An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

Interactions between biological systems and nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Anti-tumor effect of pH-sensitive drug-loaded nanoparticles optimized via an integrated computational/experimental approach

The acidic pH of tumor tissue has been used to trigger drug release from nanoparticles. However, dynamic interactions between tumor pH and vascularity present challenges to optimize therapy to particular microenvironment conditions. Despite recent development of pH-sensitive nanomaterials that can accurately quantify drug release from nanoparticles, tailoring release to maximize tumor response remains elusive. This study hypothesizes that a computational modeling-based platform that simulates the heterogeneously vascularized tumor microenvironment can enable evaluation of the complex intra-tumoral dynamics involving nanoparticle transport and pH-dependent drug release, and predict optimal nanoparticle parameters to maximize the response. To this end, SPNCD nanoparticles comprising superparamagnetic cores of iron oxide (Fe3O4) and a poly(lactide-co-glycolide acid) shell loaded with doxorubicin (DOX) were fabricated. Drug release was measured in vitro as a function of pH. A 2D model of vascularized tumor growth was calibrated to experimental data and used to evaluate SPNCD effect as a function of drug release rate and tissue vascular heterogeneity. Simulations show that pH-dependent drug release from SPNCD delays tumor regrowth more than DOX alone across all levels of vascular heterogeneity, and that SPNCD significantly inhibit tumor radius over time compared to systemic DOX. The minimum tumor radius forecast by the model was comparable to previous in vivo SPNCD inhibition data. Sensitivity analyses of the SPNCD pH-dependent drug release rate indicate that slower rates are more inhibitory than faster rates. We conclude that an integrated computational and experimental approach enables tailoring drug release by pH-responsive nanomaterials to maximize the tumor response.

Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model

Immune cells in the tumor microenvironment (TME) are known to affect tumor growth, vascularization, and extracellular matrix (ECM) deposition. Marked interest in system-scale analysis of immune species interactions within the TME has encouraged progress in modeling tumor-immune interactions in silico. Due to the computational cost of simulating these intricate interactions, models have typically been constrained to representing a limited number of immune species. To expand the capability for system-scale analysis, this study develops a three-dimensional continuum mixture model of tumor-immune interactions to simulate multiple immune species in the TME. Building upon a recent distributed computing implementation that enables efficient solution of such mixture models, major immune species including monocytes, macrophages, natural killer cells, dendritic cells, neutrophils, myeloid-derived suppressor cells (MDSC), cytotoxic, helper, regulatory T-cells, and effector and regulatory B-cells and their interactions are represented in this novel implementation. Immune species extravasate from blood vasculature, undergo chemotaxis toward regions of high chemokine concentration, and influence the TME in proportion to locally defined levels of stimulation. The immune species contribute to the production of angiogenic and tumor growth factors, promotion of myofibroblast deposition of ECM, upregulation of angiogenesis, and elimination of living and dead tumor species. The results show that this modeling approach offers the capability for quantitative insight into the modulation of tumor growth by diverse immune-tumor interactions and immune-driven TME effects. In particular, MDSC-mediated effects on tumor-associated immune species' activation levels, volume fraction, and influence on the TME are explored. Longer term, linking of the model parameters to particular patient tumor information could simulate cancer-specific immune responses and move toward a more comprehensive evaluation of immunotherapeutic strategies.

Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data

Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.

Kinetics of Nanomedicine in Tumor Spheroid as an In Vitro Model System for Efficient Tumor-Targeted Drug Delivery With Insights From Mathematical Models

