CD38 Antibody Daratumumab for the Treatment of Chronic Active Antibody-mediated Kidney Allograft Rejection
Bibliographic record
Abstract
BACKGROUND: Late antibody-mediated rejection (AMR) is a major cause of transplant failure. Potential therapeutic targets are plasma cells and natural killer (NK) cells, both expressing high levels of CD38. METHODS: Here, we report the use of CD38 monoclonal antibody daratumumab (9-mo course) in a kidney allograft recipient diagnosed with smoldering myeloma and anti-HLA class II donor-specific antibody-positive chronic active AMR 13 years after transplantation. Patient monitoring included serial HLA single-antigen testing, peripheral blood immune cell phenotyping, as well as follow-up allograft and bone marrow biopsies at 3 and 9 months, including analyses of rejection-related gene expression patterns. RESULTS: Daratumumab led to persistent CD138+ cell depletion in the bone marrow and blood and substantially decreased NK cells counts in blood and graft tissue. At the same time, donor-specific antibody in serum disappeared, and in vitro alloantibody production by CD138+ cells enriched from bone marrow aspirates was abrogated. A 3-month follow-up biopsy revealed a complete resolution of microcirculation inflammation (g+ptc: 3 to 0) and molecular AMR activity (AMR score: 0.79 to <0.2). The same biopsy showed (subclinical) tubulointerstitial inflammation, which prompted steroid treatment. Over an observation period of 12 months, graft function stabilized. CONCLUSIONS: Targeting CD38 for plasma cell and NK cell depletion may be an effective strategy to counteract AMR. Our results may encourage the design of future trials to clarify the role of this innovative treatment concept in organ transplantation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".