Rituximab pediatric drug development: Pharmacokinetic and pharmacodynamic modeling to inform regulatory approval for rituximab treatment in patients with granulomatosis with polyangiitis or microscopic polyangiitis
Bibliographic record
Abstract
Abstract Anti‐neutrophil cytoplasmic antibody‐associated vasculitides granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) are rare, potentially organ‐ and life‐threatening autoimmune conditions affecting adult and pediatric patients. An open‐label phase II study was conducted to determine safe and effective dosing regimens of rituximab in pediatric patients with GPA/MPA. To determine the selection of an appropriate dose regimen in children for induction and maintenance, a population pharmacokinetic approach was used (nonlinear mixed‐effect modeling), combining pediatric data with data from adults with GPA/MPA. The time course of B‐cell depletion was assessed in both populations. The exposure‐effect relationship was assessed by logistic regression. Twenty‐five pediatric patients (80% female patients; age range, 6–17 years) were enrolled in the trial and received the induction regimen of intravenous rituximab 375 mg/m2 weekly for 4 weeks, which resulted in a similar exposure to that of adults. Based on pharmacokinetic modeling, a maintenance dosing regimen of 250 mg/m2 administered twice over 14 days followed by 250 mg/m2 every 6 months is expected to result in similar rituximab exposure as that of adults receiving the approved maintenance dose of 500 mg administered twice over 14 days followed by 500 mg every 6 months. The time course of B‐cell depletion was similar between the pediatric and adult populations, supporting the similarities in response in both populations and allowing extrapolation to patients less than 6 years old. Using a partial extrapolation approach helped identify safe and effective dosing regimens of rituximab in pediatric patients with GPA/MPA and lead to regulatory approval.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".