MétaCan
Menu
Back to cohort
Record W4384131434 · doi:10.1186/s41231-023-00150-y

Feasibility of manufacture of chimeric antigen receptor-regulatory T cells from patients with end-stage renal disease

2023· article· en· W4384131434 on OpenAlexfundno aff
Hervé Bastian, Nadia Lounnas-Mourey, Pierre Heimendinger, Benjamin Hsu, K. Schreeb, Claire Chapman, Emily J Culme-Seymour, Gillian Atkinson, Diego Cantarovich

Bibliographic record

VenueTranslational Medicine Communications · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersUniversity of British ColumbiaEtablissement Français du SangSangamo Therapeutics
KeywordsTransplantationHuman leukocyte antigenChimeric antigen receptorPopulationMedicineEnd stage renal diseaseImmunologyCell therapyAntigenFOXP3Kidney transplantationT cellBiologyCellInternal medicineDiseaseImmune system

Abstract

fetched live from OpenAlex

Abstract Background Gene-modified cell therapy with regulatory T cells (Tregs) is a promising approach to prevent graft rejection and induce immunological tolerance in organ transplantation. We are developing a cell therapy comprising autologous naïve Tregs that are isolated from leukapheresate, transduced with lentiviral vector encoding a chimeric antigen receptor (CAR) recognising human leukocyte antigen class I molecule A*02 (HLA-A*02), and expanded ex vivo before cryopreservation as resultant drug product (TX200-TR101). In an ongoing first-in-human study (NCT04817774), kidney transplant recipients will receive a single infusion of TX200-TR101 2–3 months after transplantation. The phase 0 study described here evaluated the feasibility of manufacture of TX200-TR101 for the target population, i.e., end-stage renal disease (ESRD) necessitating kidney transplantation. Participants in this study did not receive an infusion of drug product. Methods Four patients with ESRD and HLA-A*02 negative typing underwent leukapheresis to collect starting material for manufacture of TX200-TR101. Manufacturing success criteria were predefined as a batch of CAR-Tregs with cell quantity in each batch ≥ 104 cells/kg body weight, cell viability ≥ 70%, transduction efficiency ≥ 20% and hypomethylation of the FoxP3 gene (Treg-specific demethylated region [TSDR]) ≥ 80%. Other manufacturing variables included Treg identity and maturation by phenotyping, residual bead count, vector copy number, endotoxin level, sterility, and presence of mycoplasma. The characteristics of leukapheresate starting material and drug product from patients with ESRD were compared with those from commercially purchased leukapheresate from 10 healthy donors. Results No safety issues were identified during leukapheresis collections. Batches of drug product were manufactured from all 4 patients with ESRD and met the predefined success criteria. There was some variability in leukapheresate starting material in terms of volume of apheresis and total leukocyte counts between patients with ESRD and healthy donors, but percentage differential white blood cell counts were comparable. The quality, quantity and functional activity of manufactured CAR-Tregs were similar between ESRD patients and healthy donors. CAR-Treg drug product from one patient with pre-existing lymphopenia had similar high quality but reduced cell quantity compared with batches from the other patients with ESRD, although yield was still above the predefined target minimum number of cells. Conclusions Manufacture of high-quality naïve CAR-Tregs from patients with ESRD is safe and feasible.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.346
Teacher spread0.279 · 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 designBench or experimental
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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTranslational Medicine CommunicationsSame topicCAR-T cell therapy researchFrench-language works237,207