Targeting CD38 in Antibody-Mediated Rejection
Bibliographic record
Abstract
Antibody-mediated rejection (AMR) remains a major challenge in clinical transplantation. Current therapies have yielded inconsistent outcomes, highlighting the need for innovative approaches. CD38, a multifunctional glycoprotein, is highly expressed on plasma cells and natural killer (NK) cells, potentially offering a dual mechanism of action that could intervene in the pathophysiologic course of AMR: depleting alloantibody-producing plasma cells and NK cells. This review focuses on recent results from CD38-targeted therapies, with felzartamab emerging as a promising option. Previous case reports and series suggested that off-label daratumumab treatment could effectively reverse AMR. Felzartamab has now demonstrated safety and efficacy in a phase 2 trial for late AMR. Reductions in microvascular inflammation, downregulation of rejection-associated transcripts, and decreases in donor-derived cell-free DNA paralleled a substantial decrease in NK cell counts. However, felzartamab did not significantly affect donor-specific antibodies, which may reflect its distinct mechanism of action, primarily involving antibody-dependent cellular cytotoxicity and phagocytosis. The effects on rejection activity may have a rapid onset, but are transient. The potential benefits of prolonged therapy are currently being investigated in a recently launched phase III trial. Future studies may expand the applications of CD38 targeting to early AMR or broader indications, such as DSA-negative microvascular inflammation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".