Evidence map›Paper›PMID 38726179›Full record

ArticleHeliyon2024

Post-marketing safety concerns of sotorasib: A disproportionality analysis based on FDA adverse event reporting system.

Yiling Ding, Hongyan Su, Yamin Shu, Jing Chen

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Yiling DingDepartment of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hongyan SuDepartment of Pharmacy, The People's Hospital of Lincang, Lincang, 677099, China.
Yamin ShuDepartment of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Jing ChenDepartment of Pharmacy, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sotorasib has been approved for the treatment of adult patients with KRAS G12C-mutated locally advanced or metastatic non-small cell lung cancer (NSCLC). Due to the limitations of clinical trials, potential adverse events (AEs) and long-term safety issues cannot be detected. The presented study aimed to evaluate sotorasib-associated AEs using the FDA Adverse Event Reporting System (FAERS) database. Methods: Post-marketing AE reports of sotorasib in the database were collected for analysis. Disproportionality analyses, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC) and empirical bayes geometric mean (EBGM) algorithms, were performed to mine the signals of sotorasib-associated AEs. The median duration, quartiles and the Weibull shape parameter (WSP) test were used to assess the onset time data. Results: The database contained 1538 cases of sotorasib as primary suspect (PS), with 27 signals detected, scattering in 5 SOCs. The SOC of hepatobiliary disorders (182, ROR 4.48, PRR 4.07, IC 2.02, EBGM 4.07) met the four methodological thresholds. The median onset time of sotorasib-associated AEs was 42 days (interquartile range [IQR] 14-86.75 days). Different SOCs had different types of risk over time. Conclusion: After obtaining marketing authorization, the study identified all potentially relevant adverse event (AE) signals expected to have a reporting frequency higher than anticipated and characterized them during sotorasib treatment.

Indexed as

AEsDisproportionality analysesFAERSSotorasibWSP

Identifiers

PMID38726179
PMCPMC11079084

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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 OpenQuestion graph.