This research develops a novel PEG-free lipid nanoparticle for gene therapy. Unlike conventional nanoparticles, which trigger immune responses after repeated use, the new formulation remains stable without PEG and performs even better. The approach could enable safer repeat dosing, improve treatment effectiveness, and expand access to life-changing gene therapies.

This research shows that the sympathetic nervous system is disrupted at the earliest stages of ALS, despite sympathetic neurons themselves remaining intact. Early loss of signalling receptors may contribute to muscle degeneration and disease progression, identifying new opportunities for earlier diagnosis and therapies that preserve muscle function and improve patient outcomes.

This research develops patient-specific digital twins of the heart to improve radiofrequency ablation for cardiac arrhythmias. By simulating heat transfer, tissue damage, and electrical activity, these computational models could improve treatment accuracy, reduce repeat procedures, accelerate medical device development, and advance the future of personalised cardiovascular medicine.

This research investigates how the choice of time interval affects reinforcement learning models for sepsis treatment in intensive care. By comparing one-, two-, four-, and eight-hour patient timelines, it demonstrates that shorter intervals better capture patient dynamics, improve estimated survival outcomes, and may enable more effective AI-assisted clinical decision-making.

This research investigates how antiviral drug resistance develops by examining structural changes in viral proteins. Using protein crystallography, it identified why SARS-CoV-2 mutations prevent Paxlovid binding and discovered two compounds capable of inhibiting resistant viruses. The findings could guide development of more effective antivirals against future drug-resistant viral infections.

This dissertation develops brain-charting methods to support precision psychiatry and neurology by measuring how an individual’s brain differs from population norms. Using normative modelling, it replaces conventional case-control averages with personalised deviation scores across age and disease progression. The research introduces warped normative models for non-Gaussian imaging data and multivariate extreme-value methods for identifying unusual patterns across brain regions. Applications to rare copy-number variants and Parkinson’s disease reveal individual differences obscured by group averages. The defence also examines longitudinal monitoring, environmental influences, resilience, ethics, stigma and clinical implementation, while emphasising that brain deviations alone cannot define pathology or determine treatment.

This research develops a targeted anti-VEGF therapy for wet age-related macular degeneration that can be injected under the skin rather than directly into the eye. In animal studies, the drug successfully reached the eye and reduced abnormal blood vessel growth, offering a safer, cheaper, and more convenient treatment for preventing blindness.

This research improves drug formulations by developing predictive tools for amorphous solid dispersions that increase drug solubility while allowing higher drug loading in a single tablet. The work aims to reduce pill burden, improve medication adherence, lower pharmaceutical development costs, and make treatments more effective for patients with chronic illnesses.

This research seeks blood-based biomarkers that predict which people infected with Chagas disease will later develop life-threatening cardiomyopathy. By analysing immune proteins in blood samples from Bolivia, it aims to enable earlier diagnosis, targeted monitoring, and preventative treatment, offering a model for predicting and preventing many chronic diseases before irreversible damage occurs.

This research investigates polyploid giant cancer cells, a highly treatment-resistant population responsible for cancer relapse. By studying their structural biology and dependence on lipid metabolism, the work identifies metabolic vulnerabilities that can be targeted alongside chemotherapy, offering a promising strategy to eliminate resistant cancer cells and improve long-term treatment outcomes.