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
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.
期刊介绍:
Drug Safety is the official journal of the International Society of Pharmacovigilance. The journal includes:
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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.
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