Evidence map›Paper›PMID 41646188›Full record

ArticleFrontiers in pharmacology2026

The post-marketing safety of venlafaxine: a real-world two-decade pharmacovigilance study using the FAERS database.

Santosh Chokkakula, Hualiang Yang, Abeer A Al-Masri, Yiquan Zhang, Bommireddy Naveen, Bing Yang

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2026. 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

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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Santosh Chokkakula *Department of Microbiology, Chungbuk National University College of Medicine and Medical Research Institute, Cheongju, Republic of Korea.
Hualiang Yang *Department of Pharmacy, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China.
Abeer A Al-MasriDepartment of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Yiquan ZhangSchool of Pharmacy, Tianjin Medical University, Tianjin, China.
Bommireddy NaveenDepartment of Chemical and Biological Engineering, Gachon University, Seongnam-Si, Gyeonggi-do, Republic of Korea.
Bing YangDepartment of Cell Biology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Venlafaxine, a serotonin-norepinephrine reuptake inhibitor widely prescribed for major depressive disorder and related conditions, remains insufficiently characterized regarding its adverse event profile. This study aimed to comprehensively evaluate the post-marketing safety of venlafaxine using data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The data were extracted from the FAERS database from the first quarter (Q1) 2004 to Q2 2025. Adverse drug events were analyzed using the Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), Proportional Reporting Ratio (PRR), and Multi-Item Gamma Poisson Shrinker (MGPS) methods. Results: A total of 47,325 venlafaxine-related adverse event reports were analyzed. Disproportionality analysis revealed significant signals for drug ineffectiveness (n = 2,108, ROR = 1.6, PRR = 1.6, χ Conclusion: This pharmacovigilance analysis systematically identified significant safety signals associated with venlafaxine. The findings provide important evidence to support safer clinical use of venlafaxine and may assist in optimizing individualized therapeutic decisions in practice.

Indexed as

disproportionality analysisFAERSmajor depressive disorderpharmacovigilancevenlafaxine

Identifiers

PMID41646188
PMCPMC12868114

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.