Blonanserin Induced Rhabdomyolysis: A Disproportionality Analysis in USFDA Adverse Event Reporting System Database

Signal detection is a key element for the early detection of new, rare reactions of a drug which enables healthcare professionals to be cautious in encountering serious adverse events associated with the drug. Blonanserin, an atypical antipsychotic agent with high binding affinity for D2, D3 and 5-HT2A receptor subtypes was intended for the treatment of schizophrenia. Our study aims in the identification of rhabdomyolysis associated with blonanserin by data mining analysis of the FDA database of Adverse Event Reporting System [FAERS] using Data Mining Algorithms (DMAs). The three most commonly used data mining algorithms, namely, Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR) and Information Component (IC) were applied retrospectively from 2012 first quarter (Q1) to 2015 fourth quarter (Q4) in USFDA Adverse Event Reporting System. A value of ROR-1.96SE>1, PRR≥2, IC-2SD>0 were considered as the positive signal. A total of 11, 365, 699 Drug Event Combinations were reported in USFDA AERS database from 2012Q1 to 2015Q4. Among which, 239 DECs were associated with blonanserin. On analysis, data mining algorithms exhibited positive signal for rhabdomyolysis as all the values were well above the pre-set threshold. This analysis helped in identification of blonanserin induced rhabdomyolysis which warrants an integration of clinical studies for the validation and quantification of possible risks of the adverse event obtained by the signal detection technique.