Zhang Ping, Wang Fei, Hu Jianying
Healthcare Analytics Research, IBM T.J. Watson Research Center, New York, USA.
AMIA Annu Symp Proc. 2014 Nov 14;2014:1258-67. eCollection 2014.
In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 diseases. Experimental results demonstrated the effectiveness of the approach. In addition, DDR ranked drug and disease information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models. Particularly, some of our novel predictions of drug-disease associations were supported by clinical trials databases, showing that DDR could serve as a useful tool in drug discovery to efficiently identify potential novel uses for existing drugs.
鉴于传统的从头开始的药物研发成本高昂且风险巨大，对现有药物潜在的其他用途进行研究，即药物重新定位，已引起制药行业和研究界越来越多的关注。在本文中，我们提出了一个名为DDR的统一计算框架，用于预测新的药物-疾病关联。DDR将药物重新定位的假设生成任务表述为一个约束非线性优化问题。它利用多个药物相似性网络、多个疾病相似性网络以及已知的药物-疾病关联，来探索药物和疾病之间潜在的新关联，这些关联此前并无已知联系。我们使用799种药物针对719种疾病进行了大规模研究。实验结果证明了该方法的有效性。此外，DDR根据药物和疾病信息源对预测的贡献进行排序，从而为对多个数据源进行优先级排序和构建更可靠的药物重新定位模型铺平了道路。特别地，我们对药物-疾病关联的一些新预测得到了临床试验数据库的支持，这表明DDR可作为药物研发中的一个有用工具，以有效地识别现有药物潜在的新用途。
