A Novel Step‐Up Dosage Regimen for Enhancing the Benefit‐to‐Risk Ratio of Mosunetuzumab in Relapsed or Refractory Follicular Lymphoma
Bibliographic record
Abstract
Mosunetuzumab, a T‐cell engaging bispecific antibody targeting CD20xCD3, is approved for treating relapsed/refractory follicular lymphoma. This research supports the approved intravenous clinical dose regimen, summarizing the exposure–response relationships for clinical safety and efficacy. A population pharmacokinetic model and Emax logistic regression exposure–response models for safety and efficacy were developed using data from 439 patients with relapsed/refractory non‐Hodgkin lymphoma and 159 patients with relapsed/refractory follicular lymphoma, respectively, from a Phase I/II study (NCT02500407). Data from 0.2 to 60 mg across fixed dosing (Cohort A) and Cycle 1 step‐up dosing (Cohort B) were used. Exposure–response models, using two‐cycle area‐under‐the‐concentration curve (AUC0–42) as the primary exposure endpoint, accurately depicted the complete response and objective response rate data across a 600‐fold AUC0–42 range. The approved clinical dose regimen of 1/2/60/30 mg achieved near‐maximal efficacy, with model‐estimated CR and ORR (90% confidence interval) of 63.1% (49.7–75.0) and 79.1% (69.1–87.7), respectively. The exposure–response analysis for Grade ≥ 2 cytokine release syndrome identified receptor occupancy (%) within the first two cycles as a driver, with CRS dissipating beyond the first dosing cycle. No exposure‐dependent increases were observed for other serious adverse events, including neutropenia and infections. The approved intravenous step‐up dose regimen (i.e., step doses of 1 and 2 mg on Day 1 and 8, respectively) mitigated severe CRS risk, allowing safe administration of loading (60 mg) and target doses (30 mg every 3 weeks) to achieve a favorable benefit–risk profile.
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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 | 0.001 |
| 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 | 0.001 |
| 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".