Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer.
Immunotherapy
Mixed effects models
Stochastic modeling
T cell exhaustion
Treatment optimization
Journal
Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342
Informations de publication
Date de publication:
07 10 2020
07 10 2020
Historique:
received:
28
03
2019
revised:
04
05
2020
accepted:
28
05
2020
pubmed:
17
6
2020
medline:
22
6
2021
entrez:
17
6
2020
Statut:
ppublish
Résumé
Adoptive Cell Transfer therapy of cancer is currently in full development and mathematical modeling is playing a critical role in this area. We study a stochastic model developed by Baar et al. (2015) for modeling immunotherapy against melanoma skin cancer. First, we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice. A Nonlinear Mixed Effects Model is estimated by the Stochastic Approximation Expectation Maximization algorithm. With the estimated parameters, we return to the stochastic model and calculate the probability of complete T cells exhaustion. We show that for some relevant parameter values, an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability. Then, focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges.
Identifiants
pubmed: 32540247
pii: S0022-5193(20)30214-9
doi: 10.1016/j.jtbi.2020.110359
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
110359Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.