Analysis of Magneto-Hyperthermia Duration in Nano-sized Drug Delivery System to Solid Tumors Using Intravascular-Triggered Thermosensitive-Liposome.

Computational models have been developed as a potential platform to identify bio-interactions that cannot be properly understood by experimental models. In the present study, a mathematical model has been employed to investigate the therapeutic response of drug-loaded thermosensitive liposome (TSL) following intravascular release paradigm. Thermal field created by an alternating magnetic field is utilized to release the drug within microvessels. Determining the time required for the application of magneto-hyperthermia is the main purpose of this study. Results show that applying a long-term continuous or pulsed hyperthermia can affect the concentration level of drugs in the extracellular space. The peak value of free and bound drug concentrations in the extracellular space is equal for all hyperthermia programs. Additionally, the concentrations of free and bound drugs are retained at a higher level in pulsed mode compared to the continuous mode (i.e., area under curve (AUC) of pulsed case is slightly higher than continuous case). However, there is no significant difference in bioavailability time. Hence, onset time of tumor growth is similar for different conditions. This study shows that the appropriate time to apply hyperthermia is post-bolus injection until reaching the peak concentration profile in extracellular space. Therefore, in clinical applications similar to the present study's circumstances, continuous hyperthermia for 30 min can be a better choice. This study can be a useful guideline for experimental studies to reduce the number of in vivo tests as well as for clinical trials to make the right decision to provide optimal medication programs.

Computational modeling of PET tracer distribution in solid tumors integrating microvasculature.

BACKGROUND: We present computational modeling of positron emission tomography radiotracer uptake with consideration of blood flow and interstitial fluid flow, performing spatiotemporally-coupled modeling of uptake and integrating the microvasculature. In our mathematical modeling, the uptake of fluorodeoxyglucose F-18 (FDG) was simulated based on the Convection-Diffusion-Reaction equation given its high accuracy and reliability in modeling of transport phenomena. In the proposed model, blood flow and interstitial flow are solved simultaneously to calculate interstitial pressure and velocity distribution inside cancer and normal tissues. As a result, the spatiotemporal distribution of the FDG tracer is calculated based on velocity and pressure distributions in both kinds of tissues. RESULTS: Interstitial pressure has maximum value in the tumor region compared to surrounding tissue. In addition, interstitial fluid velocity is extremely low in the entire computational domain indicating that convection can be neglected without effecting results noticeably. Furthermore, our results illustrate that the total concentration of FDG in the tumor region is an order of magnitude larger than in surrounding normal tissue, due to lack of functional lymphatic drainage system and also highly-permeable microvessels in tumors. The magnitude of the free tracer and metabolized (phosphorylated) radiotracer concentrations followed very different trends over the entire time period, regardless of tissue type (tumor vs. normal). CONCLUSION: Our spatiotemporally-coupled modeling provides helpful tools towards improved understanding and quantification of in vivo preclinical and clinical studies.

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.

Spatiotemporal modeling of nano-delivered chemotherapeutics for synergistic microwave ablation cancer therapy.

BACKGROUND AND OBJECTIVE: The effectiveness of current microwave ablation (MWA) therapies is limited. Administration of thermosensitive liposomes (TSLs) which release drugs in response to heat has presented a significant potential for enhancing the efficacy of thermal ablation treatment, and the benefits of targeted drug delivery. However, a complete knowledge of the mechanobiological processes underlying the drug release process, especially the intravascular drug release mechanism and its distribution in response to MWA needs to be improved. Multiscale computational-based modeling frameworks, integrating different biophysical phenomena, have recently emerged as promising tools to decipher the mechanobiological events in combo therapies. The present study aims to develop a novel multiscale computational model of TSLs delivery following MWA implantation. METHODS: Due to the complex interplay between the heating procedure and the drug concentration maps, a computational model is developed to determine the intravascular release of doxorubicin from TSL, its transvascular transport into the interstitium, transport in the interstitium, and cell uptake. Computational models can estimate the interplays among liposome and drug properties, tumor perfusion, and heating regimen to examine the impact of essential parameters and to optimize a targeted drug delivery platform. RESULTS: Results indicated that the synergy of TSLs with MWA allows more localized drug delivery with lower side effects. The drug release rate and tumor permeability play crucial roles in the efficacy of TSLs during MWA treatment. The computational model predicted an unencapsulated drug lime around the ablated zone, which can destroy more cancer cells compared to MWA alone by 40%. Administration of TSLs with a high release rate capacity can improve the percentage of killed cancer cells by 24%. Since the heating duration in MWA is less than 15 min, the presented combination therapy showed better performance for highly permeable tumors. CONCLUSION: This study highlights the potential of the proposed computational framework to address complex and realistic scenarios in cancer treatment, which can serve as the future research foundation, including advancements in nanomedicine and optimizing the pair of TSL and MWA for both preclinical and clinical studies. The present model could be as a valuable tool for patient-specific calibration of essential parameters.

