Wan Xin-Hao, Zhong Zhi-Jian, Tao Qing, Wang Zi-Qian, Liao Jia-Li, Yang Dong-Yin, Yang Ming, Luo Xiao-Rong, Wu Zhen-Feng
Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Chinese Medicine Nanchang 330004, China.
Jiangzhong Pharmaceutical Co., Ltd. Nanchang 330100, China.
Zhongguo Zhong Yao Za Zhi. 2024 Dec;49(24):6565-6573. doi: 10.19540/j.cnki.cjcmm.20241022.301.
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.
关键物料属性（CMA）的识别是健胃消食片等大规模中药产品质量控制中的关键问题。本研究聚焦于健胃消食片颗粒，将片剂抗张强度作为主要质量属性。建立了一种识别CMA的方法和颗粒的设计空间，以及基于傅里叶变换近红外光谱（FT-NIR）的颗粒CMA预测模型。首先，采用实验设计（DOE）中的部分因子设计方法制备了具有不同性质的健胃消食片颗粒。测定了颗粒的粉体性质。建立了正交偏最小二乘法（OPLS）模型，将粉体性质与抗张强度相关联。根据OPLS提取的综合变量的特征，确定了对抗张强度解释力最大的自变量。然后采用FT-NIR技术建立颗粒CMA的预测模型。最终确定的CMA为吸湿性、水分含量、D50、崩溃角、质量流率和振实密度。预测集的决定系数（R■）以及流动性、D50和水分含量预测集的相对百分比偏差（RPD）分别为：0.891、0.994和0.998；以及2.97、12.4和20.7。所建立的OPLS模型清晰地识别了各种因素对抗张强度的影响，拟合效果良好。该模型具有较高的预测准确性，可用于快速准确地测定健胃消食片颗粒中的CMA。
