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通过基于共表达数量性状基因座的分子和靶向独立疾病相关多态性的途径进行精准药物重新利用。

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms.

作者信息

Vitali Francesca, Berghout Joanne, Fan Jungwei, Li Jianrong, Li Qike, Li Haiquan, Lussier Yves A

机构信息

Center for Biomedical Informatics and Biostatistics (CB2), The University of Arizona, Tucson, AZ 85721, USA2Department of Medicine COM-T, The University of Arizona, Tucson, AZ 85721, USA†Authors contributed equally to this work,

出版信息

Pac Symp Biocomput. 2019;24:308-319.

Abstract

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.

摘要

将现有药物用于新的治疗适应症可以提高成功率并简化研发过程。利用大规模生物医学数据库,包括eQTL调控关系和全基因组疾病风险关联,为针对与两种或更多疾病相关的常见或趋同分子候选物的药物提出新适应症提供了机会。这种提出的新计算方法适用于262种复杂疾病,构建了一个多部分层次网络,整合了(i)全基因组关联研究(GWAS)得出的单核苷酸多态性(SNP)与疾病的关联,(ii)eQTL得出的SNP与e基因的关联,纳入了来自19种组织的顺式和反式关系,(iii)蛋白质靶点与药物的关联,以及(iv)药物与疾病适应症的关联,并利用基于基因本体论的信息理论语义(ITS)相似性计算蛋白质靶点功能之间的相似性。我们的假设是,如果两种疾病与一个共同的或功能相似的e基因相关,并且存在一种针对一种疾病中的该e基因/蛋白质的药物,那么第二种疾病就成为一个潜在的重新利用适应症。为了探索这一点,生成了所有可能的独立分离的GWAS衍生SNP对,并根据调控的e基因中趋同生物过程活性的无标度超量表达(ITSeGENE - eGENE)和两个SNP之间共同e基因靶点的无标度超量表达(ITSSNP - SNP)计算每个SNP - SNP对内的相似性统计网络。ITSSNP - SNP的显著性通过经验性无标度排列重采样保守估计,在每次排列中保持每个分子的节点度不变。我们确定了跨越89种GWAS疾病的26种新的药物重新利用适应症候选物,包括钙通道阻滞剂维拉帕米从冠心病到痛风的潜在重新利用。使用DrugBank作为金标准,将我们方法的预测结果与已知药物适应症进行比较(优势比 = 13.1,p值 = 2.49×10 - 8)。由于特定疾病 - SNP与候选药物靶点的关联,所提出的方法为未来针对患者特定多态性的精准药物重新定位提供了证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2874/6425966/e0b9612385c2/nihms-999818-f0001.jpg

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