Monitoring of Biopharmaceutical Processes: Present and Future Approaches - Enhance your control strategy with robust monitoring methods. - BioPharm International

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Monitoring of Biopharmaceutical Processes: Present and Future Approaches
Enhance your control strategy with robust monitoring methods.


BioPharm International
Volume 22, Issue 5

CASE STUDY INVOLVING REAL-TIME PROCESS MONITORING

As an example of real-time multivariate statistical process monitoring, a PCA-based model has been developed for monitoring a mammalian cell culture bioreactor at commercial-scale. In this setting, historical batches are mined from the manufacturing databases to develop a nominal process model for a seed bioreactor train. Eleven process variables that are measured online for 30 batches are used in model building. The model is able to explain overall process variability with only three PCs. New production batches are monitored against this model in real time.


Figure 3
During the real-time monitoring, one typically looks at high-level multivariate charts for deviation detection purposes as mentioned earlier. In Figure 3, the three main steps involved in monitoring are shown. In step 1, a T 2 chart is used to detect a deviation. Step 2 involves diagnosis at the variable level, which indicates that the pH probe is reading less than historical averages, i.e., outside of +/–3 standard deviation. Finally, in step 3, inspection of the pH trace is performed. This allows scientists and engineers to start troubleshooting the probe and other operational factors to better understand and monitor the process by this simple three-step process.20

CONCLUSIONS

Statistical techniques, such as those discussed in the article, have been demonstrated to be capable of performing rigorous analysis of complex datasets that abound in biotech applications. This, combined with the advancements in analytical tools to allow online analysis, can form the basis of real-time process monitoring. Implementation of such systems is likely to result in gains in consistency of product quality as well as efficiency in manufacturing of biotech products and bring us closer to full implementation of QbD and realizing its benefits.

Paul Konold is senior engineer, Rob Woolfenden II is principal engineer at Seattle, WA, Cenk Undey is principal engineer at West Greenwich, RI, and Anurag S. Rathore is director at Thousand Oaks, CA, 805.447.4491,
All authors are in the process development department at Amgen, Inc. Rathore is also a member of BioPharm International's editorial advisory board.

REFERENCES

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7. Bersimis S, Psarakis S, Panaretos J. Multivariate statistical process control charts: an overview. Quality Reliability Eng Int. 2007;23:517–543.

8. AT&T statistical quality control handbook. 11th ed. North Carolina: Delmar Printing Company; 1985.

9. Nelson LS. Technical aids: display tables and significant digits. J Quality Technol. 1984;16:175–176.

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

11. Johnson R, Yu O, Kirdar AO, Annamalai A, Ahuja S, Ram K, Rathore AS. Applications of multivariate data analysis (MVDA) for biotech processing. BioPharm Int. 2007;20(10):130–144.

12. Rathore AS, Branning R, Cecchini D. Design space for biotech products. BioPharm Int. 2007;20(4);36–40.

13. Rathore AS, Winkle H. Quality by Design for pharmaceuticals: regulatory perspective and approach. Nature Biotechnol. 2009;27;26–34.

14. Cinar A, Parulekar SJ, Undey C, Birol G. Batch fermentation: modeling, monitoring and control. New York: CRC Press; 2003.

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18. Undey C, Tatara E, Cinar A. Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations. J Biotechnol. 2004;108(1):61–77.

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21. Undey C. Are we there yet? An industrial perspective of evolution from post-mortem data analysis towards real-time multivariate monitoring and control of biologics manufacturing processes. IBC's BioProcess International Analytical and Quality Summit. Cambridge, MA; 2008 Jun 2–4.

Other articles from The Elements of Biopharmaceutical Production series:

1. Modeling of Microbial and Mammalian Unit Operations

2. Scaling Down Fermentation

3. Optimization, Scale-up, and Validation Issues in Filtration

4. Filter Clogging Issues in Sterile Filtration

5. Lifetime Studies for Membrane Reuse

6. Modeling of Process Chromatography Unit Operation

7. Resin Screening to Optimize Chromatographic Separations

8. Optimization and Scale-Up in Preparative Chromatography

9. Scaling Down Chromatography and Filtration

10. Qualification of a Chromatographic Column

11. Efficiency Measurements for Chromatography Columns

12. Process Validation: How Much to Do and When to Do It

13. Quality by Design for Biopharmaceuticals: Defining Design Space

14. Quality by Design for Biopharmaceuticals: Case Studies

15. Design Space for Biotech Products

16. Applying PAT to Biotech Unit Operations

17. Applications of MVDA in Biotech Processing

18. Future Technologies for Efficient Manufacturing

19. Costing Issues in the Production of Biopharmaceuticals

20. Economic Analysis as a Tool for Process Development

For the entire series of The Elements of Biopharmaceutical Production, please visit http://www.industrymatter.com/EBPseries.aspx/


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