Nishimura Yuhei, Hara Hideaki
Department of Molecular and Cellular Pharmacology, Pharmacogenomics and Pharmacoinformatics, Mie University Graduate School of Medicine, Tsu, Mie 514-8507, Japan.
Molecular Pharmacology, Department of Biofunctional Evaluation, Gifu Pharmaceutical University, Gifu 501-1196, Japan.
Oxid Med Cell Longev. 2016;2016:2370252. doi: 10.1155/2016/2370252. Epub 2016 Dec 7.
Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases.
过度的氧化应激会导致视网膜功能网络失调，进而引发青光眼、年龄相关性黄斑变性和糖尿病性视网膜病变等视网膜疾病。尽管已经开发出各种疗法来减轻视网膜疾病中的氧化应激，但大多数疗法在临床试验中均未显示出疗效。这可能是由于针对氧化应激这样复杂的网络进行靶点选择时过于简单化。高通量技术的最新进展促进了多组学数据的收集，这推动了公共数据库的增长以及生物信息学工具的开发。从组学数据库中获得的知识整合可用于生成疾病相关的生物网络，并识别网络内潜在的治疗靶点。在此，我们概述了药物发现过程中的整合方法，并提供了一些简单示例，说明如何利用这些方法来识别与视网膜疾病氧化应激相关的靶点。
