Purdue University, Discovery Park, West Lafayette, Indiana, USA.
AAPS PharmSciTech. 2011 Mar;12(1):442-8. doi: 10.1208/s12249-011-9598-x. Epub 2011 Mar 4.
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.
根据 ICH Q8 R2（1）的规定，基于过程建模预测关键工艺参数是产品开发增强型设计质量方法的一部分。在这项工作中，我们讨论了一种用于预测初级干燥阶段持续时间的贝叶斯模型。该模型基于以下前提：干燥层传质阻力是特定于产品的，并且是成核温度的函数。在实验室规模的冷冻干燥机上对初级干燥的预测持续时间进行了实验验证。建议在放大活动中使用该模型，以尽量减少试错并降低与昂贵的大规模实验相关的成本。所提出的方法通过向初级干燥建模中添加贝叶斯处理扩展了 Searles 等人的工作（2）。
