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Quality by Design for Herbal Drugs: a Feedforward Control Strategy and an Approach to Define the Acceptable Ranges of Critical Quality Attributes
Author(s) -
Yan Binjun,
Li Yao,
Guo Zhengtai,
Qu Haibin
Publication year - 2013
Publication title -
phytochemical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 72
eISSN - 1099-1565
pISSN - 0958-0344
DOI - 10.1002/pca.2463
Subject(s) - critical quality attributes , quality by design , quality (philosophy) , process (computing) , feed forward , salvia miltiorrhiza , raw material , linear regression , chemistry , process engineering , biochemical engineering , computer science , machine learning , engineering , control engineering , particle size , medicine , philosophy , alternative medicine , organic chemistry , epistemology , traditional chinese medicine , pathology , operating system
The concept of quality by design (QbD) has been widely accepted and applied in the pharmaceutical manufacturing industry. There are still two key issues to be addressed in the implementation of QbD for herbal drugs. The first issue is the quality variation of herbal raw materials and the second issue is the difficulty in defining the acceptable ranges of critical quality attributes (CQAs). Objective To propose a feedforward control strategy and a method for defining the acceptable ranges of CQAs for the two issues. Methods In the case study of the ethanol precipitation process of Danshen (Radix Salvia miltiorrhiza ) injection, regression models linking input material attributes and process parameters to CQAs were built first and an optimisation model for calculating the best process parameters according to the input materials was established. Then, the feasible material space was defined and the acceptable ranges of CQAs for the previous process were determined. Results In the case study, satisfactory regression models were built with cross‐validated regression coefficients ( Q 2 ) all above 91 %. The feedforward control strategy was applied successfully to compensate the quality variation of the input materials, which was able to control the CQAs in the 90–110 % ranges of the desired values. In addition, the feasible material space for the ethanol precipitation process was built successfully, which showed the acceptable ranges of the CQAs for the concentration process. Conclusion The proposed methodology can help to promote the implementation of QbD for herbal drugs. Copyright © 2013 John Wiley & Sons, Ltd.