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Published on: August 2, 2024
Taxanes versus platinum derivatives for ovarian cancer: a comparative disproportionality analysis using the FDA
Maria Jacob1, Alisha Roy Edakkarote1, Sriya Lingamagunta1
1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal-576104, Karnataka, India.
Objective:
Platinum derivatives and taxanes are first line in the pharmacotherapy of ovarian cancer. However, both classes are associated with a wide array of adverse events (AEs). This study aimed to assess and compare AEs associated with platinum derivatives and taxanes through disproportionality analysis.
Methods:
Data mining of the FAERS (FDA adverse event reporting system) database was conducted via OpenVigil 2.1 from 2003Q4 to 2024Q4. In frequentist methods, AE reports with a frequency of ≤3 were excluded, and duplicates were removed from the raw data. Proportional reporting ratio (PRR), reporting odds ratio (ROR), and chi-squared (χ2) were used for signal detection. Data analysis was performed using R software version 4.4.1. MedDRA version 28.0 was employed to link preferred terms (PTs) to their respective system organ classes (SOCs).
Key Findings:
After excluding irrelevant AEs, the frequency of PTs for platinum derivatives and taxanes was 322/246. The most reported AE in both classes was dyspnea, with 172 reports for taxanes and 272 for platinum derivatives. Reports were mainly from the USA and France, mostly involving patients aged 60-70 years. Disproportionality analysis flagged 150 signals for platinum derivatives and 125 for taxanes, accounting for 46.58% and 50.81% of PTs, respectively. The AEs for platinum derivatives and taxanes targeted 22 and 21 SOCs, respectively.
Conclusions:
Signals were identified for both drug classes across multiple SOCs. AE reports were higher for platinum derivatives than taxanes, with gastrointestinal and general disorders and administration site conditions most frequently reported. Overall, the findings suggest the usefulness of FAERS data mining in identifying signals.
