通过网络分析揭示药物相互作用的动态格局
Eugene Jeong1, Bradley Malin1,2,3, Scott D Nelson1
1Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
Frontiers in pharmacology
|October 20, 2023
概括
网络分析揭示了药物相互作用 (DDI) 研究趋势. 药理动力学 (PK) 和药理动力学 (PD) 相互作用主要发生在相同的药物类别内,这表明研究不足的跨类别DDI.
科学领域:
- 药理学和药物相互作用
- 计算生物学和生物信息学
- 网络科学 网络科学
背景情况:
- 药物相互作用 (DDI) 研究已经发展了60多年,需要了解复杂的药物相互作用.
- 现有的图书统计分析提供了定量洞察力,但不能完全捕捉到DDI的复杂网络.
- 网络分析提供了一种细微的方法来理解DDI研究的不断变化的结构.
研究的目的:
- 通过网络分析,确定药物相互作用 (DDI) 研究的研究趋势和未被探索的领域.
- 通过分析药理动力学 (PK) 和药理动力学 (PD) 证据来界定药物之间的复杂相互作用.
- 了解DDI研究结构随着时间的推移而不断变化的架构和转型.
主要方法:
- 使用自然语言处理 (NLP),包括ScispaCy和SciFive模型的命名实体识别 (NER),从PubMed (1962-2023) 中提取PK和PD DDI数据.
- 利用静态网络分析来识别DDI研究中的结构模式.
- 应用不断发展的网络分析来追踪随时间变化的DDI研究趋势的变化.
主要成果:
- 药理动力学,药理动力学和综合DDI网络表现出无尺度的特征,功率法指数分别为2.5,2.82和2.46.
- 网络显示了密集功率定律,表明随着新药的增加,DDI的指数式增长.
- PK和PDDDI主要涉及同一类解剖治疗化学物质 (ATC) 类别内的药物,不同类别之间观察到的相互作用较少.
结论:
- 不同类别的ATC药物之间的PK和PDDDI比同一类别内的药物研究较少.
- 本研究突出了DDI研究中尚未探索的领域,特别是涉及跨类别互动的领域.
- 研究结果为未来的DDI研究,临床实践和药物开发策略提供了宝贵的见解.
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