Selecting the right therapeutic target for kidney disease

被引:8
作者
Buvall, Lisa [1 ]
Menzies, Robert I. [1 ]
Williams, Julie [1 ]
Woollard, Kevin J. [2 ]
Kumar, Chanchal [3 ]
Granqvist, Anna B. [1 ]
Fritsch, Maria [1 ]
Feliers, Denis [2 ]
Reznichenko, Anna [3 ]
Gianni, Davide [4 ]
Petrovski, Slave [5 ]
Bendtsen, Claus [6 ]
Bohlooly-Y, Mohammad [7 ]
Haefliger, Carolina [5 ]
Danielson, Regina Fritsche [1 ]
Hansen, Pernille B. L. [1 ]
机构
[1] AstraZeneca, Biosci Renal Res & Early Dev Cardiovasc Renal & Me, BioPharmaceut R&D, Gothenburg, Sweden
[2] AstraZeneca, Biosci Renal Res & Early Dev Cardiovasc Renal & Me, BioPharmaceut R&D, Cambridge, England
[3] AstraZeneca, Translat Sci & Expt Med Res & Early Dev, Cardiovasc Renal & Metab, BioPharmaceut R&D, Gothenburg, Sweden
[4] AstraZeneca, Funct Genom, Discovery Sci, R&D, Cambridge, England
[5] AstraZeneca, Ctr Genom Res, Discovery Sci, R&D, Cambridge, England
[6] AstraZeneca, Data Sci & Quantitat Biol, Discovery Sci, R&D, Cambridge, England
[7] AstraZeneca, Translat Genom, Discovery Sci, R&D, Gothenburg, Sweden
关键词
chronic kidney disease; validation; systems biology; omics; machine learning; drug discovery; ficial intelligence; ON-A-CHIP; ENDOTHELIAL-CELLS; EPITHELIAL-CELLS; ZEBRAFISH MODEL; OMICS; GLOMERULUS; RECEPTORS; REVEALS; SYSTEM; ASSAY;
D O I
10.3389/fphar.2022.971065
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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 in silico tools (systems biology, connectivity mapping, and machine learning) and 'state-of-the-art' experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). 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.
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页数:17
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