Using big data and genomics to reduce harm from polypharmacy
Supervisors:
Prof Ewan Pearson, School of Medicine (University of Dundee)
Prof Jacob George, School of Medicine (University of Dundee)
Prof Bruce Guthrie, Advanced Care Research Centre, Usher Institute (University of Edinburgh)
Summary:
With increasing age and multimorbidity, polypharmacy is common. In 2010, 20.8% of the Tayside population were prescribed ≥ 5 drugs and 5.8% ≥ 10 drugs, with increased polypharmacy with increasing age, deprivation and care home residence. Some populations are particularly exposed to polypharmacy – in pilot data from Tayside 30% of patients with Type 2 diabetes are treated with 10 or more drugs.
It is only recently that large clinical databases with linked genomics have become available to enable a ‘big data’ approach to investigate adverse outcomes of polypharmacy, and the impact of genetic variants that alter drug pharmacokinetics and dynamics.
The overarching aim of this PhD is to identify and reduce harms from polypharmacy in an ageing multimorbid population. Specific aims will be developed by the candidate with the supervisory team including:
- Investigate the scale of known drug-drug interactions in the UK
- Identify novel beneficial and harmful drug-drug interactions in the UK, with a focus on people with diabetes
- To investigate the interaction of known genetic variants in drug metabolism genes on outcomes of commonly used drugs or drug combinations.
This work will underpin future implementation development to reduce prescribing harm, including potential routine use of genetics-guided prescribing.
The fellow will work with large data sets developing skills in Python, SQL and R, statistical modelling including pharmacoepidemiology and pharmacogenomics. The student will be linked to policy groups including Scottish Polypharmacy Working Group (Guthrie), SMC/NICE (George), and Scottish Pharmacogenomics Working group (Pearson).