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首页> 外文期刊>BMC Medical Informatics and Decision Making >Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases
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Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases

机译：临床连接地图用于药物修复：使用实验室结果弥漫武器毒品和疾病

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Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.
机译：药物重估，确定现有药物的额外治疗用途的过程引起了药品行业和研究界的越来越关注。许多现有的计算药物修复方法依赖于临床前数据（例如，化学结构，药物靶标），从而导致临床试验的翻译问题。在这项研究中，我们提出了一种基于临床连通性测绘的新型框架，用于药物批准，以分析药物对疾病的治疗效果。我们首先通过在纵向电子健康记录数据上应用连续的自控案例系列模型来建立临床药物效果载体（即药物实验室结果协会），然后建立临床疾病征兆（即疾病实验室结果协会）在大规模国家调查数据上应用Wilcoxon等级测试。最终，通过在临床疾病标志载体和临床药物效应载体上应用基于DOT产品的评分功能来计算每种药物疾病对的重新调整可能性评分。在实验期间，我们全面评估了392种慢性疾病的392种药物（包括哮喘，冠心病，充血性心力衰竭，心脏病发作，2型糖尿病和中风）。实验结果不仅反映了疾病和药物之间的已知协会，而且还包括一些隐患的药物疾病协会。本文的代码可用于：https://github.com/hoytwen/ccmdr，所提出的临床连接地图框架使用从电子临床信息中发现的实验室结果来桥接药物和疾病，这使得其关系可解释并具有更好的翻译权力比现有的计算方法。实验结果表明我们提出的框架的有效性，进一步的案例分析还证明了我们的方法可用于修复现有的药物机会。

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