预测治疗中的酸反应的翻译多模式机器学习原型
medRxiv : the preprint server for health sciences
|September 5, 2025
概括
这项研究使用遗传和体外数据开发了一个预测算法,以个性化治疗,旨在改善控制和减少不良反应.
科学领域:
- 药物基因组学
- 神经科学
- 计算生物学
背景情况:
- 影响全球1%的人口,通常需要长期抗发作药物治疗.
- 目前的ASM选择依赖于试验和错误,只有50%的患者在一线治疗中实现了持续的发作自由.
- 酸 (VPA) 是一种常见的第一线ASM,在近50%的患者中显示出低于最佳的疗效和耐受性,导致控制不足或不可接受的不良反应 (UAR).
研究的目的:
- 开发和验证一种预测算法,以优化患者的酸治疗决策.
- 整合多模式数据,包括体外神经反应和遗传变异,以进行个性化VPA治疗.
- 减少与VPA治疗相关的治疗延迟,患者负担和医疗费用.
主要方法:
- 开发了一个预测算法,整合了体外数据,遗传变异 (常见和罕见) 和先前知识.
- 使用多模式管道,专注于与VPA药理学和药理动力学相关的基因.
- 使用多族裔数据集和Epi25队列训练和验证算法,并对独立队列进行概念验证.
主要成果:
- 预测算法显示整体性能不佳,但通过预测准确性和高负预测值突出显示了潜在的临床价值.
- 该模型显示有可能显著减少成功治疗VPA的时间.
- 与治疗相关的患者负担和医疗费用估计减少.
结论:
- 一个基于转化生物标志物的算法显示了个性化治疗的潜力,
- 虽然开发的原型由于数据需求 (SNP和WES) 尚未临床准备,但它表明了提高治疗疗效的可行途径.
- 个性化VPA治疗可以通过缩短发作自由时间和尽量减少ISC和UAR来改善生活质量.
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