Fluorescence excitation-emission matrix (EEM) spectroscopy for rapid identification and quality evaluation of cell culture media components.

The application of fluorescence excitation-emission matrix (EEM) spectroscopy to the quantitative analysis of complex, aqueous solutions of cell culture media components was investigated. These components, yeastolate, phytone, recombinant human insulin, eRDF basal medium, and four different chemically defined (CD) media, are used for the formulation of basal and feed media employed in the production of recombinant proteins using a Chinese Hamster Ovary (CHO) cell based process. The comprehensive analysis (either identification or quality assessment) of these materials using chromatographic methods is time consuming and expensive and is not suitable for high-throughput quality control. The use of EEM in conjunction with multiway chemometric methods provided a rapid, nondestructive analytical method suitable for the screening of large numbers of samples. Here we used multiway robust principal component analysis (MROBPCA) in conjunction with n-way partial least squares discriminant analysis (NPLS-DA) to develop a robust routine for both the identification and quality evaluation of these important cell culture materials. These methods are applicable to a wide range of complex mixtures because they do not rely on any predetermined compositional or property information, thus making them potentially very useful for sample handling, tracking, and quality assessment in biopharmaceutical industries.

Rapid characterization and quality control of complex cell culture media solutions using raman spectroscopy and chemometrics.

The use of Raman spectroscopy coupled with chemometrics for the rapid identification, characterization, and quality assessment of complex cell culture media components used for industrial mammalian cell culture was investigated. Raman spectroscopy offers significant advantages for the analysis of complex, aqueous-based materials used in biotechnology because there is no need for sample preparation and water is a weak Raman scatterer. We demonstrate the efficacy of the method for the routine analysis of dilute aqueous solution of five different chemically defined (CD) commercial media components used in a Chinese Hamster Ovary (CHO) cell manufacturing process for recombinant proteins.The chemometric processing of the Raman spectral data is the key factor in developing robust methods. Here, we discuss the optimum methods for eliminating baseline drift, background fluctuations, and other instrumentation artifacts to generate reproducible spectral data. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were then employed in the development of a robust routine for both identification and quality evaluation of the five different media components. These methods have the potential to be extremely useful in an industrial context for "in-house" sample handling, tracking, and quality control.

Prediction of cell culture media performance using fluorescence spectroscopy.

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.