Advancing Biopharmaceutical Process Development by System-Level Data Analysis and Integration of Omics Data

  • Jochen Schaub
  • Christoph Clemens
  • Hitto Kaufmann
  • Torsten W. Schulz
Chapter
First Online:
Part of the Advances in Biochemical Engineering Biotechnology book series (ABE, volume 127)

Abstract

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential.

Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.

Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

Keywords

CHO Mammalian cell culture Biopharmaceuticals Bioprocess development Advanced data analysis Omics technologies 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Seth G, Hossler P, Yee JC et al (2006) Engineering cells for cell culture bioprocessing—physiological fundamentals. Adv Biochem Eng Biotechnol 101:119–164Google Scholar
  2. 2.
    Wlaschin KF, Hu WS (2006) Fedbatch culture and dynamic nutrient feeding. Adv Biochem Eng Biotechnol 101:43–74Google Scholar
  3. 3.
    Wurm FM (2004) Production of recombinant protein therapeutics in cultivated mammalian cells. Nat Biotechnol 22(11):1393–1398CrossRefGoogle Scholar
  4. 4.
    Griffin TJ, Seth G, Xie H et al (2007) Advancing mammalian cell culture engineering using genome-scale technologies. Trends Biotechnol 25(9):401–408CrossRefGoogle Scholar
  5. 5.
    O’Callaghan PM, James DC (2008) Systems biotechnology of mammalian cell factories. Brief Funct Genomic Proteomic 7(2):95–110CrossRefGoogle Scholar
  6. 6.
    Europa AF, Gambhir A, Fu PC et al (2000) Multiple steady states with distinct cellular metabolism in continuous culture of mammalian cells. Biotechnol Bioeng 67(1):25–34CrossRefGoogle Scholar
  7. 7.
    Gambhir A, Korke R, Lee J et al (2003) Analysis of cellular metabolism of hybridoma cells at distinct physiological states. J Biosci Bioeng 95(4):317–327Google Scholar
  8. 8.
    Clementschitsch F, Bayer K (2006) Improvement of bioprocess monitoring: development of novel concepts. Microb Cell Fact 5:19CrossRefGoogle Scholar
  9. 9.
    Read EK, Park JT, Shah RB et al (2010) Process analytical technology (PAT) for biopharmaceutical products: Part I. concepts and applications. Biotechnol Bioeng 105(2):276–284CrossRefGoogle Scholar
  10. 10.
    Read EK, Shah RB, Riley BS et al (2010) Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications. Biotechnol Bioeng 105(2):285–295CrossRefGoogle Scholar
  11. 11.
    Teixeira AP, Oliveira R, Alves PM et al (2009) Advances in on-line monitoring and control of mammalian cell cultures: supporting the PAT initiative. Biotechnol Adv 27(6):726–732CrossRefGoogle Scholar
  12. 12.
    Gnoth S, Jenzsch M, Simutis R et al (2007) Process analytical technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol 132(2):180–186CrossRefGoogle Scholar
  13. 13.
    Rathore AS (2009) Roadmap for implementation of quality by design (QbD) for biotechnology products. Trends Biotechnol 27(9):546–553CrossRefGoogle Scholar
  14. 14.
    Stephanopoulos G, Locher G, Duff MJ et al (1997) Fermentation database mining by pattern recognition. Biotechnol Bioeng 53(5):443–452CrossRefGoogle Scholar
  15. 15.
    Charaniya S, Hu WS, Karypis G (2008) Mining bioprocess data: opportunities and challenges. Trends Biotechnol 26(12):690–699CrossRefGoogle Scholar
  16. 16.
    Kamimura RT, Bicciato S, Shimizu H et al (2000) Mining of biological data II: assessing data structure and class homogeneity by cluster analysis. Metab Eng 2(3):228–238CrossRefGoogle Scholar
  17. 17.
    Karim MN, Hodge D, Simon L (2003) Data-based modeling and analysis of bioprocesses: some real experiences. Biotechnol Prog 19(5):1591–1605CrossRefGoogle Scholar
  18. 18.
    Albert S, Kinley RD (2001) Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision. Trends Biotechnol 19(2):53–62CrossRefGoogle Scholar
  19. 19.
    Lennox B, Montague GA, Hiden HG et al (2001) Process monitoring of an industrial fed-batch fermentation. Biotechnol Bioeng 74(2):125–135CrossRefGoogle Scholar
  20. 20.
