论文标题
临床连接图的药物重新利用：使用实验室测试桥接药物和疾病
Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases
论文作者
论文摘要
药物重新定位引起了制药行业和研究界的越来越多的关注。许多现有的计算药物重新利用方法依赖于临床前数据（例如，化学结构，药物靶标），导致临床试验的转化问题。在这项研究中，我们提出了一个通过利用实验室测试来分析药物和疾病之间的互补性来重新利用药物的临床连接图框架。我们通过在纵向电子健康记录数据上应用一个连续的自我控制病例系列模型来建立临床药物效应向量（即药物实验室测试关联）。我们通过在大规模的国家调查数据上应用Wilcoxon Rank Sum测试来建立临床疾病符号载体（即疾病实验室测试协会）。最后，我们通过在临床疾病符号向量和临床药物效应向量上应用基于DOT产品的评分功能来计算每个药物疾病对的重新利用可能性得分。我们全面评估了6种重要的慢性疾病（例如哮喘，冠心病，2型糖尿病等）的392种药物。我们不仅发现疾病和药物之间的已知关联，而且发现许多隐藏的药物疾病关联。此外，我们能够通过实验室效应向量和疾病符号向量之间的相应互补性来解释预测的药物疾病关联。提出的临床连接图框架使用了从电子临床信息到桥接药物和疾病的实验室测试，这是可以解释的，并且比现有计算方法具有更好的翻译能力。实验结果证明了拟议框架的有效性，并建议我们的方法可以帮助确定药物重新利用的机会，这将通过提供更有效和更安全的治疗方法来使患者受益。
Drug repurposing 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 clinical connectivity map framework for drug repurposing by leveraging laboratory tests to analyze complementarity between drugs and diseases. We establish clinical drug effect vectors (i.e., drug-laboratory test associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data. We establish clinical disease sign vectors (i.e., disease-laboratory test associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Finally, we compute a repurposing possibility score for each drug-disease pair by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. We comprehensively evaluate 392 drugs for 6 important chronic diseases (e.g., asthma, coronary heart disease, type 2 diabetes, etc.). We discover not only known associations between diseases and drugs but also many hidden drug-disease associations. Moreover, we are able to explain the predicted drug-disease associations via the corresponding complementarity between laboratory tests of drug effect vectors and disease sign vectors. The proposed clinical connectivity map framework uses laboratory tests from electronic clinical information to bridge drugs and diseases, which is explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of the proposed framework and suggest that our method could help identify drug repurposing opportunities, which will benefit patients by offering more effective and safer treatments.
