Background: Anticholinergic (AC) adverse drug events (ADEs) are caused by inhibition of muscarinic receptors as a result of designated or off-target drug-receptor interactions. In practice, AC toxicity is assessed primarily based on clinician experience. The goal of this study was to evaluate a novel concept of integrating big pharmacological and healthcare data to assess clinical AC toxicity risks.
Methods: AC toxicity scores (ATSs) were computed using drug-receptor inhibitions identified through pharmacological data screening. A longitudinal retrospective cohort study using medical claims data was performed to quantify AC clinical risks. ATS was compared with two previously reported toxicity measures. A quantitative structure-activity relationship (QSAR) model was established for rapid assessment and prediction of AC clinical risks.
Results: A total of 25 common medications, and 575,228 exposed and unexposed patients were analyzed. Our data indicated that ATS is more consistent with the trend of AC outcomes than other toxicity methods. Incorporating drug pharmacokinetic parameters to ATS yielded a QSAR model with excellent correlation to AC incident rate (R2 = 0.83) and predictive performance (cross validation Q2 = 0.64). Good correlation and predictive performance (R2 = 0.68/Q2 = 0.29) were also obtained for an M2 receptor-specific QSAR model and tachycardia, an M2 receptor-specific ADE.
Conclusions: Albeit using a small medication sample size, our pilot data demonstrated the potential and feasibility of a new computational AC toxicity scoring approach driven by underlying pharmacology and big data analytics. Follow-up work is under way to further develop the ATS scoring approach and clinical toxicity predictive model using a large number of medications and clinical parameters.
Keywords: adverse drug reactions; anticholinergic toxicity; big data; biomedical informatics; clinical toxicology; drug safety.