Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.
Nat Commun. 2021 Nov 25;12(1):6848. doi: 10.1038/s41467-021-27138-2.
Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.
传统药物发现面临严重的疗效危机。已注册药物的再利用提供了一种具有更低成本和更快药物开发时间表的替代方案。然而,识别疾病模块所需的数据(即描述复杂疾病机制的途径和子网络,其中包含潜在的药物靶点)分散在独立的数据库中。此外,现有的研究仅限于针对特定疾病的预测或非转化算法方法。因此,需要一种适应性强的工具,使生物医学研究人员能够将基于网络的药物再利用方法用于他们的个别用例。我们通过 NeDRex 填补了这一空白,这是一个用于基于网络的药物再利用和疾病模块发现的集成和交互式平台。NeDRex 集成了十个不同的数据源,涵盖基因、药物、药物靶点、疾病注释及其关系。NeDRex 允许构建异构生物网络,挖掘疾病模块,针对疾病机制对药物进行优先级排序,并进行统计验证。我们在五个具体用例中展示了 NeDRex 的实用性。