Editorial: Model-informed drug development and evidence-based translational pharmacology

Zhao, Jinxin; Zhu, Xiao; Tan, Songwen; Chen, Chuanpin; Kaddoumi, Amal; Guo, Xiuli; Lin, Yu-Wei; Cheung, S.Y. Amy

doi:10.3389/fphar.2022.1086551

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…The potential for high impact in this area is immense and is currently an active domain of research. [15][16][17] As we navigate this exciting era of AI, it is crucial to remember the lessons from the past hype-hope cycles. The potential of AI is immense, but its future growth and application within clinical pharmacology will depend on how we manage its promises and challenges, train scientists on best practices for using this tool, and disseminate successful applications to ensure that the benefits of this transformative technology are shared equitably across society.…”

Section: Artificial Intelligence -Potential Applications In the Pharm...mentioning

confidence: 99%

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Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology

Shahin,

Barth,

Podichetty

et al. 2023

Clin Pharma and Therapeutics

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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 healthcare, demonstrating their potential to empower clinical pharmacology decision‐making, revolutionize the drug development landscape, and advance patient care. While 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 datasets, screening to identify important covariates, optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, 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.

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Section: Artificial Intelligence -Potential Applications In the Pharm...mentioning

confidence: 99%

“…One of the most exciting prospects lies in using AI/ML in model‐informed drug development. The potential for high impact in this area is immense and is currently an active domain of research 15–17 …”

Section: Artificial Intelligence – Hope or Hype?mentioning

confidence: 99%