Inferring new drug indications using the complementarity between clinical disease signatures and drug effects
Inferring new drug indications using the complementarity between clinical disease signatures and drug effects
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DOI：
10.1016/j.jbi.2015.12.003
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发表时间：
2016-02-01
影响因子：
4.5
通讯作者：
Lee, Doheon
中科院分区：
文献类型：
--
作者：
Jang, Dongjin;Lee, Sejoon;Lee, Doheon
背景：药物重新定位是为现有药物寻找新适应症的过程。近年来，由于新药开发成本的大幅增加，其重要性急剧增加。然而，由于人体生理系统固有的复杂性，以往大多数以分子为中心的药物定位工作并不能反映药物的终点生理活性。在这里，我们提出了一种新的计算框架，通过使用反映最终结果的电子临床信息来推断上市药物的替代适应症，药物对人体生物活动影响的生理学结果。在这项工作中，我们使用临床疾病特征和临床药物效果之间的互补性概念。在此框架下，我们通过应用两种方法（即，统计分析和文献挖掘）。最后，我们通过计算疾病特征的临床状态（“上升”或“下降”）与药物的临床效果（“上升”、“下降”或“关联”）之间的互补性（反相关性）和关联性，为每个疾病药物对分配重新定位的可能性得分。在电子临床数据集（NHANES），共717个临床变量，被认为是在本study.Results：我们的预测结果的统计学意义的支持，通过两个基准数据集（比较毒理基因组学数据库和临床试验）。我们不仅发现了许多已知的疾病与药物之间的关系，而且还发现了许多隐藏的疾病与药物之间的关系。例如，谷胱甘肽和依地酸可作为治疗哮喘的候选药物进行研究。我们用统计实验检验了预测结果（比较毒理基因组学数据库和临床试验中的富集验证、超几何和排列检验P < 0.009），并为已发表文献提供证据。结果表明，电子临床信息是一种可行的数据资源，利用其互补性，疾病的临床特征和药物的临床效果之间的（反相关关系）是药物重新定位研究中的潜在预测概念。它使得所提出的方法可用于识别疾病和药物之间的新关系，这些关系具有很高的生物学有效性。(C)2015作者爱思唯尔公司出版
Background: Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems.Methods: Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug's effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease drug pair by the calculation of complementarity (anti-correlation) and association between clinical states ("up" or "down") of disease signatures and clinical effects ("up", "down" or "association") of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study.Results: The statistical significance of our prediction results is supported through two benchmark data sets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P < 0.009 in Comparative Toxicogenomics Database and Clinical Trials) and presented evidences for those with already published literature.Conclusion: The results show that electronic clinical information is a feasible data resource and utilizing the complementarity (anti-correlated relationships) between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research. It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid. (C) 2015 The Authors. Published by Elsevier Inc.