A real-world disproportionality analysis of adverse events associated with lenvatinib in thyroid cancer patients based on the FAERS database
W skrócie
[Preprint - wstępne wyniki] Badacze przeanalizowali ponad 2400 raportów o niepożądanych zdarzeniach u pacjentów z rakiem tarczycy leczonych lenvatinibem. Odkryli siedem nowych powikłań nie wymienionych wcześniej w instrukcji leku, w tym zapalenie nerek, epilepsję i krwawienie z tchawicy, a także stwierdzili, że większość objawów pojawia się w pierwszym miesiącu leczenia, a część dopiero po roku terapii. Te wyniki mogą pomóc lekarzom w lepszym monitorowaniu pacjentów i wczesnym rozpoznaniu poważnych skutków ubocznych lenvatinibu.
Oryginalny abstract (angielski)
Abstract Background Lenvatinib is a first-line treatment for advanced thyroid cancer, but its real-world safety profile in this population is incompletely defined. This pharmacovigilance study aimed to identify novel adverse event (AE) signals, characterize their temporal distribution, and provide a basis for stratified monitoring. Methods We analyzed FDA Adverse Event Reporting System (FAERS) reports from Q1 2015 to Q4 2025. A total of 2,484 eligible reports (lenvatinib as primary suspect drug, thyroid cancer indication) were included. Four disproportionality algorithms (ROR, PRR, BCPNN, MGPS) were used to detect safety signals at the preferred term (PT) and system organ class (SOC) levels. Time-to-onset analysis was performed. Results Strongest signals were found for vascular, renal/urinary, and cardiac disorders. Seven novel AE signals not listed in the FDA label were detected: kidney infection, ascites, hiccups, biliary colic, tracheal haemorrhage, epilepsy, and polycythaemia. Time-to-onset analysis revealed a biphasic pattern: 43.4% of events occurred within the first month, while a distinct late-onset cluster (15.3%) appeared after one year of therapy. Conclusion This real-world analysis updates the safety profile of lenvatinib in thyroid cancer, identifies previously unrecognized AEs, and reveals a biphasic time-to-onset pattern supporting stratified clinical monitoring.