Scaffold Design To Prime Soft Tissue Regeneration and Replacement - - PowerPoint PPT Presentation

Scaffold Design To Prime Soft Tissue Regeneration and Replacement

Antonio D’Amore1,2,3 and William R. Wagner1

1Department of Bioengineering, Swanson School of Engineering and

The McGowan Institute for Regenerative Medicine,University of Pittsburgh

2 Dipartimento di Ingegneria Chimica, Gestionale,Informatica e Meccanica, University of Palermo 3Fondazione RiMED, Italy

Altered tissue mechanics can lead to adverse tissue remodeling and regeneration

Image: Jessup M, Brozena S. Heart Failure. N Engl J Med 348: 2007 (2003).

Ventricular wall thinning, stiffening in ischemic cardiomyopathy with increased wall stress

Actuating arm

Bioreactor well

Media bath Stationary Pin 10 mm x 19 mm

Bioreactors for cyclic loading

Laboratory of Dr. Michael Sacks

In tissue engineering, mechanical training is often necessary to develop correctly anisotropic, mechanically robust tissue

Temporarily altering the mechanical environment of the tissue will alter remodeling, regeneration

F F

How might the ventricular wall mechanical environment be altered with localized therapy?

X

Elastomeric patch placement on 2 week old infarct (rat) examined at 8 weeks

Infarct control PEUU patch patch

P S

5mm 5mm 5mm 5mm

×

 Create myocardial infarction by ligating left anterior descending coronary artery  2 weeks post-infarct, implant PEUU scaffold to cover infarcted region of left ventricle  Examine at 8 weeks

500um H&E staining

Infarction alone PEUU patch

Explant at 8 weeks

Fujimoto KL, et al. J Am Coll Cardiol 49:2292 (2007).

Ventricular wall is significantly thicker and softer than controls

End-diastolic Area (cm2) Fractional Area Change (%)

Pre 4w 8w

Infarction control Patch

Echocardiography

Mean ±SEM, Two-factor repeated ANOVA: *; p<0.05 between groups, †; p<0.05 vs. 0w within group

EDA (end-diastolic LV cavity area) FAC (fractional area change)

Pre 4w 8w

20 30 10 † † 0.70 0.60 0.50 0.40 † †

*

†

*

Fujimoto K, et al. J Am Coll Cardiol. 49: 2292 (2007)

Could a mechanically protective elastic matrix be deposited around a saphenous vein for arterial bypass?

Mechanical support in the vascular system

In collaboration with the laboratory of Dr. David Vorp

Spinning a temporary, conformal, elastic jacket on a vein to protect from sudden expansion at arterial pressure

El-Kurdi MS, et al. Biomaterials 29:3213 (2008).

PEUU/elastin/collagen El-Kurdi MS, et al. Biomaterials 29:3213 (2008).

Tuning wrap “mechanical degradation”

Elastomeric scaffolds for the development of tissue engineered cardiovascular structures with matching mechanics (blood vessel & pulmonary valve)

Mechanical support for developing, engineered tissue

An appropriately elastic scaffold, seeded with precursor cells, will match the compliance of the native artery and exhibit higher patency

Collaboration with laboratory of Dr. David Vorp Nieponice A, et al. Biomaterials 29:825 (2008). Soletti L, et al. Biomaterials 27:4863 (2006). Soletti L, et al. Acta Biomater 5:2901 (2010). Nieponice, et al. Tissue Eng A 16:1215 (2010). He W, et al. Cardiovasc Eng Tech (2011).

Critical gap: Biodegradable materials with tunable properties to meet the hypothesized needs for soft tissue mechanical protection and tissue engineering

Structure Function  Nano (molecular)  Micro  Meso  Macro Design Scales

Molecular design: biodegradable thermoplastic elastomers

Polycaprolactone diol (Mw=2000) 1,4-diisocyanatobutane H2N(CH2)4NH2 Putrescine HO(CH2)5C C(CH2)5OH + OCN(CH2)4NCO Prepolymer 70oC, Sn(OCt)2 O O O O O O O O ...HNRNHCNH(CH2)4NHCO(CH2)5C C(CH2)5OCNH(CH2)4NHCNHRNH...

patch

Poly(ester urethane) urea (PEUU)

Tuning degradation to be faster with polyether blocks

Poly(ether ester urethane) urea

Creating enzymatically labile elastomers

HO(CH2CH2O)n H O O O(CH2CH2O) n C(CH2)5O m O O(CH2)5C m O OCN(CH2)4NCO + O(CH2CH2O) n C(CH2)5O m O O(CH2)5C m O CNH(CH2)4NCO OCN(CH2)4NHC O(CH2CH2O) n C(CH2)5O m O O(CH2)5C m O CNH(CH2)4NCNHAAKNH CN(CH2)4NHC O O HO O O O O KAANH ... ... + + AAK

(MW=600 or 1000)

OH

elastase lability

Tuning degradation to be slower with a polycarbonate blocks

Hong Y, et al. Biomaterials 31:4249 (2010)

Tuning mechanics with labile segment selection and length

=PCL, PTMC or PVLCL

PUU

Putrescine BDI

Diethylene glycol Diethylene glycol Diethylene glycol

Ma Z., et al. Biomacromolecules 12:3265 (2011)

1 2 3 4 5 6 7 10 20 30 40 50

Stress (Mpa) Strain (%)

PUU-PTMC1500 PUU-PTMC2500 PUU-PTMC5400 PUU-PVLCL6000 PUU-PVLCL2246 PUU-PCL2000

Tuning mechanics with labile segment selection and length

Ma Z., et al. Biomacromolecules 12:3265 (2011)

Structure Function  Nano (molecular)  Micro  Meso  Macro Design Scales

From the targeted mechanical behavior at micro and macro levels to the material micro-structure From the micro-structure to the mechanical response at micro and macro levels

Introduction: overview on the modeling strategy

Input: 1: Material sample 2: SEM

Image Analysis

Mechanical testing

Artificial network model generation from experimental data

FEM simulation

Output: Mechanical response 1: Macro level 2: Micro level

Output: Material fabrication parameters Optimal micro- architecture identification

FEM simulation

Artificial network model generation in the design space

Input: clinical application, targeted macro- meso mechanical response

Micro-control: electrohydrodynamic processing

Input: 1: Material sample 2: SEM

fiber intersection density can be controlled by the rastering speed fiber main angle of orientation can be controlled by the mandrel speed A

1 µm

Isotropic

10 µm 1 µm 1 µm

Anisotropic VSMCs int

Microspheres int

B C D

Methods: material fabrication

Input: 1: Material sample 2: SEM

fiber intersection density can be controlled by the rastering speed fiber main angle of orientation can be controlled by the mandrel speed

1 µm

Isotropic

10 µm 1 µm 1 µm

Anisotropic VSMCs int

Microspheres int

A

1 µm 10 µm 1 µm 1 µm

Anisotropic

Microspheres int

B C D

20 60 100 140 180 Input: 1: Material sample 2: SEM

Methods: image analysis and material characterization

ϑ

[*] D’Amore Stella, Wagner Sacks Characterization of the Complete Fiber Network Topology of Planar Fibrous Tissues and Scaffolds. Biomat 2010; 31:(20) 5345-5354

• ---- OI

___ ϑ

A B

[*] Sacks. Biaxial Mechanical Evaluation of Planar Biological Materials. Journal of Elasticity 61: 199–246, 2000. [*]

Methods: mechanical testing

Mechanical testing

Methods: mechanical testing

Mechanical testing

A

[*]

Nuclear Aspect Ratio

from confocal microscopy

NAR = 1.3 NAR = 1

λ

[*] Stella, Wagner et al et al. Tissue-to-cellular level deformation coupling in cell micro-integrated elastomeric scaffolds. Biomaterials Volume 29, Issue 22, August 2008, Pages 3228-3236 .

Anisotropic model

• Size=120 µm
• OI = 0.65
• Diameter= 0.5 µm
• Int Den=0.28 [n/ µm2]

[*] D’Amore et al. Micro Scale Based Mechanical Models for Electrospun Poly (Ester Urethane) Urea Scaffolds. Proceedings of the 7th European Solid Mechanics Conference (ESMC2009) September 7-11 2009, Lisbon, Portugal.

