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Abstract Details
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Activity Number:
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552
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Quality and Productivity Section
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| Abstract - #304424 |
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Title:
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A QBD Case Study: Bayesian Prediction of Lyophilization Cycle Parameters
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Author(s):
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David LeBlond*+ and Linas Mockus
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Companies:
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Abbott and Purdue University
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Address:
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3091 Midlane Dr, Wadsworth, IL, 60083-9528, United States
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Keywords:
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Bayesain ;
lyophilization ;
quality by design
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Abstract:
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As stipulated by ICH Q8 R2, prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (J Pharm Sci. 2001;90(7):860-71.) by adding a Bayesian treatment to primary drying modeling.
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