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

Journal
Bioprocess and biosystems engineering
Volume
42
Issue
5
Start Page
867
End Page
882
Citation
Bioprocess and biosystems engineering 5 (42): 867-882 (2019-05)
Publisher DOI
10.1007/s00449-019-02089-7
Scopus ID
2-s2.0-85062624042
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.
Subjects
Chinese hamster ovary
Feeding profile
Modeling
Response surface