Establishment of prediction model for personalized water-paste pills based on classification of traditional Chinese medicine materials / 中国中药杂志

The purpose of the present study was to investigate the relationship of the classification of traditional Chinese medicine(TCM) materials with the suitable binder concentration and dosage in the preparation of personalized water-paste pills and establish a model for predicting the binder concentration and dosage. Five representative TCM materials were selected, followed by mixture uniform design. The water-paste pills were prepared by extrusion and spheronization with hypromellose E5(HPMC E5) as the binder. The quality of intermediates and final products was evaluated, and the resulting data were subjected to multivariate statistical analysis. The prediction models for binder concentration and dosage were established as follows: binder concentration: Y_1=0.378 6 + 0.570 1X_A + 2.271 2X_B-0.894 5X_C-0.458 2X_D-1.145 4X_E(when Y_1 < 0, 10% HPMC E5 was required; when Y_1 > 0, 20% HPMC E5 was required), with the accuracy reaching up to 100%; binder dosage: Y_2=32.38 + 0.25X_A + 1.85X_B-0.013X_B~2-0.002 5X_C~2(R~2=0.932 6, P < 0.001). The results showed that the binder concentration and dosage were correlated positively with the proportion of fiber material but negatively with the proportions of sugar material and brittle material. Then the validation experiments were conducted with the prediction models and all the prescriptions could be successfully prepared at one time. These demonstrated that following the classification of TCM materials and the calculation of their proportions in the prescription, the established mathematical model could be adopted for predicting the binder concentration and dosage required in the preparation of personalized water-paste pills, which contributed to reducing the pre-formulation research and guiding the actual production of personalized water-paste pills.

Material classification of decoction pieces based on physical properties of powder / 中国中药杂志

Chinese medicinals feature different medicinal parts and enriched components, which makes their powders show obvious microscopic identification characteristics and specific physical properties. On this basis, the commonly used Chinese medicinals can be divided into several categories, such as powdery, fibrous, sugar, oil, and brittle materials, which is of great importance to the research and development of personalized Chinese medicinal preparation technology. However, the existing classification methods are highly subjective and thus difficult to meet the requirements for the development of personalized Chinese medicinal preparations with high quality. In this study, 55 representative Chinese medicinals, such as Dioscoreae Rhizoma and Leonuri Herba, were selected, and the physical properties of their powders were systematically characterized by comprehensive powder tester, torque rheometer, texture analyzer, etc., based on which a data set encompassing physical properties of these powders was built. The typical physical fingerprints of powders from the above 5 categories were established by multivariate statistical analysis. Then, the Chinese medicinals were classified according to the Euclidean distance between each of them and the typical value in the PCA score plot. For those with multiple material properties, whose classification boundary was fuzzy, the proportions of different types of materials were calculated with the combination of Euclidean distance, powder properties, microscopic identification characteristics, and chemical composition, so as to achieve the multivariate quantitative classification of Chinese medicinals. This lays the foundation for the further creation of intelligent personalized Chinese medicinal preparation technology.