Efficacy and safety of PARP inhibitors in the treatment of prostatic cancer: a systematic review and network meta-analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,021 | 0,003 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».