Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets
Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets
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DOI：
10.1208/s12249-020-01911-w
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发表时间：
2021-01
影响因子：
3.3
通讯作者：
Shojiro Shibayama;K. Funatsu
中科院分区：
文献类型：
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作者：
Shojiro Shibayama;K. Funatsu
在美国食品药品协会发布了关于加强使用过程分析技术（PAT）和连续生产的指南后，许多关于PAT和连续生产的研究已经发表。本文描述了一个案例研究，涉及造粒和包衣步骤与乙苯甲酰胺调查干扰PAT模型构建和模型管理。我们研究了在连续制造中实施PAT时应考虑和解决的因素以及应如何构建预测模型。监测的产品质量为制粒步骤中的水分含量和粒度以及包衣步骤中的片剂重量和水分含量。我们已经构建了颗粒化步骤的模型，并验证了模型对外部数据集的预测能力。手动波长选择的偏最小二乘（PLS）模型对外部验证集的干燥失重具有最佳预测准确度。我们发现，干燥失重的预测是准确的，但粒度的预测不够准确。在涂层步骤中，由于数据量小，我们进行了10次三重交叉验证和y-加扰，以选择最佳超参数并检查模型是否符合偶然相关性。我们证实，包衣剂重量、片剂重量和含水量可根据交叉验证的R2评分平均值准确预测。添加其他变量以及吸光度略微提高了预测准确度。
After the Food and Drug Association in the USA published guidelines on the enhanced use of process analytical technology (PAT) and continuous manufacturing, many studies regarding PAT and continuous manufacturing have been published. This paper describes a case study involving granulation and coating steps with ethenzamide to investigate interference for PAT model construction and model management. We investigated what factors should be considered and addressed when PAT is implemented for continuous manufacturing and how predictive models should be constructed. The product qualities that were monitored were moisture content and particle size in the granulation step and tablet weight and moisture content in the coating step. We have constructed models for the granulation step and validated the predictive capability of the models against an external dataset. A partial least squares (PLS) model with manual wavelength selection had the best predictive accuracy for loss on drying against the external validation set. We found that the prediction of loss on drying was accurate, but the prediction of particle size was not sufficiently accurate. In the coating step, because of the small amount of data, we performed three-fold cross-validation and y-scrambling 10 times, to select the optimal hyper-parameters and to check if the models were fitted to chance correlations. We confirmed that the coating agent weights, tablet weights, and water content could be accurately predicted based on the mean of the R2 score for cross-validation. Addition of other variables, as well as the absorbance, slightly improved the predictive accuracy.