Effect of tumor heterogeneity on enhancing drug delivery to vascularized tumors using thermo-sensitive liposomes triggered by hyperthermia: A multi-scale and multi-physics computational model.

In this study, a novel multi-scale and multi-physics image-based computational model is introduced to assess the delivery of doxorubicin (Dox) loaded temperature-sensitive liposomes (TSLs) in the presence of hyperthermia. Unlike previous methodologies, this approach incorporates capillary network geometry extracted from images, resulting in a more realistic physiological tumor model. This model holds significant promise in advancing personalized medicine by integrating patient-specific tumor properties. The finite element method is employed to solve the equations governing intravascular and interstitial fluid flows, as well as the transport of therapeutic agents within the tissue. Realistic biological conditions and intricate processes like intravascular pressure, drug binding to cells, and cellular uptake are also considered to enhance the model's accuracy. The results underscore the significant impact of vascular architecture on treatment outcomes. Variation in vascular network pattern yielded changes of up to 38 % in the fraction of killed cells (FKCs) parameter under identical conditions. Pressure control of the parent vessels can also improve FKCs by approximately 17 %. Tailoring the treatment plan based on tumor-specific parameters emerged as a critical factor influencing treatment efficacy. For instance, changing the time interval between the administration of Dox-loaded TSLs and hyperthermia can result in a 48 % improvement in treatment outcomes. Additionally, devising a customized heating schedule led to a 20 % increase in treatment efficacy. Our proposed model highlights the significant effect of tumor characteristics and vascular network structure on the final treatment outcomes of the presented combination treatment.

Ultrasound-mediated nano-sized drug delivery systems for cancer treatment: Multi-scale and multi-physics computational modeling.

Computational modeling enables researchers to study and understand various complex biological phenomena in anticancer drug delivery systems (DDSs), especially nano-sized DDSs (NSDDSs). The combination of NSDDSs and therapeutic ultrasound (TUS), that is, focused ultrasound and low-intensity pulsed ultrasound, has made significant progress in recent years, opening many opportunities for cancer treatment. Multiple parameters require tuning and optimization to develop effective DDSs, such as NSDDSs, in which mathematical modeling can prove advantageous. In silico computational modeling of ultrasound-responsive DDS typically involves a complex framework of acoustic interactions, heat transfer, drug release from nanoparticles, fluid flow, mass transport, and pharmacodynamic governing equations. Owing to the rapid development of computational tools, modeling the different phenomena in multi-scale complex problems involved in drug delivery to tumors has become possible. In the present study, we present an in-depth review of recent advances in the mathematical modeling of TUS-mediated DDSs for cancer treatment. A detailed discussion is also provided on applying these computational models to improve the clinical translation for applications in cancer treatment. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Radiopharmaceutical transport in solid tumors via a 3-dimensional image-based spatiotemporal model.

Lutetium-177 prostate-specific membrane antigen (177Lu-PSMA)-targeted radiopharmaceutical therapy is a clinically approved treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Even though common practice reluctantly follows "one size fits all" approach, medical community believes there is significant room for deeper understanding and personalization of radiopharmaceutical therapies. To pursue this aim, we present a 3-dimensional spatiotemporal radiopharmaceutical delivery model based on clinical imaging data to simulate pharmacokinetic of 177Lu-PSMA within the prostate tumors. The model includes interstitial flow, radiopharmaceutical transport in tissues, receptor cycles, association/dissociation with ligands, synthesis of PSMA receptors, receptor recycling, internalization of radiopharmaceuticals, and degradation of receptors and drugs. The model was studied for a range of values for injection amount (100-1000 nmol), receptor density (10-500 nmol•l-1), and recycling rate of receptors (10-4 to 10-1 min-1). Furthermore, injection type, different convection-diffusion-reaction mechanisms, characteristic time scales, and length scales are discussed. The study found that increasing receptor density, ligand amount, and labeled ligands improved radiopharmaceutical uptake in the tumor. A high receptor recycling rate (0.1 min-1) increased radiopharmaceutical concentration by promoting repeated binding to tumor cell receptors. Continuous infusion results in higher radiopharmaceutical concentrations within tumors compared to bolus administration. These insights are crucial for advancing targeted therapy for prostate cancer by understanding the mechanism of radiopharmaceutical distribution in tumors. Furthermore, measures of characteristic length and advection time scale were computed. The presented spatiotemporal tumor transport model can analyze different physiological parameters affecting 177Lu-PSMA delivery.

