Dose-dependent Pharmacological Response to Rituximab in the Treatment of Antineutrophil Cytoplasmic Antibody-associated Vasculitis
Bibliographic record
Abstract
Objective Rituximab (RTX) is effective in the induction and maintenance of remission in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). However, uncertainty remains regarding the optimal maintenance dosing regimen. This work evaluates the relationship between variability in RTX dosing and pharmacological response in AAV. Methods A prospective cohort of patients with AAV (n = 28) with either granulomatosis with polyangiitis (n = 23) or microscopic polyangiitis (n = 5) receiving maintenance RTX therapy were followed in a single tertiary care academic medical center over a 2-year period. Patient demographics, RTX dosing information, and trough plasma RTX levels were collected along with laboratory measures of pharmacologic response, including B cell counts and ANCA titers. Results RTX dosing information from 94 infusions with 59 trough samples were collected with a mean ± SD dose of 640 ± 221 mg, dosing interval of 210 ± 88 days, and trough plasma RTX concentration of 622 ± 548 ng/mL. RTX trough concentrations were associated with RTX dose (ρ = 0.60, P < 0.0001) and dosing interval (ρ = –0.55, P < 0.0001). RTX dosing intensity (mg/d) was associated with RTX trough concentrations (ρ = 0.57, P < 0.0001). Higher dosing intensities were associated with undetectable B cell repopulation (P < 0.0001), but not negative ANCA titers (P = 0.60). Stratification of dosing intensities based on the standard dosing regimen of 500 mg every 6 months (2.4–3.3 mg/d) demonstrated that this regimen was associated with B cell repopulation in 8 of 17 doses (47%) compared to 0 of 23 doses (0%) with the high-dose regimen (> 3.3 mg/d; P < 0.0001). Conclusion RTX maintenance dosing of 500 mg every 6 months may be inadequate to maintain B cell depletion in the treatment of AAV.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".