Xia Chun-Yan, Xu Bing, Xu Fang-Fang, Zhang Xin, Wang Qing, DU Hui, Bao Le-Wei, Wang Zhen-Zhong, Qiao Yan-Jiang, Xiao Wei
Nanjing University of Chinese Medicine Nanjing 210023, China Jiangsu Kanion Pharmaceutical Co., Ltd.,State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process, National & Local Joint Engineering Research Center on Intelligent Manufacturing of Traditional Chinese Medicine, Key Laboratory of New Technology for Extraction and Refining of Traditional Chinese Medicine Lianyungang 222001, China.
Department of Chinese Medicine Information Science, Beijing University of Chinese Medicine Beijing 102400, China.
Zhongguo Zhong Yao Za Zhi. 2020 Jan;45(2):250-258. doi: 10.19540/j.cnki.cjcmm.20191219.303.
In this paper, a real time release testing(RTRT) model for predicting the disintegration time of Tianshu tablets was established on the basis of the concept of quality by design(QbD), in order to improve the quality controllability of the production process. First, 49 batches of raw materials and intermediates were collected. Afterwards, the physical quality attributes of all materials were comprehensively characterized. The partial least square(PLS) regression model was established with the 72 physical quality attributes of raw materials and intermediates as input and the disintegration time(DT) of uncoated tablets as output. Then, the variable screening was carried out based on the variable importance in the projection(VIP) indexes. Moisture content of raw materials(%HR), tapped density of wet masses(D_c), hygroscopicity of dry granules(%H), moisture content of milling granules(%HR) and Carr's index of mixed granules(IC) were determined as the potential critical material attributes(pCMAs). According to the effects of interactions of pCMAs on the performance of the prediction model, it was finally determined that the wet masses' D_c and the dry granules'%H were critical material attributes(CMAs). A RTRT model of the disintegration time prediction was established as DT=34.09+2×D_c+3.59×%H-5.29×%H×D_c,with R2 equaling to 0.901 7 and the adjusted R2 equaling to 0.893 3. The average relative prediction error of validation set for the RTRT model was 3.69%. The control limits of the CMAs were determined as 0.55 g·cm(-3)<D_c<0.63 g·cm(-3) and 4.77<%H<7.59 according to the design space. The RTRT model of the disintegration time reflects the understanding of the process system, and lays a foundation for the implementation of intelligent control strategy of the key process of Tianshu Tablets.
本文基于质量源于设计(QbD)理念,建立了预测天舒片崩解时间的实时放行检测(RTRT)模型,以提高生产过程的质量可控性。首先,收集了49批原材料和中间体。随后,全面表征了所有物料的物理质量属性。以原材料和中间体的72个物理质量属性为输入、素片崩解时间(DT)为输出,建立了偏最小二乘(PLS)回归模型。然后,基于投影变量重要性(VIP)指标进行变量筛选。确定原材料水分含量(%HR)、湿物料振实密度(D_c)、干颗粒吸湿率(%H)、制粒后颗粒水分含量(%HR)和混合颗粒卡尔指数(IC)为潜在关键物料属性(pCMA)。根据pCMA相互作用对预测模型性能的影响,最终确定湿物料的D_c和干颗粒的%H为关键物料属性(CMA)。建立了崩解时间预测的RTRT模型为DT = 34.09 + 2×D_c + 3.59×%H - 5.29×%H×D_c,R²等于0.901 7,调整后的R²等于0.893 3。RTRT模型验证集的平均相对预测误差为3.69%。根据设计空间,将CMA的控制限确定为0.55 g·cm⁻³ < D_c < 0.63 g·cm⁻³和4.77 < %H < 7.59。崩解时间的RTRT模型反映了对过程系统的理解,为天舒片关键过程智能控制策略的实施奠定了基础。