Pharmaceutical Technology Management Department, Production Division, Nichi-Iko Pharmaceutical Co., Ltd, 205-1 Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan; Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan.
Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan.
Int J Pharm. 2023 Jun 25;641:123066. doi: 10.1016/j.ijpharm.2023.123066. Epub 2023 May 20.
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.
本研究旨在展示机器学习 (ML) 在分析来自不同制粒规模的片剂的材料属性数据库方面的有用性。使用高剪切湿法制粒机（规模为 30g 和 1000g），并根据不同规模的实验设计收集数据。总共制备了 38 种不同的片剂，并测量了拉伸强度 (TS) 和 10 分钟后的溶出率 (DS10)。此外，评估了 15 种与颗粒粒径分布、堆密度、弹性、塑性、表面特性和水分含量相关的材料属性 (MAs)。通过使用包括主成分分析和层次聚类分析在内的无监督学习，可视化了每个规模生产的片剂区域。随后，应用包括偏最小二乘回归与投影变量重要性和弹性网络在内的有监督学习进行特征选择。所构建的模型可以高精度地从 MAs 和压缩力预测 TS 和 DS10（分别为 0.777 和 0.748），与规模无关。此外，还成功确定了重要因素。ML 可用于更好地理解规模之间的相似性/差异性，构建关键质量属性的预测模型，并确定关键因素。
