Ivanov Sergey M, Lagunin Alexey A, Pogodin Pavel V, Filimonov Dmitry A, Poroikov Vladimir V
Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia.
Chem Res Toxicol. 2014 Jul 21;27(7):1263-81. doi: 10.1021/tx500147d. Epub 2014 Jun 18.
Drug-induced myocardial infarction (DIMI) is one of the most serious adverse drug effects that often lead to death. Therefore, the identification of DIMI at the early stages of drug development is essential. For this purpose, the in vitro testing and in silico prediction of interactions between drug-like substances and various off-target proteins associated with serious adverse drug reactions are performed. However, only a few DIMI-related protein targets are currently known. We developed a novel in silico approach for the identification of DIMI-related protein targets. This approach is based on the computational prediction of drug-target interaction profiles based on information from approximately 1738 human targets and 828 drugs, including 254 drugs that cause myocardial infarction. Through a statistical analysis, we revealed the 155 most significant associations between protein targets and DIMI. Because not all of the identified associations may lead to DIMI, an analysis of the biological functions of these proteins was performed. The Random Walk with Restart algorithm based on a functional linkage gene network was used to prioritize the revealed DIMI-related protein targets according to the functional similarity between their genes and known genes associated with myocardial infarction. The biological processes associated with the 155 selected protein targets were determined by gene ontology and pathway enrichment analysis. This analysis indicated that most of the processes leading to DIMI are associated with atherosclerosis. The revealed proteins were manually annotated with biological processes using functional and disease-related data extracted from the literature. Finally, the 155 protein targets were classified into three categories of confidence: (1) high (the protein targets are known to be involved in DIMI via atherosclerotic progression; 50 targets), (2) medium (the proteins are known to participate in biological processes related with DIMI; 65 targets), and (3) low (the proteins are indirectly involved in DIMI pathogenesis; 40 proteins).
药物性心肌梗死（DIMI）是最严重的药物不良反应之一，常导致死亡。因此，在药物研发早期识别DIMI至关重要。为此，需对类药物物质与各种与严重药物不良反应相关的脱靶蛋白之间的相互作用进行体外测试和计算机预测。然而，目前已知的与DIMI相关的蛋白质靶点很少。我们开发了一种新型的计算机方法来识别与DIMI相关的蛋白质靶点。该方法基于对约1738个人类靶点和828种药物（包括254种可导致心肌梗死的药物）信息的药物-靶点相互作用谱的计算预测。通过统计分析，我们揭示了蛋白质靶点与DIMI之间155个最显著的关联。由于并非所有已识别的关联都会导致DIMI，因此对这些蛋白质的生物学功能进行了分析。基于功能连接基因网络的重启随机游走算法用于根据与心肌梗死相关的已知基因与其基因之间的功能相似性，对所揭示的与DIMI相关的蛋白质靶点进行优先级排序。通过基因本体论和通路富集分析确定了与155个选定蛋白质靶点相关的生物学过程。该分析表明，导致DIMI的大多数过程都与动脉粥样硬化有关。利用从文献中提取的功能和疾病相关数据，对所揭示的蛋白质进行了生物学过程的人工注释。最后，将这155个蛋白质靶点分为三类可信度：（1）高（已知这些蛋白质靶点通过动脉粥样硬化进展参与DIMI；50个靶点），（2）中（已知这些蛋白质参与与DIMI相关的生物学过程；65个靶点），（3）低（这些蛋白质间接参与DIMI发病机制；40个蛋白质）。
