Dataset Information

Point-Of-Care CAR T-Cell Production (ARI-0001) Using a Closed Semi-automatic Bioreactor: Experience From an Academic Phase I Clinical Trial.

ABSTRACT: Development of semi-automated devices that can reduce the hands-on time and standardize the production of clinical-grade CAR T-cells, such as CliniMACS Prodigy from Miltenyi, is key to facilitate the development of CAR T-cell therapies, especially in academic institutions. However, the feasibility of manufacturing CAR T-cell products from heavily pre-treated patients with this system has not been demonstrated yet. Here we report and characterize the production of 28 CAR T-cell products in the context of a phase I clinical trial for CD19+ B-cell malignancies (NCT03144583). The system includes CD4-CD8 cell selection, lentiviral transduction and T-cell expansion using IL-7/IL-15. Twenty-seven out of 28 CAR T-cell products manufactured met the full list of specifications and were considered valid products. Ex vivo cell expansion lasted an average of 8.5 days and had a mean transduction rate of 30.6 ± 13.44%. All products obtained presented cytotoxic activity against CD19+ cells and were proficient in the secretion of pro-inflammatory cytokines. Expansion kinetics was slower in patient's cells compared to healthy donor's cells. However, product potency was comparable. CAR T-cell subset phenotype was highly variable among patients and largely determined by the initial product. TCM and TEM were the predominant T-cell phenotypes obtained. 38.7% of CAR T-cells obtained presented a TN or TCM phenotype, in average, which are the subsets capable of establishing a long-lasting T-cell memory in patients. An in-depth analysis to identify individual factors contributing to the optimal T-cell phenotype revealed that ex vivo cell expansion leads to reduced numbers of TN, TSCM, and TEFF cells, while TCM cells increase, both due to cell expansion and CAR-expression. Overall, our results show for the first time that clinical-grade production of CAR T-cells for heavily pre-treated patients using CliniMACS Prodigy system is feasible, and that the obtained products meet the current quality standards of the field. Reduced ex vivo expansion may yield CAR T-cell products with increased persistence in vivo.

SUBMITTER: Castella M

PROVIDER: S-EPMC7259426 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Point-Of-Care CAR T-Cell Production (ARI-0001) Using a Closed Semi-automatic Bioreactor: Experience From an Academic Phase I Clinical Trial.

Castella Maria M Caballero-Baños Miguel M Ortiz-Maldonado Valentín V González-Navarro Europa Azucena EA Suñé Guillermo G Antoñana-Vidósola Asier A Boronat Anna A Marzal Berta B Millán Lucía L Martín-Antonio Beatriz B Cid Joan J Lozano Miquel M García Enric E Tabera Jaime J Trias Esteve E Perpiña Unai U Canals Josep Ma JM Baumann Tycho T Benítez-Ribas Daniel D Campo Elías E Yagüe Jordi J Urbano-Ispizua Álvaro Á Rives Susana S Delgado Julio J Juan Manel M

Frontiers in immunology 20200320

Development of semi-automated devices that can reduce the hands-on time and standardize the production of clinical-grade CAR T-cells, such as CliniMACS Prodigy from Miltenyi, is key to facilitate the development of CAR T-cell therapies, especially in academic institutions. However, the feasibility of manufacturing CAR T-cell products from heavily pre-treated patients with this system has not been demonstrated yet. Here we report and characterize the production of 28 CAR T-cell products in the co ...[more]

PMID: 32528460

Similar Datasets

Project description:Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.

Dataset Information

Point-Of-Care CAR T-Cell Production (ARI-0001) Using a Closed Semi-automatic Bioreactor: Experience From an Academic Phase I Clinical Trial.

Publications

Point-Of-Care CAR T-Cell Production (ARI-0001) Using a Closed Semi-automatic Bioreactor: Experience From an Academic Phase I Clinical Trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets