CoChat
Sign Up
Cited by 79Viral Infectious Diseases and Gene Expression in InsectsProtein purification and stabilityFluid Dynamics and Mixing

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes

Open access
Brunner, Matthias, Fricke, Jens, Kroll, Paul, Herwig, Christoph·2017·Bioprocess and Biosystems Engineering
DOI 10.1007/s00449-016-1693-7PMC PMC5274649
Open this paper in CoChat — free accountOpen PDF

Abstract

Understanding process parameter interactions and their effects on mammalian cell cultivations is an essential requirement for robust process scale-up. Furthermore, knowledge of the relationship between the process parameters and the product critical quality attributes (CQAs) is necessary to satisfy quality by design guidelines. So far, mainly the effect of single parameters on CQAs was investigated. Here, we present a comprehensive study to investigate the interactions of scale-up relevant parameters as pH, p O 2 and p CO 2 on CHO cell physiology, process performance and CQAs, which was based on design of experiments and extended product quality analytics. The study used a novel control strategy in which process parameters were decoupled from each other, and thus allowed their individual control at defined set points. Besides having identified the impact of single parameters on process performance and product quality, further significant interaction effects of process parameters on specific cell growth, specific productivity and amino acid metabolism could be derived using this method. Concerning single parameter effects, several monoclonal antibody (mAb) charge variants were affected by process p CO 2 and pH. N -glycosylation analysis showed positive correlations between mAb sialylation and high pH values as well as a relationship between high mannose variants and process pH. This study additionally revealed several interaction effects as process pH and p CO 2 interactions on mAb charge variants and N -glycosylation pattern. Hence, through our process control strategy and multivariate investigation, novel significant process parameter interactions and single effects were identified which have to be taken into account especially for process scale-up.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s00449-016-1693-7

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Only a few studies have systematically investigated effect of varying DO on glycosylation, with most reporting minimal changes in GIx (∆GIx < 1.0%).

    “Only a few studies have systematically investigated the effect of varying DO on glycosylation,28,38,50 with most reporting minimal changes in GIx (∆GIx < 1.0%) (Figure 5A-C).”

    From Impact of process parameters on IgG glycosylation in CHO systems: a comprehensive quantitative analysis (2026) · cites this paper as [28]

    The Results and discussion section describes a study varying pO2 and measuring glycosylation, so this paper is an example of the kind of study cited. It reports GI mostly between 25% and 30% across batch processes, but does not report that varying DO changed GI by less than 1.0% or establish what most studies found. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Batch-to-batch differences under the same condition and inconsistent/contradictory glycan outcomes under similar process optimization strategies were observed.

    “In some cases, we observed batch-to-batch differences under the same condition (Figure 7B-C),47 or inconsistent/contradictory glycan outcomes under similar or comparable process optimization strategies.28”

    From Impact of process parameters on IgG glycosylation in CHO systems: a comprehensive quantitative analysis (2026) · cites this paper as [28]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 44

  1. Spadiut O, Capone S, Krainer F, Glieder A, Herwig C (2014) Microbials for the production of monoclonal antibodies and antibody fragments. Trends Biotechnol 32(1):54–60. doi: 10.1016/j.tibtech.2013.10.002 · DOI
  2. Kim JY, Kim YG, Lee GM (2012) CHO cells in biotechnology for production of recombinant proteins: current state and further potential. Appl Microbiol Biotechnol 93(3):917–930. doi: 10.1007/s00253-011-3758-5 · DOI · Paper page
  3. Hernandez R (2015) Top trends in biopharmaceutical manufacturing: 2015. Pharm Technol 39(6):24–29
  4. Omasa T, Onitsuka M, Kim WD (2010) Cell engineering and cultivation of chinese hamster ovary (CHO) cells. Curr Pharm Biotechnol 11(3):233–240 · DOI
  5. Jayapal KP, Wlaschin KF, Hu W-S, Yap MGS (2007) Recombinant protein therapeutics from CHO cells-20 years and counting. Chem Eng Prog 103:40–47
  6. International Conference of Harmonisation, ICH Harmonised Tripartite Guideline: Q8(R2) Pharmaceutical Development. (2009). http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q8_R1/Step4/Q8_R2_Guideline.pdf . Accessed 1 May 2016
  7. von Stosch M, Davy S, Francois K, Galvanauskas V, Hamelink JM, Luebbert A, Mayer M, Oliveira R, O’Kennedy R, Rice P, Glassey J (2014) Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry. Biotechnol J 9(6):719–726. doi: 10.1002/biot.201300385 · DOI
  8. Lara AR, Galindo E, Ramirez OT, Palomares LA (2006) Living with heterogeneities in bioreactors: understanding the effects of environmental gradients on cells. Mol Biotechnol 34(3):355–381. doi: 10.1385/mb:34:3:355 · DOI
  9. Nienow AW (2006) Reactor engineering in large scale animal cell culture. Cytotechnology 50(1–3):9–33. doi: 10.1007/s10616-006-9005-8 · DOI · Paper page
  10. Cromwell ME (2006) Formulation, filling and packaging. In: Ozturk SS, Hu W-S (eds) Cell culture technology for pharmaceutical and cell-based therapies, chap 14. Taylor & Francis Group, London, pp 483–522
  11. Trummer E, Fauland K, Seidinger S, Schriebl K, Lattenmayer C, Kunert R, Vorauer-Uhl K, Weik R, Borth N, Katinger H, Muller 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(6):1033–1044. doi: 10.1002/bit.21013 · DOI
  12. Kishishita S, Nishikawa T, Shinoda Y, Nagashima H, Okamoto H, Takuma S, Aoyagi H (2015) Effect of temperature shift on levels of acidic charge variants in IgG monoclonal antibodies in Chinese hamster ovary cell culture. J Biosci Bioeng 119(6):700–705. doi: 10.1016/j.jbiosc.2014.10.028 · DOI
  13. Link T, Backstrom M, Graham R, Essers R, Zorner K, Gatgens J, Burchell J, Taylor-Papadimitriou J, Hansson GC, Noll T (2004) Bioprocess development for the production of a recombinant MUC1 fusion protein expressed by CHO-K1 cells in protein-free medium. J Biotechnol 110(1):51–62. doi: 10.1016/j.jbiotec.2003.12.008 · DOI
  14. Li F, Vijayasankaran N, Shen AY, Kiss R, Amanullah A (2010) Cell culture processes for monoclonal antibody production. mAbs 2(5):466–479 · DOI · Paper page
  15. Ivarsson M, Villiger TK, Morbidelli M, Soos M (2014) Evaluating the impact of cell culture process parameters on monoclonal antibody N-glycosylation. J Biotechnol 188c:88–96. doi: 10.1016/j.jbiotec.2014.08.026 · DOI
  16. Zanghi JA, Schmelzer AE, Mendoza TP, Knop RH, Miller WM (1999) Bicarbonate concentration and osmolality are key determinants in the inhibition of CHO cell polysialylation under elevated pCO(2) or pH. Biotechnol Bioeng 65(2):182–191 · DOI
  17. Ivarsson M, Noh H, Morbidelli M, Soos M (2015) Insights into pH-induced metabolic switch by flux balance analysis. Biotechnol Prog 31(2):347–357. doi: 10.1002/btpr.2043 · DOI
  18. Goudar CT, Biener R, Konstantinov KB, Piret JM (2009) Error propagation from prime variables into specific rates and metabolic fluxes for mammalian cells in perfusion culture. Biotechnol Prog 25(4):986–998. doi: 10.1002/btpr.155 · DOI
  19. Frahm B, Blank HC, Cornand P, Oelssner W, Guth U, Lane P, Munack A, Johannsen K, Portner R (2002) Determination of dissolved CO(2) concentration and CO(2) production rate of mammalian cell suspension culture based on off-gas measurement. J Biotechnol 99(2):133–148 · DOI
  20. Klein T, Heinzel N, Kroll P, Brunner M, Herwig C, Neutsch L (2015) Quantification of cell lysis during CHO bioprocesses: impact on cell count, growth kinetics and productivity. J Biotechnol 207:67–76. doi: 10.1016/j.jbiotec.2015.04.021 · DOI
  21. Yoon SK, Choi SL, Song JY, Lee GM (2005) Effect of culture pH on erythropoietin production by Chinese hamster ovary cells grown in suspension at 32.5 and 37.0 °C. Biotechnol Bioeng 89(3):345–356. doi: 10.1002/bit.20353 · DOI
  22. Gódia F, Cairó JJ (2006) Cell metabolism. In: Ozturk SS, Hu W-S (eds) Cell culture technology for pharamceutical and cell-based therapies. Taylor & Francis Group, London, pp 81–112
