ArticlePLoS computational biology2018
Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 42 citations in OpenAlex.
- Review
- Go-or-grow models in biology: a monster on a leash.Journal of mathematical biology · 2025Review
- Multiscale mathematical model-informed reinforcement learning optimizes combination treatment scheduling in glioblastoma evolution.Science advances · 2025Article
- Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2023Article
- Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology.BMC cancer · 2023Article
- Optimized scaling of translational factors in oncology: from xenografts to RECIST.Cancer chemotherapy and pharmacology · 2022Article
- Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.IEEE transactions on medical imaging · 2022Article
- Extended transit compartment model to describe tumor delay using Coxian distribution.Scientific reports · 2022Article
- Optimizing the future: how mathematical models inform treatment schedules for cancer.Trends in cancer · 2022Review
- Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation.Molecular cancer therapeutics · 2022Article
- Article
- Updated Insights on EGFR Signaling Pathways in Glioma.International journal of molecular sciences · 2021Review
- Model-based optimization of combination protocols for irradiation-insensitive cancers.Scientific reports · 2020Article
- Towards standardization guidelines for in silico approaches in personalized medicine.Journal of integrative bioinformatics · 2020Article
- Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models.Cancers · 2020Review
- From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.PLoS computational biology · 2020Article
- Quantifying Glioblastoma Drug Response Dynamics Incorporating Treatment Sensitivity and Blood Brain Barrier Penetrance From Experimental Data.Frontiers in physiology · 2020Article
- System-based approaches as prognostic tools for glioblastoma.BMC cancer · 2019Review
- Hyaluronic acid-functionalized gelatin hydrogels reveal extracellular matrix signals temper the efficacy of erlotinib against patient-derived glioblastoma specimens.Biomaterials · 2019Article
- Opportunities for improving cancer treatment using systems biology.Current opinion in systems biology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.