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Model-Informed Drug Development: Current US Regulatory Practice and Future Considerations.

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Model-informed drug development (MIDD) uses quantitative models to improve drug decision-making. This approach supports dose optimization, efficacy evidence, trial design, and policy, with emerging technologies expanding its scope.

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Area of Science:

  • Pharmacometrics and Quantitative Pharmacology
  • Regulatory Science
  • Drug Development

Background:

  • Model-informed drug development (MIDD) applies quantitative models to enhance drug development decision-making.
  • MIDD received formal recognition in the Prescription Drug User Fee Act (PDUFA) VI.
  • Regulatory applications of MIDD address critical drug development and regulatory questions.

Purpose of the Study:

  • To review and highlight common features of MIDD applications across key categories.
  • To discuss established and emerging areas of MIDD in regulatory submissions.
  • To explore the expanding scope of model-based analysis in drug development.

Main Methods:

  • Review of case studies, literature papers, and published regulatory documents.
  • Classification of MIDD applications into four main categories: dose optimization, supportive efficacy evidence, clinical trial design, and policy informing.
  • Analysis of emerging technologies and their integration into MIDD.

Main Results:

  • MIDD applications are broadly categorized into dose optimization, supportive efficacy evidence, clinical trial design, and policy informing.
  • Established domains of MIDD application continue to see further development and investment.
  • New technologies like mechanistic models, neural networks, and real-world data are gaining traction.

Conclusions:

  • MIDD is a valuable tool in drug development, formally recognized and widely applied.
  • Established MIDD applications demonstrate success in optimizing drug development processes.
  • Emerging technologies and data sources are poised to further expand the utility and scope of MIDD.