P11.65.B GBM AGILE: A global, phase 2/3 adaptive platform trial to evaluate multiple treatment 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 Response Adaptive Randomization platform trial designed 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 a collaboration between academic investigators, patient organizations and industry, under the sponsorship of the non-profit organization, Global Coalition for Adaptive Research, to support new drug applications for newly diagnosed and recurrent GBM. Material and Methods The primary objective of GBM AGILE is to identify therapies that effectively improve overall survival in patients with newly diagnosed or recurrent GBM. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. Operating under a master protocol, GBM AGILE allows multiple drugs from different pharmaceutical companies to be evaluated simultaneously and/or over time against a common control arm. Based on performance, a drug may graduate and move to a Stage 2 (Phase 3) within the trial, and the totality of the data can be used for a new drug application and registration process. New experimental therapies are added as information about promising new drugs is identified while other therapies are removed as they complete their evaluation. The master protocol/ trial infrastructure includes efficiencies through an adaptive trial design, shared control arm and operational processes such as risk-based monitoring and enhanced remote activities. With its adaptable structure, GBM AGILE has continued trial activation, inclusion of new investigational therapies, and enrollment globally through the challenges of a global pandemic.GBM AGILE provides an efficient mechanism to screen and develop robust information regarding the efficacy of proposed novel therapeutics and associated biomarkers for GBM and to quickly move therapies and biomarkers into clinic. GBM AGILE received initial approval from the United States FDA in April 2019, and in Europe through the Voluntary Harmonization Procedure (VHP) in April, 2021. As of 2022, AGILE has screened over 1000 patients studying multiple investigational treatments. Enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/site/month. Currently, there are 41 sites activated in the US, 4 in Canada and 2 in Switzerland and an estimated 24 sites yet to open in Germany, France, Switzerland, Italy and Austria. In addition to the continued expansion in Europe, effort is undergoing to extend the trial to China and Australia as well. Clinical trial information: NCT03970447
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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 | 0.003 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".