Identifying novel & repurposing drug targets for Atrial Fibrillation MEDicines

心房颤动(房颤)是一种心脏上腔(即心房)的心律失常，其特征是失去有组织的收缩。房颤是导致持续性心律失常的最常见原因，在某种程度上会影响至少三分之一的人口。尽管在医疗保健和公共卫生方面有所改善，房颤仍是一种日益严重的全球流行病，预计到2060年，欧洲55岁以上的人口将超过1800万(是2010年的两倍多：880万人)。房颤的临床结果差异很大，从单个发作到导致严重并发症和死亡的持续性心律失常。患有房颤的人患多种合并症的风险更高，如肺部疾病、肾功能不全、心源性中风、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、阿皮沙班和依多沙班以来，没有新的化合物被批准。为了帮助重振房颤药物开发，我建议开发一条新的“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、基因组和成像模式，以及包括跨越遗传流行病学、真实世界观察性研究和生物网络分析的不同分析方法。具体地说，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(新的和重新定位的)药物靶点，以改善疾病管理。我将通过1)探索影像和生物标记物疾病替代物，2)识别蛋白药物靶标，3)识别与心房消融特征相关的可用药基因，4)评估关于重新定位候选者的真实证据，以及5)创建知识图谱，通过结合遗传和非遗传证据来确定房颤药物靶标的优先顺序，从而改进房颤的管理。

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.