Related Experiment Video
Updated: Jun 7, 2025
Assessment of Resistance to Tyrosine Kinase Inhibitors by an Interrogation of Signal Transduction Pathways by Antibody Arrays
Published on: September 19, 2018
Safety assessment of cabozantinib in patients with renal cell carcinoma: retrospective pharmacovigilance study based
Zhipeng Wang1,2, Fuchun Zheng1,2, Liangwei Wan1,2
1Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Objective:
This study was designed to conduct data mining through the Food and Drug Administration Adverse Event Reporting System (FAERS) to assess adverse events (AEs) associated with cabozantinib in the treatment of renal cell carcinoma.
Methods:
Reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were used to detect drug-related AEs signals from reporting data in FAERS database from 2016 to 2024.
Results:
A total of 32,129 AE reports identifying cabozantinib as a 'primary suspect' were retrieved from the FAERS database. Among them, there were 21,549 reports of renal cell carcinoma as an indication. AEs induced by cabozantinib were observed in 23 system organ classes (SOCs). 215 AE signals were detected in 16 SOCs after four algorithms were simultaneously met. Among them, signals related to gastrointestinal disorders, general disorders and administration site conditions, and skin and subcutaneous tissue disorders were the most common. Of note, the median time to onset of AEs was 38 days (interquartile range (IQR) 14-116 days).
Conclusion:
This study provides new insights into the monitoring, surveillance, and management of cabozantinib-related adverse drug reactions and provides a comprehensive long-term post-marketing safety assessment of cabozantinib.
Insights
This study analyzed Food and Drug Administration Adverse Event Reporting System data to identify adverse events (AEs) linked to cabozantinib for renal cell carcinoma. Gastrointestinal, general, and skin disorders were the most frequently reported AEs.
Area of Science:
- Pharmacovigilance
- Oncology
- Data Mining
Background:
- Cabozantinib is a targeted therapy used for renal cell carcinoma.
- Post-marketing surveillance is crucial for understanding drug safety profiles.
- The Food and Drug Administration Adverse Event Reporting System (FAERS) is a valuable resource for pharmacovigilance.
Purpose of the Study:
- To perform data mining on the FAERS database to assess adverse events (AEs) associated with cabozantinib in renal cell carcinoma treatment.
- To identify potential safety signals and provide a comprehensive long-term post-marketing safety assessment of cabozantinib.
Main Methods:
- Utilized data mining techniques on the FAERS database from 2016 to 2024.
- Employed statistical algorithms including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) to detect AE signals.
- Retrieved 32,129 AE reports where cabozantinib was the primary suspect, with 21,549 related to renal cell carcinoma.
Main Results:
- Identified 215 adverse event signals across 16 system organ classes (SOCs).
- The most common AE signals were related to gastrointestinal disorders, general disorders, and skin and subcutaneous tissue disorders.
- The median time to onset for adverse events was 38 days (IQR 14-116 days).
Conclusions:
- This study offers new insights into monitoring, surveillance, and management of cabozantinib-related adverse drug reactions.
- Provides a comprehensive long-term post-marketing safety assessment for cabozantinib in renal cell carcinoma treatment.
- Highlights the importance of continued pharmacovigilance for targeted cancer therapies.
More Related Videos
12:22The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
09:29Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
Published on: September 30, 2016
