An agent-based model for drug-radiation interactions in the tumour microenvironment: Hypoxia-activated prodrug SN30000 in multicellular tumour spheroids

Abstract

Multicellular tumour spheroids capture many characteristics of human tumour microenvironments, including hypoxia, and represent an experimentally tractable in vitro model for studying interactions between radiotherapy and anticancer drugs. However, interpreting spheroid data is challenging because of limited ability to observe cell fate within spheroids dynamically. To overcome this limitation, we have developed a hybrid continuum/agent-based model (ABM) for HCT116 tumour spheroids, parameterised using experimental models (monolayers and multilayers) in which reaction and diffusion can be measured directly. In the ABM, cell fate is simulated as a function of local oxygen, glucose and drug concentrations, determined by solving diffusion equations and intracellular reactions. The model is lattice-based, with cells occupying discrete locations on a 3D grid embedded within a coarser grid that encompasses the culture medium; separate solvers are employed for each grid. The generated concentration fields account for depletion in the medium and specify concentration-time profiles within the spheroid. Cell growth and survival are determined by intracellular oxygen and glucose concentrations, the latter based on direct measurement of glucose diffusion/reaction (in multilayers) for the first time. The ABM reproduces known features of spheroids including overall growth rate, its oxygen and glucose dependence, peripheral cell proliferation, central hypoxia and necrosis. We extended the ABM to describe in detail the hypoxia-dependent interaction between ionising radiation and a hypoxia-activated prodrug (SN30000), again using experimentally determined parameters; the model accurately simulated clonogenic cell killing in spheroids, while inclusion of reversible cell cycle delay was required to account for the marked spheroid growth delay after combined radiation and SN30000. This ABM of spheroid growth and response exemplifies the utility of integrating computational and experimental tools for investigating radiation/drug interactions, and highlights the critical importance of understanding oxygen, glucose and drug concentration gradients in interpreting activity of therapeutic agents in spheroid models.

Pyruvate anaplerosis is a mechanism of resistance to pharmacological glutaminase inhibition in triple-receptor negative breast cancer

“…Multicellular spheroids represent useful models of many of the microenvironmental characteristics that occur in tumours. Advanced mathematical modelling of these systems can provide key insights into cell proliferation/death, oxygen and nutrient gradients, development of necrosis, and how these compartments interact and change spatially and temporally with spheroid growth and in response to treatment [42]. It has been reported that the anchorage-independent growth conditions of spheroid culture alter glutamine metabolism, favouring reductive carboxylation of glutamine to citrate to support redox homeostasis [43].…”

Pyruvate anaplerosis is a mechanism of resistance to pharmacological glutaminase inhibition in Triple-receptor Negative Breast Cancer

“…The proportion of proliferating cells in 3D models is lower than in 2D cultures resulting in greater cellular heterogeneity within the culture [40,41] [42]. It has been reported that the anchorage-independent growth conditions of spheroid culture alter glutamine metabolism, favouring reductive carboxylation of glutamine to citrate to support redox homeostasis [43].…”

Energy dynamics for systemic configurations of virus-host co-evolution

“…There are known advantages of in silico modelling the action of therapeutic agents on known diseases through agent-based modelling 74 . However, the literature evidenced some intrinsic limitations on the choice of parameters, like the size of investigated populations 75 , while major problems are related to model validation 75,76 , also requiring to supplement the models with adequate formal ontologies 77 .…”