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Mathematical Tools for the Quantitative Definition of a Design Space

  • Amanda Rogers
  • Marianthi G. IerapetritouEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

This chapter focuses on the application of process modeling to determination of design space for pharmaceutical manufacturing processes. The first two sections define design space and related terms from a regulatory perspective and discuss how these apply in practice during pharmaceutical process development. In Sect. 3, a variety of mathematical techniques that can be used to guide design space development are introduced. An emphasis is placed on the use of feasibility analysis and the relationship between process feasibility and design space. Statistical techniques, including latent variable and Bayesian methods, are also discussed in detail. For methods that have been reported in the literature for pharmaceutical manufacturing applications, an overview of the relevant case studies is provided. If methods have not yet been applied to pharmaceutical processes, potential applications for these techniques are indicated. To illustrate the applicability of these concepts to pharmaceutical manufacturing processes, a brief case study is presented in Sect. 4. A discussion of design space verification and issues related to scale-up is provided in Sect. 5. Finally a brief summary of the concepts presented and their potential role in design space development for pharmaceutical processes is given in Sect. 6.

This chapter is intended to provide readers with an understanding of mathematical techniques that can be used during process development to assist in the determination of process design space. An overview of the problem formulation and solution approaches for each method are presented. More detailed mathematical developments for each technique are available in the references provided. Table 1 provides a list of symbols that are used throughout this chapter, while Table 2 describes acronyms used within this chapter.

Key words

Design space Feasibility analysis Flexibility analysis Black-box feasibility Derivative-free optimization Bayesian reliability Partial least squares (PLS) Principal component analysis (PCA) Pharmaceutical process development Process modeling 

Notes

Acknowledgements

The authors would like to acknowledge financial support from Bristol-Myers Squibb as well as from the Engineering Research Center for Structured Organic Particulate Systems at Rutgers University (NSF-0504497, NSF-ECC 0540855).

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Chemical and Biochemical Engineering, School of EngineeringRutgers, The State University of New JerseyPiscatawayUSA

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