FDA (CDER) posted this discussion paper regarding the use of Artificial Intelligence in pharmaceutical manufacturing.
This discussion paper presents areas for consideration and potential policy development that CDER identified based on evaluating the application of the existing regulatory framework to use of AI in pharmaceutical manufacturing. A regulatory framework for advanced manufacturing evaluation will address these areas while also considering how potential changes could affect existing technologies and facilities.
Through interactions with industry, FDA has received valuable feedback, including potential AI use cases in pharmaceutical manufacturing. Below are examples, based on these interactions and a review of published information, that forecast how AI might be used in pharmaceutical manufacturing. These examples are not exhaustive and the potential applications of AI in pharmaceutical manufacturing may continue evolving.
Process Design and Scale-up
AI models such as machine learning—generated using process development data—could be leveraged to more quickly identify optimal processing parameters or scale-up processes, reducing development time and waste.
Process Monitoring and Fault Detection
AI methods can be used to monitor equipment and detect changes from normal performance that trigger maintenance activities, reducing process downtime.
Advanced Process Control (APC)
APC allows dynamic control of the manufacturing process to achieve a desired output. AI methods can also be used to develop process controls that can predict the progression of a process by using AI in combination with real-time sensor data.
Trend Monitoring
AI can be used to examine consumer complaints and deviation reports containing large volumes of text to identify cluster problem areas and prioritize areas for continual improvement. This offers the advantage of identifying trends in manufacturing-related deviations to support a more comprehensive root cause identification.
Pharmaceutical Areas of Consideration Associated with AI (Artificial Intelligence)
1. Cloud applications may affect oversight of pharmaceutical manufacturing data and records.
2. The IOT may increase the amount of data generated during pharmaceutical manufacturing, affecting existing data management practices.
3. Applicants may need clarity about whether and how the application of AI in pharmaceutical manufacturing is subject to regulatory oversight.
4. Applicants may need standards for developing and validating AI models used for process control and to support release testing.
5. Continuously learning AI systems that adapt to real-time data may challenge regulatory assessment and oversight.
Disclaimer: This paper is for discussion purposes only and is not a draft or final guidance. It is meant to facilitate early input from stakeholders outside the Agency. The Agency intends to consider such input in developing a future regulatory framework.
