Precision Medicine and Pharmacotherapeutics
Utilising the large population bioresources available within our local health region, Tayside, to identify such subgroups of patients based upon their clinical phenotype and genotype
Precision medicine is, “the tailoring of diagnostics or therapeutics to subgroups of populations sharing similar characteristics, thereby minimizing error and risk while maximising efficacy.” In the Division of Population Health & Genomics we utilise the large population bioresources available within our local health region, Tayside, to identify such subgroups of patients based upon their clinical phenotype and genotype.
We draw upon the large population bioresources (GoDARTS, SHARE, Generation Scotland) with currently around 40K individuals with genome-wide association study (GWAS) linked to electronic medical records, and increasingly proteomics, metabolomics and RNAseq data. We are part of the Scottish HDRUK site and lead the theme on Precision Therapeutics. In addition, we utilise UK Biobank, large UK population databases including CPRD and Scottish national registries and have access to many clinical trial data sets.
Pharmacoepidemiology and medicines regulation
We are involved in a number of large international pharmacoepidemiology and pharmacogenomics consortia through the conduct of federated analyses supported by big data approaches. For example, we participate in the CVD-COVID-UK project to investigate the effects of commonly used blood pressure lowering drugs (angiotensin converting enzyme inhibitors and angiotensin receptor blockers) on COVID-19 outcomes; and we undertake causal inference approaches to generate reliable real-world evidence to help clinical decision making.
A key impact of our work is to inform medicines regulatory science on the safety and effectiveness of medicines. We have close relationships with medicines regulatory agencies to support policy and methods development through ENCePP and have conducted several commissioned multi-country studies for the European Medicines Agency (EMA), including to measure the impact of regulatory measures on public health.
Clinical Trials and trial methodology
Our School of Medicine benefits from having a UKCRC registered trials unit from 2007, the Tayside Clinical Trials Unit (TCTU) which provides design, management and analysis for PIs. Wide-ranging trials currently include:
- STOP-COVID19 with brensocatib to reduce hospitalisation
- ECLS - biomarkers for early detection of lung cancer
- cardiovascular trials such as REFORM on SGLT2 inhibitors
- VESUVIUS on electronic cigarettes
- EPITOPE, NIHR programme grant project on HCV treatment.
Dundee Epidemiology and Biostatics Unit (DEBU) is a group of medical statisticians and epidemiologists that provides teaching and methodological support across the School of Medicine, particularly in pharmaco-epidemiology and statistical modelling as well as in trials to TCTU. It also engages with industry and government bodies such as the Scottish Medicines Consortium which decides on what new drugs are to be recommended based on cost-effectiveness analyses.
Pharmacogenomic discovery and implementation
In pharmacogenomics, our work has focused on diabetes and cardiovascular disease, with major discoveries for genetic determinants of glycaemic response to metformin, sulphonylureas, and GLP-1RA and adverse outcomes with statins. Not only is this work of potential clinical relevance for genotype-based prescribing but has yielded novel biological mechanisms of actions of drugs (metformin and GLP-1RA).
The P4Me initiative is a partnership between Precision Medicine and Pharmacotherapeutics Group at the University of Dundee and NHS Tayside. P4Me is developing the know-how for translation of Precision Medicine research into real world clinical implementation based on Prediction, Prevention, Personalisation, and Participation (P4). The programme incorporates the full spectrum of translational healthcare data science from genomics and imaging-derived biomarkers to remote eHealth solutions. A fundamental component of P4Me is the concept of self-learning healthcare systems whereby evidence of clinical utility is generated as a by-product of clinical activity using novel pragmatic trial designs and natural experiments.