https://doi.org/10.1016/j.apsb.2022.08.011

This new article publication from Acta Pharmaceutica Sinica B, discusses data-driven engineering framework with AI algorithm for manufacturing Ginkgo Folium tablets.

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 provides a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

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

Graphical Abstract: available at https://ars.els-cdn.com/content/image/1-s2.0-S2211383522003628-ga1_lrg.jpg   

The innovative data-driven engineering strategy integrated artificial intelligence (Al) algorithms was proposed for process capability and end-to-end quality traceability of intelligent pharmaceutical manufacturing.

# # # # # #

The Journal of the Institute of Materia Medica, the Chinese Academy of Medical Sciences and the Chinese Pharmaceutical Association.

For more information please visit https://www.journals.elsevier.com/acta-pharmaceutica-sinica-b/

Editorial Board: https://www.journals.elsevier.com/acta-pharmaceutica-sinica-b/editorial-board

APSB is available on ScienceDirect (https://www.sciencedirect.com/journal/acta-pharmaceutica-sinica-b).

Submissions to APSB may be made using Editorial Manager® (https://www.editorialmanager.com/apsb/default.aspx).

CiteScore: 15.9

Impact Factor: 14.907

JIF without self-citation: 13.885

ISSN 2211-3835

 # # # # #

Lijuan Ma, Jing Zhang, Ling Lin, Tuanjie Wang, Chaofu Ma, Xiaomeng Wang, Mingshuang Li, Yanjiang Qiao, Yongxiang Wang, Guimin Zhang, Zhisheng Wu, Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing, Acta Pharmaceutica Sinica B, Volume 13, Issue 5, 2023, Pages 2188-2201, ISSN 2211-3835, https://doi.org/10.1016/j.apsb.2022.08.011