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Mardi 5 mai
Les atouts de la modélisation numérique : exemples de la médecine régénérative
Prof. Liesbet GERIS, ULg - Génie biomécanique ; Université de Louvain - Biomécanique
7/05/2015
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Les atouts de la modélisation numérique : exemples de la
médecine régénérative
Liesbet [email protected]
In vitro, in vivo … in silico: examples of regenerative medicine
Liesbet [email protected]
7/05/2015
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In vitro, in vivo … in silico: examples of regenerative medicine
Liesbet [email protected]
Recently in Europe
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Recently in Europe
eHealth
• refers to tools and services using information and communication technologies (ICTs) that can improve prevention, diagnosis, treatment, monitoring and management.
• can benefit the entire community by improving access to care
and quality of care and by making the health sector more efficient.
• includes information and data sharing between patients and health service providers, hospitals, health professionals and health information networks; electronic health records; telemedicine services; portable patient-monitoring devices, operating room scheduling software, robotized surgery and blue-sky research on the virtual physiological human.
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Recently in Belgium
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Recently in Belgium
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Recently in print
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Recently in Liège Créative
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Big data =
new black gold
Neelie Kroes, former Vice-President of the EC, responsible for the Digital Agenda, 2011
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117/05/2015 All rights reserved © 2014
Blogs.sas.com
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Ben Myers, Dx3 DigestGartner Hype Cycle
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Ben Myers, Dx3 DigestGartner Hype Cycle
Big data ≠
Big knowledge
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Genetics
Epigenetics
Upward
causation
Downward
causation
Disease
Multiple
diseases
Genotype
Phenotype
Marco Viceconti, university of Sheffield & President of VPH Institute
Big data =
Complex data
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In silico medicine
The Virtual
Physiological Human is
a framework of methods
and technologies that
once established will
make possible to
investigate the human
body as a whole
http://www.vph-institute.org/
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Accenture.com
Patient: personal health forecasting
Simula ResearchLaboratory
clinician: digital patient
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Roche
Industry: in silico clinical trials
In vitro, in vivo … in silico: examples of regenerative medicine
Liesbet [email protected]
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The problem
optn.transplant.hrsa.gov
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Bone Tissue Engineering
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Dag Allemaal 2012; Pannier , Orthop & Traum 2011
All rights reserved © 2015
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The solution
Tissue Engineering is an interdisciplinary field that applies the principles of
engineering and life sciences toward the development of biological substitutes that
restore, maintain, or improve tissue function or a whole organ
(Langer & Vacanti, 1994)
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Image: J.H. George
All rights reserved © 2015
The problem (bone)
• Bone = intelligent material
o Bone cells act as sensors, processors & actuators
• Capable of adapting to changes in loading
• Capable of scarless healing
• 5-10% defects do not heal
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http://www.doc.ic.ac.uk/bioinformatics/CISB/
All rights reserved © 2015
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Going biomimetic
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Various sources
Developmental Engineering
• Intramembranousossification
o Formation of bone on connective tissue
• Endochondral ossification
o Formation of bone on cartilage template
Lenas et al., TE , 2009
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Engineering?
• One of the major challenges in TE is translation of biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process
• Engineers can help by:
o quantifying and optimizing the TE product
o quantifying and optimizing the TE process
o assessing the influence of the in vivo environment on the behavior of the TE product
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Integrative approach
http://www.doc.ic.ac.uk/bioinformatics/CISB/
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In vitro, in vivo … in silico: examples of regenerative medicine
Liesbet [email protected]
Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Carriers: questions
• What happens to carrier in
vivo?
• How is biology influencedby presence of carrier?
• What are the ideal carrier properties to optimize boneformation?
CopiosTM, NuOssTM
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hPDCs + growth factors
Composite CaP-polymer scaffold
+
Cellularized scaffoldFT-IR and XRD � %HA, %TCP, %CHP
µ/nCT� porosity, pore size distribution
CaP particle size distribution,
cell wall thickness distribution,
specific surface area, interconnectivity
Carrier in vivo
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µ/nCT� amount & distribution
of newly formed bone
biology � gene & protein dataAll rights reserved © 2015
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Contrast-enhanced nanoCT
Kerckhofs et al. eCM, 2013; Cartilage 2014
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• In vivo ectopic implantation in nude mice
o Pre-implantation, 3 day explants, 12 day explants: gene expression & protein data
o 8 week explants: evaluate bone formation
Roberts et al., Biomat., 2011; Kerckhofs et al., In prep, 2015; Bolander et al., Submitted 2015,
Carrier in vivo
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Partial least square regression
• Data-driven predictive model
o Figures removed, unpublished data
• Experimental confirmation – when staying within sample
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Carrier behavior
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InterfaceIn
Out
• Investigate calcium dissolution from scaffold
• Quantify local calcium concentration
• Level-set (degradation)
• Diffusion (Ca2+ release)
• FreeFem++
All rights reserved © 2015
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Carrier degradation & Ca2+ release
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(Video)
In vitro dissolution tests
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0,16
0,18
0 5 10 15 20 25
Ca
(x 1
mM
)
Time (days)
Reprobone Exp Reprobone Mod
MBCP Exp MBCP Mod
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
0,45
0,5
0 5 10 15 20 25
Ca
(x 1
mM
)
Time (days)
Bio-Oss Exp Bio-Oss Mod
Integra Exp Integra Mod
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Manhas et al., in preparation 2015
All rights reserved © 2015
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Carrier influence on biology
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Carlier et al., Acta Biomat, 2013
• Partial & Delay Differential Equations
• Matlab/FreeFem++
All rights reserved © 2015
Carrier influence on biology
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In silico design of ideal carrier
• Design of optimal combination of calcium release profile of scaffold and seeding density of cells (cm0)
Carlier et al., Acta Biomat, 2011
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25
bo
ne
form
ati
on
(%)
calcium release rate σ (x 40)
cm0 = 0.1
cm0 = 0.5
cm0 = 1
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Culture: questions
• Qualitycontrol!
