Predictive model for hygroscopicity of contents in Guizhi Fuling Capsules.
10.19540/j.cnki.cjcmm.20191219.302
- Author:
Qing WANG
1
;
Bing XU
2
;
Fen WANG
2
;
Fang-Fang XU
3
;
Xin ZHANG
3
;
Yong-Chao ZHANG
4
;
Hui DU
4
;
Chun-Yan XIA
4
;
Le-Wei BAO
3
;
Zhen-Zhong WANG
3
;
Yan-Jiang QIAO
2
;
Wei XIAO
5
Author Information
1. Nanjing University of Chinese Medicine Nanjing 210023, China Jangsu Kanion Pharmaceutical Co., Ltd. Lianyungang 222001, China.
2. Department of Chinese Medicine Information Science, Beijing University of Chinese Medicine Beijing 102400, China.
3. Jangsu Kanion Pharmaceutical Co., Ltd. Lianyungang 222001, China State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process Lianyungang 222001, China National & Local Joint Engineering Research Center on Intelligent manufacturing of TCM Lianyungang 222001, China.
4. Nanjing University of Chinese Medicine Nanjing 210023, China.
5. Nanjing University of Chinese Medicine Nanjing 210023, China Jangsu Kanion Pharmaceutical Co., Ltd. Lianyungang 222001, China State Key Laboratory of New-tech for Chinese Medicine Pharmaceutical Process Lianyungang 222001, China National & Local Joint Engineering Research Center on Intelligent manufacturing of TCM Lianyungang 222001, China.
- Publication Type:Journal Article
- Keywords:
Guizhi Fuling Capsules;
hygroscopicity;
model robustness;
physical fingerprint;
predictive model
- MeSH:
Capsules;
Drug Compounding;
Drugs, Chinese Herbal/chemistry*;
Powders;
Wettability
- From:
China Journal of Chinese Materia Medica
2020;45(2):242-249
- CountryChina
- Language:Chinese
-
Abstract:
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.
