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 Research empowerment on solute carriers (ReSOLUTE) proposal aims at inducing a decisive acceleration in the intensity of SLC research worldwide while establishing solute carriers (SLCs) as a tractable target class. It is composed of a core consortium of seven academic/SME members (CeMM, Univ. of Oxford, Univ. of Manchester, AXXAM Spa, Univ. Leiden, Max-Planck Institut für medizinische Forschung, Univ. Wien) and six industrial partners (Pfizer Limited UK, Novartis Pharma AG, Boehringer-Ingelheim, Vifor Pharma Group, Sanofi Aventis Recherche et Développement, Bayer AG) with established and complementary expertise combining industry grade standards and systems level principle-driven analysis. ReSOLUTE merges systematic and focused approaches: 1) the generation of reliable and validated ‘hardware’ such as cell lines, proteins, antibodies/high affinity binders and large ‘omics’ datasets, 2) a central deorphanisation process coupling genomic engineering to metabolomics and supported by genetic and proteomics studies, 3) a non-redundant process to test systematically the suitability of a certain SLC to a variety of assay formats, 4) a collection of interlinked networks that cover different expertise, datasets and functional connections among SLCs, integrated into large-scale data repositories and a dedicated knowledgebase. The ReSOLUTE strategy will be applied in parallel at the super-family level and, in a more in-depth manner, to a prioritized gene list elected to be potential drug targets. To implement this and achieve the expected goals, the consortium is supported by a large network of academic and industrial partners with specific knowledge of certain SLCs and tools, willing to be accessed at any time and provide specific expertise. Together with a strong dissemination and exploitation plan, we therefore expect that ReSOLUTE will transform the landscape of SLC research for years to come.

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.

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 allows for a pragmatic approach to treatment choice, regulation and clinical research. However, without a biological rationale for these disorders, drug development has dramatically stagnated in the past decades. In a coordinated effort encompassing academic experts, SMEs, patient and family organizations, regulators, ECNP and EFPIA partners, this project aims to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments for patients. This project will concentrate on Schizophrenia (SZ), Alzheimer’s disease (AD), and Major Depression (MD), as these disorders share part of their symptomatology, in particular social withdrawal and certain cognitive deficits, such as deficits in attention, working memory and sensory processing. By applying innovative technologies (e.g. EEG, cognitive tasks, (f)MRI, smartphone monitoring, and (epi-)genetics) to deep phenotype a clinical cohort of SZ and AD patients combined with a wider analysis of existing clinical data sets from major European and global disease cohorts that also include MD, we will define a set of quantifiable biological parameters best able to cluster and differentiate SZ, AD, and MD patients that do, or do not, exhibit social withdrawal. First, by mining large European SZ, AD and MD cohort datasets with already available social and cognitive proxy measures, and, second, by obtaining objective measures of social exploration levels (using a novel smartphone application), phenotypic relationships with social and cognitive measures will be further tested. For instance we might predict that socially withdrawn individuals may have lower cognitive functioning and poorer clinical course compared to those who are more socially engaged.