Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac229.
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
网络方法正迅速成为计算方法的基本组成部分，旨在阐明药物的作用机制 (MoA) 和治疗效果。通过对药物和疾病对不同生物网络的影响进行建模，就有可能更好地解释疾病扰动和药物靶点之间的相互作用，以及药物化合物如何诱导有利的生物学反应和/或不良反应。组学技术已被广泛用于生成用于研究药物作用机制和疾病的所需数据。这些数据通常用于定义特定条件的网络，并研究药物是否可以逆转疾病扰动。在这篇综述中，我们描述了常用于研究药物 MoA 并加深我们对慢性疾病基础理解的网络数据挖掘算法。这些方法可以支持药物开发过程的基本阶段，包括潜在药物靶点的识别、药物化合物的计算机筛选以及用于治疗疾病的药物组合。我们还讨论了使用生物和组学驱动的网络来寻找可能重新利用的 FDA 批准的治疗 SARS-CoV-2 感染（COVID-19）的药物治疗的最新研究。
