The existence of high cost-consuming and high rate of drug failures suggests the promotion of drug repositioning in drug discovery. Existing drug repositioning techniques mainly focus on discovering candidate drugs for a kind of disease, and are not suitable for predicting candidate drugs for an individual sample. Type 1 diabetes mellitus (T1DM) is a disorder of glucose homeostasis caused by autoimmune destruction of the pancreatic β-cell. Here, we present a novel single sample drug repositioning approach for predicting personalized candidate drugs for T1DM. Our method is based on the observation of drug-disease associations by measuring the similarities of individualized pathway aberrance induced by disease and various drugs using a Kolmogorov-Smirnov weighted Enrichment Score algorithm. Using this method, we predicted several underlying candidate drugs for T1DM. Some of them have been reported for the treatment of diabetes mellitus, and some with a current indication to treat other diseases might be repurposed to treat T1DM. This study conducts drug discovery via detecting the functional connections among disease and drug action, on a personalized or customized basis. Our framework provides a rational way for systematic personalized drug discovery of complex diseases and contributes to the future application of custom therapeutic decisions.

中文翻译：

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一种新颖的个性化药物重新定位方法，用于预测1型糖尿病的个性化候选药物。

高成本消耗和高药物失败率的存在表明在药物开发中促进了药物重新定位。现有的药物重新定位技术主要集中于发现一种疾病的候选药物，而不适合于预测单个样本的候选药物。1型糖尿病（T1DM）是由胰岛β细胞自身免疫破坏引起的葡萄糖体内稳态疾病。在这里，我们提出了一种新型的单一样品药物重新定位方法，用于预测T1DM的个性化候选药物。我们的方法基于对药物-疾病关联的观察，方法是使用Kolmogorov-Smirnov加权富集得分算法测量由疾病和多种药物引起的个体化途径异常的相似性。使用这种方法 我们预测了T1DM的几种潜在候选药物。据报道，其中一些可用于治疗糖尿病，而某些目前可用于治疗其他疾病的适应症可能会重新用于治疗T1DM。这项研究通过在个性化或定制的基础上检测疾病和药物作用之间的功能联系来进行药物发现。我们的框架为复杂疾病的系统化个性化药物发现提供了一种合理的方法，并有助于定制治疗决策的未来应用。在个性化或定制的基础上。我们的框架为复杂疾病的系统化个性化药物发现提供了一种合理的方法，并有助于定制治疗决策的未来应用。在个性化或定制的基础上。我们的框架为复杂疾病的系统化个性化药物发现提供了一种合理的方法，并为定制治疗决策的未来应用做出了贡献。