Controllability analysis of protein glycosylation in CHO cells.
To function as intended in vivo, a majority of biopharmaceuticals require specific glycan distributions. However, achieving a precise glycan distribution during manufacturing can be challenging because glycosylation is a non-template driven cellular process, with the potential for significant uncontrolled variability in glycan distributions. As important as the glycan distribution is to the end-use performance of biopharmaceuticals, to date, no strategy exists for controlling glycosylation on-line. However, before expending the significant amount of effort and expense required to develop and implement on-line control strategies to address the problem of glycosylation heterogeneity, it is imperative to assess first the extent to which the very complex process of glycosylation is controllable, thereby establishing what is theoretically achievable prior to any experimental attempts. In this work, we present a novel methodology for assessing the output controllability of glycosylation, a prototypical example of an extremely high-dimensional and very non-linear system. We first discuss a method for obtaining the process gain matrix for glycosylation that involves performing model simulations and data analysis systematically and judiciously according to a statistical design of experiments (DOE) scheme and then employing Analysis of Variance (ANOVA) to determine the elements of process gain matrix from the resulting simulation data. We then discuss how to use the resulting high-dimensional gain matrix to assess controllability. The utility of this method is demonstrated with a practical example where we assess the controllability of various classes of glycans and of specific glycoforms that are typically found in recombinant biologics produced with Chinese Hamster Ovary (CHO) cells. In addition to providing useful insight into the extent to which on-line glycosylation control is achievable in actual manufacturing processes, the results also have important implications for genetically engineering cell lines design for enhanced glycosylation controllability.
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, United States of America.
Melissa M
MM
St Amand
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, United States of America.
Kevin
K
Tran
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, United States of America.
Devesh
D
Radhakrishnan
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, United States of America ; Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana, United States of America.
Anne S
AS
Robinson
Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, United States of America.
Babatunde A
BA
Ogunnaike
D000818:Animals; D016466:CHO Cells; D003198:Computer Simulation; D006224:Cricetinae; D003412:Cricetulus; D006023:Glycoproteins / Q000737:chemistry*; D006031:Glycosylation; D008962:Models, Theoretical; D011134:Polysaccharides / Q000032:analysis* / Q000145:classification*
D006023:Glycoproteins; D011134:Polysaccharides