用随机生存森林为孤儿药物产品首次提交仿制药申请的预测分析
Robert Hopefl1, Jing Wang1, Abhinav Ram Mohan1
1Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Clinical and translational science
|October 5, 2025
概括
开发通用孤儿药物 (ODP) 可以降低罕见病患者的成本. 这项研究确定了影响通用ODPs缩写新药应用 (ANDAs) 的关键因素,使用机器学习预测提交并为增加可用性的策略提供信息.
科学领域:
- 药物经济学 药物经济学
- 监管科学 监管科学
- 机器学习在药物开发中的作用
背景情况:
- 罕见疾病影响患者人群较少,导致对孤儿药物产品 (ODP) 开发的激励有限.
- 1983年"孤儿药物法案"旨在鼓励ODP的发展,但ODP往往需要更高的治疗费用.
- 通用ODP可以加强市场竞争,提供替代治疗,使患者受益.
研究的目的:
- 确定影响仿制孤儿药最初提交缩写新药申请 (ANDA) 的因素.
- 使用机器学习开发一个预测模型,用于ANDA提交的ODP.
主要方法:
- 从美国食品和药物管理局 (FDA) 数据库和IQVIA销售数据库收集的数据.
- 包括药物产品信息,监管因素和药物经济数据.
- 随机生存森林 (RSF) 机器学习模型用于新化学实体 (NCE) 和非NCE,内部和外部验证.
主要成果:
- RSF模型预测ANDA提交的C指数为0.675±0.0261对于NCE和0.754±0.0441对于非NCE.
- 对于NCE的ODP,销售数据是ANDA提交的最重要的预测因素.
- 对于非NCE ODP,监管数据,特别是产品特定指南 (PSG) 的可用性是最重要的.
结论:
- 机器学习,特别是RSF,可以预测ODP的ANDA提交,NCE和非NCE的关键因素不同.
- 未来的数据可用性可能会提高RSF模型的准确性,用于预测ODP ANDA提交.
- 基于模型的洞察力可以指导促进ANDA提交和增加通用ODP可用性的战略.
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