Assessing and predicting drug-induced anticholinergic risks: an integrated computational approach

Abstract

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.

Quality of anticholinergic burden scales and their impact on clinical outcomes: a systematic review

“…1 ). We excluded two studies by Aizenberg et al [ 45 ] and by Whalley et al [ 46 ], as no medication list was provided after contacting the authors, resulting in a total of 22 records describing 19 different ABS[ 23 – 44 ]. The updated search revealed no new scale.…”

Implications of Off‐Target Serotoninergic Drug Activity: An Analysis of Serotonin Syndrome Reports Using a Systematic Bioinformatics Approach

“…A 10‐μmol/L activity cutoff was used to ensure a high‐level of confidence in identifying relationships within the human interactome. This computational approach efficiently accounts for the interaction of drugs at these receptors, and has been shown to capture drug off‐target interactions effectively and measure drug‐induced anticholinergic toxicity burden …”

MSBIS: A Multi-Step Biomedical Informatics Screening Approach for Identifying Medications that Mitigate the Risks of Metoclopramide-Induced Tardive Dyskinesia

“…ChEMBL ( Gaulton et al, 2012 ), the largest bioactivity database in the world, contains > 1.5 million small molecules, 10,000 receptors, and 14 million bioactivity records. We have developed TargetSearch, an in-house bioinformatics web service ( http://dxulab.org/software ) to mine the vast amount of ChEMBL pharmacological data for relevant drug-receptor interactions including off-target polypharmacy ( Xu et al, 2017 ). Here we used TargetSearch to score the anticholinergicity of the 120 drugs.…”

“…2 , essentially accounts for the pharmacodynamic interactions of a drug with muscarinic Ach receptors. It is fast, systematic, and has been shown to effectively capture drug off-target polypharmacy ( Keiser et al, 2009 ) and measure drug-induced AC toxicity burden ( Xu et al, 2017 ).

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