# Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Continuous Perfusion Cell Cultures

#### 2.2. Estimation of Secretion and Uptake Fluxes

_{i}) and the IgG titer (T) according to:

#### 2.3. Glycosylation Flux Analysis

**S**denotes the $m\times n$ stoichiometric matrix. The (i,j)-th element of

**S**gives the number of the i-th glycoform molecule produced (if positive) or consumed (if negative) by the j-th glycosylation reaction. Since the number of reaction fluxes (i.e., the number of unknowns) typically exceeds that of glycoforms (i.e., the number of equations), the estimation of ${v}_{\mathrm{I}}$ from ${v}_{\mathrm{E}}$ in Equation (6) is underdetermined. In other words, there exist many ${v}_{\mathrm{I}}$ for the same experimentally determined ${v}_{\mathrm{E}}$. In the method Flux Balance Analysis, the most plausible ${v}_{\mathrm{I}}$ is set to the vector that maximizes a cellular objective, such as the biomass production [37]. However, the appropriate cellular objective to use for glycosylation networks is not immediately obvious.

- (1)
- generate a uniformly distributed random vector of ${v}_{\mathrm{I}}{}^{\mathrm{ref}}$ within a biologically feasible range (${\mathrm{v}}_{\mathrm{I},j}{}^{\mathrm{ref}}\in \left[0,25\right];\mathrm{unit}:\text{}\mathrm{pg}/\mathrm{cell}/\mathrm{day}$),
- (2)
- given ${v}_{I}{}^{ref}$ from step (1), solve for or update ${\alpha}_{J}$ using Equation (10),
- (3)
- given ${\alpha}_{J}$ from step (2), solve for or update ${v}_{I}{}^{ref}$ using Equation (11), and
- (4)
- repeat steps (2) and (3) until the change of $\mathsf{\Phi}$ as described in Equation (9) becomes smaller than a threshold (default 10
^{−10}).

#### 2.4. Random Forest for Regression

**p**. Note that the function $g\left(t,{\alpha}_{J,\mathrm{GalT}},p\right)$ is likely nonlinear in nature. Here, we employed Random Forest (RF) [40] to build the above regression model using data from all four perfusion cell culture experiments. RF regression involves building an ensemble of unpruned regression trees, in which each regression tree is created using a bootstrap sample of the original dataset. At each node of a tree, a subset of predictors is selected randomly to determine the best decision split of the samples. The final prediction of the regression trees in RF is obtained by averaging the predictions of the entire ensemble. Notably, RF regression is able to capture nonlinear dependencies of the response variable on the predictors.

## 3. Results

#### 3.1. Perfusion Cell Culture Experiments

#### 3.2. Glycosylation Flux Analysis

#### 3.3. Effects of Process Parameters on Glycosylation

_{IgG}, q

_{Glc}, q

_{Lac}and q

_{Amm}, respectively). Furthermore, we excluded data from the startup period of the cell culture (i.e., days one to three of each experiment), as we were more interested in the regulation of IgG glycosylation during the steady state operations and setpoint changes of the perfusion cell culture. Finally, we ranked the predictor variables in decreasing magnitudes of the impurity gains. A higher impurity gain points to a predictor variable with higher importance in explaining the response variable.

_{IgG}) and the concentration of ammonia (Amm) are the two most important predictors of the dynamical changes in the galactosyltransferase specific activity. Indeed, when we repeated the RF regression using only q

_{IgG}and Amm as the predictor variables, we observed a similar quality of data fitting to the response variables (see Supplementary Figure S6). Kolmogorov-Smirnov (KS) test and Wilcoxon rank sum test further confirmed that the residuals of the RF regression models using all 14 predictors and those using only q

_{IgG}and Amm, are not statistically different (KS test p-value = 0.857; Wilcoxon rank sum test p-value = 0.824). Following q

_{IgG}and Amm in the ranking are the glucose uptake rate (q

_{GLC}) and bleed rate (B), indicating that the specific growth rate has a moderate contribution to the changes in the IgG galactosylation activity. The influence of growth rate on IgG galactosylation is somewhat expected, as the nucleotide sugar UDP-Galactose is used for different forms of cellular glycosylation, not solely for IgG [43].

## 4. Discussion

_{IgG}is small and negative (partial correlation = −0.037), suggesting that the relationship between the two variables as revealed by RF analysis is likely to be nonlinear. The negative partial correlation further implies that keeping all other process parameters the same, a higher q

_{IgG}is concomitant with decreasing ${\alpha}_{GalT}$ with time. Such a trend is in general agreement with how ${\alpha}_{J}$ of the upstream enzymes vary with the cell-specific productivity of IgG as explained earlier.

