Considering the cumulative risk of mixtures of chemicals – A challenge for policy makers

REVIEW Open Access

Considering the cumulative risk of mixtures ofchemicals – A challenge for policy makersDenis A Sarigiannis1,2*, Ute Hansen1

From HENVINET (Health and Environment Network) final conferenceBrussels, Belgium. 14 April 2010 - 15 April 2010

Abstract

Background: The current paradigm for the assessment of the health risk of chemical substances focuses primarilyon the effects of individual substances for determining the doses of toxicological concern in order to informappropriately the regulatory process. These policy instruments place varying requirements on health and safetydata of chemicals in the environment. REACH focuses on safety of individual substances; yet all the other facets ofpublic health policy that relate to chemical stressors put emphasis on the effects of combined exposure tomixtures of chemical and physical agents. This emphasis brings about methodological problems linked to thecomplexity of the respective exposure pathways; the effect (more complex than simple additivity) of mixtures (theso-called ‘cocktail effect’); dose extrapolation, i.e. the extrapolation of the validity of dose-response data to doseranges that extend beyond the levels used for the derivation of the original dose-response relationship; theintegrated use of toxicity data across species (including human clinical, epidemiological and biomonitoring data);and variation in inter-individual susceptibility associated with both genetic and environmental factors.

Methods: In this paper we give an overview of the main methodologies available today to estimate the humanhealth risk of environmental chemical mixtures, ranging from dose addition to independent action, and fromignoring interactions among the mixture constituents to modelling their biological fate taking into account thebiochemical interactions affecting both internal exposure and the toxic potency of the mixture.

Results: We discuss their applicability, possible options available to policy makers and the difficulties and potentialpitfalls in implementing these methodologies in the frame of the currently existing policy framework in theEuropean Union. Finally, we suggest a pragmatic solution for policy/regulatory action that would facilitate theevaluation of the health effects of chemical mixtures in the environment and consumer products.

Conclusions: One universally applicable methodology does not yet exist. Therefore, a pragmatic, tiered approach toregulatory risk assessment of chemical mixtures is suggested, encompassing (a) the use of dose addition to calculate ahazard index that takes into account interactions among mixture components; and (b) the use of the connectivityapproach in data-rich situations to integrate mechanistic knowledge at different scales of biological organization.

BackgroundThe current paradigm for the assessment of the health riskof chemical substances focuses primarily on the effects ofindividual substances for determining the doses of toxico-logical concern in order to inform appropriately the

regulatory process. Given the recently increased publicawareness on the link between environmental conditionsand public health, the policy-making and environmentalmanagement processes have an enhanced need for healthand safety data. In Europe, this was signalled in the 6thEnvironmental Action Plan (2001-2010), where for thefirst time the issue of environment and health was identi-fied as a key determinant of sustainability. Since then, anumber of legislative initiatives have been undertaken inthe European Union with a scope to reducing the potential

* Correspondence: [email protected] Commission – Joint Research Centre, Institute for Health andConsumer Protection, Chemical Assessment and Testing, via E. Fermi 1,21027 (VA), ItalyFull list of author information is available at the end of the article

Sarigiannis and Hansen Environmental Health 2012, 11(Suppl 1):S18http://www.ehjournal.net/content/11/S1/S18

© 2012 Sarigiannis and Hansen; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

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adverse effect of environmental pressure on public healthas a key dimension for ensuring sustainability. Theseinclude(a) Consumer Policy and REACH, the current chemi-

cal safety regulation in the European Union, requiringsound data on chemical safety of consumer products [1];(b) Environment & Health Action Plan putting parti-

cular emphasis on mixture effects in various environ-mental matrices [2];(c) Food safety policies regarding chemicals in food

and food contact materials [3].These policy instruments place varying requirements

on health and safety data of chemicals in the environ-ment. REACH focuses on safety of individual sub-stances; yet all the other facets of public health policythat relate to chemical stressors put emphasis on theeffects of combined exposure to mixtures of chemicaland physical agents. This emphasis brings about metho-dological problems linked to the complexity of therespective exposure pathways; the effect (more complexthan simple additivity) of mixtures (the so-called ‘cock-tail effect’); dose extrapolation, i.e. the extrapolation ofthe validity of dose-response data to dose ranges thatextend beyond the levels used for the derivation of theoriginal dose-response relationship; and the integrateduse of toxicity data across species (including humanclinical, epidemiological and biomonitoring data).In this paper we give an overview of the main methodol-

ogies available today to estimate the human health risk ofenvironmental chemical mixtures, ranging from dose addi-tion to independent action, and from ignoring interactionsamong the mixture constituents to modelling their biolo-gical fate taking into account the biochemical interactionsaffecting both internal exposure and the toxic potency ofthe mixture. We discuss the possible options available topolicy makers and the difficulties and potential pitfalls inimplementing these methodologies in the frame of thecurrently existing policy framework in the EuropeanUnion. Finally, we suggest a plausible scenario for policy/regulatory action that would facilitate the evaluation of thehealth effects of chemical mixtures in the environmentand consumer products.

