Effect of tumor shape, size, and tissue transport properties on drug delivery to solid tumors

Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells' preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Hydraulic conductivity of human cancer tissue: A hybrid study

Background

Elevated tumor tissue interstitial fluid pressure (IFP) is an adverse biomechanical biomarker that predicts poor therapy response and an aggressive phenotype. Advances in functional imaging have opened the prospect of measuring IFP non-invasively. Image-based estimation of the IFP requires knowledge of the tissue hydraulic conductivity (K), a measure for the ease of bulk flow through the interstitium. However, data on the magnitude of K in human cancer tissue are not available.

Methods

We measured the hydraulic conductivity of tumor tissue using modified Ussing chambers in surgical resection specimens. The effect of the tumor microenvironment (TME) on K was investigated by quantifying the collagen content, cell density, and fibroblast density of the tested samples using quantitative immune histochemistry. Also, we developed a computational fluid dynamics (CFD) model to evaluate the role of K on interstitial fluid flow and drug transport in solid tumors.

Results

The results show that the hydraulic conductivity of human tumor tissues is very limited, ranging from approximately 10-15 to 10-14 m2/Pa∙s. Moreover, K values varied significantly between tumor types and between different samples from the same tumor. A significant inverse correlation was found between collagen fiber density and hydraulic conductivity values. However, no correlation was detected between K and cancer cell or fibroblast densities. The computational model demonstrated the impact of K on the interstitial fluid flow and the drug concentration profile: higher K values led to a lower IFP and deeper drug penetration.

Conclusions

Human tumor tissue is characterized by a very limited hydraulic conductivity, representing a barrier to effective drug transport. The results of this study can inform the development of realistic computational models, facilitate non-invasive IFP estimation, and contribute to stromal targeting anticancer therapies.

Spatiotemporal modeling of radiopharmaceutical transport in solid tumors: Application to 177Lu-PSMA therapy of prostate cancer

BACKGROUND AND OBJECTIVE

177Lu-labeled prostate-specific membrane antigen (PSMA) radiopharmaceutical therapy (RPT) represents a pivotal advancement in addressing prostate cancer. However, existing therapies, while promising, remain incompletely understood and optimized. Computational models offer potential insights into RPTs, aiding in clinical drug delivery enhancement. In this study, we investigate the impact of various physiological parameters on the delivery of 177Lu-PSMA-617 RPT using the convection-diffusion-reaction (CDR) model.

METHODS

Our investigation encompasses tumor geometry and surrounding tissue, characterized by well-defined boundaries and initial conditions. Utilizing the finite element method, we solve governing equations across a range of parameters: dissociation constant KD (1, 0.1, 0.01 [nM]), internalization rate (0.01-0.0001 [min-1]), diverse tumor shapes, and variable necrotic zone sizes. This model can provide an accurate analysis of radiopharmaceutical delivery from the injection site to the tumor cell, including drug transport in the vascular, interstitial, and intracellular spaces, and considering important parameters (e.g., drug extravasation from microvessels or to lymphatic vessels, the extracellular matrix, receptors, and intracellular space).

RESULTS

Our findings reveal significant enhancements in tumor-absorbed doses as KD decreases. This outcome can be attributed to the higher affinity of radiopharmaceuticals for PSMA receptors as KD diminishes, facilitating a more efficient binding and retention of the therapeutic agent within the tumor microenvironment. Additionally, tumor-absorbed doses for KD ∼ 1 [nM] show an upward trend with higher internalization rates. This observation can be rationalized by considering that a greater internalization rate would result in a higher proportion of radiopharmaceuticals being taken up by tumor cells after binding to receptors on the cell surface. Notably, tumor shape and necrotic zone size exhibit limited influence on tumor absorbed dose.

CONCLUSIONS

The present study employs the CDR model to explore the role of physiological parameters in shaping 177Lu-PSMA-617 RPT delivery. These findings provide insights for improving prostate cancer therapy by understanding radiopharmaceutical transport dynamics. This computational approach contributes to advancing our understanding of radiopharmaceutical delivery mechanisms and has implications for enhancing treatment efficacy.

Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth

Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor growth, and spatial heterogeneity of tumor and host tissue. This work introduces a Bayesian framework to calibrate the two-/three-dimensional spatial distribution of the parameters within a tumor growth model to quantitative magnetic resonance imaging (MRI) data and demonstrates its implementation in a pre-clinical model of glioma. The framework leverages an atlas-based brain segmentation of grey and white matter to establish subject-specific priors and tunable spatial dependencies of the model parameters in each region. Using this framework, the tumor-specific parameters are calibrated from quantitative MRI measurements early in the course of tumor development in four rats and used to predict the spatial development of the tumor at later times. The results suggest that the tumor model, calibrated by animal-specific imaging data at one time point, can accurately predict tumor shapes with a Dice coefficient 0.89. However, the reliability of the predicted volume and shape of tumors strongly relies on the number of earlier imaging time points used for calibrating the model. This study demonstrates, for the first time, the ability to determine the uncertainty in the inferred tissue heterogeneity and the model-predicted tumor shape.

Drug transport modeling in solid tumors: A computational exploration of spatial heterogeneity of biophysical properties

Inadequate uptake of therapeutic agents by tumor cells is still a major barrier in clinical cancer therapy. Mathematical modeling is a powerful tool to describe and investigate the transport phenomena involved. However, current models for interstitial flow and drug delivery in solid tumors have not yet embedded the existing heterogeneity of tumor biomechanical properties. The purpose of this study is to introduce a novel and more realistic methodology for computational models of solid tumor perfusion and drug delivery accounting for these regional heterogeneities as well as lymphatic drainage effects. Several tumor geometries were studied using an advanced computational fluid dynamics (CFD) modeling approach of intratumor interstitial fluid flow and drug transport. Hereby, the following novelties were implemented: (i) the heterogeneity of tumor-specific hydraulic conductivity and capillary permeability; (ii) the effect of lymphatic drainage on interstitial fluid flow and drug penetration. Tumor size and shape both have a crucial role on the interstitial fluid flow regime as well as drug transport illustrating a direct correlation with interstitial fluid pressure (IFP) and an inverse correlation with drug penetration, except for large tumors having a diameter larger than 50 mm. The results also suggest that the interstitial fluid flow and drug penetration in small tumors depend on tumor shape. A parameter study on the necrotic core size illustrated that the core effect (i.e. fluid flow and drug penetration alteration) was only profound in small tumors. Interestingly, the impact of a necrotic core on drug penetration differs depending on the tumor shape from having no effect in ideally spherical tumors to a clear effect in elliptical tumors with a necrotic core. A realistic presence of lymphatic vessels only slightly affected tumor perfusion, having no substantial effect on drug delivery. In conclusion, our findings illustrated that our novel parametric CFD modeling strategy in combination with accurate profiling of heterogeneous tumor biophysical properties can provide a powerful tool for better insights into tumor perfusion and drug transport, enabling effective therapy planning.

Challenges and Opportunities Associated With Drug Delivery for the Treatment of Solid Tumors

In this review, we discuss the effectiveness of drug delivery system based on metal nanoparticles, and also, describe the problems associated with their delivery to tumor cells. Throughout recent years, more reports have appeared in the literature that demonstrate promising results for the treatment of various types of cancer using metal-based nanoparticles. Due to their unique physical and chemical properties, metal nanoparticles are effectively being used for the delivery of drug to the tumor cells, for cancer diagnosis and treatment. They can also be synthesized allowing the control of size and shape. However, the effectiveness of the metal nanoparticles for cancer treatment largely depends on their stability, biocompatibility, and ability to selectively affect tumor cells after their systemic or local administration. Another major problem associated with metal nanoparticles is their ability to overcome tumor tissue barriers such as atypical blood vessel structure, dense and rigid extracellular matrix, and high pressure of tumor interstitial fluid. The review also describes the design of tumor drug delivery systems that are based on metal nanoparticles. The mechanism of action of metal nanoparticles on cancer cells is also discussed. Considering the therapeutic safety and toxicity of metal nanoparticles, the prospects for their use for future clinical applications are being currently reviewed.

