Search

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 we here propose using an 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 numerous 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.

International audience; In this review we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges than need to be addressed to allow for a better integration of the model parts and of the data into the models. And Finally, the Hopes with a focus towards making personalised medicine a reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05.

Tumour hypoxia has been associated with increased resistance to various cancer treatments, particularly radiation therapy. Conversely, tumour hypoxia is a validated and ideal target for guided cancer drug delivery. For this reason, hypoxia-activated prodrugs (HAPs) have been developed, which remain inactive in the body until in the presence of tissue hypoxia, allowing for an activation tendency in hypoxic regions. We present here an experimentally motivated mathematical model predicting the effectiveness of HAPs in a variety of clinical settings. We first examined HAP effectiveness as a function of the amount of tumour hypoxia and showed that the drugs have a larger impact on tumours with high levels of hypoxia. We then combined HAP treatment with radiation to examine the effects of combination therapies. Our results showed radiation-HAP combination therapies to be more effective against highly hypoxic tumours. The analysis of combination therapies was extended to consider schedule sequencing of the combination treatments. These results suggested that administering HAPs before radiation was most effective in reducing total cell number. Finally, a sensitivity analysis of the drug-related parameters was done to examine the effect of drug diffusivity and enzyme abundance on the overall effectiveness of the drug. Altogether, the results highlight the importance of the knowledge of tumour hypoxia levels before administration of HAPs in order to ensure positive results.

This article presents the results of a workshop held in Stirling, Scotland in June 2018, called to examine critically the effects of low-dose ionising radiation on the ecosphere. The meeting brought together participants from the fields of low- and high-dose radiobiology and those working in radioecology to discuss the effects that low doses of radiation have on non-human biota. In particular, the shape of the low-dose response relationship and the extent to which the effects of low-dose and chronic exposure may be predicted from high dose rate exposures were discussed. It was concluded that high dose effects were not predictive of low dose effects. It followed that the tools presently available were deemed insufficient to reliably predict risk of low dose exposures in ecosystems. The workshop participants agreed on three major recommendations for a path forward. First, as treating radiation as a single or unique stressor was considered insufficient, the development of a multidisciplinary approach is suggested to address key concerns about multiple stressors in the ecosphere. Second, agreed definitions are needed to deal with the multiplicity of factors determining outcome to low dose exposures as a term can have different meanings in different disciplines. Third, appropriate tools need to be developed to deal with the different time, space and organisation level scales. These recommendations permit a more accurate picture of prospective risks.

Funding: Cancer Research UK. Research at The Institute of Cancer Research is supported by Cancer Research UK under Programme C33589/A19727. Peter Ziegenhein is supported by Cancer Research UK under Programme C33589/A19908. Combined radiotherapy and hyperthermia offer great potential for the successful treatment of radio-resistant tumours through thermo-radiosensitization. Tumour response heterogeneity, due to intrinsic, or micro-environmentally induced factors, may greatly influence treatment outcome, but is difficult to account for using traditional treatment planning approaches. Systems oncology simulation, using mathematical models designed to predict tumour growth and treatment response, provides a powerful tool for analysis and optimization of combined treatments. We present a framework that simulates such combination treatments on a cellular level. This multiscale hybrid cellular automaton simulates large cell populations (up to 107 cells) in vitro, while allowing individual cell-cycle progression, and treatment response by modelling radiation-induced mitotic cell death, and immediate cell kill in response to heating. Based on a calibration using a number of experimental growth, cell cycle and survival datasets for HCT116 cells, model predictions agreed well (R2 > 0.95) with experimental data within the range of (thermal and radiation) doses tested (0–40 CEM43, 0–5 Gy). The proposed framework offers flexibility for modelling multimodality treatment combinations in different scenarios. It may therefore provide an important step towards the modelling of personalized therapies using a virtual patient tumour. Publisher PDF