loading page

Development of a Machine Learning derived Anticholinergic Burden Scale (ML-ACB scale): A Machine Learning Approach with Enhanced Drug Properties and Weighting
  • Oteng Phutietsile,
  • Nikoletta Fotaki,
  • Prasad Nishtala
Oteng Phutietsile
University of Bath
Author Profile
Nikoletta Fotaki
University of Bath
Author Profile
Prasad Nishtala
University of Bath

Corresponding Author:[email protected]

Author Profile

Abstract

Aim: This study aims to refine the anticholinergic burden (AB) scale developed in our previous research by incorporating additional drug properties, such as Lipophilicity and Molecular Weight, and implementing a new weighting approach to address the varying influence of each drug property on anticholinergic burden. The objective is to improve the scale’s predictive accuracy and compare its performance against established scales. Methods: The scale, which covers 87 drugs, was expanded to include seven drug properties, combining new properties, Lipophilicity and Molecular Weight, with previously utilised experimental and in silico ADME, physicochemical, and pharmacological properties. A weighting approach was introduced to the hierarchical clustering process to account for the differential impact of each drug property on AB. The performance of this revised scale was evaluated through 10-fold cross-validation against the clinical Anticholinergic Cognitive Burden (ACB) scale and the non-clinical Anticholinergic Toxicity Scores (ATS) scale. Results: The scale showed improved alignment with the ACB and ATS scales, agreeing with the rankings of 54 out of 87 drugs and 16 out of 25 drugs respectively. The Area Under the Receiver Operating Characteristic Curve (AUROC) indicated strong performance. The ML-ACB and ACB has an AUC of 0.99 and 0.81 respectively, whilst the ML-ACB and ATS had an AUC of 0.96 and 0.62. Conclusion: The ML-ACB scale offers improved alignment with the established ACB scale. This highlights the potential of the ML-ACB scale as a valuable tool for clinical and research applications, providing a data-driven alternative that closely correlates with existing validated scales.
02 Oct 2024Submitted to British Journal of Clinical Pharmacology
03 Oct 2024Submission Checks Completed
03 Oct 2024Assigned to Editor
03 Oct 2024Review(s) Completed, Editorial Evaluation Pending
15 Oct 2024Reviewer(s) Assigned