ArticleFrontiers in pharmacology2024
A real-world disproportionality analysis of Tivozanib data mining of the public version of FDA adverse event reporting system.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
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Who cites it
4 citing papers in PubMed.
- Disproportionality analysis comparing safety profiles of sonidegib and vismodegib based on the FAERS database.Scientific reports · 2025Article
- Adverse event reporting of faricimab: a disproportionality analysis of FDA adverse event reporting system (FAERS) database.Frontiers in pharmacology · 2025Article
- Adverse event reporting of the IGF-1R monoclonal antibody teprotumumab: a real-world study based on the US food and drug administration adverse event reporting system.Frontiers in pharmacology · 2024Article
- Adverse event signal mining and severe adverse event influencing factor analysis of Lumateperone based on FAERS database.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Tivozanib, a vascular endothelial growth factor tyrosine kinase inhibitor, has demonstrated efficacy in a phase III clinical trials for the treatment of renal cell carcinoma. However, comprehensive evaluation of its long-term safety profile in a large sample population remains elusive. The current study assessed Tivozanib-related adverse events of real-world through data mining of the US Food and Drug Administration Adverse Event Reporting System FDA Adverse Event Reporting System. Methods: Disproportionality analyses, utilizing reporting odds ratio proportional reporting ratio Bayesian confidence propagation neural network and multi-item gamma Poisson shrinker (MGPS) algorithms, were conducted to quantify signals of Tivozanib-related AEs. Weibull distribution was used to predict the varying risk incidence of AEs over time. Results: Out of 5,361,420 reports collected from the FAERS database, 1,366 reports of Tivozanib-associated AEs were identified. A total of 94 significant disproportionality preferred terms (PTs) conforming to the four algorithms simultaneously were retained. The most common AEs included fatigue, diarrhea, nausea, blood pressure increased, decreased appetite, and dysphonia, consistent with prior specifications and clinical trials. Unexpected significant AEs such as dyspnea, constipation, pain in extremity, stomatitis, and palmar-plantar erythrodysaesthesia syndrome was observed. The median onset time of Tivozanib-related AEs was 37 days (interquartile range [IQR] 11.75-91 days), with a majority (n = 127, 46.35%) occurring within the initial month following Tivozanib initiation. Conclusion: Our observations align with clinical assertions regarding Tivozanib's safety profile. Additionally, we unveil potential novel and unexpected AE signatures associated with Tivozanib administration, highlighting the imperative for prospective clinical studies to validate these findings and elucidate their causal relationships. These results furnish valuable evidence to steer future clinical inquiries aimed at elucidating the safety profile of Tivozanib.
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