Research Center of Model-Informed Drug Development, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS), Moscow, Russia.
Front Immunol. 2024 Apr 17;15:1357706. doi: 10.3389/fimmu.2024.1357706. eCollection 2024.
In vivo T cell migration has been of interest to scientists for the past 60 years. T cell kinetics are important in the understanding of the immune response to infectious agents. More recently, adoptive T cell therapies have proven to be a most promising approach to treating a wide range of diseases, including autoimmune and cancer diseases, whereby the characterization of cellular kinetics represents an important step towards the prediction of therapeutic efficacy.
Here, we developed a physiologically-based pharmacokinetic (PBPK) model that describes endogenous T cell homeostasis and the kinetics of exogenously administered T cells in mouse. Parameter calibration was performed using a nonlinear fixed-effects modeling approach based on published data on T cell kinetics and steady-state levels in different tissues of mice. The Partial Rank Correlation Coefficient (PRCC) method was used to perform a global sensitivity assessment. To estimate the impact of kinetic parameters on exogenously administered T cell dynamics, a local sensitivity analysis was conducted.
We simulated the model to analyze cellular kinetics following various T cell doses and frequencies of CCR7+ T cells in the population of infused lymphocytes. The model predicted the effects of T cell numbers and of population composition of infused T cells on the resultant concentration of T cells in various organs. For example, a higher percentage of CCR7+ T cells among exogenously administered T lymphocytes led to an augmented accumulation of T cells in the spleen. The model predicted a linear dependence of T cell dynamics on the dose of adoptively transferred T cells.
The mathematical model of T cell migration presented here can be integrated into a multi-scale model of the immune system and be used in a preclinical setting for predicting the distribution of genetically modified T lymphocytes in various organs, following adoptive T cell therapies.
过去 60 年来，体内 T 细胞迁移一直是科学家关注的焦点。T 细胞动力学对于理解针对感染因子的免疫反应非常重要。最近，过继性 T 细胞疗法已被证明是治疗广泛疾病（包括自身免疫和癌症疾病）的最有前途的方法，其中细胞动力学的特征对于预测治疗效果是重要的一步。
在这里，我们开发了一种生理相关的药代动力学（PBPK）模型，该模型描述了内源性 T 细胞稳态和外源性给予的 T 细胞在小鼠中的动力学。使用基于已发表的关于 T 细胞动力学和不同组织中 T 细胞稳态水平的非线性固定效应建模方法进行参数校准。偏秩相关系数（PRCC）方法用于进行全局敏感性评估。为了估计动力学参数对给予的 T 细胞动力学的影响，进行了局部敏感性分析。
我们模拟了该模型，以分析在输注的淋巴细胞群体中，不同 T 细胞剂量和 CCR7+T 细胞频率下的细胞动力学。该模型预测了 T 细胞数量和输注 T 细胞群体组成对各种器官中 T 细胞浓度的影响。例如，在外源性给予的 T 淋巴细胞中，CCR7+T 细胞的百分比更高会导致 T 细胞在脾脏中的积累增加。该模型预测了过继性转移 T 细胞的剂量与 T 细胞动力学之间的线性依赖性。
这里提出的 T 细胞迁移数学模型可以整合到免疫系统的多尺度模型中，并在临床前环境中用于预测各种器官中经过基因修饰的 T 淋巴细胞的分布，以进行过继性 T 细胞治疗。
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