Timing Matters: A Machine Learning Method for the Prioritization of Drug–Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

IF 4 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Vera Battini, Marianna Cocco, Maria Antonietta Barbieri, Greg Powell, Carla Carnovale, Emilio Clementi, Andrew Bate, Maurizio Sessa
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引用次数: 0

Abstract

Introduction

Current drug–drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

Objective

This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

Methods

The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression).

Results

Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency’s (EMA’s) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs.

Conclusions

Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Abstract Image

时机至关重要:通过 FDA 不良事件报告系统中的信号检测确定药物间相互作用优先顺序的机器学习方法及其与共同暴露时间的关系
引言目前的药物相互作用(DDI)检测方法往往忽略了时间合理性这一点,从而导致自发报告系统(SRS)数据库中出现假阳性比例失调信号。阳性对照的 CRESCENDDI 数据集是真实阳性 DDI 的主要来源。根据共同暴露的时间进行比例失调分析。时间合理性使用累积报告不相称性信号的屈折点进行评估。使用机器学习方法(即 Lasso 回归)确定了潜在的混杂因素。结果对 122 个有三个以上病例的三联样本进行了比例失调分析,结果确定了 61 个比例失调信号(50.0%)的优先级,涉及 13 个不良事件,其中 61.5%被列入欧洲药品管理局 (EMA) 的重要医疗事件 (IME) 列表。共有 27 个信号(44.3%)至少有 10 个病例报告了相关的三联征,其中大多数(n = 19;70.4%)在时间上是合理的。检索到的混杂因素主要是其他伴随药物。这一结果表明,我们的方法在确定更有可能得到进一步验证的信号方面具有潜力。
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来源期刊
Drug Safety
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.
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