Buvall Lisa, Menzies Robert I, Williams Julie, Woollard Kevin J, Kumar Chanchal, Granqvist Anna B, Fritsch Maria, Feliers Denis, Reznichenko Anna, Gianni Davide, Petrovski Slavé, Bendtsen Claus, Bohlooly-Y Mohammad, Haefliger Carolina, Danielson Regina Fritsche, Hansen Pernille B L
Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom.
Front Pharmacol. 2022 Nov 2;13:971065. doi: 10.3389/fphar.2022.971065. eCollection 2022.
Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution 'patient-centric' omics data sets, advanced tools (systems biology, connectivity mapping, and machine learning) and 'state-of-the-art' experimental systems (complex 3D systems , CRISPR gene editing, and various model biological systems ). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.
肾脏疾病是一种复杂的疾病,有几种不同的病因和潜在的相关病理生理学。这反映在慢性肾脏病(CKD)缺乏阻止疾病进展的有效治疗方法上。然而,新策略、近期的科学突破和技术进步揭示了在CKD中寻找新的疾病驱动因素的新可能性。本综述描述了该领域的一些最新进展,并将它们整合在一个更全面的框架中,应用于CKD疾病驱动因素的识别和验证。它使用高分辨率的“以患者为中心”的组学数据集、先进工具(系统生物学、连接图谱和机器学习)以及“最先进”的实验系统(复杂的3D系统、CRISPR基因编辑和各种模式生物系统)。预计应用这样一个框架将增加基于强大的人类靶点验证和对潜在机制的更好科学理解成功识别新型候选药物的可能性。