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 R2 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.
|Original language||English (US)|
|Number of pages||10|
|Journal||European Journal of Pharmaceutics and Biopharmaceutics|
|State||Published - May 26 2015|
Bibliographical noteFunding Information:
Milad Khorasani, is acknowledging the Drug Research Academy (University of Copenhagen) and Takeda Pharma A/S for financing of the Ph.D. studies. The authors also acknowledge the funding from The Danish Council for Independent Research (DFF), Technology and Production Sciences (FTP), Project 12-126515/0602-02670B.
© 2015 Elsevier B.V. All rights reserved.
- Near-infrared chemical imaging
- Partial least squares
- Principal component analysis
- Ribbon chemical map
- Ribbon porosity map
- Roll compaction/dry granulation
- Tablet chemical map