Advertisement

pH excursions impact CHO cell culture performance and antibody N-linked glycosylation

  • Rubin Jiang
  • Hao Chen
  • Sen Xu
Research Paper
  • 72 Downloads

Abstract

pH excursions exist due to frequent base addition and environmental heterogeneity in large-scale bioreactors. Such excursions could lead to suboptimal culture performance. Here we investigated the impact of pH excursions on cell culture performance and N-linked glycosylation for three MAb-producing Chinese hamster ovary cell lines. Frequent pH excursions were introduced by bolus base addition (in total 2–6% of initial volume, fixed bolus addition distributed from day 2 to 8) into small-scale bioreactors. Base addition led to increase in osmolality, pCO2, and lactate production. Lactate production increase was mainly caused by increased culture pH due to base addition, and bolus addition led to higher glucose and lactate metabolic rates than continuous addition. For the three cell lines studied, antibody galactosylation increased with the increase in cultivating pH, correlating to the decrease in cell-specific productivity. Interestingly, pH excursions led to significantly higher galactosylation for one cell line, which also had a higher response to different cultivating pHs. On the other hand, there was no such substantial impact of pH excursions on galactosylation for the other two cell lines, both of which also had minimal response to cultivating pH. This suggests that the impact of pH excursions on antibody N-linked glycosylation is cell line specific and is closely related to cell line response to cultivating pH.

Keywords

Heterogeneity Lactate Mixing Quality attributes Scale-up/scale-down 

Notes

Acknowledgements

We thank the Bioprocess Technical Operations group for inoculum and media preparation, Elizabeth Valente, Illysa Hom, Justin Miller, and Michael Rauscher for in-process purification and analytics support, Sonja Battle and Jaymim Patel for N-glycan analysis. We would also like to thank Gregg Nyberg and John Bowers for their constructive feedback.

Supplementary material

449_2018_1996_MOESM1_ESM.doc (348 kb)
Supplementary material 1 (DOC 348 KB)

