MITOCHONDRIAL TOXICITY AND OXIDATIVE STRESS:

CARL WESTMORELANDSARAH COOPER, JIABIN GUO, ALISTAIR MIDDLETON, JOE REYNOLDS, SHUANGQING PENG, BOB VAN DER WATER, ANDREW WHITE, HAITAO YUAN AND QIANG ZHANG

Defining the tipping point between adaptive and adverse effects for consumer safety risk assessment

CAN WE USE A NEW INGREDIENT SAFELY?

Computational Toxicology (2018) 7, 20-16

MITOCHONDRIAL TOXICITY AND OXIDATIVE STRESS

• Oxidative stress and mitochondrial toxicity are key events in several Adverse Outcome Pathways https://aopwiki.org/aops

• A large number of assays exist for oxidative stress and mitochondrial toxicity in vitro

• Where could the role be for these assays in next generation consumer safety risk assessment?

CELLULAR STRESS WORKSHOP

February 2016, London, UK

TOXCAST: COMBINING IN VITRO ACTIVITY AND DOSIMETRY

Slide from Dr Rusty Thomas, EPA, with thanksRotroff, et al. (2010) Toxicol.Sci 117, 348-58

CELL STRESS PATHWAYS AND TIPPING POINTS

Simmons et al (2009) Toxicol Sci, 111, 202-25

TF

ST

Transcription factor

Sensor Transducers

Cellular defences

C

Chemical stressor

Cell injury

Time

Re

sp

on

se

Low dose (adaptive)

Medium dose (adaptive)

High dose (adverse)

Tipping point

UNCERTAINTY AND DECISION MAKING

in vitro cell culture

Characterise stress response

What is low risk exposure?

• Which cell model? 2D or 3D? • Primary or cell line?• Which pathways/biomarkers (coverage)?• How many time points/dose points?

• How do we calculate the tipping point?• Number of pathways?• Duration of response?

• Cells in media vs tissue?

• Chronic vs acute exposure

Characterise uncertainties to facilitate decision-making

Prof B. van de Water, U. Leiden

Tier ITier II Tier III

UncertaintyMechanistic understanding

Pathway identification

• Transcriptomics• Proteomics• Receptor screens• Stress Panel

Pathway characterisation

• Live cell imaging• Systems toxicology

models• Repeat dose• Organotypic models

Hazard Identification

• Publications• In-silico alerts• MIE atlas• AOP wiki

Regression (SAR/QSAR) or Docking Models

Systems ModellingDose-Response Modelling

DEVELOPING MODELS WITHIN A TIERED STRATEGY

‘CELL STRESS PANEL’

‘Low-risk’ compounds:

Phenoxyethanol

Niacinamide

Caffeine

Known ‘high-risk’ compounds:

Doxorubicin

Diclofenac

Troglitazone

14 chemicals, including

Mitochondrial Toxicity

Oxidative Stress

DNA damage

Inflammation

ER Stress

Metal Stress

Osmotic Stress

Heat Shock

Hypoxia

Cell Health

Stress pathways

Platform

Technology: High content imaging*

Cell line: HepG2

Timepoints: 1, 6 & 24 hours*

Choosing the dosing range

Use typical exposure scenarios:

Use PBK models:

Calculate ‘free concentration’

Use in vitro exposure models:

Fo

ld c

ha

ng

e f

rom

co

ntr

ol

Dose (µM)

EXAMPLE - DOXORUBICIN (MITOCHONDRIAL-ROS)

1. Does an effect occur (within the observed dose range?)

2. If an effect does occur, what is the point of departure?

LINKING EXPOSURE AND POINT OF DEPARTURE

PBK models of systemic exposure

in vitro exposure modelQuantify evidence of a response, calculate PoD

Summarise data

GRAPHICAL SUMMARY

Blood plasma FREE concentration (shaded region indicates uncertainty)

Positive biomarkers (i.e. ‘hits’)

Mean FREE concentration PoD

(*) and 95% uncertainty range

(o’s)