Numerous strategies have been developed to treat cancer conventionally. Most importantly, chemotherapy shows its huge promise as a better treatment modality over others. Nonetheless, the very complex behavior of the tumor microenvironment frequently impedes successful drug delivery to the tumor sites that further demands very urgent and effective distribution mechanisms of anticancer drugs specifically to the tumor sites. Hence, targeted drug delivery to tumor sites has become a major challenge to the scientific community for cancer therapy by assuring drug effects to selective tumor tissue and overcoming undesired toxic side effects to the normal tissues. The application of nanotechnology to the drug delivery system pays heed to the design of nanomedicine for specific cell distribution. Aiming to limit the use of traditional strategies, the adequacy of drug-loaded nanocarriers (i.e., nanomedicine) proves worthwhile. After systemic blood circulation, a typical nanomedicine follows three levels of disposition to tumor cells in order to exhibit efficient pharmacological effects induced by the drug candidates residing within it. As a result, nanomedicine propounds the assurance towards the improved bioavailability of anticancer drug candidates, increased dose responses, and enhanced targeted efficiency towards delivery and distribution of effective therapeutic concentration, limiting toxic concentration. These aspects emanate the proficiency of drug delivery mechanisms. Understanding the potential tumor targeting barriers and limiting conditions for nanomedicine extravasation, tumor penetration, and final accumulation of the anticancer drug to tumor mass, experiments with in vivo animal models for nanomedicine screening are a key step before it reaches clinical translation. Although the study with animals is undoubtedly valuable, it has many associated ethical issues. Moreover, individual experiments are very expensive and take a longer time to conclude. To overcome these issues, nowadays, multicellular tumor spheroids are considered a promising in vitro model system that proposes better replication of in vivo tumor properties for the future development of new therapeutics. In this review, we will discuss how tumor spheroids could be used as an in vitro model system to screen nanomedicine used in targeted drug delivery, aiming for better therapeutic benefits. In addition, the recent proliferation of mathematical modeling approaches gives profound insight into the underlying physical principles and produces quantitative predictions. The hierarchical tumor structure is already well decorous to be treated mathematically. To study targeted drug delivery, mathematical modeling of tumor architecture, its growth, and the concentration gradient of oxygen are the points of prime focus. Not only are the quantitative models circumscribed to the spheroid, but also the role of modeling for the nanoparticle is equally inevitable. Abundant mathematical models have been set in motion for more elaborative and meticulous designing of nanomedicine, addressing the question regarding the objective of nanoparticle delivery to increase the concentration and the augmentative exposure of the therapeutic drug molecule to the core. Thus, to diffuse the dichotomy among the chemistry involved, biological data, and the underlying physics, the mathematical models play an indispensable role in assisting the experimentalist with further evaluation by providing the admissible quantitative approach that can be validated. This review will provide an overview of the targeted drug delivery mechanism for spheroid, using nanomedicine as an advantageous tool.

Chemosensitization of Temozolomide-Resistant Pediatric Diffuse Midline Glioma Using Potent Nanoencapsulated Forms of a N(3)-Propargyl Analogue

The lack of clinical response to the alkylating agent temozolomide (TMZ) in pediatric diffuse midline/intrinsic pontine glioma (DIPG) has been associated with O6-methylguanine-DNA-methyltransferase (MGMT) expression and mismatch repair deficiency. Hence, a potent N(3)-propargyl analogue (N3P) was derived, which not only evades MGMT but also remains effective in mismatch repair deficient cells. Due to the poor pharmacokinetic profile of N3P (t1/2 < 1 h) and to bypass the blood-brain barrier, we proposed convection enhanced delivery (CED) as a method of administration to decrease dose and systemic toxicity. Moreover, to enhance N3P solubility, stability, and sustained distribution in vivo, either it was incorporated into an apoferritin (AFt) nanocage or its sulfobutyl ether β-cyclodextrin complex was loaded into nanoliposomes (Lip). The resultant AFt-N3P and Lip-N3P nanoparticles (NPs) had hydrodynamic diameters of 14 vs 93 nm, icosahedral vs spherical morphology, negative surface charge (-17 vs -34 mV), and encapsulating ∼630 vs ∼21000 N3P molecules per NP, respectively. Both NPs showed a sustained release profile and instant uptake within 1 h incubation in vitro. In comparison to the naked drug, N3P NPs demonstrated stronger anticancer efficacy against 2D TMZ-resistant DIPG cell cultures [IC50 = 14.6 (Lip-N3P) vs 32.8 μM (N3P); DIPG-IV) and (IC50 = 101.8 (AFt-N3P) vs 111.9 μM (N3P); DIPG-VI)]. Likewise, both N3P-NPs significantly (P < 0.01) inhibited 3D spheroid growth compared to the native N3P in MGMT+ DIPG-VI (100 μM) and mismatch repair deficient DIPG-XIX (50 μM) cultures. Interestingly, the potency of TMZ was remarkably enhanced when encapsulated in AFt NPs against DIPG-IV, -VI, and -XIX spheroid cultures. Dynamic PET scans of CED-administered zirconium-89 (89Zr)-labeled AFt-NPs in rats also demonstrated substantial enhancement over free 89Zr radionuclide in terms of localized distribution kinetics and retention within the brain parenchyma. Overall, both NP formulations of N3P represent promising approaches for treatment of TMZ-resistant DIPG and merit the next phase of preclinical evaluation.

