Towards controlling the glycoform: A model framework linking extracellular metabolites to antibody glycosylation

International Journal of Molecular Sciences

Volumn 15, Issue 3, 2014, Pages 4492-4522

  Jedrzejewski, Philip M ^a   del Val, Ioscani Jimenez ^a   Constantinou, Antony ^a   Dell, Anne ^a   Haslam, Stuart M ^a   Polizzi, Karen M ^a   Kontoravdi, Cleo ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Antibody glycosylation; Metabolic modelling; Nucleotide sugars

Indexed keywords

GLUCOSE; GLUTAMINE; GLYCAN DERIVATIVE; GLYCOPROTEIN; IMMUNOGLOBULIN G1 ANTIBODY; NUCLEOTIDE; ANTIBODY; NUCLEOSIDE DIPHOSPHATE SUGAR; POLYSACCHARIDE;

ANIMAL CELL; ANTIBODY GLYCOSYLATION; ARTICLE; CELL GROWTH; COMPUTER MODEL; ENZYME IMMUNOASSAY; GLYCOLYSIS; GOLGI COMPLEX; GROWTH RATE; MATHEMATICAL MODEL; METABOLISM; MOLECULAR WEIGHT; MOUSE; NONHUMAN; PROCESS OPTIMIZATION; PROTEIN GLYCOSYLATION; SENSITIVITY ANALYSIS; SIMULATION; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CELL LINE; CELL PROLIFERATION; COMPUTER SIMULATION; EXTRACELLULAR SPACE; GLYCOSYLATION; HYBRIDOMA; MASS SPECTROMETRY; TIME;

ALGORITHMS; ANIMALS; ANTIBODIES; CELL LINE; CELL PROLIFERATION; COMPUTER SIMULATION; EXTRACELLULAR SPACE; GLUCOSE; GLUTAMINE; GLYCOPROTEINS; GLYCOSYLATION; GOLGI APPARATUS; HYBRIDOMAS; METABOLIC NETWORKS AND PATHWAYS; MICE; MODELS, BIOLOGICAL; NUCLEOSIDE DIPHOSPHATE SUGARS; NUCLEOTIDES; POLYSACCHARIDES; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION; TIME FACTORS;

EID: 84896474989     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms15034492     Document Type: Article

References (52)

