Oral Solids Development, Drug Product Development, Pharmaceutical Product Development & Supply, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium; Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Int J Pharm. 2022 Jun 10;621:121801. doi: 10.1016/j.ijpharm.2022.121801. Epub 2022 May 5.
This study developed a material and time saving method for powder characterization. Building on an earlier developed raw material property database for use towards development of pharmaceutical dry powder processes, blends were selected in an efficient way to include maximal variability of the underlying raw material dataset. For both raw materials and blends, powder characterization methods were kept to a minimum by selecting the testing methods that described the highest amount of variability in physical powder properties based on principal component analysis (PCA). This method selection was made by identifying the overarching properties described by the principal components of the PCA model. Ring shear testing, powder bed compressibility, bulk/tapped density, helium pycnometry, loss on drying and aeration were identified as the most discriminating characterization techniques from this dataset to detect differences in physical powder properties. This ensured a workload reduction while most of the powder variability that could be detected was still included. The methodology proposed in this paper could be used as a material-saving alternative to the current "Design of Experiment" approach, which will be investigated further for applicability to speed up the development of formulations and processes for new drug products and building an end-to-end predictive platform.
本研究开发了一种节省材料和时间的粉末特性分析方法。基于早期开发的用于开发药物干粉工艺的原材料特性数据库，以有效方式选择混合物，以包括基础原材料数据集的最大可变性。对于原材料和混合物，通过选择基于主成分分析（PCA）描述物理粉末特性最大可变性的测试方法，将粉末特性分析方法保持在最低限度。通过确定 PCA 模型的主成分描述的总体特性来进行方法选择。环剪测试、粉末床可压缩性、堆密度/振实密度、氦比重瓶法、干燥失重和通气性被确定为该数据集中最具区分力的特性分析技术，可用于检测物理粉末特性的差异。这确保了在减少工作量的同时，仍能包含大部分可检测的粉末变化。本文提出的方法可以作为当前“实验设计”方法的节省材料替代方案，我们将进一步研究其在加快新药产品配方和工艺开发以及构建端到端预测平台方面的适用性。
