Study Design: Human Leukocyte Antigen Class I Molecule A∗02-Chimeric Antigen Receptor Regulatory T Cells in Renal Transplantation
Bibliographic record
Abstract
Introduction Cell therapy with regulatory T cells (Tregs) in solid organ transplantation is a promising approach for the prevention of graft rejection and induction of immunologic tolerance. Previous clinical studies have demonstrated the safety of Tregs in renal transplant recipients. Antigen-specific Tregs, such as chimeric antigen receptor (CAR)-Tregs, are expected to be more efficacious than polyclonal Tregs in homing to the target antigen. We have developed an autologous cell therapy (TX200-TR101) where a human leukocyte antigen (HLA) class I molecule A ∗ 02 (HLA-A ∗ 02)-CAR is introduced into autologous naive Tregs from a patient with HLA-A ∗ 02-negative end-stage renal disease (ESRD) awaiting an HLA-A ∗ 02-positive donor kidney. Methods This article describes the design of the STEADFAST study, a first-in-human, phase I/IIa, multicenter, open-label, single-ascending dose, dose-ranging study to assess TX200-TR101 in living-donor renal transplant recipients. Up to 15 transplant recipients will receive TX200-TR101 and will be followed up for a total of 84 weeks post-transplant, alongside a control cohort of up to 6 transplant recipients. All transplant recipients will receive a standard of care immunosuppressive regimen, with the intent of intensified tapering of the regimen in the TX200-TR101 cohort. Results The primary end point is the incidence and severity of treatment-emergent adverse events (AEs) within 28 days post–TX200-TR101 infusion. Other end points include additional safety parameters, clinical and renal outcome parameters, and the evaluation of biomarkers. Conclusion The STEADFAST study represents the next frontier in adoptive cell therapies. TX200-TR101 holds great potential to prevent immune-mediated graft rejection and induce immunologic tolerance after HLA-A ∗ 02-mismatched renal 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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".