[Establishment of prediction model for personalized water-paste pills based on classification of traditional Chinese medicine materials].

2021 
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, 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.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    0
    Citations
    NaN
    KQI
    []