Post-marketing safety signals, therapeutic failure, and product-use issues reported with injectable Cabenuva: A FAERS disproportionality analysis.

Background: Cabenuva (cabotegravir/rilpivirine) is the first complete long-acting injectable regimen for HIV-1 treatment. As its use expands, post-marketing surveillance should address not only conventional adverse reactions, but also therapeutic failure and product-use or administration-related issues. This study aimed to characterize Cabenuva-associated reports submitted to the FDA Adverse Event Reporting System (FAERS) from Q4 2019 to Q3 2024. Methods: Cabenuva-associated FAERS reports were extracted and coded using MedDRA v27.1. Disproportionality analysis and Bayesian models were applied for signal detection. Subgroup analyses by sex and age were conducted using reporting odds ratios and interpreted as exploratory. Time-to-onset (TTO) was calculated from treatment start date to event onset date when valid dates were available. Results: A total of 3,497 reports including 22,738 Cabenuva-associated event records were identified. The detected PTs included established adverse reactions, therapeutic failure or disease-progression terms, product-use and administration issues, exposure circumstances, and sparse unexpected findings. Frequently reported clinically relevant events included injection-site pain, injection-site nodule, and depression. Prominent implementation-related terms included product dose omission issue, inappropriate schedule of product administration, and product administration error. Sparse unexpected PTs were interpreted as hypothesis-generating observations rather than established adverse reactions. Conclusions: This study provides a broad post-marketing signal-screening profile of Cabenuva, highlighting both known safety concerns and real-world implementation issues such as missed doses, administration problems, product issues, and therapeutic failure. The findings support careful clinical monitoring, adherence to administration guidance, staff training, and further validation of sparse unexpected signals.