Safety and Efficacy of Niraparib in Metastatic Castration-resistant Prostate Cancer: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background and Objective: In 2021, around 250,000 individuals were diagnosed with prostate cancer, making it the second leading cause of cancer-related deaths among males in the United States. Metastatic castration-resistant prostate cancer (mCRPC) is a deadly condition, underscoring the need for novel treatments. Niraparib, a powerful and highly specific inhibitor of Poly (ADP-Ribose) Polymerase (PARP)-1 and PARP-2, is approved for use in the United States, Canada, Europe and China for certain individuals with various conditions such as ovarian, fallopian tube and primary peritoneal malignancies. Niraparib is now licensed for clinical usage in ovarian cancer at a daily dosage ranging from 200 to 300 mg. 21-23 Niraparib has shown efficacy in treating mCRPC in patients with identified DNA repair gene abnormalities. Materials and Methods: Four electronic databases – PubMed, Scopus, Cochrane Library and Web of Science were searched for relevant studies until 26 February 2024. Efficacy outcomes were radiographic progression-free survival (rPFS), time to symptomatic progression (TSP) and time to cytotoxic chemotherapy (TCC). Safety outcomes were at least one serious adverse event and treatment-emergent adverse event. The extracted data were dichotomous, and R Studio software was used for the analysis using the fixed effect model. Results: Six studies were systematically reviewed, three of which were included in the meta-analysis, involving 1298 patients. The analysis showed that niraparib is statistically significant in improving rPFS; risk ratio (RR) 1.36 (95% confidence interval [CI]: 1.22–1.52, P < 0.01), and this is consistent with the results of TSP and TCC that also revealed positive impact favouring niraparib; RR 1.11 (95% CI: 1.01–1.22, P < 0.03) and 1.14 (95% CI: 1.06–1.23, P < 0.01). However, niraparib was associated with a higher incidence rate of at least one serious adverse event and treatment-emergent adverse events; RR 1.47 (95% CI: 1.21–1.79, P < 0.01) and 1.04 (95% CI: 1.02–1.07, P < 0.01). Conclusions: Niraparib has been found to have a positive impact on rPFS, TSP and TCC, but it has also been associated with some adverse events, such as anaemia, neutropenia and thrombocytopenia. Despite the adverse events and further studies which are required to assess its safety, niraparib should be considered for clinical usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".