modelling of cell stress pathways for safety decision making
TRANSCRIPT
Modelling of cell stress pathways for safety decision making
ALISTAIR MIDDLETONSAFETY & ENVIRONMENTAL ASSURANCE CENTRE (SEAC), UNILEVER R&D
CONFLICT OF INTEREST STATEMENT
Alistair Middleton is employed by Unilever and the work described inthis presentation was conducted at Unilever or funded by Unilever andconducted at the University of Leiden, the University of Emory, AMMSand Scitovation
A TIERED APPROACH FOR RISK ASSESSMENT
Tier 1: In silico MIE prediction
QSARsDocking models
MIE Atlas
Tier 2: Pathway Identification
TranscriptomicsIn-vitro screening panels
High content imaging
Tier 3: Pathway Characterisation
3D organotypic modelsSystems biology models
MD simulations
Adopting an exposure-driven risk assessment approach
Weight of Evidence
Mechanistic understanding
CELL STRESS PATHWAYS
• Toxicity driven by non-specific effects• Investigate stress-pathway responses• Stress pathways have common network motifs
Simmons, S. O. et al (2009). Cellular stress response pathway system as a sentinel ensemble in toxicological screening. Toxicological sciences, kfp140.
High concentration
TF
ST
Transcription factor
SensorTransducers
Cellular defences
C
Cell Stress
Cell injury
Low concentration
time
Expose cells to compound
Stre
ss r
esp
on
se
Prof B. van de Water, U. Leiden
TIPPING POINTS IN VITROTipping point
Vulnerable to 2nd insult?
Concentration
Ce
ll st
ress
res
po
nse
Early response
Late response
Different cell types?
• Explain response based on the molecular mechanisms• Capture uncertainties in the quantitative manner• Calculate low-risk exposures
QUANTITATIVE IN VITRO TO IN VIVOEXTRAPOLATION (QIVIVE)
In vitro cell culture
Calculate tipping point
PBPK models
Measure stress response
Use models (in vitro cell based and mathematical) to build weight of evidence to calculate low risk exposures.
Mathematical models
Uncertainties Uncertainties
Prof B. van de Water, U. Leiden
NRF2 SIGNALLING AS AN
EXEMPLAR STRESS
PATHWAY
THE NRF2 SIGNALLING NETWORK
Cellular defence against oxidative and electrophilic stress
ROS/Electrophiles
NRF2NRF2
Keap1Keap1
NRF2 NRF2
Keap1ox
Anti-oxidative stress response genes
NRF2
?
De-novo synthesis
Proteolysis
Nucleus
CytosolGSH
nuclear export
Removal
AntioxidantROS/Electrophiles
NRF2
SRXN1
NRF2
KEAP1-INDEPENDENT REGULATION
Cellular defence against oxidative and electrophilic stress
ROS/Electrophiles
NRF2NRF2
Keap1Keap1
NRF2
NRF2
NRF2
Keap1ox
Anti-oxidative stress response genes
NRF2
?
De-novo synthesis
Proteolysis
Nucleus
CytosolGSH
nuclear export
Removal
AntioxidantROS/Electrophiles
Fyn
NRF2
SRXN1
NRF2
HIGH CONTENT IN VITRO ASSAY DATA
SRXN1, NRF2 & KEAP1 ROS/GSH
Mitochondrial ROS
Chemicals
SulforaphaneDEMtBHQ
CDDO-Meetc
Prof B. van de Water, U. Leiden
Prof Peng, AMMS
MODEL FITS TO TBHQ DATA: NUCLEAR NRF231 µM 56 µM 100 µM
31 µM 56 µM 100 µM
MODEL FITS TO TBHQ DATA: SRXN1
31 µM 56 µM 100 µM
31 µM 56 µM 100 µM
MODELS AND MECHANISTIC RATIONALE
Building confidence by challenging the model with new data
Experimental perturbations
• Knockdowns• Repeat exposure response• Depletion of GSH
MODEL FITS TO TBHQ DATA: SRXN1
31 µM 56 µM 100 µM
31 µM 56 µM 100 µM
EVALUATING THE MODEL: KNOCKDOWN DATA
Prediction based on NRF2 data fits Knockdown data
IN-VITRO CELL KINETICS
Given exposure(e.g. tBHQ)
ResponseIntracellular kinetics
GSHNRF2 ROS/Electrophiles
?
(Model predictions)
~2 fold difference
Yoshimasa Nakamura et al. (2003).
Pivotal Role of Electrophilicity in Glutathione
S-Transferase Induction by tert-Butylhydroquinone
100µM tBHQRL34 hepatocytes
(Experimental data)
THE ‘TIPPING POINT’(Model prediction)
(Experimental data)
Saturation of elimination
pathway
Depletion of GSH
(Model prediction)
(Model prediction)
TOWARDS UNDERSTANDING REPEAT DOSE
Predicted effect of 1mM tBHQ repeat dose on hepatocytes
• Expose for two hours, wash and re-expose 24 hours later
Measured effect of 0.5mM DNCB on epidermis model
• 2,4-Dinitrochlorobenzene (DNCB) – similar reactivity mechanism to tBHQ
800
700
600
500
400
300
200
100
NR
F2 o
r N
QO
1 (
% o
f co
ntr
ol t
=0
h)
00 2 24 26 48 50 72
Nrf2
NQO1
800
600
400
200
0N
RF2
(%
of
con
tro
l t=
0h
)0 10 20 30 40 50 60
Nrf2 activation vs time
DISCUSSION
1. The NRF2 model• The model appears to capture the relationship between exposure in-vitro
and response• However, no model (even animal or in-vitro cell based) is ‘perfect’• Open question: when do we know the models we use are ‘good’
enough?• A challenge for the future: capture uncertaintiesR
esp
on
se
Time
Confidence region
DISCUSSION
2. Linking to risk assessment• All the models we develop are meant as tools to be used in the risk
assessment process• For many chemicals there is an exposure level that is considered to be
acceptable for humans. • How do the predicted low-risk exposures compare to currently accepted
ones?
PBPK models
Example: tBHQ
ADI (EFSA):0.7 mg/kg/day
THANKS FOR LISTENING!
ACKNOWLEDGEMENTS
Unilever• Maja Aleksic
• Paul Carmichael
• Sarah Cooper
• Carol Courage
• Stephen Glavin
• Penny Jones
• Jin Li
• Paul Russell
• Jayasujatha Vethamanickam
• Sam Windebank
• Andrew White
Leiden University
• Bob van de Water
• Stephen Winks
ScitoVation
• Melvin Andersen
• Rebecca Clewell
• Harvey Clewell
• Patrick McMullen
Emory University
• Qiang Zhang
AMMS
• Prof Peng
• Jiabin Guo
ROS & GSH
0.00.51.01.52.02.53.03.54.04.55.05.56.06.57.07.58.0
10min 30min 1h 2h 3h 4h 6h 24h
Fold-change from control
Time-course of ROS production
Control
DMSOcontrol50µMtBHQ100µMtBHQ500µMtBHQ100µMNEM
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
10min 30min 1h 2h 3h 4h 6h 24h
Fold-change from control
Time-course of GSH content
Control
DMSOcontrol50µMtBHQ100µMtBHQ500µMtBHQ100µMNEM
Computational cellular stress systems models to define the
tipping point between adaptive cellular capacity and
adverse outcomes: Implications for safety decision making.
ALISTAIR MIDDLETONSAFETY & ENVIRONMENTAL ASSURANCE CENTRE (SEAC), UNILEVER R&D