Data from Anti-CD79B Antibody–Drug Conjugate DCDS0780A in Patients with B-Cell Non-Hodgkin Lymphoma: Phase 1 Dose-Escalation Study
Bibliographic record
Abstract
AbstractPurpose: Targeting CD79B using antibody–drug conjugates (ADC) is an effective therapeutic strategy in B-cell non-Hodgkin lymphoma (B-NHL). We investigated DCDS0780A, an anti-CD79B ADC with THIOMAB technology (TDC) that consistently conjugates two anti-neoplastic molecules per antibody, in contrast with ADCs with heterogeneous loads. Patients and Methods: This phase 1 study enrolled 60 patients with histologically confirmed B-NHL that had relapsed/failed to respond following ≥1 prior treatment regimens; 41 (68%) had diffuse large B-cell lymphoma (DLBCL). Fifty-one patients received DCDS0780A monotherapy once every 3 weeks (0.3–4.8 mg/kg); 9 received combination therapy (3.6–4.8 mg/kg) with rituximab. Results: Fifty-four (90%) patients experienced adverse events related to study drug, the most common of which were blurred vision, fatigue, corneal deposits, neutropenia, nausea, and peripheral neuropathy. 4.8 mg/kg was the highest dose tested and the recommended phase II dose. The pharmacokinetic profile was linear at doses ≥1.2 mg/kg. Response rate in all-treated patients (N = 60) was 47% (n = 28), including 17 complete responses (28%) and 11 partial responses (18%). The median duration of response (15.2 months) was the same for all responders (n = 28) and patients with DLBCL (n = 20). Conclusions: DCDS0780A as the TDC format for CD79B was tested at higher doses than its ADC counterpart investigated earlier, leading to deep responses. However, dose intensity was limited by ocular toxicities seen at the higher doses indicating that the TDC format was unable, in the current study, to expand the therapeutic index for the CD79B target. The encouraging antitumor activity advocates continuation of investigations into novel ADC technologies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".