Background: Nilotinib is a leukemia drug that can treat imatinib tolerance. During the drug trial, some adverse reactions of nilotinib have been proposed, and some articles have mentioned that nilotinib may have cardiovascular-related ADR signals. However, there is no systematic and comprehensive analysis of the potential ADR of nilotinib. AIM: The purpose of this study is to use the FDA adverse event reporting system (FAERS) database to detect the potential adverse event signals of nilotinib. Method: Data from the first quarter of 2015 to the fourth quarter of 2022 were selected for analysis from in the FAERS database. Use the preferred term in the Management activity Medical Dictionary (version 24.0) to extract cases of adverse events. The reported odds ratio (ROR) and information component (IC) methods based on statistical shrinkage transformation were used for disproportional analysis. Results: There were 24,451 adverse events associated with nilotinib in 11,190,626 records.A total of 529 positive signals of adverse reactions were found in taking nilotinib. Peripheral arterial occlusive disease (〖ROR〗_025=41.74 〖IC〗_025=5.36), Arteriosclerosis(〖ROR〗_025=33.49 〖IC〗_025=5.04), Intermittent claudication (〖ROR〗_025=32.12 〖IC〗_025=4.96), Splenitis (〖ROR〗_025=29.18 〖IC〗_025=4.79), Peripheral vascular disorder (〖ROR〗_025=27.00 〖IC〗_025=4.72), Peripheral artery stenosis (〖ROR〗_025=26.95 〖IC〗_025=4.96), Carotid artery stenosis (〖ROR〗_025=22.94 〖IC〗_025=4.48) had the strongest signal intensities. Conclusion: This study found that patients with leukemia taking nilotinib may have adverse reactions such as arteriovenous adverse reactions, myocardial infarction, splenitis, intermittent claudication and so on. KEYWORDS Disproportionate analysis, Nilotinib, FAERS database, pharmacovigilance study, CML

Aim: With the widespread use of SGLT2i, various adverse events (AEs) have been reported. This study aimed to describe the distribution of SGLT2i-related AEs in different systems, quantify the association of important medical events (IMEs) and SGLT2i regimens, and build a signal profile of SGLT2i- induced IMEs. Methods: Data from 2015 Q1 to 2020 Q4 in the FDA Adverse Event Reporting System database (FAERS) were selected to conduct disproportionality analysis. Two signal indicators, the reported odds ratio (ROR) and information component (IC), were used to evaluate the correlation between SGLT2i and IMEs. The lower end of the 95% confidence interval of IC (IC025) exceeding zero was deemed a signal. For ROR, it was defined a signal if ROR025 over one, with at least 3 cases. Results: A total of 45,771,436 records were involved, including 111,564 records related to SGLT2i, with 38,366 records of SGLT2i-induced IMEs. Overall, SGLT2i was significantly associated with IMEs (IC=0.36, 95% CI: 0.35-0.38; ROR=1.44, 95% CI: 1.42-1.46). Most SGLT2i-related adverse events occurred in monotherapy (92.93%). Diabetic ketoacidosis was the most IMEs. Specifically, acute osteomyelitis has the strongest signal of all SGLT2i (IC025=7.83), and it was unique to canagliflozin. Diabetic ketoacidosis, acute kidney injury, ketoacidosis, Fournier’s gangrene, and euglycemic diabetic ketoacidosis were common to the four FDA-approved SGLT2i. Conclusion: Our study demonstrated that different SGLT2i regimens lead to different important adverse events, but there are overlapping events. Early identification and management of SGLT2i-associated IMEs are essential for clinical practice.