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Research Papers

Numerical Simulation of Particle Transport and Deposition in the Pulmonary Vasculature

[+] Author and Article Information
Salman Sohrabi

Department of Mechanical
Engineering & Mechanics,
Lehigh University,
Bethlehem, PA 18015
e-mail: Sas713@Lehigh.edu

Junda Zheng

Department of Mechanical
Engineering and Mechanics,
Lehigh University,
Bethlehem, PA 18015
e-mail: Juz212@Lehigh.edu

Ender A. Finol

Department of Biomedical Engineering,
The University of Texas at
San Antonio, TX 78249
e-mail: Ender.finol@utsa.edu

Yaling Liu

Mem. ASME
Department of Mechanical
Engineering & Mechanics,
Bioengineering Program Lehigh University,
Bethlehem, PA 18015
e-mail: Yal310@lehigh.edu

1Corresponding author.

Manuscript received April 21, 2014; final manuscript received October 6, 2014; accepted manuscript posted October 15, 2014; published online November 7, 2014. Assoc. Editor: Naomi Chesler.

J Biomech Eng 136(12), 121010 (Dec 01, 2014) (11 pages) Paper No: BIO-14-1173; doi: 10.1115/1.4028800 History: Received April 21, 2014; Revised October 06, 2014; Accepted October 15, 2014

To quantify the transport and adhesion of drug particles in a complex vascular environment, computational fluid particle dynamics (CFPD) simulations of blood flow and drug particulate were conducted in three different geometries representing the human lung vasculature for steady and pulsatile flow conditions. A fully developed flow profile was assumed as the inlet velocity, and a lumped mathematical model was used for the calculation of the outlet pressure boundary condition. A receptor–ligand model was used to simulate the particle binding probability. The results indicate that bigger particles have lower deposition fraction due to less chance of successful binding. Realistic unsteady flow significantly accelerates the binding activity over a wide range of particle sizes and also improves the particle deposition fraction in bifurcation regions when comparing with steady flow condition. Furthermore, surface imperfections and geometrical complexity coupled with the pulsatility effect can enhance fluid mixing and accordingly particle binding efficiency. The particle binding density at bifurcation regions increases with generation order and drug carriers are washed away faster in steady flow. Thus, when studying drug delivery mechanism in vitro and in vivo, it is important to take into account blood flow pulsatility in realistic geometry. Moreover, tissues close to bifurcations are more susceptible to deterioration due to higher uptake.

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Figures

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Fig. 1

(a) Artificial ideal Weibel model and (b) lung vasculature geometry reconstructed from a CT image dataset; a four-generation original (c) and oversmoothed (d) subunit of pulmonary vasculature

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Fig. 2

Illustration of a spherical particle in contact with vascular wall. δeq is the separation distance between the spheroidal particle and the substrate; h0 is the maximum distance of the spheroid from the substrate at which a specific bond can occur; d is the diameter of the particle.

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Fig. 7

Study of streamline disturbance effect on particle deposition fraction using original, oversmoothed and artificial geometry with the same inlet flow rate

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Fig. 8

Particle deposition fraction for particles of different sizes under steady and pulsatile blood flow. It is demonstrated at two different particle diameter ranges (a) 1 to 100 nm and (b) 1 nm to 3 μm.

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Fig. 9

Effect of vessel surface smoothness on particle deposition fraction under pulsatile flow condition

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Fig. 3

Instantaneous inlet velocity profile estimated from experimentally measured data [30] for left and right pulmonary arteries (LPA and RPA). 0.1 m/s is approximately the average of LPA flow which occurs at 0.1 s and 0.45 s during cardiac cycle.

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Fig. 4

Velocity contours and selected streamlines in a planar slice of original geometry in (a) steady flow case with 0.1 m/s inlet velocity and in (b), and (c) pulsatile flow case at t = 0.1 s and t = 0.45 s with 0.1 m/s instantaneous inlet velocity (refer to Fig. 6), respectively

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Fig. 5

Demonstration of shear stress on the vessel wall in (a) steady flow case with 0.1 m/s inlet velocity and pulsatile flow case at t = 0.1 s, (b) and t = 0.45 s, and (c) with 0.1 m/s instantaneous inlet velocity

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Fig. 6

Particle deposition fraction at two different ranges of particle sizes under various steady flow rates (a) 1 to 100 nm and (b) 1 nm to 3 μm

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Fig. 10

Illustrations of deposition profile versus time for various simulation parameters. (a) Accumulative profiles of particle binding under different flow conditions and geometries (b) instantaneous deposition profile of cardiac flow in geometries with different surface smoothness.

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Fig. 11

3D demonstration of deposition pattern when releasing two million particles with diameters ranging from 1 nm to 0.5 μm under pulsatile inlet flow in (a) original lung vascular geometry and (b) artificial Weibel geometry

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Fig. 12

Normalized deposited number of particles with different diameter ranges deposited on various regions of the original geometry

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Fig. 13

3D representation for trapped location of injected particles at different times (a) 1 s, (b) 2 s, and (c) 8 s after starting the simulation with cardiac inlet flow

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Fig. 14

Normalized deposited number on a bifurcating region of vasculature coated with different anti-ICAM densities for different particle sizes

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Fig. 15

Mesh models over a cross section of a lung geometry. Mesh size is large in the middle and small near boundary. The minimum mesh size limits are 0.2, 0.15, 0.125, and 0.1 mm for (a), (b), (c), and (d) cases, respectively.

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Fig. 16

Total number of deposited particle versus number of elements in different mesh models

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Fig. 17

Mesh analysis using elements of four different sizes for (a) steady inlet flow and (b) unsteady inlet flow

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Fig. 18

Mesh model of lung vasculature has been split into different regions. Gen: generation; Bif: bifurcation.

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