CDER Regulatory Science Research (RSR) Projects AI for Process Control in Advanced Manufacturing
Description
Need for better process control in continuous manufacturing and development of soft sensors for real-time release testing strategies.
Detailed example
The AI model demonstrated remarkable performance in setpoint tracking and disturbance rejection for a digital continuous manufacturing line, underscoring the potential of AI-based control strategies in enhancing product quality and regulatory assessment.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The outcomes of this work can be used to gain a better understanding of AI in advanced pharmaceutical manufacturing control, identify the associated risks, and help review future submissions involving this technology, ultimately supporting more efficient and reliable drug manufacturing.
Audit / financial statement impact
This project explores the use of AI in advanced pharmaceutical manufacturing as part of an exploratory R&D effort focused on model predictive control strategies. The AI components are used solely in a research context to improve understanding of AI-enabled control systems and inform future regulatory readiness. The project does not involve operational use, decision-making, or direct impact on the public or regulated entities. Therefore, it does not meet the definition of a high-impact AI use case under OMB M-25-21.
Controls / human review
ATO: No; PIA: Not published
Data needed
Data was generated using a digital twin of a manufacturing plant developed in-house