Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties.
anticholinergic cognitive burden
antimuscarinic
elderly
in silico
machine learning
polypharmacy
Journal
British journal of clinical pharmacology
ISSN: 1365-2125
Titre abrégé: Br J Clin Pharmacol
Pays: England
ID NLM: 7503323
Informations de publication
Date de publication:
11 Jun 2024
11 Jun 2024
Historique:
revised:
18
04
2024
received:
11
01
2024
accepted:
08
05
2024
medline:
12
6
2024
pubmed:
12
6
2024
entrez:
12
6
2024
Statut:
aheadofprint
Résumé
This study evaluated the use of machine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously published scales. Experimental and in silico ADME, physicochemical and pharmacological data were collected for antimuscarinic activity, blood-brain barrier penetration, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate profile. These five drug properties were used to train an unsupervised model to assign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (ATS) scale, which is based primarily on muscarinic binding affinity. In silico software (ADMET Predictor) used for screening drugs for their blood-brain barrier (BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five selected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (12 of 25). Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scale would misclassify drugs solely depending on muscarinic binding affinity without considering other drug properties. Machine learning models can be trained on these features to build classification models that are easy to update and have greater generalizability.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.
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