Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.
In roll compaction, the specific compaction force, the gap width and the roll speed are the most important settings as they have a high impact in the products obtained. However the mechanical properties of the mixture being compacted are also critical. For this reason, a multilevel full factorial design including these parameters as factors plus three repetitions of the center point was performed for microcrystalline cellulose, mannitol and five binary mixtures (15, 30, 50, 70 and 85% MCC). These two reference excipients were chosen in order to investigate the plastic/brittle behavior of mixtures for the roll compaction process. These materials were roll compacted in a 3-W-Polygran 250/50/3 (Gerteis) and the ribbons obtained were collected and milled into granules which were characterized regarding granule size distribution. After statistical evaluation, it was found that the most critical factors affecting the D10, D50, D90 and the fines fraction from the granules were the gap width and the specific compaction force, as well as the proportion of MCC together with its quadratic effect and the interaction between force and proportion of MCC. The microhardness of the ribbons from the center point as well as the D10, D50, D90 and the fines fraction from the granules produced at these same conditions were characterized. In all the cases, the proportion of MCC, i.e. the composition of the mixture, showed also an important effect on these properties measured. In this sense, the percolation theory was applied in order to study further the importance of the plastic/brittle ratio by calculating the percolation threshold or the limit over which the behavior of the system changes. This resulted in values of 34% for the HU (expression of microhardness), 27% and 28% for the D10 and fines, respectively (percolation of MCC) and 84% and 85% for the D50 and D90, respectively (percolation of mannitol).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.