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Experts

T H I S A C S W E B I N A R W I L L B E G I N S H O R T L Y . . .

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Slides available now! Recordings are an exclusive ACS member benefit.

“How to Predict Human CNS PK/PD: Preclinical Experiments and Advanced Mathematical Modelling”

This ACS Webinar is co-produced with the ACS Division of Medicinal Chemistry and the American Association of Pharmaceutical Scientists

13

Elizabeth CM de LangeProfessor in Predictive Pharmacology, LACDR,

Leiden University

Alexander TropshaAssociate Dean for Pharmacoinformatics and

Data Science, University of North Carolina

How to Predict Human CNS PK/PD: Preclinical

Experiments and Advanced Mathematical Modelling

V2, A2, C2

V1, A1, C1

V3, A3, C3

V4, A4, C4


Leiden University, The [email protected]

http://www.megagadgets.nl/Virtual+Laser+Keyboard-im1-377.jpg

mailto:[email protected]

10/18/2018

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Pharmacology in Human

ToxicNOT beneficial

ToxicBeneficial

NOT ToxicBeneficial

NOT ToxicNOT beneficial

Same diagnosisSame prescription

Patient group

15

Given patients having the same diagnosis and same drug prescription:

What is the most important reason for differences in effects among the patients?

A) Not all patients take the drug according to the instructions with regard to when and how to take the drug

B) Not all patients take the drug according to the instructions with regard to the amount: they take too little or too much

C) Not all patients are the same. Rate and extent of body processes differ, so do the drug effects

D) It is still unknown what the reason is for interindividual differences of drug effects between patients

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Outline

Interrelationships of body processes

Factors in CNS drug effects

Need for knowledge on unbound CNS conc-time profiles

Microdialysis as key technique

Mastermind Research Approach

Drug vs. systems properties

Prediction of the PKPD of a CNS drug in human

Prediction of human CNS PK for a single drug

Prediction of human CNS PK for multiple drugs

CNS PK prediction for any small drug without the need for in vivo data?

17

Interconnections and Relationships

Driver + = Car Performance

19

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Interconnections and Relationships

Drug + = CNS Drug Effect

20

Factors in CNS Drug Effects

Drug Dosing Target

tissue exposure

Trans-duction EFFECT

Homeostatic feedback

Plasma exposure T

arget exposure

Trans-duction


E

• Dose• Plasma PK• Target tissue (site) distribution• Target binding kinetics • Cellular response & homeotstatic feedback• Body response & body homeostatic feedback

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The Blood-Brain Barrier (BBB)

BB

B

Drug Dosing

Brain PK Trans-duction EFFECT


Plasma PK

Trans-duction


E


21

BBB transport of drugs depend on:

A) The drug’s properties

B) BBB transport just generally restricts transport of drugs into the brain

C) The BBB characteristics

D) Combination of drug properties and BBB characteristics

E) None of the above

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Blood-Brain Barrier - Modes of Transport

23


Blood-Brain Barrier - Simple Diffusion

24


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Blood-Brain Barrier - Facilitated Diffusion

25


Blood-Brain Barrier - Active Transport

26


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Blood-Brain Barrier - Vesicle Based Transport

27


Blood-Brain Barrier - Modes of Transport

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BB

B

Drug Dosing



Plasma PK

Trans-duction


E

Drug concentrations at (off) targets drive the effects of the drug

29


Hammarlund-Udenaes, Paalzow, & De Lange.

Pharm Res (1997)

Model for simulations

CLin = CLout

Clin = Clout

Varying: 1.0 - 0.01

CLout = 0.5

Varying CLin : 0.5 - 0.01

CLin ≠ CLout

Simulations on plasmau and brainu PK

BBB transport – simple cases


32

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Hammarlund-Udenaes, Paalzow, & De Lange.

Pharm Res (1997)

Model for simulations

CLin = CLout

Clin = Clout

Varying: 1.0 - 0.01

CLout = 0.5

Varying CLin : 0.5 - 0.01

CLin ≠ CLout

Simulations on plasmau and brainu PK

BBB transport – simple cases


33

Experimental Approach

Dialysate PerfusateMicrodialysis: a key technique

Tissue / external

FlowCin Cout

Reflection of unboundExtracellular tissue concentrations

Microdialysis probe- semipermeable membrane

32

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Differences in: • rate of PK and PD processes• sizes, and surfaces of physiological compartments, and flows

