(234e) Modeling the Effect of pH on Chinese Hamster Ovary Cell Metabolism and Glycosylation to Optimize the Production of Monoclonal Antibodies
AIChE Annual Meeting
Tuesday, November 9, 2021 - 9:24am to 9:45am
The work performed here aims to overcome these drawbacks of literature models by developing a detailed mechanistic model that can be used to optimize bioreactor operation while keeping track of glycosylation and change in media requirements under different conditions. More specifically this work targets the integration of the effects of pH on a model of CHO cell metabolism and glycosylation. VRC01 producing CHO cells were grown at pH of 6.75, 7 and 7.25 in a 1 L Eppendorf bioflo 120 bioreactor system. Cell density, viability, glucose, lactate, 18 amino acids, ammonia, titer, nucleotide sugar and glycan structures were measured at each condition to develop a database for model regression. The model for metabolism and glycosylation was developed by integrating a combined kinetic and stoichiometric model for metabolism with a kinetic model for glycosylation . The integrated model was developed by using semi-empirical kinetic expressions to determine uptake rates of a few metabolites and these uptake rates were used as constraints to generate the solution of the detailed stoichiometric model by using flux balance analysis . The nucleotide sugar fluxes, and the antibody production flux were fed to a kinetic model for glycosylation . The effect of pH was incorporated into the kinetic expressions of both the model for metabolism and the model for glycosylation. Through the experimental data it is evident that changes in pH led to accumulation of many metabolites in certain cases and depletion of many metabolites in certain cases. This leads to suboptimal media performance. It is also evident that operating in certain pH values led to drop in the galactosylation levels hence, producing low quality products. The proposed model is then used to determine the effect of pH on nutrient requirements as well as product quality, providing a platform to optimize bioreactor pH and media formulation as well as determining product quality.
- Shepard HM, Phillips GL, D Thanos C, Feldmann M. Developments in therapy with monoclonal antibodies and related Proteins. Clin Med (Lond). 2017;(3):220-232. doi:10.7861/clinmedicine.17-3-220
- Grilo, A. L., and Mantalaris, A. (2019). The Increasingly Human and Profitable Monoclonal Antibody Market. Trends In Biotechnology, 37(1), 9â16. https://doi.org/10.1016/j.tibtech.2018.05.014
- Shaughnessy Allen F. Monoclonal antibodies: magic bullets with a hefty price tag BMJ 2012; 345:e8346
- Bielser, J. M., Wolf, M., Souquet, J., Broly, H., and Morbidelli, M. (2018). Perfusion mammalian cell culture for recombinant protein manufacturing â A critical review. Biotechnology Advances, 36(4), 1328â1340. https://doi.org/10.1016/j.biotechadv.2018.04.011
- Batra, J., & Rathore, A. S. (2016). Glycosylation of monoclonal antibody products: Current status and Future prospects. Biotechnology Progress ,32 (5), 1091â1102. https://doi.org/10.1002/btpr.2366
- Kiparissides A, Pistikopoulos EN, Mantalaris A: On the model- based optimization of secreting mammalian cell (GS-NS0) cultures. Biotechnol Bioeng 2015, 112:536-548.
- Fouladiha, H., Marashi, S. A., Torkashvand, F., Mahboudi, F., Lewis, N. E., & Vaziri, B. (2020).
- A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal Antibody production. Bioprocess and Biosystems Engineering . https://doi.org/10.1007/s00449-020-02332-6
- Karst, D. J., Scibona, E., Serra, E., Bielser, J., Souquet, J., Stettler, M., Soos, M., Morbidelli, M., & Villiger, T. K. (2017). Modulation and Modeling of Monoclonal Antibody N-Linked Glycosylation in Mammalian Cell Perfusion Reactors. 114(9), 1978â1990. https://doi.org/10.1002/bit.26315
- Robitaille, J., Chen, J., & Jolicoeur, M. (2015). A Single Dynamic Metabolic Model Can Describe mAb Producing CHO Cell Batch and Fed-Batch Cultures on Different Culture Media. PLOS ONE, 10(9), e0136815. https://doi.org/10.1371/journal.pone.0136815
- Krambeck, F. J., Bennun, S. V., Andersen, M. R., & Betenbaugh, M. J. (2017). Model-based analysis of N-glycosylation in Chinese hamster ovary cells. PLoS ONE, 12(5), 1â30. https://doi.org/10.1371/journal.pone.0175376