Phase 1 trial of everolimus plus sunitinib in patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Simultaneous inhibition of the vascular epithelial growth factor (VEGF) and the mammalian target of rapamycin (mTOR) pathway may improve treatment response in advanced renal cell carcinoma (RCC). Everolimus, an oral mTOR inhibitor, and sunitinib, an oral tyrosine kinase inhibitor targeting VEGF, are standard agents in the management of metastatic RCC. METHODS: Sequential cohorts of 3 to 6 patients with advanced RCC received dose-escalated combinations of sunitinib (37.5 or 50 mg daily, 4 weeks on/2 weeks off) with everolimus (2.5-5 mg daily or 20-30 mg weekly). Dose-limiting toxicities (DLTs) were assessed in the first 6-week cycle to determine maximum tolerated dose (MTD). Pharmacokinetic profiles were obtained. RESULTS: Twenty patients (13 clear cell and 7 nonclear cell RCC) were enrolled in 5 cohorts. Daily everolimus was not tolerated when combined with sunitinib; the first 2 patients on the second cohort suffered DLTs. With weekly everolimus, the MTD was 30 mg everolimus on days 7, 14, 21, and 28, plus 37.5 mg sunitinib on days 1 to 28 of a 42-day cycle; however, chronic treatment was associated with grade 3 and 4 toxicities. A schedule of 20 mg everolimus weekly/37.5 mg sunitinib was tolerated as chronic therapy. Five patients (25%) had confirmed partial responses, and 3 had nonclear cell RCC. No unexpected accumulation of everolimus, sunitinib, or N-desethyl sunitinib was observed. CONCLUSIONS: The combination of everolimus and sunitinib is associated with significant acute and chronic toxicities and is only tolerated at attenuated doses. Responses were observed in nonclear cell and clear cell RCC.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".