    Kirdar AO, Conner JS, Baclaski J et al (2007) Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation. Biotechnol Prog 23(1):61–67CrossRefGoogle Scholar
  21. 21.
    Kirdar AO, Green KD, Rathore AS (2008) Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application. Biotechnol Prog 24(3):720–726CrossRefGoogle Scholar
  22. 22.
    Mandenius CF, Brundin A (2008) Bioprocess optimization using design-of-experiments methodology. Biotechnol Prog 24(6):1191–1203CrossRefGoogle Scholar
  23. 23.
    Gnoth S, Jenzsch M, Simutis R et al (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31(1):21–39CrossRefGoogle Scholar
  24. 24.
    D’haeseleer P (2005) How does gene expression clustering work? Nat Biotechnol 23(12):1499–1501CrossRefGoogle Scholar
  25. 25.
    Steuer R, Morgenthal K, Weckwerth W et al (2007) A gentle guide to the analysis of metabolomic data. Methods Mol Biol 358:105–126CrossRefGoogle Scholar
  26. 26.
    Izenman AJ (2010) Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer, New YorkGoogle Scholar
  27. 27.
    Shaw AD, Winson MK, Woodward AM et al (2000) Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics. Adv Biochem Eng Biotechnol 66:83–113Google Scholar
  28. 28.
    Skibsted E, Lindemann C, Roca C et al (2001) On-line bioprocess monitoring with a multi-wavelength fluorescence sensor using multivariate calibration. J Biotechnol 88(1):47–57CrossRefGoogle Scholar
  29. 29.
    Stark E, Hitzmann B, Schugerl K et al (2002) In situ-fluorescence-probes: a useful tool for non-invasive bioprocess monitoring. Adv Biochem Eng Biotechnol 74:21–38Google Scholar
  30. 30.
    Stephanopoulos G, Nielsen J (1998) Metabolic engineering: principles and methodologies. Academic, San DiegoGoogle Scholar
  31. 31.
    Christensen B, Nielsen J (2000) Metabolic network analysis. A powerful tool in metabolic engineering. Adv Biochem Eng Biotechnol 66:209–231Google Scholar
  32. 32.
    Iwatani S, Yamada Y, Usuda Y (2008) Metabolic flux analysis in biotechnology processes. Biotechnol Lett 30(5):791–799CrossRefGoogle Scholar
  33. 33.
    Nielsen J (2001) Metabolic engineering. Appl Microbiol Biotechnol 55(3):263–283CrossRefGoogle Scholar
  34. 34.
    Koffas M, Stephanopoulos G (2005) Strain improvement by metabolic engineering: lysine production as a case study for systems biology. Curr Opin Biotechnol 16(3):361–366CrossRefGoogle Scholar
  35. 35.
    de Graaf AA, Eggeling L, Sahm H (2001) Metabolic engineering for l-lysine production by Corynebacterium glutamicum. Adv Biochem Eng Biotechnol 73:9–29Google Scholar
  36. 36.
    Wiechert W (2002) Modeling and simulation: tools for metabolic engineering. J Biotechnol 94(1):37–63CrossRefGoogle Scholar
  37. 37.
    Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 185(9):2692–2699CrossRefGoogle Scholar
  38. 38.
    Balcarcel RR, Clark LM (2003) Metabolic screening of mammalian cell cultures using well-plates. Biotechnol Prog 19(1):98–108CrossRefGoogle Scholar
  39. 39.
    Bonarius HP, Hatzimanikatis V, Meesters KP et al (1996) Metabolic flux analysis of hybridoma cells in different culture media using mass balances. Biotechnol Bioeng 50(3):299–318CrossRefGoogle Scholar
  40. 40.
    Bonarius HP, Houtman JH, Schmid G et al (2000) Metabolic-flux analysis of hybridoma cells under oxidative and reductive stress using mass balances. Cytotechnology 32(2):97–107CrossRefGoogle Scholar
  41. 41.
    Follstad BD, Balcarcel RR, Stephanopoulos G et al (1999) Metabolic flux analysis of hybridoma continuous culture steady state multiplicity. Biotechnol Bioeng 63(6):675–683CrossRefGoogle Scholar
  42. 42.
