Comprehensive Summary
This study was done by Chen et. al and aimed to identify disproportional adverse drug reactions (ADR) signals associated with zuranolone which is a medication specifically meant to treat postpartum depression. Data was extracted from the FAERS database and the data was processed using R. Signal detection for zuranolone was performed using both the reporting odds ratio (ROR) method and the Bayesian confidence propagation neural network (BCPNN) method. 477 unique cases were identified using zuranolone. 3.56% of patients within this sample experienced serious adverse outcomes, and multiple types of adverse events not included in zuranolone's prescribing information were found. Statistical disproportionality analysis was performed and detected positive signals for zuranolone in mostly nervous system disorders and psychiatric disorders. The study was able to determine that there was a high reporting frequency for psychiatric and neurological ADRs that were associated with zuranolone use.
Outcomes and Implications
Chen et al. stated that there were a few limitations in the study, including limitations of the FAERS database data and that disproportionality analysis that was used cannot establish causal relationships between adverse events and drugs. This means that, moving forward, more clinical case-control studies should be done to fully understand and identify the adverse reaction signals of zuranolone. This study serves as the foundation for future research on the causal relationship between zuranolone and the reported adverse events.