3D modeling of vector/edge finite element method for multi-ablation technique for large tumor-computational approach

Microwave ablation (MWA) is a cancer thermal ablation treatment that uses electromagnetic waves to generate heat within the tissue. The goal of this treatment is to eliminate tumor cells while leaving healthy cells unharmed. During MWA, excess heat generation can kill healthy cells. Hence, mathematical models and numerical techniques are required to analyze the heat distribution in the tissue before the treatment. The aim of this research is to explain the implementation of the 3D vector finite element method in a wave propagation model that simulates the specific absorption rate in the liver. The 3D Nedelec elements from H(curl; Ω) space are used to discretize the wave propagation model, and this implementation is helpful in solving many real-world problems that involve electromagnetic propagation with perfect conducting and absorbing boundary conditions. One of the difficulties in ablation treatment is creating a large ablation zone for a large tumor (diameter greater than 3 cm) in a short period of time with minimum damage to the surrounding tissue. This article addresses the aforementioned issue by introducing four antennas into the different places of the tumor sequentially and producing heat uniformly over the tumor. The results demonstrated that 95.5% of the tumor cells were killed with minimal damage to the healthy cells when the heating time was increased to 4 minutes at each position. Subsequently, we studied the temperature distribution and localised tissue contraction in the tissue using the three-dimensional bio-heat equation and temperature-time dependent model, respectively. The local tissue contraction is measured at arbitrary points in the domain and is more noticeable at temperatures higher than 102°C. The thermal damage in the liver during MWA treatment is investigated using the three-state cell death model. The system of partial differential equations is solved numerically due to the complex geometry of the domain, and the results are compared with experimental data to validate the models and parameters.

Multi-dimensional modeling of nanoparticles transportation from capillary bed into the tumor microenvironment

In this study, we examined the extravasation of pharmaceutical inorganic nanoparticles (NPs) with a new approach from the leaky endothelium of tumor microvasculature (TMV) into the tumor microenvironment (TME) multi-dimensionally. We proposed a combination of prevailing macroscopic and microscopic methods and addressed the effect of interstitial fluid (IF) retention in solid tumor as an imperative parameter in drug delivery modeling. The Navier-Stokes equations and Darcy's law were utilized for blood flow and porous media, and the Starling's law was brought in for coupling effect. The blood flow was simulated as a non-Newtonian fluid alongside the Newtonian IF. We applied the Galerkin finite element method for the simulations. Our parametric study includes examining the effect of IF retention and TMV pressure on the distribution of tumor interstitial fluid pressure (TIFP), NPs concentration, and diameter on the penetration process, together with the time effect, on two-dimensional (2D) delivery of NPs. Our findings indicate that the IF retention in tumor cells increases TIFP depending on the amount of TMV pressure and IF retained. In addition to doubling pressure in the tumor necrotic region rather than the rest of TME, it enhances the TIFP which is an important parameter in drug delivery to solid tumors. By decreasing pressure drop within the TMV, pressure distribution within the TME becomes more uniform, creating a better condition for homogeneous penetration of NPs. Increasing both inlet pressure and NPs concentration leads to a nonlinear increase in the average concentration of tumor. Decreasing the diameter of NPs increases the penetration of NPs with a higher ratio in the TME.

Interstitial fluid pressure as an emerging biomarker in solid tumors

The physical microenvironment of cancer is characterized by elevated stiffness and tissue pressure, the main component of which is the interstitial fluid pressure (IFP). Elevated IFP is an established negative predictive and prognostic parameter, directly affecting malignant behavior and therapy response. As such, measurement of the IFP would allow to develop strategies aimed at engineering the physical microenvironment of cancer. Traditionally, IFP measurement required the use of invasive methods. Recent progress in dynamic and functional imaging methods such as dynamic contrast enhanced (DCE) magnetic resonance imaging and elastography, combined with numerical models and simulation, allows to comprehensively assess the biomechanical landscape of cancer, and may help to overcome physical barriers to drug delivery and immune cell infiltration. Here, we provide a comprehensive overview of the origin of elevated IFP, and its role in the malignant phenotype. Also, we review the methods used to measure IFP using invasive and imaging based methods, and highlight remaining obstacles and potential areas of progress in order to implement IFP measurement in clinical practice.

Development of a Hybrid-Imaging-Based Prognostic Index for Metastasized-Melanoma Patients in Whole-Body 18F-FDG PET/CT and PET/MRI Data

Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.

Convection-Enhanced Delivery of Antiangiogenic Drugs and Liposomal Cytotoxic Drugs to Heterogeneous Brain Tumor for Combination Therapy

Simple Summary

Although developed anticancer drugs have shown desirable effects in preclinical trials, the clinical efficacy of chemotherapy against brain cancer remains disappointing. One of the important obstacles is the highly heterogeneous environment in tumors. This study aims to evaluate the performance of an emerging treatment using antiangiogenic and cytotoxic drugs. Our mathematical modelling confirms the advantage of this combination therapy in homogenizing the intratumoral environment for better drug delivery outcomes. In addition, the effects of local microvasculature and cell density on this therapy are also discussed. The results would contribute to the development of more effective treatments for brain cancer.

Abstract

Although convection-enhanced delivery can successfully bypass the blood-brain barrier, its clinical performance remains disappointing. This is primarily attributed to the heterogeneous intratumoral environment, particularly the tumor microvasculature. This study investigates the combined convection-enhanced delivery of antiangiogenic drugs and liposomal cytotoxic drugs in a heterogeneous brain tumor environment using a transport-based mathematical model. The patient-specific 3D brain tumor geometry and the tumor’s heterogeneous tissue properties, including microvascular density, porosity and cell density, are extracted from dynamic contrast-enhanced magnetic resonance imaging data. Results show that antiangiogenic drugs can effectively reduce the tumor microvascular density. This change in tissue structure would inhibit the fluid loss from the blood to prevent drug concentration from dilution, and also reduce the drug loss by blood drainage. The comparisons between different dosing regimens demonstrate that the co-infusion of liposomal cytotoxic drugs and antiangiogenic drugs has the advantages of homogenizing drug distribution, increasing drug accumulation, and enlarging the volume where tumor cells can be effectively killed. The delivery outcomes are susceptible to the location of the infusion site. This combination treatment can be improved by infusing drugs at higher microvascular density sites. In contrast, infusion at a site with high cell density would lower the treatment effectiveness of the whole brain tumor. Results obtained from this study can deepen the understanding of this combination therapy and provide a reference for treatment design and optimization that can further improve survival and patient quality of life.

Modelling of Nanoparticle Distribution in a Spherical Tumour during and Following Local Injection

Radio-sensitizing nanoparticles are a potential method to increase the damage caused to cancerous cells during the course of radiotherapy. The distribution of these particles in a given targeted tumour is a relevant factor in determining the efficacy of nanoparticle-enhanced treatment. In this study, a three-part mathematical model is shown to predict the distribution of nanoparticles after direct injection into a tumour. In contrast with previous studies, here, a higher value of diffusivity for charged particles was used and the concentration profile of deposited particles was studied. Simulation results for particle concentrations both in the interstitial fluid and deposited onto cells are compared for different values of particle surface charges during and after injection. Our results show that particles with a negative surface charge can spread farther from the injection location as compared to uncharged particles with charged particles occupying 100% of the tumour volume compared to 8.8% for uncharged particles. This has implications for the future development of radiosensitizers and any associated trials.

