Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients

• Published in: PLoS computational biology Vol. 14; no. 1; p. e1005924
• Main Authors: Stein, Shayna, Zhao, Rui, Haeno, Hiroshi, Vivanco, Igor, Michor, Franziska
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.01.2018; Public Library of Science (PLoS)
• Subjects: Antineoplastic Agents - administration & dosage; Antineoplastic Agents - pharmacokinetics; Biocompatibility; Biology; Biology and Life Sciences; Blood-Brain Barrier; Brain; Brain cancer; Brain Neoplasms - drug therapy; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Brain research; Cancer; Cancer therapies; Cancer treatment; Cell culture; Cell death; Cell Line, Tumor; Cell migration; Chemotherapy; Clinical trials; Computational Biology; Computational neuroscience; Dosage and administration; Drug Administration Schedule; Drug dosages; Drug resistance; Drug therapy; Enzyme inhibitors; Epidermal growth factor; Epidermal growth factor receptors; ErbB Receptors - genetics; ErbB-2 protein; Glioblastoma; Glioblastoma - drug therapy; Glioblastoma - genetics; Glioblastoma - metabolism; Glioblastomas; Humans; Hypoxia; In vivo methods and tests; Kinases; Lapatinib; Logistic Models; Mathematical analysis; Mathematical models; Maximum Tolerated Dose; Medical research; Medicine and Health Sciences; Methods; Microenvironments; Models, Biological; Mutation; Neurosurgery; Oncology; Optimization; Partial differential equations; Pharmacokinetics; Protein Kinase Inhibitors - administration & dosage; Protein Kinase Inhibitors - pharmacokinetics; Protein-tyrosine kinase; Public health; Quinazolines - administration & dosage; Quinazolines - pharmacokinetics; Regression analysis; Schedules; Statistical analysis; Stem cells; Studies; Targeted cancer therapy; Toxicity; Tyrosine; United States > US; Massachusetts
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005924

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.