Numerical modeling of drug delivery in a dynamic solid tumor microvasculature

Sefidgar, Mostafa; Soltani, M.; Raahemifar, Kaamran; Sadeghi, Mahmood; Bazmara, Hossein; Bazargan, Majid; Naeenian, Mojtaba Mousavi

doi:10.1016/j.mvr.2015.02.007

Cited by 137 publications

(135 citation statements)

References 38 publications

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“…The blood flow estimation model by Fry et al [39] has been thoroughly tested using 202 mesenteric networks [50] in which blood flow measurements were taken in individual 203 vessels and used to inform parameter estimation [39,42,48,51]. Darcy's law has been effectively used to describe the passage of fluids [3,14,[30][31][32][33]52] or 206 solutes [40,45] through tissues. In this study, we use Darcy's law to describe the 207 relationship between the volume-averaged IFP, p, and interstitial fluid velocity (IFV), u, 208 within the porous interstitial domain:…”

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confidence: 99%

Modelling the transport of fluid through heterogeneous, whole tumours in silico

Sweeney

d’Esposito

Walker‐Samuel

et al. 2019

Preprint

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It is critically important to understand and predict fluid transport within both physiological and pathological tissues in order to develop effective treatment strategies.Recent advances in high-resolution optical imaging allow the acquisition of whole tumour vascular networks which can be used to parameterise computational models to predict the fluid dynamics at all length scales across the tissue. This enables hypothesis testing around the role of the tumour microenvironment in determining transport characteristics, which would otherwise be unavailable using traditional experiments.In this study, we present a novel computational framework for the efficient simulation of vascular blood flow and interstitial fluid transport based on complete three-dimensional, whole tumour vasculature obtained using high-resolution optical imaging. This framework comprises a Poiseuille flow model which simulates vascular blood flow within the vessel network, coupled via point sources of flux to a porous medium model describing interstitial fluid transport. We develop a computational algorithm for prescription of network boundary conditions and validation of tissue-scale fluid transport against measured in vivo perfusion data acquired using biomedical imaging tools. We present simulations of the model on orthoptic murine glioma and December 24, 2018 1/36 human colorectal carcinoma xenograft data (GL261 and LS147T, respectively), and perform sensitivity analysis on key unknown parameters relating to the tissue microenvironment, to understand their impact in predicting vascular and interstitial flow. Finally, we simulate radially varying vascular normalisation in a LS147T tumour and hypothesise that uniform normalisation is required to lower tumour interstitial fluid pressure.Our computational framework permits predictions of whole tumour fluid dynamics which incorporate the inherent architectural heterogeneities appearing at the micron-scale, and outputs three-dimensional spatial maps detailing these flow properties from micro to macro length scales. This provides vital information on the tumour microenvironment which could enable the design and delivery of future anti-cancer therapies. Author summaryThe structure of tumours varies widely, with dense and chaotically-formed networks of blood vessels that differ between each individual tumour and even between different regions of the same tumour. This atypical environment can inhibit the delivery of anti-cancer therapies. Computational tools are urgently required which incorporate micron-scale tumour biomechanics to predict tissue-scale fluid dynamics, and consequently the efficacy of cancer therapies.We have developed a computational framework which integrates the complex tumour vascular architecture to predict fluid transport across all lengths scales in whole tumours. This enables computationally efficient hypothesis testing of cancer therapies which manipulate the tumour microenvironment in order to improve drug delivery to tumours. Introduction 1 Architectural heterogeneities in...

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confidence: 99%

Modelling the transport of fluid through heterogeneous, whole tumours in silico

Sweeney

d’Esposito

Walker‐Samuel

et al. 2019

Preprint

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“…The model parameters required to simulate drug transport from equations (2.4) -(2.6) were taken from relevant works in the literature (e.g. [12,29]). We note that the adopted model parameters for the Solid mechanics, Fluid mechanics and Biochemics compartments are summarized in the electronic supplementary material.…”

Section: Resultsmentioning

confidence: 99%

“…where K int and L int are the average hydraulic conductivity and the relative distance between two material points in the interstitium whose (interstitial fluid) pressure difference is denoted by Dp int , while A int denotes the cross-sectional area of the interstitium. Finally, biofluid transport across the endothelium of the capillaries is modelled using Starling's law [12], with the transvascular flow rate expressed by _…”

Section: Fluid Mechanics Model Compartment 231 Microvascular and Imentioning

confidence: 99%

A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours

Wijeratne

Vavourakis

2019

Interface Focus.

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One contribution of 15 to a theme issue 'Multiresolution simulations of intracellular processes'.The role of tumour-host mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs have been extensively studied using in vitro and in vivo models. A complementary approach is offered by in silico models, which can also potentially identify the main factors affecting the transport of tumour-targeting molecules. Here, we present a generalized three-dimensional in silico modelling framework of dynamic solid tumour growth, angiogenesis and drug delivery. Crucially, the model allows for drug properties-such as size and binding affinity-to be explicitly defined, hence facilitating investigation into the interaction between the changing tumour-host microenvironment and cytotoxic and nanoparticle drugs. We use the model to qualitatively recapitulate experimental evidence of delivery efficacy of cytotoxic and nanoparticle drugs on matrix density (and hence porosity). Furthermore, we predict a highly heterogeneous distribution of nanoparticles after delivery; that nanoparticles require a high porosity extracellular matrix to cause tumour regression; and that post-injection transvascular fluid velocity depends on matrix porosity, and implicitly on the size of the drug used to treat the tumour. These results highlight the utility of predictive in silico modelling in better understanding the factors governing efficient cytotoxic and nanoparticle drug delivery.

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“…In [18], Kim et al advocate integrating mechanistic models with clinical data to improve understanding and prediction. In addition, quite sophisticated transport models have been developed for penetration into a solid tumor [19,20,21]. However, it is evident that drug distribution from an internal injection has not been investigated mathematically.…”

Section: Introductionmentioning

confidence: 99%

Modeling of drug diffusion in a solid tumor leading to tumor cell death

2018

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It has been shown recently that changing the fluidic properties of a drug can improve its efficacy in ablating solid tumors. We develop a modeling framework for tumor ablation, and present the simplest possible model for drug diffusion in a spherical tumor with leaky boundaries and assuming cell death eventually leads to ablation of that cell effectively making the two quantities numerically equivalent. The death of a cell after a given exposure time depends on both the concentration of the drug and the amount of oxygen available to the cell. Higher oxygen availability leads to cell death at lower drug concentrations. It can be assumed that a minimum concentration is required for a cell to die, effectively connecting diffusion with efficacy. The concentration threshold decreases as exposure time increases, which allows us to compute dose-response curves. Furthermore, these curves can be plotted at much finer time intervals compared to that of experiments, which is used to produce a dose-threshold-response surface giving an observer a complete picture of the drug's efficacy for an individual. In addition, since the diffusion, leak coefficients, and the availability of oxygen is different for different individuals and tumors, we produce artificial replication data through bootstrapping to simulate error. While the usual data-driven model with Sigmoidal curves use 12 free parameters, our mechanistic model only has two free parameters, allowing it to be open to scrutiny rather than forcing agreement with data. Even so, the simplest model in our framework, derived here, shows close agreement with the bootstrapped curves, and reproduces well established relations, such as Haber's rule.

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Numerical modeling of drug delivery in a dynamic solid tumor microvasculature

Cited by 137 publications

References 38 publications

Modelling the transport of fluid through heterogeneous, whole tumours in silico

Modelling the transport of fluid through heterogeneous, whole tumours in silico

A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours

Modeling of drug diffusion in a solid tumor leading to tumor cell death

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