LTBK-02. EVALUATION OF PAXALISIB IN GBM AGILE PHASE 3 REGISTRATION PLATFORM TRIAL FOR NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract GBM AGILE(NCT03970447) is a phase 2/3 Bayesian adaptive registration platform trial testing multiple therapies against a common control. Paxalisib, a PI3K/mTOR inhibitor, is the 3rd arm in the trial to conclude evaluation. METHODS Paxalisib was open to patients with newly diagnosed unmethylated (NDU) and recurrent (RD) glioblastoma, with three possible signatures: NDU, RD, and All(NDU+RD). Arm enrollment occurred Dec2020 through May2022. Control patients were enrolled from study initiation (July2019) and were treated with temozolomide(NDU) or lomustine(RD). GBM AGILE investigational arms have 1 or 2 stages, with adaptive randomization in stage 1 and fixed randomization if arms continue to stage 2. Efficacy is based on OS hazard ratio(HR) of Arm/Control. Efficacy goal is final Bayesian probability ≥ 98% for HR<1.00 in combined Stages. An Arm continues to Stage 2 if Bayesian predictive power (PP) ≥ 0.8. An Arm stops accruing in Stage 1 if it reaches maximal sample size(N) or does not meet a minimum efficacy threshold (PP<0.25 for all signatures when N>50). Clinical cut-off is 12 months after accrual stops. The maximum N for paxalisib was approximately 150(Stage 1) and 50(Stage 2). RESULTS After paxalisib reached >150 patients in Stage 1, accrual stopped [Paxalisib/control N 54/75(NDU), 100/188(RD)]. Neither PP thresholds for moving to Stage 2 nor stopping for minimum efficacy were met. Following an unplanned public disclosure that the arm did not continue to stage 2, clinical cut-off for final analysis (planned May2023) was updated to public disclosure date (August2022). At final analysis mean HRs were 0.89(NDU), 1.25(RD), 1.05(All), probabilities of HR<1.00 of 0.72(NDU), 0.076(RD), 0.398(All). Model estimated median OS paxalisib/control (months) were 14.77/13.84(NDU) and 8.58/10.06(RD). CONCLUSION Paxalisib did not show survival benefit over cumulative control in final primary analysis; additional secondary analyses are being considered. GBM AGILE continues to rapidly and efficiently assess therapies in ND and RD glioblastoma.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".