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


Applications of Multivariate Data Analysis in Biotech Processing

BioPharm International
Volume 20, Issue 10

Figure 10. Multivariate exponentially weighted moving average chart during training
The mean square of successive differences (MSSD) was used to remove the influence of process drifts in the covariance estimates.12–14 Hotelling's T 2 chart in Figure 13 based on MSSD suggests that a process shift occurred during the second half of the training data and persisted throughout. MSSD appears to be too sensitive for T 2 but we do not know if this will be true for a large training data set or for other biopharmaceutical processes. At present, a control limit for MEWMA calculated with MSSD can be set only arbitrarily, as done in Figure 14.

Figure 11. Hotelling's T 2 chart during monitoring
We have shown that multivariate statistical process control (SPC) tools are useful in ongoing monitoring of manufacturing processes. These tools can provide early warning of process problems before they become severe. In this case study, Hotelling's T 2 identified an unusual production batch (observation 71) during monitoring that would have otherwise gone unnoticed. This scenario is possible for multiple consecutive batches also. MEWMA revealed small process drifts that were previously hidden. Understanding the origins of these drifts will provide opportunities to improve the process further.


Sanjeev Ahuja and Kripa Ram, MedImmune

Figure 12. Multivariate exponentially weighted moving average chart during monitoring
Many industrial mammalian-cell-growth media rely upon the inclusion of a serum fraction, commonly known as the lipoprotein fraction, to ensure the availability of cholesterol to the cells. The cholesterol contained in such serum fractions is associated with various lipoproteins that act as carriers of cholesterol. The lipoproteins also provide a means by which the cholesterol can be solubilized in a hydrophilic environment. Since these fractions are derived from serum, it is likely that the compositions of these fractions are highly variable, which, in turn, can contribute to the variation in process outcome and product quality attributes. To understand this raw material further and how it might affect process productivity, detailed analytical characterization and cell culture experiments were carried out. This case study shows how multivariate analysis can be used to understand various lipoprotein fraction products and to assist in raw material selection and control for a cell culture process.

Figure 13. Mean square of successive differences based Hotelling's T 2 chart during monitoring
Analytical characterization of various lipoprotein fraction lots involved four distinct assays: lipid profiling, fatty acid analysis, lipoprotein analysis, and lipid oxidation. A brief description of these tests is given below.

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