Multi-Scale and Multi-Physics Models of the Transport of Therapeutic/Diagnostic Cancer Agents.

The effectiveness of tumor treatment heavily relies on the successful delivery of anticancer drugs [...].

A spatiotemporal computational model of focused ultrasound heat-induced nano-sized drug delivery system in solid tumors.

Focused Ultrasound (FUS)-triggered nano-sized drug delivery, as a smart stimuli-responsive system for treating solid tumors, is computationally investigated to enhance localized delivery of drug and treatment efficacy. Integration of thermosensitive liposome (TSL), as a doxorubicin (DOX)-loaded nanocarrier, and FUS, provides a promising drug delivery system. A fully coupled partial differential system of equations, including the Helmholtz equation for FUS propagation, bio-heat transfer, interstitial fluid flow, drug transport in tissue and cellular spaces, and a pharmacodynamic model is first presented for this treatment approach. Equations are then solved by finite element methods to calculate intracellular drug concentration and treatment efficacy. The main objective of this study is to present a multi-physics and multi-scale model to simulate drug release, transport, and delivery to solid tumors, followed by an analysis of how FUS exposure time and drug release rate affect these processes. Our findings not only show the capability of model to replicate this therapeutic approach, but also confirm the benefits of this treatment with an improvement of drug aggregation in tumor and reduction of drug delivery in healthy tissue. For instance, the survival fraction of tumor cells after this treatment dropped to 62.4%, because of a large amount of delivered drugs to cancer cells. Next, a combination of three release rates (ultrafast, fast, and slow) and FUS exposure times (10, 30, and 60 min) was examined. Area under curve (AUC) results show that the combination of 30 min FUS exposure and rapid drug release leads to a practical and effective therapeutic response.

Ultrasound-mediated nano drug delivery for treating cancer: Fundamental physics to future directions.

The application of biocompatible nanocarriers in medicine has provided several benefits over conventional treatment methods. However, achieving high treatment efficacy and deep penetration of nanocarriers in tumor tissue is still challenging. To address this, stimuli-responsive nano-sized drug delivery systems (DDSs) are an active area of investigation in delivering anticancer drugs. While ultrasound is mainly used for diagnostic purposes, it can also be applied to affect cellular function and the delivery/release of anticancer drugs. Therapeutic ultrasound (TUS) has shown potential as both a stand-alone anticancer treatment and a method to induce targeted drug release from nanocarrier systems. TUS approaches have been used to overcome various physiological obstacles, including endothelial barriers, the tumor microenvironment (TME), and immunological hurdles. Combining nanomedicine and ultrasound as a smart DDS can increase in situ drug delivery and improve access to impermeable tissues. Furthermore, smart DDSs can perform targeted drug release in response to distinctive TMEs, external triggers, or dual/multi-stimulus. This results in enhanced treatment efficacy and reduced damage to surrounding healthy tissue or organs at risk. Integrating DDSs and ultrasound is still in its early stages. More research and clinical trials are required to fully understand ultrasound's underlying physical mechanisms and interactions with various types of nanocarriers and different types of cells and tissues. In the present review, ultrasound-mediated nano-sized DDS, specifically focused on cancer treatment, is presented and discussed. Ultrasound interaction with nanoparticles (NPs), drug release mechanisms, and various types of ultrasound-sensitive NPs are examined. Additionally, in vitro, in vivo, and clinical applications of TUS are reviewed in light of the critical challenges that need to be considered to advance TUS toward an efficient, secure, straightforward, and accessible cancer treatment. This study also presents effective TUS parameters and safety considerations for this treatment modality and gives recommendations about system design and operation. Finally, future perspectives are considered, and different TUS approaches are examined and discussed in detail. This review investigates drug release and delivery through ultrasound-mediated nano-sized cancer treatment, both pre-clinically and clinically.

A comparative study between conventional chemotherapy and photothermal activated nano-sized targeted drug delivery to solid tumor.