1. Evaluate Pharma Embracing the Patent Cliff. In World Preview 2018; EvaluatePharma Ltd.: London, UK, 2013; p. 38.
2. Kyriakopoulos, S.; Kontoravdi, C. Analysis of the landscape of biologically-derived pharmaceuticals in Europe: Dominant production systems, molecule types on the rise and approval trends. Eur. J. Pharm. Sci. 2013, 48, 428-441.
3. Shields, R. L.; Lai, J.; Keck, R.; O'Connell, L. Y.; Hong, K.; Meng, Y. G.; Weikert, S. H. A.; Presta, L. G. Lack of fucose on human IgG1 N-linked oligosaccharide improves binding to human Fc gamma RIII and antibody-dependent cellular toxicity. J. Biol. Chem. 2002, 277, 26733-26740.
4. Matsumiya, S.; Yamaguchi, Y.; Saito, J.; Nagano, M.; Sasakawa, H.; Otaki, S.; Satoh, M.; Shitara, K.; Kato, K. Structural comparison of fucosylated and nonfucosylated Fc fragments of human immunoglobulin G1. J. Mol. Biol. 2007, 368, 767-779.
5. Hodoniczky, J.; Zheng, Y. Z.; James, D. C. Control of recombinant monoclonal antibody effector functions by Fc N-glycan remodeling in vitro. Biotechnol. Progr. 2005, 21, 1644-1652.
6. Anthony, R. M.; Nimmerjahn, F.; Ashline, D. J.; Reinhold, V. N.; Paulson, J. C.; Ravetch, J. V. Recapitulation of IVIG anti-inflammatory activity with a recombinant IgG fc. Science 2008, 320, 373-376.
7. Goetze, A. M.; Liu, Y. D.; Zhang, Z. Q.; Shah, B.; Lee, E.; Bondarenko, P. V.; Flynn, G. C. High-mannose glycans on the Fc region of therapeutic IgG antibodies increase serum clearance in humans. Glycobiology 2011, 21, 949-959.
8. Mullard, A. Can next-generation antibodies offset biosimilar competition? Nat. Rev. Drug Discov. 2012, 11, 426-428.
9. Jedrzejewski, P. M.; Jimenez del Val, I.; Polizzi, K. M.; Kontoravdi, C. Applying quality by design to glycoprotein therapeutics: Experimental and computational efforts of process control. Pharm. Bioprocess. 2013, 1, 51-69.
10. Balaquer, E.; Neususs, C. Intact glycoform characterization of erythropoietin-alpha and erythropoietin-beta by CZE-ESI-TOF-MS. Chromatographia 2006, 64, 351-357.
11. 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.
12. Del Val, I. J.; Kontoravdi, C.; Nagy, J. M. Towards the implementation of quality by design to the production of therapeutic monoclonal antibodies with desired glycosylation patterns. Biotechnol. Progr. 2010, 26, 1505-1527.
13. Hills, A. E.; Patel, A.; Boyd, P.; James, D. C. Metabolic control of recombinant monoclonal antibody N-glycosylation in GS-NS0 cells. Biotechnol. Bioeng. 2001, 75, 239-251.
14. Grainger, R. K.; James, D. C. CHO cell line specific prediction and control of recombinant monoclonal antibody N-glycosylation. Biotechnol. Bioeng. 2013, 110, 2970-2983.
15. Wong, N. S. C.; Wati, L.; Nissom, P. M.; Feng, H. T.; Lee, M. M.; Yap, M. G. S. An investigation of intracellular glycosylation activities in CHO Cells: Effects of nucleotide sugar precursor feeding. Biotechnol. Bioeng. 2010, 107, 321-336.
16. Rathore, A. S.; Winkle, H. Quality by design for biopharmaceuticals. Nat. Biotechnol. 2009, 27, 26-34.
17. del Val, I. J.; 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. Progr. 2011, 27, 1730-1743.
18. gPROMS Introductory User Guideversion-Release 3. 5. 0; Process Systems Enterprise, Ltd.: London, UK, 2011.
19. Xie, L. Z.; Wang, D. I. C. Stoichiometric analysis of animal-cell growth and its application in medium design. Biotechnol. Bioeng. 1994, 43, 1164-1174.
20. Nolan, R. P.; Lee, K. Dynamic model of CHO cell metabolism. Metab. Eng. 2011, 13, 108-124.
21. Bonarius, H. P.; Hatzimanikatis, V.; Meesters, K. P.; de Gooijer, C. D.; Schmid, G.; Tramper, J. Metabolic flux analysis of hybridoma cells in different culture media using mass balances. Biotechnol. Bioeng. 1996, 50, 299-318.
22. KEGG (Kyoto Encylopedia of Genes and Genomes). Available online: http://www. genome. jp/kegg/(accessed on 10 March 2014).
23. BRENDA (BRaunschweig ENzyme DAtabase). Available online: http://www. brenda-enzymes. org (accessed on 10 March 2014).
24. Springer, M. S.; Stanhope, M. J.; Madsen, O.; de Jong, W. W. Molecules consolidate the placental mammal tree. Trends Ecol. Evol. 2004, 19, 430-438.
25. Rijcken, W. R. P.; Overdijk, B.; Vandeneijnden, D. H.; Ferwerda, W. The effect of increasing nucleotide sugar concentrations on the incorporation of sugars into glycoconjugates in rat hepatocytes. Biochem. J. 1995, 305, 865-870.
26. Apweiler, R.; Hermjakob, H.; Sharon, N. On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database. Bba-Gen. Subj. 1999, 1473, 4-8.
27. Chen, N.; Koumpouras, G. C.; Polizzi, K. M.; Kontoravdi, C. Genome-based kinetic modeling of cytosolic glucose metabolism in industrially relevant cell lines: Saccharomyces cerevisiae and Chinese hamster ovary cells. Bioproc. Biosyst. Eng. 2012, 35, 1023-1033.
