In silico vascular modeling for personalized nanoparticle delivery

Effect of Circadian Rhythm Modulated Blood Flow on Nanoparticle based Targeted Drug Delivery in Virtual In Vivo Arterial Geometries

Delivery of drug using nanocarriers tethered with vasculature-targeting epitopes aims to maximize the therapeutic efficacy of the drug while minimizing the drug side effects. Circadian rhythm which is governed by the central nervous system has implications for targeted drug delivery due to sleep-wake cycle changes in blood flow dynamics. This paper presents an advanced fluid dynamics modeling method that is based on viscous incompressible shear-rate fluid (blood) coupled with an advection-diffusion equation to simulate the formation of drug concentration gradients in the blood stream and buildup of concentration at the targeted site. The method is equipped with an experimentally calibrated nanoparticle-endothelial cell adhesion model that employs Robin boundary conditions to describe nanoparticle retention based on probability of adhesion, a friction model accounting for surface roughness of endothelial cell layer, and a dispersion model based on Taylor-Aris expression for effective diffusion in the boundary layer. The computational model is first experimentally validated and then tested on engineered bifurcating arterial systems where impedance boundary conditions are applied at the outflow to account for the downstream resistance at each outlet. It is then applied to a virtual geometric model of an in vivo arterial tree developed through MRI-based image processing techniques. These simulations highlight the potential of the computational model for drug transport, adhesion, and retention at multiple sites in virtual in vivo models. The model provides a virtual platform for exploring circadian rhythm modulated blood flow for targeted drug delivery while minimizing the in vivo experimentation.

Statement of Significance

A novel integration of nanoparticle-based drug delivery framework with shear-rate dependent blood flow model is presented. The framework is comprised of a unique combination of mechanics-based dispersion model, an asperity model for endothelium surface roughness, and a stochastic nanoparticle-endothelial cell adhesion model. Simulations of MRI based in vivo carotid artery system showcase the effects of vessel geometry on nanoparticle adhesion and retention at the targeted site. Vessel geometry and target site location impact nanoparticle adhesion; curved and bifurcating regions favor local accumulation of drug. It is also shown that aligning drug administration with circadian rhythm and sleep cycle can enhance the efficacy of drug delivery processes. These simulations highlight the potential of the computational modeling for exploring circadian rhythm modulated blood flow for targeted drug delivery while minimizing the in vivo experimentation.

A parametric study of the effect of 3D plaque shape on local hemodynamics and implications for plaque instability

The vast majority of heart attacks occur when vulnerable plaques rupture, releasing their lipid content into the blood stream leading to thrombus formation and blockage of a coronary artery. Detection of these unstable plaques before they rupture remains a challenge. Hemodynamic features including wall shear stress (WSS) and wall shear stress gradient (WSSG) near the vulnerable plaque and local inflammation are known to affect plaque instability. In this work, a computational workflow has been developed to enable a comprehensive parametric study detailing the effects of 3D plaque shape on local hemodynamics and their implications for plaque instability. Parameterized geometric 3D plaque models are created within a patient-specific coronary artery tree using a NURBS (non-uniform rational B-splines)-based vascular modeling pipeline. Realistic blood flow features are simulated by using a Navier-Stokes solver within an isogeometric finite-element analysis framework. Near wall hemodynamic quantities such as WSS and WSSG are quantified, and vascular distribution of an inflammatory marker (VCAM-1) is estimated. Results show that proximally skewed eccentric plaques have the most vulnerable combination of high WSS and high positive spatial WSSG, and the presence of multiple lesions increases risk of rupture. The computational tool developed in this work, in conjunction with clinical data, -could help identify surrogate markers of plaque instability, potentially leading to a noninvasive clinical procedure for the detection of vulnerable plaques before rupture.

Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs.

Image-guided subject-specific modeling of glymphatic transport and amyloid deposition

The glymphatic system is a brain-wide system of perivascular networks that facilitate exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) to remove waste products from the brain. A greater understanding of the mechanisms for glymphatic transport may provide insight into how amyloid beta (A β ) and tau agglomerates, key biomarkers for Alzheimer's disease and other neurodegenerative diseases, accumulate and drive disease progression. In this study, we develop an image-guided computational model to describe glymphatic transport and A β deposition throughout the brain. A β transport and deposition are modeled using an advection-diffusion equation coupled with an irreversible amyloid accumulation (damage) model. We use immersed isogeometric analysis, stabilized using the streamline upwind Petrov-Galerkin (SUPG) method, where the transport model is constructed using parameters inferred from brain imaging data resulting in a subject-specific model that accounts for anatomical geometry and heterogeneous material properties. Both short-term (30-min) and long-term (12-month) 3D simulations of soluble amyloid transport within a mouse brain model were constructed from diffusion weighted magnetic resonance imaging (DW-MRI) data. In addition to matching short-term patterns of tracer deposition, we found that transport parameters such as CSF flow velocity play a large role in amyloid plaque deposition. The computational tools developed in this work will facilitate investigation of various hypotheses related to glymphatic transport and fundamentally advance our understanding of its role in neurodegeneration, which is crucial for the development of preventive and therapeutic interventions.

Optimal heat transport induced by magnetic nanoparticle delivery in vascularised tumours

We describe a novel mathematical model for blood flow, delivery of nanoparticles, and heat transport in vascularised tumour tissue. The model, which is derived via the asymptotic homogenisation technique, provides a link between the macroscale behaviour of the system and its underlying, tortuous micro-structure, as parametrised in Penta and Ambrosi (2015). It consists of a double Darcy's law, coupled with a double advection-diffusion-reaction system describing heat transport, and an advection-diffusion-reaction equation for transport and adhesion of particles. Particles are assumed sufficiently large and do not extravasate to the tumour interstitial space but blood and heat can be exchanged between the two compartments. Numerical simulations of the model are performed using a finite element method to investigate cancer hyperthermia induced by the application of magnetic field applied to injected iron oxide nanoparticles. Since tumour microvasculature is more tortuous than that of healthy tissue and thus suboptimal in terms of fluid and drug transport, we study the influence of the vessels' geometry on tumour temperature. Effective and safe hyperthermia treatment requires tumour temperature within certain target range, generally estimated between 42 °C and 46 °C, for a certain target duration, typically 0.5h to 2h. As temperature is difficult to measure in situ, we use our model to determine the ranges of tortuosity of the microvessels, magnetic intensity, injection time, wall shear stress rate, and concentration of nanoparticles required to achieve given target conditions.

