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探索新型炎症性肠病药物小分子诱导的全基因组转录谱。

Explore Small Molecule-induced Genome-wide Transcriptional Profiles for Novel Inflammatory Bowel Disease Drug.

作者信息

Cai Xiaoshu, Chen Yang, Gao Zhen, Xu Rong

机构信息

Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

出版信息

AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:22-31. eCollection 2016.

Abstract

Inflammatory Bowel Disease (IBD) is a chronic and relapsing disorder, which affects millions people worldwide. Current drug options cannot cure the disease and may cause severe side effects. We developed a systematic framework to identify novel IBD drugs exploiting millions of genomic signatures for chemical compounds. Specifically, we searched all FDA-approved drugs for candidates that share similar genomic profiles with IBD. In the evaluation experiments, our approach ranked approved IBD drugs averagely within top 26% among 858 candidates, significantly outperforming a state-of-art genomics-based drug repositioning method (p-value < e-8). Our approach also achieved significantly higher average precision than the state-of-art approach in predicting potential IBD drugs from clinical trials (0.072 vs. 0.043, p<0.1) and off-label IBD drugs (0.198 vs. 0.138, p<0.1). Furthermore, we found evidences supporting the therapeutic potential of the top-ranked drugs, such as Naloxone, in literature and through analyzing target genes and pathways.

摘要

炎症性肠病(IBD)是一种慢性复发性疾病,影响着全球数百万人。目前的药物选择无法治愈该疾病,且可能会导致严重的副作用。我们开发了一个系统框架,利用数百万种化合物的基因组特征来识别新型IBD药物。具体而言,我们在所有FDA批准的药物中搜索与IBD具有相似基因组特征的候选药物。在评估实验中,我们的方法在858个候选药物中,将已批准的IBD药物平均排名在前26%以内,显著优于一种基于基因组学的最先进的药物重新定位方法(p值<e-8)。在从临床试验预测潜在IBD药物(0.072对0.043,p<0.1)和非标签IBD药物(0.198对0.138,p<0.1)方面,我们的方法也比最先进的方法实现了显著更高的平均精度。此外,通过分析靶基因和通路,我们在文献中找到了支持排名靠前的药物(如纳洛酮)治疗潜力的证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4915/5001780/6fb12da5cdd2/2383214f1.jpg

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