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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.

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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 (Aß) 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 Aß deposition throughout the brain. Aß 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.

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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.

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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.

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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.

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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.

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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.

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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.

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Effective treatment for glioblastoma (GBM) will likely require targeted delivery of several specific pharmacological agents simultaneously. Intra-arterial (IA) delivery is one technique for targeting the tumor site with multiple agents. Although IA chemotherapy for glioblastoma (GBM) has been attempted since the 1950s, the predicted benefits remain unproven in clinical practice. This review focuses on innovative approaches to IA drug delivery in treating GBM. Guided by novel in vitro and in vivo optical measurements, newer pharmacokinetic models promise to better define the complex relationship between background cerebral blood flow and drug injection parameters. Advanced optical technologies and tracers, unique nanoparticles designs, new cellular targets, and rational drug formulations are continuously modifying the therapeutic landscape for GBM. Personalized treatment approaches are emerging; however, such tailored approaches will largely depend on effective drug delivery techniques and on the ability to simultaneously deliver multidrug regimens. These new paradigms for tumor-selective drug delivery herald dramatic improvements in the effectiveness of IA chemotherapy for GBM. Therefore, within this context of so-called "precision medicine," the role of IA delivery for GBM is thoroughly reassessed.

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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.

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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.

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AIMS: To predict the deposition of nanoparticles in a patient-specific arterial tree as a function of the vascular architecture, flow conditions, receptor surface density and nanoparticle properties. MATERIALS & METHODS: The patient-specific vascular geometry is reconstructed from computed tomography angiography images. The isogeometric analysis framework integrated with a special boundary condition for the firm wall adhesion of nanoparticles is implemented. A parallel plate flow chamber system is used to validate the computational model in vitro. RESULTS: Particle adhesion is dramatically affected by changes in patient-specific attributes, such as branching angle and receptor density. The adhesion pattern correlates well with the spatial and temporal distribution of the wall shear rates. For the case considered, the larger (2.0 µm) particles adhere two-times more in the lower branches of the arterial tree, whereas the smaller (0.5 µm) particles deposit more in the upper branches. CONCLUSION: Our computational framework in conjunction with patient-specific attributes can be used to rationally select nanoparticle properties to personalize, and thus optimize, therapeutic interventions.

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