Computational model of silica nanoparticle penetration into tumor spheroids: Effects of methoxy and carboxy PEG surface functionalization and hyperthermia

Drug delivery to tumors suffers from poor solubility, specificity, diffusion through the tumor micro-environment and nonoptimal interactions with components of the extracellular matrix and cell surface receptors. Nanoparticles and drug-polymer complexes address many of these problems. However, large size exasperates the problem of slow diffusion through the tumor. Three-dimensional tumor spheroids are good models to evaluate approaches to mitigate these difficulties and aid in design strategies to improve the delivery of drugs to treat cancer effectively. Diffusion of drug carriers is highly dependent on cell uptake rate parameters (association/dissociation) and temperature. Hyperthermia increases molecular transport and is known to act synergistically with chemotherapy to improve treatment. This study presents a new inverse estimation approach based on Bayesian probability for estimating nanoparticle cell uptake rates from experiments. The parameters were combined with a finite element computational model of nanoparticle transport under hyperthermia conditions to explore its effect on tumor porosity, diffusion and particle binding (association and dissociation) at cell surfaces. Carboxy-PEG-silane (cPEGSi) nanoparticles showed higher cell uptake compared to methoxy-PEG-silane (mPEGSi) nanoparticles. Simulations were consistent with experimental results from Skov-3 ovarian cancer spheroids. Amorphous silica (cPEGSi) nanoparticles (58 nm) concentrated at the periphery of the tumor spheroids at 37°C but mild hyperthermia (43°C) increased nanoparticle penetration. Thus, hyperthermia may enhance cancer treatment by improving blood delivery to tumors, enhancing extravasation and penetration into tumors, trigger release of drug from the carrier at the tumor site and possibly lead to synergistic anti-cancer activity with the drug.

Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies

In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.

Chondroitin sulfate modified and adriamycin preloaded hybrid nanoparticles for tumor-targeted chemotherapy of lung cancer

Promising cancer treatment requires the assistant of drug delivery systems (DDS) with the aim to increase the accumulation of drugs in tumor tissue. Herein, a hybrid DDS was successfully developed to integrate chondroitin sulfate (CS) and calcium carbonate (CC) in to one system. Anticancer drug adriamycin (Adr) was preloaded into CC nanoparticles to obtain Adr-loaded CC nanoparticles (CC/Adr). The resulted CS-CC/Adr nanoparticles as a biocompatible DDS was able to specifically target cancer cells to enhance the chemotherapy of lung cancer due to the surface modification of CS. Intracellular uptake as well as in vivo imaging results revealed the obtained CS-CC/Adr nanoparticles (size of ~100 nm) showed CS mediated tumor specific accumulation into A549 and LLC cells than unmodified CC/Adr, in which the CD44 receptor might be involved, which finally resulted in stronger anticancer capability than Adr or CC/Adr. As a result, CS-CC/Adr nanoparticles could be further extended to clinical administration in our future works.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles

The pharmacokinetics of nanoparticle-borne drugs targeting tumors depends critically on nanoparticle design. Empirical approaches to evaluate such designs in order to maximize treatment efficacy are time- and cost-intensive. We have recently proposed the use of computational modeling of nanoparticle-mediated drug delivery targeting tumor vasculature coupled with numerical optimization to pursue optimal nanoparticle targeting and tumor uptake. Here, we build upon these studies to evaluate the effect of tumor size on optimal nanoparticle design by considering a cohort of heterogeneously-sized tumor lesions, as would be clinically expected. The results indicate that smaller nanoparticles yield higher tumor targeting and lesion regression for larger-sized tumors. We then augment the nanoparticle design optimization problem by considering drug diffusivity, which yields a two-fold tumor size decrease compared to optimizing nanoparticles without this consideration. We quantify the tradeoff between tumor targeting and size decrease using bi-objective optimization, and generate five Pareto-optimal nanoparticle designs. The results provide a spectrum of treatment outcomes - considering tumor targeting vs. antitumor effect - with the goal to enable therapy customization based on clinical need. This approach could be extended to other nanoparticle-based cancer therapies, and support the development of personalized nanomedicine in the longer term.

Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies

While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Modeling of Combination Chemotherapy and Immunotherapy for Lung Cancer

Cancer has traditionally been studied from a basic science perspective, focusing on the underlying biology, physiology, and biochemistry. Engineering has supplemented this effort via the development of technology, e.g., microscopy. In recent times, engineering and the physical sciences have positioned themselves as approaches on par with the traditional basic sciences to tackle the study of cancer. Mathematical modeling and computational simulation have become key elements of this engineering-focused effort, evaluating the growth of tumors and their response to therapy as problems that could benefit from a systems analysis perspective. Building upon previous work in this field, here is developed a modeling framework to help evaluate the response of tumors to the combination of chemotherapy and immunotherapy, focusing on non-small cell lung cancer (NSCLC). With system parameters set with patient tumor-specific parameters, the longer term goal of this work is to advance personalized cancer treatment.

