On the sunny shores of Porto, Portugal, a significant event took place on the 31st of May 2024. Representatives from all partner institutions met to discuss the progress of VAXPRED, the Subtopic 1 of Inno4Vac project (additional information here: ST1 VAXPRED | Inno4Vac). This collaborative effort aims to accelerate vaccine development building an open access and cloud-based platform to predict vaccine efficacy using artificial intelligence.
Meeting highlights
The meeting was kicked off by Gunnveig Grødeland (UiO), setting the stage for a productive day of discussions and presentations. One of the significant aspects of the meeting was the presentation by Henk-Jan van den Ham (Enpicom) who presented results on methods for generating personalised human models, in particular modelling and quantifying the heterogeneous baseline of the human adaptive immune system. Niklas Schwan (HZI) shared updates on Germinal Center Modeling, relevant for understanding immune responses and optimising vaccine design. Artur Rocha (INESC-TEC) showcased progress on the open-access, cloud-based platform. The architecture design and user stories collected will shape its features, ensuring it meets the needs of researchers and developers in the field of vaccine R&D.
During the afternoon Elisa Rosati (GSK) and Taissa de Matos Kasahara (UiO) presented the most recent results on single-cell TCR and BCR sequencing respectively. These insights inform vaccine efficacy assessments, providing valuable data for the prediction models.
In summary, the VAXPRED meeting in Porto served as a collaborative platform for partners to exchange knowledge, compare and integrate results, and plan future activities. The progress made in this meeting marks a significant step forward in the mission of Inno4Vac and VAXPRED to accelerate vaccine development.
Contact:
Dr. Luisa Borgianni (Project Manager, Sclavo Vaccines Association)
Email: borgianni@sclavo.org
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 101007799. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
This communication reflects the authors' view(s) and that neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained therein.
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