Prediction of Cell Culture Media Performance Using Fluorescence Spectroscopy
Author(s) -
Paul W. Ryan,
Boyan Li,
Michael T. Shanahan,
Kirk J. Leister,
Alan G. Ryder
Publication year - 2010
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/ac902337c
Subject(s) - partial least squares regression , process analytical technology , biochemical engineering , principal component analysis , chemistry , chinese hamster ovary cell , biological system , process engineering , product (mathematics) , computer science , bioprocess , artificial intelligence , machine learning , mathematics , engineering , chemical engineering , biochemistry , receptor , geometry , biology
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.
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