Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.

识别药物相互作用（drug-drug interaction, DDI）是研发安全药物、优化癌症、人类免疫缺陷病毒（HIV）等复杂疾病多药联合治疗方案的重要研究课题。PubMed数据库中已有约15万篇关于DDI的研究文献，是DDI研究的宝贵资源。本文提出了一种自动化计算方法，可利用PubMed文献中的医学主题词（Medical Subject Headings, MeSH）对DDI的作用机制进行系统性分析。医学主题词是由美国国家医学图书馆开发的受控词汇表，用于文献的标引与注释。本方法可高精度、有效地识别与DDI相关的医学主题词，涵盖药物、蛋白质及相关生物学现象。我们通过共现热图与社交网络分析方法，探究了这些医学主题词之间的关联关系。本方法可实现DDI相关术语间关联关系的可视化，有助于使用者更深入地理解DDI的相关机制。随着PubMed数据库收录文献量的不断增长，本用于从PubMed数据库中自动分析DDI的方法的精度将进一步提升。