Demo from FAERS database.

BackgroundObinutuzumab is the first glycosylated type II anti-CD20 monoclonal antibody for the treatment of lymphocytic leukemia and follicular lymphoma. This research aimed to identify significant and unexpected adverse events (AEs) associated with obinutuzumab by utilizing data from the US Food and Drug Administration’s Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) databases, with the intention of providing a reference for the safe and rational clinical use of the drug.Research design and methodsThe reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric average (EBGM) were employed to analyze the AEs of obinutuzumab using the registration data from the FAERS and JADER databases spanning from 2013 to 2025.ResultsThe study screened 7,868 and 1,584 AE reports related to obinutuzumab from the FAERS and JADER databases, respectively. These AEs involved 198 and 39 risk signals, respectively, and were associated with 16 and 8 system organ classes. In the analysis of the top 30 preferred terms, 19 and 15 risk signals in the FAERS and JADER databases, respectively, were not documented in the drug instruction. Moreover, when obinutuzumab is used for tumor indications, the frequency and signal strength of AEs related to infection and infusion-related reaction (IRR) are higher than those when it is used for non-tumor indications.ConclusionThe results of signal mining indicate that more attention should be paid to the risks of obinutuzumab-related AEs. Strengthening clinical medication monitoring is necessary to mitigate the impact of AEs on patients’ prognosis and quality of life.

背景：奥妥珠单抗(obinutuzumab)是首款糖基化II型抗CD20单克隆抗体，临床用于治疗淋巴细胞白血病与滤泡性淋巴瘤。本研究依托美国食品药品监督管理局不良事件报告系统（FAERS）数据库与日本药品不良事件报告（JADER）数据库的数据，旨在识别与奥妥珠单抗相关的具有统计学意义且未被预期的不良事件(adverse events, AEs)，以期为该药物的安全合理临床应用提供参考依据。
研究设计与方法：本研究采用报告比值比(reporting odds ratio, ROR)、比例报告比值比(proportional reporting ratio, PRR)、贝叶斯置信传播神经网络(Bayesian confidence propagation neural network, BCPNN)以及经验贝叶斯几何平均值(empirical Bayesian geometric average, EBGM)等方法，对2013年至2025年FAERS与JADER数据库中收录的奥妥珠单抗相关不良事件数据进行分析。
结果：本研究分别从FAERS与JADER数据库中筛选得到7868份和1584份奥妥珠单抗相关不良事件报告，对应识别出198个和39个不良事件风险信号，关联的系统器官分类分别为16类和8类。在排名前30的首选不良事件术语中，FAERS数据库中有19个、JADER数据库中有15个风险信号未在药品说明书中记载。此外，当奥妥珠单抗用于肿瘤适应症时，与感染及输液相关反应(infusion-related reaction, IRR)相关的不良事件发生频率与信号强度均高于其用于非肿瘤适应症时的水平。
结论：信号挖掘结果提示，临床需进一步关注奥妥珠单抗相关不良事件的风险，应加强临床用药监测，以减轻不良事件对患者预后与生活质量的负面影响。