  23. Carrillo-Cocom LM, Genel-Rey T, Araiz-Hernandez D, Lopez-Pacheco F, Lopez-Meza J, Rocha-Pizana MR, Ramirez-Medrano A, Alvarez MM (2015) Amino acid consumption in naive and recombinant CHO cell cultures: producers of a monoclonal antibody. Cytotechnology 67(5):809–820. doi: 10.1007/s10616-014-9720-5 · DOI
  24. Li J, Wong CL, Vijayasankaran N, Hudson T, Amanullah A (2012) Feeding lactate for CHO cell culture processes: impact on culture metabolism and performance. Biotechnol Bioeng 109(5):1173–1186. doi: 10.1002/bit.24389 · DOI · Paper page
  25. Zagari F, Jordan M, Stettler M, Broly H, Wurm FM (2013) Lactate metabolism shift in CHO cell culture: the role of mitochondrial oxidative activity. New Biotechnol 30(2):238–245. doi: 10.1016/j.nbt.2012.05.021 · DOI
  26. Wahrheit J, Nicolae A, Heinzle E (2014) Dynamics of growth and metabolism controlled by glutamine availability in Chinese hamster ovary cells. Appl Microbiol Biotechnol 98(4):1771–1783. doi: 10.1007/s00253-013-5452-2 · DOI
  27. Kim HS, Lee GM (2007) Differences in optimal pH and temperature for cell growth and antibody production between two Chinese hamster ovary clones derived from the same parental clone. J Microbiol Biotechnol 17(5):712–720
  28. Lu C, Liu D, Liu H, Motchnik P (2014) Characterization of monoclonal antibody size variants containing extra light chains. mAbs 5(1):102–113 · DOI
  29. Jing Y, Borys M, Nayak S, Egan S, Qian Y, Pan S-H, Li ZJ (2012) Identification of cell culture conditions to control protein aggregation of IgG fusion proteins expressed in Chinese hamster ovary cells. Process Biochem 47(1):69–75. doi: 10.1016/j.procbio.2011.10.009 · DOI
  30. Liu H, Gaza-Bulseco G, Faldu D, Chumsae C, Sun J (2008) Heterogeneity of monoclonal antibodies. J Pharm Sci 97(7):2426–2447. doi: 10.1002/jps.21180 · DOI
  31. Franco R, Daniela G, Fabrizio M, Ilaria G, Detlev H (1999) Influence of osmolarity and pH increase to achieve a reduction of monoclonal antibodies aggregates in a production process. Cytotechnology 29(1):11–25. doi: 10.1023/a:1008075423609 · DOI
  32. Usami A, Ohtsu A, Takahama S, Fujii T (1996) The effect of pH, hydrogen peroxide and temperature on the stability of human monoclonal antibody. J Pharm Biomed Anal 14(8–10):1133–1140 · DOI
  33. Terashima I, Koga A, Nagai H (2007) Identification of deamidation and isomerization sites on pharmaceutical recombinant antibody using H218O. Anal Biochem 368(1):49–60. doi: 10.1016/j.ab.2007.05.012 · DOI
  34. Zhong X, Wright JF (2013) Biological insights into therapeutic protein modifications throughout trafficking and their biopharmaceutical applications. Int J Cell Biol 2013:19. doi: 10.1155/2013/273086 · DOI
  35. Liu H, Ponniah G, Zhang HM, Nowak C, Neill A, Gonzalez-Lopez N, Patel R, Cheng G, Kita AZ, Andrien B (2014) In vitro and in vivo modifications of recombinant and human IgG antibodies. mAbs 6(5):1145–1154. doi: 10.4161/mabs.29883 · DOI
  36. Zheng JY, Janis LJ (2006) Influence of pH, buffer species, and storage temperature on physicochemical stability of a humanized monoclonal antibody LA298. Int J Pharm 308(1–2):46–51. doi: 10.1016/j.ijpharm.2005.10.024 · DOI
  37. Yi L, Beckley N, Gikanga B, Zhang J, Wang YJ, Chih HW, Sharma VK (2013) Isomerization of Asp–Asp motif in model peptides and a monoclonal antibody Fab fragment. J Pharm Sci 102(3):947–959. doi: 10.1002/jps.23423 · DOI
  38. Cai B, Pan H, Flynn GC (2011) C-terminal lysine processing of human immunoglobulin G2 heavy chain in vivo. Biotechnol Bioeng 108(2):404–412. doi: 10.1002/bit.22933 · DOI
  39. Dick LW Jr, Qiu D, Mahon D, Adamo M, Cheng KC (2008) C-terminal lysine variants in fully human monoclonal antibodies: investigation of test methods and possible causes. Biotechnol Bioeng 100(6):1132–1143. doi: 10.1002/bit.21855 · DOI
  40. Harris RJ (1995) Processing of C-terminal lysine and arginine residues of proteins isolated from mammalian cell culture. J Chromatogr A 705(1):129–134. doi: 10.1016/0021-9673(94)01255-D · DOI
  41. Agarabi CD, Schiel JE, Lute SC, Chavez BK, Boyne MT II, Brorson KA, Khan M, Read EK (2015) Bioreactor process parameter screening utilizing a Plackett–Burman design for a model monoclonal antibody. J Pharm Sci 104(6):1919–1928. doi: 10.1002/jps.24420 · DOI
  42. Raju TS, Jordan RE (2012) Galactosylation variations in marketed therapeutic antibodies. mAbs 4(3):385–391. doi: 10.4161/mabs.19868 · DOI
  43. Hossler P, Khattak SF, Li ZJ (2009) Optimal and consistent protein glycosylation in mammalian cell culture. Glycobiology 19(9):936–949. doi: 10.1093/glycob/cwp079 · DOI
  44. Kimura R, Miller WM (1997) Glycosylation of CHO-derived recombinant tPA produced under elevated pCO2. Biotechnol Prog 13(3):311–317. doi: 10.1021/bp9700162 · DOI

