Marizomib alone or in combination with bevacizumab in patients with recurrent glioblastoma: Phase I/II clinical trial data
Bibliographic record
Abstract
Abstract Background This phase I/II trial in patients with recurrent glioblastoma (GBM) evaluates the safety and preliminary efficacy of marizomib, an irreversible pan-proteasome inhibitor that crosses the blood–brain barrier. Methods Part A assessed the safety and efficacy of marizomib monotherapy. In Part B, escalating doses of marizomib (0.5–0.8 mg/m2) in combination with bevacizumab were evaluated. Part C explored intra-patient dose escalation of marizomib (0.8–1.0 mg/m2) for the combination. Results In Part A, 30 patients received marizomib monotherapy. The most common AEs were fatigue (66.7%), headache (46.7%), hallucination (43.3%), and insomnia (43.3%). One patient (3.3%) achieved a partial response. In Part B, the recommended phase II dose of marizomib was 0.8 mg/m2 when combined with bevacizumab 10 mg/kg. In Part C, dose escalation to 1.0 mg/m2 was not tolerated. Pooled analysis of 67 patients treated with marizomib ≤0.8 mg/m2 and bevacizumab showed a nonoverlapping safety profile consistent with the known safety profile of each agent: the most common grade ≥3 AEs were hypertension (16.4%), confusion (13.4%), headache (10.4%), and fatigue (10.4%). The overall response rate was 34.3%, including 2 patients with complete response. Six-month progression-free survival was 29.8%; median overall survival was 9.1 months. Conclusions The safety profile of marizomib as monotherapy and in combination with bevacizumab was consistent with previous observations that marizomib crosses the blood–brain barrier. Preliminary efficacy did not demonstrate a meaningful benefit of the addition of marizomib to bevacizumab for the treatment of recurrent GBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".