The FDA’s Five Areas of Consideration for AI in Drug Manufacturing
September 2024
Artificial Intelligence (AI) has been a topic of conversation in the life sciences industry for years now. While some have made progress adopting this technology, most are beginning to explore the possibilities it offers across the value chain, and just scratching the surface on the opportunities it holds for drug manufacturing.
As it transforms the manufacturing space, the FDA has been evaluating different use cases and how to regulate it in efforts to help inform the existing regulatory framework. In 2023, the Agency published a discussion paper which dove deeper into the uses and potential risks of AI in drug manufacturing, as part of the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) Initiative.
In addition to providing some potential applications of AI in pharmaceutical manufacturing, the FDA also presented some areas of consideration they were seeking feedback on. These areas focused on the manufacture of drug products that would be marketed under a New Drug Application (NDA), Abbreviated New Drug Application (ANDA), or Biologics License Application (BLA).
The FDA’s Artificial Intelligence in Drug Manufacturing paper lists the following five areas of consideration associated with AI:
- “Cloud applications may affect oversight of pharmaceutical manufacturing data and records.
- The IOT [Internet of Things] may increase the amount of data generated during pharmaceutical manufacturing, affecting existing data management practices.
- Applicants may need clarity about whether and how the application of AI in pharmaceutical manufacturing is subject to regulatory oversight.
- Applicants may need standards for developing and validating AI models used for process control and to support release testing.
- Continuously learning AI systems that adapt to real-time data may challenge regulatory assessment and oversight.”
All in all, these areas of consideration pointed to some key themes the FDA is evaluating; ensuring data integrity and quality, managing the potential influx of data, identifying which applications will require regulatory oversight, AI model development and validation, and managing expectations for continuous AI learning systems.
The discussion paper also highlights some potential associated requirements and policies. You can read the FDA’s Artificial Intelligence in Drug Manufacturing discussion paper here.
Sources:
FDA Voices: FDA releases papers on AI and Machine Learning in drug development and manufacturing