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Cytotechnology

, Volume 64, Issue 6, pp 623–634 | Cite as

Increasing batch-to-batch reproducibility of CHO-cell cultures using a model predictive control approach

  • Mathias Aehle
  • Kaya Bork
  • Sebastian Schaepe
  • Artur Kuprijanov
  • Rüdiger Horstkorte
  • Rimvydas Simutis
  • Andreas Lübbert
Original Research

Abstract

By means of a model predictive control strategy it was possible to ensure a high batch-to-batch reproducibility in animal cell (CHO-cell) suspensions cultured for a recombinant therapeutic protein (EPO) production. The general control objective was derived by identifying an optimal specific growth rate taking productivity, protein quality and process controllability into account. This goal was approached indirectly by controlling the oxygen mass consumed by the cells which is related to specific biomass growth rate and cell concentration profile by manipulating the glutamine feed rate. Process knowledge represented by a classical model was incorporated into the model predictive control algorithm. The controller was employed in several cultivation experiments. During these cultivations, the model parameters were adapted after each sampling event to cope with changes in the process’ dynamics. The ability to predict the state variables, particularly for the oxygen consumption, led to only moderate changes in the desired optimal operational trajectories. Hence, nearly identical oxygen consumption profiles, cell and protein titers as well as sialylation patterns were obtained for all cultivation runs.

Keywords

CHO cells Specific erythropoietin production rate Specific growth rate Model predictive control Optimal process design Reproducibility 

Abbreviations

IVC

Integral of viable cells (109 cells h)

XV

Viable cell density (109 cells L−1)

Xt

Total cell density (109 cells L−1)

Glc

Glucose concentration (mM)

Gln

Glutamine concentration (mM)

Lac

Lactate concentration (mM)

NH4

Ammonia concentration (mM)

P

Protein (mg)

W

Culture weight (kg)

F

Overall feed rate (kg h−1)

FBase

Base consumption rate (kg h−1)

FEvap

Evaporation rate (kg h−1)

FGlc

Glucose feed rate (kg h−1)

FGln

Glutamine feed rate (kg h−1)

FSample

Sampling rate (kg h−1)

XV0

Viable cell density at start of feeding (109 cells L−1)

W0

Culture weight at start of feeding (kg)

Sf

Substrate feed concentration (mM)

S

Substrate concentration (mM)

ρ

Liquid density (kg L−1)

t

Time (h)

t0

Start of feeding (h)

x

Viable cells (109 cells)

μ

Specific growth rate (h−1)

μnet

Net specific growth rate μnet = μ − kd (h−1)

μset

Setpoint specific growth rate (h−1)

qi

Specific consumption/production rate of state variable i (mmol 109 cells−1 h−1)

qGlcmax

Maximum specific glucose consumption rate (mmol 109 cells−1 h−1)

qGlnmax

Maximum specific glutamine consumption rate (mmol 109 cells−1 h−1)

kd

Cell decay rate (h−1)

kdmax

Maximum cell decay rate (h−1)

kdeg

Degradation constant for glutamine kdeg = 0.004 (h−1)

YSX

Yield consumed Substrate per cells formed (mmol 109 cells−1 h−1)

YGlcX

Yield glucose consumed per cells formed (mmol 109 cells−1)

YGlnX

Yield glutamine consumed per cells formed (mmol 109 cells−1 h−1)

YLacGlc

Yield lactate formed per glucose consumed (mmol mmol−1)

\( {\text{Y}}_{{{\text{NH}}_{4}{\rm X}}} \)

Yield ammonia formed per cells formed (mmol 109 cells−1 h−1)

YOGlc

Yield oxygen consumed per glucose consumed (mmol mmol−1)

YOGln

Yield oxygen consumed per glutamine consumed (mmol mmol−1)

KGlc

Glucose saturation constant (mM)

KGln

Glutamine saturation constant (mM)

KLac

Lactate inhibition constant for lactate (mM)

\( {\text{K}}_{{{\text{NH}}_{ 4}^{ + } }} \)

Ammonia inhibition constant for ammonia (mM)

KO

Inhibition constant (mM)

k1

Constant 1 for glucose (mM)

k2

Constant 2 for glucose (mM)

mGln

Glutamine maintenance constant (mmol 109 cells−1 h−1)

mO

Oxygen maintenance constant (mmol 109 cells−1 h−1)

\( {\text{n}}_{{{\text{O}}_{ 2} }} \)

Amount of oxygen consumed (mmol)

mAA

Specific ammonia production rate at low glutamine concentrations (mmol 109 cells−1 h−1) (Zeng et al. 1998)

OUR

Oxygen uptake rate (mmol L−1 h−1)

tOUR

Total oxygen uptake rate (mmol  h−1)

tcOUR

\( ={\text{n}}_{{{\text{O}}_{ 2} }} \) total cumulative oxygen uptake rate (mmol)

Notes

Acknowledgments

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

References

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Mathias Aehle
    • 1
  • Kaya Bork
    • 2
  • Sebastian Schaepe
    • 1
  • Artur Kuprijanov
    • 1
  • Rüdiger Horstkorte
    • 2
  • Rimvydas Simutis
    • 3
  • Andreas Lübbert
    • 1
  1. 1.Institute of Biochemistry and BiotechnologyMartin-Luther-University Halle-WittenbergHalle (Saale)Germany
  2. 2.Institute of Physiological ChemistryMartin-Luther-University Halle-WittenbergHalle (Saale)Germany
  3. 3.Institute of Automation and Control SystemsKaunas University of TechnologyKaunasLithuania

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