A Novel Method for Improving the Accuracy of MR-derived Patient-specific Vascular Models using X-ray Angiography

MR imaging, a noninvasive radiation-free imaging modality commonly used during clinical follow up, has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In recent work, we used patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)-a progressive steno-occlusive cerebrovascular disorder that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR) that signifies elevated stroke risk. However, sources of error such as insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. To improve the accuracy of MR-derived vascular models, we have developed a novel method for adjusting model vessel geometry utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VAs) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared with corresponding XA images to guide the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeometric analysis framework, and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA), and up to a 56% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model. To evaluate whether XA-based adjustment improves model accuracy, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal-to-noise ratio compared to MR imaging but is not commonly used in the clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate, individualized assessment of stroke risk would be of substantial help in guiding the timing of preventive surgical interventions in pediatric MMD patients.

Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke

Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothesized that local hemodynamics are predictive of future stroke and set out to noninvasively assess this stroke risk in pediatric MMD patients. MR and X-ray angiography imaging were utilized to reconstruct patient-specific models of the circle of Willis of six pediatric MMD patients who had previous strokes, along with a control subject. Blood flow simulations were performed by using a Navier-Stokes solver within an isogeometric analysis framework. Vascular regions with a wall shear rate (WSR) above the coagulation limit (>5,000 s-1) were identified to have a higher probability of thrombus formation, potentially leading to ischemic stroke(s). Two metrics, namely, "critical WSR coverage" and "WSR score," were derived to assess contralateral stroke risk and compared with clinical follow-up data. In two patients that suffered a contralateral stroke within 2 months of the primary stroke, critical WSR coverages exceeding 50% of vessel surface and WSR scores greater than 6× the control were present in multiple contralateral vessels. These metrics were not as clearly indicative of stroke in two additional patients with 3-5 year gaps between primary and contralateral strokes. However, a longitudinal study of one of these two cases, where a subsequent timepoint was analyzed, suggested disease stabilization on the primary stroke side and an elevated contralateral stroke risk, which was confirmed by patient outcome data. This indicates that post-stroke follow-up at regular intervals might be warranted for secondary stroke prevention. The findings of this study suggest that WSR-based metrics could be predictive of future stroke risk after an initial stroke in pediatric MMD patients. In addition, better predictions may be possible by performing patient-specific hemodynamic analysis at multiple timepoints during patient follow-up to monitor changes in the WSR-based metrics.

Image-based patient-specific flow simulations are consistent with stroke in pediatric cerebrovascular disease

Moyamoya disease (MMD) is characterized by narrowing of the distal internal carotid artery and the circle of Willis (CoW) and leads to recurring ischemic and hemorrhagic stroke. A retrospective review of data from 50 pediatric MMD patients revealed that among the 24 who had a unilateral stroke and were surgically treated, 11 (45.8%) had a subsequent, contralateral stroke. There is no reliable way to predict these events. After a pilot study in Acta-/- mice that have features of MMD, we hypothesized that local hemodynamics are predictive of contralateral strokes and sought to develop a patient-specific analysis framework to noninvasively assess this stroke risk. A pediatric MMD patient with an occlusion in the right middle cerebral artery and a right-sided stroke, who was surgically treated and then had a contralateral stroke, was selected for analysis. By using an unsteady Navier-Stokes solver within an isogeometric analysis framework, blood flow was simulated in the CoW model reconstructed from the patient's postoperative imaging data, and the results were compared with those from an age- and sex-matched control subject. A wall shear rate (WSR) > 60,000 s-1 (about 12 × higher than the coagulation threshold of 5000 s-1 and 9 × higher than control) was measured in the terminal left supraclinoid artery; its location coincided with that of the subsequent postsurgical left-sided stroke. A parametric study of disease progression revealed a strong correlation between the degree of vascular morphology altered by MMD and local hemodynamic environment. The results suggest that an occlusion in the CoW could lead to excessive contralateral WSRs, resulting in thromboembolic ischemic events, and that WSR could be a predictor of future stroke.

Understanding Drug Delivery to the Brain Using Liposome-Based Strategies: Studies that Provide Mechanistic Insights Are Essential

Brain drug delivery may be restricted by the blood-brain barrier (BBB), and enhancement by liposome-based drug delivery strategies has been investigated. As access to the human brain is limited, many studies have been performed in experimental animals. Whereas providing interesting data, such studies have room for improvement to provide mechanistic insight into the rate and extent of specifically BBB transport and intrabrain distribution processes that all together govern CNS target delivery of the free drug. This review shortly summarizes BBB transport and current liposome-based strategies to overcome BBB transport restrictions, with the emphasis on how to determine the individual mechanisms that all together determine the time course of free drug brain concentrations, following their administration as such, and in liposomes. Animal studies using microdialysis providing time course information on unbound drug in plasma and brain are highlighted, as these provide the mechanistic information needed to understand BBB drug transport of the drug, and the impact of a liposomal formulations of that drug on BBB transport. Overall, these studies show that brain distribution of a drug administered as liposomal formulation depends on both drug properties and liposomal formulation characteristics. In general, evidence suggests that active transporters at the BBB, either being influx or efflux transporters, are circumvented by liposomes. It is concluded that liposomal formulations may provide interesting changes in BBB transport. More mechanistic studies are needed to understand relevant mechanisms in liposomal drug delivery to the brain, providing an improved basis for its prediction in human using animal data.

Parameterization, geometric modeling, and isogeometric analysis of tricuspid valves

Approximately 1.6 million patients in the United States are affected by tricuspid valve regurgitation, which occurs when the tricuspid valve does not close properly to prevent backward blood flow into the right atrium. Despite its critical role in proper cardiac function, the tricuspid valve has received limited research attention compared to the mitral and aortic valves on the left side of the heart. As a result, proper valvular function and the pathologies that may cause dysfunction remain poorly understood. To promote further investigations of the biomechanical behavior and response of the tricuspid valve, this work establishes a parameter-based approach that provides a template for tricuspid valve modeling and simulation. The proposed tricuspid valve parameterization presents a comprehensive description of the leaflets and the complex chordae tendineae for capturing the typical three-cusp structural deformation observed from medical data. This simulation framework develops a practical procedure for modeling tricuspid valves and offers a robust, flexible approach to analyze the performance and effectiveness of various valve configurations using isogeometric analysis. The proposed methods also establish a baseline to examine the tricuspid valve's structural deformation, perform future investigations of native valve configurations under healthy and disease conditions, and optimize prosthetic valve designs.

