大规模哺乳动物细胞培养工程的现状与展望

doi:10.12211/2096-8280.2020-094

摘要/Abstract

摘要：

近年来，随着对疫苗和治疗性蛋白类药物等多种生物制品需求量增加以及产品质量要求的提高，细胞大规模培养技术也不断发展。为了增加产量、降低成本，生产更安全有效的药物，大规模细胞培养过程的开发至关重要，而动物细胞的工艺优化和规模放大具有挑战性。提高细胞培养工艺表达量、扩大细胞培养生产规模、保证表达抗体质量稳定成为目前大规模细胞培养过程中亟待解决的问题，迫切需要进一步研究和开发细胞培养工艺。本文围绕以上问题，系统综述了通过优良细胞株的构建、培养基设计与无血清培养基的开发、基于过程分析技术（PAT）培养工艺的优化与放大，建立合适的大规模培养体系，实现细胞的高密度培养和产物的高效表达。与此同时，细胞培养过程中产生的多源异质数据基本依靠低效的人工处理与判断，缺乏深层次的全局因素考虑。为此，未来希望通过人工智能深度挖掘数据之间的关系并指导细胞培养过程工艺优化与放大，实现真正的智能生物制造。

关键词: 动物细胞培养, 生长培养基, 微载体, 过程分析技术（PAT）, 过程放大, 智能化

Abstract:

With requirements for the improvement of the quantity and quality of vaccines, therapeutic protein drugs and many other biological products, large-scale cell culture technologies continue to be developed. In order to increase production and reduce cost to produce safer and more effective drugs, the development of large-scale cell culture processes is essential, while the process optimization and scale up of animal cell culture are challenging. Ongoing research and development of animal cell culture technology are urgently required to increase the expression level of target poducts, to expand the scale of cell culture and to ensure stable product quality. This article focuses on above issues and systematically summarizes the establishment of suitable large-scale culture to achieve high-density and high-efficiency production through the construction of excellent cell lines, the design of medium, particularly serum-free medium , as well as the optimization and scale-up of the culture process based on process analysis technology (PAT). Genomics and gene editing tools are frequently applied to construct an excellent cell line that supports high-density growth and secretes a large amount of therapeutic proteins. With the in-depth study of cell physiological and metabolic characteristics, serum-free culture can be designed based on multi-omics study, and through a variety of online sensing technologies to monitor and analyze the biological process, precise feedback control can thus be performed. On the other hand, the generation of a large amount of inconsistent data during the cell culture process is basically due to inefficient manual processing and judgment, and lacking of an in-depth consideration of global factors. Therefore, for the high-efficiency industrial biomanufacturing process, it is necessary to visualize a large number of process parameters, establish a database of process parameters for subsequent big data analysis, and conduct deep learning and data mining to perform real-time bioprocess intelligent analysis, diagnosis and precise control, and then realize intelligent manufacturing.

Key words: animal cell cultivation, growth medium, microcarrier, process analysis technology (PAT), process scale-up, intelligence

中图分类号:

Q813.1

朱紫瑜, 王冠, 庄英萍. 大规模哺乳动物细胞培养工程的现状与展望[J]. 合成生物学, 2021, 2(4): 612-634.

Ziyu ZHU, Guan WANG, Yingping ZHUANG. Present situation and prospect for large-scale mammalian cell culture engineering[J]. Synthetic Biology Journal, 2021, 2(4): 612-634.

图/表 4

图 1 动物细胞大规模培养技术发展史以及细胞培养技术发展的里程碑事件（从1907年体外组织培养法的建立，发展至今基于多组学数据和机器学习的智能生物制造，列出了动物大规模培养技术的发展过程以及细胞培养技术发展的突破性事件；EBRC—美国工程生物学会联盟）

Fig. 1 History of large-scale animal cell culture technology and milestone events in the development of biotechnology(From the establishment of in vitro tissue culture in 1907 to the development of intelligent biomanufacturing based on multi-omics data and machine learning, the chronological development of large-scale animal culture technology and the breakthrough events in the process are highlighted; EBRC—The Engineering Biology Research Consortium)

图 2 基于化学计量模型约束的培养基设计［以葡萄糖、20种氨基酸和10种维生素作为反应物，细胞量（包括蛋白质、脂质、DNA、RNA等混合物）、产物和ATP作为反应的产物列出化学计量方程］

Fig. 2 Media design guided by stoichiometric and the constraint-based model(Taking glucose, 20 amino acids and 10 vitamins as reactants, cell mass including the mixtures of protein, lipid, DNA, RNA, etc., products and ATP as reaction products to develop stoichiometric equations)

图3 动物细胞培养过程检测技术［69］（通过在线检测和离线检测动物细胞培养过程中的变量，并根据数据对系统进行反馈控制；DO—溶解氧）

Fig. 3 Detection technology for animal cell culture processes[69](Through online and offline detection of variables in the process of animal cell culture, and feedback control of the system based on the data; DO—dissolved oxygen)

图 4 基于大数据的工业生物过程智能制造系统（通过智能感知系统挖掘大数据，实现对生物培养过程的智能控制，同时利用文献数据、组学数据形成知识图谱并通过机器深度学习，进而指导智能感知系统，实现真正的智能生物制造）

Fig. 4 Intelligent manufacturing systems developed based on big data for industrial processes(Mining big data through the intelligent perception system to realize the intelligent control of the biological training process. On the other hand, using literature and omics data to form a knowledge map for machine deep learning, the intelligent perception system can guide to realize the real intelligent biological manufacturing)

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