Mixture toxicology methodsAs described in the text above, from a mixture toxicologypoint of view each methodology to predict cumulativeeffects or to detect interactions has limited applicabilityunder specific conditions that cannot be generalized.Information on mode and mechanism of action for eachactive substance in the mixture is needed to decidewhether to use dose or effect addition, while biochemicaland molecular pathway information regarding enzymeactivity and the ADME (absorption – distribution –metabolism – excretion) properties of the substances are

needed to determine the existence or not of these pro-cesses and the level of biochemical interactions amongthem. In risk assessment and management, however, amore pragmatic viewpoint is taken, and given the overalluncertainties of the process an approximation to thecumulative effects is usually good enough, for example aworst-case, but not overly conservative, estimate. Onekey issue is, nonetheless, the need to render the riskassessment transparent by recognizing and reportingexplicitly on the uncertainty and the assumptions asso-ciated with each of the risk assessment methodologiesdescribed below and in the next chapter (see Figure 1).

Hazard indexThe hazard index (HI) of a mixture of chemicals is thesum of the compound-specific hazard quotients (HQi),which are calculated as the ratio of the exposure (e.g.the daily intake of a substance) to the dose of no con-cern (i.e. the exposure above which adverse effects onhuman health can be expected) [4-7]. This referencedose (refi) can be, for example, the acceptable dailyintake (ADI) or the acute reference dose (ARfD).The hazard index for a mixture of several substances

Si (i = 1,2,…n) can be calculated as

HIref

i

ii

nexp

.1

(9)

The hazard index method does not allow prediction ofthe overall health effect of the combined substances butadds the strength of risk (roughly estimated), attributa-ble to each component of the mixture. If the indexexceeds unity the concern is the same as if an individualchemical exposure exceeded its acceptable level by thesame proportion [4]. The hazard index can be used toidentify the most risky substances in a mixture, i.e. thechemicals that have the highest health risks based ontoxic potential and estimated or measured exposure.Feron et al [5] propose to use the Mumtaz-Durkin andMumtaz et al [6,7] weight of evidence approach, whichis based on the hazard index concept but additionallyconsiders evidence for interactions for the selection ofthe most risky chemicals or groups of chemicals in acomplex mixture (see below).Risk prediction using the HI approach is based on a

comparison of specific exposure to the substance-speci-fic exposure limit, refi, for each component of the mix-ture. If this exposure limit is derived from testing effectson an identical physiological target, e.g. effects on a cer-tain enzyme measured under defined conditions, theresult obtained is equivalent to the outcome of the dosesummation method. A kind of effect addition is per-formed through the HI calculation if the refi values refer


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to a more general endpoint, induced by similar or dis-similar mechanisms, e.g. if refi values are threshold con-centrations above which adverse effects on the liver areexpected. It allows prediction of even more general riskindices. If for the first compound of a mixture a refi isavailable for lung cancer, for the second the refi refersto liver cancer and the third component is known toenhance the risk to a third type of cancer, a risk index,named Health Risk Index, can be calculated for the gen-eral category “cancer”. It should be noted that expi andrefi should refer to the same time scale of exposure interms of chronic versus acute exposure and toxicity, andto the same uptake route, because, for example, theamount of a toxic substance taken up with food maynot be directly compared to the toxicity thresholdderived from an inhalation study.The hazard index concept has the advantage that it is

transparent and easy to apply [8-10]. The disadvantageis that the reference to which exposure is related doesnot solely reflect the toxicity of the substance but is

derived by using uncertainty factors which are not fullydata based but may incorporate significant policy-drivenassumptions: For calculating reference concentrations ofno concern (refi) the NOAELs usually are divided by afactor accounting for the uncertainty of the generalisa-tion of the NOAEL as specified also in the respectivetechnical annex of the REACH Regulation. If theNOAEL is known only for a low number of species/trophic groups this assessment factor is high and theresulting threshold is low. If testing has been performedfor several species and trophic groups extrapolationfrom test species to humans is more reliable. In thiscase the assessment factor is lower so that the referencevalue deviates to a lesser extent from the NOAEL.

