Cai Xiaoshu, Chen Yang, Gao Zhen, Xu Rong
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.
AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:22-31. eCollection 2016.
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.
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。