剂量反应曲线的多输出预测使药物重新定位和生物标志物发现成为可能
Juan-José Giraldo Gutierrez1,2, Evelyn Lau3, Subhashini Dharmapalan4,3
1National Heart and Lung Institute, Imperial College London, London, UK. j.giraldo-gutierrez@imperial.ac.uk.
NPJ precision oncology
|September 20, 2024
概括
我们开发了一种新的概率模型,用于预测所有剂量的药物反应,并识别生物标志物. 这种方法提高了药物开发的精度,并减少了数据需求,即使在有限的实验中也是如此.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物反应预测是具有挑战性的,因为可变的响应措施和剂量选择困难.
- 准确预测药物疗效和识别预测生物标志物的准确预测对于个性化医学和药物开发至关重要.
研究的目的:
- 提出一种概率多输出模型,用于同时预测所有剂量反应和生物标志物发现.
- 通过对所有剂量的反应进行基因组特征和化学性质的建模,使用任何剂量反应指标来评估药物疗效.
主要方法:
- 开发了多输出高斯过程 (MOGP) 模型来描述基因组特征,化学性质和剂量反应之间的关系.
- 应用Kullback-Leibler分歧来测量特征的重要性和确定生物标志物.
- 在两项药物查研究和十种癌症类型中验证了该方法.
主要成果:
- MOGP模型准确地预测了不同癌症类型的剂量反应,即使训练数据有限.
- 确定了EZH2基因作为BRAF抑制剂反应的新生物标志物.
- 通过使用各种剂量反应指标,证明了该模型在评估药物疗效方面的有效性.
结论:
- 多输出高斯过程模型为瘤学中精确的剂量反应预测提供了强大的框架.
- 这种方法可以通过减少数据要求和提高预测准确性来显著提高药物开发管道.
- 鉴定EZH2突出了发现药物反应的新生物标志物的潜力.
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