Research ArticlesNew Perspectives for Visual Characterization of Pharmaceutical Solids
Section snippets
INTRODUCTION
Granulation is an important unit operation in powder handling in the pharmaceutical industry. This process is carried out primarily to produce material of desirable particle size and shape to improve processing properties, i.e., blend uniformity, the flowability, and tabletting behavior, of powder masses. Consequently, the monitoring of particle growth kinetics is important in a granulation process. The principles and mechanisms of granule growth kinetics have been thoroughly described in the
Materials
A model formulation (batch size 3500 g) consisting of 5% wt/wt of caffeine (Orion Pharma, Espoo, Finland), 475 g microcrystalline cellulose (MCC) (Emcocel 50M, Penwest Pharmaceuticals, Nastola, Finland), 2200 g lactose monohydrate (Pharmatose 200M, DMV Pharma, Veghel, The Netherlands), and 500 g pregelatinized starch (Starch 1500, Colorcon, Indianapolis, IN). Polyvinylpyrrolidone (PVP) (Kollidon K25, BASF, Ludwigshafen, Germany) was used as a binder in the formulation (5% wt/wt). Solutions in
Particle Size Measurements
The mean particle sizes of the end‐point or the final product for each batch are presented in Table 1. We can see that the end‐point mean particle size measured by sieving varies from 223 to 1413 μm. The Pearson correlation coefficient between the mean sizes measured from surface images and the mean size measured by sieving is 0.97. A similar Pearson correlation coefficient (0.98) for particle mean size using the end‐point values for a fitted curve (the fitting is explained below) and sieve
CONCLUSIONS
This study introduced a system for analysis and control of a pharmaceutical manufacturing process. The technique is a proper tool in process control of pharmaceutical solids and powerful in granule growth kinetics research. Furthermore, surface image information can be used in the prediction of functional properties granules to estimate further prosessability. Fast screening of tabletting properties can be made using the combination of multivariate visualization and image information. In
Acknowledgements
The authors wish to thank student Heidi Kettunen for technical assistance with the imaging and students Antti Eskelinen and Tina Suominen for the technical assistance with the granulations.
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