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一个完全不完美的引擎:在肺高血压中利用数字双胞胎范式

Melody Walker¹, Helen Moore¹, Ali Ataya¹

¹University of Florida College of Medicine Gainesville Florida USA.

Pulmonary circulation

|June 27, 2024

概括

通过将患者数据集成到计算模型中,可以实现个性化的肺高血压 (PH) 治疗. 这种方法,创造疾病的数字双胞胎,旨在优化干预措施和开发新的疗法,以获得更好的患者结果.

科学领域:

计算生物学是一种计算生物学.
个性化医疗是个性化的医疗.
肺高血压研究研究

背景情况:

肺高血压 (PH) 是一种严重的疾病,通常在不考虑个体机制的情况下进行治疗.
目前的治疗方法缺乏量身定制的方法,除了血管反应性肺动脉高血压 (PAH) 患者治疗通道阻塞剂.
广泛的患者数据,从分子到临床水平,可用于PH.

研究的目的:

通过使用计算模型,提出针对肺高血压的个性化治疗方法.
探索"数字双胞胎"在优化干预和开发新疗法的潜力.
审查现有的PH计算模型,并概述实施路线图.

主要方法:

整合各种患者数据 (分子,生理,临床) 与机械信息.
开发计算模型,以创建患者特定的疾病模拟 ("数字双胞胎").
基于模型的优化对个体患者的干预措施.

主要成果:

集成多尺度数据的计算模型为个性化PH治疗提供了一条道路.
针对个体患者定制的数字双胞胎可以优化现有疗法并指导新药开发.
这种方法有望显著改善PH患者的临床过程和预后.

结论:

由计算建模和数字双胞胎驱动的个性化医学是肺高血压的关键未来技术.
为实现这种方法提出了一份路线图,利用现有的数据和模型.
通过个性化方法优化干预和开发新型治疗方法对PH管理具有重大前景.

关键词:

计算建模计算建模肺高血压是一种肺高血压.治疗治疗治疗治疗治疗治疗

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