Kobayashi Yuki, Kim Sanghong, Nagato Takuya, Oishi Takuya, Kano Manabu
Department of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 6068501, Kyoto, Japan.
Department of Applied Physics and Chemical Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, 1840012 Tokyo, Japan.
Int J Pharm X. 2024 Mar 31;7:100242. doi: 10.1016/j.ijpx.2024.100242. eCollection 2024 Jun.
In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (FFPs) of two-component mixed powders with various formulations, while most previous studies have focused on single-component powders. It further aims to identify the suitable model type and to determine the significance of material properties in enhancing prediction accuracy by using several FFP prediction models with different input variables. Four datasets from the experiment were generated with different ranges of the mass fraction of active pharmaceutical ingredients (API) and the powder weight in the hopper. The candidates for the model inputs are (a) the mass fraction of API, (b) process parameters, and (c) material properties. It is desirable to construct a high-performance prediction model without the material properties because their measurement is laborious. The results show that using (c) as input variables did not improve the prediction accuracy as much, thus there is no need to use them.
在连续制药生产过程中，控制粉末流速至关重要。进料过程的特点是每螺杆旋转输送的粉末量，称为进料因子。本研究旨在建立模型，以预测具有各种配方的两组分混合粉末的进料因子曲线（FFP），而此前大多数研究都集中在单组分粉末上。它还旨在通过使用具有不同输入变量的几种FFP预测模型，确定合适的模型类型，并确定材料特性对提高预测准确性的重要性。通过在不同的活性药物成分（API）质量分数范围和料斗中的粉末重量下进行实验，生成了四个数据集。模型输入的候选因素包括（a）API的质量分数、（b）工艺参数和（c）材料特性。由于材料特性的测量繁琐，因此希望构建一个不包含材料特性的高性能预测模型。结果表明，将（c）用作输入变量并没有显著提高预测准确性，因此无需使用它们。
