Mechanistic effect modelling for environmental risk assessment of biocides

Transcription

1 Mechanistic effect modelling for environmental risk assessment of biocides Thomas G. Preuss 1, Roman Ashauer 2, Virginie Ducrot 3, Nika Galic 4, Charles Hazlerigg 5, Tjalling Jager 6, Laurent Lagadic 3, Pernille Thorbek 7, Paul van den Brink 4, Magnus Wang 8, Udo Hommen 9 1 RWTH Aachen University; 2 Eawag; 3 INRA; 4 Alterra and Wageningen University; 5 Imperial College London; 6 Vrije Universiteit Amsterdam; 7 Syngenta; 8 RIFCON GmbH; 9 Fraunhofer Institute for Molecular Biology and Applied Ecology IME

2 SETAC Europe advisory group MeMoRisk The general aim of this advisory group is to explore and evaluate the benefit of mechanistic effect modeling for the risk assessment of chemicals in Europe. Annual meetings at SETAC Europe conferences Sessions, short courses at SETAC conferences Technical workshops

3 Outline Defining Mechanistic effect models What is triggering mechanistic effect modelling in ERA of pesticides? Recent developments The potential use of effect models within ERA for biocides Advantages & Obstacles to use effect modelling in biocidal ERA Planned future activities

4 Mechanistic effect models Dynamic models that mechanistically link exposure to effects on individuals, populations or ecosystems. This includes different model techniques: E.g. differential equations, matrix models or individualbased models.

5 Effects Exposure Complexity Biotests THE ENVIRONMENTAL RISK Protein Constant TK/TD models Cell Mixtures Time variable Organism Protection goal Population Ecosystem Ecosystem models Population models DNA Relevance for ERA

6 What is triggering mechanistic effect modelling in ERA of pesticides Linking exposure to effects How to extrapolate effects to untested complex exposure scenarios (e.g. variable concentrations over time, FOCUS scenarios)? Recovery How are population dynamics affected at time variable exposure? How do sublethal effects act on population dynamics? How to consider variability of exposure and spatial distributions of populations on different spatial scales?(edge of field, landscape)

7 The potential use of effect models within ERA for biocides Simulate the time curve of toxicity and extrapolate from short-term to long-term toxicity Effects of multi-component mixtures Demonstrate effects which will occur in the environment

8 Simulate the time curve of toxicity and extrapolate from short-term to long-term toxicity

9 Time curve of toxicity 12 LC50 [µm] Azinophosmethyl Malathion Methidathion Phenthoate Phosmet Time [h] Legierse et al. (1999) ES&T 33:

10 Time curve of toxicity 10 LC50 [relative to 96h] 1 Faktor [4d/14d]: 7 Azinophosmethyl Malathion Methidathion Phenthoate Phosmet Faktor [4d/14d]: 1.5 If time dependencies are included in the safety factors: RA is overprotective for Malathion otherwise: RA is protective for Malathion but not for Phenthoate 0, Time [h] Legierse et al. (1999) ES&T 33:

11 TK/TD models for survival Toxicokinetics What the organism does with the toxicant Toxicodynamics What the toxicant does with the organism

12 TK/TD models for survival Toxicokinetics Toxicodynamics external concentration (over time) internal concentration damage survival model survival (over time) bioaccumulation distribution biotransformation elimination damage accrual (toxic mechanism) damage recovery (biochemical and physiological compensation processes) mortality Jager et al. (2011): ES&T 45,

13 TK/TD models for survival Advantages understand rather than describe effects derive time-independent toxicity parameters interpret time-varying exposure make predictions for untested situations However many models have been published CBR, DAM, DEBtox, TDM, CTO, etc., etc. how do they relate to each other?

14 How do they relate to each other? Death mechanism Why don t all animals die at the same time? differences in sensitivity (IT) death is a stochastic process (SD) Dose metric What is the relevant dose for toxicity? internal concentration (which one, where?) scaled internal concentration some form of damage GUTS = One TK/TD model integrating all approaches and explicitly states the assumptions Jager et al. (2011): ES&T 45,

15 Power of TK/TD models Fraction Surviving Time [d] TK/TD models describe the time curve of toxicity Jager et al. (2005): ES&T 39,

16 Use of TK/TD models in ERA of Biocides TK/TD models simulates the time curve on toxicity based on a mechanistic approach They can discreminate between compounds for which equilibrium was reached in the biotests and for which not They can be used to assess mortality after chronic exposure

17 Population models

18 Population models How do sublethal effects act on population dynamics? Effects of multi-component mixtures How to consider variability of exposure and the spatial distributions of organisms and populations on different spatial scales?(edge of field, landscape) Demonstrate effects which will occur in the environment

