2. OUTLINE
The EU FP6 NoMiracle project
Results & Examples:
Exposure
Effects
Risk Assessment
Conclusions

3. •
BACKGROUND
Emissions are reduced and environmental quality improves, but some environmental health issues persist or are on the rise:
Childhood brain cancer
Allergies
Infertility
Autism, attention deficit, hyperactivity disorders
Biodiversity decreases
Could exposure to cumulative stressors play a role?

4. THE CALL Integrated Risk Assessment Methodologies Realistic Exposure•
THE CALL
Integrated Risk Assessment Methodologies
Realistic Exposure
Effects of combined exposures (including non- chemicals)
Substances that provoke specific interactions
Cover different regions in Europe
Outdoor and indoor exposure
Cover environmental and human health scientists
Exposure to chemicals in products
Precautionary principle

5. NoMiracle – objectives I•
NoMiracle – objectives I
To develop new methods for assessing the cumulative risks from combined exposures to several stressors including mixtures of chemical and physical/biological agents
2. To achieve more effective integration of the risk analysis of environmental and human health effects
3. To improve our understanding of complex exposure situations and develop adequate tools for sound exposure assessment
4. To develop a research framework for the description and interpretation of cumulative exposure and effect

6. NoMiracle – objectives II•
NoMiracle – objectives II
5. To quantify, characterise and reduce uncertainty in current risk assessment methodologies, e.g. by improvement of the scientific basis for setting safety factors
6. To develop assessment methods which take into account geographical, ecological, social and cultural differences in risk concepts and risk perceptions across Europe
7. To improve the provisions for the application of the precautionary principle and to promote its operational integration with evidence-based assessment methodologies

7. Duration: 1 November 2004 – 30 October 2009 •
NoMiracle – Details
Duration: 1 November 2004 – 30 October 2009
Partners: 38 institutes from 17 European states
Total Budget: 17 mio €
EC contribution: 10 mio €

8. The NoMiracle Consortium
SYKE
ITM
LHASA Ltd
ULANC
WRc WU AU
APINI RU
UCAM
NERC VU
LIMCO
Alterra
KCL
KCL UA
RWTHA
UFZ
UJAG
ETC
LEMTEC
RIVM
USOUTH
SYMLOG
EKUT
DIA
NIHP
USALZ
EPFL
ETH
UMFT
LMC
JRC
UNIMIB
DISAV
ENVI
UAVR
CSIC
URV

9. NoMiracle – Organization

10. EXPOSURE Compound-Matrix Interactions•
EXPOSURE
Compound-Matrix Interactions
Accessibility and Chemical Activity
Metabolic Fate
Region-Specific Environmental Fate

11. COMPOUND-MATRIX INTERACTIONS
Hypothesis:
Bioconcentration Factor (BCF) can be predicted more reliably with Kmw than with Kow
Experimental Kmw determination
Theoretical model for prediction of Kmw
Optimized model to predict Kmw
Compound descriptors
Phase parameters
If Kmw is a better predictor for the BCF than Kow, the challenge is to develop good methods/models to measure/predict Kmw. These results demonstrate that we are able to predict Kmw quite accurately based on molecular descriptors.

12. CHEMICAL ACTIVITY I
Bioavailability has traditionally been quantified using (freely dissolved) concentrations
Partition coefficients (KP values or BCFs) can be difficult to quantify, especially for substances with Kow>6
Measurement techniques have been developed within the NoMiracle project to measure chemical activity
It has been shown that substances with Kow>6 contribute to baseline toxicity
Chemical activity seems to be a good dose metric for mixtures showing baseline toxicity

13. CHEMICAL ACTIVITY II

14. REGION-SPECIFIC ENVIRONMENTAL FATE
MAPPE model

15. EFFECTS Meta Analysis of Mixture Toxicity Data•
EFFECTS
Meta Analysis of Mixture Toxicity Data
Mechanistic Framework for Mixture Toxicity
Toxicokinetic Interactions
Experimental Methods for Mechanistic Mixture Studies (micro-arrays, metabolomics, proteomics)
Immunotoxicity assay for mixtures
Chemicals and Natural Stressors
The DEBtox model for mixtures

16. META-ANALYSIS OF TOXICITY DATA
Similar joint action
Dissimilar joint action
Interaction absent
Simple similar action or concentration addition
Independent action or response addition
Interaction present
Complex similar action
Dependent similar action
NoMiracle database: 322 mixture test results
65% of the studies showed interactions (antagonism, synergism, dose level deviation, dose ratio deviation)
Data can be used to derive a safety factor for mixture toxicity