Delivery of chemotherapeutic medicines to solid tumors is critical for optimal therapeutic success and minimal adverse effects. We mathematically developed a delivery method using thermosensitive nanocarriers activated by light irradiation. To assess its efficacy and identify critical events and parameters affecting therapeutic response, we compared this method to bolus and continuous infusions of doxorubicin for both single and multiple administrations. A hybrid sprouting angiogenesis approach generates a semi-realistic microvascular network to evaluate therapeutic drug distribution and microvascular heterogeneity. A pharmacodynamics model evaluates treatment success based on tumor survival cell percentage. The study found that whereas bolus injection boosted extracellular drug concentration levels by 90%, continuous infusion improved therapeutic response due to improved bioavailability. Cancer cell death increases by 6% with several injections compared to single injections due to prolonged chemotherapeutic medication exposure. However, responsive nanocarriers supply more than 2.1 times more drug than traditional chemotherapy in extracellular space, suppressing tumor development longer. Also, controlled drug release decreases systemic side effects substantial through diminishing the concentration of free drug in the circulation. The primary finding of this work highlights the significance of high bioavailability in treatment response. The results indicate that responsive nanocarriers contribute to increased bioavailability, leading to improved therapeutic benefits. By including drug delivery features in a semi-realistic model, this numerical study sought to improve drug-bio interaction comprehension. The model provides a good framework for understanding preclinical and clinical targeted oncology study outcomes.

Stimuli-sensitive nano-drug delivery with programmable size changes to enhance accumulation of therapeutic agents in tumors.

Nano-based drug delivery systems hold significant promise for cancer therapies. Presently, the poor accumulation of drug-carrying nanoparticles in tumors has limited their success. In this study, based on a combination of the paradigms of intravascular and extravascular drug release, an efficient nanosized drug delivery system with programmable size changes is introduced. Drug-loaded smaller nanoparticles (secondary nanoparticles), which are loaded inside larger nanoparticles (primary nanoparticles), are released within the microvascular network due to temperature field resulting from focused ultrasound. This leads to the scale of the drug delivery system decreasing by 7.5 to 150 times. Subsequently, smaller nanoparticles enter the tissue at high transvascular rates and achieve higher accumulation, leading to higher penetration depths. In response to the acidic pH of tumor microenvironment (according to the distribution of oxygen), they begin to release the drug doxorubicin at very slow rates (i.e., sustained release). To predict the performance and distribution of therapeutic agents, a semi-realistic microvascular network is first generated based on a sprouting angiogenesis model and the transport of therapeutic agents is then investigated based on a developed multi-compartment model. The results show that reducing the size of the primary and secondary nanoparticles can lead to higher cell death rate. In addition, tumor growth can be inhibited for a longer time by enhancing the bioavailability of the drug in the extracellular space. The proposed drug delivery system can be very promising in clinical applications. Furthermore, the proposed mathematical model is applicable to broader applications to predict the performance of drug delivery systems.

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.

No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages.

Engineered strategies to enhance tumor penetration of drug-loaded nanoparticles.

Nanocarriers have been widely employed in preclinical studies and clinical trials for the delivery of anticancer drugs. The most important causes of failure in clinical translation of nanocarriers is their inefficient accumulation and penetration which arises from special characteristics of tumor microenvironment such as insufficient blood supply, dense extracellular matrix, and elevated interstitial fluid pressure. Various strategies such as engineering extracellular matrix, optimizing the physicochemical properties of nanocarriers have been proposed to increase the depth of tumor penetration; however, these strategies have not been very successful so far. Novel strategies such as transformable nanocarriers, transcellular transport of peptide-modified nanocarriers, and bio-inspired carriers have recently been emerged as an advanced generation of drug carriers. In this study, the latest developments of nanocarrier-based drug delivery to solid tumor are presented with their possible limitations. Then, the prospects of advanced drug delivery systems are discussed in detail.

Spatiotemporal multi-scale modeling of radiopharmaceutical distributions in vascularized solid tumors.

