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Statistical Methods in Quality by Design and Process Analytical Technologies for Continuous Processes to Enable Real-Time Release

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Continuous Pharmaceutical Processing

Part of the book series: AAPS Advances in the Pharmaceutical Sciences Series ((AAPS,volume 42))

Abstract

The large data sets obtained in real-time monitoring of batch and continuous manufacturing processes require statistical methods to extract information on the state of critical process parameters and quality attributes. This chapter presents useful statistical methods such as design of experiments (DoE), principal component analysis (PCA), and partial least squares (PLS) regression for monitoring both physical and chemical properties in pharmaceuticals. A discussion of variographic analysis is also included, presenting a promising method to evaluate the mixing of pharmaceutical formulations and address sampling errors. This chapter also presents statistical methods that are currently used to improve both batch and continuous processes. These methods are now used in commercial manufacturing of pharmaceutical products.

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Acknowledgments

The authors thank Dr. Andres Roman Ospino and Dr. James Scicolone for their help with figures in this chapter. This work was funded by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855, and the Puerto Rico Science, Technology and Research Trust.

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Authors and Affiliations

  1. Department of Chemistry, Center for Structured Organic Particulate Systems (C-SOPS), Mayagüez, Puerto Rico

    Vanessa Cárdenas, Carlos Pinzón & Rodolfo J. Romañach

  2. Merck, Sharp & Dohme Limited, Cramlington, UK

    Juan G. Rosas

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  1. Vanessa Cárdenas
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  2. Juan G. Rosas
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  3. Carlos Pinzón
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  4. Rodolfo J. Romañach
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Correspondence to Rodolfo J. Romañach .

Editor information

Editors and Affiliations

  1. Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA

    Zoltan K Nagy

  2. Faculty of Pharmacy, Cairo University, Cairo, Egypt

    Arwa El Hagrasy

  3. Department of Chemical and Biochemical Engineering, The University of Sheffield, Sheffield, UK

    Jim Litster

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Cárdenas, V., Rosas, J.G., Pinzón, C., Romañach, R.J. (2020). Statistical Methods in Quality by Design and Process Analytical Technologies for Continuous Processes to Enable Real-Time Release. In: Nagy, Z., El Hagrasy, A., Litster, J. (eds) Continuous Pharmaceutical Processing. AAPS Advances in the Pharmaceutical Sciences Series, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-41524-2_10

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