OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation.

IF 3.8 2区医学 Q1 PHARMACOLOGY & PHARMACY Drug Safety Pub Date : 2025-04-01 Epub Date: 2025-02-08 DOI:10.1007/s40264-024-01503-8

Achilleas Chytas, George Gavriilides, Anargyros Kapetanakis, Alix de Langlais, Marie-Christine Jaulent, Pantelis Natsiavas

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Abstract

Introduction: Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potential links between drugs and harmful effects. Still, their unstructured format prevents this valuable information from being integrated into data-processing pipelines (e.g., to support either the investigation of drug safety signals or decision-making in the clinical context).

Objective: OpenPVSignal is a data model designed specifically to publish PVSRs via a computationally exploitable format, compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles to promote ease of access and reusability of these valuable data.

Methods: This paper outlines the procedure for converting pharmacovigilance signals published by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model, resulting in a Knowledge Graph (KG). It details each step of the process, including the technical validation by KG engineers and the qualitative verification by medical and pharmacovigilance experts, leading to the finalized KG.

Results: A total of 101 PVSRs from 2011 to 2019 were incorporated into the openly available KG.

Conclusion: The presented KG could be useful in various data-processing pipelines, including systems that support drug safety activities.

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OpenPVSignal知识图谱：药物警戒信号报告在计算可利用的公平表示。

简介：药物警戒信号报告（PVSR）文件包含由药物监测组织发布的有价值的浓缩信息，通常采用自由文本格式。它们为药物和有害影响之间的潜在联系提供了初步见解。然而，它们的非结构化格式阻碍了这些有价值的信息被整合到数据处理管道中（例如，支持药物安全信号的调查或临床环境中的决策）。目标：OpenPVSignal是一个专门设计的数据模型，通过计算可利用的格式发布pvsr，符合FAIR（可查找、可访问、可互操作、可重用）原则，以促进这些有价值数据的易于访问和可重用性。方法：本文概述了将世界卫生组织乌普萨拉监测中心（WHO-UMC）发布的药物警戒信号转换为OpenPVSignal数据模型的过程，从而产生知识图（KG）。它详细说明了过程的每一步，包括KG工程师的技术验证以及医学和药物警戒专家的定性验证，从而最终确定KG。结果：2011 - 2019年共有101例psvr被纳入公开可获得的KG。结论：本文提出的KG可用于各种数据处理管道，包括支持药物安全活动的系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Drug Safety 医学-毒理学

CiteScore

7.60

自引率

7.10%

发文量

112

审稿时长

6-12 weeks

期刊介绍： Drug Safety is the official journal of the International Society of Pharmacovigilance. The journal includes: Overviews of contentious or emerging issues. Comprehensive narrative reviews that provide an authoritative source of information on epidemiology, clinical features, prevention and management of adverse effects of individual drugs and drug classes. In-depth benefit-risk assessment of adverse effect and efficacy data for a drug in a defined therapeutic area. Systematic reviews (with or without meta-analyses) that collate empirical evidence to answer a specific research question, using explicit, systematic methods as outlined by the PRISMA statement. Original research articles reporting the results of well-designed studies in disciplines such as pharmacoepidemiology, pharmacovigilance, pharmacology and toxicology, and pharmacogenomics. Editorials and commentaries on topical issues. Additional digital features (including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations) can be published with articles; these are designed to increase the visibility, readership and educational value of the journal’s content. In addition, articles published in Drug Safety Drugs may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand important medical advances.