The PharmaLedger project will create a blockchain-based framework for the efficient digitization of the healthcare industry. The goal of the project is to provide a widely trusted platform that will support the design and adoption of blockchain-enabled healthcare solutions while accelerating delivery of innovation that will benefit the entire ecosystem, from manufacturers to patients. PharmaLedger will serve as a single source of truth for the healthcare ecosystem and will be designed for efficient decentralized governance, wide adoption by the stakeholders of the ecosystem, compliance with extant and emerging standards and regulation, and end-to-end connectivity and interoperability. Sustainability of the platform will be ensured by leveraging existing, successful blockchain technologies; open source reference implementation; and a fully documented, actionable methodology for evolutionary digitization of the healthcare industry. The project will address the key challenges of the healthcare ecosystem through prioritized delivery of applications and validation of business use cases, including but not be limited to end-to-end product tracking for combating counterfeit medicines and medical supplies; supply chain integrity; efficiency of recruitment and submission in clinical trials; and machine-learning health data marketplaces. The platform will support integrated use of medical devices across the use cases. The PharmaLedger project brings together 28 partners from 10 EU Member States, including 11 large pharmaceutical companies; highly innovative technology SMEs specializing in blockchain development, security, privacy and business intelligence; universities and research institutes specializing in pharmacoeconomic analysis, research of patient requirements, big data analytics and electronic health records; leading clinical trials companies; supply chain partners; patient representatives; and leading healthcare service providers.

By bridging an important gap between basic research and drug development, ESCulab will facilitate the translation of fundamental scientific insights into innovative drug starting points. New biological ideas will be collected by crowdsourcing, novel targets, phenotypes and pathways translated and de-risked by delivering high-quality chemical starting points for drug development. Innovators from EU academics and SMEs are offered access to state-of-the-art industry-grade facilities, drug discovery expertise, and a top-quality, curated library with over 550,000 unique compounds suitable for screening potential drug targets. The ESCulab drug discovery data generated will allow the innovators to build a proposition that is likely to attract investors and/or drug developers and stimulate them to further exploit these findings. Ultimately, this will result in novel medicines – based on basic scientific findings – that will help patients (sooner). ESCulab will achieve this by starting off the shoulders of the European Lead Factory (ELF), where this principle has been successfully implemented. 15 out of the 18 ESCulab consortium members were involved in ELF, thus ensuring maximum benefit from the ELF legacy. ESCulab will expand and enhance ELF by supporting 185 new screens. Important additions include the application of a much larger compound collection for screening, the ability to accommodate the more complex phenotypic screening assays, and the building of a sustainable business model to ensure access to Collaborative Drug Discovery activities beyond the funding period. The delivery of qualified hit lists (QHLs) is the end point for this project, but during the funding period, ESCulab will attract additonal funding for externally paid screens and hit-to-lead projects for organizations such as charities and foundations for long term sustainability of the initiative.

Haematological malignancies (HM), also known as blood cancers, are a heterogeneous and complex group of multicausal diseases that can’t be easily diagnosed nor treated. Nowadays most treatments are extremely complex, and advances in patient diagnosis and therapies slow due to the low number of patients per centre. Thus, there is a need to harmonise, store, and analyse the current HM information to speed-up and support the decision-making process for patients’ access to new therapies. HARMONY PLUS takes advantage of the capabilities of the HARMONY Big Data platform to match this unmet needs by expanding its scope to incorporate myeloproliferative neoplasms, including chronic myeloid leukemia, polycythaemia vera, essential thrombocythaemia, and myelofibrosis; and lymphoproliferative disorders, including Hodgkin’s lymphoma, Waldenström macroglobulinemia and all the other rare HMs not covered by HARMONY Project. In parallel, HARMONY PLUS will continue to refine and define the Core Outcome Sets (COS), especially for these new HMs to ensure the use by researchers of useful common outcomes relevant to all stakeholders. As previously accomplished in HARMONY, HARMONY PLUS is committed to pursue the maximum ethical and legal requirements, particularly to ensure patient’s right to privacy. Data-driven research within Europe will be enhanced by converting the current HARMONY platform into an Integrated Services Platform to serve as a valuable tool to support clinical trial design and use of available data as a control arm. This platform, combined with a HaemoDatabank repository with information about HMs patient biological samples across Europe, will facilitate a more efficient research and clinical trial design, and consequently will promote collaborations with recognised databases outside Europe. From the regulatory point of view, HARMONY PLUS will be a valuable technology tool during the evaluation of new treatments and drugs by also considering the patients’ needs.

The dramatic increase in using of Artificial Intelligence (AI) and machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro biotech, and other chemical companies. However, the application of AI in these fields is not straightforward and requires excellent knowledge of chemistry. Thus, there is a strong need to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry, which is the prime goal of the AIDD proposal. Research WPs include sixteen topics selected to cover the key innovative directions in machine learning in chemistry. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed me