144950 20260217205529.0 doi 10.1016/j.compbiomed.2024.108866 sideral 139848 ART-2024-139848 eng Pérez-Aliacar, Marina Universidad de Zaragoza (orcid)0000-0002-7909-4446 Modelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro 2024 Drug 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. info:eu-repo/grantAgreement/ES/AEI/PID2021-126051OB-C41 info:eu-repo/grantAgreement/ES/DGA/T62-230R info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 6.3 2024 BIOLOGY 7 / 107 = 0.065 2024 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 4 / 67 = 0.06 2024 Q1 T1 ENGINEERING, BIOMEDICAL 22 / 124 = 0.177 2024 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 26 / 177 = 0.147 2024 Q1 T1 1.447 2024 Health Informatics 2024 Q1 Computer Science Applications 2024 Q1 13.0 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ayensa-Jiménez, Jacobo (orcid)0000-0003-2564-6038 Randelovic, Teodora (orcid)0000-0001-7232-7588 Ochoa, Ignacio Universidad de Zaragoza (orcid)0000-0003-2410-5678 Doblaré, Manuel Universidad de Zaragoza (orcid)0000-0001-8741-6452 1003 443 Universidad de Zaragoza Dpto. Anatom.Histolog.Humanas Area Histología 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 180 (2024), 108866 [14 pp.] Comput. biol. med. Computers in biology and medicine 0010-4825 2709215 http://zaguan.unizar.es/record/144950/files/texto_completo.pdf Versión publicada 2593881 http://zaguan.unizar.es/record/144950/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:144950 articulos driver 2026-02-17-20:31:40 ARTICLE