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dc.contributor.authorStein, Sen_US
dc.contributor.authorZhao, Ren_US
dc.contributor.authorHaeno, Hen_US
dc.contributor.authorVivanco, Ien_US
dc.contributor.authorMichor, Fen_US
dc.coverage.spatialUnited Statesen_US
dc.date.accessioned2019-03-05T16:37:05Z
dc.date.issued2018-01en_US
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/29293494en_US
dc.identifierPCOMPBIOL-D-17-00016en_US
dc.identifier.citationPLoS Comput Biol, 2018, 14 (1), pp. e1005924 - ?en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3138
dc.identifier.eissn1553-7358en_US
dc.identifier.doi10.1371/journal.pcbi.1005924en_US
dc.description.abstractHuman primary glioblastomas (GBM) often harbor mutations within the epidermal growth factor receptor (EGFR). Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. However, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynamics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules currently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from proliferative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other cancer types.en_US
dc.format.extente1005924 - ?en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectAntineoplastic Agentsen_US
dc.subjectBlood-Brain Barrieren_US
dc.subjectBrain Neoplasmsen_US
dc.subjectCell Line, Tumoren_US
dc.subjectComputational Biologyen_US
dc.subjectDrug Administration Scheduleen_US
dc.subjectErbB Receptorsen_US
dc.subjectGlioblastomaen_US
dc.subjectHumansen_US
dc.subjectLapatiniben_US
dc.subjectLogistic Modelsen_US
dc.subjectMaximum Tolerated Doseen_US
dc.subjectModels, Biologicalen_US
dc.subjectMutationen_US
dc.subjectProtein Kinase Inhibitorsen_US
dc.subjectQuinazolinesen_US
dc.titleMathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients.en_US
dc.typeJournal Article
dcterms.dateAccepted2017-12-12en_US
rioxxterms.versionofrecord10.1371/journal.pcbi.1005924en_US
rioxxterms.licenseref.startdate2018-01en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfPLoS Comput Biolen_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Molecular Addictions
pubs.publication-statusPublished onlineen_US
pubs.volume14en_US
pubs.embargo.termsNot knownen_US
icr.researchteamMolecular Addictionsen_US
dc.contributor.icrauthorVivanco, Igoren_US


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/