Pharmacokinetic/Pharmacodynamics Modeling of Drug-Loaded PLGA Nanoparticles Targeting Heterogeneously Vascularized Tumor Tissue

PURPOSE

Nanoparticle-mediated drug delivery and efficacy for cancer applications depends on systemic as well as local microenvironment characteristics. Here, a novel coupling of a nanoparticle (NP) kinetic model with a drug pharmacokinetic/pharmacodynamics model evaluates efficacy of cisplatin-loaded poly lactic-co-glycolic acid (PLGA) NPs in heterogeneously vascularized tumor tissue.

METHODS

Tumor lesions are modeled with various levels of vascular heterogeneity, as would be encountered with different types of tumors. The magnitude of the extracellular to cytosolic NP transport is varied to assess tumor-dependent cellular uptake. NP aggregation is simulated to evaluate its effects on drug distribution and tumor response.

RESULTS

Cisplatin-loaded PLGA NPs are most effective in decreasing tumor size in the case of high vascular-induced heterogeneity, a high NP cytosolic transfer coefficient, and no NP aggregation. Depending on the level of tissue heterogeneity, NP cytosolic transfer and drug half-life, NP aggregation yielding only extracellular drug release could be more effective than unaggregated NPs uptaken by cells and releasing drug both extra- and intra-cellularly.

CONCLUSIONS

Model-based customization of PLGA NP and drug design parameters, including cellular uptake and aggregation, tailored to patient tumor tissue characteristics such as proportion of viable tissue and vascular heterogeneity, could help optimize the NP-mediated tumor drug response.

Efficacy of Surface-Modified PLGA Nanoparticles as a Function of Cervical Cancer Type

PURPOSE

Hypovascularization of cervical tumors, coupled with intrinsic and acquired drug resistance, has contributed to marginal therapeutic outcomes by hindering chemotherapeutic transport and efficacy. Recently, the heterogeneous penetration and distribution of cell penetrating peptide (CPP, here MPG) and polyethylene glycol (PEG) modified poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) were evaluated as a function of tumor type and morphology in cervical cancer spheroids modeling hypovascularized tumor nodules. Building upon this work, this study investigates the efficacy imparted by surface-modified Doxorubicin-loaded NPs transported into hypovascularized tissue.

METHODS

NP efficacy was measured in HeLa, CaSki, and SiHa cells. NP internalization and association, and associated cell viability, were determined in monolayer and spheroid models.

RESULTS

MPG and PEG-NP co-treatment was most efficacious in HeLa cells, while PEG NPs were most efficacious in CaSki cells. NP surface-modifications were unable to improve efficacy, relative to unmodified NPs, in SiHa cells.

CONCLUSIONS

The results highlight the dependence of efficacy on tumor type and the associated microenvironment. The results further relate previous NP transport studies to efficacy, as a function of surface-modification and cell type. Longer-term, this information may help guide the design of NP-mediated strategies to maximize efficacy based on patient-specific cervical tumor origin and characteristics.

A review of small molecules and drug delivery applications using gold and iron nanoparticles

Conventional cancer treatment techniques show several limitations including low or no specificity and consequently a low efficacy in discriminating between cancer cells and healthy cells. Recent nanotechnology developments have introduced smart and novel therapeutic nanomaterials that take advantage of various targeting approaches. The use of nanotechnology in medicine and, more specifically, drug delivery is set to spread even more rapidly than it has over the past two decades. Currently, many nanoparticles (NPs) are under investigation for drug delivery including those for cancer therapy. Targeted nanomaterials bind selectively to cancer cells and greatly affect them with only a minor effect on healthy cells. Gold nanoparticles (Au-NPs), specifically, have been identified as significant candidates for new cancer therapeutic modalities because of their biocompatibility, easy functionalization and fabrication, optical tunable characteristics, and chemophysical stability. In the last decade, there has been significant research on Au-NPs and their biomedical applications. Functionalized Au-NPs represent highly attractive and promising candidates for drug delivery, owing to their unique dimensions, tunable surface functionalities, and controllable drug release. Further, iron oxide NPs due to their "superparamagnetic" properties have been studied and have demonstrated successful employment in numerous applications. In targeted drug delivery systems, drug-loaded iron oxide NPs can accumulate at the tumor site with the aid of an external magnetic field. This can lead to incremental effectiveness in drug release to the tumor site and vanquish cancer cells without harming healthy cells. In order for the application of iron oxide NPs in the human body to be realized, they should be biodegradable and biocompatible to minimize toxicity. This review illustrates recent advances in the field drug and small molecule delivery such as fluorouracil, folic acid, doxorubicin, paclitaxel, and daunorubicin, specifically when using gold and iron oxide NPs as carriers of anticancer therapeutic agents.

