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Marcel Leist Professor for In Vitro Toxicology and Biomedicine
Chair inaugurated by the Doerenkamp-Zbinden Foundation,
University of Konstanz, Germany
EFSA conference: New approach methods
(NAM) in toxicology for mechanism-based
hazard assessment
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Need of human cell-based models in toxicology
https://joshmitteldorf.scienceblog.com/category/uncategorized/
https://www.innovativetesting.nl/news?page=3
https://thedailyblog.co.nz/2015/07/10/money-as-a-social-technology/
Mechanisms
Ressources, costs, throughput Species barriers
Predictivity
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?
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Why not the good old way…?
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fashion?
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Why not the good old way…?
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Why not the good old way…?
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Reach: > 8000 chemicals (high tonnage),
TSCA: > 8000 high production vol. chemicals,
Hundreds of pesticides,
Thousands of food additives,
etc….
- About 200 regulatory DNT studies
- About 10 industrial chemicals
- EFSA ‚claims‘ 34 pesticides tested*
- Number of positives unclear (no survey)
- About 14 substances with human evidence
* (unpublished)
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Two reasons to consider mechanisms
I. Making sense of data II. Generation of data
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A. Animal studies
Aa: eye opening delayed by 0.5 days; altered gender balance; etc.
(implication; relevance?)
Ab: hyperactivity (species extrapolation; implication?)
B. Epidemiological studies
Ba: Parkinsonism & childhood leukemia in areas of high pesticide use
(plausibility, causality?)
Bb: Methylmercury from fish intake and cognitive performance
(modulation by nutrients; causality; confounding?)
C. In vitro studies
Ca: Positive outcome in the embryonic stem cell test (EST)
(relevance; association with adverse outcome?)
Cb: Zebra fish altered movement in the dark
(relevance; association with adverse outcome?)
Two reasons to consider mechanisms
I. Making sense of data Examples
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Principle: ‚process control‘ instead of ‚end stage control‘
Assumption I: there are key neurodevelopmental processes required to
form a fully functional and intact nervous system.
Assumption II: if key neurodevelopmental processes are disturbed, functional
or structural deficits may arise.
Procedure: define and establish test methods for key neurodevelopmental
processes and evaluate interference by test chemicals
Two reasons to consider mechanisms
II. Generation of data
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Key neurodevelopmental processes
Glial
Progenitor
Neuronal
Progenitor
Apoptosis
Synaptogenesis
Network
formation and
function
Neurite outgrowth
Network formation
and function
Myelination
Migration
Microglia (CD45, CD11b etc)
Astrocytes (GFAP)
Neurons (diverse neuronal
markers)
Differentiation
Apoptosis
Oligodendrocytes (O4, GalC ,CNPase)
Proliferation
Neural
Stem Cell
(NSC)
Glial Progenitors
Erythromyeloid
Progenitor
Neuronal
Progenitors
Neuroepithelium
Radial Glia
Pluripotent
Stem Cell
(PSC)
Bal-Price (2018) ALTEX
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In vivo Finding Disturbed neurodevelopmental processes
Brain weight up/down Proliferation, Apoptosis
Holoprosencephaly Apoptosis, Neurodifferentiation
Lissencephaly Apoptosis, Neurodifferentiation, Migration
Neuroinflammation Astrocyte activation, Gliosis, Neurodegneration
Cortical layer thickness Proliferation, Migration, Myelination
Disturbed reflexes Neurodifferentiation, Myelination, Synaptic
transmission
Anxiety behaviour Neurodifferentiation, Synaptic transmission,
Synapse formation
Eventually, any DNT finding (man or animal) must be due to
a combination of disturbed neurodevelopmental processes
If a compound does not disturb at least one process, it cannot be
associated with a DNT hazard
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De novo (no prior knowledge) evaluation of a new unknown
compound for classification and labelling
Two reasons to consider mechanisms
II. Generation of data: expectations and challenges
alert: preliminary indication that there is a hazard potential; needs verification by other methods
Screening of libraries of compounds to check for ‚alerts‘ and to
prioritize for further more comprehensive (resource-consuming) testing
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De novo (no prior knowledge) evaluation of a new unknown
compound for classification and labelling
Two reasons to consider mechanisms
II. Generation of data: expectations and challenges
* similarity extended from structure to mechanisms (and metabolism)
Screening of libraries of compounds to check for ‚alerts‘ and to
prioritize for further more comprehensive (resource-consuming) testing
Read-across (RAX): 1. anchoring toxicity of unknown compound by
comparison to similar* known compound (s);
2. comparison within a category of related*
compounds
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Plausibility, relevance
Species extrapolation
Causality
Key neurodevelopmental processes
Gap-filling / screening/ prioritization
Read-across (RAX)
De novo evaluation
Two reasons to consider mechanisms
I. Making sense of data II. Generation of data
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Data always describe a model – not the reality!
always an extrapolation required (uncertainty)
poor explanation of uncertainty
implicit mechanistic assumptions (not rationalized and validated)
Example: mouse cancer bioassay
Perfect description, but wrong model (< 60% concordance)
What is wrong with descriptive data
(Often outdated technology)
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Is a mechanistic approach less direct?
Level Parameter
Direct observation Altered light-dark behaviour
Interpretation
(theoretical
construct)
Anxiety
Endophenotype
(measurable change
in structure or
connectivity)
Altered function/structure of
amygdala (limbic system)
Processes disturbed
(during
development)
Migration/Differentiation
Mechanistic
correlate / endpoint
Hit in
Migration/Differentiation
assay
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Is a mechanistic approach less direct?
