Pharmacovigilance signal detection of rilonacept using FAERS with exploratory Mendelian randomization analysis

Rilonacept, an interleukin-1 (IL-1) "trap," is FDA-approved for recurrent pericarditis, but research on its adverse reactions is limited due to its recent introduction.

This study utilized FDA Adverse Event Reporting System (FAERS) to screen potential rilonacept-related adverse events, and further performed Mendelian randomization (MR) analysis to explore genetic associations related to IL-1 pathway modulation with these adverse events.

We analyzed all adverse event reports related to rilonacept from the FAERS database between January 2021 and June 2024. Positive signals for adverse reactions were extracted using reporting odds ratios (ROR) and proportional reporting ratio (PRR). MR analysis was conducted using genetic variants of IL1RN as instrumental variables to explore associations between IL1RN genetic variation and identified adverse reactions, with sensitivity analyses performed for robustness.

A total of 419 adverse event reports were analyzed, documenting 1847 adverse events (AEs). Common events include injection site erythema, injection site pain, and others, which are categorized into 27 System Organ Classes (SOCs). Notable frequencies were found in General disorders and administration site conditions, Skin and subcutaneous tissue disorders, and Injury, poisoning and procedural complications. Disproportionality analysis identified positive signals primarily in Cardiac Disorders, and Immune System Disorders, with 4 AEs showing positive signals in both Preferred Terms (PTs) and SOCs. MR analysis revealed significant associations between IL1RN genetic variation (used to explore rilonacept-related effects) and allergic urticaria (OR: 1.56 1.08-2.26) and myocarditis (OR: 2.26 1.63-3.14).

Our study identified potential rilonacept AE signals via FAERS and IL1RN-genetic associations via MR (myocarditis was not detected in prior clinical trials). All findings serve solely to generate hypotheses for subsequent validation, and larger clinical datasets are needed to confirm these risks and optimize monitoring protocols.