Log10 scale

Colours indicate pathway

Oxidative stress Mitotox Inflammation

Cell health/other DNA damage ER stress

6 hours1 hour24 hours

Doxorubicin

6 hours1 hour 24 hours

Phenoxyethanol

TIER 3 ASSESSMENT E.G. DOXORUBICIN

Experimental data: General cellular health remain largely unchanged at low concentrations

Yuan et al, (2016) Toxicol Sci 150, 400-17

TIER 3 ASSESSMENT E.G. DOXORUBICIN

Yuan et al, (2016) Toxicol Sci 150, 400-17

Experimental data: ROS, MMP and ATP levels remain largely unchanged at low concentrations

TIER 3 ASSESSMENT E.G. DOXORUBICIN

Yuan et al, (2016) Toxicol Sci 150, 400-17

Experimental data: PGC-1a, NRF1, mtDNA, MnSOD and UCP2 are upregulated at lower concentrations

HYPOTHESIS FOR MITOCHONDRIAL HOMEOSTASIS AND DOXORUBICIN TREATMENT

DOX

ROS

PGC-1α

TFAntioxidants

MMP ATP

TFAMMitochondrial biogenesis

Electron transfer

intermediates

AMPK

TCA, FAO

Metabolic enzymes

Φ

Φ

Pro

ton

le

ak

UCP2

Mitochondrial mass

Through PGC-1α-mediated transcriptional feedback and feedforward networks, disruption of the mitochondrial electron transfer chain by doxorubicin, which leads to increased ROS production and reduced ATP synthesis, can be very limited. However, when this PGC-1α-mediated transcriptional network is maxed out at higher concentrations of doxorubicin, cells lose homeostatic control,

which is associated with a point of departure.

SIMULATION RESULTS: CONCENTRATION-RESPONSES WITH DOXORUBICIN

Yuan et al, (2016) Toxicol Sci 150, 400-17

PLASMA Cmax BASED COMPARISON: IN VIVO VS. IN VITRO

0

1

2

3

4

5

6

7

8

9

10

11

12

13

1 10 100 1000 10000

In vitro tipping points

Plasma Cmax derived by PBPK Modelling

Plasma Cmax observed in clinical study

Log Concentration (nM)

9mg/m2/day continuous i.v.infusion

Tipping point: AC16 cell line 125nM 12h

9mg/m2/day 30 min i.v.infusion

30mg/m2 30 min i.v. infusion

ihPS derived cardiomyocytes 156nM 48h

ihPS derived cardiomyocytes 156nM 144h

4.5mg/m2/day continuous i.v.infusion

30mg/m2/day 30 min i.v. infusion

No Adverse Effects

observed in clinical

For PBPK modelling, continuous infusion or 30 min infusion were given for 4 days as a cycle, repeated every 21 days, 4 cycles in total were given.

Prof S PengHaitao YuanJiabin Guo

SUMMARY

Oxidative stress and mitochondrial toxicity are key events in several Adverse Outcome Pathways (https://aopwiki.org/aops)

• Exposure-driven, Next Generation Risk Assessment following ICCR principles

• Developing a tiered approach to understanding toxicity associated with oxidative

stress and mitochondrial toxicity

• Use of dose response information for risk assessment

• Tipping points: Absolute or adaptive/adverse?

• Uncertainties: in vitro models, duration of exposure,

• Use for safety decision-making: Data quality/robustness for novel approach

methodologies (NAMs), modelling approaches

21SEAC Unilever Information: Internal Use

ACKNOWLEDGEMENTS

Unilever

The SEAC NGRA Team with special thanks to:

• Alistair Middleton

• Sarah Cooper

• Andy White

• Jin Li

• Paul Carmichael

• Joe Reynolds

Emory University

• Qiang Zhang

AMMS

• Prof Peng

• Jiabin Guo

• Haitao Yuan

Leiden University

• Bob van de Water

• Stephen Winks

Cyprotex

• Paul Walker