Data mining study on adverse events of tirzepatide based on FAERS database

ABSTRACT

Background

Tirzepatide is a novel dual gastric inhibitory polypeptide (GIP) and glucagon‐like peptide‐1 receptor agonist (GLP-1 RA) for type 2 diabetes or obesity. To explore the safety profile of tirzepatide in real-world clinical applications.

Research design and methods

A retrospective analysis of adverse events (AEs) reports associated with tirzepatide was conducted from the second quarter of 2022 through the fourth quarter of 2023, utilizing the FDA Adverse Event Reporting System (FAERS) database. Signal mining utilized the reporting odds ratio (ROR) method, and onset time was analyzed utilizing the Weibull Shape Parameter (WSP).

Results

We identified 25,215 AE reports related to tirzepatide, predominantly in the 65 to 85 age group. Four positive signals were found at the system organ classes level. Additionally,109 AEs at the preferred terms level with positive signals were indicated. Included among these are novel signals, such as the presence of thyroid mass, medullary thyroid carcinoma, and conditions affecting the reproductive system and breast. Most AEs occurred within the first 30 days. The WSP was 0.66, indicating a propensity for early failure type.

Conclusions

This study identified several novel AE signals for tirzepatide, highlighting the need for careful monitoring, especially in the early stages of treatment.

KEYWORDS:

• Tirzepatide
• adverse events
• data mining
• pharmacovigilance
• FAERS database

Declaration of interest

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

Xiaolan Liao conceived the concept of this study and formulated the research protocol. Yan Huo and Minghua Ma dedicated themselves to the normalization of drug nomenclature and data purification. The data’s summary analysis was carried out by Yan Huo and Xiaolan Liao. All authors were involved in executing the research and in the composition of this manuscript.

Ethics statement

The FAERS datasets are publicly accessible and anonymized, thus obviating the need for ethical approval in our current study.

Additional information

Funding

This paper was not funded.