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全网络可控性分析发现可解释的新冠病毒治疗药物。

Total network controllability analysis discovers explainable drugs for Covid-19 treatment.

机构信息

Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.

School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 210001, China.

出版信息

Biol Direct. 2023 Sep 5;18(1):55. doi: 10.1186/s13062-023-00410-9.

Abstract

BACKGROUND

The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets.

RESULTS

We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach's effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients.

CONCLUSIONS

Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

摘要

背景

网络医学在药物重定位方面的积极探索,特别是针对新冠病毒的研究,激发了人们对细胞网络结构可控性概念的兴趣。我们旨在扩展这一理论,将研究重点从细胞对病毒感染的控制转向防御。因此,我们将结构可控性扩展到了总结构可控性,并引入了控制枢纽的概念。干扰任何控制枢纽都可能使细胞对外源刺激(如病毒感染)失去控制,因此控制枢纽是理想的药物靶点。

结果

我们开发了一种有效的算法来识别所有的控制枢纽,并将其应用于最大的人类蛋白质相互作用同构图网络,包括人类和 SARS-CoV-2 蛋白之间的相互作用。我们的方法识别出 65 个具有丰富抗病毒功能的可成药控制枢纽。利用这些枢纽,我们将潜在药物分为四类:抗病毒和抗炎药物、作用于中枢神经系统的药物、膳食补充剂和增强免疫力的化合物。我们方法有效性的一个例证是福他替尼,一种最初为慢性免疫性血小板减少症开发的药物,现在正在临床试验中用于治疗新冠病毒。临床前试验数据表明,福他替尼可降低新冠病毒患者的死亡率、重症监护病房停留时间和疾病严重程度。

结论

我们的研究结果证实了我们的新型策略的有效性,该策略利用控制枢纽作为药物靶点。该方法深入了解了新冠病毒潜在治疗药物的分子机制,为可解释的药物发现提供了有价值的工具。我们的新方法具有通用性,可适用于其他疾病的药物再利用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb77/10478273/abf6582c2e41/13062_2023_410_Fig1_HTML.jpg

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