Assessing and predicting drug-induced anticholinergic risks: an integrated computational approach.
Dong Xu1, Heather D Anderson2, Aoxiang Tao3
1Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, Idaho State University, 1311 East Central Drive, Meridian, ID 83642, USA.
Therapeutic Advances in Drug Safety
|November 2, 2017
Summary
This study introduces a new computational approach using big data to assess anticholinergic (AC) drug toxicity risks. The developed Anticholinergic Toxicity Score (ATS) shows promise in predicting clinical outcomes more effectively than existing methods.
Area of Science:
- Pharmacology
- Computational Toxicology
- Big Data Analytics
Background:
- Anticholinergic (AC) adverse drug events (ADEs) result from muscarinic receptor inhibition.
- Current assessment of AC toxicity relies heavily on clinician experience.
- There is a need for objective methods to evaluate clinical AC toxicity risks.
Purpose of the Study:
- To evaluate a novel approach integrating big pharmacological and healthcare data for assessing clinical AC toxicity risks.
- To develop and validate a computational scoring system for AC toxicity.
Main Methods:
- Anticholinergic Toxicity Scores (ATSs) were computed using drug-receptor inhibition data.
- A retrospective cohort study using medical claims data quantified clinical AC risks.
- Quantitative structure-activity relationship (QSAR) models were established for risk prediction.
Main Results:
- Analysis included 25 medications and over 575,000 patients.
- ATS demonstrated better consistency with AC outcomes compared to other measures.
- A QSAR model incorporating pharmacokinetic parameters showed excellent correlation (R=0.83) and predictive performance (Q=0.64) for AC incident rates.
Conclusions:
- Pilot data suggest the feasibility of a computational AC toxicity scoring approach using pharmacology and big data.
- The Anticholinergic Toxicity Score (ATS) shows potential for improved clinical toxicity prediction.
- Further development with larger datasets and clinical parameters is planned.
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