改变儿科罕见病药物开发:加强与虚拟患者的临床试验和监管证据
Fianne Sips1, Marco Virgolin1, Giuseppe Pasculli2
1InSilicoTrials Technologies BV, 's-Hertogenbosch, the Netherlands.
CPT: pharmacometrics & systems pharmacology
|August 4, 2025
概括
虚拟患者 (VPs) 可以改善罕见儿科疾病的临床试验. 这些由机器学习驱动的模型解决了诸如小患者数量和异质性等挑战,提高了药物开发效率.
科学领域:
- 计算生物学是一种计算生物学.
- 临床试验方法论 临床试验方法论
- 儿科药理学 儿科药理学
背景情况:
- 针对儿科罕见病的药物开发面临重大障碍,原因是患者人群较小,异质,脆弱.
- 现有的临床试验设计对于这些特定群体来说往往是不切实际的,并且在伦理上受到限制.
研究的目的:
- 探索虚拟患者 (VPs) 在提高儿科罕见病临床试验效率和影响方面的潜力.
- 讨论机器学习和计算方法如何为药物开发创造VP.
主要方法:
- 对现有文献和实例的审查,涉及成人和儿科研究中的虚拟患者.
- 讨论机器学习 (ML),机械驱动的计算方法,以及创建VP的混合模型.
主要成果:
- 虚拟患者在克服罕见儿科疾病传统临床试验的局限性方面表现有前途.
- 应用VP可以加快药物开发过程并最大限度地提高试验影响.
结论:
- 虚拟患者代表了儿科罕见病临床试验的变革性方法.
- 进一步开发和整合VP可以显著有利于罕见疾病药物发现和开发.
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