predicting mixture effects

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Predicting mixture effects Tjalling Jager Dept. Theoretical Biology the causality chain from molecule to population

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Predicting mixture effects. the causality chain from molecule to population. Tjalling Jager Dept. Theoretical Biology. Contents. Complexity of multiple stress Classic mixture approach Following the causality chain Case studies with TKTD models Take home messages. - PowerPoint PPT Presentation

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Page 1: Predicting mixture effects

Predicting mixture effects

Tjalling JagerDept. Theoretical Biology

the causality chain from molecule to population

Page 2: Predicting mixture effects

Contents

Complexity of multiple stress

Classic mixture approach

Following the causality chain

Case studies with TKTD models

Take home messages

Page 3: Predicting mixture effects

Complexity of multi-stress

Predictions at the population/community level In the field, multiple stress is the norm Questions:

– do stresses add up?– do stressors interact?

Page 4: Predicting mixture effects

Complexity of multi-stress

insecticidalBt-proteins

xenobioticchemicals

environmentalstress

intra-speciesinteractions

inter-speciesinteractions

Page 5: Predicting mixture effects

Current status

Ecotoxicological research: descriptive at the individual level mechanistic at the molecular level sketchy at population/community level

dose

resp

onse

Page 6: Predicting mixture effects

Classic mixture models

Dose-response curve for compound A and B

dose A

resp

onse

dose B

resp

onse

Page 7: Predicting mixture effects

Classic mixture models

Combine using reference model:– “response multiplication”: chemicals act independently– “concentration addition”: chemicals act as dilutions

dose 1dose 2

resp

onse

dose Adose B

resp

onse

dose 1dose 2

resp

onse

resp

onse

dose Adose B

Page 8: Predicting mixture effects

Classic mixture models

Combine using reference model:– “response multiplication”: chemicals act independently– “concentration addition”: chemicals act as dilutions

dose A

dose

B

dose A

dose

B50% effect50% effectsynergism

antagonism

synergism

antagonism

Page 9: Predicting mixture effects

What is the relevance?

This analysis is usually done … with one reference model, for one endpoint, at one time point.

dose 1dose 2

resp

onse

resp

onse

Page 10: Predicting mixture effects

0

1

2

3

4

5

0 1 2 3 4 5 6time (weeks)

TU m

ixtu

re 5

0% e

ffect

syn.ant.

What is the relevance?Van Gestel & Hensbergen (1997)Environ. Toxicol. Chem.

reproductionbody weight

Cadmium and zincReference: conc. add.

Page 11: Predicting mixture effects

What is the relevance?

This analysis is usually done … with one reference model, for one endpoint, at one time point.

And test conducted … at constant exposure, under one set of conditions.

dose 1dose 2

resp

onse

resp

onse

Page 12: Predicting mixture effects

population dynamics

Causality chain

protectiongoals

external exposure

life-history traits

toxicitytesting

molecular targets

mechanisticstudies ?internal

exposure

toxico-kinetics

Page 13: Predicting mixture effects

Effect on reproduction

Page 14: Predicting mixture effects

Effect on reproduction

Page 15: Predicting mixture effects

Effect on reproduction

Page 16: Predicting mixture effects

Effect on reproduction

Page 17: Predicting mixture effects

Effect on reproduction

No single pathway for effects on reproduction!

Page 18: Predicting mixture effects

Energy budget

How are resources used to fuel life history? Subject of DEB theory

– dynamically linking all traits over the life cycle

growth

maintenancematuration

offspring

Kooijman (2001)Phil. Trans. B

Page 19: Predicting mixture effects

population dynamics

Causality chain

protectiongoals

external exposure

life-history traits

toxicitytesting

molecular targets

mechanisticstudies

metabolic processes

energybudget

growth

maintenancematuration

offspring

internal exposure

toxico-kinetics

Page 20: Predicting mixture effects

population dynamics

Causality chain

protectiongoals

external exposure

life-history traits

toxicitytesting

molecular targets

mechanisticstudies

metabolic processes

energybudget

internal exposure

toxico-kinetics

Fill this chain with mechanistic models …– predict impact on populations/communities– deal with time-varying exposure– extrapolate to different environments– predict impact of multiple stress ...

Page 21: Predicting mixture effects

toxicitytesting

energybudget

survivalin time

survivalmodel ‘GUTS’

growth, repro, etc.

Case studies: TKTD

external exposure

metabolic processes

internal exposure

toxico-kinetics

toxicokinetics toxicodynamics

growth

maintenancematuration

off spring

‘DEBtox’

Jager et al. (2006)Ecotoxicology

Jager et al. (2011)Environ. Sci. Technol.