An Updated Review on EPR-Based Solid Tumor Targeting Nanocarriers for Cancer Treatment

Simple Summary

One of the important efforts in the treatment of cancers is to achieve targeted drug delivery by nanocarriers to be more effective and reduce adverse effects. However, due to the adverse responses of nanocarriers in clinical trials due to the very weak EPR effects, doubts have been raised in this regard. In this study, an attempt has been made to take a critical look at EPR approaches to enable the convergence of previous papers and the EPR critics to reach an appropriate therapeutic path. Although the effectiveness of EPR is highly variable due to the complex microenvironment of the tumor, there is high hope for cancer treatment by describing new strategies to overcome the challenges of EPR effect. Furthermore, in this paper an attempt was made to provide a reliable path for future to develop cancer therapeutics based on EPR effect.

Abstract

The enhanced permeability and retention (EPR) effect in cancer treatment is one of the key mechanisms that enables drug accumulation at the tumor site. However, despite a plethora of virus/inorganic/organic-based nanocarriers designed to rely on the EPR effect to effectively target tumors, most have failed in the clinic. It seems that the non-compliance of research activities with clinical trials, goals unrelated to the EPR effect, and lack of awareness of the impact of solid tumor structure and interactions on the performance of drug nanocarriers have intensified this dissatisfaction. As such, the asymmetric growth and structural complexity of solid tumors, physicochemical properties of drug nanocarriers, EPR analytical combination tools, and EPR description goals should be considered to improve EPR-based cancer therapeutics. This review provides valuable insights into the limitations of the EPR effect in therapeutic efficacy and reports crucial perspectives on how the EPR effect can be modulated to improve the therapeutic effects of nanomedicine.

Euler-Lagrange numerical simulation of improved magnetic drug delivery in a three-dimensional CT-based carotid artery bifurcation

BACKGROUND AND OBJECTIVE

Magnetic drug targeting (MDT) is a promising method to improve the therapy efficiency for cardiovascular diseases (CVDs) and cancers. In MDT, therapeutic agents are bonded to superparamagnetic iron oxide nanoparticle (SPION) cores and then are guided toward the damaged tissue through a magnetic field. Fundamentally, it's vital to steer the SPIONs to the desired location to increase the capture efficiency at the target lesion. Hence, the present study aims to enhance the drug delivery to the desired branch in a carotid bifurcation. Besides, it is tried to decrement the particles' entry to the unwanted outlet by using four different magnet configurations (with a maximum magnetic flux density of 0.4 T) implanted adjacent to the artery wall. Also, the effect of particles' diameter -ranging from 200 nm to 2 µm- on the drug delivery performance is studied in the four cases.

METHODS

The Eulerian-Lagrangian approach with one-way coupling is employed for numerical simulation of the problem using the finite element method (FEM). The dominant forces acting on particles are drag and magnetophoretic. A computed tomography (CT) model of the carotid bifurcation is adopted to have a 3D realistic geometry. The blood flow is considered to be laminar, incompressible, pulsatile, and non-Newtonian. Boundary conditions are applied using the three-element Windkessel equation.

RESULTS

Results are presented in terms of velocity, pressure, magnetic field flux density, wall shear stress, and streamlines. Also, the number of particles in each direction is presented for the four studied cases. The results show that using proper magnets configurations makes it possible to guide more particles to the desired branch (up to 4%) while preventing particles from entering the unwanted branch (up to 13%). By defining connectivity between oscillatory shear index (OSI) value and magnetic drug delivery efficacy, it becomes clear that places with lower OSI values are more proper to place the magnets than areas with higher OSI values.

CONCLUSIONS

It was observed that increasing the diameter of particles does not necessarily result in a higher drug delivery efficiency. The configuration of the magnets and the size of particles are the main affecting parameters that should be chosen precisely to meet the most efficient drug delivery performance. Magnetic drug targeting (MDT) is a promising method to improve the therapy efficiency for cardiovascular diseases (CVDs) and cancers. Fundamentally, it's vital to steer the superparamagnetic iron oxide nanoparticles (SPIONs) to the target lesion location to increase the capture efficiency. Hence, the present study aims to enhance the drug delivery to the desired branch in a 3D carotid bifurcation. Besides, it is tried to decrement the particles' entry to the unwanted outlet by using four different magnet configurations implanted adjacent to the artery wall. The Eulerian-Lagrangian approach with one-way coupling is employed for numerical simulation of the problem using the finite element method (FEM). The dominant forces acting on particles are drag and magnetophoretic. The blood flow is laminar, incompressible, pulsatile, and non-Newtonian. The results show that it is possible to guide more particles to the desired branch (up to 4%) while preventing particles from entering the unwanted branch (up to 13%). By defining connectivity between oscillatory shear index (OSI) value and magnetic drug delivery efficacy, it becomes clear that places with lower OSI values are more proper to place the magnets than areas with higher OSI values.

Drug delivery through nanoparticles in solid tumors: a mechanistic understanding

Aim: In this study, the main goal was to apply a multi-scale computational model in evaluating nano-sized drug-delivery systems, following extracellular drug release, into solid tumors in order to predict treatment efficacy. Methods: The impact of several parameters related to tumor (size, shape, vessel-wall pore size, and necrotic core size) and therapeutic agents (size of nanoparticles, binding affinity of drug, drug release rate from nanoparticles) are examined in detail. Results: This study illustrates that achieving a higher treatment efficacy requires smaller nanoparticles (NPs) or a low binding affinity and drug release rate. Long-term analysis finds that a slow release rate in extracellular space does not always improve treatment efficacy compared with a rapid release rate; NP size as well as binding affinity of drug are also highly influential. Conclusions: The presented methodology can be used as a step forward towards optimization of patient-specific nanomedicine plans.

Numerical Investigation on the Anti-Angiogenic Therapy-Induced Normalization in Solid Tumors

This study numerically analyzes the fluid flow and solute transport in a solid tumor to comprehensively examine the consequence of normalization induced by anti-angiogenic therapy on drug delivery. The current study leads to a more accurate model in comparison to previous research, as it incorporates a non-homogeneous real-human solid tumor including necrotic, semi-necrotic, and well-vascularized regions. Additionally, the model considers the effects of concurrently chemotherapeutic agents (three macromolecules of IgG, F(ab′)2, and F(ab′)) and different normalization intensities in various tumor sizes. Examining the long-term influence of normalization on the quality of drug uptake by necrotic area is another contribution of the present study. Results show that normalization decreases the interstitial fluid pressure (IFP) and spreads the pressure gradient and non-zero interstitial fluid velocity (IFV) into inner areas. Subsequently, wash-out of the drug from the tumor periphery is decreased. It is also demonstrated that normalization can improve the distribution of solute concentration in the interstitium. The efficiency of normalization is introduced as a function of the time course of perfusion, which depends on the tumor size, drug type, as well as normalization intensity, and consequently on the dominant mechanism of drug delivery. It is suggested to accompany anti-angiogenic therapy by F(ab′) in large tumor size (Req=2.79  cm) to improve reservoir behavior benefit from normalization. However, IgG is proposed as the better option in the small tumor (Req=0.46  cm), in which normalization finds the opportunity of enhancing uniformity of IgG average exposure by 22%. This study could provide a perspective for preclinical and clinical trials on how to take advantage of normalization, as an adjuvant treatment, in improving drug delivery into a non-homogeneous solid tumor.

