Post-marketing signal detection of fruquintinib-associated adverse events using the FAERS database

利用FAERS数据库进行上市后信号检测,以发现与呋喹替尼相关的不良事件

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Abstract

Fruquintinib, a targeted therapeutic agent approved in 2018 for the treatment of metastatic colorectal cancer, is gradually being applied in an expanding range of clinical settings. Given the complexities associated with real-world dosing, a comprehensive evaluation of its safety profile is essential for optimizing clinical decision-making. We conducted a retrospective pharmacovigilance study utilizing a spontaneous reporting system. Reports related to Fruquintinib were extracted through a standardized data cleaning process and cross-validated using 3 complementary signal detection algorithms: proportional reporting ratio, Bayesian confidence propagation neural network, and reporting odds ratio (ROR). This approach was employed to systematically assess the drug's safety and identify potential adverse event signals. A total of 92 statistically significant safety signals were identified from 1188 independent cases included in the analysis, corresponding to 1836 adverse event reports. The signal distribution exhibited organ system specificity, with gastrointestinal reactions being the most prevalent category. These reactions primarily manifested as typical drug-related toxicities, including diarrhea and nausea. Other notable complications included neurological, respiratory, dermatological, renal, and cardiovascular events. Important aes not mentioned in the instructions, such as bone marrow suppression (ROR = 11.17), peripheral neuropathy (ROR = 4.24) and dehydration (ROR = 3.98), were also discovered. This study systematically characterizes the post-marketing safety profile of Fruquintinib through a multi-algorithm synergistic analysis, confirming that its safety profile aligns with the clinically expected outcomes based on its mechanism of action. Future research should focus on analyzing drug interaction mechanisms and exploring biomarkers to develop a precise risk-benefit assessment model.

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