Khorasani Milad, Amigo José M, Sun Changquan Calvin, Bertelsen Poul, Rantanen Jukka
Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
Department of Food Science, Faculty of Science, University of Copenhagen, Denmark.
Eur J Pharm Biopharm. 2015 Jun;93:293-302. doi: 10.1016/j.ejpb.2015.04.008. Epub 2015 Apr 25.
In the present study the application of near-infrared chemical imaging (NIR-CI) supported by chemometric modeling as non-destructive tool for monitoring and assessing the roller compaction and tableting processes was investigated. Based on preliminary risk-assessment, discussion with experts and current work from the literature the critical process parameter (roll pressure and roll speed) and critical quality attributes (ribbon porosity, granule size, amount of fines, tablet tensile strength) were identified and a design space was established. Five experimental runs with different process settings were carried out which revealed intermediates (ribbons, granules) and final products (tablets) with different properties. Principal component analysis (PCA) based model of NIR images was applied to map the ribbon porosity distribution. The ribbon porosity distribution gained from the PCA based NIR-CI was used to develop predictive models for granule size fractions. Predictive methods with acceptable R(2) values could be used to predict the granule particle size. Partial least squares regression (PLS-R) based model of the NIR-CI was used to map and predict the chemical distribution and content of active compound for both roller compacted ribbons and corresponding tablets. In order to select the optimal process, setting the standard deviation of tablet tensile strength and tablet weight for each tablet batch was considered. Strong linear correlation between tablet tensile strength and amount of fines and granule size was established, respectively. These approaches are considered to have a potentially large impact on quality monitoring and control of continuously operating manufacturing lines, such as roller compaction and tableting processes.
在本研究中，对由化学计量学建模支持的近红外化学成像（NIR-CI）作为监测和评估滚压和压片过程的无损工具的应用进行了研究。基于初步风险评估、与专家的讨论以及文献中的现有工作，确定了关键工艺参数（辊压和辊速）和关键质量属性（带状物孔隙率、颗粒尺寸、细粉量、片剂抗张强度），并建立了设计空间。进行了五次具有不同工艺设置的实验运行，得到了具有不同特性的中间体（带状物、颗粒）和最终产品（片剂）。应用基于主成分分析（PCA）的近红外图像模型来绘制带状物孔隙率分布。从基于PCA的近红外化学成像获得的带状物孔隙率分布用于建立颗粒尺寸分数的预测模型。具有可接受R(2)值的预测方法可用于预测颗粒粒径。基于近红外化学成像的偏最小二乘回归（PLS-R）模型用于绘制和预测滚压带状物和相应片剂中活性化合物的化学分布和含量。为了选择最佳工艺，考虑了每个片剂批次的片剂抗张强度和片剂重量的标准偏差。分别建立了片剂抗张强度与细粉量和颗粒尺寸之间的强线性相关性。这些方法被认为对连续运行的生产线（如滚压和压片过程）的质量监测和控制可能有很大影响。
