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Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients.
Stein, Shayna; Zhao, Rui; Haeno, Hiroshi; Vivanco, Igor; Michor, Franziska.
Afiliación
  • Stein S; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massasschusetts, United States of America.
  • Zhao R; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
  • Haeno H; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, United States of America.
  • Vivanco I; The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
  • Michor F; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massasschusetts, United States of America.
PLoS Comput Biol ; 14(1): e1005924, 2018 01.
Article en En | MEDLINE | ID: mdl-29293494
Human 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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Quinazolinas / Neoplasias Encefálicas / Glioblastoma / Inhibidores de Proteínas Quinasas / Antineoplásicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Quinazolinas / Neoplasias Encefálicas / Glioblastoma / Inhibidores de Proteínas Quinasas / Antineoplásicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos