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整合监管药物标签信息,以促进药物警戒中不良事件的评估。

Integrating Regulatory Drug Label Information to Facilitate Evaluation of Adverse Events in Pharmacovigilance.

机构信息

Global Patient Safety Evaluation, Takeda Pharmaceuticals, Cambridge, MA 02139, United States.

出版信息

Curr Drug Saf. 2020;15(2):124-130. doi: 10.2174/1574886315666200224101011.

DOI:10.2174/1574886315666200224101011
PMID:32091345
Abstract

BACKGROUND

Efficiency and accuracy for signal detection and evaluation activities are integral components of routine Pharmacovigilance (PV) practices. However, an Individual Case Safety Report (ICSR) may consist of a variety of confounders such as Concomitant Medications (CM), Past Medical History (PMH), and concurrent medical conditions that influence a safety officer's evaluation of a potential Adverse Event (AE). Limited pharmacovigilance systems are currently available as a tool designed to enhance the efficiency and accuracy of signal detection and management.

OBJECTIVE

To introduce a systemic approach to make critical safety information readily available for users in order to discern possible interferences from CM and make informed decisions on the signal evaluation process - saving time while improving quality.

METHODS

Oracle Empirica Signal software was utilized to extract cases with CM that are Known Implicating Medications (KIM) for each AE according to public regulatory information from drug labels - FDA Structured Product Labeling (SPL) or EMA Summary of Product Characteristics (SPC). SAS Enterprise Guide was used to further process the data generated from Oracle Empirica Signal software.

RESULTS

For any target drug being evaluated for safety purposes, a KIM reference table can be generated, which summarizes all potential causality contributions from CMs.

CONCLUSION

In addition to providing standalone KIM table as reference, adoption of this concept and automation may also be fully integrated into commercial signal detection and management software packages for easy use and accessibility and may even lead to reduced False Positive rate in signal detection within the PV space.

摘要

背景

信号检测和评估活动的效率和准确性是常规药物警戒(PV)实践的组成部分。然而,一份个例安全性报告(ICSR)可能包含各种混杂因素,如伴随用药(CM)、既往病史(PMH)和并发疾病,这些因素会影响安全官员对潜在不良事件(AE)的评估。目前可用的有限药物警戒系统是一种旨在提高信号检测和管理效率和准确性的工具。

目的

引入一种系统方法,以便为用户提供关键安全信息,以便辨别 CM 可能产生的干扰,并在信号评估过程中做出明智的决策——在提高质量的同时节省时间。

方法

利用 Oracle Empirica Signal 软件根据来自药物标签的公共监管信息(FDA 结构化产品标签或 EMA 产品特性摘要),提取出每个 AE 中与 CM 相关的已知牵连药物(KIM)的病例。使用 SAS Enterprise Guide 进一步处理从 Oracle Empirica Signal 软件生成的数据。

结果

对于任何正在评估安全性的目标药物,都可以生成一个 KIM 参考表,该表总结了 CM 对所有潜在因果关系的贡献。

结论

除了提供独立的 KIM 表作为参考外,采用这一概念和自动化还可以完全集成到商业信号检测和管理软件包中,以便于使用和访问,甚至可能降低药物警戒领域信号检测中的假阳性率。

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