Control-Oriented Models Inform Synthetic Biology Strategies in CAR T Cell Immunotherapy

TL;DR

This paper explores the use of control-oriented mathematical models to optimize CAR T cell therapy by integrating synthetic gene circuits, aiming to improve tumor clearance with fewer experiments.

Contribution

It introduces a novel application of control-oriented models to guide synthetic biology strategies in CAR T therapy, adapting existing models for control purposes.

Findings

Control-oriented models can predict CAR T therapy outcomes.

Alternative activation methods impact tumor clearance.

Models guide synthetic gene circuit design.

Abstract

Chimeric antigen receptor (CAR) T cell therapy is revolutionizing the treatment of blood cancers. Mathematical models that can predict the effectiveness of immunotherapies such as CAR T are of increasing interest due to their ability to reduce the number of experiments performed and to guide the theoretical development of new therapeutic strategies. {Following this rationale, we propose the use of control-oriented models to guide the augmentation of CAR T therapy with synthetic gene circuitry. Here we present an initial investigation where we adapt a previously developed CAR T model for control-oriented purposes. We then explore the impact of realistic alternative activation methods as control inputs to ensure effective tumor clearance.