P27.11.A GLOBAL EXPANSION OF GBM AGILE: A PHASE 2/3 ADAPTIVE PLATFORM TRIAL TO EVALUATE MULTIPLE REGIMENS IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract BACKGROUND GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 platform trial designed to continuously add new arms and to rapidly identify experimental therapies that improve overall survival and confirm efficacious experimental therapies and associated biomarker signatures to support new drug approvals and registration. It is estimated that about 25% of glioblastoma patients that enroll in clinical trials in the US participate in GBM AGILE. Given the evergreen nature of GBM AGILE, with arms entering and exiting over time, combined with the high enrollment rate, there is commitment to providing regular updates on the advancement of the trial’s progress to the broader glioblastoma community. MATERIAL AND METHODS GBM AGILE (Trial sponsor: Global Coalition for Adaptive Research, GCAReseach.org) is a collaboration between academic investigators, patient organizations, and industry to support new drug applications for newly diagnosed (ND) and recurrent glioblastoma. The primary objective of GBM AGILE is to identify therapies that improve the overall survival in patients with ND or recurrent glioblastoma. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. GBM AGILE operates under a Master Protocol, which allows multiple drugs from different companies to be evaluated simultaneously and/or over time against a common control. New investigational arms are added as potential promising new drugs are identified, while therapies are removed as they complete their evaluation. Six investigational arms have been included to date since 2019, with arm 6 entering in 2023. Evaluation of the first three arms in the trial has been completed. During 2023, GBM AGILE continued its global expansion and opened in Australia and Germany. As of April 2024, there are 41 active sites in the US, 4 active sites in Canada, 3 active sites in France, 5 active sites in Germany, 2 active sites in Switzerland, and 4 active sites in Australia, with additional sites in start-up. Along with a global reach, adaptive trial design, shared control arm and operational processes to serve the goal of optimizing patient care, GBM AGILE also incorporates design and operational elements to enhance efficiencies, cost-savings, and allow for arm-specific customizations. Regional arm activation is one such customization. Although the goal is for all agents in the trial to be available in all regions, arms may activate only in certain regions based on their clinical development plan. Through the use of improved and flexible processes, GBM AGILE continues to serve as a global trial that supports the efficient and rapid incorporation and evaluation of new experimental therapies for patients with GBM. Clinical trial information: NCT03970447.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".