    Dalm MC, Lamers PP, Cuijten SM et al (2007) Effect of feed and bleed rate on hybridoma cells in an acoustic perfusion bioreactor: metabolic analysis. Biotechnol Prog 23(3):560–569CrossRefGoogle Scholar
  43. 43.
    Bonarius HP, Ozemre A, Timmerarends B et al (2001) Metabolic-flux analysis of continuously cultured hybridoma cells using (13)CO(2) mass spectrometry in combination with (13)C-lactate nuclear magnetic resonance spectroscopy and metabolite balancing. Biotechnol Bioeng 74(6):528–538CrossRefGoogle Scholar
  44. 44.
    Altamirano C, Illanes A, Casablancas A et al (2001) Analysis of CHO cells metabolic redistribution in a glutamate-based defined medium in continuous culture. Biotechnol Prog 17(6):1032–1041CrossRefGoogle Scholar
  45. 45.
    Altamirano C, Illanes A, Becerra S et al (2006) Considerations on the lactate consumption by CHO cells in the presence of galactose. J Biotechnol 125(4):547–556CrossRefGoogle Scholar
  46. 46.
    Goudar C, Biener R, Zhang C et al (2006) Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture. Adv Biochem Eng Biotechnol 101:99–118Google Scholar
  47. 47.
    Niklas J, Schneider K, Heinzle E (2010) Metabolic flux analysis in eukaryotes. Curr Opin Biotechnol 21:63–69CrossRefGoogle Scholar
  48. 48.
    Quek LE, Dietmair S, Kromer JO et al (2010) Metabolic flux analysis in mammalian cell culture. Metab Eng 12(2):161–171CrossRefGoogle Scholar
  49. 49.
    Boghigian BA, Seth G, Kiss R et al (2010) Metabolic flux analysis and pharmaceutical production. Metab Eng 12(2):81–95CrossRefGoogle Scholar
  50. 50.
    Goudar C, Biener R, Boisart C et al (2010) Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing and 2D [13C, 1H] COSY NMR spectroscopy. Metab Eng 12(2):138–149CrossRefGoogle Scholar
  51. 51.
    Birzele F, Schaub J, Rust W et al (2010) Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Res 38(12):3999–4010CrossRefGoogle Scholar
  52. 52.
    Jacob NM, Kantardjieff A, Yusufi FN et al (2009) Reaching the depth of the Chinese hamster ovary cell transcriptome. Biotechnol Bioeng 105(5):1002–1009Google Scholar
  53. 53.
    Kantardjieff A, Nissom PM, Chuah SH et al (2009) Developing genomic platforms for Chinese hamster ovary cells. Biotechnol Adv 27(6):1028–1035CrossRefGoogle Scholar
  54. 54.
    Korke R, Gatti ML, Lau AL et al (2004) Large scale gene expression profiling of metabolic shift of mammalian cells in culture. J Biotechnol 107(1):1–17CrossRefGoogle Scholar
  55. 55.
    Schaub J, Clemens C, Schorn P et al (2010) CHO gene expression profiling in biopharmaceutical process analysis and design. Biotechnol Bioeng 105(2):431–438CrossRefGoogle Scholar
  56. 56.
    Wong VV, Nissom PM, Sim SL et al (2006) Zinc as an insulin replacement in hybridoma cultures. Biotechnol Bioeng 93(3):553–563CrossRefGoogle Scholar
  57. 57.
    Spens E, Haggstrom L (2009) Proliferation of NS0 cells in protein-free medium: the role of cell-derived proteins, known growth factors and cellular receptors. J Biotechnol 141(3–4):123–129CrossRefGoogle Scholar
  58. 58.
    Trummer E, Ernst W, Hesse F et al (2008) Transcriptional profiling of phenotypically different Epo-Fc expressing CHO clones by cross-species microarray analysis. Biotechnol J 3(7):924–937CrossRefGoogle Scholar
  59. 59.
    Clark KJ, Griffiths J, Bailey KM et al (2005) Gene-expression profiles for five key glycosylation genes for galactose-fed CHO cells expressing recombinant IL-4/13 cytokine trap. Biotechnol Bioeng 90(5):568–577CrossRefGoogle Scholar
  60. 60.
    Wong DC, Wong KT, Lee YY et al (2006) Transcriptional profiling of apoptotic pathways in batch and fed-batch CHO cell cultures. Biotechnol Bioeng 94(2):373–382CrossRefGoogle Scholar
  61. 61.
