建模路径对有效阿尔茨海默病药物重定位的重要性
Shunian Xiang1, Patrick J Lawrence1, Bo Peng2
1Biomedical Informatics Department, The Ohio State University, Columbus, OH 43210, USA.
ArXiv
|November 14, 2023
概括
这项研究介绍了MPI,这是一种基于网络的新型方法,用于阿尔茨海默病 (AD) 药物重定位. 与传统的最短路径方法相比,MPI优先考虑重要的网络路径,改善了潜在的AD药物的识别.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 神经科学是一个神经科学.
背景情况:
- 药物重新定位是阿尔茨海默病 (AD) 药物发现的有效策略.
- 基于网络的方法利用相互作用数据来识别候选药物.
- 现有的方法往往忽略了相同长度的网络路径的不同重要性.
研究的目的:
- 提出一种新的基于网络的方法,MPI (建模路径重要性),用于AD药物重新利用.
- 为了解决假设相同长度的网络路径具有相同重要性的局限性.
- 改善有效的抗老年痴呆症候选药物的鉴定.
主要方法:
- 开发了MPI,这是一种基于网络的方法,利用已学习的节点嵌入来优先考虑路径的重要性.
- 在复杂网络中集成多种交互类型.
- 与依赖最短路径的基线方法对比评估的MPI.
主要成果:
- 在识别抗老年痴呆症候选药物方面,MPI表现优异.
- 在排名前50名的药物中,MPI优先考虑了与基线相比有20.0%更多的抗AD证据的药物.
- 考克斯的比例危险模型确定了etodolac,尼古丁和BBB交叉的ACE-INH与AD风险降低有关.
结论:
- 通过区分路径的重要性,MPI提供了一种更有效的方法来重新利用AD药物.
- 学习节点嵌入对于捕捉网络结构和优先考虑治疗途径至关重要.
- 对埃托多拉克,尼古丁和BBB交叉的ACE-INH进行进一步的研究是有必要的,以寻找潜在的AD重定位策略.
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