(423g) The Pursuit of Tighter Titer: Model Predictive Control of Monoclonal Antibody Titer Under Disturbances

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, one can help ensure high product yield while maintaining near-optimal culture conditions in fed-batch bioreactors. Batch-to-batch variability in end-of-run titer is a challenge in biomanufacturing operations and can result in cost increases.

The dynamic and complex nature of cell culture systems poses a significant challenge due to the potential for unpredictable process variability. 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]. Subsequently, monoclonal antibody product titer can be affected by cell growth and metabolism. Lastly, the metabolic activity of cells changes over time, which makes it difficult to make accurate predictions. The feeding strategy can be optimized based 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]. However, when disturbances act on the process, the operation is no longer progressing under optimal feeding conditions and is expected to deviate from a desired trajectory. This leaves the process susceptible to low productivity or altered product quality.

Process control is a systematic framework designed to maintain a process within a specified setpoint and enhance its robustness against uncertainties. Simple control loops, such as proportional-integral (PI) control loops are readily implemented in cell culture processes to control the bioreactor’s temperature, dissolved oxygen, and pH. The idea of implementing advanced process control of productivity and quality attributes in biomanufacturing is increasingly attracting interest from the industry. A cell culture model can be integrated into advanced control methodologies such as model predictive control (MPC) to control titer “tightly” around a specified nominal trajectory by optimizing the cell culture feeding strategy.

In this talk, we discuss MPC strategies for fed-batch mammalian cell culture processes. MPC strategies are investigated for their potential to achieve consistent end-of-run titer in the presence of unexpected process disturbances from initial viable cell count, pH, and temperature. First, we discuss two different model structures and their integration with MPC. Then, we compare empirically the effects of process disturbances on end-of-run titer with and without MPC, showing that MPC reduces process variability and maintains consistent titers.

References

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

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