Medication Burden and Physical Function in Older Adults

About the Project

We have developed and clinically validated a novel machine learning–based anticholinergic burden scale (ML-ACB) that integrates detailed pharmacological properties, including absorption, distribution, metabolism, excretion (ADME), blood–brain barrier penetration, and receptor affinity. This innovative approach moves beyond traditional expert-derived anticholinergic scales by providing data-driven weighting of drug effects.

This PhD project will apply the ML-ACB scale to large, internationally recognised ageing cohorts, including:

• ELSA (English Longitudinal Study of Ageing)
• TILDA (The Irish Longitudinal Study on Ageing)

The primary aim is to examine the longitudinal impact of anticholinergic burden on mobility outcomes in older adults, including gait speed, physical function, frailty progression, and disability. The project will also compare the predictive performance of ML-ACB with traditional anticholinergic burden scales.

Candidate Background

This project would particularly suit postgraduate students with a background in:

• Pharmacy
• Pharmacology
• Clinical pharmacology
• Epidemiology
• Data science applied to health

Experience in statistical programming (R or Python) and an interest in ageing research would be advantageous.

Impact

The findings will inform safer prescribing in older adults, improve understanding of medication-related functional decline, and contribute to precision pharmacotherapy in ageing populations.