Chen Yinshan, Baviriseaty Sruthika, Thool Prajwal, Gautreau Jonah, Yawman Phillip D, Sluga Kellie, Hau Jonathan, Zhang Shawn, Mao Chen
Synthetic Molecule Pharmaceutical Sciences, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.
digiM Solution LLC., 500 West Cummings Park, Suite 3650, Woburn, MA, 01801, USA.
Pharm Res. 2025 Jan;42(1):203-217. doi: 10.1007/s11095-024-03812-0. Epub 2025 Jan 8.
The purpose of this study is to present a correlative microscopy-tomography approach in conjunction with machine learning-based image segmentation techniques, with the goal of enabling quantitative structural and compositional elucidation of real-world pharmaceutical tablets.
Specifically, the approach involves three sequential steps: 1) user-oriented tablet constituent identification and characterization using correlative mosaic field-of-view SEM and energy dispersive X-ray spectroscopy techniques, 2) phase contrast synchrotron X-ray micro-computed tomography (SyncCT) characterization of a large, representative volume of the tablet, and 3) constituent segmentation and quantification of the imaging data through user-guided, iterative supervised machine learning and deep learning.
This approach was implemented on a real-world tablet containing 15% API and multiple common excipients. A representative volumetric tablet image was obtained using SyncCT at a 0.36-µm resolution, from which constituent particles and pores were fully segmented and quantified. As validation, the derived tablet formulation composition and porosity agreed with the experimental values, despite the micrometer-scale particle and pore sizes. The approach also revealed the formation of ordered mixture inside the tablet. Notably, the image-derived size distributions of both the agglomerated microcrystalline cellulose and its primary particulate units matched the laser diffraction-based measurements of the as-is material. Key pore attributes including the pore size distribution, spatial anisotropy, and pore interconnectivity were also qualified.
Overall, this study demonstrated that the correlative microscopy-tomography approach, by leveraging phase contrast SyncCT and AI-based image analysis, can deliver new, practically-useful structural and compositional information and facilitate more efficient formulation and process development of tablets.
本研究的目的是提出一种关联显微镜 - 断层扫描方法，并结合基于机器学习的图像分割技术，以实现对实际药用片剂进行定量的结构和成分解析。
具体而言，该方法包括三个连续步骤：1）使用关联镶嵌视场扫描电子显微镜和能量色散X射线光谱技术进行面向用户的片剂成分识别和表征；2）对片剂的一个大的代表性体积进行相衬同步加速器X射线显微计算机断层扫描（同步CT）表征；3）通过用户引导的迭代监督机器学习和深度学习对成像数据进行成分分割和定量。
该方法应用于一片含有15%活性成分和多种常见辅料的实际片剂。使用同步CT以0.36微米的分辨率获得了代表性的片剂体积图像，从中对成分颗粒和孔隙进行了完全分割和定量。作为验证，尽管颗粒和孔隙尺寸为微米级，但得出的片剂配方组成和孔隙率与实验值相符。该方法还揭示了片剂内部有序混合物的形成。值得注意的是，团聚微晶纤维素及其初级颗粒单元的图像衍生尺寸分布与原样材料的激光衍射测量结果相匹配。关键孔隙属性，包括孔径分布、空间各向异性和孔隙连通性也得到了确定。
总体而言，本研究表明，通过利用相衬同步CT和基于人工智能的图像分析，关联显微镜 - 断层扫描方法可以提供新的、实际有用的结构和成分信息，并促进片剂更高效的配方和工艺开发。