Point of departure indexThe point of departure index (PODI) method does nothave the disadvantage of the hazard index, as exposureis compared to a concentration level reflected by toxicitydata. The point of departure index is the sum of the

Figure 1 Scheme of risk assessment approaches Scheme of risk assessment approaches. Currently methodology is available for substance bysubstance toxicology and the assessment of mixtures of substances with the same mechanism of action for which dose or concentrationaddition is applied.


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exposure of each compound divided by its respectivepoint of departure (POD),

PODIPOD

i

ii

nexp

1

(10)

where the POD can be a NOAEL, the dose at which atoxic effect becomes biologically significant, or a bench-mark dose [11,12]. The benchmark dose method is astatistical tool fitting a mathematical model to all thedose-response data within a study and thus more biolo-gical information is used for the derivation of the PODcompared to the traditional NOAEL derivation [12].The benchmark dose (BMD) method provides additionalinformation regarding its uncertainty but high qualitydose response curves are required in order to provideestimated BMDs with small confidence intervals [10].For the evaluation of potential risk the PODI of a mix-ture is compared to an agreed group safety factor. Thisfactor is often 100 and the product of PODI and theuncertainty factor should be <1 [10].

Margin of exposureThe margin of exposure (MOE) of a substance is theNOAEL divided by exposure so that the combined mar-gin of exposure of a mixture (MOEmix) can be calcu-lated as

MOEMOE MOE MOEmix

n

1 1 1

1 2

1

... (11)

The margin of exposure index of a mixture is com-pared to an agreed acceptable threshold. According toEFSA [10] there are no established criteria for the mag-nitude of an acceptable MOEmix for mixtures of chemi-cals but it is widely accepted that at a MOEmix higherthan the uncertainty factor of 100 the conclusion can bedrawn that the risk of toxicity is unlikely.

Cumulative risk indexThe cumulative risk index (CRI) combines MOEs forchemicals with different uncertainty factors. The riskindex (RI) of a single chemical is the reciprocal of thehazard quotient and is calculated as

RINOAEL

UF

refi

i

i i

i

iexp exp, (12)

where UF is the uncertainty factor mentioned above(e.g. [10]). The reference dose (refi) can be, for example,the acceptable daily intake, ADI. The cumulative riskindex (CRI) of a mixture of chemicals is defined as

CRIref

i

ii

n

11

/exp

. (13)

The cumulative risk index is the reciprocal of thehazard index (equation 9).

Toxic equivalency factorsThe Toxic Equivalence Factor (TEF) is a specific type ofrelative potency factor (see section 2.2) formed througha scientific consensus procedure [10]. Based on theassumptions of a similar mechanism of action of struc-turally related chemicals and parallel concentration (ordose) response curves, TEFs were first developed forpolychlorinated dibenzo-p-dioxins (PCDDs) and poly-chlorinated dibenzofurans (PCDFs) and polyaromatichydrocarbons (PAHs). The total toxicity of the mixtureis assessed in terms of the toxicity of an equivalent con-centration of an index compound. The total equivalentquantity TEQ is estimated by summation of the concen-trations (or doses) of mixture components ci multipliedby the respective TEFi:

TEQ c TEFi i

i

n

( )1

(14)

The TEF/RFP approach is equivalent to the MOEapproach described above (see Section 3.3). The majordifference is the stage at which to extrapolate fromexperimental data to humans: for each chemical sepa-rately (allowing different assessment factors for eachchemical) in the case of MOE; or after combining theirdoses/effects (using thus one assessment factor for allchemicals in the mixture) in the case of the TEF/RFPapproach.

Methods for risk assessment of mixtures takinginto account interactionsToxicant interactions may take place during any of theprocesses that affect the toxic potency of a single com-pound: adsorption, distribution, metabolism, excretionand activity at the receptor site(s). They may interactchemically, and they may interact by causing differenteffects at different receptor sites [4]. Interactions can beassumed to occur frequently and often are dose-depen-dent but, according to EFSA [7], there is no standardstudy design to evaluate the potential interaction ofcompounds. Evidence exists that mixtures having addi-tive toxicity at low, environmentally relevant concentra-tions show synergism at higher exposure. This wasshown for effects of pesticides mixtures on a targetenzyme extracted from tissue of salmon [13]. At EC50