Advance cardiac nanomedicine by targeting the pathophysiological characteristics of heart failure

Heart failure (HF) has continued to be a leading cause of morbidity and mortality worldwide. Nanomedicine, which can deliver therapeutic drugs/biomolecules specifically to damaged myocardium and overcome the limitations of conventional therapies, shows great potential in the treatment of HF. Although a number of preclinical studies of cardiac nanoformulations have been published, targeted nanomedicine for HF is yet to be applied in clinical practice. Therefore, it is meaningful to sum up past experiences and deepen the understanding of nanomedicine and HF. In this review, we first emphasized the key biological barriers to cardiac nanomedicine that hinder its targeting effect. Since the rational design of nanoparticles should take into account the specific characteristics of HF, we then summarized the key pathophysiological changes of HF to provide a clear understanding on HF, as well as the latest examples of nanotechnology-based delivery strategies for different pathophysiological characteristics. Finally, the major challenges are discussed in detail, aiming to provide guidance for future development of cardiac nanomedicine.

Elucidating the rheological implications of adding particles in blood

In the past few decades, nanotechnology has been employed to provide breakthroughs in the diagnosis and treatment of several diseases using drug-carrying particles (DCPs). In such an endeavor, the optimal design of DCPs is paramount, which necessitates the use of an accurate and trustworthy constitutive model in computational fluid dynamics (CFD) simulators. We herein introduce a continuum model for elaborating on the rheological implications of adding particles in blood. The model is developed using non-equilibrium thermodynamics to guarantee thermodynamic admissibility. Red blood cells are modeled as deformed droplets with a constant volume that are able to aggregate, whereas particles are considered rigid spheroids. The model predictions are compared favorably against rheological data for both spherical and non-spherical particles immersed in non-aggregating blood. It is expected that the use of this model will allow for the testing of DCPs in virtual patients and for their tailor-design in treating various diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00397-021-01289-x.

The biomedical significance of multifunctional nanobiomaterials: The key components for site-specific delivery of therapeutics

The emergence of nanotechnology has provided the possibilities to overcome the potential problems associated with the development of pharmaceuticals including the low solubility, non-specific cellular uptake or action, and rapid clearance. Regarding the biomaterials (BMs), huge efforts have been made for improving their multi-functionalities via incorporation of various nanomaterials (NMs). Nanocomposite hydrogels with suitable properties could exhibit a variety of beneficial effects in biomedicine particularly in the delivery of therapeutics or tissue engineering. NMs including the silica- or carbon-based ones are capable of integration into various BMs that might be due to their special compositions or properties such as the hydrophilicity, hydrophobicity, magnetic or electrical characteristics, and responsiveness to various stimuli. This might provide multi-functional nanobiomaterials against a wide variety of disorders. Meanwhile, inappropriate distribution or penetration into the cells or tissues, bio-nano interface complexity, targeting ability loss, or any other unpredicted phenomena are the serious challenging issues. Computational simulations and models enable development of NMs with optimal characteristics and provide a deeper knowledge of NM interaction with biosystems. This review highlights the biomedical significance of the multifunctional NMs particularly those applied for the development of 2-D or 3-D BMs for a variety of applications including the site-specific delivery of therapeutics. The powerful impacts of the computational techniques on the design process of NMs, quantitation and prediction of protein corona formation, risk assessment, and individualized therapy for improved therapeutic outcomes have also been discussed.

A converging artery-sized model for shear adhesion mapping of particles

Drug carriers for targeting cardiovascular diseases have been gaining a respectable attention, however, designing such carriers is challenging due to the biophysical complexity of the vascular system. Wall shear stress (WSS), exerted by blood flow on the endothelium surface, is a crucial factor in the circulatory system. WSS affects the adhesion and preferential accumulation of drug carriers. Here, we propose, an innovative approach to investigate particle adhesion in a converging artery-sized model, lined with human endothelial cells. Unlike widely used microfluidic and in vivo setups, our model enables to investigate particle accumulation in a continuous WSS range, performed in a single experiment, and at the right scale relevant for human arteries. First, we characterized the flow and the WSS map along the designed model, which can span along the entire arterial WSS range. We then used the model to examine the effect of particle size and the suspension buffer on particle adhesion distribution. The results demonstrated the role of particle size, where the same particles with a diameter of 2 µm exhibit shear-decreased adhesion while 500 nm particles exhibit shear-enhanced adhesion. Furthermore, under the same WSS, particles show a similar behavior when suspended in a Dextran buffer, having a viscosity analogous to blood, compared to a phosphate buffer solution without Dextran. Moreover, experiments with RBCs in the phosphate buffer, at a 40% physiological hematocrit, decreased particle adhesion and affected their deposition pattern. Altogether, our study suggests an original platform for investigating and optimizing intravascular drug carriers and their targeting properties.

Effect of Material and Population on the Delivery of Nanoparticles to an Atherosclerotic Plaque: A Patient-specific In Silico Study

Coronary artery disease (CAD) is the prevalent reason of mortality all around the world. Targeting CAD, specifically atherosclerosis, with controlled delivery of micro and nanoparticles, as drug carriers, is a very proficient approach. In this work, a patient-specific and realistic model of an atherosclerotic plaque in the left anterior descending (LAD) artery was created by image-processing of CT-scan images and implementing a finite-element mesh. Next, a fluid-solid interaction simulation considering the physiological boundary conditions was conducted. By considering the simulated force fields and particle-particle interactions, the correlation between injected particles at each cardiac cycle and the surface density of adhered particles over the atherosclerotic plaque (SDP) were examined. For large particles (800 and 1000 nm) the amount of SDP on the plaque increased significantly when the number of the injected particles became higher. However, by increasing the number of the injected particles, for the larger particles (800 and 1000 nm) the increase in SDP was about 50% greater than that of the smaller ones (400 and 600 nm). Furthermore, for constant number of particles, depending on their size, different trends in SDP were observed. Subsequently, the distribution and adhesion of metal-based nanoparticles including SiO₂, Fe₃O₄, NiO₂, silver and gold with different properties were simulated. The injection of metal particles with medium density among the considered particles resulted in the highest SDP. Remarkably, the affinity, the geometrical features, and the biophysical factors involved in the adhesion outweighed the effect of difference in the density of particles on the SDP. Finally, the consideration of the lift force in the simulations significantly reduced the SDP and consistently decreased the particle residence time in the studied domain.

