Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing.
10.1016/j.apsb.2022.08.011
- Author:
Lijuan MA
1
;
Jing ZHANG
1
;
Ling LIN
1
;
Tuanjie WANG
2
;
Chaofu MA
1
;
Xiaomeng WANG
1
;
Mingshuang LI
1
;
Yanjiang QIAO
1
;
Yongxiang WANG
3
;
Guimin ZHANG
4
;
Zhisheng WU
1
Author Information
1. Beijing University of Chinese Medicine, Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing 100029, China.
2. Jiangsu Kanion Pharmaceutical Co., Ltd., State Key Laboratory of New Technology in Pharmaceutical Process of Traditional Chinese Medicine, Lianyungang 222001, China.
3. Yangtze River Pharmaceutical (Group) Co., Ltd., State Pharmaceutical Engineering Technology Research Center of Traditional Chinese Medicine, Taizhou 225321, China.
4. Lunan Pharmaceutical Group Co., Ltd., State Key Laboratory of Generic Technology of Traditional Chinese Medicine, Linyi 276005, China.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Data-driven engineering;
End-to-end;
Information fusion;
Process capability index;
Quality traceability;
Real-world Ginkgo Folium products;
Smart manufacturing
- From:
Acta Pharmaceutica Sinica B
2023;13(5):2188-2201
- CountryChina
- Language:English
-
Abstract:
Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.
