Adv Biochem Eng Biotechnol (2021) 177: 1–28
https://doi.org/10.1007/10_2020_154
© Springer Nature Switzerland AG 2020
Published online: 31 December 2020
Potential of Integrating Model-Based
Design of Experiments Approaches
and Process Analytical Technologies
for Bioprocess Scale-Down
Peter Neubauer, Emmanuel Anane, Stefan Junne, and
Mariano Nicolas Cruz Bournazou
Contents
1 Status of Bioprocess Scale-Down: The Need for a Model-Based Design . . . . . . . . . . . . . . . . . . 2
2 The Digital Twin in Bioprocess Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Inhomogeneities in Industrial-Scale Bioreactors and Their Influence on the Biological
System . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . 7
4 Framework for Bioprocess Scale-Down Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1 Characterization of the Large Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Execution of Scale-Down Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 General Conclusions and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Abstract Typically, bioprocesses on an industrial scale are dynamic systems with a
certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale
and vice versa is not a trivial task. Traditional scale-down methodologies consider
several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial
bioreactor over the entire process, from the perspective of a cell. Supported by the
enormous increase in computing power, the latest scientific focus is on the
P. Neubauer (*), E. Anane, and S. Junne
Bioprocess Engineering, Faculty III Process Sciences, Institute of Biotechnology, Technische
Universität Berlin (TU Berlin), Berlin, Germany
e-mail: peter.neubauer@tu-berlin.de
M. N. Cruz Bournazou
DataHow AG, Zurich, Switzerland
https://doi.org/10.1007/10_2020_154
© Springer Nature Switzerland AG 2020
Published online: 31 December 2020
Potential of Integrating Model-Based
Design of Experiments Approaches
and Process Analytical Technologies
for Bioprocess Scale-Down
Peter Neubauer, Emmanuel Anane, Stefan Junne, and
Mariano Nicolas Cruz Bournazou
Contents
1 Status of Bioprocess Scale-Down: The Need for a Model-Based Design . . . . . . . . . . . . . . . . . . 2
2 The Digital Twin in Bioprocess Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Inhomogeneities in Industrial-Scale Bioreactors and Their Influence on the Biological
System . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . 7
4 Framework for Bioprocess Scale-Down Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1 Characterization of the Large Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Execution of Scale-Down Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 General Conclusions and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Abstract Typically, bioprocesses on an industrial scale are dynamic systems with a
certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale
and vice versa is not a trivial task. Traditional scale-down methodologies consider
several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial
bioreactor over the entire process, from the perspective of a cell. Supported by the
enormous increase in computing power, the latest scientific focus is on the
P. Neubauer (*), E. Anane, and S. Junne
Bioprocess Engineering, Faculty III Process Sciences, Institute of Biotechnology, Technische
Universität Berlin (TU Berlin), Berlin, Germany
e-mail: peter.neubauer@tu-berlin.de
M. N. Cruz Bournazou
DataHow AG, Zurich, Switzerland