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

Carfilzomib-Loaded Ternary Polypeptide Nanoparticles Stabilized by Polycationic Complexation

Carfilzomib (CFZ) is a second-generation proteasome inhibitor showing great efficacy in multiple myeloma treatment, yet its clinical applications for other diseases such as solid cancers are limited due to low aqueous solubility and poor biostability. Ternary polypeptide nanoparticles (tPNPs) are drug carriers that we previously reported to overcome these pharmaceutical limitations by entrapping CFZ in the core of the nanoparticles and protecting the drugs from degradation in biological media. However, preclinical studies revealed that tPNPs would require further improvement in particle stability to suppress initial burst drug release and thus achieve prolonged inhibition of proteasome activity with CFZ against tumor cells in vivo. In this study, CFZ-loaded tPNPs are stabilized by polycations which have varying pKa values and thus differently modulate nanoparticle stability in response to solution pH. Through polyion complexation, the polycations appeared to stabilize the core of tPNPs entrapping CFZ-cyclodextrin inclusion complexes while allowing for uniform particle size before and after freeze drying. Interestingly, CFZ-loaded tPNPs (CFZ/tPNPs) showed pH-dependent drug release kinetics, which accelerated CFZ release as solution acidity increased (pH < 6) without compromising particle stability at the physiological condition (pH 7.4). In vitro cytotoxicity and proteasome activity assays confirmed that tPNPs stabilized with cationic polymers improved bioactivity of CFZ against CFZ-resistant cancer cells, which would be greatly beneficial in combination with pH-dependent drug release for treatment of solid cancers with drug resistance and tumor microenvironment acidosis by using CFZ and other proteasome inhibitors.

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.

Modeling of tumor response to macrophage and T lymphocyte interactions in the liver metastatic microenvironment

The dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment exert both antagonistic and synergistic functions affecting tumor growth. Extensive experimental effort has been expended to investigate immunotherapeutic strategies targeting macrophage polarization as well as T-cell activation with the goal to promote tumor cell killing and cancer elimination. However, these interactions remain poorly understood, and cancer immunotherapeutic strategies are often disappointing. The complex system encompassing innate and adaptive immune cell activity in response to tumor growth could benefit from a systems perspective built upon mathematical modeling. This study develops a modeling system to help evaluate the effects of macrophage and T-lymphocyte interactions on tumor growth. The system enables simulating the combined cytotoxic and tumor-promoting interactions of these two immune cell populations in a vascularized organ microenvironment, such as in liver metastases. A hypothetical immunotherapeutic strategy is simulated to increase the number of tumor-suppressive (M1-phenotype) vs. tumor-promoting (M2-phenotype) macrophages to gauge their effects on CD8+ T-cells and CD4+ T-helper cells, which in turn affect the macrophage functions. The results highlight the dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment and show that with the chosen set of parameter values, the overall cytotoxic effect from macrophages and T-lymphocytes obtained by driving the M1:M2 ratio higher could saturate and fail to achieve tumor regression. Further expansion of this modeling platform to include additional tumor-immune cell interactions, coupled with parameters representing particular tumor characteristics, could enable systematic evaluation of immunotherapeutic strategies tailored to patient-tumor specific conditions, including metastatic disease.

Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could "reawaken" after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.

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.

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.

Hybrid modeling frameworks of tumor development and treatment

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments.

This article is characterized under:

Analytical and Computational Methods > Computational Methods

Translational, Genomic, and Systems Medicine > Therapeutic Methods

Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

Design Optimization of Tumor Vasculature-Bound Nanoparticles

Nanotherapy may constitute a promising approach to target tumors with anticancer drugs while minimizing systemic toxicity. Computational modeling can enable rapid evaluation of nanoparticle (NP) designs and numerical optimization. Here, an optimization study was performed using an existing tumor model to find NP size and ligand density that maximize tumoral NP accumulation while minimizing tumor size. Optimal NP avidity lies at lower bound of feasible values, suggesting reduced ligand density to prolong NP circulation. For the given set of tumor parameters, optimal NP diameters were 288 nm to maximize NP accumulation and 334 nm to minimize tumor diameter, leading to uniform NP distribution and adequate drug load. Results further show higher dependence of NP biodistribution on the NP design than on tumor morphological parameters. A parametric study with respect to drug potency was performed. The lower the potency of the drug, the bigger the difference is between the maximizer of NP accumulation and the minimizer of tumor size, indicating the existence of a specific drug potency that minimizes the differential between the two optimal solutions. This study shows the feasibility of applying optimization to NP designs to achieve efficacious cancer nanotherapy, and offers a first step towards a quantitative tool to support clinical decision making.

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.

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.

Development of Halofluorochromic Polymer Nanoassemblies for the Potential Detection of Liver Metastatic Colorectal Cancer Tumors Using Experimental and Computational Approaches

PURPOSE

To develop polymer nanoassemblies (PNAs) modified with halofluorochromic dyes to allow for the detection of liver metastatic colorectal cancer (CRC) to improve therapeutic outcomes.

METHODS

We combine experimental and computational approaches to evaluate macroscopic and microscopic PNA distributions in patient-derived xenograft primary and orthotropic liver metastatic CRC tumors. Halofluorochromic and non-halofluorochromic PNAs (hfPNAs and n-hfPNAs) were prepared from poly(ethylene glycol), fluorescent dyes (Nile blue, Alexa546, and IR820), and hydrophobic groups (palmitate), all of which were covalently tethered to a cationic polymer scaffold [poly(ethylene imine) or poly(lysine)] forming particles with an average diameter < 30 nm.

RESULTS

Dye-conjugated PNAs showed no aggregation under opsonizing conditions for 24 h and displayed low tissue diffusion and cellular uptake. Both hfPNAs and n-hfPNAs accumulated in primary and liver metastatic CRC tumors within 12 h post intravenous injection. In comparison to n-hfPNAs, hfPNAs fluoresced strongly only in the acidic tumor microenvironment (pH < 7.0) and distinguished small metastatic CRC tumors from healthy liver stroma. Computational simulations revealed that PNAs would steadily accumulate mainly in acidic (hypoxic) interstitium of metastatic tumors, independently of the vascularization degree of the tissue surrounding the lesions.

CONCLUSION

The combined experimental and computational data confirms that hfPNAs detecting acidic tumor tissue can be used to identify small liver metastatic CRC tumors with improved accuracy.