• Whathappensinside of the scaffold?
• Influence of fluid flow?
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Growth in static conditions
0,8 mm
• Level-set (growth)
• Curvature dependent
• FreeFem++
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Growth in static conditions
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Growth in static conditions
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Guyot et al, BMMB, 2014
• Level-set (growth)
• Curvature dependent
• FreeFem++
Dynamic culture conditions
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Papantoniou et al, Bioproc & Bioeng, 2014
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Brief overview of the model
• Neotissue growth via Level-Set method
o growth velocity depending on curvature only.
• Flow profile approximated via Brinkman equation
• Two evaluated wall shear stresses :
• Shear stress at the fluid neotissue interface
• Shear stress inside the neotissue
Guyot, Bioproc BioEng 2015,; Guyot et al. BMMB, 2015
Shear stress during growth
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Determination of shear stress magnitude and distribution, along the fluid neotissue interface but also within the 3D neotissue
Guyot, Bioproc BioEng 2015,; Guyot et al. BMMB, 2015
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Pore size = 50 µm
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Avera
ge s
hear
str
esses
Average shear stresses over time
All rights reserved © 2015
Growth in dynamic conditions
Guyot, Bioproc BioEng 2015,;
Guyot et al. BMMB, 2015
Shear stress influences growth rate
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Previously, the neotissue growth was only dependent on local mean curvature
Shear stress can inihibite or enhace growth rate !
New defintion of the growth velocity
Figures removed, unpublished data
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Model set up
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Results (Qualitative)
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Results (Quantitative)
• Figures removed, unpublished data
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic: questions
• What is cause of adverse fracture healing?
• How can we solve it?
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Figures removed, unpublished data
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Normal fracture healing
• Fracture healing
o Cells, tissues, growth factors
o Blood vessels, oxygen
o Intracellular variables
• Multiscale, hybrid model system
• MatlabHarrison et al., J Orthop Trauma, 2005
Geris et al., JTB, 2008; Geris et al., BMMB, 2010; Geris et al., PLoS CB 2010;
Peiffer et al., BMMB, 2011; Carlier et al., PLoS CB, 2012
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Fracture healing model
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Carlier et al., J Theor Biol, 2014
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Normal versus critical size
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active blood vessels
5 mm0.5 mm
90 days35 days
Carlier et al., PLoS CB, 2014
Normal versus critical size
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bone formation
5 mm0.5 mm
90 days35 days
low highCarlier et al., PLoS CB, 2014
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Predicted oxygen dynamics
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0.01
0
0.01
0
0.05
0
0.08
0
0.08
0
Carlier et al., PLoS CB, 2014
Treatment strategies
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 7 14 21 28 35 42 49 56 63
am
ou
nt
of
bo
ne a
fter
90 d
ays
post fracture day
100% MSC
no treatment
100% cc
75% cc
50% cc
25% cc
Carlier et al., PLoS CB, 2014
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Congenital non-unions: NF1
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Dag Allemaal 2012; Pannier , Orthop & Traum 2011
Congenital non-unions: NF1
• In silico population
o Design of Experiments (DOE) approach
o N=200
• CI = complication index
o Non-union
o Fibroblasts
o Fibrous tissue
• Figures removed: unpublished data
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Carlier et al., submitted 2015
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In silico design of therapies
• Application to non-unions
o Large bone defects
o (Congenital) pseudarthrosis
• Simulation of treatment strategies
o Surgical interventions
o Admission of cells
o Admission of growth factors
• Validation ongoing
F
F
F
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Clinic
CellsCarriers
Culture
Tissue Engineering
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Clinic
CellsCarriers
Culture
Tissue Engineering
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In vitro, in vivo … in silico: examples of regenerative medicine
Liesbet [email protected]
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In silico regenerative medicine
• Engineering contributes to
o Increase understanding of pathophysiology
o Design treatment strategies
• Engineering as part of R&D pipeline
o Quality control
o 3R’s
o Personalisation
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The 3 R’s
• Reduction
o Better planning of experiments
• Refinement
o Extrapolate experimental data using models
• Replacement
• … and translation from animal to human
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Utopia or reality?
• E.g. subcutaneous glucose sensing and insulin delivery (Kovatech – Cobelli, 2003 and later)
o Use of computer simulation for the preclinical testing of a new type of model-predictive closed-loop control of blood glucose levels
Utopia or reality?
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AcknowledgementsAll members of Prometheus,
especially:
• Aurélie Carlier• Johanna Bolander• Nick van Gastel• Morgan Germain• Yann Guyot• Johan Kerkhofs• Jeroen Leijten• Varun Manhas• Maarten Sonnaert• Yoke Chin Chai
Darmstadt
• A. Gerisch
• Greet Kerckhofs• Akash Fernando• Marina Maréchal• I. Papantoniou• Nick Van Gastel• Geert Carmeliet• Johan Lammens• Frank Luyten• H.Van Oosterwyck• Jan Schrooten