Data-driven Adaptive Quality Control Under Uncertain Conditions for a Cyber-Pharmaceutical-Development System

Pharmaceutical quality control (PQC) holds a critical position in quality by design-based pharmaceutical development, but development costs are seldom considered due to the overly important role of drugs. Furthermore, in the context of the Made in China 2025 and Industry 4.0 strategies, drug customization brings about uncertainty embodied as frequently changing critical material attributes, which presents new challenges related to development cost and efficiency in PQC compared to traditional model-based control. The pursuit of optimal cost and efficiency while ensuring high quality will always be a hot topic of discussion because it is an eternal theme of technological revolutions. In this article, we first introduce the idea of a cyber–physical system into pharmaceutical development to propose the concept of a cyber-pharmaceutical-development system (CPDS) for the first time, and then we present a general data-driven adaptive PQC framework for the CPDS. Next, a case study is presented to preliminarily apply the proposed framework in a simulated pharmaceutical granulation-tabletting process. Finally, a series of simulation experiments are designed to verify the feasibility and effectiveness of the simulation modeling and the proposed PQC framework.