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Bioprocess and Biosystems Engineering

, Volume 42, Issue 5, pp 867–882 | Cite as

Model-assisted Design of Experiments as a concept for knowledge-based bioprocess development

  • Johannes Möller
  • Kim B. Kuchemüller
  • Tobias Steinmetz
  • Kirsten S. Koopmann
  • Ralf PörtnerEmail author
Research Paper
  • 427 Downloads

Abstract

Design of Experiments methods offer systematic tools for bioprocess development in Quality by Design, but their major drawback is the user-defined choice of factor boundary values. This can lead to several iterative rounds of time-consuming and costly experiments. In this study, a model-assisted Design of Experiments concept is introduced for the knowledge-based reduction of boundary values. First, the parameters of a mathematical process model are estimated. Second, the investigated factor combinations are simulated instead of experimentally derived and a constraint-based evaluation and optimization of the experimental space can be performed. The concept is discussed for the optimization of an antibody-producing Chinese hamster ovary batch and bolus fed-batch process. The same optimal process strategies were found if comparing the model-assisted Design of Experiments (4 experiments each) and traditional Design of Experiments (16 experiments for batch and 29 experiments for fed-batch). This approach significantly reduces the number of experiments needed for knowledge-based bioprocess development.

Keywords

Chinese hamster ovary Feeding profile Response surface Modeling 

Variable

\(\alpha \)

Constant antibody production rate (\(\hbox {mg} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(\mu \)

Cell-specific growth rate (\(\hbox {h}^{-1}\))

\(\mu _{\mathrm{d,max}} \)

Maximum death rate (\(\hbox {h}^{-1}\))

\(\mu _{\mathrm{d,min}} \)

Minimum death rate (\(\hbox {h}^{-1}\))

\(\mu _{\mathrm{max}} \)

Maximum growth rate (\(\hbox {h}^{-1}\))

\(c_{{i}} \)

Concentration of component i (\(\hbox {mmol} \, \hbox {l}^{-1}\))

\(d_{{i}} \)

Desirability function (−)

D

Overall desirability function (−)

\(F_{{i}} \)

Feed concentration of component i (\(\hbox {mmol} \, \hbox {l}^{-1}\))

\(F_{\mathrm{rate}} \)

Feed rate (\(\hbox {ml} \, \hbox {d}^{-1}\))

\(\hbox {Feed-start}\)

Time of feed-start (\(\hbox {h}\))

i

Index (Glc, Gln, Amm, Lac, mAb) (−)

j

Index (lactate, ammonium)

\(k_{{i}}\)

Inhibitory constant (\(\hbox {mmol} \, \hbox {l}^{-1}\))

\(K_{\mathrm{i,Amm}}\)

Inhibitory constant of ammonia (\(\hbox {mmol} \, \hbox {l}^{-1}\))

\(K_{\mathrm{Lys}}\)

Cell lysis constant (\(\hbox {h}^{-1}\))

\(K_{\mathrm{S,i}}\)

Monod kinetic constant for component i (\(\hbox {mmol} \, \hbox {l}^{-1}\))

\(L_{{i}}\)

Lower acceptable response (−)

\(q_{\mathrm{Amm}}\)

Ammonia formation rate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{Glc}} \)

Glucose uptake rate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{Gln}} \)

Glutamine uptake rate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{i,\mathrm{max}} \)

Maximum uptake rate (component i) (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{Lac}} \)

Lactate formation rate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{Lac,uptake}} \)

Uptake rate of lactate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{Lac,uptake,max}}\)

Maximum uptake rate of lactate (\(\hbox {mmol} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(q_{\mathrm{mAb}} \)

Antibody formation rate (\(\hbox {mg} \, \hbox {cell}^{-1} \, \hbox {h}^{-1}\))

\(R^{2}\)

Coefficient of determination (−)

\({U}_{{i}}\)

Upper acceptable response (−)

V

Volume (l)

\(X_{\mathrm{d}}\)

Dead cell density (\(\hbox {cells} \, \hbox {ml}^{-1}\))

\(X_{\mathrm{t}} \)

Total cell density (\(\hbox {cells} \, \hbox {ml}^{-1}\))

\(X_{\mathrm{v}} \)

Viable cell density (\(\hbox {cells} \, \hbox {ml}^{-1}\))

\(y_{{i}} \)

Response (−)

\(Y_{\mathrm{Amm/Gln}}\)

Yield coefficient of ammonia formation to glutamine uptake (−)

\(Y_{\mathrm{Lac/Glc}}\)

Yield coefficient of lactate formation to glucose uptake (−)

Abbreviation

Amm

Ammonia

ANOVA

Analysis of variance

CHO

Chinese hamster ovary

DAPI

4,6-diamidin-2-phenylindol

DMEM/F12

Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12

DNA

Deoxyribonucleic acid

DoE

Design of Experiments

FITC

Fluorescein isothiocyanate

FSC-A

Forward scatter area

FSC-H

Forward scatter height

Glc

Glucose

Gln

Glutamine

HPLC

High-performance liquid chromatographic

IgG

Immunoglobulin G

Lac

Lactate

Long R3 IGF-1

Long arginine 3-insulin-like growth factor-1

mAb

Antibody

max

Maximum

min

Minimum

mDoE

Model-assisted Design of Experiments

mRNA

Messenger ribonucleic acid

OFAT

One-factor-at-time

opt

Optimum

PBS

Phosphate-buffered saline

QbD

Quality by Design

RMSD

Root-mean-squared deviation

RSM

Response-surface model

SSC-A

Side scatter area

UV

Ultraviolet

Notes

Acknowledgements

This work was partially funded by the project: Federal Ministry of Education and Research, Germany, under Grant nos. 031B0305 and 031B0577A “New mDoE-Software-Toolbox for model-based optimization of biotechnological processes”.

Supplementary material

449_2019_2089_MOESM1_ESM.pdf (1.7 mb)
Supplementary file1 (PDF 1781 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hamburg University of Technology, Bioprocess and Biosystems EngineeringHamburgGermany

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