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Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer

Abstract : We study the stochastic model developed by Baar et al., 2015 for the modeling of immunotherapy against melanoma skin cancer. In the first part, we estimate the parameters of the deterministic limit of the model based on the biological data of tumor growth in mice which are provided in Landsberg et al.,2012. The main statistical tools we use are the NonLinear Mixed Effects Models (NLMEM) and the Stochastic Approximation Expectation Maximization (SAEM) algorithm. With the estimated parameters, we head back to the stochastic model and calculate the probability that the T cells all get exhausted during the treatment. We show that for biologically reasonable values of the parameters, an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability. In the second part, assuming that the relapse is 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 at different stages of the disease evolution.
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Submitted on : Tuesday, June 21, 2022 - 7:56:19 AM
Last modification on : Monday, June 27, 2022 - 9:28:07 AM

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Modibo Diabate, Loren Coquille, Adeline Samson. Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer. Journal of Theoretical Biology, Elsevier, 2020, 502, pp.110359. ⟨10.1016/j.jtbi.2020.110359⟩. ⟨hal-01810143⟩

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