A real-world pharmacovigilance study of axitinib: data mining of the public version of FDA adverse event reporting system
Yamin Shu, Yufeng Ding, Bing Dai, Qilin Zhang
Abstract
BACKGROUND: Axitinib was approved for treatment of advanced renal cell carcinoma (RCC). The current study was to assess axitinib-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) algorithms, were employed to quantify the signals of axitinib-associated AEs. RESULTS: Out of 10,703,806 reports collected from the FAERS database, 9044 reports of axitinib as the 'primary suspected (PS)' AEs were identified. Axitinib induced AEs occurrence targeted 26 organ systems. A total of 95 significant disproportionality PTs conforming to the four algorithms were simultaneously retained. Rare reports and significant signals of aortic disease have emerged. Unexpected significant AEs such as scrotal swelling, scrotal ulcers, infections, and infestations might also occur. The median onset time of axitinib-associated AEs was 63.5 days (interquartile range [IQR] 20-182 days), and most of the cases occurred within the first one and 2 months after axitinib initiation. CONCLUSION: Our study found potential new AEs signals and might provide important support for clinical monitoring and risk identification of axitinib.