Apalutamide, enzalutamide, and darolutamide for non-metastatic castration-resistant prostate cancer: a systematic review and network meta-analysis
Bibliographic record
Abstract
Management of non-metastatic castration-resistant prostate cancer (nmCRPC) has undergone a paradigm shift with next-generation androgen receptor inhibitors. However, direct comparative data are not available to inform treatment decisions and/or guideline recommendations. Therefore, we performed network meta-analysis to indirectly compare the efficacy and safety of currently available treatments. Multiple databases were searched for articles published before June 2020. Studies that compared overall and/or metastasis-free and/or prostate-specific antigen (PSA) progression-free survival (OS/MFS/PSA-PFS) and/or adverse events (AEs) in nmCRPC patients were considered eligible. Three studies (n = 4117) met our eligibility criteria. Formal network meta-analyses were conducted. For MFS, apalutamide, darolutamide, and enzalutamide were significantly more effective than placebo, and apalutamide emerged as the best option (P score: 0.8809). Apalutamide [hazard ratio (HR): 0.85, 95% credible interval (CrI): 0.77-0.94] and enzalutamide (HR: 0.86, 95% CrI: 0.78-0.95) were both significantly more effective than darolutamide. For PSA-PFS, all three agents were statistically superior to placebo, and apalutamide emerged as the likely preferred option (P score: 1.000). Apalutamide (HR: 0.71, 95% CrI: 0.69-0.74) and enzalutamide (HR: 0.76, 95% CrI: 0.74-0.79) were both significantly more effective than darolutamide. For AEs (including all AEs, grade 3 or grade 4 AEs, grade 5 AEs, and discontinuation rates), darolutamide was the likely best option. Apalutamide and enzalutamide appear to be more efficacious agents for therapy of nmCRPC, while darolutamide appears to have the most favorable tolerability profile. These findings may facilitate individualized treatment strategies and inform future direct comparative trials.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".