    Wong DC, Wong KT, Nissom PM et al (2006) Targeting early apoptotic genes in batch and fed-batch CHO cell cultures. Biotechnol Bioeng 95(3):350–361CrossRefGoogle Scholar
  62. 62.
    Shen D, Kiehl TR, Khattak SF et al (2010) Transcriptomic responses to sodium chloride-induced osmotic stress: a study of industrial fed-batch CHO cell cultures. Biotechnol Prog 26(4):1104–1115Google Scholar
  63. 63.
    Wu MH, Dimopoulos G, Mantalaris A et al (2004) The effect of hyperosmotic pressure on antibody production and gene expression in the GS-NS0 cell line. Biotechnol Appl Biochem 40(Pt 1):41–46Google Scholar
  64. 64.
    Yee JC, Gerdtzen ZP, Hu WS (2009) Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells. Biotechnol Bioeng 102(1):246–263CrossRefGoogle Scholar
  65. 65.
    Al-Fageeh MB, Marchant RJ, Carden MJ et al (2006) The cold-shock response in cultured mammalian cells: harnessing the response for the improvement of recombinant protein production. Biotechnol Bioeng 93(5):829–835CrossRefGoogle Scholar
  66. 66.
    De Leon GM, Wlaschin KF, Nissom PM et al (2007) Comparative transcriptional analysis of mouse hybridoma and recombinant Chinese hamster ovary cells undergoing butyrate treatment. J Biosci Bioeng 103(1):82–91CrossRefGoogle Scholar
  67. 67.
    Wang M, Senger RS, Paredes C et al (2009) Microarray-based gene expression analysis as a process characterization tool to establish comparability of complex biological products: scale-up of a whole-cell immunotherapy product. Biotechnol Bioeng 104(4):796–808Google Scholar
  68. 68.
    Stansfield SH, Allen EE, Dinnis DM et al (2007) Dynamic analysis of GS-NS0 cells producing a recombinant monoclonal antibody during fed-batch culture. Biotechnol Bioeng 97(2):410–424CrossRefGoogle Scholar
  69. 69.
    Pascoe DE, Arnott D, Papoutsakis ET et al (2007) Proteome analysis of antibody-producing CHO cell lines with different metabolic profiles. Biotechnol Bioeng 98(2):391–410CrossRefGoogle Scholar
  70. 70.
    Seow TK, Korke R, Liang RC et al (2001) Proteomic investigation of metabolic shift in mammalian cell culture. Biotechnol Prog 17(6):1137–1144CrossRefGoogle Scholar
  71. 71.
    Kumar N, Gammell P, Meleady P et al (2008) Differential protein expression following low temperature culture of suspension CHO-K1 cells. BMC Biotechnol 8:42CrossRefGoogle Scholar
  72. 72.
    Smales CM, Dinnis DM, Stansfield SH et al (2004) Comparative proteomic analysis of GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody production rate. Biotechnol Bioeng 88(4):474–488CrossRefGoogle Scholar
  73. 73.
    Alete DE, Racher AJ, Birch JR et al (2005) Proteomic analysis of enriched microsomal fractions from GS-NS0 murine myeloma cells with varying secreted recombinant monoclonal antibody productivities. Proteomics 5(18):4689–4704CrossRefGoogle Scholar
  74. 74.
    Carlage T, Hincapie M, Zang L et al (2009) Proteomic profiling of a high-producing Chinese hamster ovary cell culture. Anal Chem 81(17):7357–7362CrossRefGoogle Scholar
  75. 75.
    Baik JY, Lee GM (2010) A DIGE approach for the assessment of differential expression of the CHO proteome under sodium butyrate addition: effect of Bcl-x(L) overexpression. Biotechnol Bioeng 105(2):358–367CrossRefGoogle Scholar
  76. 76.
    Jin M, Szapiel N, Zhang J et al (2010) Profiling of host cell proteins by two-dimensional difference gel electrophoresis (2D-DIGE): implications for downstream process development. Biotechnol Bioeng 105(2):306–316CrossRefGoogle Scholar
  77. 77.
    van der Werf MJ, Overkamp KM, Muilwijk B et al (2007) Microbial metabolomics: toward a platform with full metabolome coverage. Anal Biochem 370(1):17–25CrossRefGoogle Scholar
  78. 78.
