COMPRESSION, COMPACTION AND STATISTICAL ANALYSIS

The manufacturing of solid dosage forms, especially tablets, relies heavily on understanding the mechanical behavior of powdered materials during compression and compaction. This study explores the interplay between particle rearrangement, fragmentation, and deformation under applied pressure, using established models such as Heckel, Kawakita, and Shapiro equations. These models provide quantitative descriptors—like yield pressure, rearrangement index, and fragmentation parameters—that help classify materials based on their compressibility and compactability.

To enhance predictive capabilities, multivariate statistical analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were employed. These methods revealed latent structures in compression data, enabling the development of a classification protocol that groups materials by their mechanical response. The protocol was validated using a diverse set of pharmaceutical powders, demonstrating its utility in formulation development and process optimization.

The findings suggest that combining physical modeling with statistical analysis offers a robust framework for assessing tablet manufacturability. This approach supports the Process Analytical Technology (PAT) initiative by embedding quality into the design phase rather than relying solely on post-production testing.