Abstract: Over the past few decades, the volume of data available to support drug and biological product development have increased substantially. These increases in data volume were also accompanied by an expansion in data diversity with data originating from disparate sources including biologic data, pharmacological data, and clinical data (e.g., imaging, electronic health records, and digital health technologies). This growth in data volume and complexity combined with cutting-edge computing power and methodological advancements in artificial intelligence (AI) have the potential to transform how drugs and biological products are developed, manufactured, and utilized. Concurrent with these technological advancements, FDA’s Center for Drug Evaluation and Research (CDER) have seen an exponential increase in the number of drug submissions using AI components, with over 200 submissions reported in 2023 compared to only 1 in 2016. These submissions traverse the drug product life cycle from drug discovery to postmarket safety monitoring and cut across a range of therapeutic areas. In general, the application of AI in these submissions aims to improve the efficiency of drug discovery and our understanding of the efficacy and safety of specific treatments. The diverse uses of AI in these submissions highlight the need for an adaptive regulatory assessment of benefits and risks and emphasizes the importance of adopting a risk-based approach for establishing and evaluating the credibility of an AI model output for a particular context of use.
Speaker: Tala Fakhouri
Affiliation: FDA
Title: Tala Fakhouri’s Keynote
Abstract_Sanitzed: Over the past few decades, the volume of data available to support drug and biological product development have increased substantially. These increases in data volume were also accompanied by an expansion in data diversity with data originating from disparate sources including biologic data, pharmacological data, and clinical data (e.g., imaging, electronic health records, and digital health technologies). This growth in data volume and complexity combined with cutting-edge computing power and methodological advancements in artificial intelligence (AI) have the potential to transform how drugs and biological products are developed, manufactured, and utilized. Concurrent with these technological advancements, FDA’s Center for Drug Evaluation and Research (CDER) have seen an exponential increase in the number of drug submissions using AI components, with over 200 submissions reported in 2023 compared to only 1 in 2016. These submissions traverse the drug product life cycle from drug discovery to postmarket safety monitoring and cut across a range of therapeutic areas. In general, the application of AI in these submissions aims to improve the efficiency of drug discovery and our understanding of the efficacy and safety of specific treatments. The diverse uses of AI in these submissions highlight the need for an adaptive regulatory assessment of benefits and risks and emphasizes the importance of adopting a risk-based approach for establishing and evaluating the credibility of an AI model output for a particular context of use.