Bio-manufacturing platforms using mathematical modelling
In a nutshell
Subtopic 4 aims to develop a modular one-stop computational platform for in silico (i.e., computer-based) modelling of vaccine bio-manufacturing and stability testing.
The scientific and technical objectives of this subtopic are:
Stablish an open source, one-stop cloud-based platform for in silico modelling of vaccine bio-manufacturing processes and stability
Develop in silico models of fermentation processes for protein subunit vaccines
Develop in silico models for clarification and purification steps most common in protein subunit vaccine bio-manufacturing
Develop in silico models for stability prediction of protein subunit vaccine products
Calibrate, validate and if necessary, refine in silico models using retrospective real-life data from industrial vaccine manufacturing and stability testing
Apply in silico models in prospective studies on maintaining process robustness for unit-operation scale-up or scale-down, and for process transfer
Develop a process control system that integrates the in-silico modelling into the bio-manufacturing operations
Academic leader: Daniel Bracewell, Department of Biochemical Engineering, University College London
Industry co-leader: Eric Calvosa, Sanofi Pasteur and Antonio Gaetano Cardillo, GSK
Project Manager: Irina Meln, EVI
Daniel G. Bracewell is Professor of Bioprocess Analysis he has made major contributions to the fundamental understanding of biopharmaceutical purification operations and collaborates with colleagues in Thailand, India and the USA. He has authored more than 100 peer-reviewed journal articles in the area to date and currently supervises 15 doctoral and postdoctoral projects, many of these studies are in collaboration with industry. One such project was the basis from which the spinout Puridify a nanofibre absorption technology company now owned by Cytiva was created. He is academic lead for the UCL-Pall Biotech Centre of Excellence.
The industry co-leader Antonio Gaetano Cardillo is currently a Senior scientist in Technical Research and Development at GSK Vaccines where he leads Automation and industry 4.0 activity in the drug substance team. He holds a master’s degree and Ph.D. in Chemical engineering and has strong expertise in upstream and downstream process development, scale-up, process transfer as well as in Process modelling including computational fluid dynamics & chromatography optimization. Since December 2019, he a has been leading the process modelling Centre of excellence, working at the Global Innovation Centre.
The industry co-leader Eric Calvosa is senior scientist expert in vaccine process development and process modelling. Eric is titular of a master engineer in Biochemistry. He has more than 50 processes to its credit USP/DSP development at 1mL to 4M3 scale. Eric has already led some collaborative projects for process modelling in sillico process control (Cellpat). Eric is involved in the digital program transformation at Sanofi Pasteur for the R&D part.
Main WP objective
Stability prediction models
Develop appropriate statistical models belonging to the linear, non-linear or ODE (Ordinary Differential Equation) families. Implement these models in a global platform for the modelling of vaccine bio-manufacturing.
Led by Bruno Boulanger, PharmaLex and Didier Clenet, Sanofi
Bioprocess scale-up / scale-down models (upstream processing)
Develop a generic cloud-based in silico platform for evaluation of bio-manufacturing process performance and process robustness, here with focus on scale-up and scale-down of upstream vaccine production by E. coli.
Led by Krist V. Gernaey, Danmarks Tekniske Universitet and Giulia Gastaldo, GSK
Bioprocess scale-up / scale-down models (downstream processing)
To build and validate digital twins for core purification units used to produce common vaccine products. To apply novel in-silico process analysis and design tools for development of optimized downstream processes with process knowledge being reliably transferred across operating conditions and scales. To use predictive maintenance and control tools enabling real-time insights into the downstream processes and efficient handling of process uncertainty coming from the upstream processes using optimal feed-back control. To model and control an integrated centrifugation and chromatography capture step. To facilitate the future of downstream bio-manufacturing with cloud-enabled tools for modelling and control of a fully integrated downstream processing train.
Led by Kristian Meyer, MCT Bioseparation ApS; Luca Nompari, GSK; Stefanie Timmins, Sanofi and Daniel Bracewell, University College London
Data for model parameterization, proof of concept experimentation and validation
Obtain proof of concept of the predictive value of the in silico models for stability of both vaccine substance and final product. Obtain proof of concept of the predictive value of the in silico models for the typical unit operations used in protein sub-unit vaccine manufacturing, upstream as well as downstream. Demonstrate proof concept for the MPC control modules from WP18.
Led by Daniel Bracewell, Department of Biochemical Engineering, University College London
Regulatory dialogue for road maps of implementation of new tools in CMC dossiers
Initiating a dialogue with relevant regulatory authorities, that paves the way for future use of predictive stability and process scale-up modelling in chemistry, manufacturing and control (CMC) dossiers for new and existing vaccines.
Led by Irina Meln, European Vaccine Initiative