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Predicting tabletability flip in pharmaceutical powders via a mixture tabletability model
Zijian Wang1, Changquan Calvin Sun1
1Pharmaceutical Materials Science and Engineering Laboratory, Department of Pharmaceutics, College of Pharmacy, University of Minnesota, MN 55455, USA.
International Journal of Pharmaceutics
|November 4, 2025
Summary
A new model accurately predicts the tabletability flip phenomenon (TFP), where less inherently tabletable active pharmaceutical ingredients (APIs) perform better with excipients. This aids early tablet formulation development and solid-form selection.
Area of Science:
- Pharmaceutical Sciences
- Materials Science
- Chemical Engineering
Background:
- The tabletability flip phenomenon (TFP) complicates active pharmaceutical ingredient (API) solid-form selection and formulation.
- Existing research offers insights but lacks a predictive tool for early-stage tablet formulation development.
- A reliable method for predicting TFP is crucial for implementing quality-by-design (QbD) principles in pharmaceutical manufacturing.
Purpose of the Study:
- To evaluate the predictive capability of a novel mixture tabletability model for the tabletability flip phenomenon (TFP).
- To assess the model's feasibility as a diagnostic tool in early tablet formulation development.
- To identify areas for model improvement based on experimental data.
Main Methods:
- A recently developed mixture tabletability model was employed.
- The model's predictions were tested across two distinct API-excipient systems.
- Model predictions were compared against experimental tabletability data.
Main Results:
- The mixture tabletability model successfully predicted the presence or absence of TFP in both tested systems.
- The model demonstrated potential as a diagnostic tool for identifying TFP during formulation development.
- Deviations between predicted and experimental results indicated the influence of particle size on model accuracy.
Conclusions:
- The developed mixture tabletability model shows promise for predicting TFP, aiding solid-form selection and formulation design.
- The model's accuracy can be enhanced by further investigating the impact of particle size characteristics.
- This predictive approach supports the implementation of QbD in tablet development by enabling early identification of potential formulation challenges.
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