A mathematical model of pre-diagnostic glioma growth

Sturrock, Marc; Hao, Wenrui; Schwartzbaum, Judith A.; Rempała, Grzegorz A.

doi:10.1016/j.jtbi.2015.06.003

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…The NAA level in patients with early HD manifestations is lower than that in a control group. At the same time, the level of a gliosis marker, myo-inositol, is significantly increased in these patients [ 31 ]. A relationship between the NAA level and the disease severity opens the opportunity to use this metabolite as an identifier of neurochemical reactions in evaluating the effectiveness of potential therapeutic agents.…”

Section: Manifestation and Diagnosis Of Hdmentioning

confidence: 99%

Huntington’s Disease: Calcium Dyshomeostasis and Pathology Models

et al. 2017

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Huntington’s disease (HD) is a severe inherited neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and mental impairment. At the molecular level, HD is caused by a mutation in the first exon of the gene encoding the huntingtin protein. The mutation results in an expanded polyglutamine tract at the N-terminus of the huntingtin protein, causing the neurodegenerative pathology. Calcium dyshomeostasis is believed to be one of the main causes of the disease, which underlies the great interest in the problem among experts in molecular physiology. Recent studies have focused on the development of animal and insect HD models, as well as patient-specific induced pluripotent stem cells (HD-iPSCs), to simulate the disease’s progression. Despite a sesquicentennial history of HD studies, the issues of diagnosis and manifestation of the disease have remained topical. The present review addresses these issues.

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Section: Manifestation and Diagnosis Of Hdmentioning

confidence: 99%

Huntington’s Disease: Calcium Dyshomeostasis and Pathology Models

et al. 2017

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“…Gliomas are a type of brain tumor that are known to be particularly difficult to treat, due to their location deep in the brain and their anisotropic growth. As discussed by Sturrock et al (2015) in their review, while significant efforts have been directed towards studying the spatiotemporal growth of gliomas towards personalized treatment, there has been less attention on pre-diagnostic gliomas. These tumors-especially the glioblastoma subtype-are deadly, with high fatality rates, motivating research in this area.…”

Section: Background and Contextmentioning

confidence: 99%

“…The immune system plays a large role in fending off tumor growth, potentially providing a link between glucose metabolism and glioma growth. As immune response requires energy, an increase of serum glucose is key for providing the energy needed for the immune system (Sturrock et al, 2015). In order for immune cells to jump from rest to an active state, they use the energy from glucose metabolism.…”

Section: Background and Contextmentioning

confidence: 99%

A revised mathematical model of pre-diagnostic glioma growth incorporating vascularization and tumor mutational burden

Jain

2024

Preprint

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Gliomas, a type of brain tumor, have become increasingly important in oncology as they are difficult to treat due to their location deep in the brain. While some research has been done on spatiotemporal prediction of future glioma growth—something that can aid in surgical resections of gliomas once a patient has been diagnosed—modeling efforts for pre-diagnostic gliomas remain limited. The lack of retrospective, pre-diagnostic data makes this a challenging task; yet, pre-symptomatic serum glucose levels in patients have been shown to have a relationship with the emergence of gliomas, motivating this area of research. In 2015, Sturrock et al. presented an ordinary differential equation model of pre-diagnostic glioma growth that describes glioma-glucose-immune interactions. This report reproduces the major findings of Sturrock et al., revising their model to incorporate more biological phenomena—namely vascularization and mutational burden—testing additional medically relevant patient scenarios, and providing an extended discussion on the implications of the model.In-silicosimulations performed in this report provide further insight into models describing glioma-glucose-immune interactions, and how they can be expanded to incorporate physiologically relevant features. Future work is necessary to refine model parameters and validate predictions with the limited, albeit steadily growing, amount of longitudinal patient data.

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“…Furthermore, much work exists for tumor growth modeling (usually for xerographs) and how they can possibly be coupled with PBPK approaches regarding the estimation of chemotherapy concentrations in tumor compartments and the improved adjustment of dosing regimen (83,(130)(131)(132). Moreover, in addition to xenograft-based models, there is much effort toward the development of multi-scale in silico models aimed at the improved comprehension of tumor growth and the underlying mechanisms that lead to diverse outcomes of tumor lesions (133)(134)(135)(136)(137)(138)(139)(140)(141). Although both approaches sometimes require a tedious and time-consuming process due to the required individual extrapolation of data and validation of various factors contributing to cancer pathophysiology, diversity and modeling, they show encouraging results towards improved, personalized or stratified approaches to cancer treatment particularly for novel chemotherapeutic drug delivery schemes.…”

Section: Moving Toward the Implementation Of Pbpk Models In Nanomedicmentioning

confidence: 99%

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

et al. 2016

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The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

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A mathematical model of pre-diagnostic glioma growth

Cited by 17 publications

References 42 publications

Huntington’s Disease: Calcium Dyshomeostasis and Pathology Models

Huntington’s Disease: Calcium Dyshomeostasis and Pathology Models

A revised mathematical model of pre-diagnostic glioma growth incorporating vascularization and tumor mutational burden

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

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