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Research Papers

Computational Mechanobiology to Study the Effect of Surface Geometry on Peri-Implant Tissue Differentiation

[+] Author and Article Information
A. Andreykiv1

Faculty of Mechanical, Maritime and Material Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlandsa.andreykiv@tudelft.nl

F. van Keulen

Faculty of Mechanical, Maritime and Material Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands

P. J. Prendergast

Trinity Centre for Bioengineering, School of Engineering, Trinity College, Dublin, Ireland

1

Corresponding author.

J Biomech Eng 130(5), 051015 (Sep 15, 2008) (11 pages) doi:10.1115/1.2970057 History: Received March 22, 2007; Revised February 12, 2008; Published September 15, 2008

The geometry of an implant surface to best promote osseointegration has been the subject of several experimental studies, with porous beads and woven mesh surfaces being among the options available. Furthermore, it is unlikely that one surface geometry is optimal for all loading conditions. In this paper, a computational method is used to simulate tissue differentiation and osseointegration on a smooth surface, a surface covered with sintered beads (this simulated the experiment (Simmons, C., and Pilliar, R., 2000, Biomechanical Study of Early Tissue Formation Around Bone-Interface Implants: The Effects of Implant Surface Geometry,” Bone Engineering, J. E. Davies, ed., Emsquared, Chap. A, pp. 369–379) and established that the method gives realistic results) and a surface covered by porous tantalum. The computational method assumes differentiation of mesenchymal stem cells in response to fluid flow and shear strain and models cell migration and proliferation as continuum processes. The results of the simulation show a higher rate of bone ingrowth into the surfaces with porous coatings as compared with the smooth surface. It is also shown that a thicker interface does not increase the chance of fixation failure.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Explanation of model geometry. In their animal experiment, Simmons and Pilliar (7) used a cylindrical implant rotating inside a canine mandible, causing a uniform shear strain inside the bone-implant interface tissue (top). The presented model (bottom) simulates tissue differentiation of a small part of this interface tissue.

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Figure 2

Derivation of the geometry and FE mesh of the interface representative volume element (RVE). (a) Geometry and FE mesh of the interface tissue element, adjacent to the smooth surface. The RVE size is 120×120×100 μm3 for a 100 μm thick interface. (b) Geometry and FE mesh of the interface tissue element adjacent to the surface, covered with sintered beads. The diameter of the four large beads is 120 μm, and the small one is 100 μm. The RVE size is 240×120×270 μm3 for a 100 μm thick interface. The symmetry of the structure is taken into account. (c) Geometry and FE mesh of the interface tissue element adjacent to the porous tantalum surface. The FE mesh is mirrored in order to be able to apply periodic boundary conditions. The RVE size is 2727×1327×574 μm3 for a 100 μm thick interface (the mirrored mesh is two times larger in the X direction).

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Figure 3

Boundary conditions imposed on the mesh. The bottom of the mesh is fixed. The degrees of freedom of the sides indicated by the arrows are tied to simulate periodic boundary conditions. The symmetry conditions are applied. The horizontal micromotion is applied by prescribing corresponding displacements in the X direction to the nodes that reside on the surface of the coating (coating is not plotted).

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Figure 5

Tissue fractions for a 100 μm thick interface with a sintered bead surface. The applied level of the micromotions is 50 μm. Bone appears in the interfacial gap after four weeks of the simulated experiment.

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Figure 6

Stimulus S for a 100 μm thick interface with a sintered bead surface (simulation of animal experiment). (a) The applied level of the micromotions is 50 μm. After 25 days of the simulated experiment, the stimulus S at the interface decreases below unity, hence allowing bone differentiation. (b) The applied level of the micromotions is 75 μm. Stimulus S at the interface remains higher than 3 until the end of the simulated time. Hence only fibrous tissue differentiation in the interfacial gap is favored.

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Figure 7

Micromotions: (a) 100 μm thick interface with a sintered bead surface (simulation of the animal study). The applied micromotions levels are 50 μm and 75 μm. (b) 100 μm thick interface with an applied level of micromotions of 50 μm. Only the sintered bead surface caused a rapid decrease of micromotions by the end of the simulated time. (c) 50 μm thick interface with an applied level of micromotions of 50 μm. With this level of micromotions, only the fibrous tissue develops at all three interfaces. Hence kinetics of micromotion in all three cases is comparable. The graph for the porous tantalum case is not complete due to the convergence problems with poroelastic simulation. (d) 50 μm thick interface with an applied level of micromotions of 25 μm. Unlike the smooth surface, both sintered bead and porous tantalum surfaces cause a rapid reduction of the micromotions.

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Figure 8

Tissue fractions for a 100 μm thick interface with a porous tantalum surface. The applied level of micromotions is 50 μm. Only the fibrous tissue and some small amount of cartilage developed at the interface until the end of the simulated time.

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Figure 9

Tissue fractions for a 50 μm thick interface with a porous tantalum surface. The force controlled conditions are based on the reaction force calculated from the 100 μm thick model with 50 μm micromotions applied. The cartilage and small amount of bone appear at the interface at the end of the simulated time.

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Figure 10

Micromotions for a 50 μm thick interfaces with force BCs. The applied force is equal to the reaction force calculated from the 100 μm thick models with 50 μm micromotions applied.

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Figure 11

Cell proliferation rates as functions of stimulus S

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Figure 12

Cell differentiation rates as functions of stimulus S

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