Unveiling unexpected adverse events: post-marketing safety surveillance of gilteritinib and midostaurin from the FDA Adverse Event Reporting database.

Abstract

Background: Gilteritinib and midostaurin are FLT3 inhibitors that have made significant progress in the treatment of acute myeloid leukemia. However, their real-world safety profile in a large sample population is incomplete.

Objectives: We aimed to provide a pharmacovigilance study of the adverse events (AEs) associated with gilteritinib and midostaurin through the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.

Design: A retrospective analysis of the FAERS database was conducted by disproportionality analyses.

Methods: We conducted disproportionality analyses to identify drug-AE associations, including the reporting odds ratio and the Bayesian confidence propagation neural network. A signal was detected if both methods achieved statistical significance.

Results: There were 1887 and 2091 case reports for gilteritinib and midostaurin, respectively. We have separately retained significant disproportionality AEs across two algorithms, with a total of 53 AEs for gilteritinib and 46 for midostaurin. The common AEs observed with gilteritinib included febrile neutropenia, pyrexia, anemia, and thrombocytopenia. Similarly, the prevalent AEs associated with midostaurin were nausea, vomiting, diarrhea, pyrexia, and febrile neutropenia. The common AEs of both drugs are consistent with previous clinical trials. Notably, we also revealed unexpected significant AEs for both drugs. For gilteritinib, 29 positive signals for AEs not mentioned in its instructions were identified, such as cerebral hemorrhage, tumor lysis syndrome, and interstitial lung disease. Midostaurin exhibited 24 positive signals for AEs not listed in its instructions, including neutropenic colitis, neutropenic sepsis, and septic shock.

Conclusion: This study highlights the need for continued monitoring and evaluation of these drugs in clinical practice, as it first reveals their AEs in a large real-world sample population. Some AEs are generally consistent with the instructions and previous studies, but some unexpected AEs are detected for each drug. Due to the limitations of the spontaneous report database, such as including potential underreporting, overreporting, lack of causal relationship, unable to calculate incidence, and other confounding factors, more pharmacoepidemiology studies are needed to validate our findings.