Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021
ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2668
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such as informing drug discovery/repurposing, enhancing clinical trial design elements, dose optimization, enhancing adherence to drug regimen, end-point/biomarker assessment, and postmarketing surveillance.
In 2019, Liu et al. provided an overview of how AI/ML was used to support drug development and regulatory submissions to the US Food and Drug Administration (FDA).
landscape analysis based on drug and biologic regulatory submissions to the FDA from 2016 to 2021.
searching for submissions with key terms “machine learning” or “artificial intelligence” in Center for Drug Evaluation and Research (CDER) internal databases for Investigational New Drug applications,
Julie Hsieh and Mo Tiwari were ORISE fellows contributing to this work.
Outcome prediction
Covariate selection/confounding adjustment:
maging, video, and voice analysis
RWD phenotyping/Natural Language Processing:
Drug discovery/repurposing: AI/ML has been demonstrated to be a useful tool for drug discovery and repurposing.
safety risk of a drug based on its structure, physiochemical properties, or affinity for targets.
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