Identification of CMAs of Jianwei Xiaoshi Tablet granules based on QbD concept and construction of their predictive model.
10.19540/j.cnki.cjcmm.20241022.301
- Author:
Xin-Hao WAN
1
;
Zhi-Jian ZHONG
2
;
Qing TAO
3
;
Zi-Qian WANG
4
;
Jia-Li LIAO
1
;
Dong-Yin YANG
1
;
Ming YANG
1
;
Xiao-Rong LUO
2
;
Zhen-Feng WU
1
Author Information
1. Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Chinese Medicine Nanchang 330004, China.
2. Jiangzhong Pharmaceutical Co., Ltd. Nanchang 330100, China.
3. School of Computer Science, Jiangxi University of Chinese Medicine Nanchang 330004, China.
4. Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Chinese Medicine Nanchang 330004, China Jiangxi Drug Inspection Center Nanchang 330000, China.
- Publication Type:Journal Article
- Keywords:
Fourier transform near-infrared spectroscopy;
critical material attributes;
design space;
orthogonal partial least squares;
predictive model
- MeSH:
Drugs, Chinese Herbal/chemistry*;
Tablets/chemistry*;
Tensile Strength;
Quality Control;
Spectroscopy, Fourier Transform Infrared;
Spectroscopy, Near-Infrared
- From:
China Journal of Chinese Materia Medica
2024;49(24):6565-6573
- CountryChina
- Language:Chinese
-
Abstract:
Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
