Nanoparticle Optimization for Enhanced Targeted Anticancer Drug Delivery

Chamseddine, Ibrahim; Kokkolaras, Michael

doi:10.1115/1.4038202

Cited by 13 publications

(20 citation statements)

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“…The rationale is that the nanoparticle size affects the interaction area between the nanoparticle surface and the blood vessel wall, the number of ligand-receptor pairings, and the hemodynamic force and torque exerted on the nanoparticle. Accordingly, large nanoparticles have generally strong binding affinities to the endothelial layer 5,26,27 , and due to this interaction, they tend to adhere to the vessel walls at the tumor periphery 26 . In contrast, smaller nanoparticles have longer circulation times 35,36 that allow them to reach the tumor core.…”

Section: Resultsmentioning

confidence: 99%

“…A preclinical computational study based on numerical optimization is presented to establish a methodology to determine optimal nanotherapy parameters. Building upon our previous work 26,27 , this study investigates the effect of initial tumor size on the optimal selection of nanoparticles, and the effect of drug diffusion on nanoparticle design optimization, and quantifies the tradeoff between nanotherapy therapeutic and toxicological metrics. The results offer the potential to improve efficacy of nanoparticle-mediated anticancer drug delivery, and reveal nanoparticle sizes with the potential to efficiently treat a range of tumor sizes.…”

Section: Resultsmentioning

confidence: 99%

“…In this case, smaller nanoparticles may be advantageous. Previous work 26 provides evidence that large particles have high accumulation rates but heterogeneous spatial distributions, since they tend to attach in the tumor peripheral tissue. In contrast, small nanoparticles have low binding affinities, but may distribute more uniformly within tumor tissue.…”

Section: Discussionmentioning

confidence: 99%

“…Hybrid framework for optimizing nanoparticle mediated drug delivery. The framework couples a computational "blackbox" model of vascularized tumor growth and nanoparticle treatment 7,25 with an optimization model based on the mesh adaptive direct search (MADS) algorithm 26,27 . The computational model 25 uses a nanoparticle design (x) selected by MADS and evaluates the objective function f(x) and the constraints g(x).…”

Section: Methodsmentioning

confidence: 99%

“…In a recent study, we utilized rigorous derivative-free optimization to obtain optimal nanoparticle diameters and aspect ratios that maximize nanoparticle accumulation and spatial distribution in tumor tissue using a continuous 2D model of blood flow, nanoparticle accumulation, and drug transport 26 . We quantified the tradeoff between maximizing tumor nanoparticle accumulation and maximizing tumor tissue exposed to drug.…”

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confidence: 99%

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

Chamseddine

Frieboes

Kokkolaras

2020

Sci Rep

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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 timeand cost-intensive. We have recently proposed the use of computational modeling of nanoparticlemediated 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 twofold 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 Paretooptimal 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. Chemotherapy is the treatment of choice to control metastatic cancer-a stage often reported in patients at the time of clinical presentation. Unfortunately, patients undergoing chemotherapy may have low median survival, especially for pancreatic, lung, and liver cancer 1. Negative response to treatment is attributed to a number of factors, including tumor microenvironmental barriers, evolution of resistance to drug, and chemotherapeutic toxicity. It has been shown that nanoparticle-mediated drug delivery may substantially enhance the pharmacokinetics of anticancer drugs while addressing some of these factors 2. However, while many nano-based formulations have undergone pre-clinical and clinical evaluation, few have been translated to the clinic 3. The targeting potential of nanotherapy is strongly associated with nanoparticle biophysical and biochemical properties 4-6. These properties include size 7 , shape (e.g., sphere or ellipsoid) 5 , stiffness 4,8 , and binding affinity of nanoparticle surface ligands to receptors upregulated in cells in tumors 5. Computational studies have investigated the effect of these properties on treatment efficacy in an attempt to find nanoparticles optimized for maximal anti-tumor activity. Nanoparticle margination 6 and adhesion to tumor vasculature 5 have been modeled as a function of nanoparticle properties (size, aspect ratio, ligand surface density, and ligand-receptor binding affinity). Uncertainties in ligand surface density and ligand-receptor affinity have been quantified and incorporated in a nanoparticle-tumor adhesion model using a Bayesian hierarc...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Chamseddine

Frieboes

Kokkolaras

2020

Sci Rep

Self Cite

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Hybrid modeling frameworks of tumor development and treatment

Chamseddine

Rejniak

2019

WIREs Mechanisms of Disease

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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

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Optimal Navigation in Active Matter

Piro

2024

Springer Theses

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Motile active matter systems are composed by a collection of agents, each of which extracts energy from the surrounding environment in order to convert it into self-driven motion. At the microscopic scale, however, directed motion is hindered by both the presence of stochastic fluctuations. Living microorganisms therefore had to develop simple yet effective propulsion and steering mechanisms in order to survive.We may turn the question of how these processes work in nature around and ask how they should work in order to perform a task in the theoretically optimal way, an issue which falls under the name of the optimal navigation problem. The first formulation of this problem dates back to the seminal work of E. Zermelo in 1931, in which he addressed the question of how to steer a ship in the presence of an external stationary wind so as to reach the destination in the shortest time.Despite the considerable progress made over the years in this context, however, there are still a number of open challenges. In this thesis, we therefore aim to generalize Zermelo's solution by adding more and more ingredients in the description of the optimal navigation problem for microscopic active particles.First, borrowing theoretical tools from differential geometry, we here show how to extend the analytical solution of this problem to when motion occurs on curved surfaces and in the presence of arbitrary flows. Interestingly, we reveal that it can elegantly be solved by finding the geodesics of an asymmetric metric of general relativity, known as the Randers metric.Then, we study the case in which navigation happens in the presence of strong external forces. In this context, route optimization can be crucial as active particles may encounter trapping regions that would substantially slow-down their progress. Comparing the exploration efficiency of Zermelo's solution with a more trivial strategy in which the active agent always points in the same direction, here we highlight the importance of the optimal path stability, which turns out to be fundamental in the design of the proper navigation strategy depending on the task at hand.We then take it a step further and include a key ingredient in the comprehensive study of optimal navigation in active matter, namely stochastic fluctuations. Although methods already exist to obtain both analytically and numerically the optimal strategies even in the presence of noise, their implementation requires the presence of an external interpreter that * Despite this difference, in the following we will actually not make any distinction between these two terms since their observable behavior -when considered individually-is indistinguishable in a homogeneous environment 32 .

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Nanoparticle Optimization for Enhanced Targeted Anticancer Drug Delivery

Cited by 13 publications

References 46 publications

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

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

Hybrid modeling frameworks of tumor development and treatment

Optimal Navigation in Active Matter

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