James Larkin

I am the Project Manager for PRADA, a clinical trial exploring how antidepressant treatment can be personalised using clinical, demographic, and genetic data. The programme brings together collaborators across the UK and international partner sites, with the aim of developing a decision support tool for use in routine care.

My work focuses on how the trial operates in practice, including coordination across sites, development of recruitment pathways and clinical workflows, and ensuring that the scientific and technical elements translate into a deliverable and robust clinical protocol. A central part of the role is maintaining alignment across a large, multidisciplinary team while fitting around the constraints of real-world research settings.

My background spans academic research, clinical trials, and industry. Prior to PRADA, I co-founded a University of Oxford spinout developing biomarker-based diagnostics, where I led programme delivery and helped translate early-stage research towards a clinically viable platform.

I have worked on a range of clinical and translational studies, including multi-site trials integrating imaging and molecular data for disease classification and diagnosis. This has involved close collaboration with partners across academia, the NHS, and industry.

My interests span the broad use of data-driven approaches and translation into tools that are usable in clinical practice. I am particularly interested in the challenges of implementing decision support systems within healthcare, and how trial design can support their evaluation and adoption.

Earlier work focused on diagnostic technologies, including imaging and metabolomics, as well as tumour biology. This has led to a broader interest in how different data types can be combined to improve disease classification and clinical decision-making.

Alongside this work, I am involved in small-group undergraduate tutorials in biochemistry at Trinity College. I continue to take an interest in teaching and mentoring, particularly in interdisciplinary and translational research.