Drug delivery performance of nanocarriers based on adhesion and interaction for abdominal aortic aneurysm treatment

Targeted drug delivery using nanocarriers (NCs) is one of the novel techniques that has recently been used to improve drug delivery to the Abdominal aortic aneurysm (AAA) disease. The purpose of this study is to evaluate the surface density of NCs (SDNC) adhered via ligand-receptor binding to the inner wall of AAA. For this purpose, fluid-structure interaction (FSI) analysis was first performed for the patient-specific and ideal AAA models. Then, by injecting NCs into the aortic artery, the values of SDNC adhered to and interacted with AAA wall were obtained. Two types of NCs, liposomes, and solid particles in four different diameters, were used to investigate the effect of the diameter and the type of NCs on the drug delivery. Additionally, the effect of the number of the injected NCs to the artery on the values of SDNC adhered to and interacted with AAA wall was investigated. The simulation results showed that the interaction and adhesion values of SDNC for Liposome nanoparticles were higher than the ones for the solid particles. Furthermore, as the diameter of NCs increases, the values of SDNC adhered to AAA wall increase, but the values of SDNC interacted with the inner wall of AAA decrease. In the low number of inserted NCs in the artery (1000 NCs), the interaction and adhesion values of SDNC were very slight, and by increasing the number of NCs inserted into the artery, the drug delivery was improved. By examining different AAA models, it was found that the complexity of the shape of AAA has a minor effect on the pattern of increase or decrease of the values of SDNC adhered to and interacted with AAA wall.This study's findings can improve the understanding of NCs design and propose the appropriate amount of their injection into various AAA models.

Personalized dynamic transport of magnetic nanorobots inside the brain vasculature

Delivering specific bioactive agents with sufficient bioavailability to the targeted brain area across blood brain barrier remains a big challenge. Magnetically driven nanorobots have demonstrated their potential for controlled drug delivery. However, the dynamic transport of these nanorobots inside each individual's brain vasculature is not yet well studied. Addressing this is a critical step forward to controlled drug delivery for non-invasive brain therapeutics. In this paper, we develop an analytical model describing the personalized dynamic transport of spherical magnetic nanorobots inside the brain vasculature reconstructed from the patient's angiography images. By inverting the transporting process, we first design the patient-specific transport path based on the reconstructed vascular model, and then calculate the magnetic force required to drive these nanorobots from the analytical model. Also, a finite element model is created to simulate the inverse design process, which implies that the delivery efficiency of these magnetically driven nanorobots to the targeted brain area can be increased by 20% and almost 95% nanorobots arrive at the desired vessel walls. In the end, a simplified brain vascular model is printed using PolyJet 3D 750 to demonstrate the dynamic transport of these nanorobots toward the targeted site. The proposed theoretical modeling, numerical simulation and experimental validation lay solid foundation toward non-invasive brain therapeutics with maximal accuracy and minimal side effects.

Computational and experimental investigation of particulate matter deposition in cerebral side aneurysms

Intracranial aneurysms frequently develop blood clots, plaque and inflammations, which are linked to enhanced particulate mass deposition. In this work, we propose a computational model for particulate deposition, that accounts for the influence of field forces, such as gravity and electrostatics, which produce an additional flux of particles perpendicular to the fluid motion and towards the wall. This field-mediated flux can significantly enhance particle deposition in low-shear environments, such as in aneurysm cavities. Experimental investigation of particle deposition patterns in in vitro models of side aneurysms, demonstrated the ability of the model to predict enhanced particle adhesion at these sites. Our results showed a significant influence of gravity and electrostatic forces (greater than 10%), indicating that the additional terms presented in our models may be necessary for modelling a wide range of physiological flow conditions and not only for ultra-low shear regions. Spatial differences between the computational model and the experimental results suggested that additional transport and fluidic mechanisms affect the deposition pattern within aneurysms. Taken together, the presented findings may enhance our understanding of pathological deposition processes at cardiovascular disease sites, and facilitate rational design and optimization of cardiovascular particulate drug carriers.

Advances in mathematical models of the active targeting of tumor cells by functional nanoparticles

BACKGROUND AND OBJECTIVE

The process of nanoparticles (NPs) entering blood circulation to actively target tumor cells involves four stages-the transport of NPs in blood vessels, transvascular transport of NPs, transport of NPs in the tumor interstitial matrix and entry of NPs into tumor cells. These four stages are a complex process involving mechanical, physical, biochemical, and biophysical factors, the tumor microenvironment (TME) and properties of NPs play important roles in this process. Because this process involves a large number of factors and is very complex, it is difficult to study with conventional methods.

METHODS

Using mathematical models for simulation is suitable for addressing this complex situation and can describe the complexity well.

RESULTS

This work focuses on the theoretical simulation of NPs that target tumor cells to illustrate the effects of the abnormal microenvironment of tumors and properties of NPs on the transport process. Mathematical models constructed by different methods are enumerated. Through studying these mathematical models, different methods to overcome nanoparticle (NP) transport obstacles are illustrated.

CONCLUSIONS

It is necessary to construct a theoretical model of active targeting nanodrug delivery under the coupling of micro-flow field and specific binding force field, and to simulate and analyze the delivery process at mesoscopic scale using computational fluid dynamics (CFD) method, so as to reveal the law and characteristics of drug delivery and cell uptake in the micro-environment of tumors in vivo. The methods and techniques discussed can serve as the basis for systematic studies of active targeting of functional nanoparticles to tumor cells.

Improving nanotherapy delivery and action through image-guided systems pharmacology

Despite recent advances in the translation of therapeutic nanoparticles (TNPs) into the clinic, the field continues to face challenges in predictably and selectively delivering nanomaterials for the treatment of solid cancers. The concept of enhanced permeability and retention (EPR) has been coined as a convenient but simplistic descriptor of high TNP accumulation in some tumors. However, in practice EPR represents a number of physiological variables rather than a single one (including dysfunctional vasculature, compromised lymphatics and recruited host cells, among other aspects of the tumor microenvironment) — each of which can be highly heterogenous within a given tumor, patient and across patients. Therefore, a clear need exists to dissect the specific biophysical factors underlying the EPR effect, to formulate better TNP designs, and to identify patients with high-EPR tumors who are likely to respond to TNP. The overall pharmacology of TNP is governed by an interconnected set of spatially defined and dynamic processes that benefit from a systems-level quantitative approach, and insights into the physiology have profited from the marriage between in vivo imaging and quantitative systems pharmacology (QSP) methodologies. In this article, we review recent developments pertinent to image-guided systems pharmacology of nanomedicines in oncology. We first discuss recent developments of quantitative imaging technologies that enable analysis of nanomaterial pharmacology at multiple spatiotemporal scales, and then examine reports that have adopted these imaging technologies to guide QSP approaches. In particular, we focus on studies that have integrated multi-scale imaging with computational modeling to derive insights about the EPR effect, as well as studies that have used modeling to guide the manipulation of the EPR effect and other aspects of the tumor microenvironment for improving TNP action. We anticipate that the synergistic combination of imaging with systems-level computational methods for effective clinical translation of TNPs will only grow in relevance as technologies increase in resolution, multiplexing capability, and in the ability to examine heterogeneous behaviors at the single-cell level.

