Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
NPJ Syst Biol Appl. 2022 Apr 20;8(1):12. doi: 10.1038/s41540-022-00221-0.
Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.
尽管现代医学的进步带来了心血管结局的改善，但心血管疾病（CVD）仍然是全球死亡和发病的主要原因。因此，迫切需要新的方法来改善 CVD 药物治疗。由于药物发现到临床应用的开发时间和成本过高，因此需要替代药物开发策略。其中包括基于组学数据的药物重定位的计算方法，这些方法越来越受到关注。在这项工作中，我们开发了一种调整后的相似性度量方法，该方法由算法 SAveRUNNER 实现，用于重新定位心血管疾病的药物，同时考虑候选药物的副作用。我们分析了九种心血管疾病和两种副作用。我们将疾病紊乱和副作用都表示为人类相互作用组中的网络模块，并认为那些靠近疾病模块但远离副作用模块的候选药物是理想的。我们的方法为心血管疾病提供了一份不太可能产生常见不良反应的候选药物清单。这种纳入副作用的方法也适用于其他疾病。
