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Modeling of cell culture damage and recovery leads to increased antibody and biomass productivity in CHO cell cultures
Author(s) -
Naderi Saeideh,
Nikdel Ali,
Meshram Mukesh,
McConkey Brendan,
Ingalls Brian,
Budman Hector,
Scharer Jeno
Publication year - 2014
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201300287
Subject(s) - chinese hamster ovary cell , intracellular , cell culture , monoclonal antibody , microbiology and biotechnology , extracellular , cell growth , productivity , cell , yield (engineering) , biology , chemistry , antibody , biochemistry , immunology , genetics , materials science , macroeconomics , economics , metallurgy
The development of an efficient and productive cell‐culture process requires a deep understanding of intracellular mechanisms and extracellular conditions for optimal product synthesis. Mathematical modeling provides an effective strategy to predict, control, and optimize cell performance under a range of culture conditions. In this study, a mathematical model is proposed for the investigation of cell damage of a Chinese hamster ovary cell culture secreting recombinant anti‐RhD monoclonal antibody (mAb). Irreversible cell damage was found to be correlated with a reduction in pH. This irreversible damage to cellular function is described mathematically by a Tessier‐based model, in which the actively growing fraction of cells is dependent on an intracellular metabolic product acting as a growth inhibitor. To further verify the model, an offline model‐based optimization of mAb production in the cell culture was carried out, with the goal of minimizing cell damage and thereby enhancing productivity through intermittent refreshment of the culture medium. An experimental implementation of this model‐based strategy resulted in a doubling of the yield as compared to the batch operation and the resulting biomass and productivity profiles agreed with the model predictions.

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