Descrizione del progetto
La teoria dei sistemi dinamici potrebbe supportare i medici nella scelta di terapie ottimali
Le malattie, le comorbilità e le risposte dei pazienti ad alcune terapie sono il risultato della complessa interazione di numerosi fattori. Le relazioni che definiscono in che modo questa rete intricata di apporti conduce a risultati vari sono estremamente difficili da ricavare attraverso i mezzi convenzionali. Grazie a megadati, matematica applicata di alto livello e computer, il progetto DC-ren, finanziato dall’UE, sta sviluppando uno strumento che tenga conto di tutto ciò e non solo, e consenta farmacoterapie personalizzate per esiti significativamente migliorati. Il team si sta concentrando sulla malattia diabetica renale, una comorbilità comune del diabete di tipo 2 spesso accompagnata da malattie cardiovascolari. Attualmente, il cocktail di farmaci mirato ad affrontare tale condizione produce risposte estremamente varie. Grazie alla propria vasta banca dati di pazienti, alle tecniche sperimentali avanzate e alla teoria dei sistemi dinamici, DC-ren sta sviluppando un quadro computazionale completamente nuovo e ampiamente applicabile per il supporto alle decisioni.
Obiettivo
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.
Campo scientifico
- medical and health sciencesclinical medicineendocrinologydiabetesdiabetic nephropathy
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugs
- medical and health sciencesbasic medicinephysiologypathophysiology
- medical and health sciencesbasic medicinepathology
- medical and health sciencesclinical medicinenephrologykidney diseases
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Programma(i)
Argomento(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SC1-2019-Two-Stage-RTD
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
6020 Innsbruck
Austria