RESUMEN
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.
Asunto(s)
Excipientes , Redes Neurales de la Computación , Microscopía Electrónica de RastreoRESUMEN
An enantio- and stereoselective construction of the atisane scaffold via organocatalytic intramolecular Michael reaction and Diels-Alder reaction is described. The organocatalytic intramolecular Michael reaction has been found to stereoselectively generate a trans-stereodiad comprising an all-carbon quaternary and a tertiary stereogenic centers. Use of the chiral secondary amine bearing thiourea with benzoic acid as additive is the key to obtaining the desired product with excellent ee in synthetically acceptable yield. The prepared chiral building block has been successfully converted to the compound including the atisane scaffold via the highly stereoselective intramolecular Diels-Alder reaction.