CAR T Cells from Code to Clinic: Framing Modeling Approaches with Current Translational Research Goals

Abstract

Chimeric Antigen Receptor (CAR) T cell therapy has transformed immunotherapy for resistant cancers, yet it faces major limitations such as lack of persistence, toxicity, exhaustion, and antigen-negative relapse. Enhancing CAR T cells with genetic circuitry and synthetic receptors offers solutions to some of these problems, but often the theoretical design space is too large to explore experimentally. Mathematical modeling offers a powerful framework for addressing these translational bottlenecks by linking mechanistic understanding to design optimization and clinical application. This perspective embeds modeling methodologies within the therapeutic problems they aim to solve, framing the discussion around key translational challenges rather than modeling techniques. We critically evaluate the strengths, limitations, and data gaps of current approaches emphasizing how modeling supports the development of safer and more effective therapies. We highlight emerging approaches such as multiscale modeling, control theory, and data-driven methods that leverage high-dimensional datasets to guide predictive design, and we point toward underexplored areas in immune cell therapy including CAR NK and CAR macrophages as future modeling frontiers. We hope that the themes explored in this perspective will encourage readers to refine predictive models, enabling researchers to optimize CAR T cell therapies at the genetic, cellular, microenvironmental, and patient level to enhance their clinical performance.