Computational Modeling of Combination of Magnetic Hyperthermia and Temperature-Sensitive Liposome for Controlled Drug Release in Solid Tumor

Combination therapy, a treatment modality that combines two or more therapeutic methods, provides a novel pathway for cancer treatment, as it targets the region of interest (ROI) in a characteristically synergistic or additive manner. To date, liposomes are the only nano-drug delivery platforms that have been used in clinical trials. Here, we speculated that it could be promising to improve treatment efficacy and reduce side effects by intravenous administration of thermo-sensitive liposomes loaded with doxorubicin (TSL-Dox) during magnetic hyperthermia (MHT). A multi-scale computational model using the finite element method was developed to simulate both MHT and temperature-sensitive liposome (TSL) delivery to a solid tumor to obtain spatial drug concentration maps and temperature profiles. The results showed that the killing rate of MHT alone was about 15%, which increased to 50% using the suggested combination therapy. The results also revealed that this combination treatment increased the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy by 15% in addition to reducing side effects. Furthermore, the impacts of vessel wall pore size, the time interval between TSL delivery and MHT, and the initial dose of TSLs were also investigated. A considerable reduction in drug accumulation was observed in the tumor by decreasing the vessel wall pore size of the tumor. The results also revealed that the treatment procedure plays an essential role in the therapeutic potential of anti-cancer drugs. The results suggest that the administration of MHT can be beneficial in the TSL delivery system and that it can be employed as a guideline for upcoming preclinical studies.

A numerical study of the distribution of chemotherapeutic drug carmustine in brain glioblastoma

To cure the illness in the brain glioblastoma, the Gliadel wafer, as the first FDA-approved chemotherapy, was available on the market since 1997. Due to the complex studies in vivo, more and more researchers have paid their attention to investigate the dynamic process in the brain by numerical methods. This study aimed to simulate the drug concentration in the cavity after drug releases from Gliadel wafers into the brain tumor by a two-dimensional simulation. The government equations, the parameters, and corresponding initial and boundary conditions are specified. Then the models are discretized and solved by finite element method (FEM) and finite difference method (FDM) based on C++ library Adaptive Finite Element Package (AFEPack) and MATLAB, respectively. First of all, the numerical convergence of the method is studied by numerical results represented in several successively refined meshes, which shows the reliability of our method. In the results from FEM, a steady state of the pressure in the normal tissue can be simulated. As for FDM, the changes of drug concentration are displayed at six different times. The numerical method in this paper is an effective tool for the numerical study on drug release from polymers. Additionally, convection is a critical factor in drug transportation. Moreover, the simulation approach can be used as the guild for remedy optimization and dynamic analysis of other drugs (paclitaxel) for tumor treatment in the clinic. This mathematical model has wide applications about drug release in multiple dosage forms, such as long sustained release microspheres, oral extended release hydrophilic matrix tablets, hydrogel, and sustained release topical rings.

Impact of axisymmetric deformation on MR elastography of a nonlinear tissue-mimicking material and implications in peri-tumour stiffness quantification

Solid tumour growth is often associated with the accumulation of mechanical stresses acting on the surrounding host tissue. Due to tissue nonlinearity, the shear modulus of the peri-tumoural region inherits a signature from the tumour expansion which depends on multiple factors, including the soft tissue constitutive behaviour and its stress/strain state. Shear waves used in MR-elastography (MRE) sense the apparent change in shear modulus along their propagation direction, thereby probing the anisotropic stiffness field around the tumour. We developed an analytical framework for a heterogeneous shear modulus distribution using a thick-shelled sphere approximation of the tumour and soft tissue ensemble. A hyperelastic material (plastisol) was identified to validate the proposed theory in a phantom setting. A balloon-catheter connected to a pressure sensor was used to replicate the stress generated from tumour pressure and growth while MRE data were acquired. The shear modulus anisotropy retrieved from the reconstructed elastography data confirmed the analytically predicted patterns at various levels of inflation. An alternative measure, combining the generated deformation and the local wave direction and independent of the reconstruction strategy, was also proposed to correlate the analytical findings with the stretch probed by the waves. Overall, this work demonstrates that MRE in combination with non-linear mechanics, is able to identify the apparent shear modulus variation arising from the strain generated by a growth within tissue, such as an idealised model of tumour. Investigation in real tissue represents the next step to further investigate the implications of endogenous forces in tissue characterisation through MRE.

Enhanced Drug Delivery to Solid Tumors via Drug-Loaded Nanocarriers: An Image-Based Computational Framework

Objective

Nano-sized drug delivery systems (NSDDSs) offer a promising therapeutic technology with sufficient biocompatibility, stability, and drug-loading rates towards efficient drug delivery to solid tumors. We aim to apply a multi-scale computational model for evaluating drug delivery to predict treatment efficacy.

Methodology

Three strategies for drug delivery, namely conventional chemotherapy (one-stage), as well as chemotherapy through two- and three-stage NSDDSs, were simulated and compared. A geometric model of the tumor and the capillary network was obtained by processing a real image. Subsequently, equations related to intravascular and interstitial flows as well as drug transport in tissue were solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. Finally, the role of periodic treatments was investigated considering tumor recurrence between treatments. The impact of different parameters, nanoparticle (NP) size, binding affinity of drug, and the kinetics of release rate, were additionally investigated to determine their therapeutic efficacy.

Results

Using NPs considerably increases the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy. Tumoral FKCs for two-stage DDS with smaller NP size (20nm) is higher than that of larger NPs (100nm), in all investigate release rates. Slower and continuous release of the chemotherapeutic agents from NPs have better treatment outcomes in comparison with faster release rate. In three-stage DDS, for intermediate and higher binding affinities, it is desirable for the secondary particle to be released at a faster rate, and the drug with slower rate. In lower binding affinities, high release rates have better performance. Results also demonstrate that after 5 treatments with three-stage DDS, 99.6% of tumor cells (TCs) are killed, while two-stage DDS and conventional chemotherapy kill 95.6% and 88.5% of tumor cells in the same period, respectively.

Conclusion

The presented framework has the potential to enable decision making for new drugs via computational modeling of treatment responses and has the potential to aid oncologists with personalized treatment plans towards more optimal treatment outcomes.

A Microfluidic Multisize Spheroid Array for Multiparametric Screening of Anticancer Drugs and Blood-Brain Barrier Transport Properties

Physiological-relevant in vitro tissue models with their promise of better predictability have the potential to improve drug screening outcomes in preclinical studies. Despite the advances of spheroid models in pharmaceutical screening applications, variations in spheroid size and consequential altered cell responses often lead to nonreproducible and unpredictable results. Here, a microfluidic multisize spheroid array is established and characterized using liver, lung, colon, and skin cells as well as a triple-culture model of the blood-brain barrier (BBB) to assess the effects of spheroid size on (a) anticancer drug toxicity and (b) compound penetration across an advanced BBB model. The reproducible on-chip generation of 360 spheroids of five dimensions on a well-plate format using an integrated microlens technology is demonstrated. While spheroid size-related IC50 values vary up to 160% using the anticancer drugs cisplatin (CIS) or doxorubicin (DOX), reduced CIS:DOX drug dose combinations eliminate all lung microtumors independent of their sizes. A further application includes optimizing cell seeding ratios and size-dependent compound uptake studies in a perfused BBB model. Generally, smaller BBB-spheroids reveal an 80% higher compound penetration than larger spheroids while verifying the BBB opening effect of mannitol and a spheroid size-related modulation on paracellular transport properties.

Multiscale modeling of tumor growth and angiogenesis: Evaluation of tumor-targeted therapy

The dynamics of tumor growth and associated events cover multiple time and spatial scales, generally including extracellular, cellular and intracellular modifications. The main goal of this study is to model the biological and physical behavior of tumor evolution in presence of normal healthy tissue, considering a variety of events involved in the process. These include hyper and hypoactivation of signaling pathways during tumor growth, vessels' growth, intratumoral vascularization and competition of cancer cells with healthy host tissue. The work addresses two distinctive phases in tumor development-the avascular and vascular phases-and in each stage two cases are considered-with and without normal healthy cells. The tumor growth rate increases considerably as closed vessel loops (anastomoses) form around the tumor cells resulting from tumor induced vascularization. When taking into account the host tissue around the tumor, the results show that competition between normal cells and cancer cells leads to the formation of a hypoxic tumor core within a relatively short period of time. Moreover, a dense intratumoral vascular network is formed throughout the entire lesion as a sign of a high malignancy grade, which is consistent with reported experimental data for several types of solid carcinomas. In comparison with other mathematical models of tumor development, in this work we introduce a multiscale simulation that models the cellular interactions and cell behavior as a consequence of the activation of oncogenes and deactivation of gene signaling pathways within each cell. Simulating a therapy that blocks relevant signaling pathways results in the prevention of further tumor growth and leads to an expressive decrease in its size (82% in the simulation).

