000144950 001__ 144950
000144950 005__ 20240926114322.0
000144950 0247_ $$2doi$$a10.1016/j.compbiomed.2024.108866
000144950 0248_ $$2sideral$$a139848
000144950 037__ $$aART-2024-139848
000144950 041__ $$aeng
000144950 100__ $$0(orcid)0000-0002-7909-4446$$aPérez-Aliacar, Marina$$uUniversidad de Zaragoza
000144950 245__ $$aModelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro
000144950 260__ $$c2024
000144950 5203_ $$aDrug resistance is one of the biggest challenges in the fight against cancer. In particular, in the case of glioblastoma, the most lethal brain tumour, resistance to temozolomide (the standard of care drug for chemotherapy in this tumour) is one of the main reasons behind treatment failure and hence responsible for the poor prognosis of patients diagnosed with this disease. In this work, we combine the power of three-dimensional in vitro experiments of treated glioblastoma spheroids with mathematical models of tumour evolution and adaptation. We use a novel approach based on internal variables for modelling the acquisition of resistance to temozolomide that was observed in experiments for a group of treated spheroids. These internal variables describe the cell’s phenotypic state, which depends on the history of drug exposure and affects cell behaviour. We use model selection to determine the most parsimonious model and calibrate it to reproduce the experimental data, obtaining a high level of agreement between the in vitro and in silico outcomes. A sensitivity analysis is carried out to investigate the impact of each model parameter in the predictions. More importantly, we show how the model is useful for answering biological questions, such as what is the intrinsic adaptation mechanism, or for separating the sensitive and resistant populations. We conclude that the proposed in silico framework, in combination with experiments, can be useful to improve our understanding of the mechanisms behind drug resistance in glioblastoma and to eventually set some guidelines for the design of new treatment schemes.
000144950 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41$$9info:eu-repo/grantAgreement/ES/DGA/T62-230R
000144950 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000144950 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000144950 700__ $$0(orcid)0000-0003-2564-6038$$aAyensa-Jiménez, Jacobo
000144950 700__ $$0(orcid)0000-0001-7232-7588$$aRandelovic, Teodora
000144950 700__ $$0(orcid)0000-0003-2410-5678$$aOchoa, Ignacio$$uUniversidad de Zaragoza
000144950 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000144950 7102_ $$11003$$2443$$aUniversidad de Zaragoza$$bDpto. Anatom.Histolog.Humanas$$cArea Histología
000144950 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000144950 773__ $$g180 (2024), 108866 [14 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000144950 8564_ $$s2709215$$uhttps://zaguan.unizar.es/record/144950/files/texto_completo.pdf$$yVersión publicada
000144950 8564_ $$s2593881$$uhttps://zaguan.unizar.es/record/144950/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000144950 909CO $$ooai:zaguan.unizar.es:144950$$particulos$$pdriver
000144950 951__ $$a2024-09-26-11:42:35
000144950 980__ $$aARTICLE