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

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

wait for 21 days …

Dose-response plot

dose-ratio dependent deviation from CA

concentration A concentratio

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

Relevance for science?

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

Relevance for science?

Relevance for risk assessment?

Better questions

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

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

pentachlorobenzene

time (days)

EC10 in time

Cd and Zn in springtailsVan Gestel & Hensbergen (1997)

0 1 2 3 4 5 6

time (weeks)

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

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

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

Kooijman (2000)

mobilisation

Standard DEB animal

structurestructure

somatic maintenance

growth

reproduction

maturation

reservereserve

Toxicant effects in DEB

(in time)

toxico-kinetics

toxico-kinetics internal

concentrationin time DEB

parametersin time

DEBmodel

Kooijman & Bedaux (1996),

Jager et al. (2006, 2010)

growth

survival

feeding

hatching

over entire life cycle

assimilationmaintenanc

ematuration

Toxicant effects in DEB

(in time)

toxico-kinetics

concentrationin time DEB

parametersin time

DEBmodel

Affected DEB parameter has specific consequences for life cycle

growth

survival

feeding

hatching

Ex.1: maintenance costs

Jager et al. (2004)

Ex.2: growth costs

ng Pentachlorobenzene

Alda Álvarez et al. (2006)

Ex.3: egg costs

Chlorpyrifos

Jager et al. (2007)

Mixture analysis

A (in time)

toxico-kinetics

concentrationA in time

B (in time)

toxico-kinetics

B in time

DEBparameters

in timeDEB

DEBmodel effects on

all endpointsin time

theory implies interactions …

Mixture analysis

A (in time)

B (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

mobilisationmobilisation

structurestructure

somatic maintenance

growth

structurestructure

somatic maintenance

growth

reproduction

maturation

reproduction

maturation

reservereserve

Mixture analysis

A (in time)

B (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

growth

A (in time)

toxico-kinetics

B (in time)

toxico-kinetics

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

size reproduction

compound Acompound A

50% contours in time

t = 10

t = 15

t = 21

compound At = 5

t = 10

t = 15

t = 21

compound A

t = 10

t = 15

t = 21

compound At = 5

t = 10

t = 15

t = 21

compound A

size reproduction

Mixture effects: simulations

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

size reproduction

Contours at t=21 days

50% contours in time

t = 10

t = 15

t = 21

compound A

t = 10t = 10

t = 15

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 5 10 15 200

0 5 10 15 20

time (days)0 5 10 15 20

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

00 (solv.)0.08650.1730.346

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

pyrene fluoranthene mixtures

costs reproduction(and costs growth)

same targetsame target

Iso-effect lines

0 0.05 0.1 0.15 0.2 0.25 0.30

t = 10

t = 14

t = 18

t = 21

pyrene (μM)

50% survival

0 0.05 0.1 0.15 0.2 0.25 0.3

t = 10

t = 14

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

your mixturedata full data

interpretationX

fit not satisfactory?

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

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

TK pars tox pars DEB pars

Educated extrapolation

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

populations

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

other endpoints

other, e.g.,feeding

respiration

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

time-varying concentrations

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

food limitation

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

DEBmodel effects on

related compounds

A (in time)

toxico-kinetics

A in time

B in time

DEBparameters

in timeDEB

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)

DEBparameters

DEB theorybiochemistry

In more detail: survival

0 20 40 60 80 1000

time (hours)

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

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)

Mortality assumptionthresholds

See Newman and McClosky (2000)

t+Δtp

Tolerancedistribution

Stochastic death

survivalin time

Model Chain

(in time)

toxico-kinetics

in time

Simple modelsinformation content of standard tests is low

survivalin time

Model Chain

(in time)

toxico-kinetics

in time

1-comp.uptake elimination

external

internal

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 8time (days)

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

blank value

killin

survivalin time

Model Chain

(in time)

toxico-kinetics

in time

Straightforward statistics …

integrate hazard rate over time and take exponential …

Hazard modelling

hazard rate

scaled internal concentration

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)

)fathead minnow

Survival in time

0 20 40 60 80 1000

time (hours)

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)

NEC (mM)

narcoticsreactives

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

NEC (mM)

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

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

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

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