A Transcriptome-based model for improved CART Therapy

This project aims to evaluate how levels of key cytokines affect the growth rate of important Tcell phenotypes, so as to promote a more consistent final product. A properly scaled-down “bioreactor” will be evaluated along with RedBud’s magnetic post tech.
Categories
Cell and Gene therapies
Equipment and Supplies
Data
Project status
100% Completed

Industry Need

An estimated 10-20% of CAR-T batches fail due to slow growth rates of Tcells during the expansion step of the process. Furthermore, the starting material for every batch differs in many ways including the distribution of phenotypes in the Tcell population, yet the final product specification is fixed.

Solution

This project then aims to evaluate how levels of key cytokines effect the growth rate of important Tcell phenotypes, so as to promote a more consistent final product. To accomplish this, a properly scaled-down “bioreactor” must be used.

Outputs/Deliverables

  •  Oxygen Transfer in the wells with RedBud magnetically-actuated Posts rates were found to be comparable to larger scale bioreactors. Increasing stirrer speed (3500-9000 rpm) and decreasing volume (150-350 µL) increased oxygen transfer (kLa: 4-88 h-1).
  • Neuro-Fuzzy inference systems (ANFIS) effectively modelled the growth of human T cell phenotype as a function of levels of supplemented cytokines. The same approach was used to model differentiation of the naïve T cell phenotypes cells to memory phenotype. The CD4+ and CD8+ naïve phenotypes had higher growth rates than the memory phenotypes.
  • RNA-Sequencing techniques were used to study the cells’ gene regulation through differential gene expression analysis; specifically comparing the phenotypes with one another. For example, the CD4+ naïve and memory T cell phenotypes differentially express genes related to response to type I interferon and CD8+ naïve and memory T cell phenotypes express genes related to stem cell maintenance and epithelial cell differentiation. 

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