Li Xianbin, Liao Minzhen, Wang Bing, Zan Xiangzhen, Huo Yanhao, Liu Yue, Bao Zhenshen, Xu Peng, Liu Wenbin
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China.
Comput Struct Biotechnol J. 2023 Sep 9;21:4446-4455. doi: 10.1016/j.csbj.2023.09.007. eCollection 2023.
Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel rug epurposing method based on the nhibition ffect on gene regulatory network () to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.
众多计算药物重定位方法已成为昂贵且耗时的传统药物发现方法的有效替代方案。其中一些方法基于这样的假设，即候选药物应对疾病相关基因具有逆转作用。然而，在疾病相关基因与药物干扰基因之间重叠有限的情况下，此类方法并不适用。在本研究中，我们提出了一种基于对基因调控网络（）的抑制作用的新型药物重定位方法，以识别用于癌症治疗的潜在药物。DRIE整合了基因表达谱和基因调控网络，通过使用疾病特异性网络中的最短路径来计算抑制分数。在11个数据集上的结果表明，与其他现有最先进方法相比，DRIE具有卓越的性能。案例研究表明，我们的方法有效地发现了新的药物-疾病关联。我们的研究结果表明，排名靠前的候选药物已被CTD数据库验证。此外，它明确识别出了三种癌症（结直肠癌、乳腺癌和肺癌）的潜在药物，这在注释CTD中的药物-疾病关系时很有帮助。本研究提出了一种新型的药物重定位框架，这将有助于药物发现和开发。
