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Applying intensified design of experiments to mammalian cell culture processes

The analysis of data collected using design of experiments (DoE) is the current gold standard to determine the influence of input parameters and their interactions on process performance and product quality. In early development, knowledge on the bioprocess of a new product is limited. Many input pa...

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Detalles Bibliográficos
Autores principales: Nold, Verena, Junghans, Lisa, Bisgen, Lorenzo, Drerup, Raphael, Presser, Beate, Gorr, Ingo, Schwab, Thomas, Knapp, Bettina, Wieschalka, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731596/
https://www.ncbi.nlm.nih.gov/pubmed/36514527
http://dx.doi.org/10.1002/elsc.202100123
Descripción
Sumario:The analysis of data collected using design of experiments (DoE) is the current gold standard to determine the influence of input parameters and their interactions on process performance and product quality. In early development, knowledge on the bioprocess of a new product is limited. Many input parameters need to be investigated for a thorough investigation. For eukaryotic cell cultures, intensified DoE (iDoE) has been proposed as efficient tool, requiring fewer bioreactor runs by introducing setpoint changes during the bioprocess. We report the first successful application of iDoE to mammalian cell culture, performing sequential setpoint changes in the growth phase for the selected input parameters temperature and dissolved oxygen. The process performance data were analyzed using ordinary least squares regression. Our results indicate iDoE to be applicable to mammalian bioprocesses and to be a cost‐efficient option to inform modeling early on during process development. Even though only half the number of bioreactor runs were used in comparison to a classical DoE approach, the resulting models revealed comparable input‐output relations. Being able to examine several setpoint levels within one bioreactor run, we confirm iDoE to be a promising tool to speed up biopharmaceutical process development.