(207c) Eliminating Batch-to-Batch Variability in Monoclonal Antibody Production Using Closed-Loop Control
AIChE Annual Meeting
Monday, November 14, 2022 - 4:12pm to 4:33pm
Current means of attaining a desired performance include open-loop control via optimisation of substrate feeding profiles (e.g., ). Unlike open-loop control, closed-loop control considers process feedback therefore compensation can be applied to account for process disturbances. Yet, there are very few examples of closed loop controllers for mammalian cell cultures. This is in part because of the highly non-linear nature of the process. In addition, process data is particularly expensive and laborious to generate. As such, model and control development remain challenging. Recent research in modelling and control of bacterial cell cultures has highlighted the advantages of closed-loop control (e.g., ). Similar advances are yet to be applied to mammalian cell-based systems.
In this work, an advanced control strategy is developed to track product titre by manipulating the feed of key metabolites to the cell culture. Using limited experimental data, a reduced-order model is developed for a controller describing only the manipulated, controlled, and constrained variables. From this, a model predictive controller is developed and the results from the simulation studies used to showcase the controllerâs performance. Future implementation is expected to reduce variability between batches.
 - Kotidis, Pavlos, et al. "Modelâbased optimization of antibody galactosylation in CHO cell culture." Biotechnology and bioengineering 116.7 (2019): 1612-1626.
 - Oh, Tae Hoon, et al. "Integration of Reinforcement Learning and Model Predictive Control to Optimize Semiâbatch Bioreactor." AIChE Journal (2022): e17658.