Buparlisib plus carboplatin or lomustine in patients with recurrent glioblastoma: a phase Ib/II, open-label, multicentre, randomised study
Bibliographic record
Abstract
Background Glioblastoma relapse is associated with activation of phosphatidylinositol 3-kinase (PI3K) signalling pathway. In preclinical studies, the pan-PI3K inhibitor buparlisib showed antitumour activity in glioma models. Methods This was a two-part, multicentre, phase Ib/II study in patients with recurrent glioblastoma pretreated with radiotherapy and temozolomide standard of care. Patients received buparlisib (80 mg or 100 mg once daily) plus carboplatin (area under the curve (AUC)=5 every 3 weeks), or buparlisib (60 mg once daily) plus lomustine (100 mg/m 2 every 6 weeks). The primary endpoint was to determine the maximum tolerable dose (MTD) and/or recommended phase II dose of buparlisib plus carboplatin or lomustine. Results Between 28 February 2014 and 7 July 2016, 35 patients were enrolled and treated with buparlisib plus carboplatin (n=17; buparlisib (80 mg) plus carboplatin, n=3; and buparlisib (100 mg) plus carboplatin, n=14), or buparlisib (60 mg) plus lomustine (n=18). The MTD of buparlisib was determined to be 100 mg per day in combination with carboplatin at an AUC of 5 every 3 weeks. The MTD of buparlisib in combination with lomustine could not be determined as it did not satisfy the MTD criteria per the Bayesian logistic regression model. Conclusion The overall safety profile of buparlisib remained unchanged, and no new or unexpected safety findings were reported in this study. Preliminary assessment for both combinations did not demonstrate sufficient antitumour activity compared with historical data on single-agent carboplatin or lomustine. Trial registration number NCT01934361.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".