中国仓鼠卵巢细胞
生物过程
克隆(Java方法)
选择(遗传算法)
生物
计算生物学
克隆选择
细胞培养
生物系统
计算机科学
加权
单克隆抗体
排名(信息检索)
直线(几何图形)
效价
数据挖掘
多元统计
适应性
细胞生物学
滤波器(信号处理)
转染
下游(制造业)
遗传学
化学
作者
Jimmy Gaudreault,Petko Komsalov,Jason Kuipers,Lucas Lemire,Brian Cass,Linda Lamoureux,Julien Leroy,Christopher R. Corbeil,Traian Sulea,Robert Voyer,Simon Joubert,Yves Durocher,Olivier Henry,Phuong Lan Pham
摘要
ABSTRACT The development of robust Chinese Hamster Ovary (CHO) cell lines expressing high titers of monoclonal antibodies (MAbs) is central to bioprocess development. Following transfection and pool generation, clone selection is critical, as individual clones often behave differently in stirred‐tank bioreactors. We propose a multivariate data analysis (MVDA) approach for clone selection that integrates productivity, growth, expression stability, and metabolism, with adaptable weighting based on process priorities. This method was applied to in‐house data from CHO clones producing omalizumab. From 24 candidates, eight stable, high‐performing clones were advanced for evaluation in 0.75–1 L bioreactors. MVDA revealed that including stability and metabolic parameters alters the ranking of lead clones compared with conventional screening. To assess scalability, cultures were run with or without air overlay to modulate dissolved CO 2 . Cultures without overlay reached up to 25% pCO 2 (190 mmHg) and unexpectedly showed improved performance: 1.69‐fold higher titer, 1.43‐fold greater cell‐specific productivity, 1.11‐fold higher peak cell density, extended viability, and sustained product accumulation over 17–21 days. By integrating statistical tools and a historical dataset, our MVDA method identified a robust lead clone performing consistently across CO 2 conditions, supporting its application in early upstream bioprocess development.
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