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Comparison of viable cell concentration estimation methods for a mammalian cell cultivation process

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Abstract

Various mechanistic and black-box models were applied for on-line estimations of viable cell concentrations in fed-batch cultivation processes for CHO cells. Data from six fed-batch cultivation experiments were used to identify the underlying models and further six independent data sets were used to determine the performance of the estimators. The performances were quantified by means of the root mean square error (RMSE) between the estimates and the corresponding off-line measured validation data sets. It is shown that even simple techniques based on empirical and linear model approaches provide a fairly good on-line estimation performance. Best results with respect to the validation data sets were obtained with hybrid models, multivariate linear regression technique and support vector regression. Hybrid models provide additional important information about the specific cellular growth rates during the cultivation.

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Abbreviations

μ:

Specific growth rate (h−1)

q P :

Specific product formation rate (g e9cells−1 h−1)

X :

Cell concentration (e9cells L−1)

X V :

Viable cell concentration (e9cells L−1)

x :

Cells count (e9cells)

m P :

Mass of product (g)

W :

Culture weight (kg)

ρ:

Culture density (kg L−1)

t :

Process time (h)

OUR:

Oxygen uptake rate (mmol kg−1 h−1)

CPR:

Carbon dioxide production rate (mmol kg−1 h−1)

tOUR:

Total oxygen uptake rate (mmol h−1)

tCPR:

Total carbon dioxide production rate (mmol h−1)

tcOUR:

Total cumulative O2 uptake rate (mmol)

tcCPR:

Total cumulative CO2 production rate (mmol)

Base:

Consumed Base (g, kg)

Y OX :

Yield O2/Cells, Y OX = 10.34 (mmol e9cells−1)

Y CX :

Yield CO2/Cells, Y CX = 11.37 (mmol e9cells−1)

m O :

Maintenance for O2, m O = 0.001 (mmol e9cells−1 h−1)

m C :

Maintenance for CO2, m C = 0.001 (mmol e9cells−1 h−1)

Y bX :

Yield Base consumption/Cells Ybx = 9.0e-3 (kg e9cells−1)

RMSE:

Root mean square error (e9cells kg−1)

w.a.:

Weighted average of Base, tOUR, tCPR related models

References

  • Behrendt U, Koch S, Gooch DD, Steegmans U, Comer MJ (1994) Mass spectrometry: a tool for on-line monitoring of animal cell cultures. Cytotechnol 14:157–162

    Article  CAS  Google Scholar 

  • Bibila TA, Robinson DK (1995) In pursuit of the optimal fed-batch process for monoclonal antibody production. Biotechnol Progr 11:1–13

    Article  CAS  Google Scholar 

  • Brinkmann M, Lütkemeyer D, Gudermann F, Lehmann J (2002) New technologies for automated cell counting based on optical image analysis `The Cellscreen’. Cytotechnology 38:119–127

    Article  CAS  Google Scholar 

  • Cannizzaro C, Gügerli R, Marison I, Stockar Uv (2003) On-line biomass monitoring of CHO perfusion culture with scanning dielectric spectroscopy. Biotechnol Bioeng 84(5):597–610

    Article  CAS  Google Scholar 

  • Chang C-C, Lin CJ (2001) LIBSVM: a library for support vector machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  • Chu L, Robinson DK (2001) Industrial choice for protein production by large-scale cell culture. Curr Op Biotechnol 12:180–187

    Article  CAS  Google Scholar 

  • Dorresteijn RC, Harbrink Numan K, de Gooijer CD, Tramper J, Beuvery EC (1996) On-line estimation of the biomass activity during animal-cell cultivations. Biotechnol Bioeng 51(2):206–214

    Article  CAS  Google Scholar 

  • Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 9:155–161

    Google Scholar 

  • Ducommun P, Bolzonella I, Rhiel M, Pugeaud P, Uv Stockar, Marison IW (2001) On-line determination of animal cell concentration. Biotechnol Bioeng 72(5):515–522

    Article  CAS  Google Scholar 

  • Eyer K, Heinzle E (1996) On-line estimation of viable cells in a hybridoma culture at various DO levels using ATP balancing and redox potential measurement. Biotechnol Bioeng 49(3):277–283

    Article  CAS  Google Scholar 

  • FDA (2004) Guidance for industry: PAT—a framework for innovative pharmaceutical manufacturing and quality assurance, http://www.fda.gov/cvm/guidance/published.html