Artificial network model generation from experimental data

Methods: mechanical modeling, artificial fiber network generation

• Mesh Topology

Fiber network cast into finite element form (from 20 x 20 µm2 to150 x 150 µm2) .

• Element

Fibers idealized as truss elements (2000-3000 nodes, 10000 – 11000 elements ) ABAQUS (t2d2h)

• Solver

Static solution, Newton -Raphson method Large deformation enabled Equi-biaxial stress conditions

• Boundary conditions

Methods: mechanical modeling, finite element model

FEM simulation

Changes in isotropic ES-PEUU fiber micro- architecture under biaxial stretch.

[*] J Stella, W R Wagner et al. Scale dependent kinematics of fibrous elastomeric scaffolds for tissue engineering. Journal of Biomedical Materials Research. 2008. In press. Output: Mechanical response 1: Macro level 2: Micro level

Results: MESO LEVEL RESPONSE

ε

SEM Confocal Model

[*]

Output: Mechanical response 1: Macro level 2: Micro level

Scaffold model under strip biaxial deformation. Red dots represent the experimental data, model prediction in black

Results: MESO LEVEL RESPONSE

( n=50 cells for each data point, model prediction solid line)

Single fiber initial shear modulus prediction Results: MICRO LEVEL RESPONSE

Output: Mechanical response 1: Macro level 2: Micro level [*] Kis A. et. al. Nanomechanics of Microtubules Phys. Rev. Lett. 89, 248101 (2002)

MACRO MESO MICRO

1 1.05 1.1 1.15 1.2 1.25 1.3 1 2 3 4 5 6 x 10

Stretch Stress [Pa]

1 1.051.11.151.21.251.31.351.41.451.51.551.6 1 2 3 4 5 6 x 10

Stretch Stress [Pa] 1 1.05 1.1 1.15 1.2 1.25 1.3 0.5 1 1.5 2 2.5 3 3.5 x 10

Stretch Stress [Pa]

strain [%] NAR n=20 for each data point NAR vs strain, method used in Stella 2008

Results: 3 Levels quantitative prediction / validation

Single fiber initial shear modulus

SUMMARY

(1 cm organ level ) (50 µm cells level ) (1 µm fiber level )

Results: Recapitulating organ level mechanical response

[*] N. Amoroso, A. D’Amore, Y. Hong, W. R Wagner and M. S. Sacks. Elastomeric Electrospun Polyurethane Scaffolds: The Interrelationship Between Fabrication Conditions, Fiber Topology, and Mechanical Properties. Advanced Materials; In press.

Physiologically relevant mechanical anisotropy and cells integration

SUMMARY

Results: Recapitulating cell level micro mechanical environment

SUMMARY

[*] Stella, D’Amore, Wagner Sacks manuscript in preparation

[*]

Optimized Extra Cellular Matrix deposition

Summary: overview on the modeling strategy

SUMMARY

Models

Macroscopic Structure - function Cells Structure - function

Cells - Extracellular matrix

Structure – Fabrication Parameters Clinical application

PEUU Patch Left ventricle PEUU Leaflet Pulmonary artery

From the targeted mechanical behavior at micro and macro levels to the material micro-structure From the micro-structure to the mechanical response at micro and macro levels

Summary: overview on the modeling strategy

Input: 1: Material sample 2: SEM

Image Analysis

Mechanical testing

Artificial network model generation from experimental data

FEM simulation

Output: Mechanical response 1: Macro level 2: Micro level

Output: Material fabrication parameters Optimal micro- architecture identification

FEM simulation

Artificial network model generation in the design space

Input: clinical application, targeted macro- meso mechanical response

SUMMARY

Cardiovascular Biomechanics Laboratory Dr Michael Sacks Dr John Stella Christopher Hobson Cardiovascular Engineering Laboratory Nicholas Amoroso, Dr Yi Hong Harvard Medical School, Children Hospital Dr A. Bayoumi, Dr J. E. Mayer

Acknowledgements

• Fondazione Ri.MED, Italy
• The McGowan Institute for Regenerative Medicine
• University of Palermo, Dipartimento di Ingegneria Chimica,

Gestionale, Informatica e Meccanica

• National Institute of Health grant R01 HL-068816
• Harvard Medical School

Acknowledgements