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Efficient optimization of process strategies with model-assisted design of experiments
Publikationstyp
Book Part
Date Issued
2020
Sprache
English
Institut
TORE-URI
Journal
Start Page
235
End Page
249
Citation
Methods in Molecular Biology (2095): 235-249 (2020)
Publisher DOI
Scopus ID
Conventional design of experiments (DoE) methods require expert knowledge about the investigated factors and their boundary values and mostly lead to multiple rounds of time-consuming and costly experiments. The combination of DoE with mathematical process modeling in model-assisted DoE (mDoE) can be used to increase the mechanistic understanding of the process. Furthermore, it is aimed to optimize the processes with respect to a target (e.g., amount of cells, product titer), which also provides new insights into the process. In this chapter, the workflow of mDoE is explained stepwise including corresponding protocols. Firstly, a mathematical process model is adapted to cultivation data of first experimental data or existing knowledge. Secondly, model-assisted simulations are treated in the same way as experimentally derived data and included as responses in statistical DoEs. The DoEs are then evaluated based on the simulated data, and a constrained-based optimization of the experimental space can be conducted. This loop can be repeated several times and significantly reduces the number of experiments in process development.
Subjects
Batch
Computer-aided methods
DoE
Experimental space
Fed-batch
Response surface