Atrial fibrillation—a chaotic heart rhythm in the upper chambers—will affect one in three people at some point, yet no new drug has been approved for it since 2015. This matters because AF is a growing epidemic: by 2060, over 18 million Europeans aged 55 and older will have it, more than double the 2010 figure. Current treatments are limited, and people with AF face higher risks of stroke, dementia, kidney disease, and cognitive decline. The researcher aims to break the drug-development logjam by combining genetic data, blood proteins, medical images, and real-world patient records to find both new and repurposed drug targets. If successful, this could accelerate the pipeline for AF medicines—identifying compounds that might already be approved for other conditions, or pinpointing entirely new protein targets. That would directly affect the millions of patients who currently have few options beyond blood thinners and ablation procedures. The work also builds a knowledge graph that prioritises the most promising targets, giving drug developers a clearer roadmap. While the project is applied, its multi-modal analytical approach could also advance fundamental understanding of how AF arises and drives other diseases.
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Atrial fibrillation (AF) is an arrhythmia of the upper chambers of the heart (i.e. atria) which is characterized by loss of organized contraction. AF is the most frequent cause of sustained arrhythmia, and will affect at least a third of the population at some point. Despite improvements in healthcare delivery and public health, AF is a growing global epidemic, which is forecasted to affect over 18 million people aged 55+ in Europe by 2060 (more than double the number of individuals in 2010: 8.8 million). Clinical outcomes of AF vary considerably, from a single episode, to persistent arrythmia resulting in serious complications and mortality. People with AF are at an increased risk of developing multiple comorbidities, such as lung disease, kidney insufficiencies, cardioembolic stroke, cognitive decline and vascular dementia. Treatment for AF is limited, where since the 2015 introduction of the oral anticoagulants rivaroxaban, apixaban and edoxaban, no novel compounds have been approved. To help re-invigorate AF drug development I propose to develop a novel "multi-modal" research line, combining different data types such as proteomics, metabolomics, genomic and imaging modalities, as well as include distinct analytical methods spanning genetic epidemiology, real-world observational studies, and biological network analysis. Specifically, I propose to leverage my strong background in genetics, pharmaco-epidemiology, and machine learning to explore determinants of AF and AF-related comorbidity to identify (novel and repurposing) drug targets for improved disease management. I will improve AF management by 1) exploring imaging and biomarker disease surrogates, 2) identifying protein drug targets, 3) identify druggable genes associating with atrial ablation traits, 4) evaluating real-world evidence on repurposing candidates, and 5) create knowledge graphs to prioritize AF drug targets by combining genetic and non-genetic evidence.
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