AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference.

Journal: Clinical and translational science
PMID:

Abstract

Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real-world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs.

Authors

  • Mohamed H Shahin
    Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA.
  • Prashant Desai
    Drug Metabolism & Pharmacokinetics (DMPK), Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States.
  • Nadia Terranova
    Merck Institute for Pharmacometrics, Merck Serono S.A., Switzerland, a Subsidiary of Merck KGaA, Darmstadt, Germany. nadia.terranova@merckgroup.com.
  • Yuanfang Guan
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. gyuanfan@umich.edu.
  • Tomáš Helikar
    Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States.
  • Sebastian Lobentanzer
    Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany. sebastian.lobentanzer@gmail.com.
  • Qi Liu
    National Institute of Traditional Chinese Medicine Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • James Lu
    Modeling and Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA.
  • Subha Madhavan
    Innovation Center For Biomedical Informatics, Georgetown University, Washington D.C, United States of America.
  • Gary Mo
    Pfizer Research & Development, Groton, Connecticut, USA.
  • Flora T Musuamba
    Federal Agency for Medicines and Health Products, Brussels, Belgium.
  • Jagdeep T Podichetty
    Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA.
  • Jie Shen
    Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui 241001, China; Pharmacy School, Wannan Medical College, Wuhu, Anhui 241002, China; Department of Clinical Pharmacy, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui 241001, China; Anhui Provincial Engineering Research Center for Polysaccharides Drugs, Wannan Medical College, Wuhu, Anhui 241001, China.
  • Lei Xie
    Ph.D. Program in Computer Science, The City University of New York, New York, NY, United States.
  • Mathew Wiens
    Metrum Research Group, Boston, Massachusetts, USA.
  • Cynthia J Musante
    Translational Clinical Sciences, Pfizer Research and Development, Cambridge, Massachusetts, USA.