Phytosynthesized metal oxide nanoparticles for pharmaceutical applications

Developments in nanotechnology field, specifically, metal oxide nanoparticles have attracted the attention of researchers due to their unique sensing, electronic, drug delivery, catalysis, optoelectronics, cosmetics, and space applications. Physicochemical methods are used to fabricate nanosized metal oxides; however, drawbacks such as high cost and toxic chemical involvement prevail. Recent researches focus on synthesizing metal oxide nanoparticles through green chemistry which helps in avoiding the involvement of toxic chemicals in the synthesis process. Bacteria, fungi, and plants are the biological sources that are utilized for the green nanoparticle synthesis. Due to drawbacks such as tedious maintenance and the time needed for the nanoparticle formation, plant extracts are widely used in nanoparticle production. In addition, plants are available all over the world and phytosynthesized nanoparticles show comparatively less toxicity towards mammalian cells. Secondary metabolites including flavonoids, terpenoids, and saponins are present in plant extracts, and these are highly responsible for nanoparticle formation and reduction of toxicity. Hence, this article gives an overview of recent developments in the phytosynthesis of metal oxide nanoparticles and their toxic analysis in various cells and animal models. Also, their possible mechanism in normal and cancer cells, pharmaceutical applications, and their efficiency in disease treatment are also discussed.

Trends towards Biomimicry in Theranostics

Over the years, imaging and therapeutic modalities have seen considerable progress as a result of advances in nanotechnology. Theranostics, or the marrying of diagnostics and therapy, has increasingly been employing nano-based approaches to treat cancer. While first-generation nanoparticles offered considerable promise in the imaging and treatment of cancer, toxicity and non-specific distribution hindered their true potential. More recently, multistage nanovectors have been strategically designed to shield and carry a payload to its intended site. However, detection by the immune system and sequestration by filtration organs (i.e., liver and spleen) remains a major obstacle. In an effort to circumvent these biological barriers, recent trends have taken inspiration from biology. These bioinspired approaches often involve the use of biologically-derived cellular components in the design and fabrication of biomimetic nanoparticles. In this review, we provide insight into early nanoparticles and how they have steadily evolved to include bioinspired approaches to increase their theranostic potential.

A model-based analysis of tissue targeting efficacy of nanoparticles

Tissue targeting is a critical challenge for systemic delivery of drug nanocarriers. To overcome this challenge, major research efforts have been undertaken to design ligand-conjugated nanoparticles. However, limited work has been done to quantitatively assess the effectiveness of such approach. In this work, using a mechanistic spatio-temporal model, I investigate the effectiveness of ligand-directed tissue targeting. By applying an approach from the colloidal filtration theory, I develop a Brownian dynamics model of nanoparticle-cell interaction. The model incorporates a single cell and its surrounding flow field. It considers both specific (receptor-mediated) and non-specific (bare cell surface-mediated) recognition of nanoparticles subject to convective and diffusive motion. Using the model, I investigate how the specific and non-specific interactions compare in determining the overall targeting efficacy. My analysis provides some interesting findings that contradict the general notion that effective targeting is possible based upon the differential receptor expression in cancer and non-cancer cells. I show that such strategy may yield only a marginal gain in the targeting efficacy. Moreover, non-specific interaction may have an important influence on particle recognition by cells even at high receptor expression levels.

Liposome size and charge optimization for intraarterial delivery to gliomas

Nanoparticles such as liposomes may be used as drug delivery vehicles for brain tumor therapy. Particle geometry and electrostatic properties have been hypothesized to be important determinants of effective tumor targeting after intraarterial injection. In this study, we investigate the combined roles of liposome size and surface charge on the effectiveness of delivery to gliomas after intraarterial injection. Intracarotid injection of liposomes was performed in separate cohorts of both healthy and C6 glioma-bearing Sprague Dawley rats after induction of transient cerebral hypoperfusion. Large (200 nm) and small (60-80 nm) fluorescent dye-loaded liposomes that were either cationic or neutral in surface charge were utilized. Delivery effectiveness was quantitatively measured both with real-time, in vivo and postmortem diffuse reflectance spectroscopy. Semi-quantitative multispectral fluorescence imaging was also utilized to assess the pattern and extent of liposome targeting within tumors. Large cationic liposomes demonstrated the most effective hemispheric and glioma targeting of all the liposomes tested. Selective large cationic liposome retention at the site of glioma growth was observed. The liposome deposition pattern within tumors after intraarterial injection was variable with both core penetration and peripheral deposition observed in specific tumors. This study provides evidence that liposome size and charge are important determinants of effective brain and glioma targeting after intraarterial injection. Our results support the future development of 200-nm cationic liposomal formulations of candidate intraarterial anti-glioma agents for further pre-clinical testing.

Computational pharmacokinetic rationale for intra-arterial delivery to the brain

Intra-arterial (IA) drug delivery has been proposed for the treatment of a wide range of brain diseases, including malignant brain tumors. However, pharmacokinetic optimization for IA drug delivery to the brain remains a challenge. In this report, we apply and expand the well-established Dedrick model of IA drug delivery to the brain and test the effects of modifying drug and delivery parameters. These simulations show that altering the properties of candidate drugs and physiological variables can have profound effects on regional deposition after IA injections. We show that drug and physiological optimization aimed at rapid drug extraction and sustained retention is necessary to maximize regional deposition after of IA injections.

Nanomedicine for the cardiac myocyte: Where are we?

Biomedical achievements in the last few decades, leading to successful therapeutic interventions, have considerably improved human life expectancy. Nevertheless, the increasing load and the still suboptimal outcome for patients with cardiac dysfunction underlines the relevance of continuous research to develop novel therapeutics for these diseases. In this context, the field of nanomedicine has attracted a lot of attention due to the potential novel treatment possibilities, such as controlled and sustained release, tissue targeting, and drug protection from degradation. For cardiac myocytes, which constitute the majority of the heart by mass and are the contractile unit, new options have been explored in terms of the use of nanomaterials (NMs) for therapy, diagnosis, and tissue engineering. This review focuses on the advances of nanomedicine targeted to the cardiac myocyte: first presenting the NMs used and the principal cardiac myocyte-based afflictions, followed by an overview of key advances in the field, including NMs interactions with the cardiac myocyte, therapy delivery, diagnosis based on imaging, and tissue engineering for tissue repair and heart-on-a-chip devices.

