominance of females(78.25%)and individuals aged 18 to 65 years(45.09%).A total of 298 ADE signals were identified,involving 19 system organ classes(SOCs).The SOCs with the higher number of reports were immune system disorders,infections and infestations,respiratory,thoracic and mediastinal disorders.A total of 19 suspicious signals were not recorded in the CZP drug label,such as prolonged pregnancy,intrauterine infection,ear infection staphylococcal,etc.CONCLUSION When using CZP clinically,it is imperative to emphasize the safety assessment of women during pregnancy and those preparing for pregnancy,and to conduct relevant screening and monitoring to ensure the safety of clinical medication use.

关 键 词：培塞利珠单抗 药品不良事件 药物警戒 数据挖掘 

分 类 号：R972[医药卫生—药品]