OBJECTIVES: No head-to-head trials had been performed to estimate the relative effectiveness of poly ADP-ribose polymerase inhibitor (PARPi) and androgen receptor signaling inhibitor (ARSi) in the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC). A network meta-analysis (NMA) was conducted to evaluate the comparative effectiveness of various systemic treatment agents for patients with mCRPC.

METHODS: A comprehensive literature search was conducted for abstracts and full-text articles from the database’s inception through April 27, 2023. The study concentrated on assessing radiographic progression-free survival (rPFS) for both overall and homologous recombination repair mutation (HRRm) population. Under the Bayesian framework, the hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) from the included trials were used to estimate survival outcomes, and the overall effect was pooled using the fixed effects model in base case analysis. Scenario analysis using restricted mean survival time (RMST) methods was performed to test the robustness of the results.

RESULTS: A total of 9 randomized clinical trials (RCTs) with 6,830 patients and 8 unique treatment options were included. Network meta-analysis demonstrated that Talazoparib in combination with enzalutamide (TALA + ENZA; overall population, hazard ratio [HR], 0.20; 95% credible interval [CrI]: 0.16-0.26; RMST, 3.51; 95% CI: 2.46-4.60; HRRm population, HR, 0.15; 95% CrI: 0.09-0.23; RMST, 4.14; 95% CI: 2.84-5.39) was superior to olaparib plus abiraterone acetate and prednisone (OLAP + AAP), apalutamide plus abiraterone acetate and prednisone (APA + AAP), abiraterone acetate plus prednisone/prednisolone (AAP), and enzalutamide (ENZA) in the first-line setting in terms of rPFS. Bayesian framework and RMST models showed consistent efficacy ranks.

CONCLUSIONS: We suggest that talazoparib in combination with enzalutamide is probably a preferred treatment agent for the overall population and HRRm patients with mCRPC. Given the limitations of network framework and modeling assumptions, results should be cautiously interpreted.