Clazakizumab in late antibody-mediated rejection: study protocol of a randomized controlled pilot trial
Bibliographic record
Abstract
BACKGROUND: Late antibody-mediated rejection (ABMR) triggered by donor-specific antibodies (DSA) is a cardinal cause of kidney allograft dysfunction and loss. Diagnostic criteria for this rejection type are well established, but effective treatment remains a major challenge. Recent randomized controlled trials (RCT) have failed to demonstrate the efficacy of widely used therapies, such as rituximab plus intravenous immunoglobulin or proteasome inhibition (bortezomib), reinforcing a great need for new therapeutic concepts. One promising target in this context may be interleukin-6 (IL-6), a pleiotropic cytokine known to play an important role in inflammation and adaptive immunity. METHODS: This investigator-driven RCT was designed to assess the safety and efficacy of clazakizumab, a genetically engineered humanized monoclonal antibody directed against IL-6. The study will include 20 DSA-positive kidney allograft recipients diagnosed with ABMR ≥ 365 days after transplantation. Participants will be recruited at two study sites in Austria and Germany (Medical University of Vienna; Charité University Medicine Berlin). First, patients will enter a three-month double-blind RCT (1,1 randomization, stratification according to ABMR phenotype and study site) and will receive either clazakizumab (subcutaneous administration of 25 mg in monthly intervals) or placebo. In a second open-label part of the trial (months 4-12), all patients will receive clazakizumab at 25 mg every month. The primary endpoint is safety and tolerability. Secondary endpoints are the pharmacokinetics and pharmacodynamics of clazakizumab, its effect on drug metabolism in the liver, DSA characteristics, morphological ABMR lesions and molecular gene expression patterns in three- and 12-month protocol biopsies, serum/urinary biomarkers of inflammation and endothelial activation/injury, Torque Teno viral load as a measure of overall immunosuppression, kidney function, urinary protein excretion, as well as transplant and patient survival. DISCUSSION: Currently, there is no treatment proven to be effective in halting the progression of late ABMR. Based on the hypothesis that antagonizing the effects of IL-6 improves the outcome of DSA-positive late ABMR by counteracting DSA-triggered inflammation and B cell/plasma cell-driven alloimmunity, we suggest that our trial has the potential to provide proof of concept of a novel treatment of this type of rejection. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03444103 . Registered on 23 February 2018 (retrospective registration).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.000 |
| 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 teacher head, 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".