Efficacy and safety of PARP inhibitors in the treatment of prostatic cancer: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer-related death in men. Previous studies have shown that the poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors (PARPis) improve the treatment response of patients with metastatic castration-resistant PCa (mCRPC). However, the efficacy and safety of various PARPis in mCRPC patients remain unclear, presenting a significant challenge for clinicians when making treatment decisions. To address this, this study conducted two indirect comparisons to evaluate the efficacy and safety of four PARPis (olaparib, niraparib, rucaparib, and talazoparib) in patients with mCRPC. METHODS: A systematic review and network meta-analysis (NMA) using Bayesian statistics was conducted. A comprehensive literature search was performed of the PubMed, Web of Science, Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) databases to identify relevant studies from the inception to November 8, 2023, using search terms such as "PARP inhibitor", "olaparib", "rucaparib", "niraparib", "talazoparib", and "mCRPC". Phase 2/3 randomized controlled trials (RCTs) related to PARPi therapy and novel hormonal therapy in patients with mCRPC were included in the analysis. The targeted outcomes included radiographic progression-free survival (rPFS), overall survival (OS), adverse events (AEs), and grade ≥3 AEs. Four reviewers screened the titles and abstracts independently to assess the eligibility of each article. Two researchers independently extracted data from the included studies. The risk of bias and quality of the studies were assessed using the Risk-of-Bias 2 tool. RESULTS: Six high-quality phase 2/3 clinical trials, comprising 3,205 individuals, were selected for the systematic review and NMAs. Two NMAs were conducted due to the different designs of the six clinical trials. The indirect comparison with a random-effects model of olaparib, niraparib, and talazoparib showed that olaparib significantly improved rPFS with a hazard ratio (HR) of 0.67 [95% confidence interval (CI): 0.46-0.96]; however, no such significant difference was observed in relation to olaparib and rucaparib. In terms of OS, no significant difference was observed among olaparib, niraparib, and talazoparib. In relation to the AEs, the PARPi interventions using olaparib, niraparib, and talazoparib increased the rates of grade ≥3 AEs with odds ratios (ORs) of 2.0 (95% CI: 0.89-5.3), 3.0 (95% CI: 1.3-7.4), and 3.7 (95% CI: 1.1-12.0), respectively. In the rank probability analysis, according to the surface under the cumulative ranking (SUCRA), olaparib ranked first, followed by niraparib, and talazoparib. Most of the included studies were assessed to be at low risk of bias. CONCLUSIONS: Olaparib significantly improved rPFS among olaparib, niraparib, and talazoparib. Talazoparib exhibited the highest SUCRA value. Regarding safety, olaparib and rucaparib did not significantly increase the incidence of grade ≥3 AEs. When making personalized treatment decisions, clinicians should consider individual patient characteristics, treatment efficacy, and potential AEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".