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Considerations in Monitoring and Controlling Pharmaceutical Manufacturing

  • Anthony J. Hickey
  • Hugh D. C. Smyth
Chapter
  • 15 Downloads
Part of the AAPS Introductions in the Pharmaceutical Sciences book series (AAPSINSTR)

Abstract

Monitoring and control of complex processes involve a number of variables whose interactions are necessarily complex. Unlike many other areas of pharmaceutical development, this has long been recognized by process engineers who have the task of guaranteeing the quality and performance of the product. A variety of statistical, physical, and mathematical approaches have been adopted depending on the needs of the assessment. As tools for this purpose have evolved from the ability to manage and store data, the concept of quality by design (QbD) has gained ground and is now a central theme for industry and government regulators. QbD requires significant preparatory consideration of any process by a team of qualified individuals to map out all of the known variables that might contribute to desired attributes of the product. Since there are many variables involved in manufacturing processes and some may not be known, or not subject to control, the mathematical approach to the complexity has included artificial neural networks which have the capacity to learn from data generated and to integrate that knowledge into a predictive approach to a range of activities from research to development. Indeed, advanced mathematical modeling and control tools will be needed as the industry slowly moves from batch to continuous processing.

Keywords

Pharmaceutical processing Continuous processing Quality by design Artificial neural networks Process control In silico modeling Design of experiments 

References

  1. Agatanovic-Kustrin, S., & Berseford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application to pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22, 717–727.CrossRefGoogle Scholar
  2. Achanta, A. S., Kowalski, J. G., & Rhodes, C. T. (1995). Artificial neural networks: Implications for pharmaceutical sciences. Drug Development and Industrial Pharmacy, 21, 119–155.CrossRefGoogle Scholar
  3. Borquin, J. (1997). Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharmaceutical Development and Technology, 2, 95–109.CrossRefGoogle Scholar
  4. Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters: An introduction to design, data analysis, and model building. New York, NY: John Wiley and Sons.Google Scholar
  5. Brunaugh, A., & Smyth, H. D. C. (2018). Process optimization and particle engineering of micronized drug powders via milling. Drug Delivery and Translational Research, 8(6), 1740–1750.CrossRefGoogle Scholar
  6. Cochran, W. G., & Cox, G. M. (1957). Experimental designs (2nd ed.). New York, NY: John Wiley and Sons.Google Scholar
  7. Hickey, A. J., & Ganderton, D. (2010). Pharmaceutical process engineering (2nd ed.). New York, NY: Informa Healthcare.Google Scholar
  8. Lepore, J., & Spavins, J. (2008). PQLI design space. Journal of Pharmaceutical Innovation, 3, 79–87.CrossRefGoogle Scholar
  9. Nosal, R., & Schultz, T. (2008). PQLI definition of criticality. Journal of Pharmaceutical Innovation, 3, 69–78.CrossRefGoogle Scholar
  10. Takayama, K., Fujikawa, M., & Nagai, T. (1999). Artificial neural network (ANN) as a novel method to optimize pharmaceutical formulation. Pharmaceutical Research, 16, 1–6.CrossRefGoogle Scholar
  11. Yu, L. X. (2008). Pharmaceutical quality by design: Product and process development, understanding, and control. Pharmaceutical Research, 25(4), 781–791.CrossRefGoogle Scholar
  12. Yu, L. X., Amidon, G., Khan, M. A., Hoag, S. W., Polli, J., Raju, G. K., et al. (2014). Understanding pharmaceutical quality by design. The AAPS Journal, 16, 771–783.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2020

Authors and Affiliations

  • Anthony J. Hickey
    • 1
  • Hugh D. C. Smyth
    • 2
  1. 1.RTI InternationalResearch Triangle ParkUSA
  2. 2.College of PharmacyThe University of Texas at AustinAustinUSA

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