References

  1. 1.
    Nienow AW (2006) Reactor engineering in large scale animal cell culture. Cytotechnology 50:9–33CrossRefGoogle Scholar
  2. 2.
    Enfors SO, Jahic M, Rozkov A et al (2001) Physiological responses to mixing in large scale bioreactors. J Biotechnol 85:175–185CrossRefGoogle Scholar
  3. 3.
    Lara AR, Galindo E, Ramírez OT, Palomares LA (2006) Living with heterogeneities in bioreactors: understanding the effects of environmental gradients on cells. Mol Biotechnol 34:355–381CrossRefGoogle Scholar
  4. 4.
    Langheinrich C, Nienow AW (1999) Control of pH in large-scale, free suspension animal cell bioreactors: alkali addition and pH excursions. Biotechnol Bioeng 66:171–179CrossRefGoogle Scholar
  5. 5.
    Tramper J, Vlak JM, de Gooijer CD (1996) Oxygen gradients in small and big sparged insect-cell bioreactors. Cytotechnology 20:231–238CrossRefGoogle Scholar
  6. 6.
    Nienow AW, Langheinrich C, Stevenson NC, Emery AN, Clayton TM, Slater NKH (1996) Homogenisation and oxygen transfer rates in large agitated and sparged animal cell bioreactors: some implications for growth and production. Cytotechnology 22:87–94CrossRefGoogle Scholar
  7. 7.
    Amanullah A, McFarlane CM, Emery AN, Nienow AW (2001) Scale-down model to simulate spatial pH variations in large-scale bioreactors. Biotechnol Bioeng 73:390–399CrossRefGoogle Scholar
  8. 8.
    Neubauer P, Junne S (2010) Scale-down simulators for metabolic analysis of large-scale bioprocesses. Curr Opin Biotechnol 21:114–121CrossRefGoogle Scholar
  9. 9.
    Nienow AW, Scott WH, Hewitt CJ, Thomas CR, Lewis G, Amanullah A, Kiss R, Meier SJ (2013) Scale-down studies for assessing the impact of different stress parameters on growth and product quality during animal cell culture. Chem Eng Res Des 91:2265–2274CrossRefGoogle Scholar
  10. 10.
    Brunner M, Braun P, Doppler P, Posch C, Behrens D, Herwig C, Fricke J (2017) The impact of pH inhomogeneities on CHO cell physiology and fed-batch process performance—two-compartment scale-down modelling and intracellular pH excursion. Biotechnol J 12:1600633CrossRefGoogle Scholar
  11. 11.
    Gao Y, Ray S, Dai S, Ivanov AR, Abu-Absi NR, Lewis AM, Huang Z, Xing Z, Borys MC, Li ZJ, Karger BL (2016) Combined metabolomics and proteomics reveals hypoxia as a cause of lower productivity on scale-up to a 5000-L CHO bioprocess. Biotechnol J 11:1190–1200CrossRefGoogle Scholar
  12. 12.
    Lewis AM, Croughan WD, Aranibar N, Lee AG, Warrack B, Abu-Absi NR, Patel R, Drew B, Borys MC, Reily MD, Li ZJ (2016) Understanding and controlling sialylation in a CHO Fc-fusion process. PLoS One 11:e0157111CrossRefGoogle Scholar
  13. 13.
    Serrato JA, Palomares LA, Meneses-Acosta A, Ramírez OT (2004) Heterogeneous conditions in dissolved oxygen affect N-glycosylation but not productivity of a monoclonal antibody in hybridoma cultures. Biotechnol Bioeng 88:176–188CrossRefGoogle Scholar
  14. 14.
    Osman JJ, Birch J, Varley J (2002) The response of GS-NS0 myeloma cells to single and multiple pH perturbations. Biotechnol Bioeng 79:398–407CrossRefGoogle Scholar
  15. 15.
    Ozturk SS (1996) Engineering challenges in high density cell culture systems. Cytotechnology 22:3–16CrossRefGoogle Scholar
  16. 16.
    Sieblist C, Jenzsch M, Pohlscheidt M (2016) Equipment characterization to mitigate risks during transfers of cell culture manufacturing processes. Cytotechnology 68:1381–1401CrossRefGoogle Scholar
  17. 17.
    Xu P, Clark C, Ryder T, Sparks C, Zhou J, Wang M, Russell R, Scott C (2017) Characterization of TAP Ambr 250 disposable bioreactors, as a reliable scale-down model for biologics process development. Biotechnol Prog 33:478–489CrossRefGoogle Scholar
  18. 18.
    Bareither R, Bargh N, Oakeshott R, Watts K, Pollard D (2013) Automated disposable small scale reactor for high throughput bioprocess development: a proof of concept study. Biotechnol Bioeng 110:3126–3138CrossRefGoogle Scholar
  19. 19.
    Xu S, Gupta B, Hoshan L, Chen H (2015) Rapid early process development enabled by commercial chemically defined media and microbioreactors. Biopharm Int 28:28–33Google Scholar
  20. 20.
    Xu S, Hoshan L, Jiang R, Gupta B, Brodean E, O’Neill K, Seamans TC, Bowers J, Chen H (2017) A practical approach in bioreactor scale-up and process transfer using a combination of constant P/V and vvm as the criterion. Biotechnol Prog 33:1146–1159CrossRefGoogle Scholar
  21. 21.
    Vohwinkel CU, Lecuona E, Sun H, Sommer N, Vadász I, Chandel NS, Sznajder JI (2011) Elevated CO2 levels cause mitochondrial dysfunction and impair cell proliferation. J Biol Chem 286:37067–37076CrossRefGoogle Scholar
  22. 22.
    Oh SKW, Chua FKF, Choo ABH (1995) Intracellular responses of productive hybridomas subjected to high osmotic pressure. Biotechnol Bioeng 46:525–535CrossRefGoogle Scholar
  23. 23.
    Shen D, Kiehl TR, Khattak SF, Li ZJ, He A, Kayne PS, Patel V, Neuhaus IM, Sharfstein ST (2010) Transcriptomic responses to sodium chloride-induced osmotic stress: A study of industrial fed-batch CHO cell cultures. Biotechnol Prog 26:1104–1115Google Scholar
  24. 24.
    Zalai D, Koczka K, Párta L, Wechselberger P, Klein T, Herwig C (2015) Combining mechanistic and data-driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed-batch CHO process. Biotechnol Prog 31:1657–1668CrossRefGoogle Scholar
