The GetReal Initiative brings together partners from the IMI GetReal project to drive the adoption of tools, methodologies and best practices from IMI GetReal and increase the quality of real-world evidence (RWE) generation in medicines development and regulatory/HTA processes across Europe. We will establish, in Work Package (WP) 1, a Think Tank, a number of Task Forces and a RWE Research Community. The Think Tank will consist of international thought leaders and will discuss, assesses and give recommendations on the opportunities and barriers to the generation, use and acceptability of RWE. They will act as ambassadors for the use of RWE during the project and beyond, enagaging with key stakeholder groups to drive policy debate and facilitate the uptake of the outputs of IMI GetReal and the GetReal Initiative. The GetReal Taskforces will drive the focused development of tangible solutions to the key challenges identified in IMI GetReal and the Think Tank. The initial three task forces will be: (i) Pragmatic Trials (design, operational feasibility and analysis/the GetReal PragMagic tool), (ii) network meta-analysis and benefit risk assessment (incl. the GetReal ADDIS tool) and (iii) Statistical Approaches for enriching RCTs with real-world data. The GetReal Research Community will consist of researchers and organisations active in the field of RWE generation, regulators, HTAs, physicians and patients. The Community will review and comment on any GetReal Initiative guidelines, recommendations or white papers ahead of their finalisation, will have access to all the GetReal tools and outputs, receive regular newsletters and receive invitations to attend stakeholder events. The project will also invest in long term sustainability of the GetReal Initiative and the tools on a not-for-profit basis (WP2). The project is supported by professional experienced consortium management (WP3) and an ethics work package (WP4).

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.