Prediction of solid fraction from powder mixtures based on single component compression analysis.
Robert Schmidtke1, Daniela Schröder2, Judith Menth2
1Pharmaceutical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach, Germany; Department of Pharmaceutical Technology, University of Bonn, Gerhard-Domagk-Straße 3, 53121 Bonn, Germany.
International Journal of Pharmaceutics
|March 29, 2017
Summary
This study evaluated compression models for predicting tablet solid fraction in direct compression. The Kawakita model demonstrated superior accuracy, while the Percolation model best fit microcrystalline cellulose mixtures.
Area of Science:
- Pharmaceutical Sciences
- Materials Science
- Chemical Engineering
Background:
- Direct compression is a key tablet manufacturing process.
- Accurate prediction of tablet solid fraction is crucial for process optimization.
- Existing compression models require evaluation for diverse pharmaceutical formulations.
Purpose of the Study:
- To systematically evaluate the predictive performance of Percolation, Kawakita, and Exponential compression models.
- To assess the models' ability to predict tablet solid fraction for direct compression mixtures.
- To compare model accuracy based on single-component compression analysis of excipients.
Main Methods:
- Compression analysis of four mixtures with varying microcrystalline cellulose (MCC) and lactose monohydrate (LAC) fractions.
- Application and comparison of adjusted Percolation, Kawakita, and Exponential models.
- Evaluation of prediction accuracy using residuals.
Main Results:
- All tested models successfully predicted the solid fraction of the compressed mixtures.
- The Kawakita model exhibited the highest overall prediction accuracy.
- The Percolation model provided the best fit for mixtures containing 72%-48% MCC, with residuals below 3%.
Conclusions:
- Single-component compression analysis and established models can effectively predict the solid fraction of direct compression mixtures.
- The Kawakita model is recommended for general prediction of tablet solid fraction.
- The Percolation model offers specific advantages for formulations rich in microcrystalline cellulose.
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