Identifying shared drug targets between cardiovascular and non-cardiovascular diseases: integrating genomics with real-world evidence
Summary
Despite decades of research, coronary heart disease (CHD) and stroke remain the leading causes of death worldwide, highlighting the urgent need for novel prevention strategies.
This PhD project aims to:
- identify pharmaceutical targets shared between >300 non-cardiovascular diseases (non-CVDs) and either CHD or stroke.
- evaluate opportunities for drug repurposing that could simultaneously address non-CVD indications while reducing cardiovascular risk.
An example of this project’s translational potential is the interleukin-6 inhibitor class, approved for non-CVD indications (e.g. rheumatoid arthritis) and now under investigation for cardiovascular benefits (e.g. ziltivekimab in the ZEUS trial, NCT05021835, sponsored by Novo Nordisk, the project’s industrial partner).
The student will use a combination of statistical genetics and real-world evidence approaches to identify high-priority therapeutic targets with cross-disease potential.
For example:
- Mendelian randomization: using genetic variation as a “natural experiment” to test whether a risk factor (such as a protein) causes disease;
- Colocalization: determining whether genetic signals for two traits (e.g. CHD and another disease) likely share the same causal variant;
- Target trial emulation: analysing observational health data in a way that mimics a clinical trial.
Mendelian randomization and colocalization will be applied to genome-wide associations study data from >2M participants in large biobanks (e.g. UK Biobank, the Million Veteran Program, Our Future Health) to identify loci with shared genetic architecture across diseases, building on the supervisory team’s experience with similar work (e.g. Karjalainen, Nature, 2024).
These loci will be mapped to therapeutic agents using resources such as Open Targets and DrugBank, as in a previous study from our team which identified repurposing opportunities for approved medications (Gaziano, Nature Medicine, 2021).
Real-world prescribing and outcome data from >60M individuals will be used to assess repurposing potential, using a target trial emulation approach and leveraging the supervisory team’s experience with whole-population NHS England datasets (e.g. Kerr, Lancet, 2024; Allara, Lancet Public Health, 2025).
Project aims
The project is designed to be flexible, with scope for the student to pursue their own directions (e.g. novel target areas, incorporation of machine learning approaches, or integration of new datasets) as the PhD develops.
Findings are expected to lead to publications in high-impact peer-reviewed journals and presentations at major international conferences. The student will also contribute to open-source analytical tools and curated drug target resources.
The student will learn techniques including genome-wide association studies, Mendelian randomization, colocalization, target trial emulation, and whole-population data analysis. They will benefit from the leading expertise at the University of Cambridge and Novo Nordisk, receiving senior scientific mentorship throughout the duration of their PhD.
The student will have opportunities for placements or visits to Novo Nordisk’s research facilities, gaining first-hand experience of how genetic and epidemiological evidence informs drug discovery and development. They will also be exposed to industrial R&D practices, including target prioritization, safety evaluation, and decision-making processes.
We welcome applicants with strong quantitative skills, ideally with a solid grounding in genetics, biology, pharmacology, medicine or a related discipline. Experience in analysing large datasets and proficiency in statistical programming (e.g. R or Python) will be advantageous, though training will be provided. A curiosity about translating genetic discoveries into clinical and therapeutic advances is essential.
Contact details
Elias Allara - ea431@medschl.cam.ac.uk
Opportunities
This project is open to applicants who want to do a:
- PhD