A real-world pharmacovigilance study of axitinib: data mining of the public version of FDA adverse event reporting system

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.

KEYWORDS:

• Axitinib
• renal carcinoma
• pharmacovigilance
• data mining
• FAERS
• adverse event

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Qilin Zhang and Yamin Shu contributed to conception and study design, and took responsibility for the collection, integrity and accuracy of the data. All authors drafted the manuscript, participated in data analyses and interpretation, and revisions of the manuscript and approved the final version.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2022.2016696.

Additional information

Funding

This study was supported by grants from National Natural Science Foundation of China [No. 82104476].