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一种新型的 COVID-19 细胞内感染逻辑模型,以支持治疗方法的开发。

A novel logical model of COVID-19 intracellular infection to support therapies development.

机构信息

European Institute of Oncology IRCCS, Milan, Italy.

Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Trento, Italy.

出版信息

PLoS Comput Biol. 2022 Aug 29;18(8):e1010443. doi: 10.1371/journal.pcbi.1010443. eCollection 2022 Aug.

Abstract

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

摘要

本文建立了一个基于逻辑的数学模型,用于研究 COVID-19 感染中涉及的细胞途径,以研究各种药物治疗(单一或联合使用)在不同疾病情况下的效果,深入了解它们的作用机制。药物模拟表明,单一药物的效果有限,或者在某些情况下适得其反,而联合使用不同的治疗方法会产生更好的效果。具体来说,抗炎症药物巴利昔替尼(Baricitinib)和抗病毒药物瑞德西韦(Remdesivir)的联合使用显示出显著的益处,而巴利昔替尼、瑞德西韦和皮质类固醇地塞米松(Dexamethasone)的三联组合则产生了更强的疗效。我们结合敏感性分析,对药物的作用机制进行了分析,以揭示它们对分子途径的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc8/9462742/1a04e044ed4d/pcbi.1010443.g001.jpg

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