ChELSI Institute, Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK.
Biotechnol Bioeng. 2013 Nov;110(11):2970-83. doi: 10.1002/bit.24959. Epub 2013 Jun 6.
Here we demonstrate that it is possible to predict and control N-glycan processing of a secreted recombinant monoclonal antibody during manufacturing process development using a combination of statistical modelling and comparative measurement of cell surface glycans using fluorescent lectins. Using design of experiments--response surface modelling (DoE-RSM) methodology to adjust the relative media concentrations of known metabolic effectors of galactosylation (manganese, galactose, and uridine) we have shown that β1,4-galactosylation of the same recombinant IgG4 monoclonal antibody produced by different CHO cell lines can be precisely controlled in a cell line specific manner. For two cell lines, monoclonal antibody galactosylation could be increased by over 100% compared to control, non-supplemented cultures without a reduction in product titre and with minimal effect on cell growth. Analysis of galactosylation effector interactions by DoE-RSM indicated that Mn²⁺ alone was necessary but not sufficient to improve galactosylation, and that synergistic combinations of Gal and Urd were necessary to maximize galactosylation, whilst minimizing the deleterious effect of Urd on cell growth. To facilitate rapid cell culture process development we also tested the hypothesis that substrate-level control of cellular galactosylation would similarly affect both cell surface and secreted monoclonal antibody glycans, enabling facile indirect prediction of product glycan processing. To support this hypothesis, comparative quantitation of CHO cell surface β1,4-galactosylation by flow cytometry using fluorescent derivatives of RCA and ConA lectins revealed that substrate-controlled variation in monoclonal antibody galactosylation and cell surface galactosylation were significantly correlated. Taken together, these data show that precision control of a complex, dynamic cellular process essential for the definition of protein product molecular heterogeneity and bioactivity is possible. Moreover, real-time, or near real-time control can be enabled by facile, rapid measurement of cell surface biomarkers of cellular biosynthetic capability.
在这里,我们展示了一种使用统计建模和使用荧光凝集素比较测量细胞表面糖基化的组合,在制造过程开发过程中预测和控制分泌型重组单克隆抗体的 N-糖基化处理是可行的。使用实验设计-响应面建模 (DoE-RSM) 方法来调整已知半乳糖基化代谢效应物(锰、半乳糖和尿苷)的相对培养基浓度,我们已经表明,可以以细胞系特异性的方式精确控制不同 CHO 细胞系生产的相同重组 IgG4 单克隆抗体的β1,4-半乳糖基化。对于两种细胞系,与未添加补充剂的对照培养物相比,单克隆抗体的半乳糖基化可以增加 100%以上,而产物滴度没有降低,并且对细胞生长的影响最小。通过 DoE-RSM 对半乳糖基化效应物相互作用的分析表明,Mn²⁺单独是必要的,但不足以提高半乳糖基化水平,并且 Gal 和 Urd 的协同组合是最大化半乳糖基化、最小化 Urd 对细胞生长有害影响的必要条件。为了促进快速细胞培养过程开发,我们还测试了这样一种假设,即细胞半乳糖基化的底物水平控制也会同样影响细胞表面和分泌的单克隆抗体糖基化,从而能够方便地间接预测产物糖基化处理。为了支持这一假设,使用 RCA 和 ConA 凝集素的荧光衍生物通过流式细胞术比较定量测定 CHO 细胞表面β1,4-半乳糖基化,发现单克隆抗体半乳糖基化和细胞表面半乳糖基化的底物控制变化显著相关。总之,这些数据表明,对蛋白质产物分子异质性和生物活性定义至关重要的复杂动态细胞过程进行精确控制是可能的。此外,通过简单、快速地测量细胞生物合成能力的细胞表面生物标志物,可以实现实时或接近实时的控制。