  Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has
recently become highly instrumental in the fight against life-threatening
diseases. A variety of modeling and computational simulation efforts have
addressed different aspects of CAR T therapy, including T-cell activation, T-
and malignant cell population dynamics, therapeutic cost-effectiveness
strategies, and patient survival analyses. In this article, we present a
systematic review of those efforts, including mathematical, statistical, and
stochastic models employing a wide range of algorithms, from differential
equations to machine learning. To the best of our knowledge, this is the first
review of all such models studying CAR T therapy. In this review, we provide a
detailed summary of the strengths, limitations, methodology, data used, and
data lacking in current published models. This information may help in
designing and building better models for enhanced prediction and assessment of
the benefit-risk balance associated with novel CAR T therapies, as well as with
the data collection essential for building such models.