Evaluation of Drug-Loaded Gold Nanoparticle Cytotoxicity as a Function of Tumor Vasculature-Induced Tissue Heterogeneity

The inherent heterogeneity of tumor tissue presents a major challenge to nanoparticle-mediated drug delivery. This heterogeneity spans from the molecular (genomic, proteomic, metabolomic) to the cellular (cell types, adhesion, migration) and to the tissue (vasculature, extra-cellular matrix) scales. In particular, tumor vasculature forms abnormally, inducing proliferative, hypoxic, and necrotic tumor tissue regions. As the vasculature is the main conduit for nanotherapy transport into tumors, vasculature-induced tissue heterogeneity can cause local inadequate delivery and concentration, leading to subpar response. Further, hypoxic tissue, although viable, would be immune to the effects of cell-cycle specific drugs. In order to enable a more systematic evaluation of such effects, here we employ computational modeling to study the therapeutic response as a function of vasculature-induced tumor tissue heterogeneity. Using data with three-layered gold nanoparticles loaded with cisplatin, nanotherapy is simulated interacting with different levels of tissue heterogeneity, and the treatment response is measured in terms of tumor regression. The results quantify the influence that varying levels of tumor vascular density coupled with the drug strength have on nanoparticle uptake and washout, and the associated tissue response. The drug strength affects the proportion of proliferating, hypoxic, and necrotic tissue fractions, which in turn dynamically affect and are affected by the vascular density. Higher drug strengths may be able to achieve stronger tumor regression but only if the intra-tumoral vascular density is above a certain threshold that affords sufficient transport. This study establishes an initial step towards a more systematic methodology to assess the effect of vasculature-induced tumor tissue heterogeneity on the response to nanotherapy.

Pharmacokinetics variability: Why nanoparticles are not just magic-bullets in oncology

Developing nanoparticles to improve the specificity of anticancer agents towards tumor tissue and to better control drug delivery is a rising strategy in oncology. An increasing number of forms (e.g., conjugated nanoparticles, liposomes, immunoliposomes…) are now available on the shelves and numerous other scaffolds (e.g., dendrimeres, nanospheres, squalenes …) are currently at various stages of development. However, as of today most nanoparticles made available remain lipidic carriers. Pharmacokinetic variability is a major, yet largely underestimated issue with liposomal nanoparticles. A wide variety of causes (e.g., tumor type and disease staging, comorbidities, patient's immune system) can explain this variability, which can in return negatively impact pharmacodynamic endpoints such as poor efficacy or severe toxicities. This review aims to cover the main causes for erratic pharmacokinetics observed with most nanoparticles, especially liposomes used in oncology. Should the main causes of such variability be identified, specific studies in non-clinical or clinical development stages could be undertaken using dedicated models (i.e., mechanistic or semi-mechanistic mathematical models such as PBPK approaches) to better describe nanoparticles pharmacokinetics and decipher PK/PD relationships. In addition, identifying relevant biomarkers or parameters likely to impact nanoparticles pharmacokinetics would allow for either the modification of their characteristics to reduce the influence of the expected variability during development phases or the development of biomarker-based adaptive dosing strategies to maintain an optimal efficacy/toxicity balance. Broadly, we call for the development of comprehensive distribution studies and state-of-the-art modeling support to better understand and anticipate nanoparticle pharmacokinetics in oncology.

