The FDA has substantial experience in reviewing regulatory submissions with AI components. Since 2016, the use of AI in drug development and in regulatory submissions has exponentially increased. AI can be used in various ways to produce data or information regarding the safety, effectiveness, or quality of a drug or biological product. AI approaches can be used to predict patient outcomes, improve understanding of predictors of disease progression and process and analyze large datasets (e.g., real-world data sources or data from digital health technologies). FDA recognizes that the use of artificial intelligence (AI) in drug development is broad and rapidly evolving.
WHY SHOULD YOU ATTEND?
On January 6, 2025, the U.S. Food and Drug Administration issued draft guidance to provide recommendations on the use of artificial intelligence (AI) intended to support a regulatory decision about a drug or biological product’s safety, effectiveness, or quality. This is the first guidance the agency has issued on the use of AI for the development of drug and biological products. Understand the seven-step process for providing a risk-based credibility assessment framework. FDA cites to various “examples of AI use for producing information or data intended to support regulatory decision-making,” including the use of predictive modeling, integrating data from various sources, and processing and analyzing large sets of data.
AREA COVERED
- A key aspect to the appropriate application of AI modeling in drug development and regulatory evaluation is ensuring model credibility—trust in the performance of an AI model.
- AI models to produce information or data intended to support regulatory decision-making regarding safety, effectiveness, or quality for drugs.
- Life cycle maintenance of the credibility of AI models is a set of planned activities to continuously assess an AI model to ensure the model’s performance and its suitability throughout its life cycle for the COU.
- Guidance also emphasizes transparency, data quality, and continuous monitoring.
- FDA strongly encourages sponsors and other interested parties to engage early with the FDA in order to set expectations regarding the appropriate credibility assessment activities.
- Guidance does not apply to the use of AI models in drug discovery, or when used for operational efficiencies, such as internal workflows or resource allocation, or any other models that are not related to regulatory decision-making.
- 2025 presents the pharma industry with regulatory modernization driven by cloud-based technologies, AI-powered tools, and expanded global harmonization efforts.
- FDA and other regulatory agencies will continue fostering innovation while ensuring patient safety, ethical conduct, and data integrity.
LEARNING OBJECTIVES
- Provide a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model.
- Define the question of interest that will be addressed by the AI model.
- Define the Context of Use (COU) for the AI model.
- Assess the AI model risk.
- Develop a plan to establish the credibility of AI model output within the COU.
- Identify engagement options other than formal meetings with FDA depending on how the sponsor or other interested parties intend to use the AI model.
- Considerations for AI use in the drug product life cycle
- When finalized, this guidance is expected to help ensure that AI models used to support regulatory decision-making are sufficiently credible for the COU.
WHO WILL BENEFIT?
• Regulatory Affairs
• Drug Development and Operations
• Regulatory Operations
• R&D
• Manufacturing
• Clinical Operations
• Postmarketing/Pharmacovigilance/Safety
• Other interested parties that may be interested in the use of AI in drug and biological product development to support
regulatory decision-making regarding safety, effectiveness, or quality for drugs
On January 6, 2025, the U.S. Food and Drug Administration issued draft guidance to provide recommendations on the use of artificial intelligence (AI) intended to support a regulatory decision about a drug or biological product’s safety, effectiveness, or quality. This is the first guidance the agency has issued on the use of AI for the development of drug and biological products. Understand the seven-step process for providing a risk-based credibility assessment framework. FDA cites to various “examples of AI use for producing information or data intended to support regulatory decision-making,” including the use of predictive modeling, integrating data from various sources, and processing and analyzing large sets of data.
- A key aspect to the appropriate application of AI modeling in drug development and regulatory evaluation is ensuring model credibility—trust in the performance of an AI model.
- AI models to produce information or data intended to support regulatory decision-making regarding safety, effectiveness, or quality for drugs.
- Life cycle maintenance of the credibility of AI models is a set of planned activities to continuously assess an AI model to ensure the model’s performance and its suitability throughout its life cycle for the COU.
- Guidance also emphasizes transparency, data quality, and continuous monitoring.
- FDA strongly encourages sponsors and other interested parties to engage early with the FDA in order to set expectations regarding the appropriate credibility assessment activities.
- Guidance does not apply to the use of AI models in drug discovery, or when used for operational efficiencies, such as internal workflows or resource allocation, or any other models that are not related to regulatory decision-making.
- 2025 presents the pharma industry with regulatory modernization driven by cloud-based technologies, AI-powered tools, and expanded global harmonization efforts.
- FDA and other regulatory agencies will continue fostering innovation while ensuring patient safety, ethical conduct, and data integrity.
- Provide a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model.
- Define the question of interest that will be addressed by the AI model.
- Define the Context of Use (COU) for the AI model.
- Assess the AI model risk.
- Develop a plan to establish the credibility of AI model output within the COU.
- Identify engagement options other than formal meetings with FDA depending on how the sponsor or other interested parties intend to use the AI model.
- Considerations for AI use in the drug product life cycle
- When finalized, this guidance is expected to help ensure that AI models used to support regulatory decision-making are sufficiently credible for the COU.
• Regulatory Affairs
• Drug Development and Operations
• Regulatory Operations
• R&D
• Manufacturing
• Clinical Operations
• Postmarketing/Pharmacovigilance/Safety
• Other interested parties that may be interested in the use of AI in drug and biological product development to support
regulatory decision-making regarding safety, effectiveness, or quality for drugs
