Phase Ib study of combinations of avadomide (CC‐122), CC‐223, CC‐292, and rituximab in patients with relapsed/refractory diffuse large B‐cell lymphoma
Bibliographic record
Abstract
There is a need for additional treatment options for patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL) who do not benefit from available therapies. We examined combinations of the cereblon E3 ligase modulator (CELMoD) agent avadomide (CC-122), the selective, ATP-competitive mammalian target of rapamycin kinase inhibitor CC-223, and the potent, selective, covalent Bruton tyrosine kinase inhibitor CC-292 in patients with relapsed/refractory (R/R) DLBCL. In the multicenter, phase Ib CC-122-DLBCL-001 study (NCT02031419), the dose-escalation portion explored combinations of CC-122, CC-223, and CC-292 administered as doublets or triplets with rituximab in patients with chemorefractory DLBCL. Primary endpoints were safety, tolerability, and dose-limiting toxicities; additional endpoints included pharmacokinetics, pharmacodynamics, biomarkers, and preliminary efficacy. As of December 1, 2017, 106 patients were enrolled across four cohorts. The median age was 65 years (range 24-84 years), and patients had a median of 3 (range 1-10) prior to regimens. A total of 101 patients (95.3%) discontinued, most commonly due to disease progression (49.1%). The most common any-grade adverse events (AEs) across treatment arms were gastrointestinal and hematologic; the most common grade 3/4 AEs were hematologic. CC-122 was well tolerated, with no unexpected safety concerns. Preliminary efficacy was observed in three of four treatment arms. CC-122 plus rituximab was considered suitable for dose expansion, whereas CC-223 and CC-292 combinations were associated with enhanced toxicity and/or insufficient improvement in responses. CC-122 plus rituximab was well tolerated, with preliminary antitumor activity in patients with R/R DLBCL. This innovative study demonstrates the feasibility of assessing the tolerability and preliminary efficacy of novel combinations utilizing a multi-arm dose-finding design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".