A real-world disproportionality analysis of Everolimus: data mining of the public version of FDA adverse event reporting system

Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric mean and MedDRA was used to systematically classify the results. Results: A total of 24,575 AE reports of Everolimus were obtained using data from the FAERS database, and Everolimus-induced AEs occurrence targeted 24 system organ classes after conforming to the four algorithms simultaneously. The common significant SOCs were identified, included benign, malignant and unspecified neoplasms, reproductive system and breast disorders, etc. The significant AEs were then mapped to preferred terms such as stomatitis, pneumonitis and impaired insulin secretion, which have emerged in the study usually reported in patients with Everolimus. Of note, unexpected significant AEs, including biliary ischaemia, angiofibroma, and tuberous sclerosis complex were uncovered in the label. Conclusion: This study provided novel insights into the monitoring, surveillance, and management of adverse drug reaction associated with Everolimus. The outcome of serious adverse events and the corresponding detection signals, as well as the unexpected significant adverse events signals are worthy of attention in order to improving clinical medication safety during treatment of Everolimus.