EuroPOND will develop a data-driven statistical and computational modeling framework for neurological disease progression. This will enable major advances in differential and personalized diagnosis, prognosis, monitoring, and treatment and care decisions, positioning Europe as world leaders in one of the biggest societal challenges of 21st century healthcare. The inherent complexity of neurological disease, the overlap of symptoms and pathologies, and the high comorbidity rate suggests a systems medicine approach, which matches the specific challenge of this call. We take a uniquely holistic approach that, in the spirit of systems medicine, integrates a variety of clinical and biomedical research data including risk factors, biomarkers, and interactions. Our consortium has a multidisciplinary balance of essential expertise in mathematical/statistical/computational modelling; clinical, biomedical and epidemiological expertise; and access to a diverse range of datasets for sporadic and well-phenotyped disease types. The project will devise and implement, as open-source software tools, advanced statistical and computational techniques for reconstructing long-term temporal evolution of disease markers from cross-sectional or short-term longitudinal data. We will apply the techniques to generate new and uniquely detailed pictures of a range of important diseases. This will support the development of new evidence-based treatments in Europe through deeper disease understanding, better patient stratification for clinical trials, and improved accuracy of diagnosis and prognosis. For example, Alzheimer’s disease alone costs European citizens around €200B every year in care and loss of productivity. No disease modifying treatments are yet available. Clinical trials repeatedly fail because disease heterogeneity prevents bulk response. Our models enable fine stratification into phenotypes enabling more focussed analysis to identify subgroups that respond to putative treatments.

The stated goal of RHAPSODY is to define a molecular taxonomy of type 2 diabetes mellitus (T2D) that will support patient segmentation, inform clinical trial design, and the establishment of regulatory paths for the adoption of novel strategies for diabetes prevention and treatment. To address these goals, RHAPSODY will bring together prominent European experts, including the leaders of the diabetes-relevant IMI1 projects to identify, validate and characterize causal biomarkers for T2D subtypes and progression. Our plans are built upon: (a) access to large European cohorts with comprehensive genetic analyses and rich longitudinal clinical and biochemical data and samples; (b) detailed multi-omic maps of key T2D-relevant tissues and organs; (c) large expertise in the development and use of novel genetic, epigenetic, biochemical and physiological experimental approaches; (d) the ability to combine existing and novel data sets through effective data federation and use of these datasets in systems biology approaches towards precision medicine; and (e) expertise in regulatory approval, health economics and patient engagement. These activities will lead to the discovery of novel biomarkers for improved T2D taxonomy, to support development of pharmaceutical activities, and for use in precision medicine to improve health in Europe and worldwide.

Frontotemporal dementia (FTD) has a debilitating effect on patients and their caregivers and leads to substantial economic costs. 15-30% of patients have familial FTD caused by known pathogenetic mutations. For the other 70-85% of patients, termed sporadic FTD, diagnosis is slow (~3.6 years) with frequent misdiagnosis due to clinical, genetic and molecular heterogeneity. Thus, there is great need for biomarkers for early diagnosis of sporadic FTD and its pathological subtypes. In PREDICTFTD, we will validate a set of biomarkers and create a diagnostic tool for early diagnosis of familial and sporadic FTD, which will facilitate tailored support and symptomatic treatments and care. We will apply several new approaches to achieve this: 1) we combine 11 geographically diverse cohorts of sporadic and familial FTD with retrospective and prospective longitudinal liquid biopsy samples and extensive clinical and behavioural data; 2) we are the first to use multimodal clinical and liquid biomarker data to train an AI-algorithm as a diagnostic tool for quick and early clinical FTD diagnosis; and 3) we implement a novel robust two-stage strategy for biomarker and AI algorithm validation, where phase I validates biomarkers and algorithms on a cohort of genetic and autopsied cases and phase II assesses biomarker value for diagnosis of sporadic FTD and at-risk pre-symptomatic mutation carriers. We will apply this two-stage validation strategy to address three critical clinical challenges: i) To distinguish sporadic FTD from (non-) neurodegenerative disorders that show significant clinical/symptomatic overlap, ii) To robustly detect FTD pathological subtypes in sporadic FTD and iii) pre-symptomatic identification of FTD onset. Thus, PREDICTFTD will transform FTD diagnosis, offering potential for early disease confirmation, guiding treatment decisions, facilitating patient recruitment for clinical trials, guidance of patients and caregivers, and enabling preventive measures.

Transplantation of autologous split-thickness skin -the epidermis with a tiny layer of dermis- remains the golden standard for various skin wounds like burns and large trauma. This treatment, however, comes with a number of serious drawbacks, including pain, mobility-limiting contractures and disfiguring scars. The SkinTERM consortium will address wound healing in a completely different way, recapitulating (certain aspects of) skin embryonic development in adults, and aiming for regeneration rather than repair. Skin organogenesis will be induced by key elements taken from the extracellular matrix of foetal and non-scarring species and by employing (stem) cells from relevant cellular origins. The starting point for the study is the remarkable capability of early foetal skin and skin from the spiny mouse (Acomys) to heal perfectly without scars/ contraction and with appendices such as hair follicles. Novel biomaterials and skin substitutes will be developed and evaluated. In order to effectively embrace this new approach, the PhD students need to have knowledge in key elements of basic science, regenerative medicine and biomaterial sciences. As translation to medical devices and especially advanced therapy medicinal products is currently too limited, we will give the PhD students a solid theoretical and practical foundation on topics like regulatory affairs, GMP and GCP, as well as secondments in industry. Driven by both the enthusiasm to gain basic scientific insights and the need for efficacious and innovative therapies, the students will acquire expertise through cutting edge scientific projects and will be trained by leading experts in all required skills to further develop their scientific findings into real life-science products. The SkinTERM program will thus create a new generation of entrepreneurial, multidisciplinary and inter-sectorially trained scientists with excellent career perspectives in either academia, industry or government.

An EEG calibration toolkit for monitoring rehabilitation of stroke patients EEG records electric potential differences from electrodes attached to the human skin. Since it is a brain imaging technique that provides a direct measure of neural activity, it is an ideal device to monitor stroke rehabilitation. For instance, the amplitude of an EEG response to a fixed external stimulus on the wrist could act as a biomarker quantifying the number of active cells in the primary somatosensory cortex. In this way, an EEG system could work as a simple monitoring device rendering direct feedback to healthcare workers on the success of the applied therapeutic strategy. This would greatly help to select the most optimal therapy for the individual patient. However, with the current state of the art a direct comparison between EEG amplitudes of different subjects is not possible because these amplitudes are strongly affected by inter individual skull variations. In this PoC project a calibration step is proposed to reduce these influences on the EEG biomarker. We will develop a software toolkit, allowing to execute the required calibration step with a minimum of patient burden. We foresee that the prototype delivered by the end of the project can be developed into a commercial accessory EEG software device that can be provided to end users for relatively small add-on price. Best practices of EEG calibration will be disseminated by publications, presentations at conferences, a special workshop and business development activities.