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

Abstract

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.

Methotraxate‐Loaded Hybrid Nanoconstructs Target Vascular Lesions and Inhibit Atherosclerosis Progression in ApoE−/− Mice

“…The histological analysis with Sudan IV confirmed pockets of high lipid accumulation (red spots) within the arch, along the carotids and in the middle sections of the abdominal aorta. Interestingly, this plaque distribution pattern coincides with areas of low wall shear stress and high oscillatory index, which are well known to favor lipid deposition, monocyte infiltration, and plaque formation (Figure S3, Supporting Information) . Moreover, the observed murine pattern is compatible with the distribution of atherosclerotic plaques in humans .…”

“…Multiple mechanisms could favor the deposition of circulating nanoparticles within atherosclerotic plaques. First, it is well accepted that vascular lesions tend to develop at sites characterized by blood flow disturbance and lower shear stresses at the walls . Interestingly, these are also areas where nanoparticle deposition occurs more efficiently due to lower hemodynamic forces, as previously documented by the authors and other investigators .…”

“…First, it is well accepted that vascular lesions tend to develop at sites characterized by blood flow disturbance and lower shear stresses at the walls . Interestingly, these are also areas where nanoparticle deposition occurs more efficiently due to lower hemodynamic forces, as previously documented by the authors and other investigators . Second, macrophages are also found to sit at the edges of atherosclerotic plaques, particularly in rupture‐prone lesions, which would favor the rapid internalization of blood‐borne nanoparticles .…”

Coronary artery plaque growth: A two‐way coupled shear stress–driven model

“…To model hemodynamic‐driven plaque growth, an exponential function is assumed where growth due to hemodynamics is maximum at zero WSS magnitude and virtually zero for a sufficiently large WSS magnitude where J ( z ) is the above injury function, η is a constant parameter defining the rate of growth, τ is WSS magnitude, and k is a constant parameter controlling the dependence of growth on variations in WSS (selected based on the anticipated order of WSS during growth). The above function is similar to a Boltzmann distribution used in prior studies to model WSS‐dependent nanoparticle adhesion to vessel wall . Additionally, a similar function has been used to model WSS‐dependent EC permeability to low‐density lipoprotein (LDL) .…”

Computational modeling and engineering in pediatric and congenital heart disease

“…The application of computational fluid dynamics to cardiovascular blood flow simulation spans a wide range of adult diseases including coronary artery disease[12–15], abdominal and cerebral aneurysms[16–18], and peripheral vascular disease[19]. Image-based modeling allows for construction of patient specific anatomy directly from image data, and subsequent simulation of blood flow in these complex models using computational fluid dynamics (CFD)[20].…”