1. Approaches to Additivity
Thomas Backhaus, University of Gothenburg

2. Approaches to mixture assessment
Whole mixture testing
Sophisticated PBPK models
Lead-compound approach
Component-based approaches

3. Lead compound approach
One defined compound is selected as the “lead” compound of a given (sub)mixture
It is assumed that the whole (sub)mixture has a toxicity similar to this compound
That is, the mixture is simply assumed to comprise only the lead compound
Used in CLP, REACH

4. Approaches to mixture assessment
Whole mixture testing
Sophisticated PBPK models
Lead-compound approach
Component-based approaches

5. Approaches to Additivity
Effect Summation
Response Addition
Concentration Addition

6. Adding Effects Effect Summation E(mix) Effect of the mixture
E(ci) Effect of compound if applied singly
ci Concentraion of compound i
n number of mixture components

7. Effect Summation leads to inconsistent assessments

8. Effect Summation leads to inconsistent assessments

9. Approaches to Additivity – Adding probabilities
Response Addition, Independent Action
Mortality
Survival
E(mix) Effect of the mixture
E(ci) Effect of compound if applied singly
ci Concentraion of compound i
n number of mixture components

10. Approaches to Additivity – Adding probabilities
Response Addition, Independent Action
Joint effect is higher than each individual effect!
Subst.1 = 50%
Subst.2 = 50%
Mixture = 75%

12. Adding fractions of equi-effective concentrations
Concentration Additivity, Toxic Unit Summation, Additivity formula
RQ Risk quotient of the mixture
ECx(mix) Concentration of the mixture that
causes x% effect
cMix Concentration of the mixture
ECxi Concentration of compound I that
causes x% effect if applied singly
ci Concentration of compound i in a
mixture that gives x% effect
n number of mixture components

13. Approaches to Concentration Additivity
Simple similar action
Toxic Equivalency Factor
Relative Potency Factor
Point of Departure Index
Hazard Index
Addition of risk quotients (PEC/PNEC’s)

14. Simple similar action

15. Simple similar action
Simple Similar Action, Relative Potency Factor, Toxicity Equivalency Factor, Toxic Equivalent Quantity (TEQ)
TEFi Toxicity Equivalency Factor for
compound I
ci Concentration of compound i in the
mixture
n number of mixture components

16. Not-so-simple similar action
Faust et al Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquatic Toxicology

17. Not-so-simple similar action
Altenburger et al. Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: mixtures composed of similarly acting chemicals. Environmental Toxicology and Chemistry

18. Not-so-simple similar action
Altenburger et al. Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: mixtures composed of similarly acting chemicals. Environmental Toxicology and Chemistry

19. Point of Departure Index
Point of Departure Index (PODI)
Endpointi NOEC of compound i
recorded in a specific assay
PODi Point of Departure for compound i

20. Point of Departure Index
Point of Departure Index (PODI)
Endpointi NOEC of compound i
recorded in a specific assay
PODi Point of Departure for compound i
ci Concentraion of compound i
n number of mixture components

21. Hazard Index Hazard Index, PEC/PNEC summation
PNECi Predicted No Effect Concentration
of compound i
PNECMixture Predicted No Effect Concentration
of the mixture i
PECi Predicted Environmental
Concentration of Compound i
PECmixture Predicted Environmental
Concentration of the mixture
n number of mixture components

22. Hazard Index – Difficult to interpret
Compound 1: PEC1=0.4*10-4 EC50Algae: 1.0 EC50Daphnids: 0.1 PNEC = 10-4 EC50Fish: 1.0 Compound 2: PEC2= 0.8*10-4 EC50Algae: 0.1 PNEC = 10-4 EC50Daphnids: 1.0

23. Hazard Index – Difficult to interpret
Compound 1
Compound 2
0.4
0.8 ?

24. The following slide was added as a consequence of the discussions during the workshop. For further details on the suggested tiered approach for using additivity in the context of REACH (and similar frameworks), please see:
Backhaus, T., Faust, M. Predictive environmental risk assessment of chemical mixtures: a conceptual framework, Environmental Science and Technology, 46(5), 2012,

25. CA needs to be applied in a tiered fashion
CA using PNEC values
CA using actual toxdata for each concerned species & endpoint
Comparative application of CA and IA using full concentration-response functions toxdata for each concerned species & endpoint

26. Commonalities of the different CA incarnations
Component-based
Mixture risk is higher than risk of individual compounds
Need to know exposure level (internal or external)
Need to have a potency measure that relates to the same level of effect

27. Commonalities of the different CA incarnations
Knowledge needed on the underlying modes of action
Data demands
Ease of interpretation

28. Critical issues (in the context of this workshop)
Mixtures might be chemically poorly defined
Synergistic and/or antagonistic interactions
Handling of poorly soluble but still toxic compounds

29. CA in the context of UVCB substances
CA is component-based and can thus only be applied to defined mixture
Use in the context of UVCBs: application to the defined part in order to identify important mixture drivers, assess the (eco)toxicological impact of batch-to-batch variations, etc.
Used in TIE (Toxicity Identification and Evaluation) resp. EDA (Effect Directed Analysis) to confirm identified components and mixture drivers.

30. CA in the context of UVCB substances
Brack. Effect-directed analysis: a promising tool for the identification
of organic toxicants in complex mixtures? Anal Bioanal Chem (2003)

31. Synergistic or antagonistic interactions
Synergism: toxicity is higher than expected by toxic unit summation, i.e. less of a mixture is needed to cause a predefined toxicity.
Antagonism: toxicity is lower than expected by toxic unit summation, i.e. more of a mixture is needed to cause a predefined toxicity.
Can be cause by toxicodynamic, toxicokinetic interactions, and/or by data gaps and biases.

32. A real-world pesticide mixture
Decreasing TU
Finizio et al., Agr. Eco. Env., 111, , 2005
Junghans et al., Aquatic Toxicology, 76, , 2006

33. A real-world pesticide mixture
1 compound 10x more toxic than estimated

34. A real-world pesticide mixture
First n compound 10x more toxic than estimated

35. A real-world pesticide mixture
n randomly selected compounds
10x more toxic than estimated

36. The implications of CA-data demands

37. The implications of CA-data demands

38. The implications of CA-data demands

39. The implications of CA-data demands

40. The implications of CA-data demands

41. Final comments and conclusions
“Additivity” can mean whole lot of different things…
“Concentration Additivity can mean a whole lot of different things
Most common incarnation: toxic unit summation, hazard index
Mixtures buffer against synergistic interactions – the more compounds, the better

42. Final comments and conclusions
CA does not directly allow to calculate the expected effect of a mixture. Full concentration-response curves for each component are needed for that purpose.
Applicability is limited to the effect range that is covered by all compounds. Problem if some compounds do not reach a pre- defined effect level.

43. Final comments and conclusions
Simplifications of CA (Hazard Index etc) make data collection easier and interpretation more fuzzy.

44. Final comments and conclusions
CA, mainly in the form of toxic unit summation or hazard indices, is the only mixture toxicity concept that has found widespread application (REACH, CLP, but also pesticide & biocide regulations).
Detailed mode-of-action based approaches often discussed and included in the guidelines, but rarely used. Lack of data, lack of concern (motivation).

45. Approaches to Additivity
Thomas Backhaus, University of Gothenburg