WCN25-844 FELZARTAMAB FOR IgA NEPHROPATHY: FINAL RESULTS OF THE IGNAZ STUDY
Bibliographic record
Abstract
Methods: Serum and whole blood were collected pre/post-felza treatment in patients with aPLA2R+ PMN.Biomarker analyses were performed for aPLA2R (ELISA), total IgG (turbidimetry), anti-Tetanus Toxoid (TT) (ELISA), and B cell populations (flow cytometry).Results: Felza reduced aPLA2R comparably in patients with baseline levels <150 and >150 RU/mL (Table 1).A dose-dependent effect on aPLA2R reduction at 6mo was observed across both M-PLACE (9 infusions) and NewPLACE (5 or 2 infusions).Depletion of detectable CD38+ plasmablast cell counts was observed 1 wk after felza treatment.Treatment did not impact other B cell populations, as absolute counts of total, naïve, and memory B cells were unchanged.Total IgG decreased on treatment then recovered above baseline by end of study.Anti-TT titers decreased on treatment, however levels were maintained above the protective threshold (0.1 IU/mL).Conclusions: Felza selectively depleted CD38+ plasmablasts and plasma cells, reducing pathogenic aPLA2R to a greater extent than protective anti-TT titers and total IgG, which was dose-dependent and durable.Treatment of PMN with felza suggests an efficient and selective treatment concept with preservation of vaccine response compared to conventional immunosuppressive therapies.This abstract was also submitted for ASN Kidney Week 2023 congress.Table 1.Median baseline values and % change in key biomarkers in M-Place and NewPlace trials
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".