Bridging in vitro and in vivo research via an agent-based modelling approach: predicting tumour responses to an ATR-inhibiting drug
AbstractTranslating 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 we here propose using a data-driven, agent-based model to bridge the gap between in vitro and in vivo research. The agent-based model used in this study is governed by a set of empirically observable rules, and by adjusting only the rules when moving between in vitro and in vivo simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model can first be parameterised by in vitro data and thereafter be used to predict in vivo treatment responses.As a proof-of-concept, this modelling approach is here validated against data pertaining to LoVo cells subjected to the ATR (ataxia telangiectasia mutated and rad3-related kinase) inhibiting drug AZD6738, but the modelling approach has the potential to be expanded to other applications. In this article we also highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.