Background
Older people continue to be prescribed medicines with anticholinergic properties and are at an increased risk of experiencing adverse events with these medicines.1 Exposure to 1 or more medicines with anticholinergic properties is termed anticholinergic burden.2 Tools to quantify anticholinergic burden have been developed, but the major drawbacks of these tools are the limited consideration given to accounting for drug dosage and affinity for muscarinic receptor binding.
Objectives
The overall objective of this work is to develop a dose-response based pharmacological model to quantify the relative potencies of an anticholinergic medicine that can be used as an anticholinergic index. The specific aims for the current work are:
1. To develop an a priori model for multi-drug receptor binding
2. To train/calibrate the model
3. To validate the model based on health outcomes
This work will only consider aim 1.
Methodology
An a priori model will be developed using the principles of ligand binding modelling. The anticholinergic activity of commonly prescribed medicines will be derived from the literature and will be categorised according to high, moderate, and low anticholinergic activity. The receptor dissociation constant (Kd) values will be identified for a selection of medicines from each category. The binding will then generalised to be a function of defined daily dose (DDD), bioavailable fraction, fraction that crosses the blood brain barrier and relative potency, (ED50). Final parameters in the model will be ED50 for category 1 anticholinergics and f(2) and f(3) as the relative potency values for moderate and weak classes of anticholinergics.
Aim 2 is to calibrate the relative potencies of anticholinergic medicine by estimating the parameters in the proposed ligand binding model. This aim will be achieved by estimating the relative potency of the 3 categories of anticholinergic medicine. Estimation will be based on fitting the proposed model to data extracts derived from Pharmaceutical Claims Mart (Pharms) dataset and National Minimum Datasets (NMDS). Age, sex, medicine dose, and medicine strength will be extracted from the Pharms dataset and matched with anticholinergic events such as delirium, constipation and urinary retention, as examples of central, systemic and local anticholinergic side effects, derived from the NMDS dataset.
References
1. Nishtala PS et al., (2009), J Clin Pharmacol, 49, 1176-84.
2. Tune LE, (2001), J Clin Psychiatry, 62, 11-4.
