Lack of Histological and Molecular Signature Response to Tocilizumab in Kidney Transplants with Chronic Active Antibody Mediated Rejection: A Case Series
Bibliographic record
Abstract
Background Traditional therapies for caAbMR have unclear efficacy with significant side effects in recipients of kidney transplants (KTs). A recent single-center case series suggested tocilizumab (TCZ) could stabilize renal function and improve microvascular inflammation. Here we report our findings of the use of TCZ in patients with caAbMR. Methods Ten adult recipients of KTs with biopsy-proven caAbMR were treated with TCZ at 8 mg/kg per month. Patients were monitored for adverse events, and therapy was interrupted in the setting of serious infections. Six patients (60%) underwent post-treatment biopsies. Results Patients (mean age of 43 years) were initiated on TCZ at a median of 36 months post-KT. A majority of patients were black (70%), underwent regrafts (40%), and were sensitized (mean cPRA=41%). Patients received a median of six doses of TCZ (range=3–10). At a median follow-up of 12 months (range=8–24 months), renal function did not show improvement (mean eGFR, 42±18 ml/min per 1.73 m 2 to 37±24 ml/min per 1.73 m 2 ; P =0.27). The slope of decline in eGFR remained unchanged (−0.14±0.9 to −0.33±1.1; P =0.25). There was no improvement in mean MVI (g+ptc) (4.8±1.4 to 4.2±2.0; P =0.39) scores or Molecular Microscope Diagnostic System (MMDx) AbMR scores (0.79±0.17 to 0.78±0.26; P =0.86). There was a numeric worsening of chronicity (ci+ct) scores (2.5±0.8 to 3.3±1.7; P =0.38) and MMDx atrophy fibrosis scores (0.36±0.24 to 0.58±0.15; P =0.21). Patient survival was 90%, with one patient death due to complications from a hip infection. Overall death-censored graft survival was 80%, with two graft losses in patients who had recurrent infections requiring hospitalization. Conclusions In this early experience, we report a lack of efficacy and toxicity with the use of TCZ for caAbMR. Prospective clinical trials are needed to clarify the role of IL-6 blockade and the possibility of increased incidence of infections in patients with caAbMR who are treated with TCZ.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".