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synergy effects were higher compared to 0.4 and 0.1% ofEC50, respectively.Most commonly, dose additivity is considered the

expected outcome when testing for the effects of chemi-cal mixtures; any deviations thereof are deemed interac-tions. However, this is not necessarily the case, asdeviations from additivity may also be observed inabsence of interactions [14]. Experimental observationsof deviations from additivity have been reported,although Kortenkamp et al. [15] report them as beingquite rare. Nesnow et al. [16] analysed mixture effects offive polycyclic aromatic hydrocarbons on lung tumoursin A/J mice, with mixture ratios representative of ambi-ent air levels of these carcinogens. At low doses greaterthan additive effects were seen, whereas at high dosesthe observed responses fell short of additivity expecta-tions which were derived from independent action in aneffect surface analysis. However, the observed deviationsfrom effect addition were rather small.Another example is the study by Walker et al. [17] who

employed two year rodent cancer bioassays with femaleHarlan Sprague-Dawley rats given 2,3,7,8-tetrachlorodi-benzo-p-dioxin (TCDD), 3,3’,4,4’,5-pentachlorobiphenyl(PCB-126), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF),or a mixture of the three compounds. The three chemi-cals, both singly and in combination, induced hepatic,lung and oral mucosal neoplasms. A re-analysis of thedata, without utilizing the WHO TEF values [10] but byemploying the concept of dose addition directly, showedthat the experimentally observed tumor incidences fellshort of those anticipated by dose addition [15].There are a few examples from the area of endocrine

disruption that indicated antagonisms in the joint effectsof estrogenic agents [18], but these deviations wererather small. Similarly, the study by Hass et al. [19] onthe feminizing effects of androgen receptor antagonistson male offspring of dams dosed during gestation indi-cated a weak synergism with respect to induction of nip-ple retention. Similar deviations from additivity were notobserved with other endpoints evaluated in the samestudy.Even though on many an occasion deviation from

dose additivity may not be easy to observe at low, envir-onmentally relevant doses, this may be mostly due tothe sensitivity and the binary (effect or no effect) natureof classic toxicological tests. The advent of –omics andin particular transcriptomics opens the way to identify-ing deviations from the assumption of additive responseto co-exposure to chemical substances. Toxicogenomicsseems to be the appropriate screening method for asses-sing biological effects of complex chemical mixtures,allowing us to review the whole spectrum of potentialbiological response rather than focusing on a predefinednumber of endpoints as in classical toxicological

analysis. Applying such techniques Coccini et al. [20]showed a distinct modulation in the muscarinic receptordensity and gene expression associated with neurologicalresponse in rats co-exposed to methyl mercury andPCB153 perinatally. Cimino Reale et al. [21] studiedcomparatively the mixture effects of typical mixturesfound in indoor air in Europe and of a mixture of poly-aromatic hydrocarbons (PAHs) sampled in Milan, Italy.Large sets of genes modulated by exposure to differentair mixtures were profiled and common biochemicalpathways and specific molecular responses were identi-fied. Indoor air mixtures induced a higher gene modula-tion than PAHs, confirming major differences in thetoxic mode of action of the two mixtures. Indoor airinduced primarily modulation of genes associated withprotein targeting and localization including in particularcytoskeletal organization; PAHs modulated mostly theexpression of genes related to cell motility and gene net-works regulating cell-cell signaling, as well as cell prolif-eration and differentiation. These results providebiological information useful for articulating mechanistichypotheses of exposure to xenobiotic mixtures and phy-siological responses.One way to deal with the general lack of knowledge

about interactions in a cumulative risk assessment con-text is to use an additional uncertainty factor accountingfor potential synergy effects. As mentioned above,NOAELs usually are divided by assessment factors con-sidering, for example, the uncertainty of extrapolation ofdata gained with certain test organisms in the lab totoxicity thresholds applicable for human health protec-tion (factor = 10) and the between humans differencesin sensitivity (factor = 10). Increasing the uncertaintyfactor by a factor of 10 and thus accounting for interac-tions of chemicals in a mixture would cover a tenfoldincrease in mixture toxicity due to interactions betweenthe mixture components. Not much is known about thesignificance and extent of synergy effects and it isunclear whether an uncertainty factor of 10 would beprotective or over-protective. Currently no specificassessment factor for mixtures is employed in the tradi-tional chemical-by-chemical risk assessment [5].

