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-Based Model Process-Based Model your mixture data full data interpretation X

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 AB

wait for 21 days …

Dose-response plot dose-ratio dependent deviation from CA concentration A concentration B total offspring

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?”

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?”

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?”

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?”

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?”

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?”

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?”

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?”

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?” Relevance for science?

What question did we answer? “What is effect of constant exposure to this mixture on Daphnia reproduction after 21 days under standard OECD test conditions?” Relevance for science?

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?” Relevance for risk assessment?

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

survival body length cumul. reproduction carbendazim Alda Álvarez et al. (2006) time (days) pentachlorobenzene time (days) EC10 in time

Cd and Zn in springtails Van Gestel & Hensbergen (1997) time (weeks) TU mixture 50% effect, internal concentration TU = 1 Reproduction Dry 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?

external concentration B (in time) external concentration A (in time) effects in time Process-based causility Assumption: internal concentration is linked to the effect

external concentration B (in time) external concentration A (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics Process-based internal concentration A in time internal concentration B in time “toxicodynamic animal model” “toxicodynamic animal model” effects in 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! “toxicodynamic animal model” “toxicodynamic animal 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 structure somatic maintenance  growth maturity maintenance 1-  reproduction maturity eggs maturation p foodfeces assimilation reserve b

Kooijman (2000)

mobilisation Standard DEB animal foodfeces assimilation structure somatic maintenance  growth maturity maintenance 1-  reproduction maturity eggs maturation b p reserve

“toxicodynamic animal model” “toxicodynamic animal model” Toxicant effects in DEB external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time DEB parameters in time DEB model DEB model Kooijman & Bedaux (1996), Jager et al. (2006, 2010) repro growth survival feeding hatching … over entire life cycle assimilation maintenanc e maturation ….

Toxicant effects in DEB external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time DEB parameters in time DEB model DEB model Affected DEB parameter has specific consequences for life cycle repro growth survival feeding hatching …

Ex.1: maintenance costs time cumulative offspring time body length TPT Jager et al. (2004)

Ex.2: growth costs time body length time cumulative offspring Pentachlorobenzene Alda Álvarez et al. (2006)

Ex.3: egg costs time cumulative offspring time body length Chlorpyrifos Jager et al. (2007)

Mixture analysis external concentration A (in time) toxico- kinetics toxico- kinetics internal concentration A in time external concentration B (in time) toxico- kinetics toxico- kinetics internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time theory implies interactions …

Mixture analysis external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time theory implies interactions …

Mixture analysis external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time theory implies interactions … growth

external concentration A (in time) toxico- kinetics toxico- kinetics external concentration B (in time) toxico- kinetics toxico- kinetics DEB model DEB model internal concentration A in time DEB parameters in time internal concentration B in time effects on all endpoints in time Mixture analysis

Simple mixture rules compound‘target’ toxicity parameters linked (compare CA) maintenance costs ingestion rate growth costs DEB parameter …

Simple mixture rules compound‘target’ maintenance costs ingestion rate growth costs DEB parameter …

Simple mixture rules compound‘target’ 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 compound B sizereproduction compound A

50% contours in time t = 5 t = 10 t = 15 t = 21 compound A t = 5 t = 10 t = 15 t = 21 compound A compound B t = 5 t = 10 t = 15 t = 21 compound A t = 5 t = 10 t = 15 t = 21 compound A compound B sizereproduction

Mixture effects: simulations parameters for Daphnia ‘other target’ model (ingestion+maint.) plots for 21-days exposure

sizereproduction compound B Contours at t=21 days

50% contours in time t = 5 t = 10 t = 15 t = 21 compound A compound B t = 5 t = 10 t = 15 t = 21 compound A sizereproduction

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)

costs reproduction (and costs growth) costs reproduction (and costs growth) same target

Iso-effect lines 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-Based Model Process-Based Model your mixture data full data interpretation X

fit not satisfactory? fit Strategy for data analysis actual DEB model experimental data additional experiments literature educated guesses mechanistic hypothesis standard DEB model other interactions?

Parameter estimates external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars populations

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars other endpoints other, e.g., feeding respiration

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars time-varying concentrations

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars food limitation

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars related compounds

Educated extrapolation external concentration A (in time) external concentration B (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics internal concentration A in time internal concentration B in time DEB parameters in time DEB model DEB model effects on all endpoints in time TK parstox parsDEB pars 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 DEB model Outlook target sitetoxicant effect on life 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) DEB parameters DEB theorybiochemistry

In more detail: survival

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

external concentration B (in time) external concentration A (in time) toxico- kinetics toxico- kinetics toxico- kinetics toxico- kinetics Process-based internal concentration A in time internal concentration B in time survival as a chance process survival as a chance process survival in 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 assumption thresholds See Newman and McClosky (2000) t x t+Δt p 1-p Tolerance distribution Stochastic death

survival in time survival as a chance process Model Chain external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time Simple models information content of standard tests is low

survival in time survival as a chance process Model Chain external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time 1-comp. uptakeelimination scaled concentration external internal time

survival in time survival as a chance process Model Chain external concentration (in time) toxico- kinetics internal concentration in time Assumptions: death is a chance event for the individual the probability to die depends on the internal concentration.

Hazard modelling time (days) surviving chickens 0 cars/hr 10 cars/hr 20 cars/hr 50 cars/hr Hazard rate times Δt is the probability to get hit by a car in that interval

survival in time survival as a chance process Model Chain external concentration (in time) toxico- kinetics internal concentration in time internal concentration hazard rate NEC blank value killing rate

survival in time survival as a chance process Model Chain external concentration (in time) toxico- kinetics internal concentration 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 concentration elimination rate NEC / killing rateintegrate

Minnow, hexachloroethane concentration (μmol/L) time (hour) fathead minnow

Survival in time time (hours) fraction surviving conc. μmol/L elimination rate0.141 hr -1 NEC 5.54 ( ) μmol/L killing rate L/μmol/hr blank hazard hr -1

Simple mixture rules hazard rate compound‘target’ toxicity parameters independent (compare IA) 2 elimination rates 2 NECs 2 killing rates hazard rates added 2 elimination rates 2 NECs 2 killing rates hazard rates added

Simple mixture rules hazard rate compound‘target’ toxicity parameters linked (compare CA) 2 elimination rates 1 NEC 1 killing rate 1 “weight factor” weighted scaled int. conc. added 2 elimination rates 1 NEC 1 killing rate 1 “weight factor” weighted scaled int. conc. added Consequence: NEC and killing rate are not independent

Parameter relationships Jager & Kooijman (2009)

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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