A real-world disproportionality analysis of anti-VEGF drugs from the FDA Adverse Event Reporting System

ABSTRACTBackground The association between anti-vascular endothelial growth factor (VEGF) drugs and ocular adverse events (AEs) has been reported, but large real-world studies of their association with systemic AEs are still lacking.Methods A disproportionality analysis of reports from the FDA Adverse Event Reporting System from January 2004 to September 2021 was conducted to detect the significant ADR signals with anti-VEGF drugs (including aflibercept, bevacizumab, brolucizumab, pegaptanib, and ranibizumab).Results A total of 2980 reported cases with 7125 drug-AEs were included. Five drugs were all associated with eye disorders, and pegaptanib and ranibizumab were also associated with cardiac disorders. For ranibizumab, pegaptanib, bevacizumab and aflibercept, the proportions of cardiac AEs were 8.57%, 5.62%, 3.43% and 3.20%, respectively, and the proportions of central nervous AEs were 8.81%, 7.41, 5.86% and 5.68%, respectively. In multiple comparisons, ranibizumab was significantly higher than bevacizumab and aflibercept in the proportion of cardiac AEs (P < 0.001), and ranibizumab was significantly higher than aflibercept in central nervous AEs (P < 0.001).Conclusions Our findings support the associations between anti-VEGF drugs and ocular AEs, cardiac AEs, and central nervous AEs. After intravitreal injection, attention should not only be paid to ocular symptoms, but also to systemic symptoms.KEYWORDS: Anti-VEGF drugsdisproportionality analysisFAERS databaseadverse eventsdrug safety Declaration of interestsThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Reviewer disclosuresPeer reviewers on this manuscript have no relevant financial or other relationships to disclose.Author contribution statementRSZ developed the study protocol and research design, processed data, analyzed data, interpretated data, and edited the manuscript. PWL participated in data collection and editing the English manuscript and made critical comments. MXH, JHC and YYS revised it critically for important intellectual content. FFH participated in developing the study protocol and research design and editing the English manuscript. FFH and YMC supervised the study, administrated the project, and revised it critically for important intellectual content. All authors read and approved the final manuscript.Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2023.2250717Additional informationFundingThis paper was funded by the Research on Prediction Trend of Population Infected with COVID-19 Based on Big Data (NO. 2020KZDZX1126), the 2022 Science and Technology Innovation Project of Guangdong Medical Products Administration "Research and application of key technology and evaluation system of pharmacovigilance" (NO. 2022ZDZ06), and its sub-project named "Intelligent prediction of adverse drug reactions based on drug molecular structure-genomics reaction chain".