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


Figure 8. Bivariate plot showing an outlier
In this multivariate statistical process control (SPC) application, MVDA was performed on the data from a legacy protein purification process. Custom software was developed at Wyeth Biotech in the form of an Excel add-in to create two kinds of multivariate control charts, Hotelling's T 2 and multivariate exponentially weighted moving average (MEWMA).8–10 Hotelling's T 2 monitors individual process observations while MEWMA monitors shifts and drifts in the process. Process data residing in Excel worksheets can be used directly with the software. The software interfaces Excel with custom-developed functions and the run-time version of MATLAB (The MathWoks, Inc., Natick, MA) through a component object model (COM) object.11 Excel serves as the user interface while the MATLAB functions perform the multivariate calculations. The Excel add-in was developed using Visual Basic for Applications (VBA) and the COM object was developed using Visual C++.


Figure 9. Hotellings T 2 chart during training
Eight parameters of the protein purification process were examined initially. Those parameters were harvest volume, harvest amount, CSulf RP-HPLC, B-sepharose recovery, overall recovery, specific activity, peptide map sub-unit percent, and DS rapid acidic C4 RP-HPLC. We used 100 process vectors in the training set and then monitored the next one hundred vectors. Hotelling's T 2 produced one signal for the monitoring observation 71. The bivariate plot between harvest amount and CSulf RP-HPLC in Figure 8 shows that observation 71 undoubtedly is an outlier. Harvest amount and CSulf RP-HPLC measure the amount of protein between two successive process steps and exhibit a very high correlation. Because of the high correlation, we repeated the multivariate statistical process control calculations after excluding the parameter "harvest amount" but using the remaining seven parameters. Interestingly, this too flagged observation 71 as an outlier. During the seven-parameter training phase, the two observations above the training T 2 limit in Figure 9 were discarded and the remaining 98 observations were used to estimate the covariance matrix. It is surprising to note that the theoretical MEWMA limit for a typical ARL of 370 (α = 0.0027) with γ = 0.1 was found to be too tight in Figure 10. In Figures 11 and 12, both the first 100 training observations and the next 100 monitoring observations are plotted. As noted above, T 2 for the monitoring observation 71 exceeds the control limit in Figure 11. Despite the theoretical MEWMA limit being too tight, Figure 12 indicates that the process has drifted from the process mean calculated with the training data.


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