Affiliation:
1. Clinical Pharmacology and Bioanalytics Pfizer Inc. Groton Connecticut USA
2. Critical Path Institute Tucson Arizona USA
3. Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research US Food and Drug Administration Silver Spring Maryland USA
4. Clinical Pharmacology and Pharmacometrics Janssen Research and Development, LLC. Spring House Pennsylvania USA
5. Department of Clinical Pharmacology Genentech South San Francisco California USA
Abstract
The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition. This review dives into the burgeoning world of AI and machine learning (ML), showcasing different applications of AI in clinical pharmacology and the impact of successful AI/ML implementation on drug development and/or regulatory decisions. This review also highlights recommendations for areas of opportunity in clinical pharmacology, including data analysis (e.g., handling large data sets, screening to identify important covariates, and optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, and training). Realizing the benefits of AI in drug development and understanding its value will lead to the successful integration of AI tools in our clinical pharmacology and pharmacometrics armamentarium.
Subject
Pharmacology (medical),Pharmacology
Cited by
3 articles.
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