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Generation of CAR T Cells for Adoptive Therapy in the Context of Glioblastoma Standard of Care

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Published on: February 16, 2015

A stochastic framework for evaluating CAR T cell therapy efficacy and variability.

Chau Hoang¹, Tuan Anh Phan², Cameron J Turtle³

¹Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM 88001, USA.

Mathematical Biosciences

|January 8, 2024

Summary

This summary is machine-generated.

This study models CAR T cell therapy using a hybrid approach, accounting for patient variability to predict treatment outcomes. The findings help compare different CAR T cell protocols for improved patient cure rates.

Keywords:

CAR-T cell therapy Ergodic distribution Stochastic modeling Stopping time

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Published on: February 27, 2019

Area of Science:

Immunology
Mathematical Biology
Computational Biology

Background:

CAR T cell therapy shows promise but requires optimization for diverse patient responses.
Patient variability and heterogeneity significantly impact treatment efficacy.
Mathematical modeling is crucial for understanding complex biological systems like cancer therapy.

Purpose of the Study:

To develop and apply a hybrid deterministic-stochastic model to evaluate CAR T cell therapy protocols.
To analyze patient heterogeneity and variability in treatment outcomes.
To predict short-term and long-term efficacy of different CAR T cell treatment strategies.

Main Methods:

Constructed a hybrid model combining deterministic and stochastic processes using Ito stochastic differential equations.
Incorporated white noise to represent patient variability and a hyperparameter for cohort heterogeneity.
Simulated treatment protocols using transient dynamics and defined stopping times (time to cure, time to progress).

Main Results:

The stochastic model identified three ergodic invariant measures, acting as attractors for tumor growth under certain conditions.
Transient dynamics of the stochastic model revealed short-term chances of cure.
Probability distributions of time to cure and time to progress provided detailed outcome comparisons for three CAR T cell protocols.

Conclusions:

The developed stochastic model effectively simulates CAR T cell therapy, capturing patient variability and heterogeneity.
The model's predictions of cure and progression times offer valuable insights for optimizing treatment protocols.
This approach supports clinical studies by providing a quantitative framework for assessing CAR T cell therapy efficacy.