::April 2018::
Winner of First Prize at ANSYS Discovery Live Competition
Optimization of the geometry of a
drug-eluting stent
Drug eluting stents
Image 1: Parametric model of a coronary drug-eluting
stent.
In clinical trials stents were shown to lead to decreased
restenosis rates when compared with balloon angioplasty alone [1], [2],
[3]. Although stents have been effective in reducing the elastic recoil
and partly vascular remodeling, they may lead to adverse effects such
as in-stent restenosis, which is associated with neointimal hyperplasia.
The systemic administration of a variety of pharmacological agents has
not had a significant impact on post- angioplasty restenosis rates [4].
However, the concept of using stents as vehicles for prolonged and sufficient
intramural drug delivery is appealing. Stents represent an ideal platform
for local drug delivery because of their permanent scaffolding properties,
which prevent vessel recoil and negative remodeling. In addition, they
represent drug reservoirs, whose properties are governed by the quantity
of medications released from various coatings at certain time intervals.
The achievement of a well designed relationship between stent, coating
matrix, drug and vessel wall is extremely challenging and is known as
a drug-eluting stent.
Numerical model
The original stent
geometry was digitized and represented by smooth surfaces
in a dedicated CAD software. The software allows to fully
parameterize the stent. This was important to carry out the
optimization as a function of several design parameters.
The finite-element
model was built directly from the smooth CAD surfaces. This
procedure allowed to perform adaptive mesh refinement while
retaining even the finest details of the original model. The
adaptivity utilizes an error estimator that is suitable for
nonlinear problems.
The model is based on a hexahedral mesh because of the superior
performance when compared to tetrahedral meshes. For the stent
material a shape-memory alloy was utilized. The simulation
was carried out in Cucuma, an object oriented finite element
software that allows to deal with numerical problems on a
highly abstract (and therefore efficient) level.
For the pharmacokinetic interaction a specially developed
3D version of the model described in [5] was used.
Optimization
Image 2: Stress distribution in
the stent struts due to interaction with a human stenosis.
A large number of design parameters
had to be optimized, since the stent not only acts as mechanical but also
as a drug eluting device. Stress distributions in the struts were analyzed
to allow for maximum lateral stiffness while retaining longitudinal flexibility
(which is necessary when maneuvering through turtuous vessels). On the
pharmacokinetic side, several parameters associated with drug release
had to be optimized.
References
[1] D.L. Fischman, M.B. Leon, D.S. Baim,
R.A. Schatz, M.P. Savage, I. Penn, K. Detre, L. Veltri, D. Ricci,
M. Nobuyushi et al., A randomized comparison of coronary-stent
placement and balloon angioplasty in the treatment of coronary
artery disease. Stent Restenosis Study Investigators, New
Engl. J. Med., 331:496-501, 1994.
[2] Serruys PW, de Jaegere P, Kiemeneij F, Macaya C, Rutsch
W, Heyndrickx G, Emanuelsson H, Marco J, Legrand V, Materne
P, et al., A comparison of balloon-expandable-stent implantation
with balloon angioplasty in patients with coronary artery disease.
Benestent Study Group, New Engl. J. Med., 331:489-495,
1994.
[3] Betriu A, Masotti M, Serra A, Alonso J, Fernandez-Aviles
F, Gimeno F, Colman T, Zueco J, Delcan JL, Garcia E, Calabuig
J., Randomized comparison of coronary stent implantation and
balloon angioplasty in the treatment of de novo coronary artery
lesions (START): a four-year follow-up, Am. J. Cardiol,
34:1498-1506, 1999.
[4] M.N. Babapulle and M.J. Eisenberg. Coated stents for the
prevention of restenosis: Part I. Circ., 106:2734-2740,2002.
[5] D.V. Sakharov, L.V. Kalachev and D.C. Rijken. Numerical
simulation of local pharmacokinetics of a drug after intravascular
delivery with an eluting stent. J. Drug Target, 10:507-513,
2002.
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