Association of polymorphism of CYP3A4, ABCB1, ABCC2, ABCG2, NFKB1, POR, and PXR with the concentration of cyclosporin A in allogeneic haematopoietic stem cell transplantation recipients

Cyclosporin a (CsA) was characterized by a narrow therapeutic window and high interindividual pharmacokinetic variability. In this study, we aimed to identify the association of CYP3A4, ABCB1, ABCC2, ABCG2, NFKB1, POR, and PXR polymorphisms with CsA concentrations in patients with allogeneic haematopoietic cell transplantation (allo-HSCT) based on the route of administration.A total of 40 allo-HSCT recipients receiving CsA were genotyped for CYP3A4, ABCB1, ABCC2, ABCG2, NFKB1, POR, and PXR polymorphisms. The correlation between polymorphisms and CsA concentration was analysed.The CsA dose-adjusted trough concentration (Cssmin/D) of oral or intravenous administration was significantly different (p < 0.001). For CsA Cssmin/D of intravenous administration, CYP3A4 rs2246709 (p = 0.015), ABCC2 rs717620 (p = 0.024), ABCG2 rs2231142 (p = 0.042), PXR rs3732359 (p = 0.008), PXR rs3814058 (p = 0.028) and PXR rs6785049 (p < 0.001) had a significant effect on CsA Cssmin/D. For CsA Cssmin/D of oral administration, ABCC2 rs717620 (p = 0.009) and ABCG2 rs2231142 (p = 0.011) had a significant effect on CsA Cssmin/D.These results illustrated that the CYP3A4, ABCC2, ABCG2, and PXR genotypes were closely correlated with CsA Cssmin/D, suggesting these SNPs were suitable for determining the appropriate dose of CsA.

Tumor Solid Stress: Assessment with MR Elastography under Compression of Patient-Derived Hepatocellular Carcinomas and Cholangiocarcinomas Xenografted in Mice

Malignant tumors have abnormal biomechanical characteristics, including high viscoelasticity, solid stress, and interstitial fluid pressure. Magnetic resonance (MR) elastography is increasingly used to non-invasively assess tissue viscoelasticity. However, solid stress and interstitial fluid pressure measurements are performed with invasive methods. We studied the feasibility and potential role of MR elastography at basal state and under controlled compression in assessing altered biomechanical features of malignant liver tumors. MR elastography was performed in mice with patient-derived, subcutaneously xenografted hepatocellular carcinomas or cholangiocarcinomas to measure the basal viscoelasticity and the compression stiffening rate, which corresponds to the slope of elasticity versus applied compression. MR elastography measurements were correlated with invasive pressure measurements and digital histological readings. Significant differences in MR elastography parameters, pressure, and histological measurements were observed between tumor models. In multivariate analysis, collagen content and interstitial fluid pressure were determinants of basal viscoelasticity, whereas solid stress, in addition to collagen content, cellularity, and tumor type, was an independent determinant of compression stiffening rate. Compression stiffening rate had high AUC (0.87 ± 0.08) for determining elevated solid stress, whereas basal elasticity had high AUC for tumor collagen content (AUC: 0.86 ± 0.08). Our results suggest that MR elastography compression stiffening rate, in contrast to basal viscoelasticity, is a potential marker of solid stress in malignant liver tumors.

Modelling of combination therapy using implantable anticancer drug delivery with thermal ablation in solid tumor

Local implantable drug delivery system (IDDS) can be used as an effective adjunctive therapy for solid tumor following thermal ablation for destroying the residual cancer cells and preventing the tumor recurrence. In this paper, we develop comprehensive mathematical pharmacokinetic/pharmacodynamic (PK/PD) models for combination therapy using implantable drug delivery system following thermal ablation inside solid tumors with the help of molecular communication paradigm. In this model, doxorubicin (DOX)-loaded implant (act as a transmitter) is assumed to be inserted inside solid tumor (acts as a channel) after thermal ablation. Using this model, we can predict the extracellular and intracellular concentration of both free and bound drugs. Also, Impact of the anticancer drug on both cancer and normal cells is evaluated using a pharmacodynamic (PD) model that depends on both the spatiotemporal intracellular concentration as well as characteristics of anticancer drug and cells. Accuracy and validity of the proposed drug transport model is verified with published experimental data in the literature. The results show that this combination therapy results in high therapeutic efficacy with negligible toxicity effect on the normal tissue. The proposed model can help in optimize development of this combination treatment for solid tumors, particularly, the design parameters of the implant.

Numerical study of microwave induced thermoacoustic imaging for initial detection of cancer of breast on anatomically realistic breast phantom

BACKGROUND AND OBJECTIVE

Microwave-induced thermoacoustic imaging (MITAI) represents an innovative imaging approach for detection of breast cancer at initial phases by integrating the benefits provided by procedures of microwave and ultrasound imaging. The present investigation examines an innovative three-dimensional numerical modeling of MITAI as a problem with multi-physics nature.

METHODS

Simulations are performed by the use of COMSOL software. An anatomically realistic breast phantom representing various parts of a real breast, such as three different types of tissue, fibro-connective/glandular, transitional; and fatty, is taken into consideration along with a tumor. This breast phantom with a tumor is irradiated by a 2.45 GHz pulsed rectangular waveguide. The temperature increase and its consequent pressure caused by electromagnetic absorption are analyzed.

RESULTS

More temperature increase occurs in the tumor area than in the other parts of the breast, the fact which results in further increase in the pressure in the tumor area than other parts. This makes the tumor distinguishable. The ability of the MITAI process regarding the tumor size, shape (both geometrical shape and spatial orientation), location, the irradiation power level, and the pulse width is also investigated. It is demonstrated that tumor size does not have a significant impact on the efficiency of detection. In fact, very small tumors in the early stages of cancer development (with a radius of 0.25 cm) are also detectable by employing the MITAI technique. The geometrical shape of the tumor does not considerably affect the detecting performance just by itself. The spatial orientation of the tumor actually has a great impact on it. The location of the tumor is an essential factor involved in detection efficiency of MITAI. Tumors located in the fatty tissues would be much easier to be detected than those in the glandular tissues. Moreover, results denote that with augmentation of the irradiation power level or increasing the pulse width, stronger acoustic waves would be produced to make tumor detection easier.

CONCLUSION

These modeling and techniques may be applied aiming for determination of the amount of the generated pressure differences and acoustic pressure magnitude, and can be utilized as an effective prognosticator in practical tests.

Effect of setup error in the single-isocenter technique on stereotactic radiosurgery for multiple brain metastases

In conventional stereotactic radiosurgery (SRS), treatment of multiple brain metastases using multiple isocenters is time‐consuming resulting in long dose delivery times for patients. A single‐isocenter technique has been developed which enables the simultaneous irradiation of multiple targets at one isocenter. This technique requires accurate positioning of the patient to ensure optimal dose coverage. We evaluated the effect of six degrees of freedom (6DoF) setup errors in patient setups on SRS dose distributions for multiple brain metastases using a single‐isocenter technique. We used simulated spherical gross tumor volumes (GTVs) with diameters ranging from 1.0 to 3.0 cm. The distance from the isocenter to the target's center was varied from 0 to 15 cm. We created dose distributions so that each target was entirely covered by 100% of the prescribed dose. The target's position vectors were rotated from 0°–2.0° and translated from 0–1.0 mm with respect to the three axes in space. The reduction in dose coverage for the targets for each setup error was calculated and compared with zero setup error. The calculated margins for the GTV necessary to satisfy the tolerance values for loss of GTV coverage of 3% to 10% were defined as coverage‐based margins. In addition, the maximum isocenter to target distance for different 6DoF setup errors was calculated to satisfy the tolerance values. The dose coverage reduction and coverage‐based margins increased as the target diameter decreased, and the distance and 6DoF setup error increased. An increase in setup error when a single‐isocenter technique is used may increase the risk of missing the tumor; this risk increases with increasing distance from the isocenter and decreasing tumor size.

