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Model-directed engineering of “difficult-to-express” monoclonal antibody production by Chinese hamster ovary cells

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ABSTRACT

Despite improvements in volumetric titer for monoclonal antibody (MAb) production processes using Chinese hamster ovary (CHO) cells, some “difficult-to-express” (DTE) MAbs inexplicably reach much lower process titers. These DTE MAbs require intensive cell line and process development activity, rendering them more costly or even unsuitable to manufacture. To rapidly and rationally identify an optimal strategy to improve production of DTE MAbs, we have developed an engineering design platform combining high-yielding transient production, empirical modeling of MAb synthesis incorporating an unfolded protein response (UPR) regulatory loop with directed expression and cell engineering approaches. Utilizing a panel of eight IgG1λ MAbs varying >4-fold in volumetric titer, we showed that MAb-specific limitations on folding and assembly rate functioned to induce a proportionate UPR in host CHO cells with a corresponding reduction in cell growth rate. Derived from comparative empirical modeling of cellular constraints on the production of each MAb we employed two strategies to increase production of DTE MAbs designed to avoid UPR induction through an improvement in the rate/cellular capacity for MAb folding and assembly reactions. Firstly, we altered the transfected LC:HC gene ratio and secondly, we co-expressed a variety of molecular chaperones, foldases or UPR transactivators (BiP, CypB, PDI, and active forms of ATF6 and XBP1) with recombinant MAbs. DTE MAb production was significantly improved by both strategies, although the mode of action was dependent upon the approach employed. Increased LC:HC ratio or CypB co-expression improved cell growth with no effect on qP. In contrast, BiP, ATF6c and XBP1s co-expression increased qP and reduced cell growth. This study demonstrates that expression-engineering strategies to improve production of DTE proteins in mammalian cells should be product specific, and based on rapid predictive tools to assess the relative impact of different engineering interventions. Biotechnol. Bioeng. 2014;111: 372–385. © 2013 Wiley Periodicals, Inc.

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