Regulatory assessment and risk management of chemical mixtures: challenges and ways forward

Figures & data

Table 1. Potential use of NAMs to support the hazard and risk assessment of chemical mixtures.

Tool	Can be used to …
In vitro methods	predict the hazard of individual compounds and their combinations assess many compounds in a fast and cost-efficient manner, without testing on animals test whole mixtures and support effect-based monitoring investigate MoA and for MoA based grouping evaluate the Concentration Additivity assumption at low doses
Omics (transcriptomics, proteomics, metabolomics)	investigate affected pathways for unraveling MoAs investigate possible interactions (antagonisms or synergisms)
QSAR (Quantitative Structure Activity Relationship)	predict (missing) information on individual compounds (physico-chemical properties, toxicological effects) predict the combined effects and interactions of chemicals in a mixture support the grouping of chemicals and assess whether they will act in a similar or dissimilar way
TTC (Threshold of Toxicological Concern) or ecoTTC	establish conservative values (safe exposure levels) for use in the absence of chemical-specific toxicity data
Read-across	predict missing information for untested constituents of a mixture in a component based approach read-across the effects of similar mixtures in a whole mixture approach
TK models	model internal exposure assess the potential/probability for internal co-exposure predict potential TK interactions between mixture components facilitate the use of HBM data in toxicological risk assessment

Figure 1. Mapping of different types of toxicity information for individual chemicals in a mixture onto AOP networks. This mapping can help identify (1) which chemicals might lead to combined effects/common adverse outcomes, (2) where information is missing, and (3) further targeted testing needs. MIE: molecular initiating event; KE: key event; AO: adverse outcome.

Table 2. Uncertainties related to mixture risk assessment.

Nature of uncertainty	Description	Potential for overestimating (+) or underestimating (−) hazard/risk	Reference
Uncertainties related to combined exposure
Limited knowledge about chemicals that humans and the environment are co-exposed to	Number and identity unknown; incomplete characterization of known individual compounds	− +/−
	Number and identity falsely assumed: assuming contribution of all chemicals in a mixture to the combined effect, assuming exposure taking place to all chemicals simultaneously	+ +	Meek et al. 2011; Kortenkamp et al 2009
Not considering co-exposure to multiple chemicals correctly	Ignoring the simultaneous presence of multiple chemicals in mixtures	−	Kortenkamp et al. 2009
	Taking into account only a certain compound class and not co-exposure to other chemical classes that, for example, might lead to the same adverse outcome	−	Bopp et al. 2016; Dewalque et al. 2014,
Missing chemicals in chemical monitoring measurements	Chemicals not measured at all, because targeted measurements only include a limited set of chemicals	−	De Brouwere et al. 2014; Kortenkamp and Faust 2010; Malaj et al. 2014
	Non-detects: chemicals not detected because of insufficient analytical capacities	− (if considered absent) +/− (assuming their presence at a certain fraction of the LOD or LOQ)	De Brouwere et al. 2014; Han and Price 2011, 2013; Price et al. 2012b
Measurement uncertainties	Underlying sampling and extraction method can influence the chemicals detected	+/−	Malaj et al. 2014; Price et al. 2012b
	Representativeness of sampling time, frequency and location. Exposure might change over time. Examples: Peak times of discharge in waste water effluents; seasonal variations in pesticide applications	+/−
Using only external exposure concentrations	Potential bioaccumulation in the organism might be neglected, also over time	−	Kortenkamp and Faust 2010
Different exposure duration for different mixture components	Due to different persistence in the body or environment	+/−	IGHRC 2009
Uncertainties in exposure estimations/modeling	Assumptions made and underlying data to develop the models Example: Assumptions made on effluent dilution in receiving waters can underestimate concentrations for small streams receiving several discharges	+/− −
Lack of knowledge of chemical fate		+/−
Variability	In the sources of chemical exposure, including product formulations or mixture composition, in the concentrations (changing the assumed dose-response of the chemicals)	+/−
	In the populations considered as target	+/−
Uncertainties related to the chemical effects
Lack of knowledge on the effects of the mixture components	Potential effects may not be included	−
	Estimation of effects by predictive models	+/−	Backhaus et al. 2010; Altenburger et al. 2013
	Use of TTC as conservative estimation	+ (too conservative) − (possible synergies not included)	Price et al. 2009; Mons et al. 2013
Measured effects, reliability	Measurement uncertainty, database uncertainty	+/−
Measured effects, relevance	Relevance of the assay for the endpoint considered, Relevance of the effect for humans (extrapolation from other species) Relevance of the effect for environment, ecosystem, sensitive species (extrapolation from simplified laboratory tests on selected species) Coverage of relevant exposure time windows	+/−	IGHRC 2009
Lack of knowledge of the (multiple) modes of action	Criteria for considering chemicals as having a similar mode of action	+/−	SCHER/SCENIHR/SCCS 2012; IGHRC 2009
	Assessment of a mixture by CA if the components are not all similarly acting	+	Kortenkamp et al. 2009
Assumptions and extrapolations for environmental MRA	Different modes of action occur for chemicals in different organisms, species might have different sensitivities; PNEC values based on datasets for different trophic levels, organisms, acute or chronic data, using different assessment factors difficult to compare	+/−	SCHER/SCENIHR/SCCS 2012 Backhaus and Faust 2012
Metabolism not considered	Metabolites more toxic than the parent chemicals	−	Malaj et al. 2014
	Detoxification	+
Interactions – at TK (ADME) or toxicodynamic level – between the components of a mixture leading to (a potency of) effects not anticipated	Synergistic effects	−	Kortenkamp and Faust 2010; Marx et al. 2015
	Antagonistic effects	+	Kortenkamp and Faust 2010; Marx et al. 2015
Contribution and impact of non-chemical stressors		+/−
Uncertainties related to assessment groups
Criteria for grouping of chemicals by similar mode of action	No harmonized criteria for defining the similarity; using different criteria in different (geographical) legislative areas, the assessment of the different groups may result in different regulatory conclusions	+/−	IGHRC 2009
	Grouping based on toxic effects rather than on mode of action leads to more uncertainties in predicting possible combination effects	+/−	Kienzler et al. 2014, EFSA PPR Panel 2013, 2014
	Basing grouping only on known mode of action (and excluding all other chemicals) also impacts on uncertainty	+/−

Figure 2. Mapping of chemicals and their mixtures to the risks they pose for various toxicological effects. For each chemical (1–9) the individual Risk Quotients are presented for different types of effect (effect 1-X, e.g. hepatotoxicity, neurotoxicity, etc.). Chemicals are grouped according to the legislative sector they are regulated under (e.g. REACH, pesticides, cosmetics, food contaminants, etc.). The Sum of Risk Quotients is illustrated for mixtures within each sector and in the last column for the cross-sectorial mixture.

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