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
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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".