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Ecker, D.M.; Jones, S.D.; Levine, H.L. The therapeutic monoclonal antibody market. MAbs
**2015**, 7, 9–14. [Google Scholar] [CrossRef] [PubMed] - Kelley, B. Industrialization of MAb production technology. MAbs
**2009**, 1, 443–452. [Google Scholar] [CrossRef] [PubMed] - Li, F.; Vijayasankaran, N.; Shen, A.; Kiss, R.; Amanullah, A. Cell culture processes for monoclonal antibody production. MAbs
**2010**, 2, 466–479. [Google Scholar] [CrossRef] [PubMed][Green Version] - Rathore, A.S. Roadmap for implementation of quality by design (QbD) for biotechnology products. Trends Biotechnol.
**2009**, 27, 546–553. [Google Scholar] [CrossRef] [PubMed] - Food and Drug Administration. FDA Guidance for Industry. PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance; Food and Drug Administration: Rockville, MD, USA, 2004.
- Reay, D.; Ramshaw, C.; Harvey, A. Process Intensification: Engineering for Efficiency, Sustainability and Flexibility, 2nd ed.; Elsevier: New York, NY, USA, 2013. [Google Scholar]
- Boedeker, B.G.D. Recombinant factor VIII (Kogenate
^{®}) for the treatment of Hemophilia A: The first and only world-wide licensed recombinant protein produced in high-throughput perfusion culture. In Modern Biopharmaceuticals; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2013; pp. 429–443. [Google Scholar] - Clincke, M.F.; Molleryd, C.; Zhang, Y.; Lindskog, E.; Walsh, K.; Chotteau, V. Very high density of CHO cells in perfusion by ATF or TFF in WAVE bioreactorTM, Part I: Effect of the cell density on the process. Biotechnol. Prog.
**2013**, 29, 754–767. [Google Scholar] [CrossRef] [PubMed] - Meuwly, F.; Weber, U.; Ziegler, T.; Gervais, A.; Mastrangeli, R.; Crisci, C.; Rossi, M.; Bernard, A.; von Stockar, U.; Kadouri, A. Conversion of a CHO cell culture process from perfusion to fed-batch technology without altering product quality. J. Biotechnol.
**2006**, 123, 106–116. [Google Scholar] [CrossRef] [PubMed] - Lee, S.-Y.; Kwon, Y.-B.; Cho, J.-M.; Park, K.-H.; Chang, S.-J.; Kim, D.-I. Effect of process change from perfusion to fed-batch on product comparability for biosimilar monoclonal antibody. Process Biochem.
**2012**, 47, 1411–1418. [Google Scholar] [CrossRef] - Ryll, T.; Dutina, G.; Reyes, A.; Gunson, J.; Krummen, L.; Etcheverry, T. Performance of small-scale CHO perfusion cultures using an acoustic cell filtration device for cell retention: Characterization of separation efficiency and impact of perfusion on product quality. Biotechnol. Bioeng.
**2000**, 69, 440–449. [Google Scholar] [CrossRef] - Lüllau, E.; Kanttinen, A.; Hassel, J.; Berg, M.; Haag-Alvarsson, A.; Cederbrant, K.; Greenberg, B.; Fenge, C.; Schweikart, F. Comparison of Batch and Perfusion Culture in Combination with Pilot-Scale Expanded Bed Purification for the Production of Soluble Recombinant β-Secretase. Biotechnol. Prog.
**2003**, 19, 37–44. [Google Scholar] [CrossRef] [PubMed] - Lipscomb, M.L.; Palomares, L.A.; Hernández, V.; Ramírez, O.T.; Kompala, D.S. Effect of production method and gene amplification on the glycosylation pattern of a secreted reporter protein in CHO cells. Biotechnol. Prog.
**2005**, 21, 40–49. [Google Scholar] [CrossRef] [PubMed] - Zhuang, C.; Zheng, C.; Chen, Y.; Huang, Z.; Wang, Y.; Fu, Q.; Zeng, C.; Wu, T.; Yang, L.; Qi, N. Different fermentation processes produced variants of an anti-CD52 monoclonal antibody that have divergent in vitro and in vivo characteristics. Appl. Microbiol. Biotechnol.
