biology-based approaches for mixture ecotoxicology

Biology-based approaches for mixture ecotoxicology

Tjalling Jager

Contents

14:00-18:00 (coffee at 16:00) Lecture

• limitations of descriptive approaches• framework for a process-based approach

Dynamic Energy Budget (DEB) theory sub-lethal effects

• simplified survival modelling in more detail

Practical exercise• Play with a “toy model” in Excel (survival only)

18:00-18:30 Open discussion

Disclaimer!

Process-BasedModel

Process-BasedModel

your mixturedata full data

interpretationX

Interest in mixtures

Scientific• why are effects of mixtures the way

they are?

Practical• how can we predict the

environmental impact of mixtures?

Practical challenge

Some 100,000 man-made chemicals Large range of natural toxicants For animals alone, >1 million species described Complex exposure situations

Typical approach

A B

Typical approach

Typical approach

wait for 21 days …

Dose-response plot

dose-ratio dependent deviation from CA

dose-ratio dependent deviation from CA

concentration A concentratio

n B

tota

l o

ffsp

rin

g

What question did we answer?

“What is effect of constant exposure to this mixture on total Daphnia reproduction after 21 days under standard OECD test conditions?”


dose-ratiodependentdeviation from CA



n B

tota

lo

ffsp

rin

g


n B

tota

lo

ffsp

rin

g







n B

tota

lo

ffsp

rin

g


n B

tota

lo

ffsp

rin

g

Relevance for science?


“What is effect of constant exposure to this mixture on Daphnia reproduction after 21 days under standard OECD test conditions?”

“What is effect of constant exposure to this mixture on Daphnia reproduction after 21 days under standard OECD test conditions?”




n B

tota

lo

ffsp

rin

g


n B

tota

lo

ffsp

rin

g

Relevance for science?







n B

tota

lo

ffsp

rin

g


n B

tota

lo

ffsp

rin

g

Relevance for risk assessment?

Better questions

do we see:• time patterns of effects on different endpoints …

0.5

1

1.5

2

2.5

0 5 10 15 200

survival

body length

cumul. reproductioncarbendazim

Alda Álvarez et al. (2006)

time (days)0 2 4 6 8 10 12 14 16

0

20

40

60

80

100

120

140

pentachlorobenzene

time (days)

Cl

Cl

Cl Cl

Cl

EC10 in time

Cd and Zn in springtailsVan Gestel & Hensbergen (1997)

0

1

2

3

4

5

0 1 2 3 4 5 6

time (weeks)

TU

mix

ture

50%

eff

ect,

inte

rnal

co

nce

ntr

atio

n

TU = 1ReproductionDry weight

Better questions

do we see:• time patterns of effects on different endpoints …• interactions between compounds and with environment …• differences between species and between compounds …

• can we make useful predictions for risky situations?

externalconcentration

B (in time)


A (in time)

effectsin time

Process-based

causility

Assumption: internal concentration is linked to the effect


B (in time)


A (in time)

toxico-kinetics

toxico-kinetics

toxico-kinetics

toxico-kinetics

Process-based

internalconcentration

A in time


B in time

“toxicodynamicanimal model”


effectsin time

Assumption: internal concentration is linked to the effect

Demands on toxicokinetics model

Complexity should match the level of detail in data

• simplest: scaled one-compartment model one parameter (elimination rate) estimated from effects data only

• most complex: PBPK model … requires detailed measurements …

toxico-kinetics

toxico-kinetics

Demands on animal model

Explain endpoints of interest over entire life cycle• growth, start of reproduction, reproduction rate, survival, …

Explain effects of toxicants on these endpoints Allow to interpret effects of multiple stressors

• combination of chemicals• chemicals and non-chemical stressors

As little chemical- and species-specific as possible• comparison and extrapolation

All organisms obey conservation

of mass and energy!“toxicodynamicanimal model”


Look closer at individual

Natural role for energetics

Understanding toxic effects on growth and reproduction requires understanding how food is acquired and used to produce traits

Rules for metabolic organisation Start of Dynamic Energy Budget (DEB) theory 30

years ago

What is DEB?Quantitative theory for metabolic

organisation; ‘first principles’• time, energy and mass balance

Life-cycle of the individual• links levels of organisation: molecule

ecosystems

Fundamental; many practical applications• (bio)production, (eco)toxicity, climate change,

evolution …

Kooijman (2000)