17. MECHANISTIC FRAMEWORK
A significant part of the work (and budget) in NoMiracle focused on effect studies:
Studies on interactions in the uptake & elimination of chemicals (e.g. Nickel and Chlorpyriffos);
Studies on metabolic interactions, such as the impact of substances on the metabolism of other substances;
Studies on interactions at the target site, i.e. by developing molecular techniques (micro-arrays, metabolomics and proteomics) to establish the mode(s) of action of single and multiple chemical exposures.
Although these studies resulted in valuable scientific insights, which add to the general body of knowledge on mixture effects. However, it will take time before the general implications for risk assessment practice will become clear. The ultimate goal (prediction of mixture effects based on the molecular structure and concentration levels in the mixture) has not yet been realized.

18. CHEMICALS AND NATURAL STRESSORS
META-ANALYSIS:
150 data sets
heat, cold, desiccation, oxygen depletion, pathogens, immunomodulatory factors
> 50% synergistic interactions
Are current laboratory studies sufficiently representative for field conditions?
Dendrobaena octaedra
Ambient temperature (°C, 8 days) following 28 days cold acclimation at 0°C in Cu spiked soil (95% credibility interval) Bindesbøl et al. 2009: ET&C 28: 2341–2347
We managed to show the relevance of including potential interaction effects between chemical and natural stressors. However, we could not propose general methods to account for the impact of natural stressors on chemical stress (and vice versa).

19. The DEBMIXTOX MODEL Organism DEB parameters toxicity parameters
Metabolic Processes
(maintenance, growth, repro)
DEB parameters
(energy budget)
External Concentration A in time
Internal Concentration A in time
Internal Concentration B in time
kinetic parameters
(e.g. elimination and uptake)
Effect points in time
(survival, movement, repro)
toxicity parameters
(e.g. NEC, hazard rate)
External Concentration B in time
Advantages:
mechanistic framework
toxicity is considered a process in time
limited number of model parameters
extrapolation over species and time

20. DEBMIXTOX RESULTS Predictions:
Westland
Green Houses
Predictions:
survival was correctly predicted in 19 out of 20 cases
mortality was correctly predicted in 15 out of 17 cases
92% correct predictions!
Mixture:
14 PAHs
13 biphenyls
8 metals
36 pesticides
10 nutrients/salst
22 env. parameters
Baas et al., 2009 ES&T 43:

21. RISK ASSESSMENT Identification of Fundamental Uncertainties•
RISK ASSESSMENT
Identification of Fundamental Uncertainties
Human and Ecological Uncertainty Factors
Probabilistic Risk Assessment
Individual-based Exposure Models
Trait-based Vulnerability Assessment
Risk Perception and Management
Risk Maps for Mixtures
Master Cases

22. ECOLOGICAL UNCERTAINTY FACTORS
Database with NOECs
5,2
4.1
4.9
3,7
0.3
1.5
0.9
9.9
15.4
14.6
12.0
11.5
10.2
HC5
Ratio = AFHC5
Resampling
Lowest
More conservative
Current Assessment Factor of 10 for small datasets (n=3) is conservative
Less conservative

23. PROBABILISTIC RISK ASSESSMENT
What are the benefits / disadvantages of performing an extra ecotoxicity test?
Anthracene:
50% probability € million could be saved
long-term ecotoxicity test costs €

24. INDIVIDUAL-BASED EXPOSURE

25. RISK PERCEPTION AND MANAGEMENT
The variation in opinions of experts (on complex risks) is comparable to that of the general public
The general public does not (yet) have a particularly strong opinion on cumulative risks

26. RISK MAPS

27. Urbania (a hypothetical city)
MASTER CASE
Urbania (a hypothetical city)
PM10 (outdoor & indoor)
4 VOCs
(outdoor & indoor)
6 Pesticides
(food)
3 Target Groups
Children (0-6)
Working Àdults (18-64)
Elderly (65 and older)

28. GENERAL CONCLUSIONS EC: develop focused calls•
GENERAL CONCLUSIONS
EC: develop focused calls
Exposure assessment can be improved using concepts like Kmw, chemical activity and spatially explicit fate models
There is sufficient experimental data to develop a safety factor for mixture assessment
More mechanistic studies on mixture toxicity are required (experimental & modeling)
Time is a crucial factor in (mixture) toxicity
Probabilistic approaches can make risk assessment and management more efficient
Cumulative risk assessment requires an receptor- oriented approach (individual/ecosystem)