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Digital Twins and Their Role in Model-Assisted Design of Experiments

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Digital Twins

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 177))

Abstract

Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined. This includes mathematical model structures and a selection scheme for the choice of DoE designs. Finally, the application of mDoE is discussed in a case study for the medium optimization in an antibody-producing Chinese hamster ovary cell culture process.

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Abbreviations

A:

Average

Amm:

Ammonium

ANOVA:

Analysis of variance

BBD:

Box-Behnken design

CCC:

Central composite circumscribed

CCD:

Central composite designs

CCF:

Central composite face centered

CCI:

Central composite inscribed

CHO:

Chinese hamster ovary

D :

Determinant

DoE:

Design of experiments

E:

Eigenvalue

G:

Global

Glc:

Glucose

Gln:

Glutamine

GMP:

Good Manufacturing Practice

I:

Variance

Lac:

Lactate

LHSD:

Latin hypercube sampling design

mAb:

Antibody

max:

Maximum

MBDoE:

Model-based design of experiments

mDoE:

Model-assisted design of experiments

min:

Minimum

PAT:

Process analytical technology

QbD:

Quality by design

VPA:

Valproic acid

α :

Distance to center point (-)

β i :

Unknown constants (-)

ε i :

Random error (-)

γ :

Constant antibody production rate (mg cell−1 h−1)

μ :

Cell-specific growth rate (h−1)

μ d,max :

Maximum death rate (h−1)

μ d,min :

Minimum death rate (h−1)

μ max :

Maximum growth rate (h−1)

c i :

Concentration of component i (mmol L−1)

d i :

Desirability function (−)

D :

Overall desirability function (−)

i:

Index (−)

k :

Factors (−)

k Lys :

Cell lysis constant (h−1)

K S,i :

Monod kinetic constant for component i (mmol L−1)

L i :

Lower acceptable response (−)

n :

Steps (−)

q Amm :

Ammonium formation rate (mmol cell−1 h−1)

q Glc :

Glucose formation rate (mmol cell−1 h−1)

q Gln :

Glutamine formation rate (mmol cell−1 h−1)

q i,max :

Maximum uptake rate of component i (mmol cell−1 h−1)

q Lac :

Lactate formation rate (mmol cell−1 h−1)

q Lac,uptake :

Uptake rate of lactate (mmol cell−1 h−1)

q Lac,uptake,max :

Maximum uptake rate of lactate (mmol cell−1 h−1)

q mAb :

Antibody formation rate (mmol cell−1 h−1)

R 2 :

Coefficient of determination (−)

U i :

Upper acceptable response (−)

x i :

Independent variables (−)

X t :

Total cell density (cells mL−1)

X v :

Viable cell density (cells mL−1)

V i :

Viability (−)

Y Amm/Gln :

Yield coefficient of ammonium formation to glutamine uptake (−)

y i :

Response (−)

Y Lac/Glc :

Yield coefficient of lactate formation to glucose uptake (−)

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Authors and Affiliations

  1. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Kim B. Kuchemüller, Ralf Pörtner & Johannes Möller

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  1. Kim B. Kuchemüller
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  2. Ralf Pörtner
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  1. Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Wien, Austria

    Christoph Herwig

  2. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Ralf Pörtner

  3. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Johannes Möller

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Kuchemüller, K.B., Pörtner, R., Möller, J. (2020). Digital Twins and Their Role in Model-Assisted Design of Experiments. In: Herwig, C., Pörtner, R., Möller, J. (eds) Digital Twins. Advances in Biochemical Engineering/Biotechnology, vol 177. Springer, Cham. https://doi.org/10.1007/10_2020_136

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