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

Abstract

The Orphan Drug Act defines a rare disease as a condition affecting fewer than 200,000 people in the United States. However, most rare diseases are categorized as ultrarare or hyper-rare, impacting fewer than 100 individuals worldwide. Developing drugs for these conditions involves multiple challenges, such as geographically dispersed and small patient populations, limited natural history data, and poor disease characterization. Issues related to small patient numbers, scarce natural history information, and clinical heterogeneity within rare diseases can be addressed by various strategies, including using artificial intelligence and advanced analytical methods, leveraging detailed individual-level data, and exploring synthetic data generation to overcome the limitations of small datasets. Moreover, establishing centralized databases and promoting public-private partnerships can help build a more comprehensive repository of available data.