Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs. We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset. Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs. We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care. http://lishenlab.com/d3i/explorer.html

中文翻译：

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使用FAERS数据库挖掘和可视化高阶定向药物相互作用效应

药物不良反应（ADE）通常是药物相互作用（DDI）的结果。使用数据挖掘来检测药物组合对ADE的影响已引起越来越多的关注和兴趣，但是，大多数研究集中在分析成对DDI。最近的努力已探索高维药物组合之间的方向关系，并已显示出对ADE风险预测的有效性。然而，当考虑三种以上的药物时，从计算和说明的角度来看，现有方法都变得效率低下。我们提出了一种有效的方法，可以通过频繁的项目集挖掘来估算高阶DDI的定向效应，并进一步开发了一种新颖的可视化方法，以交互，简洁和全面的方式组织和展示涉及三种以上药物的高阶定向DDI效应。我们通过使用公开可用的FAERS数据集挖掘与肌病相关的定向DDI来证明其性能。据报道涉及多达七种药物的DDI的定向作用。我们的分析证实了先前报道的与肌病相关的DDI，包括夫西地酸与辛伐他汀和阿托伐他汀之间的相互作用。此外，我们发现了许多新的DDI，这些新的DDI导致肌病的风险增加，例如唑来膦酸盐与不同类型的药物（包括抗生素（环丙沙星，左氧氟沙星）和镇痛药（对乙酰氨基酚，芬太尼，加巴喷丁，羟考酮）共同使用）。最后，我们通过提出的工具可视化了DDI定向结果，该工具可以交互选择任何药物组合作为基线，并放大/缩小以获得感兴趣的药物的详细和整体信息。我们开发了一种更有效的数据挖掘策略来识别高阶定向DDI，并设计了可扩展的工具以可视化高阶DDI发现。所提出的方法和工具具有促进药物相互作用研究并最终影响患者医疗保健的潜力。http://lishenlab.com/d3i/explorer.html 并设计了可扩展的工具以可视化高阶DDI发现。所提出的方法和工具具有促进药物相互作用研究并最终影响患者医疗保健的潜力。http://lishenlab.com/d3i/explorer.html 并设计了可扩展的工具以可视化高阶DDI发现。所提出的方法和工具具有促进药物相互作用研究并最终影响患者医疗保健的潜力。http://lishenlab.com/d3i/explorer.html