(458f) A Dynamic Metabolic Flux Analysis (DMFA) Model for the Production of Monoclonal Antibodies in CHO Cell Perfusion Culture.

Monoclonal antibodies (mAbs) are a class of recombinant proteins that have revolutionized medicine since the 1980’s providing life-saving therapies for a multitude of diseases.¹ Chinese hamster ovary (CHO) cells are the preferred mammalian cell platform for mAb production given their robust growth in defined mediums, easy transfectability, and native machinery which induce complex post-translational modifications.^2,3 Perfusion bioreactors facilitate continuous upstream cell culture and therefore process intensification by enabling high viable cell densities (VCDs), increased productivities, reduced equipment sizes, and improved product quality. However, the increased operational intensity relative to fed batch mode poses a significant challenge to attain optimal operation and control of perfusion bioreactors.

Mechanistic models are a valuable tool for the biopharmaceutical industry as they leverage the physical laws underpinning the process to link the key parameters with the Critical Quality Attributes (CQAs) of the product. Advancement of mechanistic models in biomanufacturing has also been heavily emphasized by the Food and Drug Administration (FDA) as a platform to implement the Quality by Design (QbD) process development principle.⁴ With respect to upstream biopharmaceutical production, modeling cell metabolism is imperative to analyze nutrient demands and physiologic objectives under different operating conditions. Specifically for continuous perfusion culture, modeling CHO cell metabolism is in its infancy compared to the extensive efforts in the literature for fed batch.

In this work, a comprehensive Dynamic Metabolic Flux Analysis (DMFA) model is developed for CHO cell perfusion culture based on prior analysis of fed batch operation. The model accounts for the effects of the perfusion rate and the cell bleed rate on the bulk mass transport and cell growth dynamics of the system. The bleed rate is simulated as a discrete event to align with its manual intermittent use in the experimental data collected for model regression. The required inputs are the initial extracellular metabolite concentrations, media amino acid and glucose profile, process conditions, and a robust kinetic parameter set. The outputs include simulated extracellular concentration profiles, intracellular and exchange fluxes, and the production and growth rates. The framework consists of a network of 67 biochemical reactions and 42 metabolites representing the key metabolic pathways in CHO cells while maintaining an overdetermined system and thus allowing application of MFA to estimate the intracellular fluxes. The pathways include glycolysis, the TCA cycle, amino acid metabolism, oxidative phosphorylation, the urea cycle, and biomass and antibody synthesis. Metabolite mass balances under the pseudo-steady state assumption yield the stoichiometric component of the model. The kinetic component has been developed to include Monod expressions describing the exchange fluxes as a function of the extracellular metabolite concentrations.

Experimental data from the CHO VRC01 cell line grown in perfusion culture has validated the model’s application to perfusion and allowed estimation of the kinetic parameters. The experimental platform consisted of a 1 L vessel controlled by the Eppendorf BioFlo 120 system. The cell retention device was a Spectrum® Hollow Fiber Filter Module from Repligen with a 0.65 μm pore size operated in tangential flow filtration mode. A manual, intermittent cell bleed was used to control the VCD to within a ±4Ë£10⁶ cells mL^-1 deadband about the setpoint of 20Ë£10⁶ cells mL^-1. The perfusion rate was set to 1 vvd^-1 while all other process parameters were maintained at industrially relevant values typical for CHO cells. The media formulation was based on an in-house fed-batch recipe which blends commercially available basal and feed medias, along with supplemental glucose. Future work seeks to improve the mathematical framework developed here with additional experimental data and subsequently apply it towards perfusion process optimization objectives, such as metabolic control and media reformulation.

References:

[1] Ecker, D. M., Jones, S. D., & Levine, H. L. (2015, January). The therapeutic monoclonal antibody market. In MAbs (Vol. 7, No. 1, pp. 9-14). Taylor & Francis.

[2] Trill, J. J., Shatzman, A. R., & Subinay, G. (1995). Production of monoclonal antibodies in COS and CHO cells. Current Opinion in Biotechnology, 6(5), 553-560.

[3] Wurm, F. M. (2004). Production of recombinant protein therapeutics in cultivated mammalian cells. Nature biotechnology, 22(11), 1393-1398.

[4] Yu, L. X., Amidon, G., Khan, M. A., Hoag, S. W., Polli, J., Raju, G. K., & Woodcock, J. (2014). Understanding pharmaceutical quality by design. The AAPS journal, 16(4), 771-783.