Stimuli-responsive and cellular targeted nanoplatforms for multimodal therapy of skin cancer

Interdisciplinary applications of nanopharmaceutical sciences have tremendous potential for enhancing pharmacokinetics, efficacy and safety of cancer therapy. The limitations of conventional therapeutic platforms used for skin cancer therapy have been largely overcome by the use of nanoplatforms. This review discusses various nanotechnological approaches experimented for the treatment of skin cancer. The review describes various polymeric, lipidic and inorganic nanoplatforms for efficient therapy of skin cancer. The stimuli-responsive nanoplatforms such as pH-responsive as well as temperature-responsive platforms have also been reviewed. Different strategies for potentiating the nanoparticles application for cancer therapy such as surface engineering, conjugation with drugs, stimulus-responsive and multimodal effect have also been discussed and compared with the available conventional treatments. Although, nanopharmaceuticals face challenges such as toxicity, cost and scale-up, efforts put-in to improve these drawbacks with continuous research would deliver exciting and promising results in coming days.

Numerical simulation of the effect of necrosis area in systemic delivery of magnetic nanoparticles in hyperthermia cancer treatment

In a magnetic hyperthermia treatment, malignant cancerous cells are ablated by the heat production of magnetic nanoparticles (MNP) under an external magnetic field. This novel approach is a promising tool to eliminate the tumor cells by a higher temperature inside the tumor microenvironment. MNPs are needed inside the tumor microenvironment to increase the heat, and this could be possible with intravenous drug injection. However, tumors with necrosis regions are more resistant to drug penetration, and this can cause inadequate and non-homogeneous temperature distribution in the tumor. Hence, in this study, we used numerical methods to investigate the Spatio-temporal temperature field distribution in the necrotic tumor and its surrounding tissue. To this end, an intravenous bolus injection is used to simulate the effect of systemic drug delivery in tumors with necrosis region. Results show that the temperature field with the necrosis region with 10% of the tumor radius is more prone to higher temperature values. The hypoxia region is affected by the high temperature despite the necrosis region in the tumor. However, a broader necrosis region impedes drug penetration inside the inner layers of tumors, which leads to a lower heat generation by the MNPs. Results also demonstrate that only 15.5% of MNP concentration distributed to the necrosis with 50% of tumor radius, leading a temperature of 42^∘C in the necrosis region, which is not sufficient for the tumor ablation. Therefore, the temperature distribution is dependant on the sizes of necrosis regions in tumors, and tumors with a larger necrotic region (over 20% of tumor radius) are challenging to treat with hyperthermia treatment. This study could help the future in vitro and in vivo studies of hyperthermia treatment in necrotic tumors.

Use of microwave ablation for thermal treatment of solid tumors with different shapes and sizes-A computational approach

Microwave Ablation (MWA) is one of the most recent developments in the field of thermal therapy. This approach is an effective method for thermal tumor ablation by increasing the temperature above the normal physiological threshold to kill cancer cells with minimum side effects to surrounding organs due to rapid heat dispersive tissues. In the present study, the effects of the shape and size of the tumor on MWA are investigated. To obtain the temperature gradient, coupled bio-heat and electromagnetic equations are solved using a three-dimensional finite element method (FEM). To extract cellular response at different temperatures and times, the three-state mathematical model was employed to achieve the ablation zone size. Results show that treatment of larger tumors is more difficult than that of smaller ones. By doubling the diameter of the tumor, the percentage of dead cancer cells is reduced by 20%. For a spherical tumor smaller than 2 cm, applying 50 W input power compared to 25 W has no significant effects on treatment efficiency and only increases the risk of damage to adjacent tissues. However, for tumors larger than 2 cm, it can increase the ablation zone up to 21%. In the spherical and oblate tumors, the mean percentage of dead cells at 6 GHz is nearly 30% higher than that at 2.45GHz, but for prolate tumors, treatment efficacy is reduced by 10% at a higher frequency. Moreover, the distance between two slots in the coaxial double slot antenna is modified based on the best treatment of prolate tumors. The findings of this study can be used to choose the optimum frequency and the best antenna design according to the shape and size of the tumor.

Drug delivery to solid tumors with heterogeneous microvascular networks: Novel insights from image-based numerical modeling

The present study examines chemotherapy by incorporating multi-scale mathematical modeling to predict drug delivery and its effects. This approach leads to a more-realistic physiological tumor model than is possible with previous approaches, as it obtains the capillary network geometry from an image, and also considers the tumor's necrotic core, drug binding, and cellular uptake. Modeling of the fluid flow and drug transport is then performed in the extracellular matrix. The results demonstrate a 10% drop in the fraction of killed cancer cells 69% rather than the 79% reported earlier for a tumor of similar geometry a more-accurate value. This study examines how tumor-related parameters including the necrotic core size and tumor size, and also drug-related parameters drug dosage, binding affinity of drug, and drug degradation can affect the delivery of the drug to solid tumors. Results indicate that concentration of drug are high in the tumor, low in normal tissue, and remarkably low in the necrotic core. Results also offer a treatment of tumors with smaller necrotic core. Tumor size, which implies the tumor progression, has a considerable impact on treatment outcomes, so to be more effective, treatment should be applied at a specific size of tumor. It is demonstrated that binding affinity of drugs to cell-surface receptors and drug dosage have significant impact on treatment efficacy, so they should be regulated based on a balanced quantification between maximum treatment efficacy and minimum side effects. On the other hand, considering the effects of drug degradation in the model has not significant effect on treatment efficacy. The findings of the present study provide insight into the mechanism of drug delivery to solid tumors based on analyzing the effective parameters and modeling how their behavior in the tumor microenvironment affects treatment efficacy.

Evaluation of inverse methods for estimation of mechanical parameters in solid tumors

To treat cancer, knowledge of mechanical parameters can be essential. This study proposes a new approach for estimating hydraulic conductivity (k) and hydraulic conductivity ratio (α) of a living tissue, based on inverse methods, allowing tissue parameter estimation using only a limited set of measurements. First, two population-based algorithms (Levenberg-Marquardt (LM) method and conjugate gradient (CG) method) and two gradient-based algorithms (genetic algorithm (GA) and particle swarm optimization (PSO) algorithm) are considered, and a comparative study between these different inverse methods is performed to determine which methods have a good performance in terms of convergence rate and stability. CG method is shown to perform well in the case of noise-free input data; however, in the case of noisy input data, it fails to converge. The other three methods (LM, GA, and PSO) converge with estimation errors <10% in both noise-free and noisy input data, suggesting their utility to tackle this problem. In the second part, the effectiveness and good accuracy of these robust algorithms (LM, GA, and PSO) are validated with experimental results. The hydraulic conductivity and hydraulic conductivity ratio of a specific living tumor tissue are then estimated for mammary adenocarcinoma (R3230AC). Moreover, assuming measurement of only one-point interstitial pressure inside the tumor, the effect of the location of this one-point on estimation accuracy of hydraulic conductivity is investigated. We show that estimation errors for points measured near the surface and center of the tumor are greater than at other points.