BB

B

Drug Dosing



Plasma PK

Trans-duction


EBBB

Drug

Dosing

Brain

PK

Trans-

ductionEFF

ECT

Homeostatic

feedback

Plasma

PK T

r

a

ns

-

d

u

c

t

i

o

n

Home

ostat

ic

feed

bac

k

E

Prediction of CNS Drug Effects in Human

33

Systems Parameters:u Blood flow

u Barrier permeabilities

u Transporter/ enzyme function

u Volumes (intra- / extracellular)

u Blood / tissue pH

u Capillary surface area

u Receptor density

u Signal transduction

u Homeostatic feedback

Drug Characteristics:u Molecular weight

u LogP / logD

u pKa / charge at pH 7.4

u PSA (polar surface area)

u H-bond donor / acceptor

u P-gp / MRP (etc) substrate

u Receptor affinity

u etc

Pharmacodynamics• Target occupancy

•Efficacy

Pharmacokinetics• Plasma kinetics• Barrier transport

• Intractissue distribution

Drug vs. CNS Systems Properties

34

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u Blood / tissue pH


u Receptor density




u LogP / logD





u Receptor affinity

u etc


•Efficacy




35





u Blood / tissue pH


u Receptor density




u LogP / logD





u Receptor affinity

u etc


•Efficacy




36

10/18/2018

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37Gary Larsson


De Lange. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects. Fluids Barriers CNS. 2013

• Move away from reductionism and face complexity• Obtain connected data at multiple levels• Reveal interactions & interdependency

Apply

• Cross-compare designed studies

• Advanced mathematical modeling

to dissect contributions of individual mechanisms in animals to provide information that can be used for extrapolation to the human situation.

To crack the code: need for an integrated

systems approach

38

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De Lange. The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects. Fluids Barriers CNS. 2013

• Move away from reductionism and face complexity• Obtain connected data at multiple levels• Reveal interactions & interdependency

Apply

• Cross-compare designed studies

• Advanced mathematical modeling

to dissect contributions of individual mechanisms in animals to provide information that can be used for extrapolation to the human situation.

To crack the code: need for an integrated

systems approach

39

1. Prediction of PKPD of a CNS Drug in Human

V2, A2, C2

V1, A1, C1

V3, A3, C3

V4, A4, C4

42


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Prolactine as a translational biomarker of the dopaminergic system

Pituitary lactotrophs release prolactin into blood

Prediction of Human PKPD of a CNS Drug

Dopamine high inhibition of release of prolactin

Dopamine low induction of release of prolactin (~ use of DA antagonist )

42

1

10

100

1000

10000

0 100 200

Pla

sma

Co

nce

ntr

atio

nR

EM (

ng/

ml)

Time (minutes)

4 mg/kg, n=9

8 mg/kg, n=10

16 mg/kg, n=6

0.1

1

10

100

1000

10000

0 100 200

Bra

inEC

F R

EM C

on

cen

tra

tio

n(n

g/m

l)

Time (minutes)

4 mg/kg n=4

8 mg/kg n=4

16 mg/kg n=4

0,1

1

10

100

1000

0 100 200

PR

L p

lasm

a co

nce

ntr

atio

ns

(ng/

ml)

Time (min)

4 mg/kg

8 mg/kg

16 mg/kg

Remoxipride plasma Remoxipride brainECF Prolactin plasma

Remoxipride = Dopamine D2 antagonist Induces Prolactin Release

Remoxipride plasma and brain PK in the rat

Intravenous administration

42


Stevens et al. Systemic and Direct Nose-to-Brain Transport PK Model for Remoxipride after IV and IN Administration. DMD 2011

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Rat plasma PRL concentrations (+/-SEM) after different interval dosing regimens of 3.8 mg/kg REM IV (IV, 30 min)

Insight into rate of synthesis of prolactin in lactotrophs in rats

Time

Remoxipride PD in the rat

Movin-Osswald and Hammarlund-Udenaes. Prolactin release after remoxipride by an integrated PKPD model with intra- and interindividual aspects. JPET, 1995

0

10

20

30

40

50

0 100 200 300 400 500 600

PR

L (n

g/m

l)

Time (minutes)

0-40-60-1JS01EX012-64-60-2

43


Unbound brain concentrations of Remoxipride represent “target site” concentrations for prolactine release

Prolactin plasma concentrations increase

synthesis rate of prolactine

PK-PD Model Remoxipride in rat

Brain unbound concentration = target site concentration

44


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Translation on species:

Prediction of PKPD relationship of REM in human

45



POP-PK Model of remoxipride in rat versus human

Ob

serv

ed (

o)

and

pre

dic

ted

(

) re

mo

xip

rid

e p

lasm

a co

nce

ntr

atio

ns

in h

um

an(u

g/m

l)

Time (hours)Time (hours)

