Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug鈥揟arget Interactions and Analysis of Biological Processes

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• 作者：Sergey M. Ivanov ; Alexey A. Lagunin ; Pavel V. Pogodin ; Dmitry A. Filimonov ; Vladimir V. Poroikov
• 刊名：Chemical Research in Toxicology
• 出版年：2014
• 出版时间：July 21, 2014
• 年：2014
• 卷：27
• 期：7
• 页码：1263-1281
• 全文大小：710K
• ISSN：1520-5010

文摘

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鈥搕arget 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).