Cited by

Sorted by citations

Metadata

Authors
Brunner, M., Fricke, J., Kroll, P., & Herwig, C.
Affiliations
TU Wien (AT)
Corresponding author
Herwig, Christoph
ORCID
Venue
Bioprocess and Biosystems Engineering
Publisher
Springer Science and Business Media LLC
Published
2016-10-17
Volume
40
Issue
2
Pages
251-263
ISSN
  • 1615-7591 (ISSN-L)
  • 1615-7605
DOI
10.1007/s00449-016-1693-7
PMC
PMC5274649
OpenAlex
W2539013878
Cited by
79
References
3
Citations by year
2026: 4 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 8 · 2021: 11 · 2020: 5 · 2019: 15 · 2018: 8 · 2017: 4
FWCI
4.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioprocess and Biosystems Engineering (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • reposiTUm (TU Wien) (submittedVersion, cc-by)
Funders
  • Bundesministerium für Wissenschaft, Forschung und Wirtschaft
  • Technische Universität Wien
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
  • Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Fluid Dynamics and Mixing
Keywords
CHO cell culture, process parameter interaction, pH, dissolved oxygen, dissolved CO2, critical quality attributes, monoclonal antibodies, charge variants, N-glycosylation, sialylation, high-mannose glycans, amino acid metabolism, specific productivity, process scale-up, quality by design
MeSH terms
Animals, Carbon Dioxide, Cricetulus, Cricetinae, Hydrogen-Ion Concentration, Oxygen, Oxygen Consumption, CHO Cells, Cell Culture Techniques

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review