Flow arrest intra-arterial delivery of small TAT-decorated and neutral micelles to gliomas

The cell-penetrating trans-activator of transcription (TAT) is a cationic peptide derived from human immunodeficiency virus-1. It has been used to facilitate macromolecule delivery to various cell types. This cationic peptide is capable of crossing the blood-brain barrier and therefore might be useful for enhancing the delivery of drugs that target brain tumors. Here we test the efficiency with which relatively small (20 nm) micelles can be delivered by an intra-arterial route specifically to gliomas. Utilizing the well-established method of flow-arrest intra-arterial injection we compared the degree of brain tumor deposition of cationic TAT-decorated micelles versus neutral micelles. Our in vivo and post-mortem analyses confirm glioma-specific deposition of both TAT-decorated and neutral micelles. Increased tumor deposition conferred by the positive charge on the TAT-decorated micelles was modest. Computational modeling suggested a decreased relevance of particle charge at the small sizes tested but not for larger particles. We conclude that continued optimization of micelles may represent a viable strategy for targeting brain tumors after intra-arterial injection. Particle size and charge are important to consider during the directed development of nanoparticles for intra-arterial delivery to brain tumors.

Predicting different adhesive regimens of circulating particles at blood capillary walls

A fundamental step in the rational design of vascular targeted particles is the firm adhesion at the blood vessel walls. Here, a combined lattice Boltzmann-immersed boundary model is presented for predicting the near-wall dynamics of circulating particles. A moving least squares algorithm is used to reconstruct the forcing term accounting for the immersed particle, whereas ligand-receptor binding at the particle-wall interface is described via forward and reverse probability distributions. First, it is demonstrated that the model predicts with good accuracy the rolling velocity of tumor cells over an endothelial layer in a microfluidic channel. Then, particle-wall interactions are systematically analyzed in terms of particle geometries (circular, elliptical with aspect ratios 2 and 3), surface ligand densities (0.3, 0.5, 0.7 and 0.9), ligand-receptor bond strengths (1 and 2) and Reynolds numbers (Re = 0.01, 0.1 and 1.0). Depending on these conditions, four different particle-wall interaction regimens are identified, namely not adhering, rolling, sliding and firmly adhering particles. The proposed computational strategy can be efficiently used for predicting the near-wall dynamics of particles with arbitrary geometries and surface properties and represents a fundamental tool in the rational design of particles for the specific delivery of therapeutic and imaging agents.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Link between deviations from Murray's Law and occurrence of low wall shear stress regions in the left coronary artery

Murray developed two laws for the geometry of bifurcations in the circulatory system. Based on the principle of energy minimization, Murray found restrictions for the relation between the diameters and also between the angles of the branches. It is known that bifurcations are prone to the development of atherosclerosis, in regions associated to low wall shear stresses (WSS) and high oscillatory shear index (OSI). These indicators (size of low WSS regions, size of high OSI regions and size of high helicity regions) were evaluated in this work. All of them were normalized by the size of the outflow branches. The relation between Murray's laws and the size of low WSS regions was analysed in detail. It was found that the main factor leading to large regions of low WSS is the so called expansion ratio, a relation between the cross section areas of the outflow branches and the cross section area of the main branch. Large regions of low WSS appear for high expansion ratios. Furthermore, the size of low WSS regions is independent of the ratio between the diameters of the outflow branches. Since the expansion ratio in bifurcations following Murray's law is kept in a small range (1 and 1.25), all of them have regions of low WSS with similar size. However, the expansion ratio is not small enough to completely prevent regions with low WSS values and, therefore, Murray's law does not lead to atherosclerosis minimization. A study on the effect of the angulation of the bifurcation suggests that the Murray's law for the angles does not minimize the size of low WSS regions.

Magnetic resonance imaging-based computational modelling of blood flow and nanomedicine deposition in patients with peripheral arterial disease

Peripheral arterial disease (PAD) is generally attributed to the progressive vascular accumulation of lipoproteins and circulating monocytes in the vessel walls leading to the formation of atherosclerotic plaques. This is known to be regulated by the local vascular geometry, haemodynamics and biophysical conditions. Here, an isogeometric analysis framework is proposed to analyse the blood flow and vascular deposition of circulating nanoparticles (NPs) into the superficial femoral artery (SFA) of a PAD patient. The local geometry of the blood vessel and the haemodynamic conditions are derived from magnetic resonance imaging (MRI), performed at baseline and at 24 months post intervention. A dramatic improvement in blood flow dynamics is observed post intervention. A 500% increase in peak flow rate is measured in vivo as a consequence of luminal enlargement. Furthermore, blood flow simulations reveal a 32% drop in the mean oscillatory shear index, indicating reduced disturbed flow post intervention. The same patient information (vascular geometry and blood flow) is used to predict in silico in a simulation of the vascular deposition of systemically injected nanomedicines. NPs, targeted to inflammatory vascular molecules including VCAM-1, E-selectin and ICAM-1, are predicted to preferentially accumulate near the stenosis in the baseline configuration, with VCAM-1 providing the highest accumulation (approx. 1.33 and 1.50 times higher concentration than that of ICAM-1 and E-selectin, respectively). Such selective deposition of NPs within the stenosis could be effectively used for the detection and treatment of plaques forming in the SFA. The presented MRI-based computational protocol can be used to analyse data from clinical trials to explore possible correlations between haemodynamics and disease progression in PAD patients, and potentially predict disease occurrence as well as the outcome of an intervention.

