William Marshall
Dr William Marshall is a Clinical Research Training Fellow based at the University of Manchester who will be working with GSK. He will use real world clinical data, multi-omics data types and articifial intelligence machine learning to better understand chronic kidney disease, identify candidate biomarkers for cardiorenal metabolic disease and identify targets for drug discover trials.
When did your Fellowship start and how long will it last?
My Fellowship starts in October 2025, and it will last three years.
What were you doing prior to your Fellowship?
Prior to starting this Fellowship, I was an academic internal medicine trainee in Glasgow.
Why did you choose this Fellowship Programme?
Several of the core themes in this Fellowship (health data science, artificial intelligence machine learning and cardiovascular sciences) align perfectly with my research interests and proposed project.
This Fellowship also feels very supported, with mentorship available from my PhD supervisory team, the industry partner and other world leading academics not directly connected with the Programme.
What is the aim of your research?
Chronic kidney disease is expected to become the 5th leading cause of deaths globally by 2030. It often coexists with other significant multiple long-term conditions in cardiorenal metabolic disease. Cardiorenal metabolic disease has a one-year mortality of >1 in 3 and is estimated to exceed >5% total NHS healthcare expenditure.
Current approaches to therapies in cardiorenal metabolic disease are a one size fits all approach; an acute rise in the creatinine or potassium after initiation will mean they are stopped. Furthermore, our understanding of disease mechanisms is limited.
For this project, I will use real world clinical data, multi-omics data types and artificial intelligence machine learning to:
- Gain a better understanding of the disease mechanisms underlying this debilitating disease.
- Identify candidate biomarkers for cardiorenal metabolic disease (both prognostic and predictive of long-term response to current cardiorenal therapies).
- Identify future targets for drug discovery trials.
What inspired you to look at this field?
Witnessing the adverse outcomes of patients with cardiorenal metabolic disease has motivated me to be at the forefront of helping understand the mechanisms of this debilitating disease. The opportunity to use artificial intelligence machine learning and multi omics data types to deliver this makes it an exciting time to be a researcher in this area.
Which industry partner are you working with and how will they support you in achieving your goals? What will your partner gain from working with you?
My industry partner is GlaxoSmithKline (GSK). GSK will help create a single data platform to facilitate sharing and transfer of data from the University of Manchester. During my industry placement at GSK, I will then work with the artificial intelligence machine learning team to perform an integrated analysis of the data.
Why did you choose University of Manchester as your HEI partner?
Registering for my PhD at University of Manchester has allowed me to build a supervisory team which consists of world leading academic nephrologists in real world clinical data and artificial intelligence machine learning. They bring a wealth of experience in utilising data from the Salford Kidney Study and the NURTuRE programme; these databases form the observation and validation cohorts in my project.
What do you plan to do when you have completed your Fellowship?
After this Fellowship, I will go on to apply for bridge funding and test the candidate biomarkers on larger, international and multi-ethnic populations. Continued collaboration with GSK after this Fellowship can provide opportunities to test the candidate biomarkers in clinical trials using techniques such as CRISPR.