(234e) Modeling the Effect of pH on Chinese Hamster Ovary Cell Metabolism and Glycosylation to Optimize the Production of Monoclonal Antibodies

Monoclonal antibodies (mAbs) based treatments have been established as one of the most successful strategies to treat cancer, autoimmune diseases and other diseases over the past 20 years [1]. The number of mAbs that are approved by the FDA has been increasing every year and the number of mAbs that are falling off patents is leading to a rise in the biosimilar market [2]. Monoclonal antibodies are an expensive therapeutic option due to cost and complexity of manufacturing and the need for high doses [3]. The increase in demand of mAbs, the large cost of mAbs and the rise of the biosimilar market shows the need to optimize the production to meet demand, reduce costs and make these therapeutic options more affordable. The key aspects of process development are process monitoring and control, media development, scale down models, cell line development and careful monitoring of product’s critical quality attributes [4]. Chinese Hamster Ovary (CHO) cells are the preferred choice for production of mAbs because the system has been well characterized over the past two decades. Optimization of CHO cell processes require the optimization of each of the above-mentioned key aspects in order to sufficiently control product quality including glycosylation [5]. Media optimization typically involves specifying the concentrations of more than 50 components. Optimal bioreactor operation requires optimizing the pH, temperature, dissolved oxygen and feeding schedule. Optimizing the process performance using experiments is a very expensive proposition. It has been demonstrated in the literature that the use of models can optimize the process while minimizing experimental effort [6,7,8]. However, it is difficult to use models to optimize the overall process as majority of models in the literature are built with the goal of specifically optimizing one of the above key aspects. There is a need to develop models describing more than just one key aspect mentioned above due to the interlinked nature of these key aspects.

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 [9]. 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 [10]. The nucleotide sugar fluxes, and the antibody production flux were fed to a kinetic model for glycosylation [11]. 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.

References

1. 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

1. Grilo, A. L., and Mantalaris, A. (2019). The Increasingly Human and Profitable Monoclonal Antibody Market. Trends In Biotechnology, 37(1), 9–16. https://doi.org/10.1016/j.tibtech.2018.05.014

1. Shaughnessy Allen F. Monoclonal antibodies: magic bullets with a hefty price tag BMJ 2012; 345:e8346

1. Bielser, J. M., Wolf, M., Souquet, J., Broly, H., and Morbidelli, M. (2018). Perfusion mammalian cell culture for recombinant protein manufacturing – A critical review. Biotechnology Advances, 36(4), 1328–1340. https://doi.org/10.1016/j.biotechadv.2018.04.011

1. Batra, J., & Rathore, A. S. (2016). Glycosylation of monoclonal antibody products: Current status and Future prospects. Biotechnology Progress ,32 (5), 1091–1102. https://doi.org/10.1002/btpr.2366

1. Kiparissides A, Pistikopoulos EN, Mantalaris A: On the model- based optimization of secreting mammalian cell (GS-NS0) cultures. Biotechnol Bioeng 2015, 112:536-548.

1. Fouladiha, H., Marashi, S. A., Torkashvand, F., Mahboudi, F., Lewis, N. E., & Vaziri, B. (2020).

1. 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

1. 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), 1978–1990. https://doi.org/10.1002/bit.26315

1. 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

1. 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), 1–30. https://doi.org/10.1371/journal.pone.0175376