Iwata Hiroaki, Sawada Ryusuke, Mizutani Sayaka, Yamanishi Yoshihiro
Division of System Cohort, Multi-Scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University , 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.
J Chem Inf Model. 2015 Feb 23;55(2):446-59. doi: 10.1021/ci500670q. Epub 2015 Jan 28.
Drug repositioning, or the application of known drugs to new indications, is a challenging issue in pharmaceutical science. In this study, we developed a new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference. We defined a descriptor for each drug-disease pair based on the phenotypic features of drugs (e.g., medicinal effects and side effects) and various molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related pathways, and environmental factors) and constructed a statistical model to predict new drug-disease associations for a wide range of diseases in the International Classification of Diseases. Our results show that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity. Finally, we performed a comprehensive prediction of a drug-disease association network consisting of 2349 drugs and 858 diseases and described biologically meaningful examples of newly predicted drug indications for several types of cancers and nonhereditary diseases.
药物重新定位，即将已知药物应用于新的适应症，是药学领域中一个具有挑战性的问题。在本研究中，我们开发了一种新的计算方法，用于在监督网络推理框架下预测未知药物适应症，以进行系统性的药物重新定位。我们基于药物的表型特征（如药用效果和副作用）以及疾病的各种分子特征（如致病基因、诊断标志物、疾病相关途径和环境因素）为每个药物-疾病对定义了一个描述符，并构建了一个统计模型，以预测《国际疾病分类》中广泛疾病的新的药物-疾病关联。我们的结果表明，所提出的方法在准确性和适用性方面优于先前的方法，并且其性能不依赖于药物化学结构相似性。最后，我们对一个由2349种药物和858种疾病组成的药物-疾病关联网络进行了全面预测，并描述了几种癌症和非遗传性疾病新预测的药物适应症的生物学意义显著的例子。
