A QbD Case Study: Bayesian Prediction of Lyophilization Cycle Parameters |
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Authors: | Linas Mockus David LeBlond Prabir K Basu Rakhi B Shah Mansoor A Khan |
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Institution: | (1) Purdue University, Discovery Park, West Lafayette, Indiana, USA;(2) Global Pharmaceutical Exploratory Statistics, Abbott Laboratories, Abbott Park, Illinois, USA;(3) NIPTE, Inc., Rosemont, IL 60018, USA;(4) FDA/CDER/OPS/OTR, Silver Spring, Maryland, USA;(5) Purdue University, 480 Stadium Mall Drive, West Lafayette, IN 4907-2100, USA |
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Abstract: | As stipulated by ICH Q8 R2 (1), 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. (2) by adding a Bayesian treatment to primary drying modeling. |
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