Nanocarrier Hydrodynamics and Binding in Targeted Drug Delivery: Challenges in Numerical Modeling and Experimental Validation

Multiphysics pharmacokinetic model for targeted nanoparticles

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.

Heat transfer study of a new hybrid photovoltaic/thermal direct absorption parabolic solar collector by two-phase Buongiorno model

Solar energy is one of the cleanest sources of renewable energy that is easily accessible in vast geographical areas. As the efficiency of the common solar collectors is very low and is limited by the absorption properties of working fluid, enhancing the thermal performance of these collectors is one of the major challenges of developing parabolic solar thermal power plants. In recent decades, researches revealed that utilizing nanofluid as a novel working fluid has a dramatic effect on the thermophysical and optical properties of the fluid. In this study, the flow and temperature fields of water/magnetite and water/aluminum nanofluids are evaluated by solving the steady form of governing equations including conservation laws of mass, volume fraction transport equation, momentum equation, energy equation, and radiation transfer equation. Moreover, the two-phase Buongiorno model is utilized and Brownian motion, thermophoresis effects, and magnetophoresis movement are taken into account in the nanofluid simulation. The numerical results demonstrate that increasing nanofluid volume fraction and flow rate can increase the thermal performance of the collector tube. It is found that the thermal efficiencies reach maximum values of 151.03% and 158.58% for water/aluminum and water/magnetite nanofluids, respectively. Furthermore, increasing the volume fraction from 0 to 0.3% leads to rise of 24.41% and 21.36% in the maximum temperature of the collector. The effects of different parameters such as nanoparticle volume fraction, flow rate, and nanoparticle kind on the collector thermal and electrical efficiencies, thermal distribution, and entropy production have been studied.

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

Multiscale modeling of protein membrane interactions for nanoparticle targeting in drug delivery

Nanoparticle (NP)-based imaging and drug delivery systems for systemic (e.g. intravenous) therapeutic and diagnostic applications are inherently a complex integration of biology and engineering. A broad range of length and time scales are essential to hydrodynamic and microscopic molecular interactions mediating NP (drug nanocarriers, imaging agents) motion in blood flow, cell binding/uptake, and tissue accumulation. A computational model of time-dependent tissue delivery, providing in silico prediction of organ-specific accumulation of NPs, can be leveraged in NP design and clinical applications. In this article, we provide the current state-of-the-art and future outlook for the development of predictive models for NP transport, targeting, and distribution through the integration of new computational schemes rooted in statistical mechanics and transport. The resulting multiscale model will comprehensively incorporate: (i) hydrodynamic interactions in the vascular scales relevant to NP margination; (ii) physical and mechanical forces defining cellular and tissue architecture and epitope accessibility mediating NP adhesion; and (iii) subcellular and paracellular interactions including molecular-level targeting impacting NP uptake.

Targeting drug delivery in the vascular system: Focus on endothelium

The bloodstream is the main transporting pathway for drug delivery systems (DDS) from the site of administration to the intended site of action. In many cases, components of the vascular system represent therapeutic targets. Endothelial cells, which line the luminal surface of the vasculature, play a tripartite role of the key target, barrier, or victim of nanomedicines in the bloodstream. Circulating DDS may accumulate in the vascular areas of interest and in off-target areas via mechanisms bypassing specific molecular recognition, but using ligands of specific vascular determinant molecules enables a degree of precision, efficacy, and specificity of delivery unattainable by non-affinity DDS. Three decades of research efforts have focused on specific vascular targeting, which have yielded a multitude of DDS, many of which are currently undergoing a translational phase of development for biomedical applications, including interventions in the cardiovascular, pulmonary, and central nervous systems, regulation of endothelial functions, host defense, and permeation of vascular barriers. We discuss the design of endothelial-targeted nanocarriers, factors underlying their interactions with cells and tissues, and describe examples of their investigational use in models of acute vascular inflammation with an eye on translational challenges.

Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Nanoparticle transport phenomena in confined flows

Nanoparticles submerged in confined flow fields occur in several technological applications involving heat and mass transfer in nanoscale systems. Describing the transport with nanoparticles in confined flows poses additional challenges due to the coupling between the thermal effects and fluid forces. Here, we focus on the relevant literature related to Brownian motion, hydrodynamic interactions and transport associated with nanoparticles in confined flows. We review the literature on the several techniques that are based on the principles of non-equilibrium statistical mechanics and computational fluid dynamics in order to simultaneously preserve the fluctuation-dissipation relationship and the prevailing hydrodynamic correlations. Through a review of select examples, we discuss the treatments of the temporal dynamics from the colloidal scales to the molecular scales pertaining to nanoscale fluid dynamics and heat transfer. As evident from this review, there, indeed has been little progress made in regard to the accurate modeling of heat transport in nanofluids flowing in confined geometries such as tubes. Therefore the associated mechanisms with such processes remain unexplained. This review has revealed that the information available in open literature on the transport properties of nanofluids is often contradictory and confusing. It has been very difficult to draw definitive conclusions. The quality of work reported on this topic is non-uniform. A significant portion of this review pertains to the treatment of the fluid dynamic aspects of the nanoparticle transport problem. By simultaneously treating the energy transport in ways discussed in this review as related to momentum transport, the ultimate goal of understanding nanoscale heat transport in confined flows may be achieved.

