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 5. Performance of campaign 4 shows sustained high culture performance with low variability; Relative standard deviation = 9.8%.
Campaign 4 reliably achieved its production goals and produced 110% of the campaign goal with a productivity improvement of 10% over campaign 3, sustained product quality, and a 15% decrease in process variability. This is shown in Figure 5.

Figure 6A. Partial least squares loadings plots for a 2-L bioreactor3
As shown in this application, MVDA can be a useful tool for continuous process improvement and long-term process understanding. Furthermore, it can be used for process optimization to reduce process variability and achieve predictable performance.


Alime Ozlem Kirdar and Anurag Rathore, Amgen Inc.

This application involved multivariate analysis of data from small-scale (2-L) and large- scale (2000-L) cell culture batches.3 A commercially available MVDA software package, SIMCA P+ 11 version (Umetrics AB, Kinnelon, NJ), was used to perform the multivariate analysis. Daily offline metabolic and cell growth measurements from 14 center point runs (2-L scale) and 11 center point runs (2000-L scale batches) were analyzed separately by partial least squares (PLS) modeling. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (percent purity, viable cell density, percent viability, osmolality) were included in the analysis.3 Loadings plot and variable importance for the projection plots were used to evaluate process comparability across scales.

Figure 6B. Partial least squares loadings plots for a 2,000-L bioreactor3
The loadings plot shows the PLS loadings computed for each of the x variables. The variables with the largest absolute values of principal components (p1 or p2) are situated far away from the origin (on the positive or negative side) on the plot and dominate the projection. The farther we are from the center (0,0) in the loadings plot, the greater the impact of input parameters on the performance of the cell culture or the greater the impact of the cell culture process on the output parameter. Also, variables near each other (in the same quadrant) are positively correlated and those opposite to each other (opposite quadrants) are negatively correlated.3

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