Detecting adverse high-order drug combinations from individual case safety reports using computational statistics on disproportionality measures

Abstract

Adverse drug reactions linked to the intake of drug combinations are a critical concern in pharmacovigilance, particularly as the controlled environment of clinical trials often lacks the scale and diversity to detect rare events involving multiple medications. While spontaneous reporting systems provide the necessary breadth for post-market surveillance, identifying overrepresented drug cocktails within such large-scale data remains a significant computational challenge. This study introduces a computational framework for the detection of drug cocktails associated with adverse events, leveraging disproportionality analysis on individual case safety reports. By integrating the Anatomical Therapeutic Chemical classification, the framework extends beyond individual drugs to capture hierarchical pharmacological relationships, enabling exploration of the space of drug combinations beyond pairwise analysis. To address biases inherent in existing disproportionality measures, we employ a hypergeometric risk metric, while a Markov Chain Monte Carlo algorithm provides robust empirical p-value estimation for the risk associated with cocktails. A genetic algorithm further facilitates efficient identification of high-risk drug cocktails. A post-treatment step based on penalized logistic regression allows distinguishing true pharmacological interactions from combined individual effects for cocktails of any size. Validation on synthetic and FDA Adverse Event Reporting System data demonstrates the method's efficacy in detecting established drugs and drug combinations associated with myopathy-related adverse events. Implemented as an R package, this framework offers a reproducible, scalable tool for post-market drug safety surveillance.