Cheng Xi, Zhao Wensi, Zhu Mengdi, Wang Bo, Wang Xuege, Yang Xiaoyun, Huang Yuqi, Tan Minjia, Li Jing
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
The Chemical Proteomics Center and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Cancer Biol Med. 2021 Apr 24;19(1):74-89. doi: 10.20892/j.issn.2095-3941.2020.0218.
Drug repurposing, the application of existing therapeutics to new indications, holds promise in achieving rapid clinical effects at a much lower cost than that of drug development. The aim of our study was to perform a more comprehensive drug repurposing prediction of diseases, particularly cancers.
Here, by targeting 4,096 human diseases, including 384 cancers, we propose a greedy computational model based on a heterogeneous multilayer network for the repurposing of 1,419 existing drugs in DrugBank. We performed additional experimental validation for the dominant repurposed drugs in cancer.
The overall performance of the model was well supported by cross-validation and literature mining. Focusing on the top-ranked repurposed drugs in cancers, we verified the anticancer effects of 5 repurposed drugs widely used clinically in drug sensitivity experiments. Because of the distinctive antitumor effects of nifedipine (an antihypertensive agent) and nortriptyline (an antidepressant drug) in prostate cancer, we further explored their underlying mechanisms by using quantitative proteomics. Our analysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication, the cell cycle, and RNA transport. Moreover, experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth of prostate tumors in a xenograft model.
Our predicted results, which have been released in a public database named The Predictive Database for Drug Repurposing (PAD), provide an informative resource for discovering and ranking drugs that may potentially be repurposed for cancer treatment and determining new therapeutic effects of existing drugs.
药物重新利用，即将现有治疗方法应用于新的适应症，有望以比药物开发低得多的成本实现快速临床效果。我们研究的目的是对疾病，特别是癌症进行更全面的药物重新利用预测。
在此，我们针对包括384种癌症在内的4096种人类疾病，提出了一种基于异构多层网络的贪心计算模型，用于药物银行中1419种现有药物的重新利用。我们对癌症中主要的重新利用药物进行了额外的实验验证。
该模型的整体性能得到了交叉验证和文献挖掘的有力支持。聚焦于癌症中排名靠前的重新利用药物，我们在药物敏感性实验中验证了5种临床上广泛使用的重新利用药物的抗癌效果。由于硝苯地平（一种降压药）和去甲替林（一种抗抑郁药）在前列腺癌中具有独特的抗肿瘤作用，我们通过定量蛋白质组学进一步探索了它们的潜在机制。我们的分析表明，硝苯地平和去甲替林均影响DNA复制、细胞周期和RNA转运等与癌症相关的途径。此外，实验证明硝苯地平和去甲替林在异种移植模型中显著抑制前列腺肿瘤的生长。
我们的预测结果已在一个名为药物重新利用预测数据库（PAD）的公共数据库中发布，为发现和排名可能潜在重新用于癌症治疗的药物以及确定现有药物的新治疗效果提供了信息资源。
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