Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery.

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY
Arda Halu, Sarvesh Chelvanambi, Julius L Decano, Joan T Matamalas, Mary Whelan, Takaharu Asano, Namitra Kalicharran, Sasha A Singh, Joseph Loscalzo, Masanori Aikawa
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引用次数: 0

Abstract

Background: Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes to prioritize drugs in a cell type-dependent manner.

Methods: We build cell type-specific gene-drug perturbation networks from CMap data using a statistical procedure we call Quantile-based Instance Z-score Consensus (QUIZ-C). Using these networks and a large-scale disease-gene network consisting of 569 disease signatures from the Enrichr database, we calculate Pathophenotypic Congruity Scores (PACOS) between input gene signatures and drug perturbation signatures and combine these scores with cheminformatic data from ChEMBL to prioritize drugs. We benchmark our approach by calculating area under the receiver operating characteristic curves (AUROC) for 73 gene sets from the Molecular Signatures Database (MSigDB) using target gene expression profiles from the Comparative Toxicogenomics Database (CTD). We validate the drugs predicted in our proofs-of-concept using real-time polymerase chain reaction (qPCR) experiments.

Results: Cell type-specific gene-drug perturbation networks built using QUIZ-C are topologically distinct, reflecting the biological uniqueness of the cell lines in CMap, and are enriched in known drug targets. Pathopticon demonstrates a better prediction performance than solely cheminformatic measures as well as state-of-the-art network and deep learning-based methods. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. In proof-of-concept applications on vascular diseases, we demonstrate that Pathopticon helps guide in vitro experiments by identifying pathways that are potentially regulated by the predicted therapeutic candidates.

Conclusions: Our network-based analytical framework integrating pharmacogenomics and cheminformatics (available at https://github.com/r-duh/Pathopticon ) provides a feasible blueprint for a cell type-specific drug discovery and repositioning platform with broad implications for the efficiency and success of drug development.

将药物基因组学和化学信息学与多种疾病表型相结合,用于细胞类型导向的药物发现。
背景:大规模的药物基因组资源,如连接图(CMap),极大地帮助了计算药物发现。然而,尽管基于cmap的方法被广泛使用,但迄今为止,它们对药物的生物活性以及药物在多种疾病背景下的基因组效应都是不可知的。在这里,我们提出了一种基于网络的统计方法,Pathopticon,它使用CMap构建细胞类型特异性基因-药物扰动网络,并将这些网络与化学信息数据和不同的疾病表型相结合,以细胞类型依赖的方式优先考虑药物。方法:我们使用基于分位数的实例Z-score Consensus (quizc)的统计程序从CMap数据中构建细胞类型特异性基因-药物扰动网络。利用这些网络和由来自enrichment数据库的569种疾病特征组成的大规模疾病基因网络,我们计算了输入基因特征和药物扰动特征之间的病理表型一致性分数(PACOS),并将这些分数与ChEMBL的化学信息学数据相结合,以确定药物的优先级。我们通过使用比较毒物基因组学数据库(CTD)的靶基因表达谱,计算来自分子特征数据库(MSigDB)的73个基因集的接受者工作特征曲线(AUROC)下的面积,对我们的方法进行基准测试。我们使用实时聚合酶链反应(qPCR)实验验证了在概念验证中预测的药物。结果:利用quizc构建的细胞类型特异性基因-药物微扰网络具有不同的拓扑结构,反映了CMap中细胞系的生物学独特性,并丰富了已知的药物靶点。Pathopticon的预测性能比单纯的化学信息测量以及最先进的网络和基于深度学习的方法更好。Pathopticon预测的顶级化合物具有很高的化学结构多样性,这表明它们具有构建化合物库的潜力。在血管疾病的概念验证应用中,我们证明了Pathopticon通过识别可能由预测的治疗候选物调节的途径来帮助指导体外实验。结论:我们基于网络的分析框架整合了药物基因组学和化学信息学(可在https://github.com/r-duh/Pathopticon上获得),为细胞类型特异性药物发现和重新定位平台提供了可行的蓝图,对药物开发的效率和成功具有广泛的影响。
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来源期刊
Genome Medicine
Genome Medicine GENETICS & HEREDITY-
CiteScore
20.80
自引率
0.80%
发文量
128
审稿时长
6-12 weeks
期刊介绍: Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.
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