Analytical Sciences

Theoretical Analysis of Tablet Hardness Prediction Using Chemoinformetric Near-Infrared Spectroscopy

Hideaki TANABE,* Kuniko OTSUKA,** and Makoto OTSUKA***

*Kobe Pharmaceutical University, Motoyamakitamachi 4-17-1, Higashinada, Kobe 658-8558, Japan
**Department of Pharmacology, School of Medicine, Showa University, Hatanodai 1-5-8, Shinagawa, Tokyo 142-8855, Japan
***Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, Shinmachi 1-1-20, Nishi-Tokyo, Tokyo 202-8585, Japan

In order to clarify the theoretical basis of the variability in the measurement of tablet hardness by compression pressure, NIR spectroscopic methods were used to predict tablet hardness of the formulations. Tablets (200 mg, 8 mm in diameter) consisting of berberine chloride, lactose, and potato starch were formed at various compression pressures (59, 78, 98, 127, 195 MPa). The hardness and the distribution of micropores were measured. The reflectance NIR spectra of various compressed tablets were used as a calibration set to establish a calibration model to predict tablet hardness by principal component regression (PCR) analysis. The distribution of micropores was shifted to a smaller pore size with increasing compression pressure. The total pore volume of tablets decreased as the compression pressure increased. The hardness increased as the compression pressure increased. The hardness could be predicted using a calibration model consisting of 7 principal components (PCs) obtained by PCR. The relationship between the predicted and the actual hardness values exhibited a straight line, an R² of 0.925. In order to understand the theoretical analysis (scientific background) of calibration models used to evaluate tablet hardness, the standard error of cross validation (SEV) values, the loading vectors of each PC and the regression vector were investigated. The result obtained with the calibration models for hardness suggested that the regression vector might involve physical and chemical factors. In contrast, the porosity could be predicted using a calibration model composed of 2 PCs. The relationship between the predicted and the actual total pore volume showed a straight line with R² = 0.801. The regression vector of the total pore volume might be due to physical factors.

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