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  1. Vaccines, Blood & Biologics

Artificial Intelligence and Machine Learning (AI/ML) for Biological and Other Products Regulated by CBER

Artificial Intelligence is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.  Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action. Machine Learning means a set of techniques that can be used to train AI algorithms to improve performance at a task based on data .

CBER participated in public workshops and discussions organized by FDA and by other national and international organizations to better understand the uses of AI/ML in biologics and inform an appropriate regulatory framework that advances its safe and responsible use and promotes innovation.  For example: 

In addition, along with other medical products Centers, CBER continues to solicit input from stakeholders regarding a range of considerations for the use of AI/ML in medical product development, see Using Artificial & Machine Learning in the Development of Drug and Biological Products: Discussion Paper and Request For Feedback.   

Addressing the full potential of AI/ML is a coordinated effort within FDA. Centers and other FDA components are working cooperatively to understand, review, and implement the various aspects of AI/ML both internally and externally. See Artificial Intelligence and Medical Products | FDA for comprehensive information on AI/ML for Medical Products, including initiatives, documents, and other information. 

CBER, in coordination with others in FDA, is developing a regulatory framework for the safe and responsible use of AI throughout the biological product lifecycle following the approach and guidelines in the Executive Order on the Safe, Secure and Trustworthy Development and Use of Artificial Intelligence, October 30, 2023 and other guiding frameworks. CBER, along with FDA, is closely monitoring developments in the AI/ML field especially evolving technologies and their application to see how they may affect the field of biologics and our public health mission.

For non-biological products regulated by CBER, such as devices, CBER will use the regulatory paradigms developed for those products.  

Facilitating AI/ML usage in Regulation of Biological Products

CBER supports the validated use of AI/ML throughout the product life cycle to expedite product development and approval, and support effective product oversight. CBER’s approach follows the collaborative process outlined in the Artificial Intelligence & Medical Products: how CBER, CDER CDRH and OCP are Working Together.  In collaboration with other medical product centers and FDA organizational components, CBER is implementing the AI Areas of Focus described in this paper. 

Since 2016, CBER has identified an increasing use of AI/ML in IND submissions of vaccines, cellular products, and gene therapies.  Currently, we have over 70 IND applications with AI/ML, used in various disciplines (e.g., clinical, CMC, pharmacovigilance) that involve prediction, classification, clustering, and anomaly detection. 

CBER, in collaboration with other medical product centers, continues to develop and apply a risk-based approach to review of AI/ML in regulatory submissions. This approach will incorporate frameworks, best practices, and consider the AI/ML technology, its specific application, and product and clinical considerations.

CBER Contacts:

Internal Coordination and Exploration of AI/ML

CBER coordinates its AI/ML activities through the Artificial Intelligence Coordinating Committee (AICC). The AICC is executing a strategy focusing on staff education, communication, facilitating review of AI/ML in submissions, and project tracking and submission landscaping. 

Within CBER, we are exploring AI/ML technologies to develop capabilities that can facilitate internal operations (e.g., information identification and extraction, information assimilation) and to provide some first-hand experience with applying AI/ML, while cautiously guarding against the well-known risks of AI.

 
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