Executable network of SARS-CoV-2-host interaction predicts drug combination treatments.
Executable network of SARS-CoV-2-host interaction predicts drug combination treatments.
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DOI：
10.1038/s41746-022-00561-5
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发表时间：
2022-02-14
影响因子：
15.2
通讯作者：
Fisher J
中科院分区：
文献类型：
--
作者：
Howell R;Clarke MA;Reuschl AK;Chen T;Abbott-Imboden S;Singer M;Lowe DM;Bennett CL;Chain B;Jolly C;Fisher J
新冠肺炎疫情已将全球医疗体系推向崩溃边缘。对有效和负担得起的新冠肺炎治疗的迫切需要要求重新调整已批准药物的组合。挑战是确定哪些组合可能是最有效的，以及在疾病的哪个阶段。在这里，我们提出了第一个疾病阶段可执行的SARS-CoV-2-宿主相互作用的信号网络模型，用于预测治疗早期和晚期严重疾病的有效再利用药物组合。使用我们的可执行模型，我们对140个潜在靶点的9870对进行了电子筛选，并确定了9种新的药物组合。Camostat和Apilimod被预测为在严重疾病的早期阶段有效抑制病毒复制的最有希望的组合，并在人类Caco-2细胞中得到实验验证。我们的研究进一步证明了可执行的机械建模的力量，使得能够根据疾病进展量身定做的联合疗法的快速临床前评估。它还提出了一种新的资源和可扩展的模型系统，可以对大流行中的进一步需求做出反应。
The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.
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影响因子：
8.8
作者：
Dittmar M;Lee JS;Whig K;Segrist E;Li M;Kamalia B;Castellana L;Ayyanathan K;Cardenas-Diaz FL;Morrisey EE;Truitt R;Yang W;Jurado K;Samby K;Ramage H;Schultz DC;Cherry S
通讯作者：
Cherry S
影响因子：
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作者：
Dogra P;Ruiz-Ramírez J;Sinha K;Butner JD;Peláez MJ;Rawat M;Yellepeddi VK;Pasqualini R;Arap W;Sostman HD;Cristini V;Wang Z
通讯作者：
Wang Z
影响因子：
56.9
作者：
Broggi, Achille;Ghosh, Sreya;Zanoni, Ivan
通讯作者：
Zanoni, Ivan
影响因子：
64.5
作者：
Bouhaddou, Mehdi;Memon, Danish;Krogan, Nevan J.
通讯作者：
Krogan, Nevan J.
影响因子：
2.6
作者：
Foucquier, Julie;Guedj, Mickael
通讯作者：
Guedj, Mickael