Cell Population Dynamics Model of CAR T Cell Survival, Cytotoxicity, and Exhaustion

Identification: 3006


Description

Cell Population Dynamics Model of CAR T Cell Survival, Cytotoxicity, and Exhaustion

Nicole Piscopo1,2, Christian Capitini3, Krishanu Saha1,2*

1Department of Biomedical Engineering, University of Wisconsin- Madison

2Wisconsin Institute for Discovery, University of Wisconsin-Madison

3.Department of Hematology and Oncology, University of Wisconsin- Madison

Chimeric antigen receptor (CAR) T cells are genetically engineered cells that are in clinical trials to target and kill tumors. The profound success of CAR T-cells in treating hematological malignancies (e.g., leukemia by targeting CD19) has lead many companies to attempt to scale up and manufacture these engineered cell therapies [1]–[3]. Even once companies have devised ways to manufacture these therapies, there must be a specific set of characteristics that will define their product. These characteristics can include the percentage of CAR+ cells, expression levels of the CAR, activation levels of the T-cells prior to implantation, and exhaustion levels of the CAR T-cells. Further, the functional influence of different sources of heterogeneity in CAR T-cells needs to be taken into account. To address these issues, we have developed mathematical models to track CAR T-cell and tumor cell interactions. These differential equation models use inputs such as number of CAR T-cells and their surface density, number of target cells and their antigen density, and presence or lack of T-cell stimulants while predicting the decline in tumor cells number. Results with this model incorporating T-cell exhaustion indicates that defined populations of CAR T-cells with high and low CAR activity can efficiently kill cancer cells while reducing off-target healthy cell toxicity. Model predictions will be of high clinical relevance to better define CAR T-cell mixtures to minimize the amount of off-target cytotoxicity and rate of exhaustion of the CAR T-cells before tumor regression can be achieved.

References: [1] M. Sadelain et al., Nat. Rev. Cancer, 2003. [2] G. Welstead et al., Editas Medicine /Juno Therapeutics, 2016. [3] L. Poirot et al., Cancer Res., 2015.

Funding: NSF EAGER Biomanufacturing CBET-1645123.

Credits

Credits: None available.

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