Nanoparticle-mediated drug delivery to treat infections in the female reproductive tract: evaluation of experimental systems and the potential for mathematical modeling

A variety of drug-delivery platforms have been employed to deliver therapeutic agents across cervicovaginal mucus (CVM) and the vaginal mucosa, offering the capability to increase the longevity and retention of active agents to treat infections of the female reproductive tract (FRT). Nanoparticles (NPs) have been shown to improve retention, diffusion, and cell-specific targeting via specific surface modifications, relative to other delivery platforms. In particular, polymeric NPs represent a promising option that has shown improved distribution through the CVM. These NPs are typically fabricated from nontoxic, non-inflammatory, US Food and Drug Administration-approved polymers that improve biocompatibility. This review summarizes recent experimental studies that have evaluated NP transport in the FRT, and highlights research areas that more thoroughly and efficiently inform polymeric NP design, including mathematical modeling. An overview of the in vitro, ex vivo, and in vivo NP studies conducted to date – whereby transport parameters are determined, extrapolated, and validated – is presented first. The impact of different NP design features on transport through the FRT is summarized, and gaps that exist due to the limitations of iterative experimentation alone are identified. The potential of mathematical modeling to complement the characterization and evaluation of diffusion and transport of delivery vehicles and active agents through the CVM and mucosa is discussed. Lastly, potential advancements combining experimental and mathematical knowledge are suggested to inform next-generation NP designs, such that infections in the FRT may be more effectively treated.

Pharmacokinetic/pharmacodynamic modeling of combination-chemotherapy for lung cancer

Chemotherapy for non-small cell lung cancer (NSCLC) typically involves a doublet regimen for a number of cycles. For any particular patient, a course of treatment is usually chosen from a large number of combinational protocols with drugs in concomitant or sequential administration. In spite of newer drugs and protocols, half of patients with early disease will live less than five years and 95% of those with advanced disease survive for less than one year. Here, we apply mathematical modeling to simulate tumor response to multiple drug regimens, with the capability to assess maximum tolerated dose (MTD) as well as metronomic drug administration. We couple pharmacokinetic-pharmacodynamic intracellular multi-compartment models with a model of vascularized tumor growth, setting input parameters from in vitro data, and using the models to project potential response in vivo. This represents an initial step towards the development of a comprehensive virtual system to evaluate tumor response to combinatorial drug regimens, with the goal to more efficiently identify optimal course of treatment with patient tumor-specific data. We evaluate cisplatin and gemcitabine with clinically-relevant dosages, and simulate four treatment NSCLC scenarios combining MTD and metronomic therapy. This work thus establishes a framework for systematic evaluation of tumor response to combination chemotherapy. The results with the chosen parameter set indicate that although a metronomic regimen may provide advantage over MTD, the combination of these regimens may not necessarily offer improved response. Future model evaluation of chemotherapy possibilities may help to assess their potential value to obtain sustained NSCLC regression for particular patients, with the ultimate goal of optimizing multiple-drug chemotherapy regimens in clinical practice.

Mathematical modeling of tumor-associated macrophage interactions with the cancer microenvironment

Background

Immuno-oncotherapy has emerged as a promising means to target cancer. In particular, therapeutic manipulation of tumor-associated macrophages holds promise due to their various and sometimes opposing roles in tumor progression. It is established that M1-type macrophages suppress tumor progression while M2-types support it. Recently, Tie2-expressing macrophages (TEM) have been identified as a distinct sub-population influencing tumor angiogenesis and vascular remodeling as well as monocyte differentiation.

Methods

This study develops a modeling framework to evaluate macrophage interactions with the tumor microenvironment, enabling assessment of how these interactions may affect tumor progression. M1, M2, and Tie2 expressing variants are integrated into a model of tumor growth representing a metastatic lesion in a highly vascularized organ, such as the liver. Behaviors simulated include M1 release of nitric oxide (NO), M2 release of growth-promoting factors, and TEM facilitation of angiogenesis via Angiopoietin-2 and promotion of monocyte differentiation into M2 via IL-10.

Results

The results show that M2 presence leads to larger tumor growth regardless of TEM effects, implying that immunotherapeutic strategies that lead to TEM ablation may fail to restrain growth when the M2 represents a sizeable population. As TEM pro-tumor effects are less pronounced and on a longer time scale than M1-driven tumor inhibition, a more nuanced approach to influence monocyte differentiation taking into account the tumor state (e.g., under chemotherapy) may be desirable.

Conclusions

The results highlight the dynamic interaction of macrophages within a growing tumor, and, further, establish the initial feasibility of a mathematical framework that could longer term help to optimize cancer immunotherapy.

