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基于本体的与神经病变诱导药物相关的药物不良反应文献挖掘及类别效应分析

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

作者信息

Hur Junguk, Özgür Arzucan, He Yongqun

机构信息

Department of Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, 58202, USA.

Department of Computer Engineering, Bogazici University, 34342, Istanbul, Turkey.

出版信息

J Biomed Semantics. 2018 Jun 7;9(1):17. doi: 10.1186/s13326-018-0185-x.

Abstract

BACKGROUND

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs).

RESULTS

We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems.

CONCLUSIONS

Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

摘要

背景

药物不良反应(ADR),也称为药物不良事件(AE),在美国食品药品监督管理局(FDA)的药品标签中有报告;然而,从文本数据中正确检索和分析ADR及其潜在关系是一项巨大挑战。此前,我们通过挖掘公共药物相关数据库和药品标签,识别并对240多种可诱发周围神经病变的药物进行了本体建模。然而,这些药物的ADR机制仍不清楚。在本研究中,我们旨在开发一个基于本体的文献挖掘系统,以从药品标签中识别ADR,并阐明诱发神经病变药物(NID)的潜在机制。

结果

我们开发并应用了一种基于本体的SciMiner文献挖掘策略,从2017年文本分析会议(TAC)提供的药品标签中挖掘ADR,其中包括53种诱发神经病变药物(NID)的药品标签。我们为每种NID平均识别出243种ADR,并构建了一个ADR-ADR网络,该网络由29个ADR节点和149条边组成,其中仅包括在至少50%的NID中发现的ADR-ADR对。与非NID的ADR-ADR网络相比,发现瘙痒、发热、血小板减少、紧张、乏力、急性淋巴细胞白血病等ADR在NID网络中高度富集。我们基于ChEBI的本体分析识别出三种苯并咪唑类NID(即兰索拉唑、奥美拉唑和泮托拉唑),它们与43种ADR相关。基于基于本体的药物类别效应定义,苯并咪唑类药物组对所有这43种ADR都有药物类别效应。这43种ADR中的许多也存在于富集的NID ADR网络中。我们的不良事件本体(OAE)分类进一步发现,这43种与苯并咪唑相关的ADR分布在许多系统中,主要在行为和神经、消化、皮肤和免疫系统中。

结论

我们的研究表明,基于本体的文献挖掘和网络分析可以有效地识别和研究特定药物组及其相关的ADR。此外,我们对药物类别效应的分析识别出3种共享43种ADR的苯并咪唑类药物,从而产生了新的假设,并有助于理解药物诱发周围神经病变的可能机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1175/5991464/6670d3a2304d/13326_2018_185_Fig1_HTML.jpg

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