Kooijman (2010)

mobilisation

Standard DEB animal

structurestructure

somatic maintenance

growth

maturity maintenance1-

reproduction

maturitymaturity eggseggs

maturation p

food fecesassimilation

reservereserve

b

Kooijman (2000)

mobilisation

Standard DEB animal


structurestructure

somatic maintenance

growth


reproduction


maturation

b

p

reservereserve



Toxicant effects in DEB


(in time)

toxico-kinetics

toxico-kinetics internal

concentrationin time DEB

parametersin time

DEBmodel

DEBmodel

Kooijman & Bedaux (1996),

Jager et al. (2006, 2010)

repro

growth

survival

feeding

hatching

…

over entire life cycle

assimilationmaintenanc

ematuration

….

Toxicant effects in DEB


(in time)

toxico-kinetics


concentrationin time DEB

parametersin time

DEBmodel

DEBmodel

Affected DEB parameter has specific consequences for life cycle

repro

growth

survival

feeding

hatching

…

Ex.1: maintenance costs

time

cum

ula

tive

off

spri

ng

time

bo

dy

len

gth

TPT

Jager et al. (2004)

Ex.2: growth costs

time

bo

dy

len

gth

time

cum

ula

tive

off

spri

ng Pentachlorobenzene

Alda Álvarez et al. (2006)

Ex.3: egg costs

time

cum

ula

tive

off

spri

ng

time

bo

dy

len

gth

Chlorpyrifos

Jager et al. (2007)

Mixture analysis


A (in time)

toxico-kinetics


concentrationA in time


B (in time)

toxico-kinetics

toxico-kinetics


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on

all endpointsin time

theory implies interactions …

Mixture analysis


A (in time)


B (in time)

toxico-kinetics

toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



mobilisationmobilisation


structurestructure

somatic maintenance

growth

structurestructure

somatic maintenance

growth


reproduction


maturation


reproduction


maturation

b

p

reservereserve

Mixture analysis


A (in time)


B (in time)

toxico-kinetics

toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



growth


A (in time)

toxico-kinetics

toxico-kinetics


B (in time)

toxico-kinetics

toxico-kinetics

DEBmodel

DEBmodel


A in time DEBparameters

in timeinternalconcentration

B in time effects onall endpoints

in time

Mixture analysis

Simple mixture rules

compound ‘target’

toxicity parameters linked (compare CA)

maintenance costs

ingestion rate

growth costs

DEB parameter

…



maintenance costs

ingestion rate

growth costs

DEB parameter

…



toxicity parameters independent (compare IA)

maintenance costs

ingestion rate

growth costs

DEB parameter

…

Mixture rules

‘same target’ and ‘different target’ are concepually similar to CA and IA, but:

CA and IA are prescriptions for combining dose-response curves (at a single time point)

here, applied at target level, yielding mixture effects on all endpoints over entire life cycle

they yield deviations from standard CA and IA (apparent interactions)

Mixture effects: simulations

• parameters for Daphnia• ‘same target’ model (ingestion)• plots for 21-days exposure

Contours at t=21 days

5

20

30

30

50

50

5

20

20

30

30

30

50

50

50

50

com

po

un

d B

size reproduction

compound Acompound A

50% contours in time

t = 5

t = 5

t = 5

t = 10

t = 10

t = 10

t = 15

t = 15

t = 15

t = 21

t = 21

compound At = 5

t = 5

t = 10

t = 10

t = 10

t = 15

t = 15

t = 15

t = 15

t = 21

t = 21

t = 21

t = 21

compound A

com

po

un

d B

t = 5

t = 5

t = 5

t = 10

t = 10

t = 10

t = 15

t = 15

t = 15

t = 21

t = 21

compound At = 5

t = 5

t = 10

t = 10

t = 10

t = 15

t = 15

t = 15

t = 15

t = 21

t = 21

t = 21

t = 21

compound A

com

po

un

d B

size reproduction

Mixture effects: simulations

• parameters for Daphnia• ‘other target’ model (ingestion+maint.)• plots for 21-days exposure

size reproduction

5

5

20

20

30

3030

50

5050

5

5

20

20

20

30

30

30

50

50

50

50

co

mp

ou

nd

B

Contours at t=21 days

50% contours in time

t = 5

t = 10

t = 10

t = 10

t = 15

t = 15

t = 15

t = 1

5

t = 21

t = 21

t = 21

t = 2

1

compound A

co

mp

ou

nd

B

t = 5

t = 5

t =

5

t = 1

0

t = 10t = 10

t = 1

5

t = 15

t = 15

t = 2

1

t = 21

t = 21

compound A

size reproduction

fluoranthene pyrene

PAHs in Daphnia

Based on standard 21-day OECD test• 10 animals per treatment• length, reproduction and survival every 2 days• no body residues (TK inferred from effects)

