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引用本文:陈琪莹,李毅敏,张吟.基于FAERS对钠-葡萄糖协同转运蛋白-2抑制剂不良反应的分析研究[J].中国现代应用药学,2021,38(14):1729-1733.
CHEN Qiying,LI Yimin,ZHANG Yin.Analysis and Study of Adverse Reactions of Sodium-glucose Cotransporter-2 Based on FAERS[J].Chin J Mod Appl Pharm(中国现代应用药学),2021,38(14):1729-1733.
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基于FAERS对钠-葡萄糖协同转运蛋白-2抑制剂不良反应的分析研究
陈琪莹, 李毅敏, 张吟
福建医科大学附属第二医院药学部, 福建 泉州 362000
摘要:
目的 通过美国食品药品监督管理局不良事件呈报系统(FDA Adverse Events Reporting System,FAERS)对钠-葡萄糖协同转运蛋白-2抑制剂(卡格列净、达格列净、恩格列净)的不良反应进行信号挖掘,探讨该抑制剂重要不良反应信号的强弱关系,为临床安全用药提供依据。方法 检索FAERS数据库从2013年第2季度—2019年第4季度共27个季度关于钠-葡萄糖协同转运蛋白-2抑制剂的6个重要不良反应。采用比例失衡法中的报告比值比法和比例报告比值法进行信号检测,并进一步对最强的警戒信号进行研究,分别从性别、年龄、剂量、适应证和用药时间这5个因素进行分析。结果 通过信号值计算,在低血压方面,恩格列净>卡格列净>达格列净;在糖尿病酮症酸中毒方面,恩格列净>卡格列净>达格列净;在急性肾损伤/肾损伤方面,卡格列净>达格列净>恩格列净;在生殖器真菌感染方面,卡格列净>达格列净>恩格列净;在尿路感染方面,达格列净>卡格列净>恩格列净;在低密度脂蛋白胆固醇升高方面,达格列净>卡格列净>恩格列净。结论 利用挖掘FAERS数据可较全面深入地分析研究钠-葡萄糖协同转运蛋白-2上市后的重要不良反应,进而有效地降低临床用药风险。
关键词:  钠-葡萄糖协同转运蛋白-2  卡格列净  达格列净  恩格列净  不良反应  数据挖掘  比例失衡法
DOI:10.13748/j.cnki.issn1007-7693.2021.14.012
分类号:R969.3
基金项目:福建省自然科学基金项目(2020J01213)
Analysis and Study of Adverse Reactions of Sodium-glucose Cotransporter-2 Based on FAERS
CHEN Qiying, LI Yimin, ZHANG Yin
Department of Pharmacy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
Abstract:
OBJECTIVE To explore the relationship between the strength and weakness of the important adverse reaction signals of these drugs, the adverse reactions of sodium-glucose cotransporter-2 inhibitors(canagliflozin, dapagliflozin and empagliflozin) were mined through the FDA Adverse Events Reporting System(FAERS) database, and to provide a basis for clinical safe medication. METHODS The FAERS database was retrieved from the second quarter of 2013 to the fourth quarter of 2019 for a total of 27 quarters regarding six major adverse reactions to sodium-glucose cotransporter-2 inhibitors. The measurse of disproportionality include reporting odds ratio and proportional reporting ratio were used for signal detection, further study the strongest warning signal, and five factors including gender, age, dose, indications and medication time were analyzed. RESULTS According to the calculation of the signal value, in terms of hypotension, empagliflozin> canagliflozin>dapagliflozin; in terms of diabetic ketoacidosis, empagliflozin>canagliflozin>dapagliflozin; for acute kidney injury/renal injury, canagliflozin>dapagliflozin>empagliflozin; for genital fungal infections, canagliflozin>dapagliflozin> empagliflozin; for urinary tract infections, dapagliflozin>canagliflozin>empagliflozin; in terms of elevated low-density cholesterol, dapagliflozin>canagliflozin>empagliflozin. CONCLUSION The mining of FAERS data can be used to comprehensively and deeply analyze and study the main adverse reactions of sodium-glucose cotransporter-2 after it is marketed, thereby effectively reducing the risk of clinical medication.
Key words:  sodium-glucose cotransporter-2  canegliflozin  dapagliflozin  empagliflozin  adverse reactions  data mining  measurse of disproportionality
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