**2017**, 101, 5997–6006. [Google Scholar] [CrossRef] [PubMed] - Karst, D.J.; Steinebach, F.; Morbidelli, M. Continuous integrated manufacturing of therapeutic proteins. Curr. Opin. Biotechnol.
**2018**, 53, 76–84. [Google Scholar] [CrossRef] [PubMed] - Berger, M.; Kaup, M.; Blanchard, V. Protein glycosylation and its impact on biotechnology. In Genomics and Systems Biology of Mammalian Cell Culture; Hu, W.S., Zeng, A.-P., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 165–185. [Google Scholar]
- Aebi, M. N-linked protein glycosylation in the ER. Biochim. Biophys. Acta-Mol. Cell Res.
**2013**, 1833, 2430–2437. [Google Scholar] [CrossRef] [PubMed] - Solá, R.J.; Griebenow, K. Glycosylation of therapeutic proteins: An effective strategy to optimize efficacy. BioDrugs
**2010**, 24, 9–21. [Google Scholar] [CrossRef] [PubMed] - Jefferis, R. Glycosylation as a strategy to improve antibody-based therapeutics. Nat. Rev.
**2009**, 8, 226–234. [Google Scholar] [CrossRef] [PubMed] - Goh, J.S.Y.; Liu, Y.; Liu, H.; Chan, K.F.; Wan, C.; Teo, G.; Zhou, X.; Xie, F.; Zhang, P.; Zhang, Y.; et al. Highly sialylated recombinant human erythropoietin production in large-scale perfusion bioreactor utilizing CHO-gmt4 (JW152) with restored GnT I function. Biotechnol. J.
**2014**, 9, 100–109. [Google Scholar] [CrossRef] [PubMed] - Harding, F.A.; Stickler, M.M.; Razo, J.; DuBridge, R.B. The immunogenicity of humanized and fully human antibodies: Residual immunogenicity resides in the CDR regions. MAbs
**2010**, 2, 256–265. [Google Scholar] [CrossRef] [PubMed] - Matasci, M.; Hacker, D.L.; Baldi, L.; Wurm, F.M. Protein therapeutics Recombinant therapeutic protein production in cultivated mammalian cells: Current status and future prospects. Drug Discov. Today Technol.
**2008**, 5, 37–42. [Google Scholar] [CrossRef] [PubMed] - Jayapal, K.P.; Wlaschin, K.F.; Hu, W.-S.; Yap, M.G.S. Recombinant protein therapeutics from CHO cells—20 years and counting. Chem. Eng. Prog.
**2007**, 103, 40–47. [Google Scholar] - Wright, A.; Morrison, S.L. Effect of glycosylation on antibody function: Implications for genetic engineering. Trends Biotechnol.
**1997**, 15, 26–32. [Google Scholar] [CrossRef] - Moremen, K.W.; Ramiah, A.; Stuart, M.; Steel, J.; Meng, L.; Forouhar, F.; Moniz, H.A.; Gahlay, G.; Gao, Z.; Chapla, D.; et al. Expression system for structural and functional studies of human glycosylation enzymes. Nat. Chem. Biol.
**2017**, 14, 156–162. [Google Scholar] [CrossRef] [PubMed] - Radhakrishnan, D.; Robinson, A.S.; Ogunnaike, B.A. Controlling the glycosylation profile in mAbs using time-dependent media supplementation. Antibodies
**2018**, 7, 1. [Google Scholar] [CrossRef] - Ivarsson, M.; Villiger, T.K.; Morbidelli, M.; Soos, M. Evaluating the impact of cell culture process parameters on monoclonal antibody N-glycosylation. J. Biotechnol.
**2014**, 188, 88–96. [Google Scholar] [CrossRef] [PubMed] - Varki, A. Biological roles of oligosaccharides: All of the theories are correct. Glycobiology
**1993**, 3, 97–130. [Google Scholar] [CrossRef] [PubMed] - Umaña, P.; Bailey, J.E. A Mathematical model of N-linked glycoform biosynthesis. Biotechnol. Bioeng.
**1997**, 55, 890–908. [Google Scholar] [CrossRef] - Krambeck, F.J.; Bennun, S.V.; Narang, S.; Choi, S.; Yarema, K.J.; Betenbaugh, M.J. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data. Glycobiology
**2009**, 19, 1163–1175. [Google Scholar] [CrossRef] [PubMed] - Jimenez del Val, I.; Nagy, J.M.; Kontoravdi, C. A dynamic mathematical model for monoclonal antibody N-linked glycosylation and nucleotide sugar donor transport within a maturing Golgi apparatus. Biotechnol. Prog.
