Identifying novel & repurposing drug targets for Atrial Fibrillation MEDicines
Lead Research Organisation:
University College London
Department Name: Institute of Cardiovascular Science
Abstract
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.
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.