Interactions based hazard indexA modified hazard index approach has been proposed[10], using the hazard index developed for additiveeffects as a basis and accounting for interactions bymultiplying the HI with a factor reflecting both theuncertainty and the strength of evidence that interac-tions take place. The interaction-based hazard index(HII) is calculated as:

HI HI UFI IWOEN (15)


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where UFI is the uncertainty factor for interactions.WOEN is a numerical weight-of-evidence score reflect-ing the strength and consistency of evidence for interac-tions scaled to reflect the relative importance of thecomponent exposure levels. The method is based on theprocedure proposed by Mumtaz and Durkin [6]. Furtherdevelopments of the HI approach yielded the followingequation for HIINT, the hazard index modified by binaryinteractions data [10], applying the weight-of-evidenceprocedure to modify each HQ instead of modifying thesum of the HQ values of the mixture:

HI HQ f MINT i ij ijB

j i

n

i

nij ij( )

1

(16)

where HIINT is the HI modified by binary interactionsdata, HQi is the hazard quotient for chemical i, fij is thetoxic hazard of the jth chemical relative to the totalhazard from all chemicals potentially interacting withchemical i (thus j cannot equal i), Mij is the interactionmagnitude, the influence of chemical j on the toxicity ofchemical i, Bij is the score for the strength of evidencethat chemical j will influence the toxicity of chemical i,and θij represents the degree to which chemicals i and jare present in equitoxic amounts. How to derive Bij, fij,Mij and θij is described in detail in EPA [10].

Excluding interactions – the isobole methodSince both the dose addition and the effect summationmethods are applicable only under the precondition thatinteractions are absent, methods suitable for examiningwhether all mixture components act without diminish-ing or enhancing the effects of each other play animportant role in current state of the art mixture toxi-cology. The isobole method [10] is the most commoncriterion for judging whether interactions between simi-larly acting chemicals have taken place in a mixtureexperiment [13]. Bosgra et al. [13] give an overview ofthe method and discuss its applicability. The isobolemethod is based on the following approach:In the case of zero-interaction

d

d

d

d1

1

2

2

1’ ’

(17)

where d1 and d2 are doses of the substances S1 and S2,respectively, present in a particular combination, and d1’and d2’ represent the doses that, if applied individually,result in the same magnitude of effect as the combina-tion. If the mixture has the same effect but the effect isreached with lower d1/d1’ and/or d2/d2’ ratio(s) the leftpart of equation 17 is smaller than 1, reflecting synergyas an enhancement of toxic potency due to interaction

of the mixture components. Any deviation from 1denotes a deviation from additivity [11].

Modelling interacting mixturesIn the past 15 years or so, physiologically based pharma-cokinetic/pharmacodynamic (PBPK/PD) modeling hasbeen applied to the toxicological interactions of chemi-cal mixtures. A comprehensive review on these studieswas conducted by Reddy et al. [22]. Progress in theapplication of PBPK modeling to chemical mixtures hasfollowed different phases from simple binary pharmaco-kinetic and pharmacodynamic interactions to more andmore complex mixtures. First, PBPK modeling of binarychemical mixtures became necessary because of phar-macological or toxicological interactions. Second, asinvestigators became interested in mechanisms of toxi-cological interactions, the advances of physiologicallybased pharmacodynamic (PBPD) modeling formed anatural course of development of this area. Third, whenmore and more sophistication was incorporated intoPBPK modeling, novel approaches towards modelingcomplex chemical mixtures were developed. Since 1996when the Food Quality and Protection Act was enacted,the United States Environmental Protection Agency (USEPA) began to give active consideration to cumulativerisk assessment taking into account toxicological inter-actions. Thus, the application of PBPK modeling incumulative risk assessment became an active area ofresearch [23].Mumtaz et al. [6] in an earlier review of PBPK model-

ing of chemical mixtures indicated that the “first exam-ple” of PBPK modeling of a “chemical mixture” actuallyinvolved one chemical, n-hexane, and its metabolites,methyl n-butyl ketone (MnBK) and 2,5-hexanedione(2,5-HD); thus, it is a kind of “one-chemical mixture.”This particular PBPK model for n-hexane and its meta-bolites incorporated three inhibitory interactions: (1)hexane and MnBK are competitive substrates for w-1oxidation; (2) MnBK and 2,5-HD are competitive sub-strates for oxidation; and (3) 2,5-HD acts as a productfeedback inhibitor [24]. The findings of this modelingstudy were intriguing and they explained some of themost interesting and complex toxicological and pharma-cokinetic behaviors of n-hexane in animals [7].Pioneering efforts in the PBPK modeling of more

complex chemical mixtures were from a research groupled by Krishnan and various colleagues in Canada; twocomprehensive reviews of work up to 1994 are available[25,26]. Earlier work from this group concentrated oninteractions and PBPK modeling between two chemicals[27,28] . As progress was made, these investigatorsbegan to build up the mixtures and devoted their effortto PBPK modeling of more and more complex chemicalmixtures [29-32]. PBPK modeling of a ternary mixture