**2011**, 27, 1730–1743. [Google Scholar] [CrossRef] [PubMed] - Jiménez del Val, I.; Constantinou, A.; Dell, A.; Haslam, S.; Polizzi, K.M.; Kontoravdi, C. A quantitative and mechanistic model for monoclonal antibody glycosylation as a function of nutrient availability during cell culture. BMC Proc.
**2013**, 7, O10. [Google Scholar] [CrossRef] - Jedrzejewski, P.M.; Jiménez del Val, I.; Constantinou, A.; Dell, A.; Haslam, S.M.; Polizzi, K.M.; Kontoravdi, C. Towards controling the glycoform: A model framework linking extracellular metabolites to antibody glycosylation. Int. J. Mol. Sci.
**2014**, 15, 4492–4522. [Google Scholar] [CrossRef] [PubMed] - Spahn, P.N.; Hansen, A.H.; Henning, G.; Arnsdorf, J.; Kildegaard, H.F.; Lewis, N.E.; Arnsdorf, J.; Kildegaard, H.F.; Lewis, N.E.; Markov, A. A markov chain model for N-linked protein glycosylation—Towards a low-parameter tool for model-driven. Metab. Eng.
**2015**, 33, 52–66. [Google Scholar] [CrossRef] [PubMed] - Spahn, P.N.; Hansen, A.H.; Kol, S.; Voldborg, B.; Lewis, N.E. Predictive glycoengineering of biosimilars using a Markov chain glycosylation model. Biotechnol. J.
**2017**, 12, 1–8. [Google Scholar] [CrossRef] [PubMed] - Hutter, S.; Villiger, T.K.; Brühlmann, D.; Stettler, M.; Broly, H.; Soos, M.; Gunawan, R. Glycosylation flux analysis reveals dynamic changes of intracellular glycosylation flux distribution in Chinese hamster ovary fed-batch cultures. Metab. Eng.
**2017**, 43, 9–20. [Google Scholar] [CrossRef] [PubMed] - Antoniewicz, M.R. Methods and advances in metabolic flux analysis: A mini-review. J. Ind. Microbiol. Biotechnol.
**2015**, 42, 317–325. [Google Scholar] [CrossRef] [PubMed] - Wolf, M. Development and Optimization of Mammalian Cell Perfusion Cultures for Continuous Biomanufacturing. Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2018. [Google Scholar]
- Karst, D.J.; Serra, E.; Villiger, T.K.; Soos, M.; Morbidelli, M. Characterization and comparison of ATF and TFF in stirred bioreactors for continuous mammalian cell culture processes. Biochem. Eng. J.
**2016**, 110, 17–26. [Google Scholar] [CrossRef] - Breiman, L. Random forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] - Villiger, T.K.; Scibona, E.; Stettler, M.; Broly, H.; Morbidelli, M.; Soos, M. Controlling the time evolution of mAb N-linkedglycosylation—Part II: Model-based predictions. Biotechnol. Prog.
**2016**, 32, 1135–1148. [Google Scholar] [CrossRef] [PubMed] - Egea, J.A.; Henriques, D.; Cokelaer, T.; Villaverde, A.F.; MacNamara, A.; Danciu, D.-P.; Banga, J.R.; Saez-Rodriguez, J. MEIGO: An open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinform.
**2014**, 15, 136. [Google Scholar] [CrossRef] [PubMed] - Del Val, I.J.; Polizzi, K.M.; Kontoravdi, C. A theoretical estimate for nucleotide sugar demand towards Chines Hamster Ovary cellular glycosylation. Sci. Rep.
**2016**, 6, 28547. [Google Scholar] [CrossRef] [PubMed] - Gawlitzek, M.; Ryll, T.; Lofgren, J.; Sliwkowski, M.B. Ammonium alters N-glycan structures of recombinant TNFR-IgG: Degradative versus biosynthetic mechanisms. Biotechnol. Bioeng.
**2000**, 68, 637–646. [Google Scholar] [CrossRef] - Walther, J.; Lu, J.; Hollenbach, M.; Yu, M.; Hwang, C.; McLarty, J.; Brower, K. Perfusion cell culture decreases process and product heterogeneity in a head-to-head comparison with fed-batch. Biotechnol. J.
**2018**, e1700733. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**Analysis procedure of IgG glycosylation in CHO perfusion cell cultures. (

