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Mapping CAR T-Cell Design Space Using Agent-Based Models.

Alexis N Prybutok¹, Jessica S Yu^1,2, Joshua N Leonard^1,3,4,5

¹Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States.

Frontiers in Molecular Biosciences

|July 29, 2022

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This summary is machine-generated.

This study introduces CARCADE, a computational model for chimeric antigen receptor (CAR) T-cell therapy. CARCADE simulates treatment variables to optimize CAR T-cell strategies for cancer, accelerating solid tumor therapy development.

Area of Science:

Immunology
Computational Biology
Oncology

Background:

Chimeric antigen receptor (CAR) T-cell therapy is a promising cancer treatment, but challenges remain for solid tumors.
Testing CAR T-cell design strategies in vitro and in vivo is costly and time-consuming.

Purpose of the Study:

To develop a computational framework, CARCADE, to simulate CAR T-cell therapy.
To investigate the impact of design choices and tumor features on treatment outcomes using in silico experiments.

Main Methods:

Extended the Agent-based Representation of Cells And Dynamic Environments (ARCADE) framework to include CAR T-cell agents (CARCADE).
Conducted in silico experiments varying CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, and cancer/healthy cell antigen expression.

Keywords:

CAR T-cell agent-based model cell population dynamics emergent dynamics model-guided design simulation

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Main Results:

Tuning CAR affinity influences IL-2 production by balancing CAR T-cell proliferation and effector function.
Identified a novel multi-feature treatment strategy for balancing selectivity and efficacy.
Revealed the impact of spatial effects on treatment performance in different contexts.

Conclusions:

CARCADE facilitates a deeper understanding of CAR T-cell treatment design.
The model can accelerate the identification of promising CAR T-cell strategies for solid tumors.
CARCADE aids in optimizing the design-build-test cycle for CAR T-cell therapies.