Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA.
Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, Ohio, 43210, USA.
BMC Med Inform Decis Mak. 2021 Sep 24;21(Suppl 8):263. doi: 10.1186/s12911-021-01617-4.
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 CONCLUSIONS: 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.
药物再利用，即确定现有药物的其他治疗用途的过程，已引起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再利用方法都依赖于临床前数据（例如化学结构，药物靶标），这导致临床试验存在转化问题。
在这项研究中，我们提出了一种基于临床连接图的药物再利用新框架，用于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自对照病例系列模型来建立临床药物效应向量（即药物-实验室结果关联），然后通过在大规模全国性调查数据上应用Wilcoxon 秩和检验来建立临床疾病特征向量（即疾病-实验室结果关联）。最终，通过在临床疾病特征向量和临床药物效应向量上应用基于点积的评分函数来计算每个药物-疾病对的再利用可能性评分。在实验过程中，我们综合评估了 392 种药物对 6 种重要慢性病（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）的作用。实验结果不仅反映了疾病与药物之间的已知关联，还包括一些隐藏的药物-疾病关联。本文的代码可在：https://github.com/HoytWen/CCMDR 上获得
所提出的临床连接图框架使用从电子临床信息中找到的实验室结果来连接药物和疾病，使它们的关系具有可解释性，并且比现有的计算方法具有更好的转化能力。实验结果证明了我们提出的框架的有效性，进一步的案例分析也证明了我们的方法可用于挖掘现有药物的再利用机会。
