Inno4Vac sub-topic 4 (VaXinS) aims to develop a hybrid model (a combination of a Compartmental Model [CM] and Kinetic mechanistic/knowledge-driven model [KM]) to optimise and better control upstream processing in vaccine biomanufacturing. The project develops the combination of computational fluid dynamics (CFD) derived compartment models CMs with biological KMs as an improved hybrid tool to enable fast development, scale-up and control of vaccine manufacture.
In 2023, Inno4Vac was selected from a long list of applicants to present their hybrid model during the 2nd Quality Innovation Group (QIG) ‘Listen and Learn Focus Group’ meeting organised by the European Medicines Agency (EMA), which took place online on 12-13 October. Bioprocess scale-up / scale-down models (upstream processing) work package lead Prof Krist V. Gernaey (DTU) and EFPIA contributor Mónica Perea-Vélez (GSK) represented the consortium and discussed with the QIG group, industry and academic representatives the model’s key parameters and questions related to its expected impact on the product quality attributes.
The meeting report is still to be published by EMA QIG. However, continuous information exchange and close interactions with European regulators were encouraged by meeting participants. The Innno4Vac team plans to continue the dialogue to ensure alignment and future usability of the CM, KM and hybrid models (i.e., CM+KM) to support vaccine products, process development, and understanding.
“Participation in the QIG ‘Listen and Learn Focus Group’ was a recognition of Inno4Vac’s WP17 ongoing efforts to develop innovative modelling approaches for vaccine development and manufacturing. Future interactions are encouraged to contribute to shaping the regulatory framework to support the development of reliable and predictable innovative tools and technologies." Mónica Perea-Vélez
Dr. Irina Meln (Project and Innovation Manager at the European Vaccine Initiative)
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 101007799 (Inno4Vac). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA (www.imi.europa.eu). This communication reflects the author´s view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.