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Goh, Y. F.; Kong, Hwai Loong; Wang, Chi-Hwa

doi:10.1023/a:1011076110317

Cited by 72 publications

(28 citation statements)

References 14 publications

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“…Nevertheless, time-varying plasma drug concentrations can be readily simulated. The baseline plasma drug concentration was set to d 0 = 2.13 mol/m 3 , as commonly used for doxorubicin [52]. In our simulation, the concentration of drug in the tumor interstitium was influenced by the pressure difference within capillary blood and extracellular space, as well as vessel mass exchange surface per unit volume (as determined by vessel diameter and/or age).…”

Section: Computational Simulation Resultsmentioning

confidence: 99%

Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation

et al. 2014

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Solid tumors develop abnormally at spatial and temporal scales, giving rise to biophysical barriers that impact anti-tumor chemotherapy. This may increase the expenditure and time for conventional drug pharmacokinetic and pharmacodynamic studies. In order to facilitate drug discovery, we propose a mathematical model that couples three-dimensional tumor growth and angiogenesis to simulate tumor progression for chemotherapy evaluation. This application-oriented model incorporates complex dynamical processes including cell- and vascular-mediated interstitial pressure, mass transport, angiogenesis, cell proliferation, and vessel maturation to model tumor progression through multiple stages including tumor initiation, avascular growth, and transition from avascular to vascular growth. Compared to pure mechanistic models, the proposed empirical methods are not only easy to conduct but can provide realistic predictions and calculations. A series of computational simulations were conducted to demonstrate the advantages of the proposed comprehensive model. The computational simulation results suggest that solid tumor geometry is related to the interstitial pressure, such that tumors with high interstitial pressure are more likely to develop dendritic structures than those with low interstitial pressure.

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Section: Computational Simulation Resultsmentioning

confidence: 99%

Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation

et al. 2014

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“…These studies were not of CED, but rather of polymeric drug delivery. The intravascular delivery of a drug for hepatoma and subsequent distribution in the tumor was studied in [19]. All these as well as recent studies ([23], [22], but these were subsequent to our early reports referenced in the Introduction) have all used finite element methods.…”

Section: Interstitial Transport Modelmentioning

confidence: 99%

Predictive models for pressure-driven fluid infusions into brain parenchyma

Raghavan¹,

Brady²

2011

Phys. Med. Biol.

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Direct infusions into brain parenchyma of biological therapeutics for serious brain diseases have been, and are being, considered. However, individual brains, as well as distinct cytoarchitectural regions within brains, vary in their response to fluid flow and pressure. Further, the tissue responds dynamically to these stimuli, requiring a nonlinear treatment of equations that would describe fluid flow and drug transport in brain. We here report in detail on an individual–specific model and a comparison of its prediction with simulations for living porcine brains. Two critical features we introduced into our model — absent from previous ones, but requirements for any useful simulation — are the infusion-induced interstitial expansion and the backflow. These are significant determinants of the flow. Another feature of our treatment is the use of cross–property relations to obtain individual–specific parameters that are coefficients in the equations. The quantitative results are at least encouraging, showing a high fraction of overlap between the computed and measured volumes of distribution of a tracer molecule, and are potentially clinically useful. Several improvements are called for; principally a treatment of the interstitial expansion more fundamentally based on poroelasticity, and a better delineation of the diffusion tensor of a particle confined to the interstitial spaces.

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“…The osmotic pressure contribution for the lymph vessels is neglected due to the highly permeable lymphatics. Also, the pressure inside the lymphatics is taken to be 0 mm Hg [49]. By substituting Darcy’s law and Starling’s law into the continuity equation, we obtain the equation for IFP in a solid tumor: …”

Section: Methodsmentioning

confidence: 99%

Quantifying the effects of antiangiogenic and chemotherapy drug combinations on drug delivery and treatment efficacy

et al. 2017

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Tumor-induced angiogenesis leads to the development of leaky tumor vessels devoid of structural and morphological integrity. Due to angiogenesis, elevated interstitial fluid pressure (IFP) and low blood perfusion emerge as common properties of the tumor microenvironment that act as barriers for drug delivery. In order to overcome these barriers, normalization of vasculature is considered to be a viable option. However, insight is needed into the phenomenon of normalization and in which conditions it can realize its promise. In order to explore the effect of microenvironmental conditions and drug scheduling on normalization benefit, we build a mathematical model that incorporates tumor growth, angiogenesis and IFP. We administer various theoretical combinations of antiangiogenic agents and cytotoxic nanoparticles through heterogeneous vasculature that displays a similar morphology to tumor vasculature. We observe differences in drug extravasation that depend on the scheduling of combined therapy; for concurrent therapy, total drug extravasation is increased but in adjuvant therapy, drugs can penetrate into deeper regions of tumor.

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Untitled

Cited by 72 publications

References 14 publications

Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation

Computational Modeling of 3D Tumor Growth and Angiogenesis for Chemotherapy Evaluation

Predictive models for pressure-driven fluid infusions into brain parenchyma

Quantifying the effects of antiangiogenic and chemotherapy drug combinations on drug delivery and treatment efficacy

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