Automated Microbioreactors and the Characterization of Media Dependent Changes in Antibody Product Glycosylation and Aglycosylation

David N. Powers, Sai Rashmika Velugula-Yellela, Nicholas Trunfio, Phillip Angart, Anneliese M. Faustino, C. Agarabi
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引用次数: 1

Abstract

The glycosylation state of therapeutic antibodies is a critical quality attribute due to its effect on product efficacy and safety. With the advent of the biosimilar development and the need to match the glycan profile of the originator product, better understanding of how the variables of the bioprocessing procedure control glycosylation is critical. To this effect, we used automated microbioreactors with our in-house model CHO DG 44 cell line and different media types to study differences in antibody outcomes, specifically focusing on N-glycosylation profiles and aglycosylation rates. We observed that different media types resulted in vastly different amounts of high mannose and terminal galactosylation of N-glycans. By measuring the percentage of antibodies that was not N-glycosylated we observed that high mannose outcomes were not correlated to aglycosylation rates. For further analysis, we utilized multivariate data analysis to determine the process variables that best explained our glycan profile findings. Factors linked to glutamine consumption were determined to be the most important in predicting high mannose outcomes, while factors related to the temporal aspects of cell growth rate were linked to terminal galactosylation. Our work discovered in-process parameters in the cell culture process that have significant effects on the glycan profile of an antibody product, further elucidating the link between the biomanufacturing process and product quality outcomes.
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自动微生物反应器和抗体产物糖基化和糖基化介质依赖性变化的表征
治疗性抗体的糖基化状态是一个关键的质量属性,因为它影响到产品的有效性和安全性。随着生物仿制药的发展和对初始产品糖基谱匹配的需求,更好地了解生物加工过程的变量如何控制糖基化是至关重要的。为此,我们使用自动化微生物反应器和我们的内部模型CHO DG 44细胞系和不同的培养基类型来研究抗体结果的差异,特别关注n-糖基化谱和糖基化率。我们观察到,不同的培养基类型导致高甘露糖和n -聚糖末端半乳糖基化的数量差异很大。通过测量非n -糖基化抗体的百分比,我们观察到高甘露糖结局与糖基化率无关。为了进一步分析,我们利用多变量数据分析来确定最能解释多糖谱发现的过程变量。与谷氨酰胺消耗相关的因素被确定为预测高甘露糖结果最重要的因素,而与细胞生长速度的时间方面相关的因素与半乳糖基化有关。我们的工作发现细胞培养过程中的工艺参数对抗体产品的聚糖谱有显著影响,进一步阐明了生物制造过程与产品质量结果之间的联系。
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