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“First-In-Human” Clinical Trial Employing Adoptive Transfer of Autologous Thymus-Derived Treg Cells (thyTreg) to Prevent Graft Rejection in Heart-Transplanted Children

2018· article· en· W2883766009 on OpenAlexaff
E. Bernaldo de Quirós, Manuela Camino, Nuria Gil, Esther Panadero, Constancio Medrano, Juan‐Miguel Gil‐Jaurena, Marjorie Pion, Giovanna Lombardi, Megan K. Levings, Lori J. West, Rafael Correa‐Rocha

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsBC Children's HospitalCNIB Foundation
Fundersnot available
KeywordsFOXP3Adoptive cell transferIL-2 receptorMedicineImmune systemImmunologyClinical trialImmunotherapyCell therapyTransplantationT cellCellBiologyInternal medicine

Abstract

fetched live from OpenAlex

Immune allograft rejection remains the main obstacle to reach successful transplants. Transfer of regulatory T cells (Treg) has acquired growing interest in attempts to prevent rejection. However, the limited number and the differentiated phenotype of Tregs isolated from blood constitute important drawbacks for the effectiveness of this strategy. In collaboration with several teams, we have explored the use of the thymic tissue, which is routinely discarded during pediatric cardiac surgery, as an alternative source of Tregs to be used as cellular immunotherapy in heart-transplanted children. Material and Methods We developed a novel GMP-compatible protocol to obtain massive amounts of thymus-derived Tregs (thyTreg) from thymuses discarded from infants (<3 years old). Several quality tests were performed on the final thyTreg cell product. A “first-in-human” clinical trial (phase 1/2a) will be initiated in January 2018 to test the safety, feasibility and effectiveness of the adoptive transfer of autologous thyTregs in heart-transplanted children. Results/Discussion ThyTreg purified from thymuses were activated and cultured ex-vivo for 7–10 days with our protocol, and the final product showed a very high purity, with >95% of CD25+Foxp3+ cells, and a viability >90% (Fig 1). Importantly, the number of thyTreg cells obtained from one single thymus reached values of more than 13x109 (billions) cells. Considering the Treg doses employed in previous clinical trials (1–10 x106 Treg/kg), in the case of transplanted infants, this amount will be enough to prepare more than 1000 doses of thyTreg treatment. The final product of thyTreg showed great expression of CTLA-4, CD39, HLA-DR and Helios. In comparison to blood-derived Tregs, the frequency of IL-10-secreting cells was markedly higher in thyTregs (Fig 2). Besides, the final thyTreg product showed a very high suppressive capacity, decreasing the proliferation of CD4+ and CD8+ T cells by more than 80% (Fig 3). The thyTreg product will be employed as immunotherapy to prevent rejection in a clinical trial. Infants younger than 3 years old included in the waiting list for a heart transplant will be enrolled. A single dose of autologous thyTreg cells purified from the thymus discarded in the surgery will be infused back to the children at day +10 post-transplant, when doses of immunosuppressants are reduced (Fig 4). The rest of the thyTreg doses will be cryopreserved in a Biobank for potential reinfusions in the future to the patient. Conclusion Massive quantities of highly suppressive and pure Thy-Tregs obtained with our novel GMP-compatible protocol are suitable to be employed as cellular immunotherapy to prevent rejection in heart-transplanted children. At the beginning of 2018, we will initiate the first clinical trial to test the safety of the procedure, the feasibility and the effect of the thyTreg therapy in the context of solid organ transplantation.Grant from Instituto de Salud Carlos III (ISCIII) co-financed by FEDER funds (PI15/00011). Grant from Instituto de Salud Carlos III (ISCIII) co-financed by FEDER funds (ICI14/00282).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.024
GPT teacher head0.304
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations4
Published2018
Admission routes1
Has abstractyes

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