Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing  

作　　者：Lijuan Ma Jing Zhang Ling Lin Tuanjie Wang Chaofu Ma Xiaomeng Wang Mingshuang Li Yanjiang Qiao Yongxiang Wang Guimin Zhang Zhisheng Wu 

机构地区：[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

出　　处：《Acta Pharmaceutica Sinica B》2023年第5期2188-2201,共14页药学学报（英文版）

基　　金：co-National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(Grant No.82022073,China);Major scientific and technological R&D projects in Jiangxi Province(Grant No.20203ABC28W018,China);National Key Research and Development Program of China(Grant No.2018YFC1706900,China)。

摘　　要：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 C_(pk)integrated Bootstrap-t.The C_(pk)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.

关 键 词：Smart manufacturing Data-driven engineering Artificial intelligence Information fusion Process capability index End-to-end Quality traceability Real-world Ginkgo Folium products 

分 类 号：R9[医药卫生—药学]