Drug delivery to solid tumors: the predictive value of the multicellular tumor spheroid model for nanomedicine screening

The increasing number of publications on the subject shows that nanomedicine is an attractive field for investigations aiming to considerably improve anticancer chemotherapy. Based on selective tumor targeting while sparing healthy tissue, carrier-mediated drug delivery has been expected to provide significant benefits to patients. However, despite reduced systemic toxicity, most nanodrugs approved for clinical use have been less effective than previously anticipated. The gap between experimental results and clinical outcomes demonstrates the necessity to perform comprehensive drug screening by using powerful preclinical models. In this context, in vitro three-dimensional models can provide key information on drug behavior inside the tumor tissue. The multicellular tumor spheroid (MCTS) model closely mimics a small avascular tumor with the presence of proliferative cells surrounding quiescent cells and a necrotic core. Oxygen, pH and nutrient gradients are similar to those of solid tumor. Furthermore, extracellular matrix (ECM) components and stromal cells can be embedded in the most sophisticated spheroid design. All these elements together with the physicochemical properties of nanoparticles (NPs) play a key role in drug transport, and therefore, the MCTS model is appropriate to assess the ability of NP to penetrate the tumor tissue. This review presents recent developments in MCTS models for a better comprehension of the interactions between NPs and tumor components that affect tumor drug delivery. MCTS is particularly suitable for the high-throughput screening of new nanodrugs.

Pancreatic adenocarcinoma response to chemotherapy enhanced with non-invasive radio frequency evaluated via an integrated experimental/computational approach

Although chemotherapy combined with radiofrequency exposure has shown promise in cancer treatment by coupling drug cytotoxicity with thermal ablation or thermally-induced cytotoxicity, limited access of the drug to tumor loci in hypo-vascularized lesions has hampered clinical application. We recently showed that high-intensity short-wave capacitively coupled radiofrequency (RF) electric-fields may reach inaccessible targets in vivo. This non-invasive RF combined with gemcitabine (Gem) chemotherapy enhanced drug uptake and effect in pancreatic adenocarcinoma (PDAC), notorious for having poor response and limited therapeutic options, but without inducing thermal injury. We hypothesize that the enhanced cytotoxicity derives from RF-facilitated drug transport in the tumor microenvironment. We propose an integrated experimental/computational approach to evaluate chemotherapeutic response combined with RF-induced phenotypic changes in tissue with impaired transport. Results show that RF facilitates diffusive transport in 3D cell cultures representing hypo-vascularized lesions, enhancing drug uptake and effect. Computational modeling evaluates drug vascular extravasation and diffusive transport as key RF-modulated parameters, with transport being dominant. Assessment of hypothetical schedules following current clinical protocol for Stage-IV PDAC suggests that unresponsive lesions may be growth-restrained when exposed to Gem plus RF. Comparison of these projections to experiments in vivo indicates that synergy may result from RF-induced cell phenotypic changes enhancing drug transport and cytotoxicity, thus providing a potential baseline for clinically-focused evaluation.

Evaluating the influence of mechanical stress on anticancer treatments through a multiphase porous media model

Drug resistance is one of the leading causes of poor therapy outcomes in cancer. As several chemotherapeutics are designed to target rapidly dividing cells, the presence of a low-proliferating cell population contributes significantly to treatment resistance. Interestingly, recent studies have shown that compressive stresses acting on tumor spheroids are able to hinder cell proliferation, through a mechanism of growth inhibition. However, studies analyzing the influence of mechanical compression on therapeutic treatment efficacy have still to be performed. In this work, we start from an existing mathematical model for avascular tumors, including the description of mechanical compression. We introduce governing equations for transport and uptake of a chemotherapeutic agent, acting on cell proliferation. Then, model equations are adapted for tumor spheroids and the combined effect of compressive stresses and drug action is investigated. Interestingly, we find that the variation in tumor spheroid volume, due to the presence of a drug targeting cell proliferation, considerably depends on the compressive stress level of the cell aggregate. Our results suggest that mechanical compression of tumors may compromise the efficacy of chemotherapeutic agents. In particular, a drug dose that is effective in reducing tumor volume for stress-free conditions may not perform equally well in a mechanically compressed environment.

A Computational/Experimental Assessment of Antitumor Activity of Polymer Nanoassemblies for pH-Controlled Drug Delivery to Primary and Metastatic Tumors

PURPOSE

Polymer nanoassemblies (PNAs) with drug release fine-tuned to occur in acidic tumor regions (pH < 7) while sparing normal tissues (pH = 7.4) were previously shown to hold promise as nanoparticle drug carriers to effectively suppress tumor growth with reduced systemic toxicity. However, therapeutic benefits of pH-controlled drug delivery remain elusive due to complex interactions between the drug carriers, tumor cells with varying drug sensitivity, and the tumor microenvironment.

METHODS

We implement a combined computational and experimental approach to evaluate the in vivo antitumor activity of acid-sensitive PNAs controlling drug release in pH 5 ~ 7.4 at different rates [PNA1 (fastest) > PNA2 > PNA3 (slowest)].

