In this study, a dynamic model of a Vero cell culture-based dengue vaccine production process is developed. The approach consists in describing the process dynamics as functions of the whole living (uninfected and infected) biomass whereas previous works are based on population balance approaches. Based on the assumption that infected biomass evolves faster than other variable, the model can be simplified using a slow-fast approximation. The structural identifiability of the model is analysed using differential algebra as implemented in the software DAISY. The model parameters are inferred from experimental datasets collected from an actual vaccine production process and the model predictive capability is confirmed both in direct and cross-validation. The model prediction shows the impact of the metabolism on virus yield and confirms observations reported in previous studies. Multi-modality and sensitivity analysis complement the parameter estimation, and allow to obtain confidence intervals on both parameters and state estimates. Finally, the model is used to compute the maximum infectious virus yield that can be obtained for different combinations of multiplicity of infection (MOI) and time of infection (TOI). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2687, 2019.
Keywords: identification; mathematical modelling; optimisation; sensitivity analysis; vero cells; virus amplification.
© 2018 American Institute of Chemical Engineers.