Combinability of epcoritamab CD20-targeting T-cell engager and CD20 antibody-targeted therapies in B-cell non-Hodgkin lymphoma
Bibliographic record
Abstract
Epcoritamab, a subcutaneous CD3xCD20 bispecific antibody approved for relapsed/refractory diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma, is being evaluated in regimens containing CD20-targeted monoclonal antibodies (e.g. rituximab plus cylophosphamide, doxorubicin, vincristine, and prednisone [R-CHOP]). To demonstrate combinability of epcoritamab with CD20 monoclonal antibodies (mAbs), potential interference of rituximab or obinutuzumab with epcoritamab was investigated. While there was dose-dependent binding interference between CD20 mAbs and epcoritamab through steric hindrance, ex vivo assays using tumor cell lines, R-CHOP-treated patient samples, and an animal model showed this did not impair tumor cell killing. In a pharmacokinetic model, >90% maximal cytotoxicity was predicted after the first full epcoritamab dose in the presence of therapeutic rituximab concentrations due to effective tumor-epcoritamab-T-cell trimer formation. Immunoprofiling of R-CHOP-treated DLBCL patient samples showed emergence of less-differentiated CD8 memory T cells, further supporting the feasibility of the combination in ongoing studies of epcoritamab with rituximab-containing chemoimmunotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".