Ob

der

vati

on

s (0

) an

d in

div

idu

al (

___

_ ) a

nd

po

pu

lati

on

p

red

ic-t

ion

s (-

----

) of

rat

rem

oxi

pri

de

pla

sma

con

c p

lasm

a (u

g/m

l)

46

http://www.shutterstock.com/subscribe.mhtml

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POP-PK Model of remoxipride in rat versus human

Ob

serv

ed (

o)

and

pre

dic

ted

(

) re

mo

xip

rid

e p

lasm

a co

nce

ntr

atio

ns

in h

um

an(u

g/m

l)

Time (hours)Time (hours)

Ob

der

vati

on

s (0

) an

d in

div

idu

al (

__

__ )

an

d p

op

ula

tio

n

pre

dic

-tio

ns

(---

--) o

f ra

tre

mo

xip

rid

e p

lasm

a co

nc

pla

sma

(ug

/ml)

47

PRL human – data and translational model prediction

Ob

serv

ed (

o)

and

pre

dic

ted

(

) p

rola

ctin

e p

lasm

a co

nce

ntr

atio

ns

in r

at (

ng

/ml)

Time (h) Time (h)

Ob

serv

ed (

o)

and

pre

dic

ted

(

) p

rola

ctin

ep

lasm

a co

nce

ntr

atio

ns

in h

um

an(n

g/m

l)

48


Stevens et al. MBPKPD model for the prolactin biological system response following acute dopamine inhibition challenge: quantitative extrapolation to humans. JPKPD 2012

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PRL human – data and translational model predictionO

bse

rved

(o

) an

d p

red

icte

d (

)

pro

lact

ine

pla

sma

con

cen

trat

ion

s in

rat

(n

g/m

l)

Time (h) Time (h)

Ob

serv

ed (

o)

and

pre

dic

ted

(

) p

rola

ctin

ep

lasm

a co

nce

ntr

atio

ns

in h

um

an(n

g/m

l)

49


Stevens et al. MBPKPD model for the prolactin biological system response following acute dopamine inhibition challenge: quantitative extrapolation to humans. JPKPD 2012

IN: Brain Distribution enhancement

IV: Same model for rat and human

PK

PDRat: unbound brain PK of REM = linked to the effect

Human: In vitro values + allometric scaling giveprediction of human plasma PRL concentrations


51

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26

Which concentration in the humanbrain is most representative to the

brain target site concentration?

blood

What CNS sites in human are accessible to obtain

information about brain PK?

target

CSFCSF

De Lange. Utility of CSF in translational neuroscience. JPKPD. 2013

Use of CSF to predict CNS target site PK?

51

For prediction of human CNS target site PK for a target that is facing the brainECF:

A) CSF concentrations can be used as it is in quick equilibrium with brainECF

B) We can use in vitro and animal data to build a mathematical model by which we can calculate brainECF concentrations

C) We can make direct use brainECF concentrations as measured in animals

D) CSF concentrations can be used, if taken from the ventricles in the brain, as CSF in the brain ventricles is the closest to the brainECF

E) We can make use of brainECF concentrations measured in humans

• CSF = cerebrospinal fluid• BrainECF = brain extracellular fluid

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2. Prediction of Human CNS PK for a Single Drug

V2, A2, C2

V1, A1, C1

V3, A3, C3

V4, A4, C4

53

Which concentration in the humanbrain is most representative to the

brain target site concentration?

blood

What CNS sites in human are accessible to obtain

information about brain PK?

target

CSFCSF

De Lange. Utility of CSF in translational neuroscience. JPKPD. 2013

Use of CSF to predict CNS target site PK?

54


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Cerebral blood

BCSFBBBB

CSF

, Cis

tern

a M

agn

a

CSF

, Lat

eral

Ven

tric

les

CSF

, Su

bar

ach

no

idal

Epen

dym

alce

llla

yer

Cerebral blood

BCSFB BBB

BrainECF

BrainECF

CSF

, lu

mb

ar

MetabolismFaciliated /active transport

Diffusion Fluid flow

BRAIN CELLS

Brain Cells

Physiological brain compartments, flows, membranes, active transporters, metabolic enzymes, subcellular compartments, pH values, targets

CNS Properties

55

Cerebral blood

BCSFBBBB

CSF

, Cis

tern

a M

agn

a

CSF

, Lat

eral

Ven

tric

les

CSF

, Su

bar

ach

no

idal

Epen

dym

alce

llla

yer

Cerebral blood

BCSFB BBB

BrainECF

BrainECF

CSF

, lu

mb

ar

MetabolismFaciliated /active transport

Diffusion Fluid flow

BRAIN CELLS

Brain Cells

Experimental Approach

56

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Animal experiment

Overview- How to Predict CNS PK?

57

Westerhout et al. PBPK Modeling to Investigate Regional Brain Distribution Kinetics in Rats. AAPSJ. 2012

Animal experiment Animal PK profiles

58



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0.2 ul/min

2.2 ul/min

10.6 ml

290 ml

50 ml

17 ml

50 ml

180 ml

2.2 ul/min

2.2 ml/min

2.2 ul/min

Animal PBPK

model



59


2.9 l

0.175 ml/min

0.4 ml/min

240 ml

22.5 ml

7.5 ml

22.5 ml

90 ml

0.4 ml/min

0.4 ml/min

0.4 ml/min

0.2 ul/min

2.2 ul/min

10.6 ml

290 ml

50 ml

17 ml

50 ml

180 ml

2.2 ul/min

2.2 ml/min

2.2 ul/min

Animal PBPK

model


Translation to human model


60

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2.9 l

0.175 ml/min

0.4 ml/min

240 ml

22.5 ml

7.5 ml

22.5 ml

90 ml

0.4 ml/min

0.4 ml/min

0.4 ml/min

0.2 ul/min

2.2 ul/min

10.6 ml

290 ml

50 ml

17 ml

50 ml

180 ml

2.2 ul/min

2.2 ml/min

2.2 ul/min

Animal PBPK

model




61

100

1000

10000

100000

0 120 240 360 480 600 720

Pred

ictedHum

anAcetaminoph

en

Con

centra

on(ng/ml)

Time(min)

Plasmaobserved

CSF(SAS)observed

Plasmapredicted

SAS(CSF)predicted

BrainECFpredicted

LV

CM

Validation on human data

Observed (Bannwarth et al. Br J Clin Pharmacol. 1992) and predicted human acetaminophen concentrations in plasma (l, ….) and CSF (о, - -- -)


2.9 l

0.175 ml/min

0.4 ml/min

240 ml

22.5 ml

7.5 ml

22.5 ml

90 ml

0.4 ml/min

0.4 ml/min

0.4 ml/min

0.2 ul/min

2.2 ul/min

10.6 ml

290 ml

50 ml

17 ml

50 ml

180 ml

2.2 ul/min

2.2 ml/min

2.2 ul/min

Animal PBPK

model




62

100

1000

10000

100000

0 120 240 360 480 600 720

Pred

ictedHum

anAcetaminoph

en

Con

centra

on(ng/ml)

Time(min)

Plasmaobserved

CSF(SAS)observed

Plasmapredicted

SAS(CSF)predicted

BrainECFpredicted

LV

CM

Validation on human data

Observed (Bannwarth et al. Br J Clin Pharmacol. 1992) and predicted human acetaminophen concentrations in plasma (l, ….) and CSF (о, - -- -)

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3. Prediction of Human CNS PK for Multiple Drugs

V2, A2, C2

V1, A1, C1

V3, A3, C3

V4, A4, C4

63

CNS target site concentration-time profiles (PK) depends on:

A) CNS target site concentration-time profiles (PK) depends on

B) BBB permeability and all aspects of intra-brain distribution

C) BBB permeability and cellular accumulation (brain binding)

D) The ratio between unbound plasma and brain PK


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33

10.6 ml

Plasma

Periphery 1

CSFSAS

CSFCM

CSFTFV

CSFLV

Brain

ECF

QDIFF

QDIFF

Deep brain

Periphery 2

QDIFF

QDIFF

CLPL-ECF

QPL-PER1 QPL-PER2

CLE

QDIFF

CLCSF_PL

QECF_ICF

Generic drug translational model (mult. drugs with distinctivephys-chem properties)

Individual drug translational models

Generic Drug Modeling Approach

65

Yamamoto et al, A generic multi-compartmental CNS distributionmodel structure for 9 drugs allows prediction of human brain target site concentrations. Pharm Res, 2016

Overview Data Modeling

66

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Plasma

Healthy (better) Brain ECF

Diseased (Worse) Brain ECF

Plasma

Periphery 1

CSFSAS

CSFCM

CSFTFV

CSFLV

Brain

ECF

QDIFF

QDIFF

Deep brain

Periphery 2

QDIFF

QDIFF

CLPL-ECF

QPL-PER1 QPL-PER2

CLE

QDIFF

CLCSF_PL

QECF_ICF

penumbra zone surrounding the evacuated haematoma

Cortex tissue with normal appearance at CT scan

Prediction: Human CNS Morphine

67

Yamamoto et al, A generic multi-compartmental CNS distribution model structure for 9 drugs allowsprediction of human brain target site concentrations. Pharm Res, 2016

Patient 1 (focal TBI)

plasma brainECF plasma brainECFplasma brainECF

Patient 2 (Focal TBI) Patient 4 (Focal TBI)

68Patient 5 (Focal TBI, only 2 blood samples) Patient 6 (Diffuse TBI)

Pedriatic TBI patients

68

Prediction: Human CNS Morphine

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Rat Human

Rat Human

Yamamoto Y, Danhof M, de Lange EC. Microdialysis: the Key to PBPK Model Prediction of Human CNS Target Site Concentrations.. AAPS J. 2017

Use of CSF to Predict CNS PK?

69

4. CNS PK prediction for any small drug without the

need for in vivo data?

V2, A2, C2

V1, A1, C1

V3, A3, C3

V4, A4, C4

70


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Human prediction

0.2 ul/min

2.2 ul/min

10.6 ml

290 ml

50 ml

17 ml

50 ml

180 ml

2.2 ml/min

2.2 ml/min

2.2 ul/min

Animal PBPK

model

100

1000

10000

100000

0 120 240 360 480 600 720

Pre

dic

ted

Hu

ma

n A

ceta

min

op

hen

Co

nce

ntr

ati

on

(n

g/m

l)

Time (min)

Plasma observed

CSF (SAS) observed

Plasma predicted

SAS (CSF) predicted

Brain ECF predicted

LV

CM

2.9 l

0.175 ml/min

0.4 ml/min

240 ml

22.5 ml

7.5 ml

22.5 ml

90 ml

0.4 ml/min

0.4 ml/min

0.4 ml/min

Prediction without in vivo data?

71

72

Full PBPK CNS Model

72

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73

Neutral compound

Zwitterionic compound

Neutral P-gp substrate

Acidic compound

Basic compounds

Basic P-gp substrates

Simulations and Actual Data

73Yamamoto et al, CPT: PSP, 2017; Yamamoto et al, EJPS, 2018

Human PBPK CNS Model

74Yamamoto et al, EJPS, 2018

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75

Simulations – systems changes


75Yamamoto et al, EJPS, 2018

76

Simulations and Actual Data

Phenytoin

Yamamoto et al, EJPS, 2018 76


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Can we use animal data on brainECF, and CSF and/or human CSF PK to predict human brainECF (off) target PK?

Relation between drug concentrations and their time course in brainECF, CSF in lateral ventricles, CSF in cisterna Magna, and CSF in lumbar region are

• Drug dependent

• Species dependent

• Time dependent

General Conclusions (1)

77

78

• We need to distinguish between drug properties and system (CNS) characteristics for being able to translate between species and/or conditions

• Inter-relationships between PK and PD processes of drugs can be revealedby mathematical modelling if experiments using in individual animalsinclude• Measurements with time-resolution (multiple time-points)• Measurements that reflect different processes within one single

animal (multi-level measurements)

• Such information from animals should be stored in mathematical models, so that it provides knowledge, and reduces the need for animals in research.

General Conclusions

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79

Final Food for Thought

• Reductionists approaches will not bring us further ….

• We should face the complexity of processes in the living body, and design our experiments accordingly in order to unravel interrelationships for true understanding and translation

• Medicinal chemist need to realize that many PK processes govern CNS target site PK- it is not only “BBB permeability”

• Thus, for optimization of drug properties, all aspects need to be considered

• The CNS PBPK model provides a very useful tool for investigating the relationship between drug properties and drug distribution into and within the CNS

Dirk-Jan van den Berg

Francesco Bellanti

Willem vd Brink

Sinziana Cristea

Meindert Danhof

Nathalie Doorenweerd

Tony Figaji

Janna Geuer

Piet Hein van der Graaf

Margareta Hammarlund-Udenaes

Thomas Hankemeier

Robin Hartman

Coen van Hasselt

Sandra den Hoedt

Laura Kervezee

Naomi Ketharanathan

Maaike Labots

Victor Mangas

Ron Mathôt

Nick van Oijen

Shinji Shimizu

Jasper Stevens

Stina Syvanen

Dick Tibboel

Pyry Valito

Willem vd Brink

Yumi Yamamoto

Joost Westerhout

Wilbert de Witte

Eric Wong

Ursula Rohlwink

Enno Wildschut

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Acknowledgements

http://www.lapp.nl/homepage.htm

http://www.uu.se/

https://www.google.nl/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwir3a3Bj-PdAhVF46QKHQ_WCmsQjRx6BAgBEAU&url=https://publons.com/author/907524/coen-van-hasselt&psig=AOvVaw1ocJqQPc8d9gdUS5Ir2r-9&ust=1538410399862311

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