https://doi.org/10.1016/j.apsb.2025.06.001
This new article publication from
Acta Pharmaceutica Sinica B, discusses an AI-integrated quality prediction and diagnostics framework that could transform small-sample pharmaceutical manufacturing from experience-driven practices to data-driven precision.
The pharmaceutical industry faces challenges in quality digitization for complex multi-stage processes, especially in small-sample systems. This article discusses how an intelligent quality prediction and diagnostic (IQPD) framework was developed and applied to Tong Ren Tang's Niuhuang Qingxin Pills, utilizing four years of data collected from four production units, covering the entire process from raw materials to finished products. In this framework, a novel path-enhanced double ensemble quality prediction model (PeDGAT) is proposed, which combines a graph attention network and path information to encode inter-unit long-range and sequential dependencies. Additionally, the double ensemble strategy enhances model stability in small samples. Compared to global traditional models, PeDGAT achieves state-of-the-art results, with an average improvement of 13.18% and 87.67% in prediction accuracy and stability on three indicators. Additionally, a more in-depth diagnostic model leveraging grey correlation analysis and expert knowledge reduces reliance on large samples, offering a panoramic view of attribute relationships across units and improving process transparency. Finally, the IQPD framework integrates into a Human–Cyber–Physical system, enabling faster decision-making and real-time quality adjustments for Tong Ren Tang's Niuhuang Qingxin Pills, a product with annual sales exceeding 100 million CNY. This facilitates the transition from experience-driven to data-driven manufacturing.
Keywords: Smart manufacturing, Artificial intelligence, Intelligent quality prediction and diagnostics, Small-sample multi-unit manufacturing, Data-driven manufacturing, Real-world Tong Ren Tang's Niuhuang Qingxin Pills
Graphical Abstract: available at
https://ars.els-cdn.com/content/image/1-s2.0-S2211383525003806-ga1_lrg.jpg
AI-integrated IQPD framework for intelligent quality prediction and diagnostics in small-sample multi-unit pharmaceutical manufacturing.
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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
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CiteScore: 24.3
Impact Factor: 14.6 (Top 6 journal in the category of Pharmacology and pharmacy)
JIF without self-citation: 13.8
ISSN 2211-3835
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Kaiyi Wang, Xinhai Chen, Nan Li, Huimin Feng, Xiaoyi Liu, Yifei Wang, Yanfei Wu, Yufeng Guo, Shuoshuo Xu, Lu Yao, Zhaohua Zhang, Jun Jia, Zhishu Tang, Zhisheng Wu, AI-integrated IQPD framework of quality prediction and diagnostics in small-sample multi-unit pharmaceutical manufacturing: Advancing from experience-driven to data-driven manufacturing, Acta Pharmaceutica Sinica B, Volume 15, Issue 8, 2025, Pages 4193-4209, ISSN 2211-3835,
https://doi.org/10.1016/j.apsb.2025.06.001