Phase I/II study of sorafenib in combination with erlotinib for recurrent glioblastoma as part of a 3-arm sequential accrual clinical trial: NABTC 05-02
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Receptor tyrosine kinases such as epidermal growth factor receptors (EGFRs) and their downstream signaling pathways such as the Ras-Raf-mitogen-activated protein kinase (MAPK) pathway play important roles in glioblastoma (GBM). This study investigated the safety, pharmacokinetics, and efficacy of sorafenib (Ras/Raf/MAPK inhibitor) in combination with erlotinib (EGFR inhibitor) for treatment of recurrent GBMs. METHODS: Patients with recurrent GBM were eligible. A novel sequential accrual trial design was used, where patients were sequentially accrued into separate treatment arms in phase I and phase II investigations to optimize recruitment efficiency. In phase I, a standard 3 + 3 format was used to identify dose-limiting toxicities (DLTs), determine maximum tolerated dose (MTD), and investigate pharmacokinetics. Phase II followed a 2-stage design with the primary endpoint being 6-month progression-free survival (PFS6). RESULTS: Sixteen patients were recruited for phase I, and the MTD was determined to be sorafenib 200 mg twice daily and erlotinib 100 mg once daily. DLTs include Grade 3 hypertension, Grade 3 elevated liver transaminases, and Grade 4 elevated lipase. While erlotinib did not affect sorafenib levels, sorafenib reduced erlotinib levels. In phase II, 3 of 19 stage 1 participants were progression free at 6 months. This did not meet the predetermined efficacy endpoint, and the trial was terminated. CONCLUSION: This study identified the MTD and DLTs for sorafenib and erlotinib combination therapy for recurrent GBMs; however, efficacy data did not meet the primary endpoint. This study also demonstrates the feasibility of a novel sequential accrual clinical trial design that optimizes patient recruitment for multiarm studies, which is particularly effective for multicenter clinical trials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it