A Heuristic Framework for Comparative Deformation Mapping of Pharmaceutical Excipients Using Compaction Simulator Data
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Published on: April 16, 2017
Sara Fathollahi1, Pauline H M Janssen2,3, Arpan Patel2
1DFE Pharma, Klever Strasse 187, 47568, Goch, Germany. sara.fathollahi@dfepharma.com.
Abstract:
Understanding the material deformation behavior in tablet dies is crucial for pharmaceutical development, as it directly influences manufacturability and final tablet properties. Heckel analysis provides a theoretical framework for assessing material deformation during compaction, but its direct correlation with real-world tableting behavior remains an area of interest. This study introduces a framework for characterizing the deformation mechanisms of pharmaceutical powders. It addresses limitations in conventional methods that rely solely on yield pressure (Py) and strain rate sensitivity (SRS), which fail to fully differentiate elastic and plastic deformation. The heuristic framework for comparative deformation mapping enables differentiation of excipients across the brittle-plastic-elastic space using two parameters from a compaction simulator: Py (from Heckel analysis) and pressure drop (ΔP, from an in-die stress relaxation test). ΔP is treated as a descriptor of time-dependent (viscoplastic and viscoelastic) deformation rather than as a direct measure of elasticity, and its use as an empirical surrogate for elastic character was benchmarked against independent force-displacement elastic energy. Applied to ten common pharmaceutical excipients, the framework differentiated materials that Py and SRS alone did not resolve (SRS was determined for the four reference materials). Across the ten materials, ΔP tracked elastic energy mainly between excipient classes, whereas within the six lactose grades the relationship was reversed, indicating that ΔP differences within a class reflect time-dependent deformation. The framework is therefore proposed as a comparative, material-sparing screening tool for early-stage excipient selection, not as a predictor of tableting performance or scale-up behavior.
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