Malka-Markovitz Alon, Camara Dit Pinto Stelian, Cherkaoui Mohammed, Levine Steven M, Anandasabapathy Sharmila, Sood Gagan K, Dhingra Sadhna, Yujia Gao, Vierling John M, Gallo Nicolas R
Center of Excellence, Long Island University, Brooklyn, NY, USA.
Dassault Systèmes, Vélizy-Villacoublay, France.
NPJ Digit Med. 2025 Jun 23;8(1):383. doi: 10.1038/s41746-025-01736-6.
Drug-induced liver injury poses significant challenges in drug development and in clinical care. This study builds on prior work developing a Human Liver Virtual Twin by creating a Multiscale Computational Fluid Dynamics framework that integrates patient-specific anatomical data to predict acetaminophen-induced liver injury as a demonstration of its capability. The model bridges vascular, lobular, and cellular scales to simulate dynamic blood flow, drug transport, and injury mechanisms that accurately reflect clinically observed spatial heterogeneity. Results demonstrate accurate blood flow dynamics, predictions of hepatocellular damage, and a scalable framework for studying spatial heterogeneity applicable to other hepatic pathologies. This work establishes the foundational principles for a whole-organ virtual liver simulation methodology, potentially becoming a powerful tool to guide safety in therapeutic development and clinical treatment strategies, ultimately reducing reliance translation from animal models for preclinical drug testing.
药物性肝损伤在药物研发和临床护理中带来了重大挑战。本研究基于之前开发人类肝脏虚拟模型的工作,创建了一个多尺度计算流体动力学框架,该框架整合了患者特异性解剖数据,以预测对乙酰氨基酚诱导的肝损伤,作为其能力的一个例证。该模型连接了血管、小叶和细胞尺度,以模拟动态血流、药物转运和损伤机制,准确反映临床上观察到的空间异质性。结果表明血流动力学准确、肝细胞损伤预测准确,且有一个可扩展的框架用于研究适用于其他肝脏疾病的空间异质性。这项工作确立了全器官虚拟肝脏模拟方法的基本原理,有可能成为指导治疗开发和临床治疗策略安全性的有力工具,最终减少临床前药物测试对动物模型的依赖。