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 7A. Variable importance for the projection (VIP) plots for a 2-L bioreactor. Adapted from reference 3.
Figure 6A presents this plot for data from the 2-L scale. It is seen that the cell culture process has a significant impact on viable cell density (VCD), titer, and viability. Titer and VCD are in the same quadrant and this is supported by the observation that both of them are lower in the earlier stages of the culture and increase as the culture progresses. In contrast, viability and VCD are in the opposite quadrants and this implies that at earlier stages of the culture, when the VCD is lower, the viability is higher and this reverses in the latter part of the process. Of all the input parameters examined, pH, pCO2, glucose, and lactate levels have a significant effect on the performance of the cell culture process. Also, pH, glucose, and pCO2 levels have a similar effect on the performance of the cell culture process, whereas lactate has the reverse effect. Figure 6B presents the loadings plot for the data from the 2000-L scale. For most of the output parameters—namely titer, VCD, and purity—the plot in Figure 6B is quite comparable with that in Figure 6A. However, differences are seen for the loadings of pO2, osmolality, NH4+, and lactate levels, suggesting changes in the cell-culture metabolism upon scale up. It is well known in the literature that gas transfer is less efficient at large scale leading to a build up of CO2 in the vessel. This results in an increased use of base in order to maintain pH at the intended set point, and the increased base addition leads to higher osmolality. Metabolic response of cells can also be amplified, which may lead to higher NH4+ and lactate levels.6–7 These observations led to further investigation of the differences observed during scale up and correction of some of those differences.

Figure 7B. Variable importance for the projection (VIP) plots for a 2,000-L bioreactor. Adapted from reference 3.
Variable importance for the projection (VIP) plot shows the relative importance of each variable included in the analysis. Figure 7A presents this plot for the 2-L scale data set. Consistent with the observations made from the corresponding loadings plot (Figure 6A), it is seen that this cell-culture process has a strong influence on the titer, VCD, and viability of the broth at the end of the process. Of the input parameters, pH, lactate, and glucose levels were found to have the greatest effect on process performance. On the contrary, osmolality has one of the lowest loadings in both principal component directions (p1 and p2). Figure 7B presents the VIP plot for the the 2000-L scale data set. Comparison of the VIP plot for the 2-L and 2,000-L data yields similar conclusions as mentioned above for discussion of the loadings plots. The most significant difference is seen in the VIP score for pO2 for the reasons mentioned above.

In summary, it is shown that MVDA can be a useful tool for evaluating process comparability across scales, equipment, or facilities. Although the loadings plot provides a qualitative assessment, the VIP plot is more quantitative. Data analysis can easily be used for troubleshooting issues encountered during scale up or technology transfer by identifying the differences and helping focus the investigation.


Alagappan Annamalai, Wyeth Biotech

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