Network medicine framework for identifying drug-repurposing opportunities for COVID-19.
Network medicine framework for identifying drug-repurposing opportunities for COVID-19.
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DOI：
10.1073/pnas.2025581118
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发表时间：
2021-05-11
影响因子：
11.1
通讯作者：
Barabási AL
中科院分区：
文献类型：
--
作者：
Morselli Gysi D;do Valle Í;Zitnik M;Ameli A;Gan X;Varol O;Ghiassian SD;Patten JJ;Davey RA;Loscalzo J;Barabási AL
新冠肺炎大流行突显了优先批准用于治疗严重急性呼吸综合征冠状病毒2型感染的药物的重要性。在这里，我们部署了依赖于人工智能、网络扩散和网络邻近的算法，根据对SARS-CoV-2的预期疗效对6,340种药物进行了排名。我们对918种药物进行了实验筛选，使我们能够评估现有药物再利用方法的性能，并使用共识算法来提高预测的准确性。最后，我们在人体细胞中筛选出排名最靠前的药物，确定了六种降低病毒感染的药物，其中四种可能会被重新用于治疗新冠肺炎。制定的战略具有超越新冠肺炎的意义，使我们能够为被忽视的疾病确定药物再用途的候选者。新冠肺炎大流行突显了需要快速和可靠地优先考虑临床批准的化合物，以确定其对严重急性呼吸综合征冠状病毒2型感染的潜在有效性。在这里，我们部署了依赖于人工智能、网络扩散和网络邻近的算法，让它们中的每一个都根据对抗SARS-CoV-2的预期疗效对6,340种药物进行排名。为了验证预测，我们使用了在VeroE6细胞中进行实验筛选的918种药物作为基本事实，以及临床试验中的药物列表，这些列表捕捉了医学界对具有潜在新冠肺炎疗效的药物的评估。我们发现，没有一个单一的预测算法在所有数据集和指标上提供一致可靠的结果。这一结果促使我们开发了一种融合所有算法预测的多模式技术，发现不同预测方法之间的共识一致地超过了最好的单个管道的性能。我们在人体细胞中筛选排名最高的药物，获得了62%的成功率，而非引导筛选的命中率为0.8%。在六种降低病毒感染的药物中，有四种可以直接用于治疗新冠肺炎，为新冠肺炎提供了新的治疗方法。我们还发现，在成功降低病毒感染的77种药物中，有76种没有结合SARS-CoV-2靶向的蛋白质，这表明这些网络药物依赖于基于网络的机制，而使用基于对接的策略无法识别这些机制。这些进展为为未来的病原体和被忽视的疾病确定可重复使用的药物提供了一条方法学途径，这些疾病没有得到新药物开发的成本和延长的时间表的满足。
The COVID-19 pandemic has highlighted the importance of prioritizing approved drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We experimentally screened 918 drugs, allowing us to evaluate the performance of the existing drug-repurposing methodologies, and used a consensus algorithm to increase the accuracy of the predictions. Finally, we screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19. The developed strategy has significance beyond COVID-19, allowing us to identify drug-repurposing candidates for neglected diseases. The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
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影响因子：
7.6
作者：
Lin, Kai;Gallay, Philippe
通讯作者：
Gallay, Philippe
影响因子：
4.6
作者：
Guala D;Sonnhammer ELL
通讯作者：
Sonnhammer ELL
影响因子：
16.6
作者：
Cheng F;Desai RJ;Handy DE;Wang R;Schneeweiss S;Barabási AL;Loscalzo J
通讯作者：
Loscalzo J
影响因子：
64.8
作者：
Riva L;Yuan S;Yin X;Martin-Sancho L;Matsunaga N;Pache L;Burgstaller-Muehlbacher S;De Jesus PD;Teriete P;Hull MV;Chang MW;Chan JF;Cao J;Poon VK;Herbert KM;Cheng K;Nguyen TH;Rubanov A;Pu Y;Nguyen C;Choi A;Rathnasinghe R;Schotsaert M;Miorin L;Dejosez M;Zwaka TP;Sit KY;Martinez-Sobrido L;Liu WC;White KM;Chapman ME;Lendy EK;Glynne RJ;Albrecht R;Ruppin E;Mesecar AD;Johnson JR;Benner C;Sun R;Schultz PG;Su AI;García-Sastre A;Chatterjee AK;Yuen KY;Chanda SK
通讯作者：
Chanda SK
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer