Quality Evaluation of Humidified Magnesium Oxide Tablet Formulations with Respect to Disintegration Time Prolongation.

In the present study, we conducted a detailed evaluation of the effects of humidification on the quality of five types of commercial magnesium oxide (MgO) tablet formulations. When near-IR spectroscopy was performed, a peak derived from the first overtone of the stretching vibration of the hydroxyl group was observed at approximately 7200 cm-1 in a humidified MgO tablet formulation. To visually evaluate the effect of this humidification, a mapping image was created using microscopic IR spectroscopy. In the IR spectrum, a peak derived from the stretching vibration of the hydroxyl group appears at approximately 3700 cm-1, so we created a mapping image using the absorbance ratio of 3700 and 3400 cm-1 as an index. In the mapping image of humidified MgO tablet formulations, many areas had a higher absorbance ratio than the dried tablet formulations. From these results, it is qualitatively confirmed that the MgO was changed to magnesium hydroxide (Mg(OH)2) by humidification. Although these results were observed in the four types of MgO tablet formulations, only one type of tablet formulation was less affected by humidification. In addition, although most tablet formulations tended to prolong disintegration time due to humidification, there was almost no effect of humidification on the disintegration time in one type of tablet formulation, which had little change in the above evaluation. Thus, in most commercial MgO tablet formulations, humidification prolongs the disintegration time, and Mg(OH)2 significantly contributes to this factor.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks.

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.

Time-domain NMR analysis for the determination of water content in pharmaceutical ingredients and wet granules.

The application of time-domain NMR (TD-NMR) analysis to quantify water content in pharmaceutical ingredients is demonstrated. The initial phase of the study employed a range of disintegrants with defined amounts of added water (0-30% of the total weight) as samples; the disintegrants included croscarmellose sodium, corn starch, low-substituted hydroxypropyl cellulose, and crospovidone. After acquisition of the T2 relaxation curves of the samples by TD-NMR measurements, these curves were analyzed by partial least squares (PLS) regression. According to the analysis, accurate and reliable PLS models were created that enabled accurate assessment of water content in the samples. A powder blend consisting of acetaminophen (paracetamol) and tablet excipients was also examined. Both a physical mixture of the powder blend and a wet granule prepared with a high-speed granulator were tested as samples in this study. Precise determination of water content in the powder blend was achieved by using the TD-NMR method. The accuracy of water content determination was equivalent to or better than that of the conventional loss on drying method. TD-NMR analysis samples were measured nondestructively and rapidly with low cost; thus, it could be a powerful quantitative method for determining water content in pharmaceuticals.