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疫苗制品下游生物加工技术的研究进展综述

A review on current downstream bio-processing technology of vaccine products.

机构信息

School of Chemistry & Biotechnology, Suzhou University of Science and Technology, Gao Xin District, Ke Rui Road, No. 1, Suzhou, China.

出版信息

Vaccine. 2013 Feb 18;31(9):1264-7. doi: 10.1016/j.vaccine.2012.12.056. Epub 2013 Jan 9.

Abstract

An effective downstream bio-processing of vaccine products requires complete chemical knowledge of the contaminants that may arise from a given vector expression system. Whether the vaccine is made from the traditional egg-based or the new cell-cultured process, it is the expression system that determines the types of impurities that need to be identified and removed from the vaccine product. There are mechanical and chemical factors that can either reduce the yield or render a vaccine product to be irreversibly inactive. The choice of equipment and solvents is therefore important in minimizing product loss, and for maintaining an efficient and optimized manufacturing process. The frequent out-of-specification, irreproducible data and inefficiency in the manufacturing of biologics were the basis for FDA to propose the "cGMP for the 21st Century" initiative in the year of 2000. Effective 2004, the concept of quality by design (QbD) has been imposed in the manufacturing of biologics. To facilitate the implementation of QbD FDA has encouraged the use of process analytical technology (PAT). Further, FDA believes that an optimized manufacturing scheme requires one to identify and to control the variables that can negatively affect the yield and quality of the desired product, and PAT can reveal wrongful data and alert the operator for immediate correction during processing.

摘要

疫苗产品的有效下游生物加工需要完全了解可能由特定载体表达系统产生的污染物的化学知识。无论疫苗是由传统的基于鸡蛋的方法还是新的细胞培养工艺制成,表达系统决定了需要识别和从疫苗产品中去除的杂质类型。机械和化学因素会降低产量或使疫苗产品不可逆地失活。因此,选择设备和溶剂对于最小化产品损失以及保持高效和优化的制造工艺非常重要。生物制品制造中经常出现不符合规格、不可重现的数据和效率低下,这是 FDA 在 2000 年提出“21 世纪 cGMP”倡议的基础。自 2004 年起,质量源于设计(QbD)的概念已被强制应用于生物制品的制造。为了促进 QbD 的实施,FDA 鼓励使用过程分析技术(PAT)。此外,FDA 认为,优化的制造方案需要识别和控制可能对所需产品的产量和质量产生负面影响的变量,而 PAT 可以揭示错误的数据,并在处理过程中提醒操作人员立即进行纠正。

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