Abstract
AimsThis 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.MethodsExperimental 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.ResultsIn 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).ConclusionsOur 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.