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Compression equations in pharmaceutical tableting: from classical compaction models to data-driven and mechanistic
1Department of Pharmaceutical Technology, Ankara University, Faculty of Pharmacy, Ankara, Türkiye.
Pharmaceutical Development and Technology
|June 23, 2026
Summary
This review assesses powder compaction models for pharmaceutical tablet manufacturing. It integrates classical and advanced methods, offering guidance for selecting appropriate models to predict tablet properties and improve manufacturing processes.
Area of Science:
- Pharmaceutical Manufacturing
- Materials Science
- Chemical Engineering
Background:
- Powder compaction is crucial for tablet manufacturing but challenging to predict due to its complex nature.
- Classical compression equations (Heckel, Kawakita-Lüdde, Cooper-Eaton) are widely used but have limitations in accuracy and mechanistic insight.
- Emerging models offer enhanced capabilities for understanding and predicting powder compaction behavior.
Purpose of the Study:
- To critically review and assess classical and advanced powder compaction models.
- To integrate these models into a hierarchical framework for pharmaceutical manufacturing.
- To provide guidance on model selection for formulation development and process control.
Main Methods:
- Review and critical assessment of classical empirical compression equations.
- Evaluation of advanced modeling techniques including finite-element method (FEM) and discrete-element method (DEM) simulations.
- Analysis of data-driven approaches like process analytical technology (PAT) and machine learning.
Main Results:
- Classical models have limitations in pressure-range dependence and mechanistic interpretation.
- Advanced models like FEM and DEM offer improved mechanistic understanding and prediction capabilities.
- PAT and machine learning frameworks provide data-driven insights for process optimization and control.
Conclusions:
- A hierarchical framework integrating classical and emerging models is proposed for pharmaceutical compaction.
- Linking densification behavior to critical quality attributes and scale-up is emphasized.
- Guidance is provided for selecting appropriate models to enhance tablet manufacturing and enable digital-twin approaches.
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