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单细胞水平的数字双胞胎用于早产预防策略

Jakob Einhaus¹, Peter Neidlinger¹, Olivier Fondeur¹

¹Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.

bioRxiv : the preprint server for biology

|November 19, 2025

概括

数字双胞胎加速了早产 (PTL) 药物发现. 一个新的单细胞框架SIMCO通过模拟免疫反应确定了四基酸盐,黄氨酸和阿司匹林/兰索普拉作为有前途的PTL干预措施.

科学领域:

计算生物学是一种计算生物学.
免疫学免疫学免疫学
药理学药理学是指药理学的学科.

背景情况:

过早分娩 (PTL) 是有限的治疗选择的主要并发症.
目前的数字双胞胎模型缺乏单细胞分辨率,阻碍了个性化的PTL治疗开发.
免疫失调是PTL的关键驱动因素,需要针对性的免疫调节疗法.

研究的目的:

引入临床结果的模拟免疫组建模型 (SIMCO),这是PTL的单细胞数字双胞胎框架.
模拟免疫调节治疗对分娩时间的影响.
确定用于PTL预防的新药候选药物.

主要方法:

开发了SIMCO,一个单细胞数字双胞胎框架,利用免疫组范围的模拟.
经过SIMCO的训练和验证,对暴露于九种候选药物的孕妇免疫组的质量细胞计图谱进行了训练和验证.
应用SIMCO模拟药物对妊娠时间的影响在一个独立的队列.

主要成果:

SIMCO确定了四基酸盐,氨酸和阿司匹林和兰索普拉的组合作为最重要的PTL预防候选者.
模拟显示先天免疫细胞中增强的mTOR信号传递和CD4+T细胞中减弱的JAK/STAT信号传递是关键机制.
该研究为每个候选药物提供了系统级的机制性见解.

结论:

在PTL中,SIMCO提供了一个强大的工具,用于低风险的,加速的治疗开发.
已识别的候选药物显示了通过特定的免疫路径调节延迟分娩的潜力.
这种单细胞方法使得针对PTL的个性化干预设计成为可能.

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