A real-world disproportionality analysis of ospemifene: data mining of the public version of FDA adverse event reporting system

ABSTRACT

Background

Ospemifene has been authorized for the treatment of vulvovaginal atrophy (VVA). This study wasto evaluate adverse events (AEs) associated with ospemifene by data mining the US Food and Drug Administration Adverse Event Reporting System (FAERS).

Methods

The signals of AEs linked to ospemifene were measured using disproportionality analyses, such as 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.

Results

There were 2283 events of ospemifene being the ’primary suspected (PS)’ AE out of the 12,692,824 reports from the FAERS database. Ospemifene-induced AEs hit 25 organ systems. There were 726 severely disproportional preferred terms (PTs) that complied with the four algorithms. The investigation turned up a number of anticipated adverse drug reactions (ADRs), and significant unanticipated ADRs linked to eye and renal problems were found, indicating potential side effects not yet included in the prescription instructions.

Conclusion

We detected novel AEs signals for ospemifene, and the results of our investigation were compatible with clinical observations. This suggests that further prospective clinical trials are required to confirm these findings and demonstrate their link. Our findings might be useful supporting data for ospemifene safety research in the future.

KEYWORDS:

• BCPNN
• EBGM
• ospemifene
• PRR
• ROR

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 contribution statement

Xingling Qi and Haixiao Wen came up with the concept, planned the investigation, and wrote the initial draft. Results were validated by Meng Zhang and Chong Lu. Xingling Qi and Haixiao Wen carried out the statistical analysis. The paper was reviewed and revised by Xingling Qi. Each author contributed to the final manuscript after discussing the findings and approved the final version.

Data availability statement

The datasets used in this study can be found in online repositories. Any inquiries can be directed to the corresponding authors, whose original contributions are reflected in the article/Supplementary Material.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2023.2247971

Additional information

Funding

This paper was funded by grants from the Shanghai Health Commission Grant [No.20224Y0086].