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Updated: Jan 8, 2026

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Mitigating Limited Data Challenges to Improve Artificial Intelligence Integration in Rare Disease Drug Development.

Atasi Poddar1, Gabriel K Innes1, Qi Liu2

  • 1Office of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.

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Summary

Developing drugs for rare and ultrarare diseases faces challenges due to small patient populations and limited data. Strategies like AI, advanced analytics, and data sharing can overcome these hurdles for drug development.

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Area of Science:

  • Medical Science
  • Pharmacology
  • Biotechnology

Background:

  • Rare diseases affect fewer than 200,000 people in the US, with ultrarare conditions impacting fewer than 100 globally.
  • Drug development for rare diseases is hindered by small, dispersed patient groups, scarce natural history data, and poor disease characterization.

Purpose of the Study:

  • To explore strategies for overcoming challenges in rare disease drug development.
  • To identify methods for addressing limitations posed by small patient numbers and data scarcity.

Main Methods:

  • Utilizing artificial intelligence and advanced analytical techniques.
  • Leveraging detailed individual-level patient data.
  • Exploring synthetic data generation to augment small datasets.
  • Establishing centralized databases and fostering public-private partnerships.

Main Results:

  • Proposed strategies offer solutions for data limitations in rare disease research.
  • AI and advanced analytics can enhance understanding and drug development for rare conditions.
  • Collaborative data-sharing initiatives can create comprehensive data repositories.

Conclusions:

  • Innovative approaches are crucial for advancing drug development in rare and ultrarare diseases.
  • Addressing data scarcity through technological and collaborative strategies is key to improving outcomes for rare disease patients.