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on toluene, m-xylene, and ethylbenzene was studied andreported by Tardif et al. [29], and the interactionmechanism involved was competitive inhibition. Subse-quently, Haddad et al. [30]applied this interactive PBPKmodel to the calculation of the biological hazard index(BHI). BHI, defined as the biological level tolerable toexposure to mixtures, is traditionally calculated in ananalogous way to the hazard index under the additivityassumption [30-33]. However, Haddad et al. [30] incor-porated toxicological interaction by using PBPK model-ing to obtain “simulation concentrations” (SC) andmodified the hazard index calculation according to thefollowing equation:

BHISC

BEIi

ii

n

1

(18)

where BHI and SC are as defined before and BEIrefers to the concentration or excretion rate of a bio-marker in a healthy worker exposed to the occupationalthreshold limit value (TLV). In doing so, Haddad et al.[30] applied interactive PBPK modeling of a chemicalmixture into the risk assessment process. Using thesame principle and similar technique, researchers atColorado State University studied PBPK modeling oftwo ternary mixtures [trichloroethylene (TCE), tetra-chloroethylene (PERC), methyl chloroform (MC) andtoluene, ethyl-benzene, and xylenes] to respectivelyenhance the concept of “interaction thresholds” andmodify and improve the “Mixture Formula” risk assess-ment by using an interactive PBPK modeling approach[34-36].Haddad et al. [31]also studied the PBPK modeling of a

four-chemical mixture involving benzene, toluene, ethyl-benzene, and m-xylene. In general, the incorporation ofthe interaction mechanism, at the level of the livermetabolic enzyme inhibition, is similar to thosedescribed above for binary and ternary mixtures, albeit abit more complicated. In Europe, Sarigiannis and Gotti[37] studied a similar group of volatile organic chemicalsincluding benzene, ethylbenzene, toluene and all thefamily of xylenes (m-, o-, p-) extending the previouswork to tackle the chemical interactions and the bio-chemical processes influencing the rate of metabolismof the mixture compounds. They brought the methodol-ogy one step further by taking into account the mor-phology of the liver and its relationship with metabolicfunction, as well as the mechanism of competitive inhi-bition of metabolism to estimate the biologically effec-tive dose (BED) of the mixture components and theirprimary metabolites and then related BED for the sumof benzene metabolites (the main carcinogenic com-pounds in the mixture) to epidemiological and clinical

observations of health outcomes (leukemia incidence).This allowed them to derive new dose-response relation-ships relating the observed health response to the totalinternal dose of the metabolites at the target tissue(bone marrow in this case), rather than to the externalenvironmental concentrations (doses) of the chemicalsin the mixture. The result was a more biologically plau-sible dose-response curve that captures better the phy-siological response to toxic insults at low doses andlong-term (chronic) exposure.As PBPK modeling of chemical mixtures progresses

towards involving more and more components, a nat-ural course of development is that investigators willattempt to tackle the real-world complex chemical mix-tures. Verhaar et al. [38] proposed the incorporation oflumping analyses (a chemical engineering techniqueused in petroleum engineering processes) and quantita-tive structure-activity relationships (QSAR) to PBPKmodeling. The idea was that each of the three techni-ques would serve its unique function in the overall goalof predicting some aspects of the chemical mixtures ofinterest. Thus, QSAR analysis can be used to predictneeded physicochemical and toxicological parametersfor unknown compounds or for surrogate compounds(from lumping); lumping analysis can drastically reducethe complexity of the description of a mixture; andPBPK/PD modeling can be used to describe the pharma-cokinetics, and possibly pharmacodynamics, of anensemble of compounds or lumped pseudocompounds,including possible interaction effects.

Grouping of chemicals by health endpointThere is no established methodology available allowingassessment of the endpoint-specific health risk due to acombination of chemicals having different modes ofaction and affecting the same endpoint. Although theindependent action method for response or effect addi-tion described in detail earlier in this paper allows calcu-lation of summed effects of mixtures of chemicals actingvia independent pathways, the applicability of thismethod for integrated health impact assessment of envir-onmental chemical stressors is limited for the followingreasons. First, dose-response relationships for the respec-tive effect are required for each compound of the mix-ture; these are, in many cases, available for effects on invitro systems or test organisms but lacking for humanhealth endpoints. A further complication results from thespecificity of the uptake route of the dose response rela-tionship. A further reason for the limited applicability isthat the response addition approach can be used underthe precondition that interactions can be excluded.Methods used to test whether no-interaction can beassumed usually require dose-response-algorithms, which


