Identifying novel & repurposing drug targets for Atrial Fibrillation MEDicines

心房颤动（AF）是心脏上腔（即心房）的一种心律失常，其特征是失去有组织的收缩。房颤是导致持续心律失常的最常见原因，至少有三分之一的人会受到房颤的影响。尽管医疗保健服务和公共卫生有所改善，但房颤是一种日益严重的全球流行病，预计到2060年，欧洲将有1800多万55岁以上的人受到影响（比2010年的880万增加了一倍多）。房颤的临床结果差异很大，从单次发作到导致严重并发症和死亡的持续性心律失常。房颤患者发生多种合并症的风险增加，如肺病、肾功能不全、心栓塞性中风、认知能力下降和血管性痴呆。房颤的治疗是有限的，自2015年引入口服抗凝剂利伐沙班、阿哌沙班和依多沙班以来，没有新的化合物被批准。为了帮助重新振兴房颤药物开发，我建议开发一种新的“多模式”研究路线，结合不同的数据类型，如蛋白质组学，代谢组学，基因组学和成像模式，以及包括遗传流行病学，现实世界观察研究和生物网络分析的独特分析方法。具体来说，我建议利用我在遗传学，药物流行病学和机器学习方面的强大背景来探索房颤和房颤相关合并症的决定因素，以确定（新的和重新利用的）药物靶点，以改善疾病管理。我将通过以下方面改善房颤的管理：1)探索影像学和生物标志物疾病替代品；2)识别蛋白药物靶点；3)识别与心房消融特征相关的可用药基因；4)评估重新定位候选药物的现实证据；5)通过结合遗传和非遗传证据创建知识图谱来优先考虑房颤药物靶点。

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.