Generalization of Dynamic Metabolic Flux Models and their Application for Economic Optimization of Bioprocesses

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The market for biopharmaceuticals is increasing at an unprecedented rate. For example, the world sales increased from US $99 billion in 2009 to US $228 billion in 2017. Monoclonal antibodies (mAb) produced in Chinese Hamster Ovary (CHO) cell cultures, which is one of the focal applications of this proposal, are the top five best-selling products. To respond to this demand, the pharma industry in collaboration with the FDA is actively seeking novel robust design, control and optimization strategies. Mathematical models are the heart of such strategies. The focus of the current application is to develop novel dynamic metabolic flux models (DMFM) for CHO cells and to study their use for process optimization. The proposed modelling approach and the robust optimization algorithms to be based on these models will confer a distinctive advantage to the Canadian pharma industry in the development of optimized culture media and cell lines and for robust economically optimal operation of bioprocesses. The application of these techniques will be the key for competitiveness and compliance with regulatory constraints. The DMFM is based on the solution of a constrained optimization subject to constraints. It has been postulated that DMFM requires a smaller number of parameters to describe data and thus the resulting models are much less prone to over-fitting. Although DMFM has been studied for bacteria and algae, the study for DMFM for CHO is very preliminary. Since DMFM are based on genomic information, and in view of the recognized knowledge-gaps of the mammalian cells' genome, model error is expected. Thus, to ensure adoption of the proposed techniques by the industry, robustness to model error must be addressed. Following the above, the proposal comprises two components: 1- development of DMFM models for mammalian cells and 2- development of robust strategies where the model is progressively learned while the process is driven towards the optimum. The modeling component includes: i-the search for suitable biological objective functions for DMFM of mammalian cells, ii-the inclusion of thermodynamic and genetic regulatory constraints and iii-the effect of heterogeneity of cells in culture . The models will be validated with data to be collected in our animal cell laboratory. The optimization component will investigate: i-the formulation of economic optimization with a DMFM, ii- Batch-to-Batch approach to optimize the media while updating the model, and iii- the investigation of the use of economic model predictive control (EMPC) based on a DMFM for perfusion and continuous operations. The research program will train and support 3 PhDs and 1 MSc who will acquire a unique combination of theoretical and experimental skills that are in very high demand in industry, such as genome based dynamic modeling and robust optimization, cell culturing and process analytical technologies.