Stiffness can mediate balance between hydrodynamic forces and avidity to impact the targeting of flexible polymeric nanoparticles in flow

We report computational investigations of deformable polymeric nanoparticles (NPs) under colloidal suspension flow and adhesive environment. We employ a coarse-grained model for the polymeric NP and perform Brownian dynamics (BD) simulations with hydrodynamic interactions and in the presence of wall-confinement, particulate margination, and wall-adhesion for obtaining NP microstructure, shape, and anisotropic and inhomogeneous transport properties for different NP stiffness. These microscopic properties are utilized in solving the Fokker-Planck equation to obtain the spatial distribution of NP subject to shear, margination due to colloidal microparticles, and confinement due to a vessel wall. Comparing our computational results for the amount of NP margination to the near-wall adhesion regime with those of our binding experiments in cell culture under shear, we found quantitative agreement on shear-enhanced binding, the effect of particulate volume fraction, and the effect of NP stiffness. For the experimentally realized polymeric NP, our model predicts that the shear and volume fraction mediated enhancement in targeting has a hydrodynamic transport origin and is not due to a multivalent binding effect. However, for ultrasoft polymeric NPs, our model predicts a substantial increase in targeting due to multivalent binding. Our results are also in general agreement with experiments of tissue targeting measurements in vivo in mice, however, one needs to exercise caution in extending the modeling treatment to in vivo conditions owing to model approximations. The reported combined computational approach and results are expected to enable fine-tuning of design and optimization of flexible NP in targeted drug delivery applications.

Enhanced antimicrobial effect of berberine in nanogel carriers with cationic surface functionality

We report a strong enhancement in the antimicrobial action of berberine encapsulated into polyacrylic acid-based nanogels followed by further surface functionalisation with a cationic polyelectrolyte (PDAC). Due to the highly developed surface area, the nanogel carrier amplifies the contact of berberine with microbial cells and increases its antimicrobial efficiency. We show that such cationic nanogel carriers of berberine can adhere directly to the cell membranes and maintain a very high concentration of berberine directly on the cell surface. We demonstrated that the antimicrobial action of the PDAC-coated nanogel loaded with berberine on E. coli and C. reinhardtii is much higher than that of the equivalent solution of free berberine due to the electrostatic adhesion between the positively charged nanogel particles and the cell membranes. Our results also showed a marked increase in their antimicrobial action at shorter incubation times compared to the non-coated nanogel particles loaded with berberine under the same conditions. We attribute this boost in the antimicrobial effect of these cationic nanocarriers to their accumulation on the cell membranes which sustains a high concentration of released berberine causing cell death within much shorter incubation times. This study can provide a blueprint for boosting the action of other cationic antimicrobial agents by encapsulating them into nanogel carriers functionalised with a cationic surface layer. This nanotechnology-based approach could lead to the development of more effective wound dressings, disinfecting agents, antimicrobial surfaces, and antiseptic and antialgal/antibiofouling formulations.

Motion of a nano-spheroid in a cylindrical vessel flow: Brownian and hydrodynamic interactions

We study the motion of a buoyant or a nearly neutrally buoyant nano-sized spheroid in a fluid filled tube without or with an imposed pressure gradient (weak Poiseuille flow). The fluctuating hydrodynamics approach and the deterministic method are both employed. We ensure that the fluctuation-dissipation relation and the principle of thermal equipartition of energy are both satisfied. The major focus is on the effect of the confining boundary. Results for the velocity and the angular velocity autocorrelations (VACF and AVACF), the diffusivities and the drag and the lift forces as functions of the shape, the aspect ratio, the inclination angle and the proximity to the wall are presented. For the parameters considered, the boundary modifies the VACF and AVACF such that three distinct regimes are discernible - an initial exponential decay followed by an algebraic decay culminating in a second exponential decay. The first is due to the thermal noise, the algebraic regime is due both to the thermal noise and the hydrodynamic correlations, while the second exponential decay shows the effect of momentum reflection from the confining wall. Our predictions display excellent comparison with published results for the algebraic regime (the only regime for which earlier results exist). We also discuss the role of the off-diagonal elements of the mobility and the diffusivity tensors that enable the quantifications of the degree of lift and margination of the nanocarrier. Our study covers a range of parameters that are of wide applicability in nanotechnology, microrheology and in targeted drug delivery.

