Optimization of the Prescription of Xiakucao Kaiyin Granules Based on Mixture Design Combined with G1-Entropy Weight Method and Neural Network
10.14148/j.issn.1672-0482.2024.1404
- VernacularTitle:基于混料设计结合G1-熵权法和神经网络的夏枯草开音颗粒制剂处方优化
- Author:
Zhengzheng WANG
1
;
Shenghua WANG
;
Chenfeng ZHANG
;
Ming YAN
;
Enli ZHOU
;
Wei XIAO
Author Information
1. 中药制药过程控制与智能制造技术全国重点实验室(江苏康缘药业股份有限公司/南京中医药大学),江苏 南京 210000;南京中医药大学康缘中药学院,江苏 南京 210023
- Publication Type:Journal Article
- Keywords:
mixture design;
dry granulation;
G1-entropy weight method;
neural network;
Physical fingerprint
- From:
Journal of Nanjing University of Traditional Chinese Medicine
2024;40(12):1404-1413
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To optimize the preparation of Xiakucao Kaiyin Granules(XKG)and control the quality of its intermedi-ates.METHODS The physical characteristics of spray dry powder of XKG were determined by powder evaluation method,and the physical fingerprint composed of 9 secondary physical quality indexes,such as bulk density,tap density,angle of repose and Hausner ratio,was established to determine the stability of the previous process and the quality consistency of spray dry powder.Taking the parti-cle forming rate,dissolution rate,moisture absorption rate and angle of repose as evaluation indexes,dry granulation was carried out,and the auxiliary materials of XKG were screened.The mixture design experiment combined with G1-entropy weight method and neural network method were used to optimize the proportion of the selected excipients,and the best preparation technology of XKG was deter-mined by comparing the two methods.The physical fingerprint of particles was established to evaluate the consistency of particle quality among different batches.RESULTS The similarity of physical fingerprints of 9 batches of spray dry powder was greater than 0.970,and the physical properties were stable.The comprehensive score of the best proportion of auxiliary materials obtained through the anal-ysis of mixture design was higher than that obtained by neural network modeling and optimization,so it was finally determined that the proportion of medicine and auxiliary materials of XKG was 7:3 and 29%maltodextrin and 71%lactose were added for dry granulation.The similarity of physical fingerprints of five batches of granule was greater than 0.994.CONCLUSION The estab-lished physical fingerprint of intermediates can be used to control the quality process of XKG,and the optimized prescription of XKG can improve the physical properties of granules and improve the consistency of granule quality.
