Identifying a robust design space for glycosylation during monoclonal antibody production. (1st July 2016)

• Record Type: Journal Article
• Authors: St. Amand, Melissa M.; Hayes, James; Radhakrishnan, Devesh; Fernandez, Janice; Meyer, Bill; Robinson, Anne S.; Ogunnaike, Babatunde A.
• Is Part Of: Biotechnology progress. Volume 32:Number 5(2016:Sep./Oct.)
• Page Start: 1149
• Page End: 1162
• Publication Date: 2016-07-01
• Subjects: glycosylation -- design space -- QbD -- bioreactor -- CHO -- DoE -- enzyme expression -- sugar nucleotide; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2316
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract : Glycan distribution has been identified as a "critical quality attribute" for many biopharmaceutical products, including monoclonal antibodies. Consequently, determining quantitatively how process variables affect glycan distribution is important during process development to control antibody glycosylation. In this work, we assess the effect of six bioreactor process variables on the glycan distribution of an IgG1 produced in CHO cells. Our analysis established that glucose and glutamine media concentration, temperature, pH, agitation rate, and dissolved oxygen (DO) had small but significant effects on the relative percentage of various glycans. In addition, we assessed glycosylation enzyme transcript levels and intracellular sugar nucleotide concentrations within the CHO cells to provide a biological explanation for the observed effects on glycan distributions. From these results we identified a robust operating region, or design space, in which the IgG1 could be produced with a consistent glycan distribution. Since our results indicate that perturbations to bioreactor process variables will cause only small (even if significant) changes to the relative percentage of various glycans (<±1.5%)—changes that are too small to affect the bioactivity and efficacy of this IgG1 significantly—it follows that the glycan distribution obtained will be consistent even with relatively large variations in bioreactor process variables. However, for therapeutic proteins where bioactivity and efficacy are affected by small changes to the relative percentage of glycans, the same analysis would identify the manipulated variables capable of changing glycan distribution, and hence can be used to implement a glycosylation control strategy. © 2016 American Institute of Chemical Engineers Biotechnol. Prog ., 32:1149–1162, 2016