Computational Models for Nanoscale Fluid Dynamics and Transport Inspired by Nonequilibrium Thermodynamics

Traditionally, the numerical computation of particle motion in a fluid is resolved through computational fluid dynamics (CFD). However, resolving the motion of nanoparticles poses additional challenges due to the coupling between the Brownian and hydrodynamic forces. Here, we focus on the Brownian motion of a nanoparticle coupled to adhesive interactions and confining-wall-mediated hydrodynamic interactions. We discuss several techniques that are founded on the basis of combining CFD methods with the theory of nonequilibrium statistical mechanics in order to simultaneously conserve thermal equipartition and to show correct hydrodynamic correlations. These include the fluctuating hydrodynamics (FHD) method, the generalized Langevin method, the hybrid method, and the deterministic method. Through the examples discussed, we also show a top-down multiscale progression of temporal dynamics from the colloidal scales to the molecular scales, and the associated fluctuations, hydrodynamic correlations. While the motivation and the examples discussed here pertain to nanoscale fluid dynamics and mass transport, the methodologies presented are rather general and can be easily adopted to applications in convective heat transfer.

A Review of Multiscale Computational Methods in Polymeric Materials

Polymeric materials display distinguished characteristics which stem from the interplay of phenomena at various length and time scales. Further development of polymer systems critically relies on a comprehensive understanding of the fundamentals of their hierarchical structure and behaviors. As such, the inherent multiscale nature of polymer systems is only reflected by a multiscale analysis which accounts for all important mechanisms. Since multiscale modelling is a rapidly growing multidisciplinary field, the emerging possibilities and challenges can be of a truly diverse nature. The present review attempts to provide a rather comprehensive overview of the recent developments in the field of multiscale modelling and simulation of polymeric materials. In order to understand the characteristics of the building blocks of multiscale methods, first a brief review of some significant computational methods at individual length and time scales is provided. These methods cover quantum mechanical scale, atomistic domain (Monte Carlo and molecular dynamics), mesoscopic scale (Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann method), and finally macroscopic realm (finite element and volume methods). Afterwards, different prescriptions to envelope these methods in a multiscale strategy are discussed in details. Sequential, concurrent, and adaptive resolution schemes are presented along with the latest updates and ongoing challenges in research. In sequential methods, various systematic coarse-graining and backmapping approaches are addressed. For the concurrent strategy, we aimed to introduce the fundamentals and significant methods including the handshaking concept, energy-based, and force-based coupling approaches. Although such methods are very popular in metals and carbon nanomaterials, their use in polymeric materials is still limited. We have illustrated their applications in polymer science by several examples hoping for raising attention towards the existing possibilities. The relatively new adaptive resolution schemes are then covered including their advantages and shortcomings. Finally, some novel ideas in order to extend the reaches of atomistic techniques are reviewed. We conclude the review by outlining the existing challenges and possibilities for future research.

Nanoparticle stochastic motion in the inertial regime and hydrodynamic interactions close to a cylindrical wall

We have carried out direct numerical simulations (DNS) of the fluctuating Navier-Stokes equation together with the particle equations governing the motion of a nanosized particle or nanoparticle (NP) in a cylindrical tube. The effects of the confining boundary, its curvature, particle size, and particle density variations have all been investigated. To reveal how the nature of the temporal correlations (hydrodynamic memory) in the inertial regime is altered by the full hydrodynamic interaction due to the confining boundaries, we have employed the Arbitrary Lagrangian-Eulerian (ALE) method to determine the dynamical relaxation of a spherical NP located at various positions in the medium over a wide span of time scales compared to the fluid viscous relaxation time τ_v = a²/v, where a is the spherical particle radius and v is the kinematic viscosity. The results show that, as compared to the behavior of a particle in regions away from the confining boundary, the velocity autocorrelation function (VACF) for a particle in the lubrication layer initially decays exponentially with a Stokes drag enhanced by a factor that is proportional to the ratio of the particle radius to the gap thickness between the particle and the wall. Independent of the particle location, beyond time scales greater than a²/v, the decay is always algebraic followed by a second exponential decay (attributed to the wall curvature) that is associated with a second time scale D²/v, where D is the vessel diameter.

