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

In summary, multivariate analysis for raw material selection and control serves multiple purposes. First, it categorizes an incoming product based on the analytical data alone, without the need to evaluate it using time-consuming cell culture studies. This classification is quite important because scale-down studies may not result in picking up the differences in raw materials even though they are different analytically. Performing the multivariate analysis on the analytical data provides another criterion for deciding if a raw material from a particular vendor or source is acceptable for use in manufacturing. Second, MVDA can be applied to the combined experimental and analytical data to identify the critical components required for desired outcome, e.g., productivity. After sufficient analytical and experimental data are gathered, multivariate analysis can be used as the sole criterion for assessing the raw material quality. It can also assist in directing the efforts to improve the quality of a suboptimal raw material (e.g., product C in the current study). Finally, the multivariate analysis also helps limit the scope of analytical testing for raw material control. For example, in the case study described here, only two assays may be needed for future products (or lots) instead of the four used to develop the model ealier.

SUMMARY

This article demonstrates the usefulness of the MVDA with respect to various activities involved in biopharmaceutical manufacturing, including scale up, process comparability, process optimization, process monitoring, and raw material testing. Currently, a lot of data collected at small and large scale do not undergo the rigorous data analysis presented here. We hope to convince the readers that MVDA allows us to extract useful process information through analysis of the readily available data, in order to maximize our understanding of the process. As the biotech industry implements Quality by Design, multivariate analysis will become a necessity.

REFERENCES:

1. Kourti T. Process analytical technology and multivariate statistical control. Process Analytical Technol. Part 1: 2004;1(1):13–19. Part 2: 2005;2(1):24–28. Part 3: 2006;3(3):18–24.

2. Martin EB, Morris AJ. Enhanced bio-manufacturing through advanced multivariate statistical technologies. J Biotechnol. 2002;99(3):223–235.

3. Kirdar AO, Conner JS, J. Baclaski J, Rathore AS. Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation. Biotechnol Prog. 2007;23(1):61–67.

4. Cunha CCF, Glassey J, Montague GA, Albert S, Mohan P. An assessment of seed quality and its influence on productivity estimation in an industrial antibiotic fermentation. Biotech Bioeng. 2002;78(6):658–669.

5. Ündey C, Ertunc S, Cinar A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Ind Eng Chem Res. 2003;42(20):4645-4658.

6. Mostafa SS, Gu X. Strategies for improved dCO2 removal in large-scale fed-batch cultures. Biotechnol Prog. 2003;19(1):45–51.

7. Zhu MM, Goyal A, Rank DL, Gupta SK, Boom TV, Lee SS. Effect of elevated pCO2 and osmolality on growth of CHO cells and production of antibody-fusion protein B1: a case study. Biotech Prog. 2005;21(1):70–77.

8. Hotelling H. Multivariate quality control, techniques of statistical analysis. Eisenhart C, Hastay HW, Wallis WA, editors. New York: McGraw-Hill; 1947. p. 111–184.

9. Mason RL, Young JC. Multivariate statistical process control with industrial applications. Philadelphia: ASA-SIAM; 2002.

10. Lowry CA, Woodall WH, Champ CW, Rigdon SE. A multivariate exponentially weighted moving average control chart. Technometrics.1992;34(1):46–53.

11. Annamalai A, Lewis J. Statistical process control using multivariate exponentially weighted moving average and MATLAB-to-Excel software interface. Proc Amer Statistical Assoc, Quality and Productivity Section. 2006;1776–81.

12. Holmes DS, Mergen AE. Improving the performance of the T 2 control chart. Qual Engin. 1993;5(4):619–625.


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