Imagine the cells in your kidneys have tiny antennas that tell the body how to grow and stay healthy. In some children, these antennas are broken, leading to severe kidney failure. This work finds new ways to fix these antennas using existing drugs or custom-made genetic 'patches' to stop the disease from progressing.
Pediatric kidney ciliopathies currently lack effective treatments beyond dialysis and transplantation, and doctors cannot accurately predict which children will progress to total kidney failure.
A machine learning prediction model, a common data model for ciliopathies, and validated preclinical ASO and drug-repurposing therapies.
If you are a drug developer dealing with high R&D costs for rare diseases — this project developed a list of repurposed molecules and ASO-mediated therapies that target specific gene variants. This reduces the time to find a lead candidate for pediatric renal ciliopathies.
If you are a lab dealing with imprecise kidney disease staging — this project developed a validated biomarker using urinary DKK3 levels. This allows for more accurate risk prediction of kidney failure in children.
If you are a software company dealing with fragmented genetic data — this project developed a machine learning-based prediction model and a common data model for ciliopathies. This enables better patient stratification for clinical trials.
Based on available project data, there is no information regarding the cost or pricing of the therapies.
Based on available project data, the project is currently at the preclinical stage with results in human cells and mouse models; industrial scaling details are not provided.
Based on available project data, specific IP or licensing terms are not mentioned, though the project involves 4 industry partners who may hold interests.
Based on available project data, the project runs from 2023-07-01 to 2027-06-30, indicating the tools are still under development and validation.
The project established a common data model (CDM) to integrate patient biological and genetic data across European databases.
The consortium is well-balanced for translation, featuring 17 partners across 6 countries. With a 24% industry ratio (4 companies, including 2 SMEs), there is a strong bridge between the 9 universities and 3 research institutes, suggesting a clear path from lab discovery to commercial application.
Contact Imagine Institut des Maladies Genetiques Necker Enfants Malades Fondation
Contact us to explore licensing opportunities for the DKK3 biomarker or ASO therapies.
