Möller, JohannesJohannesMöllerKuchemüller, Kim BeatriceKim BeatriceKuchemüllerSteinmetz, TobiasTobiasSteinmetzKoopmann, KirstenKirstenKoopmannPörtner, RalfRalfPörtner2019-05-212019-05-212019-05Bioprocess and biosystems engineering 5 (42): 867-882 (2019-05)http://hdl.handle.net/11420/2682Design 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.en1615-759120195867882Chinese hamster ovaryFeeding profileModelingResponse surfaceJournal Article10.1007/s00449-019-02089-7Other