We present comprehensive mathematical modeling of radiopharmaceutical spatiotemporal distributions within vascularized solid tumors. The novelty of the presented model is at mathematical level. From the mathematical viewpoint, we provide a general modeling framework for the process of radiopharmaceutical distribution in the tumor microenvironment to enable an analysis of the effect of various tumor-related parameters on the distribution of different radiopharmaceuticals. We argue that partial differential equations (PDEs), beyond conventional methods, including ODE-based kinetic compartment modeling, can be used to evaluate radiopharmaceutical distribution in both time and space. In addition, we consider the spatially-variable dynamic structure of tumor microvascular networks to simulate blood flow distribution. To examine the robustness of the model, the effects of microvessel density (MVD) and tumor size, as two important factors in tumor prognosis, on the radiopharmaceutical distribution within the tumor are investigated over time (in the present work, we focus on the radiopharmaceutical [18F]FDG, yet the framework is broadly applicable to radiopharmaceuticals). Results demonstrate that the maximum total uptake of [18F]FDG at all time frames occurs in the tumor area due to the high capillary permeability and lack of a functional lymphatic system. As the MVD of networks increases, the mean total uptake in the tumor is also enhanced, where the rate of diffusion from vessel to tissue has the highest contribution and the rate of convection transport has the lowest contribution. The results of this study can be used to better investigate various phenomena and bridge a gap among cancer biology, mathematical oncology, medical physics, and radiology.

Towards principled design of cancer nanomedicine to accelerate clinical translation.

Nanotechnology in medical applications, especially in oncology as drug delivery systems, has recently shown promising results. However, although these advances have been promising in the pre-clinical stages, the clinical translation of this technology is challenging. To create drug delivery systems with increased treatment efficacy for clinical translation, the physicochemical characteristics of nanoparticles such as size, shape, elasticity (flexibility/rigidity), surface chemistry, and surface charge can be specified to optimize efficiency for a given application. Consequently, interdisciplinary researchers have focused on producing biocompatible materials, production technologies, or new formulations for efficient loading, and high stability. The effects of design parameters can be studied in vitro, in vivo, or using computational models, with the goal of understanding how they affect nanoparticle biophysics and their interactions with cells. The present review summarizes the advances and technologies in the production and design of cancer nanomedicines to achieve clinical translation and commercialization. We also highlight existing challenges and opportunities in the field.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach.

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.

Computational modeling of thermal combination therapies by magneto-ultrasonic heating to enhance drug delivery to solid tumors.

For the first time, inspired by magnetic resonance imaging-guidance high intensity focused ultrasound (MR-HIFU) technology, i.e., medication therapy and thermal ablation in one session, in a preclinical setting based on a developed mathematical model, the performance of doxorubicin (Dox) and its encapsulation have been investigated in this study. Five different treatment methods, that combine medication therapy with mild hyperthermia by MRI contrast ([Formula: see text]) and thermal ablation via HIFU, are investigated in detail. A comparison between classical chemotherapy and thermochemistry shows that temperature can improve the therapeutic outcome by stimulating biological properties. On the other hand, the intravascular release of ThermoDox increases the concentration of free drug by 2.6 times compared to classical chemotherapy. The transport of drug in interstitium relies mainly on the diffusion mechanism to be able to penetrate deeper and reach the cancer cells in the inner regions of the tumor. Due to the low drug penetration into the tumor center, thermal ablation has been used for necrosis of the central areas before thermochemotherapy and ThermoDox therapy. Perfusion of the region around the necrotic zone is found to be damaged, while cells in the region are alive and not affected by medication therapy; so, there is a risk of tumor recurrence. Therefore, it is recommended that ablation be performed after the medication therapy. Our model describes a comprehensive assessment of MR-HIFU technology, taking into account many effective details, which can be a reliable guide towards the optimal use of drug delivery systems.

Effects of hypoxia and nanocarrier size on pH-responsive nano-delivery system to solid tumors.

One of the special features of solid tumors is the acidity of the tumor microenvironment, which is mainly due to the presence of hypoxic regions. Therefore, pH-responsive drug delivery systems have recently been highly welcomed. In the present study, a comprehensive mathematical model is presented based on extravascular drug release paradigm. Accordingly, drug delivery system using pH-responsive nanocarriers is taken into account to examine the impacts of hypoxic regions as well as the size of nanocarriers for cancerous cell-death. The extent of hypoxic regions is controlled by vascular density. This means that regions with very low vascular density represent regions of hypoxia. Using this mathematical model, it is possible to simulate the extracellular and intracellular concentrations of drug by considering the association/disassociation of the free drug to the cell-surface receptors and cellular uptake. Results show that nanocarriers with smaller sizes are more effective due to higher accumulation in the tumor tissue interstitium. The small size of the nanocarriers also allows them to penetrate deeper, so they can expose a larger portion of the tumor to the drug. Additionally, the presence of hypoxic regions in tumor reduces the fraction of killed cancer cells due to reduced penetration depth. The proposed model can be considered for optimizing and developing pH-sensitive delivery systems to reduce both cost and time of the process.

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.