A mathematical model of granulopoiesis incorporating the negative feedback dynamics and kinetics of G-CSF/neutrophil binding and internalisation

TL;DR

This paper presents a comprehensive mathematical model of granulopoiesis that incorporates G-CSF kinetics, including binding and internalization, to predict neutrophil responses under various treatment scenarios.

Contribution

It introduces a novel state-dependent delay based on cytokine concentration derived from an age-structured PDE model, enhancing the physiological accuracy of granulopoiesis modeling.

Findings

Successfully predicts neutrophil and G-CSF responses to treatments

Reproduces combined chemotherapy and G-CSF effects without extra fitting

Demonstrates the importance of cytokine binding dynamics in neutrophil regulation

Abstract

We develop a physiological model of granulopoiesis which includes explicit modelling of the kinetics of the cytokine granulocyte colony-stimulating factor (G-CSF) incorporating both the freely circulating concentration and the concentration of the cytokine bound to mature neutrophils. G-CSF concentrations are used to directly regulate neutrophil production, with the rate of differentiation of stem cells to neutrophil precursors, the effective proliferation rate in mitosis, the maturation time, and the release rate from the mature marrow reservoir into circulation all dependent on the level of G-CSF in the system. The dependence of the maturation time on the cytokine concentration introduces a state-dependent delay into our differential equation model, and we show how this is derived from an age-structured partial differential equation model of the mitosis and maturation, and also detail the derivation of the rest of our model. The model and its estimated parameters are shown to successfully predict the neutrophil and G-CSF responses to a variety of treatment scenarios, including the combined administration of chemotherapy and exogenous G-CSF. This concomitant treatment was reproduced without any additional fitting to characterise drug-drug interactions.