Drug‐Induced Acute Pancreatitis: A Real‐World Pharmacovigilance Study Using the FDA Adverse Event Reporting System Database

Abstract

Timely identification and discontinuation of culprit‐drug is the cornerstone of clinical management of drug‐induced acute pancreatitis (AP), but the comprehensive landscape of AP culprit‐drugs is still lacking. To provide the current overview of AP culprit‐drugs to guide clinical practice, we reviewed the adverse event (AE) reports associated with AP in the FDA Adverse Event Reporting System (FAERS) database from 2004 to 2022, and summarized a potential AP culprit‐drug list and its corresponding AE report quantity proportion. The disproportionality analysis was used to detect adverse drug reaction (ADR) signals for each drug in the drug list, and the ADR signal distribution was integrated to show the risk characteristic of drugs according to the ADR signal detection results. In FAERS database, a total of 62,206 AE reports were AP‐related, in which 1,175 drugs were reported as culprit‐drug. On the whole, metformin was the drug with the greatest number of AE reports, followed by quetiapine, liraglutide, exenatide, and sitagliptin. Drugs used in diabetes was the drug class with the greatest number of AE reports, followed by immunosuppressants, psycholeptics, drugs for acid‐related disorders and analgesics. In disproportionality analysis, 595 drugs showed potential AP risk, while 580 drugs did not show any positive ADR signal. According to the positive‐negative distribution of ADR signal for drug classes, the drug class with the greatest number of positive drugs was antineoplastic agents. In this study, we provided the current comprehensive landscape of AP culprit‐drugs from the pharmacovigilance perspective, which can provide reference information for clinical practice.

Safety of tildrakizumab: a disproportionality analysis based on the FDA adverse event reporting system (FAERS) database from 2018–2023

“…Disproportionality analysis refers to the assessment of spontaneously reported cases to detect signals in order to hypothesise a potential causal relationship between a specific drug and its ADRs, using all ADRs as a statistical background ( Zou et al, 2023 ; Li et al, 2024 ). The detected signals indicate an association between medications and ADRs, The detected signals indicating an association between medications and ADRs can be quantitatively identified by two main statistical methods and four algorithms: 1).…”