中国中药杂志

2020, v.45(02) 242-249

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桂枝茯苓胶囊内容物吸湿性预测建模研究
Predictive model for hygroscopicity of contents in Guizhi Fuling Capsules

王晴;徐冰;王芬;徐芳芳;张欣;张永超;杜慧;夏春燕;包乐伟;王振中;乔延江;肖伟;
WANG Qing;XU Bing;WANG Fen;XU Fang-fang;ZHANG Xin;ZHANG Yong-chao;DU Hui;XIA Chun-yan;BAO Le-wei;WANG Zhen-zhong;QIAO Yan-jiang;XIAO Wei;Nanjing University of Chinese Medicine;Jangsu Kanion Pharmaceutical Co., Ltd.;Department of Chinese Medicine Information Science, Beijing University of Chinese Medicine;State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process;National & Local Joint Engineering Research Center on Intelligent manufacturing of TCM;

摘要(Abstract):

为控制桂枝茯苓胶囊内容物结块和囊壳变脆风险,该文以建立桂枝茯苓胶囊内容物吸湿性预测模型为目标,共收集了90批次桂枝茯苓胶囊制剂成型过程原料、中间体粉末和胶囊成品。按照生产时间自然排序,以47批作为校正集,采用物理指纹图谱的方法对分别制剂原料和4种中间体粉末进行了物性综合表征,以内容物吸湿性为响应变量,采用偏最小二乘(PLS)算法,结合变量重要性投影(VIP)、方差膨胀因子(VIF)和回归系数指标,从54个物性参数中筛选出5个潜在关键物料属性(pCMAs)。进一步结合校正模型对验证集43个生产批次的预测稳健性评价,最终确定湿法制粒所得湿颗粒的振实密度(D_c)和原料细粉的休止角(α)2个物性参数为影响桂枝茯苓胶囊内容物吸湿性的关键物料属性(CMAs)。以CMAs为自变量建立的预测模型,对验证集样本的平均相对预测误差为2.68%,表明预测精度良好。该文初步证明了中药口服固体制剂智能制造中,以产品质量持续改进为导向、以生产大数据为驱动、以工艺模型为核心的研究思路的可行性,为其他中药口服固体制剂智能生产研究提供参考。
To control the risks of powder caking and capsule shell embrittlement of Guizhi Fuling Capsules, a predictive model for hygroscopicity of contents in Guizhi Fuling Capsules was built. A total of 90 batches of samples, including raw materials, intermediate powders and capsules, were collected during the manufacturing of Guizhi Fuling Capsules. According to the production sequence, 47 batches were used as the calibration set, and the properties of raw materials and the four intermediate powders were comprehensively characterized by the physical fingerprint. Then, the partial least squares(PLS) model was developed with the content hygroscopicity as the response variable. The variable importance in projection(VIP), variance inflation factor(VIF) and regression coefficients were used to screen out potential critical material attributes(pCMAs). As a result, five pCMAs from 54 physical parameters were screened out. Furthermore, different models were built by different combinations of pCMAs, and their predictive robustness of 43 batches was evaluated on the basis of the validation set. Finally, the tap density(D_c) of wet granules obtained from wet granulation and the angle of repose(α) of raw materials were identified as the critical material attributes(CMAs) affecting the hygroscopicity of the contents of Guizhi Fuling Capsules. The prediction model established with the two CMAs as independent variables had an average relative prediction error of 2.68% for samples in the validation set, indicating a good accuracy of prediction. This paper proved the feasibility of predictive modeling toward the control of critical quality attributes of Chinese medicine oral solid dosage(OSD). The combination of the continuous quality improvement, the industrial big data and the process modeling technique paved the way for the intelligent manufacturing of Chinese medicine oral solid preparations.

关键词(KeyWords): 桂枝茯苓胶囊;吸湿性;预测模型;物理指纹图谱;模型稳健性
Guizhi Fuling Capsules;hygroscopicity;predictive model;physical fingerprint;model robustness

Abstract:

Keywords:

基金项目(Foundation): 国家“重大新药创制”科技重大专项(2018ZX09201010-004);; 国家工信部智能制造综合标准化与新模式应用项目(KYYY20170820);; 国家中药标准化专项(ZYBZH-C-J-28)

作者(Author): 王晴;徐冰;王芬;徐芳芳;张欣;张永超;杜慧;夏春燕;包乐伟;王振中;乔延江;肖伟;
WANG Qing;XU Bing;WANG Fen;XU Fang-fang;ZHANG Xin;ZHANG Yong-chao;DU Hui;XIA Chun-yan;BAO Le-wei;WANG Zhen-zhong;QIAO Yan-jiang;XIAO Wei;Nanjing University of Chinese Medicine;Jangsu Kanion Pharmaceutical Co., Ltd.;Department of Chinese Medicine Information Science, Beijing University of Chinese Medicine;State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process;National & Local Joint Engineering Research Center on Intelligent manufacturing of TCM;

Email:

DOI: 10.19540/j.cnki.cjcmm.20191219.302

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