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.

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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.

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.

アブストラクト	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.
ジャーナル名	Drug safety
Pubmed追加日	2024/4/30
投稿者	Battini, Vera; Cocco, Marianna; Barbieri, Maria Antonietta; Powell, Greg; Carnovale, Carla; Clementi, Emilio; Bate, Andrew; Sessa, Maurizio
組織名	Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej;160, 2100, Copenhagen, Denmark. vera.battini@unimi.it.;Pharmacovigilance and Clinical Research, International Centre for Pesticides and;Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC),;ASST Fatebenefratelli, Sacco University Hospital, Universita degli Studi di;Milano, Milan, Italy. vera.battini@unimi.it.;160, 2100, Copenhagen, Denmark.;Department of Clinical and Experimental Medicine, University of Messina, Messina,;Italy.;Safety Innovation and Analytics, GSK, Durham, NC, USA.;Milano, Milan, Italy.;Scientific Institute, IRCCS E. Medea, Bosisio Parini, LC, Italy.;GSK, London, UK.;London School of Hygiene and Tropical Medicine, University of London, London, UK.
Pubmed リンク	https://www.ncbi.nlm.nih.gov/pubmed/38687463/