科学家已确定了160种可用于治疗COVID-19的新药

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Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies. Credit: Winnie Lei

Fig. 1 Construction of a SIP network.

(A) Overview and workflow of the in silico drug repurposing pipeline. (B) Schematic depicts our strategy of constructing a SIP hidden network through data integration and network construction of DIPs and DEPs, followed by identification of drugs that target key pathways in this network. (C) The SARS-CoV-2 Orf8 subnetwork shows the extent of the hidden layer that is revealed through the network analysis. (D) Percentage of the shortest paths between the DIP and DEP that are via zero to three proteins at 6 hours versus 24 hours.

Fig. 2 SARS-CoV-2 viral protein subnetwork analysis shows an enrichment of viral replication pathways.

(A) Venn diagram of key proteins in 6- and 24-hour SIP networks. (B) A circos plot depicting interactions between DIPs and DEPs revealed through the SIP network at 6 hours after infection. DIPs were subdivided into the genomic organization of SARS-CoV-2. Proteins in the hidden layer were also subdivided into major pathways. Inner colored circles demonstrate the subcellular localization of the proteins, and details are shown in the dotted box. The colored lines show PPI. (C) Twenty-four hours after infection. (D) Top 30 enriched GO terms of the key proteins in the SIP network at 24 hours (black). The enrichment P values of 30 terms at 6 hours are also shown as a control (gray).

Fig. 3 Machine learning predicts MoAs for the 200 drug repurposing candidates.

(A) U-matrix is shown of the trained unsupervised SOM used to analyze the relationship between the 200 drugs and the 148 key pathways. This contains the distance (similarity) between the neighboring SOM neurons (pathways) and shows data density (drug-pathway association scores) in input space. Each hexagon is colored according to distance between corresponding data vectors of neighbor neurons, with low-distance areas (dark purple) indicating high data density (clusters). Each smaller hexagon on the U-matrix (A) indicates the data vector distance between larger hexagons in the SOM cluster arrangements (B to E). Thus, a smaller hexagon on the U-matrix corresponds to every adjacent larger hexagon on the SOM cluster arrangements (B to E). (B) The selected clustering arrangement was based on the U-matrix and DBI to separate the 148 key pathways into nine clusters. The names of nine clusters are shown in the figure. Clusters of each SOM neuron are distinguishable by color. The size of the black hexagon in each neuron indicates distance. Larger hexagons have a low distance to neighboring neurons, hence forming a stronger cluster with neighbors. (C) Two MoA categories were identified on the basis of the pathway clustering and the drug mapping. (D) Mapping of the 200 identified drugs to each neuron (pathway) based on matching rates and inspection of examples from each cluster. (E) SOM component map shows mapping results of the 200 drugs into nine pathway clusters. The names of the nine clusters are shown in the figure, and the drugs with asterisk are already in COVID-19 clinical trials.

Fig. 4 Proguanil and sulfasalazine reduce SARS-CoV-2 replication and p38/MAPK signaling activity.

(A) RT-qPCR analysis of the indicated mRNA (envelope, E-protein) from Vero E6 cells pretreated with the indicated drugs and concentrations for 3 hours before infection with SARS-CoV-2 for 24 hours. Student’s t test. Means + SD of three independent replicates are shown. (B and C) RT-qPCR analysis of indicated mRNA (envelope, E-protein) from Vero E6 cells pretreated with proguanil or sulfasalazine at indicated concentrations for 3 hours before infection with SARS-CoV-2 for 24 hours. Student’s t test. Means + SD of three independent replicates are shown. (D and E) RT-qPCR analysis of indicated mRNA (envelope, E-protein) from Calu-3 cells pretreated with proguanil or sulfasalazine at indicated concentrations for 3 hours before infection with SARS-CoV-2 for 24 hours. Student’s t test. Means + SD of three independent replicates are shown. (F) Western blot analysis of phosphorylated MAPKAPK2 (Thr³³⁴) in mock-, DMSO-, sulfasalazine-, or proguanil-treated Vero E6 cells at indicated concentrations for 3 hours before infection with SARS-CoV-2 for 24 hours. (G to J) RT-qPCR analysis of the indicated mRNAs from Calu-3 cells pretreated with proguanil or sulfasalazine at indicated concentrations for 3 hours before infection with SARS-CoV-2 for 24 hours. Student’s t test. Means + SD of three independent replicates are shown.

Fig. 5 Schematics depicting the pathways mediating NO production that are targeted by the five tested drugs.

The black boxes indicate key proteins in SIP network, and those targeted by the five drugs are highlighted in red color. Sulfasalazine and proguanil target proteins in both pathways that directly and indirectly (via NADP production) affect NO production (58–61). 

