Mead, Emma J, Chiverton, Lesley M., Spurgeon, Sarah K., Martin, Elaine B., Montague, Gary A., Smales, Christopher Mark, von der Haar, Tobias (2012) Experimental and In Silico Modelling Analyses of the Gene Expression Pathway for Recombinant Antibody and By-Product Production in NS0 Cell Lines. PLoS ONE, 7 (10). e47422. ISSN 1932-6203. (doi:10.1371/journal.pone.0047422) (KAR id:34165)
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Official URL: http://www.plosone.org/article/info%3Adoi%2F10.137... |
Abstract
Monoclonal antibodies are commercially important, high value biotherapeutic drugs used in the treatment of a variety of diseases. These complex molecules consist of two heavy chain and two light chain polypeptides covalently linked by disulphide bonds. They are usually expressed as recombinant proteins from cultured mammalian cells, which are capable of correctly modifying, folding and assembling the polypeptide chains into the native quaternary structure. Such recombinant cell lines often vary in the amounts of product produced and in the heterogeneity of the secreted products. The biological mechanisms of this variation are not fully defined. Here we have utilised experimental and modelling strategies to characterise and define the biology underpinning product heterogeneity in cell lines exhibiting varying antibody expression levels, and then experimentally validated these models. In undertaking these studies we applied and validated biochemical (rate-constant based) and engineering (nonlinear) models of antibody expression to experimental data from four NS0 cell lines with different IgG4 secretion rates. The models predict that export of the full antibody and its fragments are intrinsically linked, and cannot therefore be manipulated individually at the level of the secretory machinery. Instead, the models highlight strategies for the manipulation at the precursor species level to increase recombinant protein yields in both high and low producing cell lines. The models also highlight cell line specific limitations in the antibody expression pathway.
Item Type: | Article |
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DOI/Identification number: | 10.1371/journal.pone.0047422 |
Subjects: |
Q Science Q Science > QR Microbiology |
Divisions: |
Divisions > Division of Natural Sciences > Biosciences Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Mark Smales |
Date Deposited: | 06 Jun 2013 11:00 UTC |
Last Modified: | 16 Nov 2021 10:11 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/34165 (The current URI for this page, for reference purposes) |
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