19 Individual-based population models Simulate the individual based on the life cycle Parameterised on individual level Simulates interaction of the individuals themselves and the environment Population dynamics emerge from the model

20 Individual-based population models

21 Extrapolate effects to population level Example: 3,4-Dichloroanilline Inhibition of reproduction Mortality 1,2 1, Data Function 95%-CL Offspring [% control] 0,8 0,6 0,4 % Inhibition , , Concentration [µg/l] Concentration [µg/l] Preuss et al. (2010):J Env Mon 39,

22 Extrapolate effects to population level Example: 3,4-Dichloroanilline Inhibition of reproduction 1,2 1,0 Offspring [% control] 0,8 0,6 0,4 0,2 0, Mortality Concentration [µg/l] 100 Data Function 90 95%-CL % Inhibition Concentration [µg/l] Preuss et al. (2010):J Env Mon 39,

23 Extrapolate effects to population level Example: 3,4-Dichloroanilline Inhibition of reproduction Offspring [% control] 1,2 1,0 0,8 0,6 0,4 IDamP Population size Population size Control 2.5 µg/l 5 µg/l 10 µg/l 0,2 50 0, Mortality Concentration [µg/l] 100 Data Function 90 95%-CL % Inhibition Population size µg/l 40 µg/l Concentration [µg/l] Time [d] Time [d] Preuss et al. (2010):J Env Mon 39,

24 Survival [%] Time [h] THE ENVIRONMENTAL RISK Extrapolate to untested exposure scenarios Calibration of GUTS GUTS + IDamP h vs CW h vs CW h vs S h vs Col 6 Col 7 vs Col Survival 7 vs Survival measured measured Time [h] Abundance Time [h] 50 Example: Triphenyltin Concentration [µg/l] Concentration [µg/l] Model prediction measured stream 1 pond Time [days] Time [days] pond 3 Time [days] Concentration [µg/l] Preuss et al. (in prep)

25 Extrapolate from lab to field Daphnids/L Time [d] Time [d] Daphnids 300 Algae Daphnids/l Exposition [% Mortality] Chl-a [µg/l] Mesocosm data Model prediction 0, Time [d] Time [d] Preuss et al. (in prep)

26 Calculate population level endpoints LC50 Population EC50 Population Preuss et al. (2010):J Env Mon 39,

27 Effects of multi-component mixtures

28 Effects of multi-component mixtures It is generally accepted that concentration addition should be used to assess effects of mixtures Silva et al. (2002): ES&T 36:

29 Effects of multi-component mixtures Time is of essence also for mixture effects Time curve of toxicity Different persistence of compounds Metabolites What to do if compounds within a biocidal product have different mechanisms of action? e.g. c1 leads to inhibition of growth, c2 to inhibition of reproduction, c3 mainly mortality NOEC <> No effect, Additionally NOEC should be replaced by ECx

30 Mixture effects on population level Population Abundance (N) Default Juvenile Survival Growth Survival and Growth Exposure Time (days) Hazlerigg et al. (in prep)

31 Spatial explicit effect modelling

32 Spatial explicit effect modelling % mortality - effects of landscape connectivity Population abundance Reference Fragmented Stream - drift Time [d] Galic et al. (in prep)

33 Spatial explicit effect modelling Wang & Grimm (2010) EC&T 29(6):

34 Advantages & Obstacles to use effect modelling in biocidal ERA + Integrate scientific knowledge in every day risk assessment and reduce uncertainty in ERA + Time curve of toxicity + Population level effects + Mixture toxicity - Modelling need clearly defined questions from risk assessors & managers - Specific protection goals - Lack of guidance nowadays - Lack of confidence by regulators

35 Outlook EU Marie Curie ITN CREAM will deliver : models and case studies (2013) guidance on model documentation (2013) training for industry & regulators EFSA Opinion on Good modelling practice EFSA Guidance document Modelling in Aquatic Risk Assessment for PPP MeMoRisk will facilitate comunication with different stakeholders, will provide training A SETAC workshop How to use ecological effect models to link ecotoxicological tests to protection goals is planned for

36 Important message the aim of modelling is not to replace the current ERA practices, but to help reducing uncertainties and to provide regulators/industry with predictions those two items cannot be obtained from data sets alone, so that models are seen as complementary approach to solve complex issues.

37

38 Current status Mechanistic modelling was recommended as a valuable higher tier tool in several SETAC workshops (AMPERE, ESCORT III, AMRAP, ELINK, LEMTOX). EFSA Opinion on Specific Protection goals mentioned mechanistic modeling as valuable tool Marie Curie ITN CREAM ( ) MeMoRisk (SETAC Europe advisory group)

39 Risk assessment of fluctuating concentrations Example: Diazinon from WWTP urban runoff Ashauer et al. (2011), ES&T, in press.