Anti-interleukin-6 Antibody Clazakizumab in Antibody-mediated Renal Allograft Rejection: Accumulation of Antibody-neutralized Interleukin-6 Without Signs of Proinflammatory Rebound Phenomena
Bibliographic record
Abstract
BACKGROUND: Blockade of interleukin-6 (IL-6) has emerged as a promising therapeutic option for antibody-mediated rejection. Subtherapeutic anti-IL-6 antibody level or treatment cessation following prolonged cytokine neutralization may result in proinflammatory rebound phenomena via accumulation of IL-6 and/or modulated gene expression of major components of the IL-6/IL-6 receptor (IL-6R) axis. METHODS: We evaluated biologic material obtained from a randomized controlled, double-blind phase 2 trial designed to evaluate the safety and efficacy of the anti-IL-6 monoclonal antibody clazakizumab in late antibody-mediated rejection. Twenty kidney transplant recipients, allocated to clazakizumab or placebo, received 4-weekly doses over 12 wks, followed by a 40-wk extension where all recipients received clazakizumab. Serum proteins were detected using bead-based immunoassays and RNA transcripts using quantitative real-time polymerase chain reaction (peripheral blood) or microarray analysis (serial allograft biopsies). RESULTS: Clazakizumab treatment resulted in a substantial increase in median total (bound and unbound to drug) serum IL-6 level (1.4, 8015, and 13 600 pg/mL at 0, 12, and 52 wks), but median level of free (unbound to drug) IL-6 did not increase (3.0, 2.3, and 2.3 pg/mL, respectively). Neutralization of IL-6 did not boost soluble IL-6R or leukocyte or allograft expression of IL-6, IL-6R, and glycoprotein 130 mRNA. Cessation of treatment at the end of the trial did not result in a meaningful increase in C-reactive protein or accelerated progression of graft dysfunction during 12 mo of follow-up. CONCLUSION: Our results argue against clinically relevant rebound phenomena and modulation of major components of the IL-6/IL-6R axis following prolonged IL-6 neutralization with clazakizumab.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".