Jager et al. (2010)

0 5 10 15 20

0

0.2

0.4

0.6

0.8

1

frac

tio

n s

urv

ivin

g

0 5 10 15 200

0.2

0.4

0.6

0.8

1

frac

tio

n s

urv

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

ula

tiv

e o

ffsp

rin

g p

er f

ema

le

0

0.5

1

1.5

2

2.5

3b

od

y le

ng

th (

mm

)

00 (solv.)0.08650.1730.346

0

0.5

1

1.5

2

2.5

3b

od

y le

ng

th (

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)

costs reproduction(and costs growth)

same targetsame target

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

0.8

t = 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)

flu

ora

nth

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

Conclusions PAH mixture

Mixture effect consistent with ‘same target’• as expected for these PAHs• explains all three endpoints, over time

Iso-effect lines are functions of time• which differ between endpoints• in this case: little deviation from CA

Few parameters for all data in time• 14 parameters (+4 Daphnia defaults)

(descriptive would require >100 parameters)

Disclaimer!

Process-BasedModel

Process-BasedModel

your mixturedata full data

interpretationX

fit not satisfactory?

fit

Strategy for data analysis

actualDEB model

experimentaldata

additionalexperiments

literature

educatedguesses

mechanistichypothesis

standardDEB model

other interactions?

Parameter estimates


A (in time)


B (in time) toxico-kinetics

toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on


TK pars tox pars DEB pars

Educated extrapolation


A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



populations



A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



other endpoints

other, e.g.,feeding

respiration



A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



time-varying concentrations



A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



food limitation



A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



related compounds



A (in time)



toxico-kinetics

toxico-kinetics

toxico-kinetics


A in time


B in time

DEBparameters

in timeDEB

model

DEBmodel effects on



other (related) species

Final words

A process-based approach is essential …• to progress the science of mixture toxicity• to make useful predictions for RA

Key elements DEB approach• one framework for all endpoints over time• not specific for particular species or compounds• certain interactions are unavoidable …

Of course, more work is needed …• validate predicted interactions and extrapolations• find out if we can explain other interactions

Limitations

A DEB-based analysis cannot be done routinely!• almost every dataset requires additional hypotheses …• DEB offers a framework, not a “foolproof software”

Data requirements are not trivial• basic life history information of the species• body size and repro over a considerable part of the life cycle• preferably survival, feeding rates, egg size, hatching time …

For mixtures, experimental effort may rapidly become excessive

There is help …

DEB pars• depart from defaults (e.g., ‘add_my_pet’ or standard animal with ‘zoom factor’)• hopefully vary little between experiments

TK pars• depart from QSARs …• extrapolate between species or toxicants

tox pars• at this moment, little help …• extrapolate between species or toxicants?

species specific

DEBmodel

Outlook

target sitetoxicant

effect onlife cycle?

number of chemicals and species is very large … but number of target sites and processes is limited!

Once we know the normal biological processes, all external stressors are merely perturbations of these processes (Yang et al., 2004)

Once we know the normal biological processes, all external stressors are merely perturbations of these processes (Yang et al., 2004)

DEBparameters

DEB theorybiochemistry

In more detail: survival

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

time (hours)

frac

tio

n s

urv

ivin

g

01.331.843.325.819.25

conc. μmol/L

Introduction

For survival, DEB can be simplified• in most acute tests, animals are not growing• survival can be treated (largely) independent from metabolic

organisation

Simple mixture version in Excel• only survival• only datasets from Baas et al., 2007• no interactions


B (in time)


A (in time)

toxico-kinetics

toxico-kinetics

toxico-kinetics

toxico-kinetics

Process-based


A in time


B in time

survival as achance process


survivalin time

Tolerance distribution• McCarty et al (1992)• Lee & Landrum (2006)Stochastic death• Ashauer et al. (2007)• Baas et al. (2007, 2009)

Tolerance distribution• McCarty et al (1992)• Lee & Landrum (2006)Stochastic death• Ashauer et al. (2007)• Baas et al. (2007, 2009)

Mortality assumptionthresholds

See Newman and McClosky (2000)

tx

t+Δtp

1-p

Tolerancedistribution

Stochastic death

survivalin time


Model Chain


(in time)

toxico-kinetics

toxico-kinetics


in time

Simple modelsinformation content of standard tests is low

survivalin time


Model Chain


(in time)

toxico-kinetics

toxico-kinetics


in time

1-comp.uptake elimination

sc

ale

d c

on

ce

ntr

ati

on

external

internal

time

survivalin time


Model Chain


(in time)

toxico-kinetics


in time

Assumptions:

death is a chance event for the individual

the probability to die depends on the internal concentration.

