Chronic pain is a complex disease suffered by about 20% of Europeans. Up to 60% of these patients do not experience adequate pain relief from currently available analgesic combinational therapies and/or suffer confounding adverse effects. Of the many conceivable combinations only a few have been studied in formal clinical trials. Thus, physicians have to rely on clinical experience when treating chronic pain patients. The vision of the QSPainRelief consortium is that alternative novel drug combinations with improved analgesic and reduced adverse effects can be identified and assessed by mechanism-based Quantitative Systems Pharmacology in silico modelling. This is far cheaper and less time-consuming than clinical trials. We will develop an in silico QSPainRelief platform which integrates recently developed 1) physiologically based pharmacokinetic model to quantitate and adequately predict drug pharmacokinetics in human CNS, 2) target-binding kinetic models; 3) cellular signalling models and 4) a proprietary neural circuit model to quantitate the drug effects on the activity of relevant brain neuronal networks, that also adequately predicts clinical outcome. This platform will include patient characteristics such as age, sex, disease status and genotypes, and will predict efficacy and tolerability of a wide range of analgesic and other centrally active drug combinations, and rank these. The best combinations will then be validated in a suitable animal model, in two clinical studies in healthy volunteers, as well as in real world clinical practice. Quantitative insights and confirmed effective combinational treatments will result in a game-changer by improving the management of pain in individuals and stratified sub-populations of chronic pain patients, and reduce the large burden on health-care providers greatly. It would also increase the understanding of chronic pain in general, and trigger the development of even better combination therapies in the future.

Researchers worldwide have been working to develop a universal flu vaccine, but no breakthrough has yet been achieved. FLUniversal is not "another costly universal flu project". It is an opportunity to create a genuine universal flu vaccine that will set the standard for rapid, efficient vaccine development, and generate know-how and tools to develop next-generation vaccines. We plan to exploit our increased understanding of molecular mechanisms of influenza infection and immunity to develop a vaccine effective against all flu virus strains. Our innovation uses genetically modified flu strains administered intranasally in a prime-boost regimen. This approach rapidly induces interferon and broadly cross-neutralising antibodies in the nasal passages and a systemic immune response directed to the conserved HA stalk. Proof of concept for universal protection was demonstrated in the ferret; we propose now to show proof of concept in humans. Previous clinical studies established safety and immunogenicity in humans. The strains are efficiently produced in Vero cells. FLUniversal's objectives align with the Expected Outcomes: comprehensive immunological assessment of preclinical models, development of a human challenge model, and assisting healthcare stakeholders’ decision-making about support for vaccine development. Consortium members’ world-leading expertise in preclinical models, clinical trials, immunology and validated assays will provide valuable insights on mechanisms of protection. Proposed clinical studies will provide crucial clinical proof of concept to advance the vaccine toward commercialisation.

Inno4Vac proposes an ambitious programme that will harness the latest advances in immunology, disease modelling, and modelling for tackling persistent scientific bottlenecks in vaccine development and for de-risking and accelerating this process. To reach this aim the project is divided into four interlinked subtopics. In Subtopic 1, artificial intelligence in combination with big data analysis and computational modelling will be used to build an open-access and cloud-based platform for in silico vaccine efficacy assessment and development. Subtopic 2 will develop new and improved controlled human infection models (CHIM) against influenza, RSV and C. difficile that will enable early vaccine efficacy evaluation. Subtopic 3 will contribute to the development of cell-based human in vitro 3D models that resemble the in vivo situation of an infection at the mucosa and more reliably predict immune protection. These models will be combined with the development of related functional immune assays for clinically relevant (surrogate) endpoints. Finally, Subtopic 4 will develop a modular one-stop computational platform for in silico modelling of vaccine bio-manufacturing and stability testing. In parallel to the scientific-technical work, the partners will develop strategies and roadmaps for positioning the newly developed models in the regulatory framework and integrating them into pharmaceutical vaccine development. The overall workplan is underpinned by horizontal activities on coordination/management and dissemination/communication, including data management and future sustainability. To achieve these ambitious objectives, Inno4Vacc has assembled a multidisciplinary consortium from academic and research institutions, industries, regulatory bodies, and vaccine R&D alliances. This unique partnership brings together clinical, immunological, microbiological, systems biology, mathematical models, and regulatory expertise and includes world-leaders in each respective field.

The treatment for cancer patients is shifting from population-based treatment to the more precise personalized medicine. Tumor-specific molecular diagnostics and treatment are an example of this phenomena to assist clinical decision making and to improve treatment strategies for patients. This change in management will lead to societal benefits such as treatments that fits the patient’s needs and therefore, increased health benefits and increased quality-of-life. The targeted agent we have developed has both an imaging version to improve the detection and resection of cancer and an cytotoxic variant to eliminate potential remaining tumor cells. At this point, there is no agent available that can contribute to both the diagnostic phase and the subsequent treatment in a tumor-specific manner. Near infrared (NIR) fluorescence imaging is a technique that has gained a lot of attention over the last decade because of its role in intra-operative guidance to improve resection, and in, for example, endoscopy to improve detection. Combining a NIR-dye to a specific tumor-targeting ligand, like an antibody or a peptide, dramatically enhances the specificity, providing a solid real-time identification and demarcation technique. In TAcTIC, we propose the clinical introduction of a targeted fluorescent imaging agent against EpCAM, which can also be used for response monitoring during endoscopy, and the initiation of a New Company (in the Netherlands) via a joint venture between LUMC and ElevenBio (Boston, United States) for the future commercialization of this agent. The expected outcomes of this proof-of-concept project are the successful introduction of a novel targeted fluorescent agent for in multiple cancer types, a joint venture between LUMC and ElevenBio for the commercialization of this agent, and societal benefits for the patient in the form of increased health benefits and quality of life due to a treatment strategy that fits the individual patient’s needs.