    Chong WP, Goh LT, Reddy SG et al (2009) Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture. Rapid Commun Mass Spectrom 23(23):3763–3771CrossRefGoogle Scholar
  79. 79.
    Oldiges M, Lutz S, Pflug S et al (2007) Metabolomics: current state and evolving methodologies and tools. Appl Microbiol Biotechnol 76(3):495–511CrossRefGoogle Scholar
  80. 80.
    Bradley SA, Ouyang A, Purdie J et al (2010) Fermentanomics: monitoring mammalian cell cultures with NMR spectroscopy. J Am Chem Soc 132(28):9531–9533CrossRefGoogle Scholar
  81. 81.
    Ma N, Ellet J, Okediadi C et al (2009) A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: improved productivity and lactate metabolism. Biotechnol Prog 25(5):1353–1363CrossRefGoogle Scholar
  82. 82.
    Dietmair S, Timmins NE, Gray PP et al (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404(2):155–164CrossRefGoogle Scholar
  83. 83.
    Sellick CA, Hansen R, Maqsood AR et al (2009) Effective quenching processes for physiologically valid metabolite profiling of suspension cultured Mammalian cells. Anal Chem 81(1):174–183CrossRefGoogle Scholar
  84. 84.
    Baik JY, Lee MS, An SR et al (2006) Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin. Biotechnol Bioeng 93(2):361–371CrossRefGoogle Scholar
  85. 85.
    Kantardjieff A, Jacob NM, Yee JC et al (2010) Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J Biotechnol 145(2):143–159CrossRefGoogle Scholar
  86. 86.
    Doolan P, Meleady P, Barron N et al (2010) Microarray and proteomics expression profiling identifies several candidates, including the valosin-containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines. Biotechnol Bioeng 106(1):42–56Google Scholar
  87. 87.
    Nissom PM, Sanny A, Kok YJ et al (2006) Transcriptome and proteome profiling to understanding the biology of high productivity CHO cells. Mol Biotechnol 34(2):125–140CrossRefGoogle Scholar
  88. 88.
    Jolliffe IT (1986) Principal component analysis. Springer, New YorkGoogle Scholar
  89. 89.
    Zhang J, Martin EB, Morris AJ (1997) Process monitoring using non-linear statistical techniques. Chem Eng J 67(3):181–189CrossRefGoogle Scholar
  90. 90.
    Schaub J, Mauch K, Reuss M (2008) Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic stationary 13C labeling data. Biotechnol Bioeng 99(5):1170–1185CrossRefGoogle Scholar
  91. 91.
    Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35(Database issue):D61–D65Google Scholar
  92. 92.
    van der Heijden RT, Heijnen JJ, Hellinga C et al (1994) Linear constraint relations in biochemical reaction systems: I. Classification of the calculability and the balanceability of conversion rates. Biotechnol Bioeng 43(1):3–10CrossRefGoogle Scholar
  93. 93.
    van der Heijden RT, Romein B, Heijnen J et al (1994) Linear constrain relations in biochemical reaction systems III. Sequential application of data reconciliation for sensitive detection of systematic errors. Biotechnol Bioeng 44(7):781–791CrossRefGoogle Scholar
  94. 94.
    Haggstrom L, Ljunggren J, Ohman L (1996) Metabolic engineering of animal cells. Ann N Y Acad Sci 782:40–52CrossRefGoogle Scholar
  95. 95.
    Wlaschin KF, Hu WS (2007) Engineering cell metabolism for high-density cell culture via manipulation of sugar transport. J Biotechnol 131(2):168–176CrossRefGoogle Scholar
  96. 96.
    Schneider M, Marison IW, von Stockar U (1996) The importance of ammonia in mammalian cell culture. J Biotechnol 46(3):161–185CrossRefGoogle Scholar
  97. 97.
    Stouthamer AH (1973) A theoretical study on the amount of ATP required for synthesis of microbial cell material. Antonie Van Leeuwenhoek 39(3):545–565CrossRefGoogle Scholar
  98. 98.
    Xie L, Wang DI (1996) Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network. Biotechnol Bioeng 52(5):591–601CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jochen Schaub
    • 1
  • Christoph Clemens
    • 1
  • Hitto Kaufmann
    • 1
  • Torsten W. Schulz
    • 1
  1. 1.Department of Biopharmaceutical Process ScienceBoehringer Ingelheim Pharma GmbH & Co. KGBiberach an der RissGermany

Personalised recommendations