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基于FAERS的培唑帕尼不良事件信号挖掘

Signal Mining of Adverse Drug Events of Pazopanib Based on FAERS
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摘要 目的为临床合理使用培唑帕尼提供参考。方法通过美国食品和药物管理局不良事件报告系统(FAERS)获取2009年1月1日至2023年4月30日以培唑帕尼为首要怀疑药物的ADE报告,利用报告比值比(ROR)法和贝叶斯置信递进神经网络(BCPNN)法对ADE信号进行挖掘。结果共得到ADE报告24141份,检测到ADE信号273个,共涉及21个系统器官分类(SOC),其中各类检查(62个,22.71%)、胃肠系统疾病(41个,15.02%)、肝胆系统疾病(17个,6.23%)等涉及信号数较多。首选语(PT)报告例数排前3的分别为腹泻(4065例)、食欲减退(1648例)、高血压(1395例);ADE信号强度排前3的为肛门直肠溃疡、毛发颜色改变、睫毛脱色。结论培唑帕尼ADE信号挖掘结果与其药品说明书记载基本一致。对于培唑帕尼药品说明书中未提及的部分ADE,如肿块(多部位)、黄视症等,目前虽尚无研究证实与使用该药有直接关联,但临床使用时也需留意。 Objective To provide a reference for the rational use of pazopanib in clinical practice.Methods The adverse drug event(ADE)reports with pazopanib as the primary suspicious drug in the FDA Adverse Event Reporting System(FAERS)from January 1,2009 to April 30,2023 were obtained,the ADE signals were mined by the reporting odds ratio(ROR)and Bayesian confidence propagation neural network(BCPNN)methods.Results A total of 24141 ADE reports were obtained,and 273 ADE signals were detected,involving 21 system organ classifications(SOCs),in which various examinations(22.71%),gastrointestinal disorders(15.02%)and hepatobiliary disorders(6.23%)involving more signals,with 62,41,17 signals respectively.The top three preferred terms(PTs)in terms of reported cases were diarrhea(4065 cases),anorexia(1648 cases)and hypertension(1395 cases).The top three ADEs in terms of signal intensity were anorectal ulcers,hair color changes and eyelash discoloration.Conclusion The results of pazopanib-related ADE signal mining are basically consistent with those recorded in the drug instructions.For some ADEs such as lumps(multiple parts)and xanthopsia that are not mentioned in the drug instructions of pazopanib,although there is currently no research to confirm their direct association with the use of the drug,attention should also be paid in clinical use.
作者 游宏勇 李卫平 王强 YOU Hongyong;LI Weiping;WANG Qiang(The Second Affiliated Hospital of Army Medical University,Chongqing,China 400037)
出处 《中国药业》 CAS 2024年第12期105-109,共5页 China Pharmaceuticals
基金 重庆市临床药学重点专科建设项目[渝卫办发〔2020〕68号]。
关键词 培唑帕尼 药品不良事件 美国食品和药物管理局不良事件报告系统 信号挖掘 报告比值比法 贝叶斯置信递进神经网络法 pazopanib adverse drug event FDA Adverse Event Reporting System signal mining reporting odds ratio Bayesian confidence propagation neural network
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