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Adverse events with pemigatinib in the real world: a pharmacovigilance study based on the FDA Adverse Event Reporting System

Dehua Zhao, Xiaoqing Long, Jisheng Wang

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Abstract

BACKGROUND: To data, there is insufficient large-scale data on the adverse events (AEs) of pemigatinib. Consequently, we conducted a pharmacovigilance study utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) database to investigate these AEs. RESEARCH DESIGN AND METHODS: The OpenVigil 2.1 was used to extract AE data from the FAERS database. Proportional reporting ratio (PRR), reporting odds ratios (ROR), and bayesian analysis confidence propagation neural network (BCPNN) were used to assess the association between pemigatinib and AEs. The clinical importance of AE signals were prioritized using a rating scale. RESULTS: A total of 848 AE reports were retrieved from the FAERS database, and 421 AE reports were identified after the data cleaning process. After accounting for indication bias and removal of medication errors, 59 positive signals were finally included. The 59 positive signals emerged in 11 system organ classes (SOCs). Besides, 19 positive AEs were classified as moderate clinical priority, while 40 were deemed weak in terms of priority. 9 positive AEs were not included in the drug label. CONCLUSIONS: This study provided valuable evidence for clinicians to mitigate the risk of pemigatinib-related toxicities in the real world.

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What this paper is about

BACKGROUND: To data, there is insufficient large-scale data on the adverse events (AEs) of pemigatinib. Consequently, we conducted a pharmacovigilance study utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) database to investigate these AEs. RESEARCH DESIGN AND METHODS: The OpenVigil 2.1 was used to extract AE data from the FAERS database. Proportional reporting ratio (PRR), reporting odds ratios (ROR), and bayesian analysis confidence propagation neural network (BCPNN) were used to assess the association between pemigatinib and AEs. The clinical importance of AE signals were prioritized using a rating scale. RESULTS: A total of 848 AE reports were retrieved from the FAERS database, and 421 AE reports were identified after the data cleaning process. After accounting for indication bias and removal of medication errors, 59 positive signals were finally included. The 59 positive signals emerged in 11 system organ classes (SOCs). Besides, 19 positive AEs were classified as moderate clinical priority, while 40 were deemed weak in terms of priority. 9 positive AEs were not included in the drug label. CONCLUSIONS: This study provided valuable evidence for clinicians to mitigate the risk of pemigatinib-related toxicities in the real world.

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Available abstract

BACKGROUND: To data, there is insufficient large-scale data on the adverse events (AEs) of pemigatinib. Consequently, we conducted a pharmacovigilance study utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) database to investigate these AEs. RESEARCH DESIGN AND METHODS: The OpenVigil 2.1 was used to extract AE data from the FAERS database. Proportional reporting ratio (PRR), reporting odds ratios (ROR), and bayesian analysis confidence propagation neural network (BCPNN) were used to assess the association between pemigatinib and AEs. The clinical importance of AE signals were prioritized using a rating scale. RESULTS: A total of 848 AE reports were retrieved from the FAERS database, and 421 AE reports were identified after the data cleaning process. After accounting for indication bias and removal of medication errors, 59 positive signals were finally included. The 59 positive signals emerged in 11 system organ classes (SOCs). Besides, 19 positive AEs were classified as moderate clinical priority, while 40 were deemed weak in terms of priority. 9 positive AEs were not included in the drug label. CONCLUSIONS: This study provided valuable evidence for clinicians to mitigate the risk of pemigatinib-related toxicities in the real world.

Key concepts: Adverse Event Reporting System, Pharmacovigilance, Medicine, Adverse effect, Odds ratio, Confidence interval, Pharmacoepidemiology, Database

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Adverse events with pemigatinib in the real world: a pharmacovigilance study based on the FDA Adverse Event Reporting System — Research Paper | ScholarLens