Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigación en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, México City, México.
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Unidad Académica Yucatán, Mérida, Yucatán, México.
PLoS One. 2021 Mar 17;16(3):e0247018. doi: 10.1371/journal.pone.0247018. eCollection 2021.
In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.
在这项工作中，我们对药物与其靶标之间相互作用的网络进行了分析，以评估这些化合物的连接程度。为此，我们从 DrugBank 数据库中下载了这些相互作用，并考虑了所有经 FDA 批准的药物。基于对这个相互作用网络的拓扑分析，我们获得了关于这些相互作用的度数、聚类系数、连通分量和中心性的信息。我们发现，这个药物-靶标相互作用网络不能分为两个不相交且独立的集合，也就是说，它不是二分的。此外，通过对每对节点的连接性或关联性的识别，我们发现药物-靶标网络由 165 个连通分量组成，其中一个巨型分量包含 4376 个相互作用，占所有元素的 89.99%。在这方面，属于视紫红质样 G 蛋白偶联受体家族并被生物胺组胺激活的组胺 H1 受体，被发现是输入度数中心性中最重要的节点。在输出度数中心性方面，发现 fostamatinib 是最重要的节点，因为这种药物与 300 个不同的靶标相互作用，包括细胞中主要参与免疫反应调节的花生四烯酸 5-脂氧合酶或 ALOX5。前 10 个枢纽与 33%的靶基因相互作用。 fostamatinib 之所以脱颖而出，是因为它被用于治疗成人慢性免疫性血小板减少症。最后，还确定了 187 个高度连接的节点集，这些节点集以社区的形式组织在一起。事实上，最大的社区有超过 400 个元素，与代谢疾病、精神疾病和癌症有关。我们的研究结果表明，可以探索这些化合物及其靶标，以改善药物重新定位并应对新兴疾病。
