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.

Five leading European supercomputing centres are committed to develop, within their respective national programs and service portfolios, a set of services that will be federated across a consortium. The work will be undertaken by the following supercomputing centres, which form the High Performance Analytics and Computing (HPAC) Platform of the Human Brain Project (HBP): ▪ Barcelona Supercomputing Centre (BSC) in Spain, ▪ The Italian supercomputing centre CINECA, ▪ The Swiss National Supercomputing Centre CSCS, ▪ The Jülich Supercomputing Centre in Germany, and ▪ Commissariat à l'énergie atomique et aux énergies alternatives (CEA), France (joining in April 2018). The new consortium will be called Fenix and it aims at providing scalable compute and data services in a federated manner. The neuroscience community is of particular interest in this context and the HBP represents a prioritised driver for the Fenix infrastructure design and implementation. The Interactive Computing E-Infrastructure for the HBP (ICEI) project will realise key elements of this Fenix infrastructure that are targeted to meet the needs of the neuroscience community. The participating sites plan for cloud-like services that are compatible with the work cultures of scientific computing and data science. Specifically, this entails developing interactive supercomputing capabilities on the available extreme computing and data systems. Key features of the ICEI infrastructure are: ▪ Scalable compute resources; ▪ A federated data infrastructure; and ▪ Interactive Compute Services providing access to the federated data infrastructure as well as elastic access to the scalable compute resources. The ICEI e-infrastructure will be realised through a coordinated procurement of equipment and R&D services. Furthermore, significant additional parts of the infrastructure and R&D services will be realised within the ICEI project through in-kind contributions from the participating supercomputing centres.

Real-world evidence derived from real-world data (RWD) has a promising role to inform regulatory decision-making. Based on highly relevant use cases from regulatory practice and across the product lifecycle Real4Reg develops AI-based data-driven methods and tools for the assessment of medicinal products. Findings will inform training activities on good practice examples and will be implemented in existing and emerging guidelines for both health regulatory authorities and health technology assessment (HTA) bodies across Europe. There is urgent need to enable the use and establish the value of the application of RWD across the spectrum of regulatory use cases. The use of RWD is established in regulatory processes such as safety monitoring, but evidentiary value for further use cases, especially in the pre-authorisation and evaluation phase of medicinal products, is rudimentary. The use of RWD in post-authorisation steps is constrained by data variability and by challenges in analysing data from different settings and sources. Thus, the development of new and optimised methods for RWD analyses is essential. Real4Reg addresses the challenges and opportunities of RWD analyses across different health care systems by involving multiple stakeholders to work together in a collaborative approach, also outreaching to already established European initiatives. Our consortium assembles partners with outstanding excellence in the field of RWD analyses, including experts from regulatory agencies/ HTA (BfArM, DKMA, Infarmed), academia (Fraunhofer, UEF, CSC, AU, DZNE) and patient organisations (EUpALS, EIWH). In an advisory board stakeholders provide input and guidance to the project, including patients, industry, payers, HTA bodies and healthcare professionals. The structure and approach of our project facilitates the successful implementation of the effective use of RWD in regulatory decision-making and HTA, and ultimately supports the application of better medicines for patients.