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How AI Transforms Regulatory Submission: Current Clinical Implementation and Future Prospects.

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Artificial Intelligence (AI) revolutionizes drug development and regulatory processes. It enhances efficiency and patient outcomes by bridging model-informed drug development with clinical practice.

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Area of Science:

  • Pharmacology
  • Regulatory Science
  • Computer Science

Background:

  • Artificial Intelligence (AI) is increasingly integrated into pharmaceutical research and development.
  • Traditional drug development pathways face challenges in efficiency and precision.
  • The convergence of data analytics and regulatory science necessitates advanced tools.

Purpose of the Study:

  • To explore the transformative impact of AI on drug development and regulatory submissions.
  • To highlight AI's role in bridging model-informed drug development (MIDD) and clinical implementation.
  • To discuss current applications, regulatory integrations, and future prospects of AI in pharmaceuticals.

Main Methods:

  • Review of current AI applications in drug discovery and development.
  • Analysis of AI's integration into regulatory frameworks.
  • Perspective on future trends and potential of AI in patient-centered drug development.

Main Results:

  • AI enables advanced data analytics, predictive modeling, and intelligent decision support.
  • AI facilitates a translational bridge between MIDD and clinical practice.
  • AI enhances therapeutic precision and improves patient outcomes through data-driven insights.

Conclusions:

  • AI is a key driver for accelerating efficient, data-driven, and patient-centered drug development.
  • Regulatory bodies are increasingly considering AI-driven evidence.
  • The future of drug development will be significantly shaped by AI advancements.