Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

Zhengsong Wang; Shengnan Tang; Yanqiu Yang; Yeqiu Chen; Le Yang

doi:10.1021/acsomega.3c02199

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

ACS Omega. 2023 Jun 29;8(27):24441-24453. doi: 10.1021/acsomega.3c02199. eCollection 2023 Jul 11.

Authors

Zhengsong Wang^{1

2}, Shengnan Tang^{1

2}, Yanqiu Yang³, Yeqiu Chen¹, Le Yang^{1

2}

Affiliations

¹ School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
² College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
³ College of Life and Health Sciences, Northeastern University, Shenyang 110169, China.

Abstract

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.