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

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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.

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Abbreviations

\(\alpha \) :

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 (−)

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

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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”.

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  1. Hamburg University of Technology, Bioprocess and Biosystems Engineering, Denickestr. 15, 21073, Hamburg, Germany

    Johannes Möller, Kim B. Kuchemüller, Tobias Steinmetz, Kirsten S. Koopmann & Ralf Pörtner

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Möller, J., Kuchemüller, K.B., Steinmetz, T. et al. Model-assisted Design of Experiments as a concept for knowledge-based bioprocess development. Bioprocess Biosyst Eng 42, 867–882 (2019). https://doi.org/10.1007/s00449-019-02089-7

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  • DOI: https://doi.org/10.1007/s00449-019-02089-7

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