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are not available for many substance-endpointcombinations.The hazard index and the interactions-based hazard

index method, described above, are applied for assessingpotential health risks due to mixtures of chemicalsaffecting a given health endpoint by acting with thesame or differing mode of action. Exposure is comparedto a reference exposure assumed to be acceptable interms of risk to human health. Although the hazardindex and related indices mentioned above are not sui-table for giving information about the number of casesof impaired health to be expected for a certain complexexposure or policy scenario they represent an importanttool for risk assessment and help to decide whetherexposure of the population or subgroups to a certainmixture of chemicals should give rise to concern.For pesticides the suitable substance specific limit

value, i.e. the reference level (refi) of no concern, usuallyis the accepted daily intake rate (ADI) relevant tohuman health but not to certain health endpoints. Cal-culating the endpoint specific health risk index for amixture of pesticides by summing up the exposure tolimit value ratios requires health endpoint specific limitvalues assumed to be protective for the selected end-point. This kind of reference value, e.g. cancer healthrisk limit values for humans, is usually not available forpesticides. There are two possibilities to deal with thisproblem: The first is to identify compounds of the mix-ture affecting the same health endpoint on the basis ofavailable evaluations, to relate exposure to generic ADIsinstead of endpoint specific limit values, and then sumup the ratios to derive the index value. The second pos-sibility is to identify compounds of the mixture showingeffects when tested with a certain indicator system (e.g.genotoxicity tests), to relate exposure to the substancespecific NOAEL derived with this test and sum up theseratios for all components of the mixture.

Grouping of unknown mixtures of unknown substancesGrouping pesticides by effects on indicator systems is ofhigh importance because to date combination toxicologyis facing a generic problem: for many potentially toxicsubstances produced or just present at relevant amountsthe mechanism of action is unknown and their toxicityhas not been evaluated. According to data from the USEPA, for about 80% of substances produced at signifi-cant amounts no toxicity information is available. Forpesticides this percentage goes down to about 40%. Forpesticides the knowledge level is relatively good but farfrom being sufficient. The U.S. EPA has launched a setof projects on chemicals risk assessment with the aim ofdeveloping methods for the prediction of chemical toxi-cology suitable for dealing with a high number of sub-stances, to improve incorporation of molecular

toxicology and computational science, to reduce stan-dard rodent toxicology tests and to increase cost effi-ciency [39]. Complex data on chemical structures,results of high throughput screening and rodent testdata are evaluated in order to identify groups of chemi-cals characterised by their effects on cellular pathwaysand more generic health endpoints.With respect to mixtures the approach is based on the

identification of relationships between the structure of asubstance and its toxicity. If structural features of a sub-stance group are correlated with toxicity revealed fromin vitro assays determined using high throughputscreening, and the same structural features are corre-lated with in vivo toxicity test results, in vitro assayresults or even structural features might be sufficient topredict the mechanism of action and health effects. Theapproach aims at predicting the mode of action and/ortoxicity of a high number of chemicals within a shorttime period. In the context of mixtures of chemicalswith unknown mode of action the methods might besuitable to sort the compounds of a mixture by pre-dicted modes of action in order to define groups of che-micals for which additive combination toxicologyapproaches, such as concentration or dose addition, orhazard index related methods, can be applied.

DiscussionIn tackling the health risk associated with combinedexposure to mixtures of chemicals in the environment,the food chain and consumer goods, regulatory bodieswill have to deal with several problems:• Setting maximum acceptable threshold concentra-

tions or acceptable daily intakes (ADIs) for certain che-mical groups with a common mechanism of action suchas organophosphates would be a first step forward, butwould disregard most of the mixture constituents as themajority of active substances in a sample usually belongto different chemical groups (an example is given inTable 1).• Additive approaches based on grouping substances

by effects (e.g. summing up doses of compounds caus-ing dysfunction of the nervous system in humans butacting via differing mechanisms) requires testingresults obtained under comparable conditions andevaluating effects on a common human health end-point. Such data are not available in most cases, and,again, only a part of the mixture constituents would beconsidered (Table 1).• Using the hazard index approach for assessing the

risk to human health is practicable and transparent butthe question arises whether HQs of all mixture com-pounds should be added, or, alternatively, HQs of sub-stances grouped on the basis of their relevance for acertain health effect. Krautter et al [40]


