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dc.contributor.authorHamis, Sara
dc.contributor.authorYates, James
dc.contributor.authorChaplain, Mark A. J.
dc.contributor.authorPowathil, Gibin G.
dc.date.accessioned2021-08-31T09:30:02Z
dc.date.available2021-08-31T09:30:02Z
dc.date.issued2021-10
dc.identifier.citationHamis , S , Yates , J , Chaplain , M A J & Powathil , G G 2021 , ' Targeting cellular DNA damage responses in cancer : an in vitro -calibrated agent-based model simulating monolayer and spheroid treatment responses to ATR-inhibiting drugs ' , Bulletin of Mathematical Biology , vol. 83 , no. 10 , 103 . https://doi.org/10.1007/s11538-021-00935-yen
dc.identifier.issn0092-8240
dc.identifier.otherPURE: 275515925
dc.identifier.otherPURE UUID: c1704657-424a-4321-b841-aa4431a07cb5
dc.identifier.otherORCID: /0000-0001-5727-2160/work/99466417
dc.identifier.otherScopus: 85113726386
dc.identifier.otherWOS: 000692473500001
dc.identifier.urihttp://hdl.handle.net/10023/23870
dc.descriptionFunding: Medical Research Council (MR/R017506/1).en
dc.description.abstractWe 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.
dc.format.extent21
dc.language.isoeng
dc.relation.ispartofBulletin of Mathematical Biologyen
dc.rightsCopyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en
dc.subjectDNA damage response inhibitionen
dc.subjectAgent-based modelen
dc.subjectMathemathical oncologyen
dc.subjectAZD6738en
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subject3rd-DASen
dc.subject.lccRC0254en
dc.titleTargeting cellular DNA damage responses in cancer : an in vitro-calibrated agent-based model simulating monolayer and spheroid treatment responses to ATR-inhibiting drugsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.identifier.doihttps://doi.org/10.1007/s11538-021-00935-y
dc.description.statusPeer revieweden


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