Obinutuzumab in hematologic malignancies: Lessons learned to date
Bibliographic record
Abstract
The routine use of anti-CD20 monoclonal antibodies (mAbs) has improved patient outcomes in CD20-positive non-Hodgkin's lymphoma (NHL) and chronic lymphocytic leukemia (CLL). Despite the clinical success achieved with rituximab, relapses are still common with further improvements in anti-CD20 mAb efficacy required. Many novel anti-CD20 antibodies are in development, but obinutuzumab is currently the only type II glycoengineered anti-CD20 mAb in clinical testing. Obinutuzumab has increased antibody-dependent cell-mediated cytotoxicity, reduced complement-dependent cytotoxicity and enhanced direct non-apoptotic cell death. In preclinical models, obinutuzumab induced superior tumor remission compared with rituximab at the equivalent dose levels, and was active in rituximab-refractory tumors. Obinutuzumab exhibits encouraging efficacy as monotherapy in NHL, and combined with chemotherapy in relapsed/refractory NHL and treatment-naïve symptomatic CLL. In a recent randomized, phase III trial in patients with untreated comorbid CLL, overall response rate was significantly greater (78% vs. 65%, P<0.0001) and median progression-free survival was significantly prolonged (26.7 vs. 15.2months, P<0.0001) for obinutuzumab plus chlorambucil vs. rituximab plus chlorambucil. Obinutuzumab is a type II anti-CD20 antibody that utilizes distinct mechanisms of action relative to type I antibodies like rituximab and has led to significant clinical improvement over rituximab in a phase III trial in CLL. Further trials are ongoing to determine whether such improvements in outcome will be seen in CD20-positive B-cell malignancies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".