Project description
Driving forward vaccine development
Vaccines protect millions from death, illness and disability. The COVID-19 pandemic has highlighted many challenges hampering vaccine development. The EU funded Inno4Vac project will address scientific bottlenecks in vaccine development. Inno4Vac proposes to develop predictive biological and mathematical models of vaccine performance and manufacturing. Artificial intelligence combined with big data and computational modelling will be used to build an open-access and cloud-based platform for in silico vaccine efficacy assessment and development. Controlled human infection models and cell-based human in vitro 3D models will be developed to enable early evaluation of vaccine efficacy and prediction of immune protection. Finally, an open source in silico simulation platform will guide the production of vaccine candidates and associated stability testing.
Objective
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.
Fields of science
- natural sciencescomputer and information sciencesdata sciencebig data
- medical and health scienceshealth sciencesinfectious diseasesRNA virusesinfluenza
- medical and health sciencesbasic medicineimmunology
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsvaccines
- natural sciencesmathematicsapplied mathematicsmathematical model
Keywords
Programme(s)
Topic(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
69115 Heidelberg
Germany
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Participants (40)
53100 Siena
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7400 Herning
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
2333 CL Leiden
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
72074 Tuebingen
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5211 DA S-Hertogenbosch
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
52428 Julich
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38124 Braunschweig
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SW7 2AZ LONDON
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70563 Stuttgart
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
4200 465 Porto
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69393 Lyon
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2333 ZA Leiden
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22100 Lund
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2800 Kongens Lyngby
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
E14 4PU London
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3721 MA Bilthoven
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63225 Langen
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1435 Mont Saint Guibert
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1050 Elsene
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2800 Kongens Lyngby
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2829 516 Caparica
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WC1E 6BT London
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07747 Jena
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50937 Koeln
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9713 GZ Groningen
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5020 Bergen
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405 30 Goeteborg
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NG7 2RD Nottingham
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0313 Oslo
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OX1 2JD Oxford
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53100 Siena
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30559 Hannover
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97070 Wuerzburg
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3584 CS Utrecht
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3029 AK Rotterdam
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
53100 Siena Si
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1330 Rixensart
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69007 Lyon
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8152 Glattpark
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72076 Tubingen
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