缓解有限数据挑战,以改善人工智能在罕见疾病药物开发中的整合
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.
NEJM AI
|December 12, 2025
概括
开发用于罕见和超罕见疾病的药物面临着挑战,原因是患者人数较少和数据有限. 像人工智能,先进分析和数据共享等策略可以克服药物开发的这些障碍.
科学领域:
- 医学科学 医学科学 医学科学
- 药理学 药理学是指药理学的学科.
- 生物技术是生物技术.
背景情况:
- 罕见疾病在美国影响不到20万人,而超罕见疾病在全球影响不到100人.
- 针对罕见疾病的药物开发受到小,分散的患者群体,稀缺的自然史数据和疾病特征的不良表征的阻碍.
研究的目的:
- 探索克服罕见病药物开发挑战的策略.
- 确定解决患者数量少和数据稀缺造成的局限性的方法.
主要方法:
- 使用人工智能和先进的分析技术.
- 利用详细的个体级患者数据.
- 探索合成数据生成以增强小型数据集.
- 建立中央数据库,促进公私合作伙伴关系.
主要成果:
- 建议的策略为罕见病研究中的数据限制提供解决方案.
- 人工智能和先进的分析可以增强对罕见疾病的理解和药物开发.
- 协作数据共享倡议可以创建全面的数据存储库.
结论:
- 创新方法对于推进罕见和超罕见疾病的药物开发至关重要.
- 通过技术和协作策略解决数据稀缺问题是改善罕见病患者治疗结果的关键.
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