Investigation of Design Space for Freeze-Drying: Use of Modeling for Primary Drying Segment of a Freeze-Drying Cycle

In this work, we explore the idea of using mathematical models to build design space for the primary drying portion of freeze-drying process. We start by defining design space for freeze-drying, followed by defining critical quality attributes and critical process parameters. Then using mathematical...

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Published inAAPS PharmSciTech Vol. 12; no. 3; pp. 854 - 861
Main Authors Koganti, Venkat Rao, Shalaev, Evgenyi Y., Berry, Mark R., Osterberg, Thomas, Youssef, Maickel, Hiebert, David N., Kanka, Frank A., Nolan, Martin, Barrett, Rosemary, Scalzo, Gioval, Fitzpatrick, Gillian, Fitzgibbon, Niall, Luthra, Sumit, Zhang, Liling
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.09.2011
Subjects
Algorithms
Biochemistry
Biomedical and Life Sciences
Biomedicine
Biotechnology
Computer Simulation
Freeze Drying - methods
Indexing in process
Models, Chemical
Pharmacology/Toxicology
Pharmacy
Quality Control
Research Article
Technology, Pharmaceutical - methods
Temperature
Volatilization
Water
mathematical modeling
quality by design
freeze-drying
design space
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Summary:In this work, we explore the idea of using mathematical models to build design space for the primary drying portion of freeze-drying process. We start by defining design space for freeze-drying, followed by defining critical quality attributes and critical process parameters. Then using mathematical model, we build an insilico design space. Input parameters to the model (heat transfer coefficient and mass transfer resistance) were obtained from separate experimental runs. Two lyophilization runs are conducted to verify the model predictions. This confirmation of the model predictions with experimental results added to the confidence in the insilico design space. This simple step-by-step approach allowed us to minimize the number of experimental runs (preliminary runs to calculate heat transfer coefficient and mass transfer resistance plus two additional experimental runs to verify model predictions) required to define the design space. The established design space can then be used to understand the influence of critical process parameters on the critical quality attributes for all future cycles.
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Guest Editors: Lavinia Lewis, Jim Agalloco, Bill Lambert, Russell Madsen, and Mark Staples
ISSN:1530-9932
1530-9932
DOI:10.1208/s12249-011-9645-7