Research Papers

Modeling of Spatially Controlled Biomolecules in Three-Dimensional Porous Alginate Structures

[+] Author and Article Information
Ibrahim T. Ozbolat

Department of Industrial and Systems Engineering, University at Buffalo, State University of New York, 438 Bell Hall, Buffalo, NY 14260

Bahattin Koc1

 Sabanci University, Faculty of Engineering and Natural Sciences, Orhanli-Tuzla, Istanbul/Turkey 34956; Department of Industrial and Systems Engineering, University at Buffalo, State University of New York, 438 Bell Hall, Buffalo, NY 14260bahattinkoc@sabanciuniv.edu


Corresponding author.

J. Med. Devices 4(4), 041003 (Nov 03, 2010) (11 pages) doi:10.1115/1.4002612 History: Received March 26, 2010; Revised September 22, 2010; Published November 03, 2010; Online November 03, 2010

This paper presents a computer-aided design (CAD) of 3D porous tissue scaffolds with spatial control of encapsulated biomolecule distributions. A localized control of encapsulated biomolecule distribution over 3D structures is proposed to control release kinetics spatially for tissue engineering and drug release. Imaging techniques are applied to explore distribution of microspheres over porous structures. Using microspheres in this study represents a framework for modeling the distribution characteristics of encapsulated proteins, growth factors, cells, and drugs. A quantification study is then performed to assure microsphere variation over various structures under imaging analysis. The obtained distribution characteristics are mimicked by the developed stochastic modeling study of microsphere distribution over 3D engineered freeform structures. Based on the stochastic approach, 3D porous structures are modeled and designed in CAD. Modeling of microsphere and encapsulating biomaterial distribution in this work helps develop comprehensive modeling of biomolecule release kinetics for further research. A novel multichamber single nozzle solid freeform fabrication technique is utilized to fabricate sample structures. The presented methods are implemented and illustrative examples are presented in this paper.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

SFF technique: (a) layer-by-layer deposition and (b) filament orientation

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Figure 2

(a) A schematic of multichamber single nozzle assembly. (b) Printed 3.3% (w/v) alginate solution with 0.2% (w/v) microsphere bead concentration crosslinked with 0.6% (w/v) calcium chloride.

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Figure 3

Sample microscope image for single filament calculations

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Figure 4

Dark field images for 3.3% (m/v) sodium alginate structure crosslinked with 0.5% calcium chloride (m/v) enclosing (a) 0.2% (m/v) microsphere concentration with 20x zoom, (b) 0.66% microsphere concentration with 5x zoom, and (c) 1% microsphere concentration with 5x zoom

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Figure 12

Region based presentation of incorporated microsphere volume: (a) 3D surface plot of the quantification study results over the top surface of the first porous structure, (b) comparison of analytical and quantification study for the first structure, (c) 3D surface plot of the quantification study results over the top surface of the second porous structure, and (d) comparison of analytical and quantification study for the second structure

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Figure 13

Limitation of dark field images to capture hidden microspheres

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Figure 14

Concentration gradient over porous structure: (a) 3%, (b) 3.1%, (c) 3.3%, (d) 3.6%, (e) 3.9%, and (f) 4%

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Figure 5

Normal distribution analysis over specific filament width in MINITAB software (39): ((a) and (d)) 0.2% (m/v) microsphere concentration with 164 microspheres over 107,412 μm2 area with p-value<0.05, ((b) and (e)) 0.66% microsphere concentration with 520 microspheres over 109,257 μm2 area with p-value<0.005, and ((c) and (f)) 1% microsphere concentration with 365 microspheres over 589,645 μm2 area with p-value<0.005

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Figure 6

Schematic diagram of (a) nozzle system with fluid velocity through the cross section of the nozzle tip (42) where RN is nozzle radius and (b) discretizing filament to approximate the (c) normal distribution

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Figure 7

Methodology of locating a microsphere stochastically starting with (a) modeling of 3D layer-by-layer manufacturing that (b) deposits each layer enclosing a number of regions. (c) Each Regionp is occupied by cylindrical filaments in 0 deg and 90 deg filament printing directions. (d) Finally, microspheres are placed stochastically inside the filament.

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Figure 8

Locating a microsphere stochastically inside a filament: (a) top view of the filament and (b) cross-sectional view of a partially demonstrated unit shell Si

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Figure 9

Generated filament with stochastic distribution of 849 microspheres with radii between 3 μm and 13 μm over 250 μm filament diameter: (a) top view and (b) 3D perspective view

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Figure 10

(a) A CAD model of 3D complex porous structure with (b) the fabrication of top layer over (c) four regions with increasing microsphere and alginate concentrations, and (d) increasing microsphere distribution is highlighted

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Figure 11

(a) A CAD model of 3D complex porous structure with (b) the fabrication of top layer over (c) two regions with decreasing microsphere and increasing alginate concentrations, and (d) decreasing microsphere distribution is highlighted




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