总网络可控性分析发现了可解释的Covid-19治疗药物
Xinru Wei1,2, Chunyu Pan3, Xizhe Zhang4,5
1Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
Biology direct
|September 5, 2023
概括
我们确定了蜂网络中的控制中心是治疗COVID-19等病毒感染的潜在药物目标. 针对这些中心,正如Fostamatinib所示,为药物重新定位和发现提供了一个新的策略.
科学领域:
- 网络医学 网络医学
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
背景情况:
- 网络医学和药物重定向对于对抗COVID-19等疾病至关重要.
- 细胞网络中的结构可控性是理解疾病机制的关键概念.
- 扩展可控性理论以识别细胞防御机制对病毒感染具有显著的兴趣.
研究的目的:
- 将结构可控性扩展到完全的结构可控性.
- 在蜂网络中引入和定义控制中心的概念.
- 确定控制中心作为潜在的药物点,用于对抗病毒感染的治疗干预.
主要方法:
- 开发了一种高效的算法来识别网络中的所有控制中心.
- 将算法应用于一个大型的人类蛋白质-蛋白质相互作用网络,包括SARS-CoV-2相互作用.
- 分析了针对抗病毒功能的药物和丰富性而识别的控制中心.
主要成果:
- 确定了65个具有显著抗病毒功能的可用药物的控制中心.
- 针对这些中心的潜在药物被分为四组:抗病毒/抗炎,中枢神经系统作用,食补充剂和免疫增强剂.
- 在临床前的Covid-19研究中证明了Fostamatinib的疗效,这是一种重新设计的药物,显示了死亡率和疾病严重程度的降低.
结论:
- 验证了一种使用控制中心作为可解释药物发现的药物目标的新策略.
- 提供了对潜在的Covid-19治疗方法的分子机制的见解.
- 确定了这种方法在其他疾病中对药物重新利用的一般适用性.
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