Advancing process control toward intelligent continuous biopharmaceutical manufacturing: Challenges, strategies, and future directions

Continuous biomanufacturing is gaining significant attention in biopharmaceutical production, offering enhanced productivity, scalability, and process consistency compared with conventional batch and fed-batch operations. Regulatory initiatives, including the FDA's Quality by Design (QbD) framework and the ICH Q13 guideline, emphasize the need for robust operational control to ensure consistent product quality under continuous processing. Within this context, process control has advanced from conventional feedback approaches to advanced model-based and data-driven strategies, such as model predictive control (MPC) and reinforcement learning (RL). This review provides a comprehensive and systematic analysis of control strategies for continuous biopharmaceutical manufacturing, across unit operations from upstream cell culture to downstream purification. Key control objectives and representative case studies are discussed for each unit operation, emphasizing their roles in maintaining stable operation and product quality. Furthermore, this review discusses how advanced process analytical technologies (PAT), model-based control, and digital twin (DT) frameworks can be integrated into sensing-modeling-control architectures tailored to interconnected and long-duration continuous biomanufacturing. This perspective provides a basis for developing predictive, adaptive, and risk-aware control systems that support a sustained state of control in continuous biopharmaceutical manufacturing.