This article describes the optimization of cultivation factor settings, that is the shaking rate and working volume in 50 mL spin tubes for a Chinese hamster ovary cell line expressing recombinant human alpha-erythropoietin, using a response D-optimal surface method. The main objectives of the research were, firstly, to determine a setting in which the product titer and product quality attributes in spin tubes are equivalent to those in 250 mL shake flasks in a seven day batch and, secondly, to find a setting in which the product titer is maximal. The model for product titer prediction as a function of shaking rate and working volume in the defined design space was successfully applied to the optimization of cultivation conditions in spin tubes for the tested cell line. Subsequently, validation experiments were carried out simultaneously in spin tubes, shake flasks and bench scale bioreactors to compare cell culture performance parameters such as growth, productivity and product quality attributes in the form of isoform profiles and glycan antennarity structures. The results of the experiments showed that similar cell culture performance and product quality could be achieved in spin tubes when compared to shake flasks. Additionally, bioreactor titers could be reproduced in spin tubes at high shaking rates and low working volumes, but with differing product quality. Cultivation at lower shaking rates in spin tubes and shake flasks produced a glycoprotein with a product quality slightly comparable to that from bioreactors, but with titers being only two thirds.
Extracellular vesicles (EVs) are membrane vesicles that are produced by cells to be released into their microenvironment. In this study, we present the EV concentration as a new factor for optimization of industrial bioprocess control. The release of EVs depends on many cell properties, including cell activation and stress status, and cell death. Therefore, the EV concentration might provide a readout for identification of the cell state and the conditions during a bioprocess. Our data show that the EV concentration increased during the bioprocess, which indicated deteriorating conditions in the bioreactor. This increase in EV concentration in the fermentation broth was the consequence of two different processes: cell activation, and cell death. However, the release of EVs from activated living cells had a much weaker impact on EV concentration in the bioreactor than those released during cell death. EVs and cells in the bioprocess environment were quantified by flow cytometry. The most accurate data were obtained directly from unprocessed samples, making the monitoring of the EV concentration a rapid, easy, and cheap method. These EV concentrations reflect the conditions in the bioreactor and provide new information regarding the state of the bioprocess. Therefore, we suggest EV concentration as a new and important parameter for the monitoring of industrial bioprocesses.
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