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在iPSC衍生的细胞中基于网络的查揭示了治疗心脏膜疾病的候选药物

Christina V Theodoris^1,2,3, Ping Zhou^1,2, Lei Liu^1,2

¹Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.

Science (New York, N.Y.)

|December 11, 2020

概括

研究人员开发了一种机器学习方法, 这种方法成功地发现了一种治疗大动脉病的化合物.

科学领域:

心血管生物学
系统生物学
药物发现

背景情况:

基因调节网络 (GRN) 的失调是许多人类疾病的基础.
目前的小分子查方法有限,往往只关注少数输出,阻碍了疾病修饰疗法的发现.
针对核心疾病机制需要系统层面的网络纠正方法.

研究的目的:

开发和应用机器学习 (ML) 策略来识别大致纠正失调GRN的小分子.
使用人类诱导多能干细胞 (iPSC) 模型,发现对大动脉 (AV) 疾病的新疗法.

主要方法:

使用人类iPSC衍生的AV疾病模型.
采用机器学习方法来选小分子以纠正网络范围内的基因表达模式.
验证了化合物 (XCT790) 在患者衍生细胞和体内小鼠模型中的疗效.

主要成果:

确定XCT790是一种强大的小分子,能够广泛纠正涉及AV疾病的GRNs.
证明 XCT790 的网络校正效应从 iPSC 模型泛化到患者的初级细胞.
在临床前的小鼠模型中显示,XCT790可以预防和治疗AV疾病.

结论:

开发的ML驱动策略有效地识别复杂疾病的网络校正小分子.
人类iPSC技术结合网络分析和机器学习为发现疾病修饰疗法提供了强大的平台.
这种方法有助于推进心血管和其他遗传疾病的药物发现.

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