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.

Aim: This study evaluated the use of machine learning in leveraging drug ADME data to develop a novel anticholinergic burden (AB) scale and compared its performance to previously published scales. Methods: Experimental and in silico ADME data were collected for antimuscarinic activity, blood-brain barrier penetration, bioavailability, chemical structure and P-gp substrate profile. These five ADME properties were used to train an unsupervised model to assign anticholinergic burden scores to drugs. The performance of the model was evaluated through 10-fold cross-validation and compared with the clinical ACB scale and non-clinical ATS scale which is based primarily on muscarinic binding affinity. Results: 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 AUC for the unsupervised and ACB scale based on five selected features 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 (n=49 of m=88) and agreed on the classification of less than half the drugs in the ATS scale (n=12/25). Conclusion: Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. On the other hand, the ATS scale would misclassify drugs solely depending on muscarinic binding affinity without considering ADME properties. Machine learning models can be trained on these features to build classification models that are easy to update and have greater generalizability.

Purpose: The purpose of this study was to ascertain antibiotic-associated acute kidney injury (AKI) in older adults aged 65 years or above in New Zealand using a case-crossover study design. Methods: We used the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification code N17.9 to identify all individuals aged 65 years and above with a diagnosis of incident AKI between January 01, 2005, and December 31, 2020, from the New Zealand National Minimum Data Set. We created a case-crossover cohort for antibiotic exposures, with a 3-day observation period and two 30 days washout periods, summed up to a 66-day study period. We calculated the changed odds of AKI due to exposures to an antibiotic as matched odds ratios and their 95% confidence intervals, using conditional logistic regression. Results: We identified a total of 2399 incident cases of AKI between 2005 and 2020 among older adults. The adjusted odds of consuming a sulphonamide antibiotic during the case period was 3.57 times (95% CI: 2.86 to 4.46) higher than the reference period among the incident AKI cases. Fluoroquinolone utilisation was also associated with incident AKI (adjusted OR = 2.56; 95% CI: 1.90 to 3.46). The number needed to harm for sulphonamides and fluroquinolones were 6.55 (95% CI: 5.15 to 8.65) and 21.38 (95% CI: 13.97 to 36.41), respectively. Conclusion: The potential of sulphonamides and fluoroquinolones to be associated with AKI raises the significant need for vigilant prescribing of these antibiotics in frail older adults.