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全网络可控性分析发现用于新冠治疗的可解释性药物。

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

作者信息

Wei Xinru, Pan Chunyu, Zhang Xizhe, Zhang Weixiong

机构信息

The Affiliated Brain Hospital of Nanjing Medical University.

Northeastern University.

出版信息

Res Sq. 2023 Jul 14:rs.3.rs-3147521. doi: 10.21203/rs.3.rs-3147521/v1.

Abstract

Background The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural control capability 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 the 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.

摘要

背景 积极探索用于药物再利用的网络医学,尤其是用于对抗新冠病毒,激发了人们对细胞网络结构控制能力概念的兴趣。我们试图扩展这一理论,重点关注细胞对病毒感染的防御而非控制。因此,我们将结构可控性扩展到全结构可控性,并引入了控制枢纽的概念。干扰任何一个控制枢纽可能会使细胞在受到病毒感染等外源性刺激时无法被控制,所以控制枢纽是理想的药物靶点。结果 我们开发了一种高效算法来识别所有控制枢纽,并将其应用于最大的人类蛋白质相互作用同质性网络,包括人类与新冠病毒蛋白质之间的相互作用。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、膳食补充剂以及增强免疫力的化合物。我们方法有效性的一个例证是,最初为治疗慢性免疫性血小板减少症而开发的药物福斯替尼,目前正在进行治疗新冠病毒的临床试验。临床前试验数据表明,福斯替尼可以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以控制枢纽作为药物靶点的新策略的有效性。这种方法为新冠病毒潜在治疗药物的分子机制提供了见解,使其成为可解释药物发现的有价值工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edcc/10371104/938dbab5341e/nihpp-rs3147521v1-f0001.jpg

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