  • Gunther JC, Conner JS, Seborg SE (2008) PLS pattern matching in design of experiment, batch process data. Chemomet Intell Lab Syst 94(1):43–50

    Article  CAS  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feed-forward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Hossler P, Khattak SF, Li ZJ (2009) Optimal and consistent protein glycosylation in mammalian cell culture. Glycobiology 19(9):936–949

    Article  CAS  Google Scholar 

  • Jenzsch M, Simutis R, Eisbrenner G, Stückrath I, Lübbert A (2006) Estimation of biomass concentrations in fermentation processes for recombinant protein production. Bioproc Biosyst Eng 29(1):19–27

    Article  CAS  Google Scholar 

  • Joeris K, Frerichs J-G, Konstantinov K, Scheper T (2002) In-situ microscopy: on-line process monitoring of mammalian cell cultures. Cytotechnology 38:129–134

    Article  CAS  Google Scholar 

  • Junker BH, Wang HY (2006) Bioprocess monitoring and computer control: key roots of the current PAT initiative. Biotechnol Bioeng 95(2):226–261

    Article  CAS  Google Scholar 

  • Konstantinov KB, Pambayun R, Matanguihan R, Yoshida T, Perusich CM, Hu W-S (1992) On-line monitoring of hybridoma cell growth using a laser turbidity sensor. Biotechnol Bioeng 40:1337–1342

    Article  CAS  Google Scholar 

  • Luedeking R, Piret EL (1959) A kinetic study of lactic acid fermentation. Batch Process at controlled pH. J Biochem Microb Technol 1(4):393–412

    Article  CAS  Google Scholar 

  • Oeggerli A, Eyer K, Heinzle E (1994) On-line gas analysis in animal cell cultivation: I. Control of dissolved oxygen and pH. Biotechnol Bioeng 45:42–53

    Article  Google Scholar 

  • Schubert J, Simutis R, Dors M, Havlik I, Lübbert A (1994) Bioprocess optimization and control: application of hybrid modeling. J Biotechnol 35:51–68

    Article  CAS  Google Scholar 

  • Schügerl K (2001) Progress in monitoring, modeling and control of bioprocesses during the last 20 years. J Biotechnol 85(2):149–173

    Article  Google Scholar 

  • Shah D, Naciri M, Clee P, Al-Rubeai M (2006) NucleoCounter—an efficient technique for the determination of cell number and viability in animal cell culture processes. Cytotechnology 51:39–44

    Article  Google Scholar 

  • Simutis R, Oliveira R, Manikowski M, Feyo de Azevedo S, Lübbert A (1997) How to increase the performance of models for process optimization and control. J Biotechnol 59:73–89

    Article  CAS  Google Scholar 

  • Teixeira AP, Portugal CAM, Carinhas N, Dias JML, Crespo JP, Alves PM, Carrondo MJT, Oliveira R (2009) In situ 2D fluorometry and chemometric monitoring of mammalian cell cultures. Biotechnol Bioeng 102(4):1098–1106

    Article  CAS  Google Scholar 

  • Walsh G (2006) Biopharmaceutical benchmarks. Nat Biotechnol 24(7):769–776

    Article  CAS  Google Scholar 

  • Wlaschin KF, Hu W-S (2006) Fed-batch culture and dynamic nutrient feeding. Adv Biochem Eng/Biotechnol 101:43–74

    Article  CAS  Google Scholar 

  • Zabriskie DW, Humphrey AE (1978) Real-time estimation of aerobic batch fermentation biomass concentration by component balancing. AIChE J 24:138–146

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The financial support from Ministry of Science and Education by means of the Excellence-Initiative Sachsen-Anhalt is gratefully acknowledged.

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Authors and Affiliations

  1. Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, Weinbergweg 22, 06120, Halle (Saale), Germany

    M. Aehle & A. Lübbert

  2. Institute of Automation and Control Systems, Kaunas University of Technology, Studentu g. 48, LT-3028, Kaunas, Lithuania

    R. Simutis

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  1. M. Aehle
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  2. R. Simutis
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Correspondence to A. Lübbert.

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Aehle, M., Simutis, R. & Lübbert, A. Comparison of viable cell concentration estimation methods for a mammalian cell cultivation process. Cytotechnology 62, 413–422 (2010). https://doi.org/10.1007/s10616-010-9291-z

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