Our Mission

Standardization and harmonization of existing concepts and tools (methods, reagents drugs, etc.) for the study of NRF2 biology, pharmacology and medical use.
Confirmation of the set of NCDs where NRF2-based therapy will be most significant either as monotherapy or as add-on therapy in combination with other medicines.
Creation of a database of relevant patient metadata, which will address NRF2-related genetic and biochemical biomarkers for disease monitoring and drug-target engagement in NRF2-related NCDs.

Somatic mutations in the KEAP1/NRF2 system in non-small lung cancer can be detected by sequencing of tumour-derived cell free DNA (cfDNA). A targeted sequencing panel of genetic and somatic mutations in the KEAP1/NRF2 system to be exploited clinically for personalized NRF2-targeted treatments.
Development of a catalogue of optimized animal models of NCDs, that combine NRF2 alterations with other disease-specific pathophenotypes such as hyperglycemia, insulin resistance, hypertension, hypercholesterolemia, cardiomyopathy, fibrosis, amyloidopathy, etc.

Locating the NRF2 regulatory pathway in the human interactome. NRF2 plays a key role in pathologic ROS formation as well as inflammatory and metabolic responses by coordinating the activity of various proteins. These interactions constitute a molecular interaction network, the NRF2 interactome. The known physical interactions between proteins involved in the NRF2 regulatory pathway (i.e., regulator proteins, brown circles) and the proteins through which they are connected (i.e., mediator proteins, green circles) are shown with a gray link. The regulator proteins that involve more than one mediator protein to connect to NRF2 are shown with a blue link. The regulator proteins are curated from literature, and their interactions are retrieved from various resources, including IntAct, MINT, BioGRID, HPRD, KEGG, and PhosphoSite (source, Cuadrado et al. Pharmacol Rev. 2018 Apr;70(2):348-383).

Development of a virtual technology platform that will comprise research infrastructures and advanced technologies existing in the network, as well as a metaarchive of biologic samples highly relevant for NRF2 research, available in several EU biobanks.
Documentation of new compounds regulating NRF2/KEAP1, with higher specificity and superior pharmadynamics profile, to be achieved by integrating results from “machine learning” in network pharmacology, structural biology and systems medicine, and a strategy of repurposing existing drugs that have proven clinical benefits in particular diseases underlined by NRF2-related pathomechanisms.

Documentation for future clinical trials with lead NRF2-targeting compounds.
Economic exploitation of outcomes in the form of provision of knowledge and innovative services in collaboration with participating SMEs.
All these approaches will be transversally addressed in the training of young researchers.

Systems medicine view of NRF2 from the perspective of the human interactome. Diseases are triggered by mutations that perturb proteins and their interactions, affecting a certain neighborhood in the NRF2 interactome. Interactome-based proximity measures the distance of genes to those disease neighborhoods. The bars show the proximity of NRF2 gene (NFE2L2) to known disease genes that participate in NRF2-related pathophenotypes. These bars highlight the role of NRF2 in digestive system diseases and cancers. For a given disease, the proximity first calculates the distance from NRF2 to the closest known disease gene and then compares that distance to a random expectation that is estimated using average distances between randomly selected proteins in the interactome. The z-score reported by interactome-based proximity corresponds to the significance of the observed distance between NRF2 and the disease genes. A negative value indicates that the observed distance is lower than what would be expected by chance. The bars are colored in varying tones of orange based on the z-score: significantly proximal (dark orange), proximal (orange), not proximal (light orange), respectively. The known disease genes are taken from DisGeNet, OMIM, and GWAS Catalog (source, Cuadrado et al. Pharmacol Rev. 2018 Apr;70(2):348-383).

Our Mission

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COST

BenBedPhar CA20121

Action details