MELLODDY will demonstrate how the pharmaceutical industry can better leverage its data assets to virtualize the Drug Discovery (DD) process with world-leading Machine Learning (ML) technologies as an answer to the ever-increasing challenges and stricter regulatory requirements it is facing. The lack of a tested, secure and privacy-preserving platform for federated machine learning that enables pharmaceutical partners to extract DD-relevant information from all types of, not only their own but even each other’s competitive data, without mutual disclosure of the chemistry and biology each partner has worked on, has previously held back such demonstration, to the detriment of patients in the EU and beyond. MELLODDY’s ten pharmaceutical partners will enable this demonstration with an unprecedented volume of more than a billion highly private and competitive DD-relevant data points, and hundreds of Tbs of image data that annotate the biological effects of more than 10 million small molecules. The successful demonstration of the predictive benefits, i.e. increased predictive model performance and chemical applicability domain, of unlocking this data volume, while strictly preserving the privacy of all underlying data and the resulting predictive models, will shape best practices and translate into substantial efficiency gains in the DD process, and in the future, drug development. Finally, MELLODDY will prepare and exploit a service-for-fee vehicle to ensure the MELLODDY technologies are available to the rest of the pharmaceutical sector.

Atrial fibrillation (AF) and stroke are major health care problems in Europe. They are most often the clinical expression of atrial cardiomyopathy, which is under-recognised due to the lack of specific diagnostic tools. Multidisciplinary research and stratified approaches are urgently needed to prevent, diagnose, and treat AF and stroke and preempt the AF-related threat to healthy ageing in Europe. MAESTRIA is a European consortium of 18 clinicians, scientists and Pharma industrials who are at the forefront of research and medical care of AF and stroke patients. It will create multi-parametric digital tools based on a new generation of biomarkers that integrate artificial intelligence (AI) processing and big data from cutting edge imaging, electrocardiography and omics technologies. It will develop novel biomarkers, diagnostic tools and personalized therapies for atrial cardiomyopathy. Digital Twin technologies, a rich data integrator combining biophysics and AI will be used to generate virtual twins of the human atria using patient-specific data. Unique experimental large-animal models, ongoing patient cohorts and a prospective MAESTRIA cohort of patients will provide rigorous validation for new biomarkers and newly developed tools. A dedicated core lab will collect and homogenize clinical data. MAESTRIA will be organized as a user-centered platform, easily accessible via clinical parameters routinely used in European hospitals. A Scientific Advisory Board comprising potential clinician users will help MAESTRIA meet clinical and market needs. Dissemination and visibility of the MAESTRIA consortium mission will benefit from participation of the German Competence Network on Atrial Fibrillation (AFNET), and support from the European Society of Cardiology, clinicians, scientists, and other professional societies. MAESTRIA will be ready to tackle the major challenges of data integration and personalized medicine focused on atrial cardiomyopathy, AF and stroke.

Systemic autoinflammatory diseases (SAID) encompass several rare disorders characterised by extensive clinical and biological inflammation. SAID are caused by the dysregulation of the innate immune system. Due to numerous and unspecific symptoms, tentative diagnosis often leads to failure/delay and inadequate treatments. ImmunAID will deliver a method for rapid and accurate diagnosis across all the spectrum of SAID, in order to improve clinical management of SAID patients. Thanks to parallel analyses run on samples from more than 600 patients with monogenic or undiagnosed SAID collected throughout Europe, Immunaid will generate a unique and comprehensive set of data, based on unbiased multiomics approaches (gene, transcript, protein, microbiome), and hypothesis-driven assays exploring inflammasome, inflammation resolution and immune networks. A centralised data management strategy will enable to conduct integrated analyses for diagnostic biomarker identification. In a discovery phase, semi-supervised clustering of omics data will be combined to supervised analysis of pathway-related data to provide robust classification and strong link to clinical features/impact. The related biomarkers will further be validated externally on independent samples and cohorts. Overall, ImmunAID will disentangle the spectrum of SAID, and propose a new omics- and pathogenesis-based SAID classification associated to a clinical decision making algorithm implementable in daily practice. An efficient dissemination plan will target e.g. guideline-forming bodies, the medical community and patients with the help of the ERN RITA and with the objective of turning our results into clinical practice. To further support this, proactive innovation management will be implemented. To reach its ambitious goals, ImmunAID interdisciplinary consortium gathers high-level partners, including the founder of SAID concept, experts in omics science, immunology, bioinformatics, and involves clinicians and patient advocacy groups.

IMMUcan proposes an inclusive and integrated European immuno-oncology platform. IMMUcan will access high-quality human biological material (tissue, blood, stool and saliva) and clinical data from patients with colorectal, lung, head & neck, breast, gastric cancers and immune checkpoints inhibitors failures. We have assembled a strong consortium with ten expert clinical centers, access to large volumes of the required tumor types. IMMUcan will mobilize the majority of academic trials running or expected to start recruiting patients during the project period. A centralized workflow for samples, via a state-of-the-art biobank will increase reproducibility as all tissues will be processed and stored in a uniform way, following proofed SOPs. The project will perform in depth immunoprofiling with cutting edge technologies including CyTOFF, single cell RNA-seq, peptidogenomics or microbiome analysis. IMMUcan will analyze the data in order to understand the host/tumour interaction in the absence of treatment (naïve population) and with treatment (patients in follow-up) to identify potential predictive biomarker for response to immunotherapy, or develop rationale for combination therapies. IMMUcan will provide an IT platform and legal/ethical contractual framework where participants from both academia and industry can pursue their own independent investigations utilizing the IMMUcan data and effectively testing and improving the functioning and relevance of the database. A sustainability plan will be developed to ensure the collection of follow-up data and the accessibility of the data platform. The use of the data generating throughout this project, for future research, will be supported.