Ocular adverse events with immune checkpoint inhibitors

• Main Authors: Tony Fang, David A. Maberley, Mahyar Etminan
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2019-09-01
• Series: Journal of Current Ophthalmology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2452232519300885

Purpose: To quantify the risk of ocular adverse events with immune checkpoint inhibitors (ICIs) as reported to the Food and Drug Administration (FDA). Methods: Disproportionality analysis using data from U.S. FDA's Adverse Events Reporting System (FAERS) database 2003 to 2018. Data from pharmaceutical manufacturers, healthcare providers, consumers in the U.S., and post-marketing clinical trial reports from U.S. and non-U.S. studies. All cases of uveitis, dry eye syndrome, ocular myasthenia and eye inflammation with use of the following ICIs: atezolizumab, avelumab, cemiplimab, durvalumab, ipilimumab, nivolumab and pembrolizumab. Reported odds ratios (RORs) and corresponding 95% confidence intervals (CIs) were computed for all drugs as a group or as individual agents. Results: We identified 113 ocular adverse events for all ICIs of interest including uveitis, dry eye, ocular myasthenia and eye inflammation. Nivolumab had the highest number of adverse events (N = 68) associated with use of the ICI. Nivolumab had the highest association with ocular myasthenia [ROR = 22.82, 95% CI (7.18–72.50)] followed by pembrolizumab [ROR = 20.17, 95% CI (2.80–145.20)]. Among all ICIs approved in North America, atezolizumab had the highest association with eye inflammation [ROR = 18.89, 95% CI (6.07–58.81)] and ipilmumab had the highest association with uveitis [ROR = 10.54, 95% CI (7.30–15.22)]. Conclusion: The results of this disproportionality analysis suggest use of ICIs is associated with an increase risk for ocular adverse reactions. Future epidemiologic studies are needed to better quantify these adverse events. Keywords: Immune checkpoint inhibitors, Immunotherapy, Drug-induced, Uveitis, Eye inflammation, Disproportionality analysis