Phase 1 Trial of Gefitinib Plus Sirolimus in Adults with Recurrent Malignant Glioma
Bibliographic record
Abstract
PURPOSE: To determine the maximum tolerated dose (MTD) and dose-limiting toxicity (DLT) of gefitinib, a receptor tyrosine kinase inhibitor of the epidermal growth factor receptor, plus sirolimus, an inhibitor of the mammalian target of rapamycin, among patients with recurrent malignant glioma. PATIENTS AND METHODS: Gefitinib and sirolimus were administered on a continuous daily dosing schedule at dose levels that were escalated in successive cohorts of malignant glioma patients at any recurrence who were stratified based on concurrent use of CYP3A-inducing anticonvulsants [enzyme-inducing antiepileptic drugs, (EIAED)]. Pharmacokinetic and archival tumor biomarker data were also assessed. RESULTS: Thirty-four patients with progressive disease after prior radiation therapy and chemotherapy were enrolled, including 29 (85%) with glioblastoma multiforme and 5 (15%) with anaplastic glioma. The MTD was 500 mg of gefitinib plus 5 mg of sirolimus for patients not on EIAEDs and 1,000 mg of gefitinib plus 10 mg of sirolimus for patients on EIAEDs. DLTs included mucositis, diarrhea, rash, thrombocytopenia, and hypertriglyceridemia. Gefitinib exposure was not affected by sirolimus administration but was significantly lowered by concurrent EIAED use. Two patients (6%) achieved a partial radiographic response, and 13 patients (38%) achieved stable disease. CONCLUSION: We show that gefitinib plus sirolimus can be safely coadministered on a continuous, daily dosing schedule, and established the recommended dose level of these agents in combination for future phase 2 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".