Abstract CT062: GBM AGILE: A global, 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 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. GBM AGILE is a collaboration between academic investigators, patient organizations and industry to support new drug applications for newly diagnosed and recurrent GBM. Methods: The primary objective of GBM AGILE is to identify therapies that improve the overall survival in patients with newly diagnosed or recurrent GBM. Operating under a Master Protocol, GBM AGILE allows multiple drugs from different pharmaceutical/biotech companies to be evaluated simultaneously and/or over time against a common control. New experimental therapies are added as new information about promising new drugs is identified while other therapies are removed as they complete their evaluation. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. GBM AGILE has screened over 1300 patients and enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/site/month. There are 41 active sites in the US, 4 active sites in Canada and 3 active sites in Europe with 12 more sites anticipated to open across 3 countries in Europe. Expansion to Australia is in progress. Stratification of the newly diagnosed patients is based on methylation status per the MGMT assay provided by a central CLIA lab. Maintaining reasonable timelines for MGMT read out to support eligibility and screening requires ongoing operational oversight. In order to continue to improve MGMT testing times, the percentage discordance between results from central testing and local lab testing is being evaluated to assess if local lab testing can be utilized for randomization within GBM AGILE. Exploratory analyses are ongoing to expand knowledge of GBM. For example, information from imaging and clinical assessments will be used to build a longitudinal model, which may inform randomization by providing earlier and continuous information regarding how a given experimental arm is performing. Citation Format: Ingo K. Mellinghoff, Brian Alexander, Donald Berry, Nicholas Blondin, Meredith Buxton, Webster Cavenee, Howard Colman, John de Groot, Macarena I. de la Fuente, Benjamin Ellingson, Gary Gordon, Emma M.V. Hyddmark, Mustafa Khasraw, Andrew Lassman, Eudocia Lee, Wenbin Li, Michael Lim, Tom Mikkelsen, Apoorva Nelli, James Perry, Erik Sulman, Kirk Tanner, Michael Weller, Patrick Y. Wen, Timothy Cloughesy. GBM AGILE: A global, phase 2/3 adaptive platform trial to evaluate multiple regimens in newly diagnosed and recurrent glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT062.
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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) | 0.005 | 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".