A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy

TL;DR

This systematic review comprehensively analyzes existing mathematical and computational models of CAR T-cell therapy, highlighting their methodologies, strengths, limitations, and data needs to improve future modeling efforts and therapeutic outcomes.

Contribution

First comprehensive review of all modeling efforts for CAR T therapy, detailing methodologies, data, and limitations to guide future research and model development.

Findings

Models use diverse algorithms from differential equations to machine learning

Current models have notable gaps in data and predictive accuracy

Insights can inform better model design and clinical decision-making

Abstract

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.