Huang Liang-Chin, Soysal Ergin, Zheng W, Zhao Zhongming, Xu Hua, Sun Jingchun
BMC Syst Biol. 2015;9 Suppl 4(Suppl 4):S2. doi: 10.1186/1752-0509-9-S4-S2. Epub 2015 Jun 11.
Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations.
In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP K(i) Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance.
The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing.
计算药理学通过提供宏观视角和对药物作用的更深入理解，能够独特地解决药物研发过程中的一些问题。具体而言，网络辅助方法在药物重新利用的推断方面具有前景。然而，来自不同来源和各种检测方法的药物 - 靶点关联存在大量噪声，导致推断误差增大。为了减少推断误差，创建一个全面且加权的药物 - 靶点关联数据集是必要且关键的。
在本研究中，我们创建了一个加权整合的药物 - 靶点相互作用组（WinDTome），为计算药理学提供全面的药物 - 靶点关联资源。我们首先从六个常用的以药物 - 靶点为中心的数据源收集药物 - 靶点相互作用，包括DrugBank、KEGG、TTD、MATADOR、PDSP K(i)数据库和BindingDB。然后，我们采用记录链接方法，利用包括PubChem、Entrez Gene和UniProt在内的公共数据源，将药物和靶点标准化为唯一标识符。为了评估药物 - 靶点关联的可靠性，我们根据其数据源和发表参考文献为每个药物 - 靶点关联分配两个分数（Score_S和Score_R）。因此，WinDTome包含303,018种化合物和4,113个基因之间的546,196个药物 - 靶点关联。为了评估WinDTome的应用，我们设计了一种基于网络的药物重新利用方法，以精神疾病精神分裂症（SCZ）为例。从41种已知的SCZ药物及其靶点出发，我们通过WinDTome中Score_S高于2的药物 - 靶点关联以及人类蛋白质 - 蛋白质相互作用，总共推断出264种潜在的SCZ药物。在这264种与SCZ相关的药物中，分别有39种药物已在SCZ治疗的临床试验中进行了研究，74种药物用于治疗其他精神疾病。与使用其他Score_S截止值、单一数据源或来自STITCH的数据的结果相比，264种与SCZ相关药物的推断具有最高的性能。
本研究中生成的WinDTome包含具有置信度分数的全面药物 - 靶点关联。其在SCZ药物重新利用中的应用表明，WinDTome有望成为药物重新利用的有用资源。
