首页 | 本学科首页   官方微博 | 高级检索  
   检索      


A QbD Case Study: Bayesian Prediction of Lyophilization Cycle Parameters
Authors:Linas Mockus  David LeBlond  Prabir K Basu  Rakhi B Shah  Mansoor A Khan
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
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.
Keywords:
本文献已被 PubMed SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号