Evidence map›Paper›PMID 42589332›Full record

ArticleInternational journal of molecular sciences2026

Investigation of Adverse Events Associated with Predicted GnRHR Agonists Using the FAERS Database.

Yui Migura, Yoshihiro Uesawa

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Yui MiguraDepartment of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan.ORCID 0009-0004-0372-6955
Yoshihiro UesawaDepartment of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan.ORCID 0000-0002-5773-991X

Funding

Japan Society for the Promotion of Science 22K06707
6 · The paper itself

Abstract

Gonadotropin-releasing hormone receptor (GnRHR) agonists are widely used therapeutically, yet their adverse event profile remains insufficiently characterized. We developed a machine-learning model using Tox21 GnRHR agonist activity data and molecular descriptors to predict GnRHR agonist activity among FDA Adverse-Event Reporting System (FAERS)-listed drugs. BalancedRandomForest achieved the highest ROC-AUC (0.810) and was applied to 5523 FAERS-listed drugs. Following applicability-domain assessment, 1191 drugs were retained, of which 367 were predicted to have GnRHR agonist activity. FAERS data from 2004 Q1 through 2024 Q3 were analyzed using reporting odds ratios (RORs) and Fisher's exact test for MedDRA Preferred Terms (PTs). Overall, 330 unique PTs corresponding to 531 PT-SOC assignments met prespecified criteria: at least 1000 reports, ln(ROR) > 1, and

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsReceptors, LHRHDatabases, FactualHumansMachine LearningUnited StatesReceptors, LHRHanatomical therapeutic chemicalandrogen deprivation therapypreferred termsprimary suspectprincipal component analysis

Identifiers

PMID42589332
PMCPMC13467096

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.