(787b) Intelligent Control Strategies Towards the Intensification of Monoclonal Antibody Production Via Continuous Operation
This shift could benefit the market of high value biologics, such as monoclonal antibodies (mAbs), as it would lead to shorter production times, decreased costs, as well as significantly less energy consumption compared to batch [1, 2]. In particular, mAb production comprises two main steps: the culturing of the cells (upstream) and the purification of the targeted product (downstream). Both processes are highly complex and their performance depends on various parameters. In particular, the efficiency of the upstream depends highly on cell growth and the longevity of the culture, while the product quality can be jeopardized in case the culture is not terminated timely. Similarly, downstream processing, whose main step is the chromatographic separation, relies highly on the setup configuration, as well as on the composition of the upstream mixture. Therefore, in order to be able achieve a sustainable and efficient continuous operation, it is of vital importance to design intelligent computational tools that form a solid basis for the: (i) execution of cost-free comparisons of various operating strategies, (ii) design of optimal operation profiles and (iii) development of advanced, intelligent control systems that can maintain the process under optimal operation, rejecting disturbances.
This work focuses on the development of advanced control strategies for: (i) a cell culture system in a bioreactor and (ii) a semi-continuous purification process. More specifically, we consider fed-batch culturing of GS-NS0 cells producing a chimeric IgG4 antibody (based on the feeding of five key nutrients) and the semi-continuous Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) [3-5]Â process for the purification of the upstream mixture. For the controller development we follow the generic framework & software platform PAROC that considers: (i) development of a high-fidelity process model, (ii) approximation of the complex, process model, (iii) design of the multi-parametric controller, (iv) â??closed-loopâ??, in-silico validation of the controller against the process model . PAROC allows â??in-silicoâ?? testing of the advanced controllers against the high-fidelity process model and evaluate their performance before operating them online. The results from this study indicate that the in-silico upstream/downstream integration can be achieved and the designed controllers succeed in maintaining both systems under optimal operation. In particular, the upstream system is characterized by a significantly increased mAb titer under the operation of the controller. Moreover, the controller indicates the time point that the culture needs to be terminated in order to minimize the risk of product degradation. Similarly, the downstream control scheme manages to maintain a stable operation throughout the process cycle, tracking efficiently the predefined setpoints. In addition, the controller is immunized against the uncertainty that results from the composition of the upstream mixture by considering the latter as measured disturbance. Finally, the suggested controller input indicates decreased use of utilities, while maintaining process efficiency.
The authors would like to thank Mr R. Oberdieck and Miss A. Quiroga-Campano for their contribution in the assessment of the controller validation. The authors would also like to thank Mr. Fabian Steinebach & Prof. M. Morbidelli from ETH Zurich, as well as Dr. Thomas Mueller-Spaeth from ChomaCon AG for their valuable input on the understanding of the MCSGP process. Financial support from the European Commission (OPTICO/G.A. No.280813) & Texas A&M University are also gratefully acknowledged.
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