  25. 25.
    Ivarsson M, Noh H, Morbidelli M, Soos M (2015) Insights into pH-induced metabolic switch by flux balance analysis. Biotechnol Prog 31:347–357CrossRefGoogle Scholar
  26. 26.
    Trummer E, Fauland K, Seidinger S, Schriebl K, Lattenmayer C, Kunert R, Vorauer-Uhl K, Weik R, Borth N, Katinger H, Müller D (2006) Process parameter shifting: part I. Effect of DOT, pH, and temperature on the performance of Epo-Fc expressing CHO cells cultivated in controlled batch bioreactors. Biotechnol Bioeng 94:1033–1044CrossRefGoogle Scholar
  27. 27.
    Halperin ML, Connors HP, Relman AS, Karnovsky ML (1969) Factors that control the effect of pH on glycolysis in leukocytes. J Biol Chem 244:384–390Google Scholar
  28. 28.
    Xu S, Chen H (2016) High-density mammalian cell cultures in stirred-tank bioreactor without external pH control. J Biotechnol 231:149–159CrossRefGoogle Scholar
  29. 29.
    Hartley F, Walker T, Chung V, Morten K (2018) Mechanisms driving the lactate switch in Chinese hamster ovary cells. Biotechnol Bioeng.  https://doi.org/10.1002/bit.26603 Google Scholar
  30. 30.
    Xu S, Jiang R, Mueller R, Hoesli N, Kretz T, Bowers J, Chen H (2018) Probing lactate metabolism variations in large-scale bioreactors. Biotechnol Prog.  https://doi.org/10.1002/btpr.2620 Google Scholar
  31. 31.
    Schilling BM, Abu-Absi S, Thompson P (2012) Metabolic process engineering—a novel technology platform applied to industrial cell culture production processes. Bioprocess Int 10:42–49Google Scholar
  32. 32.
    Xu S, Abu-Absi S, Itzcoatl P, Maranga L (2014) Scale dependence of lactate metabolism in mammalian cell cultures. ACS Spring MeetGoogle Scholar
  33. 33.
    Pacis E, Yu M, Autsen J, Bayer R, Li F (2011) Effects of cell culture conditions on antibody N-linked glycosylation-what affects high mannose 5 glycoform. Biotechnol Bioeng 108:2348–2358CrossRefGoogle Scholar
  34. 34.
    Schmelzer AE, Miller WM (2002) Hyperosmotic stress and elevated pCO2 alter monoclonal antibody charge distribution and monosaccharide content. Biotechnol Prog 18:346–353CrossRefGoogle Scholar
  35. 35.
    Borys MC, Linzer DIH, Papoutsakis ET (1993) Culture pH affects expression rates and glycosylation of recombinant mouse placental lactogen proteins by Chinese hamster ovary (CHO) cells. Nat Biotechnol 11:720–724CrossRefGoogle Scholar
  36. 36.
    Müthing J, Kemminer SE, Conradt HS, Šagi D, Nimtz M, Kärst U, Peter-Katalinić J (2003) Effects of buffering conditions and culture pH on production rates and glycosylation of clinical phase I anti-melanoma mouse IgG3 monoclonal antibody R24. Biotechnol Bioeng 83:321–334CrossRefGoogle Scholar
  37. 37.
    Ivarsson M, Villiger TK, Morbidelli M, Soos M (2014) Evaluating the impact of cell culture process parameters on monoclonal antibody N-glycosylation. J Biotechnol 188:88–96CrossRefGoogle Scholar
  38. 38.
    Aghamohseni H, Ohadi K, Spearman M, Krahn N, Moo-Young M, Scharer JM, Butler M, Budman HM (2014) Effects of nutrient levels and average culture pH on the glycosylation pattern of camelid-humanized monoclonal antibody. J Biotechnol 186:98–109CrossRefGoogle Scholar
  39. 39.
    Seo JS, Kim YJ, Cho JM, Baek E, Lee GM (2013) Effect of culture pH on recombinant antibody production by a new human cell line, F2N78, grown in suspension at 33.0 and 37.0 °C. Appl Microbiol Biotechnol 97:5283–5291CrossRefGoogle Scholar
  40. 40.
    Gawlitzek M, Estacio M, Fürch T, Kiss R (2009) Identification of cell culture conditions to control N-glycosylation site-occupancy of recombinant glycoproteins expressed in CHO cells. Biotechnol Bioeng 103:1164–1175CrossRefGoogle Scholar
  41. 41.
    Gawlitzek M, Ryll T, Lofgren J, Sliwkowski MB (2000) Ammonium alters N-glycan structures of recombinant TNFR-IgG: degradative versus biosynthetic mechanisms. Biotechnol Bioeng 68:637–646CrossRefGoogle Scholar
  42. 42.
    Chen P, Harcum SW (2006) Effects of elevated ammonium on glycosylation gene expression in CHO cells. Metab Eng 8:123–132CrossRefGoogle Scholar
  43. 43.
    Sou SN, Sellick C, Lee K, Mason A, Kyriakopoulos S, Polizzi KM, Kontoravdi C (2015) How does mild hypothermia affect monoclonal antibody glycosylation? Biotechnol Bioeng 112:1165–1176CrossRefGoogle Scholar
  44. 44.
    Zalai D, Hever H, Lovasz K, Molnar D, Wechselberger P, Hofer A, Parta L, Putics A, Herwig C (2016) A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells. Appl Microbiol Biotechnol 100:7011–7024CrossRefGoogle Scholar
  45. 45.
    Godoy-Silva R, Chalmers JJ, Casnocha SA, Bass LA, Ma N (2009) Physiological responses of CHO cells to repetitive hydrodynamic stress. Biotechnol Bioeng 103:1103–1117CrossRefGoogle Scholar
  46. 46.
    Sieck JB, Cordes T, Budach WE, Rhiel MH, Suemeghy Z, Leist C, Villiger TK, Morbidelli M, Soos M (2013) Development of a scale-down model of hydrodynamic stress to study the performance of an industrial CHO cell line under simulated production scale bioreactor conditions. J Biotechnol 164:41–49CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Biologics Process Research and Development, Process Research and DevelopmentMerck & Co., Inc.KenilworthUSA
  2. 2.Cell & Viral Drug Substance, Global Vaccines Technical Research & Development, GlaxoSmithKline VaccinesRixensartBelgium

Personalised recommendations