Rituximab and Pyoderma Gangrenosum: An Investigation of Disproportionality Using a Systems Biology-Informed Approach in the FAERS Database

Abstract

Background: Studies have found an increased risk of pyoderma gangrenosum associated with rituximab. The structural properties and pharmacological action of rituximab may affect the risk of pyoderma gangrenosum. Additionally, pyoderma gangrenosum is associated with autoimmune disorders for which rituximab is indicated.

Objective: We aimed to determine whether rituximab is disproportionally associated with pyoderma gangrenosum using a systems biology-informed approach.

Methods: Adverse event reports were extracted from the US Food and Drug Administration Adverse Event Reporting System (FAERS, 2013-20). The Bayesian Confidence Propagation Neural Network Information Component was used to test for disproportionality. Comparators used to determine potential causal pathways included all other medicines, all medicines with a similar structure (monoclonal antibodies), all medicines with the same pharmacological target (CD20 antagonists) and all medicines used for the same indication(s) as rituximab.

Results: Thirty-two pyoderma gangrenosum cases were identified, 62.5% were female, with a median age of 48 years. There was an increased association of pyoderma gangrenosum with rituximab compared with all other medicines (exponentiated Information Component 6.75, 95% confidence interval (CI) 4.66-9.23). No association was observed when the comparator was either monoclonal antibodies or CD20 antagonists. Conditions for which an association of pyoderma gangrenosum with rituximab was observed were multiple sclerosis (6.68, 95% CI 1.63-15.15), rheumatoid arthritis (2.67, 95% CI 1.14-4.80) and non-Hodgkin's lymphoma (2.94, 95% CI 1.80-3.73).

Conclusions: Pyoderma gangrenosum was reported more frequently with rituximab compared with all other medicines. The varying results when restricting medicines for the same condition suggest the potential for confounding by indication. Post-market surveillance of biologic medicines in FAERS should consider a multi-faceted approach, particularly when the outcome of interest is associated with the underlying immune condition being treated by the medicine of interest.

Fig. 1

Signal detection estimates (ratio scale information component [RSIC]) of rituximab vs other comparators for pyoderma gangrenosum. All all other medicines, BCPNN Bayesian Confidence Propagation Neural Network, CI confidence interval, mAbs monoclonal antibodies, MCMC Markov Chain Monte Carlo, MS multiple sclerosis, NHL non-Hodgkin’s lymphoma, RA rheumatoid arthritis