Level Parameter Human situation
Direct observation Altered light-dark behaviour Meaningless
Interpretation
(theoretical
construct)
Anxiety Maybe; Maybe something else
(little species correlation of actual adverse
outcomes)
Endophenotype
(measurable change
in structure or
connectivity)
Altered function/structure of
amygdala (limbic system)
Altered function/structure of amygdala
(limbic system)
Processes disturbed
(during
development)
Migration/Differentiation Migration/Differentiation
Mechanistic
correlate / endpoint
Hit in
Migration/Differentiation
assay
Predictivity for problem
Uncertainty about type of problem
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17 forms spinal cord
Example:
Test of early brain/spinal cord development
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neural plate
NC NC ED ED
Valproic acid (VPA)
(anti-epileptic)
Failure of neural
tube closure
18 forms spinal cord
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Cellular model: Neural differentiation from hiPSC
iPSC (Oct4, Nanog) day -3
10 DoD: 0 1 2 4 6 8
Medium:
Supplements:
KCM N2
Substrate: Matrigel
-3 -2 -1
ROCK, bFGF Noggin, dorsomorphin, SB 431542
11 13 14
FGF2, AA
DMEM-F12 KSR
human pluripotent stem cells
neural plate neural fold
NEP (Pax6, Otx2)
day 6
neuroectodermal progenitors
Rosettes
day 14
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Cellular model: Neural differentiation from iPSC
hiPSC NEP
ectoderm / neuroectoderm
day 6 day -3
Oct4
Pax6 / Nestin Pax6 / Nestin GM130/ ZO1
Rosettes day 15
lineage specification
differentiation
functional anchoring
Pax6 / Nestin
Oct4
Pax6 / Nestin
PAX6/Nestin
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Relevant concentration range – concentration response of valproic acid (VPA)
num
ber
of
PS
0
500
1000
1500
2000
2500
25 µM 150 µM 350 µM 450 µM 550 µM 800 µM 1000 µM 25 150 350 450 550 800 1000
VPA [µM]
500
1000
1500
2000 up-regulated PS25 µM 150 µM
350 µM
450 µM
800 µM
1000 µM
550 µM
Cytotoxicity
350 µM
450 µM
550 µM
Gene Expression
non-cytotoxic
cytotoxic
teratogen?
Gene expression changes start with 350 µM VPA no cytotoxicity observed in this range
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Functional anchoring after VPA treatment
Control (solvent only)
ZO1/GM130/DNA
VPA DoD 0-6 (0.6 mM)
10 DoD: 0 1 2 4 6 8
Medium:
Supplements:
KCM N2
Substrate: Matrigel
-3 -2 -1
ROCK, bFGF Noggin, dorsomorphin, SB 431542
11 13 14
FGF2, AA
DMEM-F12 KSR
untr
TSA
0
200
400
600
Nu
mb
er
of
rose
tte
s/w
ell
Ctrl VPA
After 6 days of treatment rosettes formation is disturbed
The gene expression changes have functional consequences on differentiation
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Valproate (VPA)
analogues and
their in vivo
response
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Testing Strategy
Concentration – Response C1 = 5 mM Endpoint: Resazurin reduction
Curve fitting (Graph Pad, 4 parameter fit) Determination of EC10
Concentrations around EC10 Endpoint day 6: RT-qPCR, gene expression
Concentrations around EC10 Endpoint day 15: rosettes formation
PAX6, OTX2 & AP2 changed?
Rosettes reduced > 75% of control?
EC10 > 2.5 mM? NO
No hit
Hit YES YES
NO NO
YES
NO
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Example for a hit: 4-ene-VPA
Gene expression: 1.2 & 0.625 mM
Viability
(G) 2-Propyl-4-pentenoic Acid = 4 ene VPA
0
50
100
-2 0 2 4untr
log Concentration [µM]
Via
bilit
y [
%u
ntr
]
G
pax6
otx2
nanog
oct4
emx2
msx
1
cacn
a
cdh2
prune
prkcd
p
tfap
2b
-2
-1
0
1
2
3
G4
G3
log
ge
ne
ex
pre
ss
ion
[%
un
tr]
1.2 mM
0.6 mM
Testing in non-cytotoxic range Expected gene expression changes Inhibition of rosettes formation
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Summary Table
3 clear hits: Valproic acid in vivo positive ✓ 2-Ethylhexanoic acid in vivo positive ✓ 4 ene VPA in vivo positive ✓
2 clear Negatives: 2 Ethylbutyric acid in vivo negative✓ 2,2-Dimethylvaleric acid in vivo negative✓
3 are unclear: Hexanoic acid in vivo unknown 2-Methylhexanoic acid in vivo negative 2-methyl-pentanoic acid in vivo unknown
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Results from a test battery
(note: data without PBPK correction)
negative (in vitro / in vivo)
unclear/ intermediate (in vitro / in vivo)
positive (in vitro / in vivo)
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Summary
1. Mechanistic risk assessment adds value to data
2. Mechanistic risk assessment allows for new NAM-based approaches
3. A battery of tests for key neurodevelopmental processes is available and has been
successfully used in case studies
4. There is an educational need on all sides to understand strengths and weaknesses
of the new approaches; discussions of case studies can provide a platform
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Acknowledgement
Bob van de Water
Hennicke Kamp and many others
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