Kang De, Pang Xiaocong, Lian Wenwen, Xu Lvjie, Wang Jinhua, Jia Hao, Zhang Baoyue, Liu Ai-Lin, Du Guan-Hua
Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College Xian Nong Tan Street Beijing 100050 China
Beijing Key Laboratory of Drug Target Research and Drug Screening, Chinese Academy of Medical Sciences, Peking Union Medical College Beijing 100050 China.
RSC Adv. 2018 Jan 30;8(10):5286-5297. doi: 10.1039/c7ra12259d. eCollection 2018 Jan 29.
The high morbidity and mortality of cancer make it one of the leading causes of global death, thus it is an urgent need to develop effective drugs for cancer therapy. Vascular endothelial growth factor receptor-2 (VEGFR2) acts as a central modulator of angiogenesis, and is therefore an important pharmaceutical target for developing anti-angiogenic agents. In this study, ligand-based naïve Bayesian (NB) models and structure-based molecular docking were combined to develop a virtual screening (VS) pipeline for identifying potential VEGFR2 inhibitors from FDA-approved drugs. The best validated naïve Bayesian model (NB-c) gave Matthews correlation coefficients of 0.966 and 0.951 for the test set and external validation set, respectively. 1841 FDA-approved drugs were sequentially screened by the optimal model NB-c and molecular docking module LibDock. By analyzing the results of VS, 9 top ranked drugs with EstPGood value ≥ 0.6 and LibDock Score ≥ 120 were chosen for biological validation. VEGFR2 kinase assay results demonstrated that flubendazole, rilpivirine and papaverine showed VEGFR2 inhibitory activities with IC values ranging from 0.47 to 6.29 μM. Binding mode analysis with CDOCKER revealed the action mechanism of the 3 hit drugs binding to VEGFR2. In summary, we not only proposed an integrated VS pipeline for potential VEGFR2 inhibitors screening, but also identified 3 FDA-approved drugs as novel VEGFR2 inhibitors, which could be used to design and develop new antiangiogenic agents.
癌症的高发病率和死亡率使其成为全球主要死因之一，因此迫切需要开发有效的癌症治疗药物。血管内皮生长因子受体2（VEGFR2）是血管生成的核心调节因子，因此是开发抗血管生成药物的重要药学靶点。在本研究中，基于配体的朴素贝叶斯（NB）模型和基于结构的分子对接相结合，开发了一种虚拟筛选（VS）流程，用于从FDA批准的药物中识别潜在的VEGFR2抑制剂。经过最佳验证的朴素贝叶斯模型（NB-c）对测试集和外部验证集的马修斯相关系数分别为0.966和0.951。通过最优模型NB-c和分子对接模块LibDock对1841种FDA批准的药物进行了依次筛选。通过分析虚拟筛选结果，选择了9种EstPGood值≥0.6且LibDock评分≥120的排名靠前的药物进行生物学验证。VEGFR2激酶测定结果表明，氟苯达唑、利匹韦林和罂粟碱具有VEGFR2抑制活性，IC值范围为0.47至6.29μM。与CDOCKER的结合模式分析揭示了这3种活性药物与VEGFR2结合的作用机制。总之，我们不仅提出了一种用于筛选潜在VEGFR2抑制剂的综合虚拟筛选流程，还鉴定出3种FDA批准的药物作为新型VEGFR2抑制剂，可用于设计和开发新的抗血管生成药物。
