DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy
DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy
复制标题
DrugCombDB：用于发现组合疗法的药物组合综合数据库
DOI：
10.1093/nar/gkz1007
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发表时间：
2020-01-08
影响因子：
14.9
通讯作者：
Deng, Lei
中科院分区：
文献类型：
--
作者：
Liu, Hui;Zhang, Wenhao;Deng, Lei
与单一药物治疗相比，药物联合治疗在癌症治疗中具有较高的疗效和较低的不良反应，因此受到研究人员和制药企业的广泛关注。由于高通量筛选（HTS）的快速发展，近年来可获得的药物联合数据集的数量急剧增加。因此，迫切需要一个全面的数据库，这对于协同药物组合的实验和计算筛选至关重要。在本文中，我们介绍了DrugCombDB，这是一个致力于从各种数据源中管理药物组合的综合数据库：(i)药物组合的HTS分析；（ii）文献手工策展；（iii） FDA橙皮书和外部数据库。具体而言，DrugCombDB包括448555种来自HTS试验的药物组合，涵盖2887种独特药物和124种人类癌细胞系。特别是，DrugCombDB有超过600000个定量剂量反应，我们从中计算了多个协同作用评分，以确定药物组合的总体协同或拮抗作用。除了从现有数据库中提取的组合外，我们还从数千份PubMed出版物中手动筛选了457种药物组合。为了便于进一步的实验验证和计算模型的开发，构建了多个准备用于训练分类和回归分析的预测模型的数据集，并收集了其他重要的相关数据。一个用户友好的图形可视化网站已经开发，供用户访问丰富的数据和下载预构建的数据集。我们的数据库可在http://drugcombdb.denglab.org/上找到。
Abstract Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.