Hydrodynamic interactions of deformable polymeric nanocarriers and the effect of crosslinking

We report theoretical as well as numerical investigations of deformable nanocarriers (NCs) under physiologically relevant flow conditions. Specifically, to model the deformable lysozyme-core/dextran-shell crosslinked polymer based NC with internal nanostructure and subject it to external hydrodynamic shear, we have introduced a coarse-grained model for the NC and have adopted a Brownian dynamics framework, which incorporates hydrodynamic interactions, in order to describe the static and dynamic properties of the NC. In order to represent the fluidity of the polymer network in the dextran brush-like corona, we coarse-grain the structure of the NC based on the hypothesis that Brownian motion, polymer melt reptations, and crosslinking density dominate their structure and dynamics. In our model, we specify a crosslinking density and employ the simulated annealing protocol to mimic the experimental synthesis steps in order to obtain the appropriate internal structure of the core-shell polymer. We then compute the equilibrium as well as steady shear rheological properties as functions of the Péclet number and the crosslinking density, in the presence of hydrodynamic interactions. We find that with increasing crosslinking, the stiffness of the nanocarrier increases, the radius of gyration decreases, and as a consequence the self-diffusivity increases. The nanocarrier under shear deforms and orients along the direction of the applied shear and we find that the orientation and deformation under shear are dependent on the shear rate and the crosslinking density. We compare various dynamic properties of the NC as a function of the shear force, such as orientation, deformation, intrinsic stresses etc., with previously reported computational and experimental results of other model systems. The computational approach described here serves as a powerful tool for the rational design of NCs by taking both the physiological as well as the hydrodynamic environments into consideration. Development of such models is essential in order to gain useful insights that may be translated into the optimal design of NCs for diagnostic as well as targeted drug delivery applications.

Flow shear stress differentially regulates endothelial uptake of nanocarriers targeted to distinct epitopes of PECAM-1

Targeting nanocarriers (NC) to endothelial cell adhesion molecules including Platelet-Endothelial Cell Adhesion Molecule-1 (PECAM-1 or CD31) improves drug delivery and pharmacotherapy of inflammation, oxidative stress, thrombosis and ischemia in animal models. Recent studies unveiled that hydrodynamic conditions modulate endothelial endocytosis of NC targeted to PECAM-1, but the specificity and mechanism of effects of flow remain unknown. Here we studied the effect of flow on endocytosis by human endothelial cells of NC targeted by monoclonal antibodies Ab62 and Ab37 to distinct epitopes on the distal extracellular domain of PECAM. Flow in the range of 1-8dyn/cm(2), typical for venous vasculature, stimulated the uptake of spherical Ab/NC (~180nm diameter) carrying ~50 vs 200 Ab62 and Ab37 per NC, respectively. Effect of flow was inhibited by disruption of cholesterol-rich plasmalemma domains and deletion of PECAM-1 cytosolic tail. Flow stimulated endocytosis of Ab62/NC and Ab37/NC via eliciting distinct signaling pathways mediated by RhoA/ROCK and Src Family Kinases, respectively. Therefore, flow stimulates endothelial endocytosis of Ab/NC in a PECAM-1 epitope specific manner. Using ligands of binding to distinct epitopes on the same target molecule may enable fine-tuning of intracellular delivery based on the hemodynamic conditions in the vascular area of interest.

Composite generalized Langevin equation for Brownian motion in different hydrodynamic and adhesion regimes

We present a composite generalized Langevin equation as a unified framework for bridging the hydrodynamic, Brownian, and adhesive spring forces associated with a nanoparticle at different positions from a wall, namely, a bulklike regime, a near-wall regime, and a lubrication regime. The particle velocity autocorrelation function dictates the dynamical interplay between the aforementioned forces, and our proposed methodology successfully captures the well-known hydrodynamic long-time tail with context-dependent scaling exponents and oscillatory behavior due to the binding interaction. Employing the reactive flux formalism, we analyze the effect of hydrodynamic variables on the particle trajectory and characterize the transient kinetics of a particle crossing a predefined milestone. The results suggest that both wall-hydrodynamic interactions and adhesion strength impact the particle kinetics.

Modelling of Binding Free Energy of Targeted Nanocarriers to Cell Surface

We have developed a numerical model based on Metropolis Monte Carlo (MC) and the weighted histogram analysis method (WHAM) that enables the calculation of the absolute binding free energy between functionalized nanocarriers (NC) and endothelial cell (EC) surfaces. The binding affinities are calculated according to the free energy landscapes. The model predictions quantitatively agree with the analogous measurements of specific antibody coated NCs (100∼nm in diameter) to intracellular adhesion molecule-1 (ICAM-1) expressing EC surface in in vitro cell culture experiments. The model also enables an investigation of the effects of a broad range of parameters that include antibody surface coverage of NC, glycocalyx in both in vivo and in vitro conditions, shear flow and NC size. Using our model we explore the effects of shear flow and reproduce the shear-enhanced binding observed in equilibrium measurements in collagen-coated tube. Furthermore, our results indicate that the bond stiffness, representing the specific antibody-antigen interaction, significantly impacts the binding affinities. The predictive success of our computational protocol represents a sound quantitative approach for model driven design and optimization of functionalized nanocarriers in targeted vascular drug delivery.