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用于对乙酰氨基酚肝毒性预测的肝小叶虚拟可扩展模型。

A virtual scalable model of the Hepatic Lobule for acetaminophen hepatotoxicity prediction.

作者信息

Camara Dit Pinto Stelian, Cherkaoui Jalal, Ghosh Debarshi, Cazaubon Valentine, Benzeroual Kenza E, Levine Steven M, Cherkaoui Mohammed, Sood Gagan K, Anandasabapathy Sharmila, Dhingra Sadhna, Vierling John M, Gallo Nicolas R

机构信息

Department of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.

Institut National des Sciences Appliquées, Lyon, France.

出版信息

NPJ Digit Med. 2024 Nov 28;7(1):340. doi: 10.1038/s41746-024-01349-5.

Abstract

Addressing drug-induced liver injury is crucial in drug development, often causing Phase III trial failures and market withdrawals. Traditional animal models fail to predict human liver toxicity accurately. Virtual twins of human organs present a promising solution. We introduce the Virtual Hepatic Lobule, a foundational element of the Living Liver, a multi-scale liver virtual twin. This model integrates blood flow dynamics and an acetaminophen-induced injury model to predict hepatocyte injury patterns specific to patients. By incorporating metabolic zonation, our predictions align with clinical zonal hepatotoxicity observations. This methodology advances the development of a human liver virtual twin, aiding in the prediction and validation of drug-induced liver injuries.

摘要

解决药物性肝损伤问题在药物研发中至关重要,因为它常常导致三期临床试验失败和药物撤市。传统动物模型无法准确预测人类肝脏毒性。人体器官虚拟孪生体提供了一个有前景的解决方案。我们引入了虚拟肝小叶,它是活体肝脏(一种多尺度肝脏虚拟孪生体)的基础元素。该模型整合了血流动力学和对乙酰氨基酚诱导的损伤模型,以预测特定患者的肝细胞损伤模式。通过纳入代谢分区,我们的预测与临床区域肝毒性观察结果相符。这种方法推动了人类肝脏虚拟孪生体的发展,有助于预测和验证药物性肝损伤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/049b/11603025/5ffcd063865c/41746_2024_1349_Fig1_HTML.jpg

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