Clinical grade expansion protocol for the manufacture of thymus-derived Treg cells for clinical application
Bibliographic record
Abstract
BACKGROUND: Adoptive transfer of regulatory T cells (Tregs) has provided promising results in treating autoimmune disorders, transplant rejection and graft versus-host disease in early clinical trials. However, major challenges remain for developing a standardized and robust good manufacturing practice (GMP)-compliant cell product which is severely hampered by low frequency of Tregs in circulation and laborious ex vivo expansion. METHODS: Paediatric thymuses routinely obtained during heart surgery have been shown by us and others to be a valuable source of large numbers of pure Tregs (Thy-Tregs). Here we show results from our process development approach including systematic laboratory-scale testing of activation reagents, restimulation timing, and cryopreservation to translate our expansion protocol of Thy-Tregs into a clinical grade cell product. RESULTS: enrichment were expanded with αCD3/αCD28 beads in the presence of Rapamycin and IL-2 for 10-23 days using G-Rex bioreactors. We successfully embedded bead removal and final formulation of a cryopreserved cell product ready to be used at bedside transfusion. CONCLUSION: This process has proved the capability of efficiently producing high number of functional Thy-Tregs, which will be administered as cell therapy in children undergoing heart transplantation (ATT-Heart, ISRCTN15374803), and enhancing the potential of using expanded Thy-Tregs for broad-ranging therapeutic applications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.023 |
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".