Spatio-temporal investigation of doxorubicin in a 3D heterogeneous tumor microenvironment

Doxorubicin (Adriamycin) is a type of chemotherapy drugs using to treat diseases such as breast cancer, bladder cancer, Kaposi's sarcoma, and lymphoma. Additionally, it can be first prescribed to reduce tumor size. The ratio of killed cells is varied depending on the clinical dosage regimen. Hence, the exact dosage of the drug must be administered to prevent the toxicity that could impair the immune system or leading to heart failure. In the present study, a 3D heterogeneous geometry with a solid tumor and healthy tissue is modeled for the drug delivery investigation. At the first stage, the physical properties of tumor microenvironment are obtained. Then, a five-compartmental model is used to evaluate the free, bound and internalized drug via the convection-diffusion-reaction (CDR) equation. Results are shown that a percent increase of 37.5% and 47.1% for the 75 mg m^-2 to 50 mg m^-2 in the AUC of bound drug and free drug concentration, respectively. The free and bound drugs have the same trend in time showing an apex at the earliest time of injection and then drops to the lowest amount about 9 hours after treatment. Moreover, the internalized drug has a different trend in time. It increases and reaches a constant amount of drug concentration in the cells. Besides, the fraction of surviving cells is also evaluated for both tumor and healthy tissues showing a 88.62% and 97.76% of surviving cells with 50 mg m^-2 of doxorubicin after the treatment, respectively. This model is developed to predict the heterogenous distribution of doxorubicin in three different drug concentrations for patient-specific drug treatment. This model could be used for different drugs to show the rate of perfusion and the ability to kill cancerous cells regarding their different doses and toxicity effects.

Estimation of drug and tumor properties using novel hybrid meta-heuristic methods

One of the major drawbacks in mathematical modeling of the drug delivery in living species is application of a common value for a specific property such as diffusion coefficient of drug in tissue, while this property is unique for each person or species. Therefore, knowledge on the species-specific values of these properties can improve the process of drug delivery and treatment. Inverse problem methods can achieve these unique properties for each specimen. Estimation of the individual-specific drug and tumor parameters requires the evaluation of the drug concentration (the concentration of medical images) within the tumor tissue. Accordingly, in this paper, first, the drug transport equation in tumor is determined. Then, the sensitivity analysis is conducted to determine the appropriate area for selecting the drug concentration to estimate the drug and tumor parameters. Finally, the parameters estimated by meta-heuristic and hybrid meta-heuristic methods are compared. To enhance the validity of the methods, two different drug transport models are examined. The results indicate that the hybrid methods gave rise to more precise estimations, especially the hybrid particle swarm optimization (PSO) method with whale optimization algorithm (WOA) which offer more appropriate responses in the parameters estimation of two models.

Effect of tissue permeability and drug diffusion anisotropy on convection-enhanced delivery

Although convection-enhanced delivery (CED) can successfully facilitate a bypass of the blood brain barrier, its treatment efficacy remains highly limited in clinic. This can be partially attributed to the brain anisotropic characteristics that lead to the difficulties in controlling the drug spatial distribution. Here, the responses of six different drugs to the tissue anisotropy are examined through a parametric study performed using a multiphysics model, which considers interstitial fluid flow, tissue deformation and interlinked drug transport processes in CED. The delivery outcomes are evaluated in terms of the penetration depth and delivery volume for effective therapy. Simulation results demonstrate that the effective penetration depth in a given direction can be improved with the increase of the corresponding component of anisotropic characteristics. The anisotropic tissue permeability could only reshape the drug distribution in space but has limited contribution to the total effective delivery volume. On the other hand, drugs respond in different ways to the anisotropic diffusivity. The large delivery volumes of fluorouracil, carmustine, cisplatin and doxorubicin could be achieved in relatively isotropic tissue, while paclitaxel and methotrexate are able to cover enlarged regions into anisotropic tissues. Results obtained from this study serve as a guide for the design of CED treatments.

Numerical modeling of high-intensity focused ultrasound-mediated intraperitoneal delivery of thermosensitive liposomal doxorubicin for cancer chemotherapy

Although intraperitoneal chemotherapy (IPC) has been suggested as a promising method for the management of peritoneal dissemination (PD) of ovarian or colorectal cancers, the actual clinical use of this method has been restricted due to such problems as poor drug penetration into the tumor and high side effects. It is, therefore, necessary to develop new strategies to improve the efficacy of this approach. In the present work, a new strategy is proposed based on intraperitoneal (IP) injection of thermosensitive liposomal doxorubicin (TSL-Dox) with triggered release by mild hyperthermia induced by high intensity focused ultrasound (HIFU). A computational model is developed to evaluate the proposed drug delivery system. Results show an order of magnitude increase in drug penetration depth into the tumor compared to the conventional IP delivery. Furthermore, the effects of thermal conditions applied to the tumor, TSL size, tumor vessel permeability, and tumor size are investigated. Results indicate an improved efficiency of the drug delivery by expanding the heated region, yet, it increases the risk of unintentional TSL drug load release in the peritoneal cavity. Results also indicate that smaller TSLs have better treatment outcome. However, there is a significant reduction in treatment efficacy for TSLs with sizes smaller than the vessel wall pore size. Thus, tuning the size of TSL should be based on the tumor microvascular permeability. The simulation results suggest that the TSL-Dox delivery system in smaller tumors is far advantageous than larger ones. Results of our model can be used as guidelines for future preclinical studies.

Personalized image-based tumor growth prediction in a convection-diffusion-reaction model

Inter-individual heterogeneity of tumors leads to non-effectiveness of unique therapy plans. This issue has caused a growing interest in the field of personalized medicine and its application in tumor growth evaluation. Accordingly, in this paper, a framework of personalized medicine is presented for growth prediction of brain glioma tumors. A convection-diffusion-reaction model is used as the patient-specific tumor growth model which is associated with multimodal magnetic resonance images (MRIs). Two parameters of intracellular area fraction (ICAF) and metabolic rate have been used to incorporate the physiological data obtained from medical images into the model. The framework is tested on the data of two cases of glioma tumors to document the approach; parameter estimation is made using particle swarm optimization (PSO) and genetic algorithm (GA) and the model is evaluated by comparing the predicted tumors with the observed tumors in terms of root mean square error of the ICAF maps (IRMSE), relative area difference (RAD) and Dice's coefficient (DC). Results show the differences of IRMSE, RAD and DC in 4.1 ∓ 1.15%, 0.099 ∓ 0.041 and 85.5 ∓ 7.5%, respectively. Survival times are estimated by assuming the tumor radius of 35 mm as the fatal burden. Results confirm that less-diffusive tumors lead to higher survival times. The represented framework makes it possible to personally predict the growth behavior of glioma tumors only based on patients' routine MRIs and provides a basis for modeling the personalized therapy and walking in the path of personalized medicine.

The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors

The ideal in vitro recreation of the micro-tumor niche-although much needed for a better understanding of cancer etiology and development of better anticancer therapies-is highly challenging. Tumors are complex three-dimensional (3D) tissues that establish a dynamic cross-talk with the surrounding tissues through complex chemical signaling. An extensive body of experimental evidence has established that 3D culture systems more closely recapitulate the architecture and the physiology of human solid tumors when compared with traditional 2D systems. Moreover, conventional 3D culture systems fail to recreate the dynamics of the tumor niche. Tumor-on-chip systems, which are microfluidic devices that aim to recreate relevant features of the tumor physiology, have recently emerged as powerful tools in cancer research. In tumor-on-chip systems, the use of microfluidics adds another dimension of physiological mimicry by allowing a continuous feed of nutrients (and pharmaceutical compounds). Here, we discuss recently published literature related to the culture of solid tumor-like tissues in microfluidic systems (tumor-on-chip devices). Our aim is to provide the readers with an overview of the state of the art on this particular theme and to illustrate the toolbox available today for engineering tumor-like structures (and their environments) in microfluidic devices. The suitability of tumor-on-chip devices is increasing in many areas of cancer research, including the study of the physiology of solid tumors, the screening of novel anticancer pharmaceutical compounds before resourcing to animal models, and the development of personalized treatments. In the years to come, additive manufacturing (3D bioprinting and 3D printing), computational fluid dynamics, and medium- to high-throughput omics will become powerful enablers of a new wave of more sophisticated and effective tumor-on-chip devices.

