Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks.
Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks.
复制标题
DOI：
10.1186/s12967-023-04223-2
复制
发表时间：
2023-06-26
影响因子：
7.4
通讯作者：
Kim, Dokyoon
中科院分区：
文献类型：
--
作者：
Nam, Yonghyun;Lucas, Anastasia;Yun, Jae-Seung;Lee, Seung Mi;Park, Ji Won;Chen, Ziqi;Lee, Brian;Ning, Xia;Shen, Li;Verma, Anurag;Kim, Dokyoon
计算药物再利用对于确定候选治疗药物以解决开发新发传染病治疗方法的迫切需要至关重要。最近的COVID-19大流行教会了我们快速发现候选药物并将其提供给医疗和制药专家进行进一步研究的重要性。基于网络的方法可以利用生物成分之间的综合关系，快速提供可重复使用的药物。然而，对于新出现的疾病，由于疾病的新颖性导致信息流不足，仅使用已有知识网络的重新利用方法可能会被证明是不够的。我们提出了一种基于网络的药物再利用的互补链接方法，以解决知识网络中缺乏新的疾病特异性信息的问题。我们在COVID-19大流行早期面临的受控重新利用情景下模拟了我们的方法。首先，通过融合综合知识库，构建疾病-基因-药物多层网络作为主干网络；然后，从截至2020年5月的出版物或预印本服务器收集COVID-19的补充信息，包括18种共病和17种相关蛋白质的数据。我们估计新型COVID-19节点与骨干网络之间的连接，以构建互补网络。通过应用基于图的半监督学习，对COVID-19进行基于网络的药物评分，所得分数用于验证基于人群规模的电子健康记录的药物分析的优先药物。基于大流行前的知识，主干网络包括591种疾病、26681种蛋白质和2173个药物节点。在将35个实体组成的补充信息纳入骨干网络后，药物评分筛选了前30名潜在的COVID-19可重复利用药物。随后，在截至2021年10月从宾夕法尼亚大学医学COVID-19登记处获得的患者电子健康记录中分析了优先药物，发现其中8种与COVID-19表型在统计学上相关。我们发现，在补充网络上基于图的评分确定的30种药物中，有8种作为COVID-19再利用的潜在候选药物，在随访分析中得到了真实患者数据的额外支持。这些结果表明，我们基于网络的互补链接方法和药物评分算法是在新出现的疾病暴发时识别候选可重复利用药物的有希望的策略。在线版本包含补充材料，可在10.1186/s12967-023- 04222 -2获得。
Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks. The online version contains supplementary material available at 10.1186/s12967-023-04223-2.
登录
查看更多内容
影响因子：
5.7
作者：
Baran A;Fırat Baran M;Keskin C;Hatipoğlu A;Yavuz Ö;İrtegün Kandemir S;Adican MT;Khalilov R;Mammadova A;Ahmadian E;Rosić G;Selakovic D;Eftekhari A
通讯作者：
Eftekhari A
影响因子：
6
作者：
Singh N;Villoutreix BO
通讯作者：
Villoutreix BO
影响因子：
14.9
作者：
Gillespie M;Jassal B;Stephan R;Milacic M;Rothfels K;Senff-Ribeiro A;Griss J;Sevilla C;Matthews L;Gong C;Deng C;Varusai T;Ragueneau E;Haider Y;May B;Shamovsky V;Weiser J;Brunson T;Sanati N;Beckman L;Shao X;Fabregat A;Sidiropoulos K;Murillo J;Viteri G;Cook J;Shorser S;Bader G;Demir E;Sander C;Haw R;Wu G;Stein L;Hermjakob H;D'Eustachio P
通讯作者：
D'Eustachio P
影响因子：
14.9
作者：
Davis AP;Grondin CJ;Johnson RJ;Sciaky D;McMorran R;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者：
Mattingly CJ
DOI：
10.1016/j.patter.2021.100396
发表时间：
2022-01-14
期刊：
Patterns (New York, N.Y.)
影响因子：
--
作者：
Santos SS;Torres M;Galeano D;Sánchez MDM;Cernuzzi L;Paccanaro A
通讯作者：
Paccanaro A