Hazard modelling

0

2

4

6

8

10

12

0 2 4 6 8time (days)

surv

ivin

g c

hic

ken

s

0 cars/hr10 cars/hr20 cars/hr50 cars/hr

Hazard rate times Δt is the probability to get hit by a car in that interval

survivalin time


Model Chain


(in time)

toxico-kinetics


in time

internal concentration

haza

rd r

ate

NEC

blank value

killin

g ra

te

survivalin time


Model Chain


(in time)

toxico-kinetics


in time

Straightforward statistics …

integrate hazard rate over time and take exponential …

Hazard modelling

time

hazard rate

time

scaled internal concentration

NEC

time

survival probability

external concentrationelimination rate

NEC / killing rate integrate

Minnow, hexachloroethane

0 1.33 1.84 3.32 5.81 9.25

0 20 20 20 20 20 20

24 20 20 20 20 20 4

48 20 20 20 20 15 0

72 20 20 19 20 12 0

96 20 20 19 20 10 0

concentration (μmol/L)

time

(h

our

)fathead minnow

Survival in time

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

time (hours)

fra

cti

on

su

rviv

ing

01.331.843.325.819.25

conc. μmol/L

elimination rate 0.141 hr-1

NEC 5.54 (5.26-5.68) μmol/Lkilling rate 0.0408 L/μmol/hrblank hazard 0.000124 hr-1


hazard rate


toxicity parameters independent (compare IA)

2 elimination rates2 NECs2 killing rates

hazard rates added

2 elimination rates2 NECs2 killing rates

hazard rates added


hazard rate


toxicity parameters linked (compare CA)

2 elimination rates1 NEC1 killing rate1 “weight factor”

weighted scaledint. conc. added

2 elimination rates1 NEC1 killing rate1 “weight factor”

weighted scaledint. conc. added

Consequence:NEC and killing rate are not independent

Parameter relationships

Jager & Kooijman (2009)

10-4

10-2

100

102

10-4

10-2

100

102

104

NEC (mM)

killi

ng

rate

(m

M-1

h-1)

narcoticsreactives

Narcotic: log b† = -1 log c0 – 0.27 (r2=0.61)Reactive: log b† = -1 log c0 – 1.2 (r2=0.85)

10-4

10-2

100

102

10-4

10-2

100

102

104

NEC (mM)

killi

ng

rate

(m

M-1

h-1)

narcoticsreactives

Narcotic: log b† = -1 log c0 – 0.27 (r2=0.61)Reactive: log b† = -1 log c0 – 1.2 (r2=0.85)

Visual representation

For binary mixture, model represents surface that changes in time …

Baas et al. (2007)

Data needs

Several observations in time• standard acute test protocols prescribe daily scoring

Note: • body residues are not needed, but can be used• exposure need not be constant• test setup may be non-standard• when animals grow, DEB will be needed …

Improvements• more observations in time is always better• optimal test design depends on chemical and species/size

An Excel exercise

Disclaimer:• Excel is not really suited (unless you have an ODE solver)• you can only use the data from Baas et al., 2007• I only use a part of the data set (you select which part)• I did not include interactions• at this moment, there is no user-friendly software

there is user-unfriendly software though … if you have a nice data set, contact me for collaboration!

Take home message

Realise that …• mixture effects change with exposure time• life-history traits are not independent• descriptive approaches will never explain why

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Vacancies• PhD student in Rennes (France), Marie Curie training

network (CREAM)

Courses• International DEB Tele Course 2011

Symposia• 2nd International DEB Symposium 2011 in Lisbon

More information: http://www.bio.vu.nl/thb

biology-based approaches for mixture ecotoxicology

Documents

effect of constant exposure

standard oecd test conditions

total daphnia reproduction

time weekstu mixture

cawhat question

time patterns of effects

time dayscd

effects of mixtures