Studying on purification technology of Resina Draconis phenol extracts based on design space method.
10.19540/j.cnki.cjcmm.20170801.002
- Author:
Jian ZHANG
1
;
Xin ZHANG
2
;
Yu-An BI
2
;
Gui-Hong XU
2
;
Wen-Zhe HUANG
2
;
Zhen-Zhong WANG
2
;
Wei XIAO
1
Author Information
1. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
2. Jiangsu Kanion Pharmaceutical Co., Ltd., Lianyungang 222001, China.
- Publication Type:Journal Article
- Keywords:
Resina Draconis phenol extracts;
critical process parameters;
critical quality attributes;
design space;
refining process
- From:
China Journal of Chinese Materia Medica
2017;42(18):3546-3552
- CountryChina
- Language:Chinese
-
Abstract:
The "design space" method was used to optimize the purification process of Resina Draconis phenol extracts by using the concept of "quality derived from design" (QbD). The content and transfer rate of laurin B and 7,4'-dihydroxyflavone and yield of extract were selected as the critical quality attributes (CQA). Plackett-Burman design showed that the critical process parameters (CPP) were concentration of alkali, the amount of alkali and the temperature of alkali dissolution. Then the Box-Behnken design was used to establish the mathematical model between CQA and CPP. The variance analysis results showed that the P values of the five models were less than 0.05 and the mismatch values were all greater than 0.05, indicating that the model could well describe the relationship between CQA and CPP. Finally, the control limits of the above 5 indicators (content and transfer rate of laurine B and 7,4'-dihydroxyflavone, as well as the extract yield) were set, and then the probability-based design space was calculated by Monte Carlo simulation and verified. The results of the design space validation showed that the optimized purification method can ensure the stability of the Resina Draconis phenol extracts refining process, which would help to improve the quality uniformity between batches of phenol extracts and provide data support for production automation control.