Target Site Delivery and Residence of Nanomedicines: Application of Quantitative Systems Pharmacology

Quantitative systems pharmacology (QSP), an emerging field that entails using modeling and computation to interpret, interrogate, and integrate drug effects spanning from the molecule to the whole organism to forecast treatment outcomes, is expected to enhance the efficiency of drug development. Since late 2017, the U.S. Food and Drug Administration has advocated the use of an analogous approach of model-informed drug development. This review focuses on issues pertaining to nanosized medicines (NP) and the potential utility of QSP to determine NP delivery and residence at extracellular or intracellular targets in vivo. The kinetic processes governing NP disposition and transport, interactions with biologic matrix components, binding and internalization in cells, and intracellular trafficking are determined, sometimes jointly, by NP properties (e.g., dimension, materials, surface charge and modifications, shape, and geometry) and target tissue properties (e.g., perfusion status, vessel pore size and wall thickness, vessel and cell density, composition of extracellular matrix, and void volume fraction). These various determinants, together with the heterogeneous tissue structures and microenvironment factors in solid tumors, lead to environment-, spatial-, and time-dependent changes in NP concentrations that are difficult to predict. Adding to the complexity is the recent discovery that NP surface-coating protein corona, whose composition depends on NP properties and which undergoes continuous evolution with time and local protein environments, is yet another unpredictable variable. Examples are provided to demonstrate the potential utility of QSP-based multiscale modeling to capture the physicochemical and biologic processes in equations to enable computational studies of the key kinetic processes in cancer treatments.

Image-based spatio-temporal model of drug delivery in a heterogeneous vasculature of a solid tumor - Computational approach

The solute transport distribution in a tumor is an important criterion in the evaluation of the cancer treatment efficacy. The fraction of killed cells after each treatment can quantify the therapeutic effect and plays as a helpful tool to evaluate the chemotherapy treatment schedules. In the present study, an image-based spatio-temporal computational model of a solid tumor is provided for calculation of interstitial fluid flow and solute transport. Current model incorporates heterogeneous microvasculature for angiogenesis instead of synthetic mathematical modeling. In this modeling process, a comprehensive model according to Convection-Diffusion-Reaction (CDR) equations is employed due to its high accuracy for simulating the binding and the uptake of the drug by tumor cells. Based on the velocity and the pressure distribution, transient distribution of the different drug concentrations (free, bound, and internalized) is calculated. Then, the fraction of killed cells is obtained according to the internalized concentration. Results indicate the dependence of the drug distribution on both time and space, as well as the microvasculature density. Free and bound drug concentration have the same trend over time, whereas, internalized and total drug concentration increases over time and reaches a constant value. The highest amount of concentration occurred in the tumor region due to the higher permeability of the blood vessels. Moreover, the fraction of killed cells is approximately 78.87% and 24.94% after treatment with doxorubicin for cancerous and normal tissues, respectively. In general, the presented methodology may be applied in the field of personalized medicine to optimize patient-specific treatments. Also, such image-based modeling of solid tumors can be used in laboratories that working on drug delivery and evaluating new drugs before using them for any in vivo or clinical studies.

A model-based approach to investigate the effect of elevated interstitial fluid pressure on strain elastography

Finite element (FE) modeling provides a useful tool to understand the mechanical behavior of complex tissues, such as cancers, in a variety of testing conditions. Although a number of numerical and analytical models for cancerous tumors are retrievable in the literature, none of these models is capable of completely describing the behavior of a cancer embedded in a normal tissue in the conditions typical for an ultrasound elastography experiment. In this paper, we first design and implement a realistic FE model of the mechanical behavior of a cancer embedded in a normal tissue under ultrasound elastography testing conditions. In addition to the commonly used tissue mechanical properties, for the cancer, elevated interstitial fluid pressure (IFP) is incorporated in the model. IFP is a parameter of great clinical significance, but it is not typically considered in elastographic models of tumors. The developed model is then used to thoroughly study the effect of IFP on the axial, lateral and volumetric strains inside the tumor. The results of this study demonstrate that the presence of the IFP affects both the temporal and spatial distributions of the axial, lateral, volumetric strains and related elastographic parameters. Thus, these results lead to two important considerations: (1) that a correct interpretation of experimental elastographic data need a clear understanding of the effect of the IFP on the obtained elastograms and (2) that this IFP-dependent alteration of the elastographic parameters may provide an opportunity to non-invasively gain localized information about this clinically relevant parameter.

In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability

The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels’ endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting.

One of the main challenges in optimising cancer therapy is understanding the in-vivo cancer environment and how it changes over time. The efficacy of chemotherapeutic drugs is known to be strongly dependent on blood vessel wall properties and the architecture of the developing tumour vasculature, which in turn are dependent on biochemical and mechanical interactions between cancer cells and their microenvironment. Here we present a novel in-silico modelling framework of dynamic tumour growth, angiogenesis and drug delivery, and we use it to explore biophysical factors governing the efficient delivery of cytotoxic drugs to solid tumours. We find that the time of treatment and vessel permeability are important factors for the efficacy of chemical agents with low binding affinity, that high affinity drugs can impact the tumour vasculature remodelling and bring vascular structure back to a more normalised state, and that the combination of large-sized vessel wall pores and high affinity enhances cytotoxic drug delivery and efficacy. These results have implications for treatment planning and optimisation, and show how in-silico models can be used to help understand and optimise cancer therapy.

Magnetically assisted intraperitoneal drug delivery for cancer chemotherapy

Intraperitoneal (IP) chemotherapy has revived hopes during the past few years for the management of peritoneal disseminations of digestive and gynecological cancers. Nevertheless, a poor drug penetration is one key drawback of IP chemotherapy since peritoneal neoplasms are notoriously resistant to drug penetration. Recent preclinical studies have focused on targeting the aberrant tumor microenvironment to improve intratumoral drug transport. However, tumor stroma targeting therapies have limited therapeutic windows and show variable outcomes across different cohort of patients. Therefore, the development of new strategies for improving the efficacy of IP chemotherapy is a certain need. In this work, we propose a new magnetically assisted strategy to elevate drug penetration into peritoneal tumor nodules and improve IP chemotherapy. A computational model was developed to assess the feasibility and predictability of the proposed active drug delivery method. The key tumor pathophysiology, including a spatially heterogeneous construct of leaky vasculature, nonfunctional lymphatics, and dense extracellular matrix (ECM), was reconstructed in silico. The transport of intraperitoneally injected magnetic nanoparticles (MNPs) inside tumors was simulated and compared with the transport of free cytotoxic agents. Our results on magnetically assisted delivery showed an order of magnitude increase in the final intratumoral concentration of drug-coated MNPs with respect to free cytotoxic agents. The intermediate MNPs with the radius range of 200-300 nm yield optimal magnetic drug targeting (MDT) performance in 5-10 mm tumors while the MDT performance remains essentially the same over a large particle radius range of 100-500 nm for a 1 mm radius small tumor. The success of MDT in larger tumors (5-10 mm in radius) was found to be markedly dependent on the choice of magnet strength and tumor-magnet distance while these two parameters were less of a concern in small tumors. We also validated in silico results against experimental results related to tumor interstitial hypertension, conventional IP chemoperfusion, and magnetically actuated movement of MNPs in excised tissue.