Background: Olaparib, the world's first poly ADP-ribose polymerase (PARP) inhibitor (PARPi), has been approved for treatment of ovarian cancer, breast cancer, pancreatic cancer and prostate cancer by FDA. The current study was to assess olaparib-related adverse events (AEs) of real-world through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS).
Methods: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of olaparib-associated AEs.
Results: Out of 8,450,009 reports collected from the FAERS database, 6402 reports of olaparib-associated AEs were identified. A total of 118 significant disproportionality preferred terms (PTs) conforming to the four algorithms simultaneously were retained. The most common AEs included anemia, thrombocytopenia, nausea, decreased appetite, blood creatinine increased and dermatomyositis, which were corresponding to those reported in the specification and clinical trials. Unexpected significant AEs as interstitial lung disease, Pneumocystis jirovecii pneumonia, folate deficiency, renal impairment and intestinal obstruction might also occur. The median onset time of olaparib-related AEs was 61 days (interquartile range [IQR] 14-182 days), and most of the cases occurred within the first 1 month after olaparib initiation.
Conclusion: Results of our study were consistent with clinical observations, and we also found potential new and unexpected AEs signals for olaparib, suggesting prospective clinical studies were needed to confirm these results and illustrate their relationship. Our results could provide valuable evidence for further safety studies of olaparib.
Keywords: FAERS; PARP inhibitor; data mining; olaparib; pharmacovigilance.
© 2022 Shu et al.