**a**) Four perfusion cell cultures were performed (see Section 2.1). Measurements of glucose/lactate/ammonia concentrations, viable cell density, IgG titer, glycoform fractions, bleed rates and harvest rates were taken from each cell culture run. (

**b**) In the data preprocessing step (see Section 2.2), the cell-specific secretion fluxes of IgG glycoforms, as well as the cell-specific secretion/uptake rates of IgG, glucose, lactate and ammonia, were computed. (

**c**) The glycosylation flux analysis (GFA) was applied to the secretion fluxes of IgG and its glycoforms (see Section 2.3). The GFA is based on a constraint-based model of the glycosylation network where the glycosylation fluxes vary with time according to two multiplicative factors: the enzyme-specific factor ${\alpha}_{J}\left(t\right)$ and the cell-specific factor $\beta \left(t\right)$. The factor ${\alpha}_{J}\left(t\right)$ captures the dynamic changes in the enzyme-specific glycosylation activities, while the factor $\beta \left(t\right)$ describes the dynamic changes in the cell-specific IgG productivity. (

**d**) A random forest regression analysis was carried out to rank the process parameters (

**p**) based on their ability to predict the dynamic changes of ${\alpha}_{J}\left(t\right)$.

**Figure 2.**A schematic of perfusion cell culture reactor (Figure adapted from [39] with the permission from the authors). CHO cells were cultivated in suspension in a continuous stirred tank reactor with continuous feeding of fresh nutrients. Cell-free spent media was constantly collected in the harvest stream, while cells remained in the stirred tank reactor. A bleed stream removed a small fraction of the reactor mixture, including biomass, which was used to regulate viable cell density.

**Figure 3.**Perfusion cell culture experiments. Four perfusion cell culture experiments, labeled (

**A**), (

**B**), (

**C**) and (

**D**), were conducted with varying VCD and PR given set-points (solid lines). The experimental VCD and PR are shown as blue filled squares and red empty triangles, respectively.

**Figure 4.**Cell-specific productivity. The cell-specific productivity generally decreases over the course of the four perfusion cell culture experiments (

**A**), (

**B**), (

**C**) and (

**D**).

**Figure 5.**Secretion fluxes of the main IgG glycoforms. The solid symbols show the experimental secretion fluxes computed in the data preprocessing step, as outlined in Section 2.2 (Experiment A: black squares, Experiment B: blue circles, Experiment C: green triangle, Experiment D: red diamonds). The lines show the secretion fluxes from the fitting of ${\alpha}_{J}$ in the GFA, as outlined in Section 2.3.

**Figure 6.**Glycosylation network for the GFA of immunoglobulin G in CHO-S. The enzyme names are abbreviated as follows: α-Mannosidase I and II (Man I/II), N-Acetylglucosaminyltransferase I and II (GnT I/II) and Fucosyltransferase (FucT), Galactosyltransferase (GalT) and Sialyltransferase (SiaT). The glycan labels are provided in Supplementary Table S2.

**Figure 7.**Predicted enzyme-specific factors. The activity of fluxes catalyzed by Man I (black), Man II (grey), GnT I (red), GnT II (orange) and FucT (green) remain relatively constant in all experiments. However, the fluxes catalyzed by GalT (purple) shows significant variation over the course of the four perfusion cell culture experiments (

**A**), (

**B**), (

**C**) and (

**D**).

**Figure 8.**Ranking of predictors based on importance. The predictors are sorted in decreasing impurity gains as measured by the improvement in the split criterion.

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## Share and Cite

**MDPI and ACS Style**

Hutter, S.; Wolf, M.; Papili Gao, N.; Lepori, D.; Schweigler, T.; Morbidelli, M.; Gunawan, R.
Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture. *Processes* **2018**, *6*, 176.
https://doi.org/10.3390/pr6100176

**AMA Style**

Hutter S, Wolf M, Papili Gao N, Lepori D, Schweigler T, Morbidelli M, Gunawan R.
Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture. *Processes*. 2018; 6(10):176.
https://doi.org/10.3390/pr6100176

**Chicago/Turabian Style**

Hutter, Sandro, Moritz Wolf, Nan Papili Gao, Dario Lepori, Thea Schweigler, Massimo Morbidelli, and Rudiyanto Gunawan.
2018. "Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture" *Processes* 6, no. 10: 176.
https://doi.org/10.3390/pr6100176