CTNI-08. DB102-01 ENGAGE STUDY: A BIOMARKER-GUIDED, RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED, MULTI-CENTER PHASE 3 CLINICAL TRIAL OF DB102 IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA (GBM)
Bibliographic record
Abstract
Abstract Precision medicine is vital for treating many cancers. Lack of valid biomarkers might contribute to the failure of drug therapy for GBM. The Denovo Genomic Marker 1 (DGM1), a novel pharmacogenomic biomarker, has been discovered by a genome-wide screen of patients treated with DB102 (enzastaurin) in a trial for lymphoma. Similarly, retrospective analyses showed that DB102 significantly improved outcomes in the biomarker positive GBM patients treated with DB102, regardless of MGMT promoter methylation status. The ENGAGE Study (DB102-01, NCT03776071) is a global Phase 3 clinical trial to confirm clinical benefits in patients with newly diagnosed GBM who are DGM1 biomarker positive. This is a prospective, randomized, double-blind, placebo-controlled, multi-center study. A total of 318 patients with newly diagnosed GBM will be enrolled. After screening, patients will be randomized to receive radiation therapy (RT) and temozolomide (TMZ) plus either DB102 or a matched placebo for 6 weeks in the Concurrent Phase, followed by DB102 or placebo for approximately 5 weeks in the Single-Agent Phase and then TMZ plus DB102 or placebo in the Adjuvant Phase (up to 12 cycles). Thereafter DB102 or placebo may be continued as a single agent for up to 2 years. The primary endpoint is overall survival (OS). The secondary endpoints include progression free survival (PFS), objective response rate (ORR) and drug safety. By April 2021, the safety-run-in part was completed. The study is now open for enrollment in the US and soon in Canada and China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".