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arXiv 2311.09596q-bio.BMcs.LGcs.SI

通过疾病特异性超图组合生成药物重定位假设

Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs

Duke University(杜克大学);Broad Institute of MIT and Harvard(麻省理工学院和哈佛大学布罗德研究所);MIT(麻省理工学院);UC Berkeley(加州大学伯克利分校);UC San Francisco(加州大学旧金山分校)

Ayush Jain, Marie Laure-Charpignon, Irene Y. Chen, Anthony Philippakis, Ahmed Alaa

AI总结:

提出疾病特异性超图表示学习方法,结合多疾病信息生成药物重定位假设,并在AD及其风险因素中识别出达格列净和异喹胍等潜力药物。

摘要:

一种新化合物的药物开发流程可能持续10-20年,成本超过100亿美元。药物重定位提供了一种更省时且更具成本效益的替代方案。基于生物医学知识图谱表示的计算方法近期已产生新的药物重定位假设。在本研究中,我们提出了一种新颖的疾病特异性超图表示学习技术,用于推导不同长度的生物通路的上下文嵌入,这些通路均从任一给定药物出发,并终止于目标疾病。此外,我们将该方法扩展至多疾病超图。为确定1,522种药物中每种药物的重定位潜力,我们推导了药物特异性的余弦相似度值分布,并最终考虑中位数进行排序。余弦相似度值计算于(1)从所考虑药物出发并终止于目标疾病的所有生物通路,与(2)从当前针对该疾病处方的药物出发并终止于目标疾病的所有生物通路之间。我们以阿尔茨海默病(AD)及其两个风险因素:高血压(HTN)和2型糖尿病(T2D)为例展示了我们的方法。我们比较了每种药物在四种超图设置(单疾病或多疾病)中的排名:仅AD、AD+HTN、AD+T2D以及AD+HTN+T2D。值得注意的是,我们的框架识别出两种有前景的药物,其在结合两种疾病的超图中的重定位潜力显著更高:达格列净(抗糖尿病药;从所有考虑药物的前32%上升至前7%)和异喹胍(抗高血压药;从所有考虑药物的前76%上升至前23%)。我们的方法作为一种假设生成工具,需与依赖实验室实验和生物医学文献半自动解析的验证流程配合使用。

英文摘要:

The drug development pipeline for a new compound can last 10-20 years and cost over 10 billion. Drug repurposing offers a more time- and cost-effective alternative. Computational approaches based on biomedical knowledge graph representations have recently yielded new drug repurposing hypotheses. In this study, we present a novel, disease-specific hypergraph representation learning technique to derive contextual embeddings of biological pathways of various lengths but that all start at any given drug and all end at the disease of interest. Further, we extend this method to multi-disease hypergraphs. To determine the repurposing potential of each of the 1,522 drugs, we derive drug-specific distributions of cosine similarity values and ultimately consider the median for ranking. Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest. We illustrate our approach with Alzheimer's disease (AD) and two of its risk factors: hypertension (HTN) and type 2 diabetes (T2D). We compare each drug's rank across four hypergraph settings (single- or multi-disease): AD only, AD + HTN, AD + T2D, and AD + HTN + T2D. Notably, our framework led to the identification of two promising drugs whose repurposing potential was significantly higher in hypergraphs combining two diseases: dapagliflozin (antidiabetic; moved up, from top 32$\%$ to top 7$\%$, across all considered drugs) and debrisoquine (antihypertensive; moved up, from top 76$\%$ to top 23$\%$). Our approach serves as a hypothesis generation tool, to be paired with a validation pipeline relying on laboratory experiments and semi-automated parsing of the biomedical literature.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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