Abstract
We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia–telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.
Highlights
Introduction1.1 Bridging In Vitro and In Vivo Research
1.1 Bridging In Vitro and In Vivo ResearchMathematical models, and their corresponding in silico tools, can be used to simulate both in vitro and in vivo scenarios that involve cancer cell populations, or tumours, and their responses to anti-cancer treatments (Rockne et al 2019; Bruno et al 2020; Stephanou et al 2018; Brady-Nicholls et al 2020; Scott et al 2020)
In the mathematical model discussed in this paper, we use agentattributed clocks to model cell cycle progression and in order to achieve a longer G1-phase under hypoxic conditions, we introduce a G1 delay factor (G1DF) (Hamis et al 2020a) where
Summary
1.1 Bridging In Vitro and In Vivo Research Mathematical models, and their corresponding in silico tools, can be used to simulate both in vitro and in vivo scenarios that involve cancer cell populations, or tumours, and their responses to anti-cancer treatments (Rockne et al 2019; Bruno et al 2020; Stephanou et al 2018; Brady-Nicholls et al 2020; Scott et al 2020). Cancer cells in an in vitro cell culture experience a microenvironment that is significantly different from the microenvironment experienced by cancer cells in a solid tumour in vivo. As these microenvironments influence cell proliferation and the delivery of oxygen, drug and nutrient molecules to cells, it follows that the dynamics of a cancer cell population in vitro differs from the dynamics of a solid tumour in vivo. A number of open-source in silico tools, such as Chaste (Mirams et al 2013), CompuCell3D (Swat et al 2012) and PhysiCell (Ghaffarizadeh et al 2018), are freely available to facilitate the implementation of ABMs
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