Mathematical modelling for bioprocess understanding and optimisation

Abstract

Recombinant proteins have been extensively studied for their wide therapeutic and research applications. The main therapeutic product category is that of monoclonal antibodies (mAbs), which have been widely approved to treat a variety of chronic and life-threatening diseases. Increasing mAb titre has been achieved mainly by cell culture medium improvement and genetic engineering to increase cell density and productivity. However, this improvement has caused many technical issues in both upstream (USP) and downstream (DSP) processes. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant has proved to negatively affect product integrity and immunogenicity in addition to increasing the subsequent cost of capture and polishing steps. It has severely affected the performance of antibody drug candidates in at least two cases in which clinical trials have been put on hold as a result of HCP-related problems. Certain HCPs are naturally secreted, while others are inevitably released because of cell death and lysis. Exploring the relationship between critical process parameters (CPPs) and critical quality attributes (CQAs) in the context of HCP dynamics at a minimum cost is a highly important factor from an industrial point of view. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. A single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. The model includes two operating modes; the first at physiological temperature and the second, which represents a common industrial practice, with a shift to mild hypothermic conditions (32 ℃) in mid-exponential growth phase. The model considers the dynamic profile of substrates and metabolites, product titre and HCPs. Culture osmolality is also considered as a determining factor for cell growth rate and cell volume increase. The model was then used to optimise titre by controlling CPPs such as feed volume and frequency, the time point of temperature downshift as well as the harvesting time. The optimisation is subject to constraints such as maintaining culture viability above 80% and no feeding in the first 48 hours interval in all model optimisation runs. Four specific optimisation scenarios have been explored based on optimising titre and the titre/HCP ratio. This has been done on both operating modes; physiological temperature and initial physiological temperature with the possibility of temperature downshift after the second culture day. Total nutrients volume can be efficiently minimised by changing feeding volume and time point to satisfy the cellular metabolic need. This approach yields higher purity and more economical operating conditions. In general, higher product titres, up to 30%, and prolonged culture viability can be attained at the expense of higher feeding pulses. However, when a constraint on HCP concentration is also applied model-based optimisation results in shorter culture duration and, in turn, overall lower antibody titre. This thesis shows the usefulness of mathematical modelling for exploring trade-offs in bioprocess performance. Integrating this model with a downstream purification model to evaluate the cost of removing these fractions of impurities, can help determine what concentration of HCPs can be economically tolerated in the cell culture supernatant and aid whole bioprocess design.