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Using Molecular Markers to Characterize Productivity in Chinese Hamster Ovary Cell Lines

机译:使用分子标记表征中国仓鼠卵巢细胞系的生产力

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摘要

Selection of high producing cell lines to produce maximum product concentration is a challenging and time consuming task for the biopharmaceutical industry. The identification of early markers to predict high productivity will significantly reduce the time required for new cell line development. This study identifies candidate determinants of high productivity by profiling the molecular and morphological characteristics of a panel of six Chinese Hamster Ovary (CHO) stable cell lines with varying recombinant monoclonal antibody productivity levels ranging between 2 and 50 pg/cell/day. We examined the correlation between molecular parameters and specific productivity (qp) throughout the growth phase of batch cultures. Results were statistically analyzed using Pearson correlation coefficient. Our study revealed that, overall, heavy chain (HC) mRNA had the strongest association with qp followed by light chain (LC) mRNA, HC intracellular polypeptides, and intracellular antibodies. A significant correlation was also obtained between qp and the following molecular markers: growth rate, biomass, endoplasmic reticulum, and LC polypeptides. However, in these cases, the correlation was not observed at all-time points throughout the growth phase. The repeated sampling throughout culture duration had enabled more accurate predictions of productivity in comparison to performing a single-point measurement. Since the correlation varied from day to day during batch cultivation, single-point measurement was of limited use in making a reliable prediction.
机译:对于生物制药行业而言,选择高产细胞系以产生最大的产品浓度是一项艰巨而费时的任务。识别早期标记以预测高生产力将大大减少新细胞系开发所需的时间。这项研究通过分析6种中国仓鼠卵巢(CHO)稳定细胞系的分子和形态学特征,确定了高生产率的候选决定因素,这些细胞系的重组单克隆抗体生产率水平在2至50 pg /细胞/天之间变化。我们检查了分批培养物整个生长阶段的分子参数与比生产率(qp)之间的相关性。使用Pearson相关系数对结果进行统计分析。我们的研究表明,总体而言,重链(HC)mRNA与qp的关联最强,其次是轻链(LC)mRNA,HC细胞内多肽和细胞内抗体。在qp和以下分子标记之间也获得了显着的相关性:生长速率,生物量,内质网和LC多肽。但是,在这些情况下,在整个生长阶段的所有时间点均未观察到相关性。与执行单点测量相比,在整个培养过程中进行重复采样可以更准确地预测生产率。由于批处理中的相关性每天都在变化,因此单点测量在做出可靠预测中的用途有限。

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