Phase I study of vinblastine in combination with nilotinib in children, adolescents, and young adults with refractory or recurrent low-grade glioma
Bibliographic record
Abstract
Abstract Background New rescue regimens are needed for pediatric refractory/recurrent low-grade glioma. Nilotinib is a tyrosine kinase inhibitor that has potential synergistic effects with vinblastine on angiogenesis, tumor cell growth, and immunomodulation. Methods This phase I trial aimed to determine the recommended doses of this combination for phase II trials (RP2D) using the dual-agent Bayesian continual reassessment method. Nilotinib was given orally twice daily (BID) in combination with once-weekly vinblastine injections for a maximum of 12 cycles of 28 days (clinicaltrials.gov, NCT01884922). Results Thirty-five pediatric patients were enrolled across 4 dose levels. The median age was 7 years and 10 had neurofibromatosis type 1. Patients had received a median of 3 prior treatment lines and 25% had received more than 4 previous treatment lines. Dose-limiting toxicity (DLT) during cycle 1 was hematologic, dermatologic, and cardiovascular. The RP2D was identified at 3 mg/m2 weekly for vinblastine with 230 mg/m2 BID for nilotinib (estimated probability of DLT = 18%; 95% credibility interval, 7–29%). Fifteen patients completed the 12 cycles; 2 stopped therapy prematurely due to toxicity and 18 due to disease progression. Three patients achieved a partial response leading to an objective response rate of 8.8% (95% confidence interval [CI], 1.9–23.7), and the disease control rate was 85.3% (95% CI, 68.9–95.1). The 12-month progression-free survival was 37.1% (95% CI, 23.2–53.67). Conclusions Vinblastine and nilotinib combination was mostly limited by myelosuppression and dermatologic toxicity. The efficacy of the combination at the RP2D is currently evaluated in a randomized phase II trial comparing this regimen to vinblastine alone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".