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 17. Coefficient plot (partial least squares model of titer)
To assess the feasibility of using an alternate raw material for the process, the analytical data of four lots from a new vendor (called product C) was integrated in the PCA model developed earlier. The resulting PCA model resulted in two principal components and had a cumulative goodness of fit (R 2 X) and predictability (Q 2 ) values of 0.71 and 0.57, respectively. Remarkably, the new product did not appear similar to either product tested earlier and belonged to a distinct cluster in M-space (Figure 15). A closer analysis showed that the new product appeared closer to product A in fatty acid composition and closer to product B in phosphatidylcholine content and lipoprotein analysis. The loading plot (Figure 16) showed that product A was uniquely identified by phosphatidylcholine content, LDL, LDL:HDL ratio, VLDL, and VLDL:HDL ratio. Also, product B was uniquely identified by the content of n-6 fatty acids and the [n-6]:[n-3] ratio. Because product C appeared analytically different from the other two products, a cell culture use test was performed. Two lots of products A and B each and one lot of product C were tested in parallel. Results indicated that product C resulted in significantly lower titer compared to products A and B. The average titer with product C was approximately 30% lower than that obtained with products A and B. Because of lower titer obtained with product C, it was considered unacceptable for use.

Figure 18. Variable importance plots (partial least squares model of titer)
Although product C was deemed unacceptable for use in manufacturing, the experimental data provided an opportunity to establish the critical components required to attain desired productivity. To identify these components, a partial least squares (PLS) model was developed using the data in the experiment described earlier. The PLS model used 10 experimental observations (five lots described above, tested in duplicate) and analytical data of 16 analytes. The model resulted in three principal components that could explain 96% of the variation in X data (R 2 (X) = 0.96) and resulted in cumulative R 2 (Y) and cumulative Q 2 (Y) values of 0.96 and 0.89, respectively. The critical quality attributes were identified by reviewing the coefficient plot (Figure 17) and the variable importance plot (Figure 18). Note that the coefficients in the coefficient plot indicate the scaled and centered data with confidence intervals derived from jackknifing. The variable importance for the projection (VIP) values reflect the importance of terms in the model with respect to Y, i.e., its correlation to titer, and with respect to X (the projection). Terms with VIP values greater than 1 are the most relevant in explaining Y. For the PLS model described above, the terms with VIP values larger than 1 were n-3 fatty acids, LO, HDL, and FC. However, a review of the coefficient plot pointed to the large variation associated with HDL and FC data. As a result, only n-3 fatty acids and lipid oxidation (LO) were determined to be the critical quality attributes. The effects of n-3 fatty acids and lipid oxidation on process productivity are plausible as literature references of such effects on certain cell types are available.15–16

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