Evaluation of Receptor-Ligand Mechanisms of Dual-Targeted Particles to an Inflamed Endothelium

Vascular-targeted carriers (VTCs) are designed as leukocyte mimics, decorated with ligands that target leukocyte adhesion molecules (LAMs) and facilitate adhesion to diseased endothelium. VTCs require different design considerations than other targeted particle therapies; adhesion of VTCs in regions with dynamic blood flow requires multiple ligand-receptor (LR) pairs that provide particle adhesion and disease specificity. Despite the ultimate goal of leukocyte mimicry, the specificity of multiple LAM-targeted VTCs remains poorly understood, especially in physiological environments. Here, we investigate particle binding to an inflamed mesentery via intravital microscopy using a series of particles with well-controlled ligand properties. We find that the total number of sites of a single ligand can drive particle adhesion to the endothelium, however, combining ligands that target multiple LR pairs provides a more effective approach. Combining sites of sialyl Lewis A (sLeA) and anti-intercellular adhesion molecule-1 (aICAM), two adhesive molecules, resulted in ~3-7-fold increase of adherent particles at the endothelium over single-ligand particles. At a constant total ligand density, a particle with a ratio of 75% sLeA: 25% aICAM resulted in more than 3-fold increase over all over other ligand ratios tested in our in vivo model. Combined with in vitro and in silico data, we find the best dual-ligand design of a particle is heavily dependent on the surface expression of the endothelial cells, producing better adhesion with more particle ligand for the lesser-expressed receptor. These results establish the importance of considering LR-kinetics in intelligent VTC ligand design for future therapeutics.

Characterization of Nanoparticle Dispersion in Red Blood Cell Suspension by the Lattice Boltzmann-Immersed Boundary Method

Nanodrug-carrier delivery in the blood stream is strongly influenced by nanoparticle (NP) dispersion. This paper presents a numerical study on NP transport and dispersion in red blood cell (RBC) suspensions under shear and channel flow conditions, utilizing an immersed boundary fluid-structure interaction model with a lattice Boltzmann fluid solver, an elastic cell membrane model and a particle motion model driven by both hydrodynamic loading and Brownian dynamics. The model can capture the multiphase features of the blood flow. Simulations were performed to obtain an empirical formula to predict NP dispersion rate for a range of shear rates and cell concentrations. NP dispersion rate predictions from the formula were then compared to observations from previous experimental and numerical studies. The proposed formula is shown to accurately predict the NP dispersion rate. The simulation results also confirm previous findings that the NP dispersion rate is strongly influenced by local disturbances in the flow due to RBC motion and deformation. The proposed formula provides an efficient method for estimating the NP dispersion rate in modeling NP transport in large-scale vascular networks without explicit RBC and NP models.

An analytical model for nanoparticles concentration resulting from infusion into poroelastic brain tissue

We consider the infusion of a diluted suspension of nanoparticles (NPs) into poroelastic brain tissue, in view of relevant biomedical applications such as intratumoral thermotherapy. Indeed, the high impact of the related pathologies motivates the development of advanced therapeutic approaches, whose design also benefits from theoretical models. This study provides an analytical expression for the time-dependent NPs concentration during the infusion into poroelastic brain tissue, which also accounts for particle binding onto cells (by recalling relevant results from the colloid filtration theory). Our model is computationally inexpensive and, compared to fully numerical approaches, permits to explicitly elucidate the role of the involved physical aspects (tissue poroelasticity, infusion parameters, NPs physico-chemical properties, NP-tissue interactions underlying binding). We also present illustrative results based on parameters taken from the literature, by considering clinically relevant ranges for the infusion parameters. Moreover, we thoroughly assess the model working assumptions besides discussing its limitations. While not laying any claims of generality, our model can be used to support the development of more ambitious numerical approaches, towards the preliminary design of novel therapies based on NPs infusion into brain tissue.

Cerebral hypoperfusion-assisted intra-arterial deposition of liposomes in normal and glioma-bearing rats

BACKGROUND

Optimizing liposomal vehicles for targeted delivery to the brain has important implications for the treatment of brain tumors. The promise of efficient, brain-specific delivery of chemotherapeutic compounds via liposomal vehicles has yet to be achieved in clinical practice. Intra-arterial injection of specially designed liposomes may facilitate efficient delivery to the brain and to gliomas.

OBJECTIVE

To test the hypothesis that cationic liposomes may be effectively delivered to both normal and glioma-bearing brain tissue utilizing a strategy of intra-arterial injection during transient cerebral hypoperfusion.

METHODS

Cationic, anionic, and neutral liposomes were separately injected via the internal carotid artery of healthy rats during transient cerebral hypoperfusion. Rats bearing C6 gliomas were similarly injected with cationic liposomes. Liposomes were loaded with DilC18(5) dye whose concentrations can be measured by light absorbance and fluorescence methods.

RESULTS

After intra-arterial injection, a robust uptake of cationic in comparison with anionic and neutral liposomes into brain parenchyma was observed by diffuse reflectance spectroscopy. Postmortem multispectral fluorescence imaging revealed that liposomal cationic charge was associated with more efficient delivery to the brain. Cationic liposomes were also readily observed within glioma tissue after intra-arterial injection. However, over time, cationic liposomes were retained longer and at higher concentrations in the surrounding, peritumoral brain than in the tumor core.

CONCLUSION

This study demonstrates the feasibility of cationic liposome delivery to brain and glioma tissue after intra-arterial injection. Highly cationic liposomes directly delivered to the brain via an intracarotid route may represent an effective method for delivering antiglioma agents.

Hemodynamic analysis in an idealized artery tree: differences in wall shear stress between Newtonian and non-Newtonian blood models

Development of many conditions and disorders, such as atherosclerosis and stroke, are dependent upon hemodynamic forces. To accurately predict and prevent these conditions and disorders hemodynamic forces must be properly mapped. Here we compare a shear-rate dependent fluid (SDF) constitutive model, based on the works by Yasuda et al in 1981, against a Newtonian model of blood. We verify our stabilized finite element numerical method with the benchmark lid-driven cavity flow problem. Numerical simulations show that the Newtonian model gives similar velocity profiles in the 2-dimensional cavity given different height and width dimensions, given the same Reynolds number. Conversely, the SDF model gave dissimilar velocity profiles, differing from the Newtonian velocity profiles by up to 25% in velocity magnitudes. This difference can affect estimation in platelet distribution within blood vessels or magnetic nanoparticle delivery. Wall shear stress (WSS) is an important quantity involved in vascular remodeling through integrin and adhesion molecule mechanotransduction. The SDF model gave a 7.3-fold greater WSS than the Newtonian model at the top of the 3-dimensional cavity. The SDF model gave a 37.7-fold greater WSS than the Newtonian model at artery walls located immediately after bifurcations in the idealized femoral artery tree. The pressure drop across arteries reveals arterial sections highly resistive to flow which correlates with stenosis formation. Numerical simulations give the pressure drop across the idealized femoral artery tree with the SDF model which is approximately 2.3-fold higher than with the Newtonian model. In atherosclerotic lesion models, the SDF model gives over 1 Pa higher WSS than the Newtonian model, a difference correlated with over twice as many adherent monocytes to endothelial cells from the Newtonian model compared to the SDF model.