Page 22: Predicting mixture effects

Simple mixture rules

compound ‘target’

add internal concentrations (with weights)

maintenance costs

growth costs

survival prob.

metabolic process

Jager et al. (2010)Ecotoxicology

Page 23: Predicting mixture effects

Simple mixture rules

compound ‘target’

maintenance costs

growth costs

survival prob.

metabolic process

Jager et al. (2010)Ecotoxicology

Page 24: Predicting mixture effects

Simple mixture rules

combine independent effectsin the energy budget

compound ‘target’

maintenance costs

growth costs

survival prob.

metabolic process

Jager et al. (2010)Ecotoxicology

Page 25: Predicting mixture effects

Case study: survival

Baas et al. (2007)Environ. Toxicol. Chem

Mixture of Cd and CuModel fit to all survival

data in time …

Page 26: Predicting mixture effects

Case study: survival

Baas et al. (2007)Environ. Toxicol. Chem

Mixture of Cd and CuModel fit to all survival

data in time …

Page 27: Predicting mixture effects

Case study: sub-lethal

Insecticide fenvalerate and food stress Based on standard 21-day reproduction test

– survival, size and reproduction over time– pulse exposure in first 24 hours– two food levels

Pieters et al. (2006)Ecotoxicology

Page 28: Predicting mixture effects

Fenvalerate and food

Same model parameters– for all endpoints over

time – for 2 food levels

Apparent synergism …

Pieters et al. (2006)Ecotoxicology

Page 29: Predicting mixture effects

life-history traits

TKTD models

external exposure

toxicitytesting

metabolic processes

growth

maintenancematuration

off spring

energybudget

internal exposure

toxico-kinetics

toxicokinetics toxicodynamics

Page 30: Predicting mixture effects

fluoranthene pyrene

PAHs in Daphnia Based on standard 21-day reproduction test

– 10 animals per treatment– length, reproduction and survival every 2 days

Jager et al. (2010)Ecotoxicology

Page 31: Predicting mixture effects

0 5 10 15 200

0.2

0.4

0.6

0.8

1

frac

tion

surv

ivin

g

0 5 10 15 200

0.2

0.4

0.6

0.8

1

frac

tion

surv

ivin

g

0 5 10 15 20

time (days)0 5 10 15 20

time (days)0 5 10 15 200 5 10 15 20

0

10

20

30

40

50

60

70

80

90

cum

ulat

ive

offs

prin

g pe

r fem

ale

0

0.5

1

1.5

2

2.5

3bo

dy le

ngth

(mm

)

00 (solv.)0.08650.1730.346

0

0.5

1

1.5

2

2.5

3bo

dy le

ngth

(mm

)

00 (solv.)0.08650.1730.346

00 (solv.)0.2130.4260.853

00 (solv.)0.2130.4260.853

0.0865 0.2130.173 0.4260.260 0.6400.0865 0.6400.260 0.2130.346 0.853

0.0865 0.2130.173 0.4260.260 0.6400.0865 0.6400.260 0.2130.346 0.853

pyrene fluoranthene mixtures

costs reproduction(and costs growth)

same target

Page 32: Predicting mixture effects

Iso-effect lines

0 0.05 0.1 0.15 0.2 0.25 0.30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8t = 10

t = 10

t = 14

t = 14

t = 14

t = 14

t = 18

t = 18

t = 18

t = 18

t = 21

t = 21

t = 21

t = 21

pyrene (μM)

fluor

anth

ene

(μM

)

50% survival

0 0.05 0.1 0.15 0.2 0.25 0.3

t = 10

t = 14

t = 14

t = 21

t = 21

pyrene (μM)

t = 10

t = 18

t = 18t = 10

50% reproduction

for body length <50% effect

Page 33: Predicting mixture effects

population dynamics

Causality chain

Requires inter-disciplinary research

molecular target

metabolic process

life-history traits

internal exposure

external exposure

environmentalchemistry

toxicokinetics

molecularbiology

survival models andenergy budgets

population biology

toxicodynamics

toxicity testing

?

Page 34: Predicting mixture effects

Take-home messages

More steps in the causality chain– not just toxicity testing and molecular mechanisms– e.g., toxicokinetics, energy budgets, population dynamics

Each step requires mechanistic models– effects change with time, environment, etc.– standardisation is not a solution ...

Interactions occur anywhere in the chain– strong synergistic effects are rare– interaction is very difficult to predict or to exclude