Les atouts de la modélisation numérique : exemples de la médecine régénérative par Liesbet...

Mardi 5 mai

Les atouts de la modélisation numérique : exemples de la médecine régénérative

Prof. Liesbet GERIS, ULg - Génie biomécanique ; Université de Louvain - Biomécanique

Avec le soutien de :

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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


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Recently in Europe

47/05/2015 All rights reserved © 2015

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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.


Recently in Belgium


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Recently in Belgium


Recently in print


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Recently in Liège Créative


Big data =

new black gold

Neelie Kroes, former Vice-President of the EC, responsible for the Digital Agenda, 2011

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Blogs.sas.com


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



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The problem

optn.transplant.hrsa.gov


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


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/


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Going biomimetic

27 277/05/2015 All rights reserved © 2015

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


Integrative approach

http://www.doc.ic.ac.uk/bioinformatics/CISB/


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Clinic

CellsCarriers

Culture

Tissue Engineering


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Clinic

CellsCarriers

Culture

Tissue Engineering


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


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


• 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


Carrier behavior

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InterfaceIn

Out

• Investigate calcium dissolution from scaffold

• Quantify local calcium concentration

• Level-set (degradation)

• Diffusion (Ca2+ release)

• FreeFem++


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Carrier degradation & Ca2+ release

7/05/2015 41All rights reserved © 2015

(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


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Carrier influence on biology

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Carlier et al., Acta Biomat, 2013

• Partial & Delay Differential Equations

• Matlab/FreeFem++


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


Clinic

CellsCarriers

Culture

Tissue Engineering


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Clinic

CellsCarriers

Culture

Tissue Engineering


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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Guyot et al, BMMB, 2014

• Level-set (growth)

• Curvature dependent

• FreeFem++

Dynamic culture conditions


Papantoniou et al, Bioproc & Bioeng, 2014

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7/05/2015 All rights reserved © 2015 53

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


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


Growth in dynamic conditions

Guyot, Bioproc BioEng 2015,;

Guyot et al. BMMB, 2015

Shear stress influences growth rate


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


Results (Qualitative)


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Results (Quantitative)

• Figures removed, unpublished data


Clinic

CellsCarriers

Culture

Tissue Engineering


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Clinic

CellsCarriers

Culture

Tissue Engineering


Clinic: questions

• What is cause of adverse fracture healing?

• How can we solve it?


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


Fracture healing model


Carlier et al., J Theor Biol, 2014

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Normal versus critical size


active blood vessels

5 mm0.5 mm

90 days35 days

Carlier et al., PLoS CB, 2014

Normal versus critical size


bone formation

5 mm0.5 mm

90 days35 days

low highCarlier et al., PLoS CB, 2014

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Predicted oxygen dynamics


0.01

0

0.01

0

0.05

0

0.08

0

0.08

0


Treatment strategies


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


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Congenital non-unions: NF1


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


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


Clinic

CellsCarriers

Culture

Tissue Engineering


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Clinic

CellsCarriers

Culture

Tissue Engineering




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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


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

Les atouts de la modélisation numérique : exemples de la médecine régénérative par Liesbet...

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