Preclinical assessment of antigen-specific chimeric antigen receptor regulatory T cells for use in solid organ transplantation
Bibliographic record
Abstract
A primary goal in transplantation medicine is the induction of a tolerogenic environment for prevention of transplant rejection without the need for long-term pharmacological immunosuppression. Generation of alloantigen-specific regulatory T cells (Tregs) by transduction with chimeric antigen receptors (CARs) is a promising strategy to achieve this goal. This publication reports the preclinical characterization of Tregs (TR101) transduced with a human leukocyte antigen (HLA)-A*02 CAR lentiviral vector (TX200) designated to induce immunosuppression of allograft-specific effector T cells in HLA-A*02-negative recipients of HLA-A*02-positive transplants. In vitro results demonstrated specificity, immunosuppressive function, and safety of TX200-TR101. In NOD scid gamma (NSG) mice, TX200-TR101 prevented graft-versus-host disease (GvHD) in a xenogeneic GvHD model and TX200-TR101 Tregs localized to human HLA-A*02-positive skin transplants in a transplant model. TX200-TR101 persisted over the entire duration of a 3-month study in humanized HLA-A*02 NSG mice and remained stable, without switching to a proinflammatory phenotype. Concomitant tacrolimus did not impair TX200-TR101 Treg survival or their ability to inhibit peripheral blood mononuclear cell (PBMC) engraftment. These data demonstrate that TX200-TR101 is specific, stable, efficacious, and safe in preclinical models, and provide the basis for a first-in-human study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".