RESULTS

Computational simulations projecting the transport, drug release, and antitumor activity of PNAs in primary and metastatic tumor models of colorectal cancer correspond well with experimental observations in vivo. The simulations also reveal that all PNAs could reach peak drug concentrations in tumors at 11 h post injection, while PNAs with slower drug release (PNA2 and PNA3) reduced tumor size more effectively than fast drug releasing PNA1 (24.5 and 20.3 vs 7.5%, respectively, as fraction of untreated control).

CONCLUSION

A combined computational/experimental approach may help to evaluate pH-controlled drug delivery targeting aggressive tumors that have substantial acidity.

Enhanced uptake and transport of PLGA-modified nanoparticles in cervical cancer

BACKGROUND

Uncoordinated cellular proliferation and dysregulated angiogenesis in solid tumors are coupled with inadequate tissue, blood, and lymphatic vascularization. Consequently, tumors are often characterized by hypoxic regions with limited access to vascular-borne substances. In particular, systemically administered nanoparticles (NPs) targeting tumor cells and relying on vascular access to reach tumor tissue can suffer from limited therapeutic efficacy due to inhomogeneous intra-tumor distribution and insufficient cellular internalization of NPs. To circumvent these challenges, NP surfaces can be modified to facilitate tumor interstitial transport and cellular uptake.

RESULTS

We create poly(lactic-co-glycolic) acid NPs modified with MPG, polyethylene glycol (PEG), MPG/PEG, and Vimentin (VIM), and evaluate their cellular uptake in 2D (monolayer) cell culture of human cervical carcinoma (HeLa). We compare NP performance by evaluating uptake by non-cancerous vaginal (VK2) cells. We further assess NP interstitial transport in hypo-vascularized lesions by evaluating the effect of the various modifications on NP penetration in 3D cell culture of the HeLa cells. Results show that after 24 h incubation with HeLa cells in monolayer, MPG, MPG/PEG, PEG, and VIM NPs were internalized at 66×, 24×, 30×, and 15× that of unmodified NPs, respectively. In contrast, incubation with VK2 cells in monolayer showed that MPG , MPG/PEG , PEG , and VIM NPs internalized at 6.3×, 4.3×, 12.4×, and 3.0× that of unmodified NPs, respectively. Uptake was significantly enhanced in tumorigenic vs. normal cells, with internalization of MPG NPs by HeLa cells being twice that of PEG NPs by VK2 cells. After 24 h incubation in HeLa 3D cell culture, MPG and MPG/PEGNPs were internalized 2× and 3× compared to PEG and VIM NPs, respectively. Whereas MPG NPs were internalized mostly in the cell culture periphery (1.2×, 1.4×, and 2.7× that of PEG, MPG/PEG, and VIM NPs, respectively), PEG NPs at 250 μm penetrated 2× farther into the tissue culture than MPG NPs. For all NP types, cellular internalization was severely hindered in 3D compared to monolayer.

CONCLUSIONS

Although MPG surface modification enhances internalization and uptake in hypo-vascularized cervical tissue culture, coating with PEG reduces this internalization while enhancing penetration. A delivery strategy combining NPs with either modification may balance cellular internalization vs. tissue penetration in hypo-vascularized cervical cancer lesions.

The Tumor Microenvironment as a Barrier to Cancer Nanotherapy

Although extensive research effort and resources have been dedicated to the development of nanotherapeutics to treat cancer, few formulations have reached clinical application. A major reason is that the large number of parameters available to tune nanotherapy characteristics coupled with the variability in tumor tissue precludes evaluation of complex interactions through experimentation alone. In order to optimize the nanotechnology design and gain further insight into these phenomena, mathematical modeling and computational simulation have been applied to complement empirical work. In this chapter, we discuss modeling work related to nanotherapy and the tumor microenvironment. We first summarize the biology underlying the dysregulated tumor microenvironment, followed by a description of major nano-scale parameters. We then present an overview of the mathematical modeling of cancer nanotherapy, including evaluation of nanotherapy in multi-dimensional tumor tissue, coupling of nanotherapy with vascular flow, modeling of nanotherapy in combination with in vivo imaging, modeling of nanoparticle transport based on in vitro data, modeling of vasculature-bound nanoparticles, evaluation of nanotherapy using pharmacokinetic modeling, and modeling of nano-based hyperthermia. We conclude that an even tighter interdisciplinary effort between biological, material, and physical scientists is needed in order to eventually overcome the tumor microenvironment barrier to successful nanotherapy.