Discover great EU-funded Innovations
INNOVATION
Urinary proteomics-based methods for in silico prediction of drug response
SHARE:
Market Maturity: Market Ready
These are innovations that are outperforming in innovation management and innovation readiness, and are considered to be "Ready for the market". Learn more
Market Creation Potential
This innovation was assessed by the JRC’s Market Creation Potential indicator framework as having a High” level of Market Creation Potential. Only innovations that are showing multiple signals of market creation potential are assigned a value under this indicator system. Learn more
Women-led innovation
A woman had a leadership role in developing this innovation in at least one of the Key Innovator organisations listed below.
Go to Market needs
Needs that, if addressed, can increase the chances this innovation gets to (or closer to) the market incude:
  • Secure capital
  • Scale-up market opportunities
Location of Key Innovators developing this innovation
Key Innovators
UN Sustainable Development Goals(SDG)
This innovation contributes to the following SDG(s)
SUSTAINABLE DEVELOPMENT GOAL 3
Ensure healthy lives and promote well-being for all at all ages

The UN explains: "Significant strides have been made in increasing life expectancy and reducing some of the common killers responsible for child and maternal mortality.

Major progress has also been made on increasing access to clean water and sanitation, reducing malaria, tuberculosis, polio and the spread of HIV/AIDS.

However, many more efforts are needed to control a wide range of diseases and address many different persistent and emerging health issues."

The EU-funded Research Project
This innovation was developed under the Horizon 2020 project DC-ren with an end date of 31/12/2024
  • Read more about this project on CORDIS
Description of Project DC-ren
Diabetic Kidney Disease (DKD) is highly prevalent in type 2 diabetes, with major impact on patients and healthcare systems. The complex disorder, further modulated by cardiovascular comorbidities, presents as an accumulation of risk factors, which we treat with drug combinations. While the overall benefit of this approach is evident on a cohort level, individual patients show remarkable heterogeneity in drug response, and lack of guidance on personalized medication results in suboptimal control of the disorder. For resolving variability, we propose a new concept for personalization of drug combinations beyond the cohort-centric perspective. We improve patient stratification based on equivalence relations of clinical presentation, disease pathophysiology and drug combinations. The approach is derived from dynamical systems theory, aimed at reducing probabilistic assignment of patient-specific disease evolution and matching drug combinations. The availability of a large European repository holding DKD patients in routine care with diverse drug combinations, complemented by high-throughput screening for improving patient phenotyping, and molecular network modelling of pathology, embedded risk factor combinations and consequence of drug effect allows a systems representation of patient groups. Integrating clinical presentation and molecular architecture in a novel computational framework will establish a decision support software prototype. We will validate this tool for predicting optimized personalized drug combinations in a study using given clinical trial repositories. Demonstration will expand to other available drugs, which in combination with approved drugs promise benefit for groups of DKD patients. With a clear route toward uptake in the clinical setting, and generalization capacity of our approach to other complex disorders we foster next steps in personalization, anticipate major patient benefit, and see novel translation and business opportunities.

Innnovation Radar's analysis of this innovation is based on data collected on 06/11/2024.
The unique id of this innovation in the European Commission's IT systems is: 126761