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.

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.

Wide sharing of knowledge and data drives the progression of science. Shared data allows other researchers to reproduce findings and benchmark quality of experiments. Sharing data so that other researchers can Find, Access and Interoperate – i.e. integrate the data with the outcomes of their own experiments - allows Reuse and an opportunity to build the large aggregated cohorts we need to detect rare signals and manage the many confounding factors in translational research. This project will develop the guidelines and tools needed to make data FAIR. Through worked examples using IMI and EFPIA data and application and extension of existing methods we will improve the level of discovery, accessibility, interoperability and reusability of selected IMI and EFPIA data. In addition, through disseminated guidelines and tailored training for data handlers in academia, SMEs and pharmaceuticals, data management culture will change and be sustained and datasets will be reused by pharmaceutical companies, academia and SMEs. Our FAIR SME & Innovation programme will enable wide data reuse and foster an innovation ecosystem around these data that power future re-use, knowledge generation, and societal benefit. We call this approach ‘FAIRplus’.

The current nosology of neuropsychiatric disorders provides a pragmatic approach to diagnosis and treatment choice but lacks reference to quantitative biological underpinnings of disease. This weakness impedes innovative drug development. To test whether a quantitative biological approach to the understanding and classification of neuropsychiatric disorders is both feasible and useful the PRISM 1 consortium was formed by academics, SMEs, patient organizations, regulators, ECNP, and EFPIA partners. PRISM 1 has now successfully identified quantitative biological parameters related to diagnosis (Schizophrenia (SZ) and Alzheimer Disease (AD)) as well as to social functioning irrespective of diagnosis. From the relationships between social function, neuroimaging, and cognitive endpoints a new neurobiological framework has emerged now needing further validation. Genetic studies of social functioning outcomes revealed known and novel loci for this phenotype. In addition, a preclinical test battery was developed, based on homologs of the clinical paradigms, to allow effective back-translation and a deepening of our neurobiological knowledge. Finally, a novel digital tool for assessing social function provided a novel, objective characterization that transcended the initial diagnostic classification and the digital readouts were associated with other study parameters. To build on outcomes of PRISM 1, PRISM 2 has three objectives. First, to determine the reproducibility of the transdiagnostic and pathophysiological relationship between DMN integrity and social dysfunction in SZ and AD that emerged from PRISM 1 and determine its potential to generalise to Major Depressive Disorders. Second, to test the causality between the quantitative variation in DMN integrity and social dysfunction. Third, to translate and communicate project results to the benefit of stakeholders, such as regulators, patients and their families, and health care providers.

The Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) pandemic has emerged as the largest global health threat to humanity in this century. According to the World Health Organisation Situation Report of March 28th 2020, 571,659 patients were diagnosed with Coronavirus Disease 2019 (COVID-19) and 26,493 deaths were reported globally. The wide spectrum of clinical symptoms, disease severity in high risk individuals, transmission efficiency and high mortality, raises an immediate need for vaccines or therapeutics. Given that the viral variant is new in the human population and emerged less than 4 months ago, there is no vaccine or approved therapies. The Corona Accelerated R&D in Europe (CARE) consortium is a coalition of 37 globally renowned academic institutions, pharmaceutical companies and non-profit research organizations who have committed to rapidly and efficiently address this emergent health threat, and the main objectives are: the development of therapeutics (i) to provide an emergency response towards the current COVID-19 pandemic by drug repositioning and (ii) to address the current and/or future coronavirus outbreaks by broad-spectrum small-molecule drug discovery and/or virus-neutralizing antibody discovery. To achieve this, a collection of repurposed drugs, focused libraries and small molecule libraries will be screened against SARS-CoV-2, other emerging SARS-CoV-2 clades and related coronavirus genera in phenotypic or target-based assays. A focused medicinal chemistry campaign will identify small-molecule hits, and Absorption, Distribution, Metabolism and Excretion (ADME), pharmacokinetic/pharmacodynamic (PK/PD), potency and safety of these therapeutic candidates will be assessed in vitro and in animal models. Virus-neutralizing monoclonal antibodies will be generated and further characterized. Immune markers will be identified contributing to the host immune responses to SARS-CoV-2 infections, and the correlation with clinical and virological outcomes will be determined. Finally, lead candidates will be advanced into Phase1 and Phase 2 clinical trials in humans. With this reactive response, the CARE consortium is dedicated to win the fight against coronavirus.