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Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods

机译:使用药物和药物靶标相互作用以及基于图的方法分析药物不良反应

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摘要

Objective: The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA). Methods: We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA.rnResults: We picked 1952 ADR cases from the years 2005-2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039.rnConclusions: We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system can help not only in analysing ADRs in terms of drug-drug interactions but also by providing drug target assessments early in the drug discovery process.
机译:目的:本研究的目的是整合有关药物,药物靶标和拓扑方法的知识。目的是建立一个有助于研究不良药物事件的系统,使其更容易找到可能的解释,并在美国食品和药物管理局(FDA)的药物不良反应报告中将相似的药物相互作用实例分组。方法:我们开发了一个系统来分析FDA报告的药物不良反应(ADR)病例。该系统包含四个模块。首先,我们整合了毒品和毒品目标数据库,这些数据库提供了与毒品不良反应相关的信息。其次,我们根据解剖治疗化学分类(ATC)和药物靶标本体(DTO)对药物和药物靶标进行分类。第三,我们基于毒品和毒品目标数据库建立毒品目标网络。最后,我们应用拓扑分析揭示了FDA报告的每个ADR病例的药物相互作用复杂性。结果:我们从2005-2006年间选择了1952个ADR病例。我们的数据集包括1952例病例,其中1471例涉及ADR靶标,845例涉及吸收,分布,代谢和排泄(ADME)靶标,507例涉及一些作用于相同靶标的药物,即共同靶标(CTs) 。然后,我们使用ATC系统和DTO调查了涉及ADR目标,ADME目标和CT的案例。在导致死亡的案例中,共同目标(NCT)的平均数为0.879,平均聚类系数(ACC)的平均数为0.067。在未导致死亡的病例中,平均NCT为0.551,ACC的平均值为0.039。结论:我们实施了一个系统,该系统可以找到可能的解释,并将FDA报告的类似ADR病例归类。我们发现,导致死亡的病例的平均ACC和平均NCT高于未导致死亡的病例,这表明导致死亡的病例中的相互作用通常比未导致死亡的病例复杂。这表明我们的系统不仅可以帮助在药物相互作用方面分析ADR,而且可以在药物发现过程的早期提供药物靶标评估。

著录项

  • 来源
    《Artificial intelligence in medicine》 |2010年第3期|161-166|共6页
  • 作者单位

    830 Room, EECS Building, Institute of Information Systems and Applications, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan;

    Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan;

    Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan;

    Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan;

    Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 300, Taiwan Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    adverse drug reaction; drug target; drug target ontology; clustering coefficient; drug-drug interaction;

    机译:药物不良反应;药物目标药物靶标本体聚类系数药物相互作用;

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