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Shake tube perfusion cell cultures are suitable tools for the prediction of limiting substrate, CSPR , bleeding strategy, growth and productivity behavior
Author(s) -
Mayrhofer Patrick,
Castan Andreas,
Kunert Renate
Publication year - 2021
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.6848
Subject(s) - bioreactor , perfusion , bioprocess , process (computing) , computer science , scale (ratio) , process engineering , biomedical engineering , chemistry , chemical engineering , engineering , medicine , physics , radiology , operating system , organic chemistry , quantum mechanics
BACKGROUND Scale‐down models (SDM) are crucial for efficient setting of different experimental conditions in cell culture media‐ or clone‐screening applications and to predict the expected performance in larger bioreactor cultivations. However, the availability of such models for continuous bioprocessing are scarce and hampered by the requirement for proper cell retention in perfusion applications. RESULTS In this study, perfusion scale‐down models were optimized for increased cell performance in shake tubes by a semi‐continuous operation mode without any instrumental monitoring. A 40 L perfusion bioreactor was simulated with 10 mL shake tubes by daily medium exchange with or without cell bleeding for process fine‐tuning. Specifically, the optimal cell‐specific perfusion rate (CSPR) was efficiently identified using the perfusion SDM. The cellular growth and productivity were nearly identical to those of the large‐scale experiments. The 24‐h interval of metabolite analysis identified the limits of the SDMs and underscores the need for perfusion processes in large‐scale animal cell bioprocesses. CONCLUSION Optimized perfusion scale‐down models in semi‐continuous operation mode are highly suitable tools to define important process parameters of larger perfusion bioreactor cultures and are predictive of cellular growth and production behavior.