Doctoral Candidate 8
Digital twin and multi-parametric optimisation of upstream unit operations for rAAV manufacturing
Host institution: Aristotle University of Thessaloniki (AUTh), Greece, and Antleron, Belgium
Supervisory team: Prof. Alexandros Kiparissides (PhD promoter, AUTh), Dr. Jan Schrooten (Antleron), Dr. Sarah Fadda (Siemens Process Systems Engineering)
With more than 350 potential therapies in clinical trials in the first half of 2020, the ability to reliably and sustainably manufacture large quantities of rAAVs is increasingly becoming a bottleneck in the commercialisation of gene therapy products. However, AAV manufacturing still heavily relies on transient transfection production methods using primarily adherent cell lines, both of which are processes heavily reliant on heuristic or statistics-driven optimisation methods. The aim of this project is to develop a detailed digital twin of upstream unit operations for AAV-based gene therapy manufacturing in order to enable model-based optimisation of the manufacturing process.
The developed digital twin will comprise both kinetic/mechanistic models, where sufficient knowledge of the underlying mechanisms exists as well as data driven (and hybrid) models where knowledge is currently lacking.Rather than reporting just titre or productivity metrics the model output will provide quantitative descriptors for key Critical Quality Attributes (CQAs) developed in collaboration with WP1 and WP2. The digital twin will be calibrated and validated against experimental data from literature, data produced in house at Antleron and data from WP1 and WP3. State-of-the-art parametric uncertainty analysis techniques will be used to investigate the correlations between uncertainty/variability in Critical Process Parameters (CPPs) and their effect on product CQAs. Finally, multi-parametric optimisation techniques will be applied in order to define the optimal ‘process operating window’ while taking into account variability both in the efficacy of preceding unit operations and well as in the employed manufacturing technologies.
A successful project will result in a validated and calibrated digital twin model for upstream unit operations of rAAV production.
Enrolment in Doctoral School: AUTh
Siemens Process Systems Engineering, UK (Months 9-11): Whole process modelling, model development and parameter estimation
KU Leuven, Belgium (Month 23): Wet lab expertise for rAAV production
You hold a master’s degree in physics, (bioscience) engineering or a related field.
You are ambitious, well organized and have excellent communication skills.
You are proficient in English both spoken and written
You have the ability to work independently and have a critical mindset.
You are an enthusiastic and motivated person, eager to participate in network wide training events, international travel and public awareness activities.
Willingness to travel
SKILLS AND EXPERTISE
Solid knowledge of Mathematical modelling, basic programming, system identification
An understanding of Bioprocessing, cell culture