Patient-specific computational modeling and magnetic nanoconstructs: tools for maximizing the efficacy of stem cell-based therapies

Stem cell transplantation has the potential to restore heart function following myocardial infarction. However, the success of any stem cell-based therapy is critically linked to the effective homing and early engraftment of the injected cells at the infarcted site. Here, a hierarchical multiscale computational model is proposed for predicting the patient-specific vascular transport and intratissue homing and migration of stem cells injected either systemically or locally. Starting with patient-specific data, such as the vascular geometry, blood flow, and location of the infarcted area, the computational model can be used to perform parametric analysis to identify optimal injection conditions in terms of administration route, injection site, catheter type, and infusion velocity. In addition to this, a new generation of magnetic nanoconstructs is introduced for labeling stem cells and monitoring their behavior in vivo via magnetic resonance imaging. These nanoconstructs also can be used for multimodal imaging, merging MRI and nuclear imaging, and the intracellular delivery of active agents to support stem cell differentiation. The convergence of computational modeling and novel nanoconstructs for stem cell labeling could improve our understanding in cell homing and early engraftment at the infarcted site and thus pave the way to more effective stem cell-based therapies.

Evaluation of gold nanoparticles toxicity towards human endothelial cells under static and flow conditions

A new in vitro model system, adding advection and shear stress associated with a flowing medium, is proposed for the investigation of nanoparticles uptake and toxicity towards endothelial cells, since these processes are normally present when nanoparticles formulations are intravenously administered. In this model system, mechanical forces normally present in vivo, such as advection and shear stress were applied and carefully controlled by growing human umbilical vein endothelial cells inside a microfluidic device and continuously infusing gold nanoparticle (Au NPs) solution in the device. The tests performed in the microfluidic device were also run in multiwells, where no flow is present, so as to compare the two model systems and evaluate if gold nanoparticles toxicity differs under static and flow culture conditions. Full characterization of Au NPs in water and in culture medium was accomplished by standard methods. Two-photon fluorescence correlation spectroscopy was also employed to map the flow speed of Au NPs in the microfluidic device and characterize Au NPs before and after interactions with the cells. Au NPs uptake in both in vitro systems was investigated through electron and fluorescence microscopy and ICP-AES, and NPs toxicity measured through standard bio-analytical tests. Comparison between experiments run in multiwells and in microfluidic device plays a pivotal role for the investigation of nanoparticle-cell interaction and toxicity assessment: our work showed that administration of equal concentrations of Au NPs under flow conditions resulted in a reduced sedimentation of nanoparticle aggregates onto the cells and lower cytotoxicity with respect to experiments run in ordinary static conditions (multiwells).

USNCTAM perspectives on mechanics in medicine

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies.

Vascular distribution of nanomaterials

Once considered primarily occupational, novel nanotechnology innovations, and applications have led to widespread domestic use and intentional biomedical exposures. With these exciting advances, the breadth and depth of toxicological considerations must also be expanded. The vascular system interacts with every tissue in the body, striving to maintain homeostasis. Engineered nanomaterials (ENM) have been reported to distribute in many different tissues and organs. However, these observations have tended to use approaches requiring tissue homogenization and/or gross organ analyses. These techniques, while effective in establishing presence, preclude an exact determination of where ENM are deposited within a tissue. If nanotechnology is to achieve its full potential, it is necessary to identify this exact distribution and deposition of ENM throughout the cardiovascular system, with respect to vascular hemodynamics and in vivo ENM modifications taken into account. Distinct levels of the vasculature will first be described as individual compartments. Then the vasculature will be considered as a whole. These unique compartments and biophysical conditions will be discussed in terms of their propensity to favor ENM deposition. Understanding levels of the vasculature will also be discussed. Ultimately, future studies must verify the mechanisms speculated on and presented herein.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles

The complex vascular dynamics and wall deposition of systemically injected nanoparticles is regulated by their geometrical properties (size, shape) and biophysical parameters (ligand-receptor bond type and surface density, local shear rates). Although sophisticated computational models have been developed to capture the vascular behavior of nanoparticles, it is increasingly recognized that purely deterministic approaches, where the governing parameters are known a priori and conclusively describe behaviors based on physical characteristics, may be too restrictive to accurately reflect natural processes. Here, a novel computational framework is proposed by coupling the physics dictating the vascular adhesion of nanoparticles with a stochastic model. In particular, two governing parameters (i.e. the ligand-receptor bond length and the ligand surface density on the nanoparticle) are treated as two stochastic quantities, whose values are not fixed a priori but would rather range in defined intervals with a certain probability. This approach is used to predict the deposition of spherical nanoparticles with different radii, ranging from 750 to 6,000 nm, in a parallel plate flow chamber under different flow conditions, with a shear rate ranging from 50 to 90 sec^-1. It is demonstrated that the resulting stochastic model can predict the experimental data more accurately than the original deterministic model. This approach allows one to increase the predictive power of mathematical models of any natural process by accounting for the experimental and intrinsic biological uncertainties.

Vascular deposition patterns for nanoparticles in an inflamed patient-specific arterial tree

Inflammation, a precursor to many diseases including cancer and atherosclerosis, induces differential surface expression of specific vascular molecules. Blood-borne nanoparticles (NPs), loaded with therapeutic and imaging agents, can recognize and use these molecules as vascular docking sites. Here, a computational model is developed within the isogeometric analysis framework to understand and predict the vascular deposition of NPs within an inflamed arterial tree. The NPs have a diameter ranging from 0.1 to 2.0 μm and are decorated with antibodies directed toward three endothelial adhesion molecules, namely intravascular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin, whose surface density depends on the local wall shear stress. Results indicate VCAM-1 targeted NPs adhere more, with ICAM-1 directed NPs adhering least efficiently, resulting in approximately an order-of-magnitude lower average particle surface density. ICAM-1 and E-selectin directed 0.5 μm NPs are distributed more uniformly (heterogeneity index ≈ 0.9 and 1.0, respectively) over the bifurcating vascular branches compared to their VCAM-1 counterparts (heterogeneity index ≈ 1.4). When the NPs are coated with antibodies for VCAM-1 and E-selectin in equal proportions, a more uniform vascular distribution is achieved compared with VCAM-1-only targeted particles, thus demonstrating the advantage of NP multivalency in vascular targeting. Furthermore, the larger NPs (2 μm) adhere more (≈ 200%) in the lower branches compared to the upper branch. This computational framework provides insights into how size, ligand type, density, and multivalency can be manipulated to enhance NP vascular adhesion in an individual patient.