Abstract
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.
Highlights
With a US market exceeding $99 billion in 2011 and an expected steady increase in future sales [1], biopharmaceuticals represent the largest growing class of therapeutics
To obtain the glycosylation process gain matrix, we propose that model simulations be performed according to a systematic statistical design of experiments (DOE) scheme where the combination of input perturbations are implemented according to an appropriately chosen experimental design, and the resulting steady state responses analyzed using standard analysis of variance (ANOVA)
Controlling Glycan Classes Visual inspection of the process gain matrix can provide some intuition about the potential controllability of glycosylation at the micro-scale. This is because only those process output variables connected via significant, non-zero elements of the gain matrix to process input variables can be affected by manipulating the input variables, and are potentially controllable
Summary
Background and Motivation With a US market exceeding $99 billion in 2011 and an expected steady increase in future sales [1], biopharmaceuticals represent the largest growing class of therapeutics. Many biopharmaceuticals, such as the therapeutic monoclonal antibodies Herceptin and Avastin, are produced as recombinant proteins from Chinese hamster ovary (CHO) cells that are cultivated in bioreactors [2]. Glycan formation and attachment to the protein are subject to variability and both are often non-uniform Regulatory agencies, such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA), are encouraging biopharmaceutical manufacturers to control glycosylation on-line during production [8]. On-line glycosylation control has yet to be implemented in the biopharmaceutical industry for a variety of reasons, mostly attributable to the complexity of these bioprocesses, the non-availability of on-line measurements, and the lack of comprehensive control paradigms tailor-made for such processes
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