据英国剑桥大学（University of Cambridge）2021年6月30日报道，剑桥大学和德国吉森大学（Justus-Liebig University）的科研人员合作，已经确定了160种可用于治疗COVID-19的新药。相关研究结果于2021年6月30日已经在《科学进展》（Science Advances）杂志网站发表——Namshik Han, Woochang Hwang, Konstantinos Tzelepis, Patrick Schmerer, Eliza Yankova, Méabh MacMahon, Winnie Lei, Nicholas M. Katritsis, Anika Liu, Ulrike Felgenhauer, Alison Schuldt, Rebecca Harris, Kathryn Chapman, Frank McCaughan, Friedemann Weber, Tony Kouzarides. Identification of SARS-CoV-2–induced pathways reveals drug repurposing strategies. Science Advances,  30 Jun 2021: Vol. 7, no. 27, eabh3032. DOI: 10.1126/sciadv.abh3032.附原文详见

2021-06-scientists-drugs-repurposed-covid-.pdf

参与此项研究的还有英国伦敦的葛兰素史克(Glaxo Smith Kline)制药公司（GSK, London, UK.）和英国斯蒂夫尼奇的生命爱科（LifeArc, Stevenage, UK）。上图显示SARS-CoV-2诱导途径的鉴定揭示了药物的重新利用策略。

英德科学家联合已经确定了200种预计对COVID-19有效的获批药物，其中目前只有40种正在进行COVID-19临床试验。发表在《科学进展》上的一项研究中，剑桥大学的米尔纳疗法研究所和古尔研究所（University of Cambridge's Milner Therapeutics Institute and Gurdon Institute）的研究人员领导的研究小组，采用计算生物学和机器学习相结合创建一个全面的蛋白质谱图。这些蛋白质都涉及到SARS-CoV-2感染，使它们帮助新冠病毒进入宿主细胞（host cell），导致最终形成感染。通过使用人工智能(artificial intelligence简称AI)方法检查这个网络，他们能够识别参与感染的关键蛋白质，以及可能被药物靶向的生物途径。

迄今为止，治疗COVID-19的小分子和抗体方法大多是目前正在进行临床试验或已经通过临床试验并获得批准的药物。许多焦点都集中在几个关键的病毒或宿主靶点上，或在炎症等途径上，药物治疗可以作为一种干预手段。该团队利用计算机建模对近2000种获批药物进行了“虚拟屏幕（virtual screen）”，并确定了可能有效对抗COVID-19的200种获批药物。其中40种药物已经进入临床试验，研究人员认为这支持了他们所采取的方法。用于识别COVID-19药物再利用靶点的数据驱动计算方法（参见上述图示）。

Data-driven computational approaches for identifying drug repurposing targets for COVID-19. Credit: Winnie Lei

当研究人员对涉及病毒复制的一类药物进行测试时，他们发现有两种药物，特别是一种抗疟疾药物（antimalarial drug）和一种用于治疗风湿性关节炎（rheumatoid arthritis）的药物能够抑制新冠病毒，这为他们基于数据的方法提供了初步验证。

米尔纳疗法研究所（Milner Therapeutics Institute）所长、领导这项研究的Tony Kouzarides教授（Professor Tony Kouzarides）说:“通过数以千计的蛋白质观察,在SARS-CoV-2感染方面发挥一些作用，即是否在感染过程中表现活跃，我们已经能够创建一个网络发现这些蛋白之间的关系。然后，我们使用最新的机器学习和计算机建模技巧，确定了200种可能帮助我们治疗COVID-19的批准药物。其中，160种以前未被发现与这种感染有关。这将给我们提供更多的武器来对抗新冠病毒。”

利用人工神经网络分析（artificial neural network analysis），该团队根据药物在SARS-CoV-2感染中的首要作用对药物进行分类:针对病毒复制的药物和针对免疫反应的药物。然后，他们提取了一些参与病毒复制的细胞，并用来自人类和非人灵长类动物（non-human primates）的细胞系进行测试。人工神经网络在训练数据集中学习药物和它们的靶蛋白之间的关系，以预测重要的作用机制（参见下图所示）。

Artificial Neural Network learned relationships between drugs and their target proteins in the training dataset to predict important mechanism of action, Credit: Winnie Lei

特别值得注意的是两种药物，柳氮磺胺吡啶(sulfasalazine，用于治疗类风湿性关节炎和克罗恩病等疾病)和氯胍(proguanil，抗疟药)。研究小组发现，这两种药物减少了SARS-CoV-2病毒在细胞中的复制，提高了它们潜在用于预防感染或治疗COVID-19的可能性。

米尔纳疗法研究所(Milner Therapeutics Institute)计算研究和人工智能负责人Namshik Han博士（Dr. Namshik Han）补充说:“我们的研究为我们提供了有关COVID-19潜在机制的意外信息，并为我们提供了一些有前途的药物，它们可能被重新用于治疗或预防新冠病毒感染。虽然我们采用了一种数据驱动的方法，基本上允许人工智能算法对数据集进行查询，但我们随后在实验室验证了我们的发现，证实了我们方法的效力。我们希望这一潜在药物资源将加快抗COVID-19新药物的开发。我们相信，我们的方法将有助于迅速应对SARS-CoV2的新变种和其他可能导致未来大流行的新病原体。”上述介绍仅供参考，欲了解更多信息敬请注意浏览原文（2021-06-scientists-drugs-repurposed-covid-.pdf）或者相关报道。

Targeting cellular response to SARS-CoV-2 holds promise as new way to fight infection

Abstract

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2–induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2–induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.