28. Wong, D. C. F.; Wong, N. S. C.; Goh, J. S. Y.; May, L. M.; Yap, M. G. S. Profiling of N-glycosylation gene expression in CHO cell fed-batch cultures. Biotechnol. Bioeng. 2010, 107, 516-528.
29. Frame, K. K.; Hu, W. S. Cell-Volume Measurement as an estimation of mammalian-cell biomass. Biotechnol. Bioeng. 1990, 36, 191-197.
30. Lee, M. S.; Lee, G. M. Hyperosmotic pressure enhances immunoglobulin transcription rates and secretion rates of KR12H-2 transfectoma. Biotechnol. Bioeng. 2000, 68, 260-268.
31. Fujita, N.; Tamura, A.; Higashidani, A.; Tonozuka, T.; Freeze, H. H.; Nishikawa, A. The relative contribution of mannose salvage pathways to glycosylation in PMI-deficient mouse embryonic fibroblast cells. FEBS J. 2008, 275, 788-798.
32. Kontoravdi, C.; Pistikopoulos, E. N.; Mantalaris, A. Systematic development of predictive mathematical models for animal cell cultures. Comput. Chem. Eng. 2010, 34, 1192-1198.
33. Sobol, I. M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simulat. 2001, 55, 271-280.
34. Kontoravdi, C.; Asprey, S. P.; Pistikopoulos, E. N.; Mantalaris, A. Application of global sensitivity analysis to determine goals for design of experiments: An example study on antibody-producing cell cultures. Biotechnol. Progr. 2005, 21, 1128-1135.
35. Kiparissides, A.; Kucherenko, S. S.; Mantalaris, A.; Pistikopoulos, E. N. Global sensitivity analysis challenges in biological systems modeling. Ind. Eng. Chem. Res. 2009, 48, 7168-7180.
36. Goudar, C.; Biener, R.; Boisart, C.; Heidemann, R.; Piret, J.; de Graaf, A.; Konstantinov, K. Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing and 2D [C-13, H-1] COSY NMR spectroscopy. Metab. Eng. 2010, 12, 138-149.
37. Li, G. Y.; Wang, S. W.; Rabitz, H. Practical approaches to construct RS-HDMR component functions. J. Phys. Chem. A 2002, 106, 8721-8733.
38. Li, G. Y.; Hu, J. S.; Wang, S. W.; Georgopoulos, P. G.; Schoendorf, J.; Rabitz, H. Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions. J. Phys. Chem. A 2006, 110, 2474-2485.
39. Li, G. Y.; Rabitz, H. Ratio control variate method for efficiently determining high-dimensional model representations. J. Comput. Chem. 2006, 27, 1112-1118.
40. Feil, B.; Kucherenko, S.; Shah, N. Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation. In Proceedings of the Simul: 2009 First International Conference on Advances in System Simulation, Porto, Portugal, 20-25 September 2009; pp. 12-17.
41. Zuniga, M. M.; Kucherenko, S.; Shah, N. Metamodelling with independent and dependent inputs. Comput. Phys. Commun. 2013, 184, 1570-1580.
42. Geiger, T.; Velic, A.; Macek, B.; Lundberg, E.; Kampf, C.; Nagaraj, N.; Uhlen, M.; Cox, J.; Mann, M. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell. Proteomics 2013, 12, 1709-1722.
43. Baycin-Hizal, D.; Tabb, D. L.; Chaerkady, R.; Chen, L.; Lewis, N. E.; Nagarajan, H.; Sarkaria, V.; Kumar, A.; Wolozny, D.; Colao, J.; et al. Proteomic analysis of Chinese hamster ovary cells. J. Proteome Res. 2012, 11, 5265-5276.
44. Xu, X.; Nagarajan, H.; Lewis, N. E.; Pan, S.; Cai, Z.; Liu, X.; Chen, W.; Xie, M.; Wang, W.; Hammond, S.; et al. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat. Biotechnol. 2011, 29, 735-741.
45. Milla, M. E.; Clairmont, C. A.; Hirschberg, C. B. Reconstitution into proteoliposomes and partial purification of the Golgi apparatus membrane UDP-galactose, UDP-xylose, and UDP-glucuronic acid transport activities. J. Biol. Chem. 1992, 267, 103-107.
46. Dietmair, S.; Timmins, N. E.; Gray, P. P.; Nielsen, L. K.; Kromer, J. O. Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol. Anal. Biochem. 2010, 404, 155-164.
47. Del Val, I. J.; Kyriakopoulos, S.; Polizzi, K. M.; Kontoravdi, C. An optimized method for extraction and quantification of nucleotides and nucleotide sugars from mammalian cells. Anal. Biochem. 2013, 443, 172-180.
48. Dell, A.; Reason, A. J.; Khoo, K. H.; Panico, M.; McDowell, R. A.; Morris, H. R. Mass spectrometry of carbohydrate-containing biopolymers. Methods Enzymol. 1994, 230, 108-132.
49. Jang-Lee, J.; North, S. J.; Sutton-Smith, M.; Goldberg, D.; Panico, M.; Morris, H.; Haslam, S.; Dell, A. Glycomic profiling of cells and tissues by mass spectrometry: Fingerprinting and sequencing methodologies. Methods Enzymol. 2006, 415, 59-86.
50. Damerell, D.; Ceroni, A.; Maass, K.; Ranzinger, R.; Dell, A.; Haslam, S. M. The GlycanBuilder and GlycoWorkbench glycoinformatics tools: Updates and new developments. Biol. Chem. 2012, 393, 1357-1362.
51. Ceroni, A.; Maass, K.; Geyer, H.; Geyer, R.; Dell, A.; Haslam, S. M. GlycoWorkbench: A tool for the computer-assisted annotation of mass spectra of glycans. J. Proteome Res. 2008, 7, 1650-1659.
52. Williamson, D. H.; Brosnan, J. T. Methods of Enzymatic Analysis; Bergmeyer, H. U., Gawehn, K., Eds.; Verlag Chemie: New York, NY, USA; London, UK, 1974; Volume 4.