Project description
Dynamical systems theory could support physicians to select optimal therapies
Diseases, co-morbidities, and patients responses to certain therapies are the result of a complicated interplay of numerous factors. The relationships defining how this intricate network of inputs leads to various outputs is extremely difficult to derive using conventional means. Thanks to Big Data, high-level applied mathematics and computers, the EU-funded DC-ren project is developing a tool that will take all this and more into account, and enable optimised personalised drug therapies for significantly enhanced outcomes. The team is focusing on diabetic kidney disease, a common co-morbidity of type 2 diabetes often accompanied by cardiovascular disease. Currently, the drug cocktails targeting it produce highly varied responses. Thanks to its vast patient database, advanced experimental techniques and dynamical systems theory, DC-ren is developing a completely new and widely applicable computational framework for decision support.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencessoftware
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugs
- medical and health sciencesbasic medicinephysiologypathophysiology
- medical and health sciencesclinical medicineendocrinologydiabetes
- medical and health sciencesbasic medicinepathology
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Keywords
Programme(s)
Call for proposal
(opens in new window) H2020-SC1-BHC-2018-2020
See other projects for this callSub call
H2020-SC1-2019-Two-Stage-RTD
Funding Scheme
RIA - Research and Innovation actionCoordinator
6020 Innsbruck
Austria