Impact of Concomitant Prostate Cancer Medications on Efficacy and Safety of Relugolix Versus Leuprolide in Men With Advanced Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: To characterize the impact of concomitant prostate cancer treatments with the use of relugolix, the oral GnRH receptor antagonist, in advanced prostate cancer, a subgroup and pharmacokinetic/pharmacodynamic analyses of the HERO study was undertaken. PATIENTS AND METHODS: Overall, 934 patients were randomized 2:1 to receive relugolix 120 mg orally once daily or leuprolide injections every 12 weeks for 48 weeks. In the setting of rising PSA, patients could receive enzalutamide or docetaxel 2 months after study initiation. Assessments included sustained testosterone suppression to castrate levels (<50 ng/dL) through 48 weeks and safety parameters. Subgroups analyzed included patients with or without concomitant enzalutamide or docetaxel. A sensitivity analysis of the primary endpoint was performed excluding patients who received concomitant therapies that may affect testosterone. Pharmacokinetic/pharmacodynamic analyses of 20 participants in the relugolix treatment group assessed the net effect of enzalutamide on exposure to relugolix. RESULTS: or testosterone concentrations were observed, suggesting that any induction or inhibition properties of enzalutamide on relugolix metabolism result in a neutral net effect on relugolix exposure and testosterone suppression. CONCLUSION: Treatment with relugolix was associated with similar efficacy and safety profiles with and without concomitant enzalutamide or docetaxel. Standard-of-care use of relugolix in combination with these agents is supported by these data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".