Signals mining and analysis of adverse drug events of dapagliflozin based on FAERS database

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Author: LI Jiaoyue ^1# LIU Yan ^2# YANG Qiubo ³ YANG Tianyi ⁴ DAI Jinlin ¹ ZHAO Qiyao ¹ LUO Yunpeng ¹ LI Ping ¹ HUANG Zifan ¹  ZHANG Li ⁵  YANG Xiaohui ¹

Affiliation: 1. Department of Nephrology and Endocrinology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China 2. Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China 3. Department of Preventive Medicine, Weihai Wendeng District Traditional Chinese and Western Medicine Hospital, Weihai Shandong 264400, China 4. Rutgers University, New Brunswick, New Jersey 08901, USA 5. Department of Scientific Research, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China

Keywords: Dapagliflozin Sodium-glucose cotransporter 2 inhibitors Adverse drug event FAERS database Signal mining Pharmacovigilance

DOI: 10.12173/j.issn.1005-0698.202407063

Reference: LI Jiaoyue, LIU Yan, YANG Qiubo, YANG Tianyi, DAI Jinlin, ZHAO Qiyao, LUO Yunpeng, LI Ping, HUANG Zifan, ZHANG Li, YANG Xiaohui.Signals mining and analysis of adverse drug events of dapagliflozin based on FAERS database[J]. Yaowu Liuxingbingxue Zazhi, 2024, 33(12): 1336-1345. DOI: 10.12173/j.issn.1005-0698.202407063.[Article in Chinese]  Copied

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Abstract

Objective To mine signals of post-marketing adverse drug events (ADEs) associated with dapagliflozin, and to provide insights for safe medication in clinical settings.

Methods Data on ADEs related to dapagliflozin from U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) were collected from the first quarter of 2013 to the third quarter of 2024. The analyses involved data mining and signal monitoring using disproportionality analysis techniques including the reporting odds ratio (ROR) method, Medicines and Healthcare Products Regulatory Agency (MHRA) method, Bayesian confidence propagation neural network (BCPNN) method, and multi-item gamma Poisson shrinker (MGPS) method.

Results After data cleaning, a total of 55 832 qualified dapagliflozin case reports were obtained, involving 25 090 patients, with a slightly higher percentage of males (44.99%) than females (41.18%). The predominant age group was 45 to 64 years (20.78%). A total of 379 ADE signals were detected across 22 system-organ classes (SOC). The ADEs of dapagliflozin were mainly concentrated in the SOC such as infections and infestations, general disorders and administration sites conditions, and metabolism and nutrition disorders, aligning with information provided in the drug instructions. Additionally, the ADE signals were not documented in drug inserts such as scrotal gangrene, periperineal cellulitis, scrotal abscess, hyperglycemia, ketonuria, and pancreatitis.

Conclusion When clinically using dapagliflozin, it is essential to conduct a thorough medication assessment. In addition to closely monitoring diabetes ketoacidosis, fungal infection, and acute renal injury. The latent ADEs that are not mentioned in the instructions to should be noticed ensure safe medication.

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