Summary
Diabetic kidney disease (DKD) is a rapidly growing worldwide health problem and represents one of the most serious threats in current medicine. DKD is the most common cause of chronic kidney disease (CKD) with 20% of DKD patients progressing to end-stage renal disease, which is associated with tremendously increased morbidity and mortality. The pathophysiology of DKD is complex, incompletely understood and the number of treatment options is low. The vision of DECODE-DKD is to utilize a patient-centric research approach to identify novel pathways and druggable targets in patients suffering from DKD. Concrete objectives are: (1) to establish a spatially resolved multi-omic landscape of human DKD; (2) to dissect and identify therapeutic pathways and signalling networks for novel drug target identification; (3) to incorporate patient-derived in-vitro models for target validation. It is envisaged that novel spatial and single-cell multi-omic technologies will generate a blueprint and predictive model of DKD. This unbiased map will serve to generate testable hypotheses with spatial and temporal coordinates at single-cell resolution. To identify disease-relevant pathways and novel drugable targets in-vitro and in-vivo genome editing approaches will be employed combined with high-throughput screens. In-vitro assays with human-derived kidney organoids will be used to screen potential compounds facilitating the development of novel therapeutics. This highly ambitious interdisciplinary proposal requires the expertise of biomedical engineers, computational biologists, biomedical researchers and physician-scientists. The generated knowledge and outcomes of DECODE-DKD will - alone and especially together - be truly transformative and provide an incremental step forward towards novel drug targets and precision medicine for the treatment of diabetic kidney disease using a systems medicine approach.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101040726 |
| Start date: | 01-04-2022 |
| End date: | 31-03-2027 |
| Total budget - Public funding: | 1 783 319,00 Euro - 1 783 319,00 Euro |
Cordis data
Original description
Diabetic kidney disease (DKD) is a rapidly growing worldwide health problem and represents one of the most serious threats in current medicine. DKD is the most common cause of chronic kidney disease (CKD) with 20% of DKD patients progressing to end-stage renal disease, which is associated with tremendously increased morbidity and mortality. The pathophysiology of DKD is complex, incompletely understood and the number of treatment options is low. The vision of DECODE-DKD is to utilize a patient-centric research approach to identify novel pathways and druggable targets in patients suffering from DKD. Concrete objectives are: (1) to establish a spatially resolved multi-omic landscape of human DKD; (2) to dissect and identify therapeutic pathways and signalling networks for novel drug target identification; (3) to incorporate patient-derived in-vitro models for target validation. It is envisaged that novel spatial and single-cell multi-omic technologies will generate a blueprint and predictive model of DKD. This unbiased map will serve to generate testable hypotheses with spatial and temporal coordinates at single-cell resolution. To identify disease-relevant pathways and novel drugable targets in-vitro and in-vivo genome editing approaches will be employed combined with high-throughput screens. In-vitro assays with human-derived kidney organoids will be used to screen potential compounds facilitating the development of novel therapeutics. This highly ambitious interdisciplinary proposal requires the expertise of biomedical engineers, computational biologists, biomedical researchers and physician-scientists. The generated knowledge and outcomes of DECODE-DKD will - alone and especially together - be truly transformative and provide an incremental step forward towards novel drug targets and precision medicine for the treatment of diabetic kidney disease using a systems medicine approach.Status
SIGNEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
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