Applications of Multivariate Data Analysis in Biotech Processing - - BioPharm International

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Applications of Multivariate Data Analysis in Biotech Processing


BioPharm International
Volume 20, Issue 10

  • Lipid profiling results were obtained via an HPLC-based method for cholesterol esters (CE), free fatty acids (FFA), free cholesterol (FC), and phosphatidylcholine (PC). The total cholesterol (TC) values were derived from the results of free cholesterol and cholesterol esters. The levels of total cholesterol for all the tested lots lay in the presepecified range required for raw material release.
  • Fatty acid analysis was performed via a GC method and quantitative results were obtained for non-essential, n-6 essential, and n-3 essential fatty acids. The results for [n-6]:[n-3] ratios and total fatty acids (TF) were subsequently calculated.
  • Various lipoproteins were identified by an SEC technique. The three distinct species in these fractions included high density lipids (HDL), low density lipids (LDL), and very low density lipids (VLDL). The ratios LDL:HDL and VLDL:HDL were derived from the original data.
  • The extent of lipid oxidation (LO) was also determined because it can affect the cell productivity.


Figure 14. Mean square of successive differences based multivariate exponentially weighted moving average chart during monitoring
A total of 16 analytes were tested for each lot using the above-mentioned assays.


Figure 15. Principal component analysis (t[1] versus t[2])
It is difficult to predict how the composition might be related to cell productivity because the individual effects of each of the tested analytes on the cell culture performance is poorly understood. Also, it is impractical to conduct experiments to evaluate the effects of various components because of their biological complexity and solubility issues. The use of multivariate analysis, however, offers a methodology by which the lot-to-lot variability of the above-mentioned complex raw material can be assessed. It also provides a method to integrate all the analytical data and to understand the key analytical differences between products from different vendors. Moreover, the number of available lots and analytical and experimental resources are often limited, which makes the use of multivariate analysis particularly useful. Finally, as will be discussed later, it can be used to identify the key raw material quality attributes required for desired productivity. These quality attributes can help focus efforts in the right direction to continually improve a raw material and hence the manufacturing process it is used in. For the multivariate analysis described in this case study, the software SIMCA-P+ (Version 11) from Umetrics, Inc. (Kinnelon, NJ) was used.


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