Evidence map›Paper›PMID 40808677›Full record

ArticleFrontiers in pharmacology2025

Identification of potential drug-induced neuralgia signals through disproportionality analysis of the FAERS database.

Yating An, Ying Zheng, Ziwei Jiang, Meng Meng, Jintuo Yin, Yahui An

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yating AnDepartment of Pharmacy, Beijing Health Vocational College, Beijing, China.
Ying ZhengDepartment of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ziwei JiangDepartment of Pharmacy, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Meng MengDepartment of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Jintuo YinDepartment of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Yahui AnDepartment of Pharmacy, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Drug-induced neuralgia is a common and significant adverse reaction. This study analyzed the United States food and drug administration adverse event reporting system (FAERS) database (2004-2024) to identify relevant drugs and potential mechanisms. Methods: We conducted an association analysis between drugs and neuralgia using the FAERS database. Disproportionality analysis methods, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM), were applied. Data from 2004 to 2024 were analyzed to identify drugs potentially associated with neuralgia. Results: Among the 103,678 reports of neuralgia-related adverse events, 60.29% involved female patients, and 30.40% were aged between 41 and 64 years. The most common underlying medical conditions were plasma cell myeloma (14.28%) and multiple sclerosis (10.65%). The analysis revealed significant associations between neuralgia and several classes of drugs, including chemotherapeutic agents, certain antibiotics, and immunosuppressants, potentially attributable to neurotoxicity, immune-mediated mechanisms, or metabolic disruptions. Notably, lenalidomide exhibited the strongest association with neuralgia, followed by sodium citrate. These findings underscore the importance of early recognition, safer prescribing strategies, and further investigation to mitigate neurotoxic risks. Conclusion: This study identifies key drugs, including chemotherapeutics, antibiotics, and immunosuppressants, associated with drug-induced neuralgia through FAERS data analysis, highlighting the need for early detection, safer prescribing practices, and further research into mitigating neurotoxicity.

Indexed as

adverse drug eventFAERS databaseneuralgianeurotoxicitysignal detection

Identifiers

PMID40808677
PMCPMC12343520

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.