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利用荧光光谱法预测细胞培养介质的性能。

Prediction of cell culture media performance using fluorescence spectroscopy.

机构信息

Nanoscale Biophotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.

出版信息

Anal Chem. 2010 Feb 15;82(4):1311-7. doi: 10.1021/ac902337c.

Abstract

Cell culture media used in industrial mammalian cell culture are complex aqueous solutions that are inherently difficult to analyze comprehensively. The analysis of media quality and variance is of utmost importance in efficient manufacturing. We are exploring the use of rapid "holistic" analytical methods that can be used for routine screening of cell culture media used in industrial biotechnology. The application of rapid fluorescence spectroscopic techniques to the routine analysis of cell culture media (Chinese hamster ovary cell-based manufacture) was investigated. We have developed robust methods which can be used to identify compositional changes and ultimately predict the efficacy of individual fed batch media in terms of downstream protein product yield with an accuracy of +/-0.13 g/L. This is achieved through the implementation of chemometric methods such as multiway robust principal component analysis (MROBPCA), and n-way partial least-squares-discriminant analysis and regression (NPLS-DA and NPLS). This ability to observe compositional changes and predict product yield before media use has enormous potential and should permit the effective elimination of one of the major process variables leading to more consistent product quality and improved yield. These robust and reliable methods have the potential to become an important part of upstream biopharmaceutical quality control and analysis.

摘要

用于工业哺乳动物细胞培养的细胞培养基是复杂的水溶液,很难全面分析。分析培养基的质量和变化对于高效生产至关重要。我们正在探索使用快速的“整体”分析方法,这些方法可用于常规筛选工业生物技术中使用的细胞培养基。研究了快速荧光光谱技术在细胞培养基(基于中国仓鼠卵巢细胞的生产)常规分析中的应用。我们已经开发了强大的方法,可以用于识别成分变化,并最终根据下游蛋白质产物的产率准确预测单个分批补料培养基的功效(+/ - 0.13 g/L)。这是通过实施化学计量方法(如多向稳健主成分分析(MROBPCA)和 n 向偏最小二乘判别分析和回归(NPLS-DA 和 NPLS))来实现的。在使用培养基之前观察成分变化和预测产物产率的这种能力具有巨大的潜力,应该允许有效消除导致更一致的产品质量和提高产量的主要过程变量之一。这些强大且可靠的方法有可能成为上游生物制药质量控制和分析的重要组成部分。

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