(563c) Optimization of Glucose Feeding Strategies of Fed-Batch Bioreactors for Monoclonal Antibody Production

Fed-batch bioreactors are particularly useful for processes that require long cultivation times or where it is difficult to predict the nutrient requirements of the cells [1]. By incrementally adding nutrients, fed-batch bioreactors can help ensure high product yield while maintaining near-optimal culture conditions.

The dynamic and complex nature of the cell culture system is the biggest challenge to identifying an optimal glucose feeding strategy. Cell growth and metabolism are affected by a wide range of factors, such as nutrient availability, dissolved oxygen levels, pH, temperature, and shear stress [2]. Additionally, monoclonal antibody (titer) production can be affected by factors such as cell density and viability. Lastly, the metabolic activity of the cells changes over time, which makes it difficult to make accurate predictions. The optimal feeding strategy can vary depending on the specific cell line in addition to culture conditions and therefore it requires a customized approach tailored to the specific requirements of the production process [3].

In an industrial context, historical data analysis and empirical testing are the widely accepted methodologies for developing a feeding strategy. Given the high complexity of the phenomena taking place inside the bioreactor, the amount of data resulting from empirical testing that could be deemed sufficient for identifying an optimal feeding strategy is large. As an alternative, mathematical models can be used to formalize the knowledge of how the cells behave in response to different glucose feeding strategies. The models should adequately capture the cells’ growth rate, viability, titer production, as well as the concentration and consumption of glucose and other nutrients and metabolites [4],[5]. These models can then be used to determine an optimal strategy.

In this talk, we will discuss methodologies that can be adopted to optimize the feeding strategy to achieve optimal bioreactor performance hence maximizing end-of-run titer. We discuss ways to address one of the main challenges in developing accurate models, which is the sparsity of the available data. For example, for a bioreactor run of twelve days, glucose feeding and measurement of metabolites often occurs only once a day. This makes it difficult to capture the causality between the event and potential outcomes. Then the open-loop optimal control problem is addressed. The amount of added glucose is optimized while the feeding time intervals are kept constant. Both first-principles and data-driven model-based methodologies are explored. Lastly, potential advantages from closing the loop in these operations are discussed.

References

[1] Xu et al., Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling. MAbs 11, 191-204 (2019).

[2] Chen et al., Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes 8, 1088 (2020).

[3] C. Yee et al., Advances in process control strategies for mammalian fed-batch cultures. Current Opinion in Chemical Engineering 22, 34-41 (2018).

[4] Xing, N. Bishop, K. Leister, Z. J. Li, Modeling kinetics of a large-scale fed-batch CHO cell culture by Markov chain Monte Carlo method. Biotechnology Progress 26, 208-219 (2010).

[5] Jandt, O. Platas Barradas, R. Pörtner, A.-P. Zeng, Synchronized mammalian cell culture: Part II—population ensemble modeling and analysis for development of reproducible processes. Biotechnology Progress 31, 175-185 (2015).