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• describe an evaluation scheme for pesticide mixturesconsidering both threshold exceedances (ARfD, ADI,MRLs) and HIs, the latter calculated as the sum of HQsof all substances in the sample (See http://www.green-peace.de/fileadmin/gpd/user_upload/themen/umwelt-gifte/greenpeace_bewertung_pestizide_neu.pdf)• Adding HQs of all compounds found has the advan-

tage that it is transparent and easy, but has the disad-vantage that improving the detection limit of theanalysis and thus identifying a high number of com-pounds present at very small amounts might increasethe overall hazard index, whereas an analysis of thesame sample with higher detection limit would indicatea lower risk. Thus, the overall estimated risk of the mix-ture is only dependent on the detection limit and noton the actual biochemical mechanisms that determinetoxic potency.• Adding HQs of compounds within a common health

effect would probably reveal additional information onspecific risk of the mixture, but has the disadvantagethat for most active substances currently in use thedataset on health effects is incomplete: for three of the13 compounds in Table 1 it is known that they have noendocrine disrupting effects, for three it is known thatthey might have these effects, but it is not clear, and forseven others no data on endocrine disruption areavailable.• A further problem connected with the use of the

hazard index approach for the risk classification of mix-tures is that a suitable reference has to be chosen. It hasto be decided whether the HQs should be calculated as

a residue versus MRL or a predicted daily dose versusthe ADI or both, and, in the case of dose versus ADI,whether the doses and ADIs for adults or childrenshould be applied.• The lack of knowledge about actual or potential

synergistic interactions is a general problem. Legislationcan deal with this problem by diminishing referenceconcentrations or doses of no concern by a factorassumed to be protective, but whether this factor shouldbe 2, 3 or 10 is a matter of speculation.• Existence of toxic metabolites and potential bio-

chemical interaction among the mixture componentsinfluencing the level of metabolic activity complicatesfurther the risk assessment of mixtures. This complica-tion is further increased when the biological half-life ofthe mixture components varies significantly. Under-standing the nature of component interactions and therelated biokinetics is a key requirement for assessingeffectively chemical mixtures with these characteristics.To date, the effects of chemical mixtures are dealt

with by the regulatory framework in the USA, but notin the European Union. The International Programmeon Chemical Safety of the World Health Organization(WHO IPCS) has developed guidelines with a view toenlarging their applicability to its domain of competence(worldwide); both of these initiatives, however, are lim-ited to human health effects. The current thinkingwithin the European Commission and in the relevantscientific and industrial community in Europe is thatany European initiative towards the establishment ofguidelines regarding the assessment of chemical

Table 1 Plant protection products found in German fruit with corresponding health effects

activesubstance

chemical group* pesticide type* target health issues*

carc end repr AChE neuro resp

Endosulfan-sulfate

organochlorine acaricide,insecticide

neurotoxic, affects the transfer of nerve impulses ininsects and mammals

? ? - no yes -

Chlorpyrifos-ethyl

organophosphate insecticide AChE inhibitor, causes dysfunction of the nerval system no ? yes yes no no

Chlorpyrifos-methyl

organophosphate acaricide,insecticide

AChE inhibitor, causes dysfunction of the nerval system no no - yes ? no

Triadimefon triazole fungicide disrupts membrane function ? ? yes no ? -

Trifloxystrobin strobilurin fungicide inhibits electron transfer and respiration no - yes no no -

Boscalid carboxamide fungicide inhibits sperm germination ? no ? no no -

Cyprodinil anilinopyrimidine fungicide blocks certain synthesis pathways within the cells no - ? no no yes

Dimethomorph morpholine fungicide lipid synthesis inhibitor no - ? no no no

Fenhexamid hydroxyanilide fungicide Disrupts membrane function, inhibits spore germination no - no no no no

Fludioxonil phenylpyrrole fungicide inhibits phosphorylation of glucose ? - ? no no no

Fluopicolide benzamide fungicide protectant ? - no no no no

Metalaxyl phenylamide fungicide protectant suppressing infections, sporangial formationand mycelial growth

no - no no no -

Thiametoxam neonicotinoid insecticide affect the central nervous system by binding to anpostsynaptic ACh receptor

? no no no no ?


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mixtures should address equally the health of bothhumans and the environment [41]. Such guidelinescould be built around a core of tools, methods andapproaches common to mammalian and ecological toxi-cology, the most important of which are described inthis paper.What type of general principles can be included in a

European Commission recommendation in order toaddress combination effects?Clearly, currently there is a need to strengthen the

legal mandate for risk assessment of mixtures. In thiscontext, mixture effects need to be taken into accountholistically, drawing useful lessons from the CIRCLAlegislation in the United States, targeting Superfundsites. If an overarching framework is set based on gen-eral guidelines and underpinned by as much scientificrigor as possible, then more sectorial implementationcould vary to adapt to the twenty one different pieces ofEuropean Community legislation pertinent to the issueof chemical mixture safety, including legislation on che-micals (REACH), consumer articles (Consumer Strat-egy), plant protection products (Plant ProtectionProducts Directive), biocides (Biocides Directive), waterquality (Water Framework Directive and Marine Strat-egy), and air pollution (Air Quality Framework Direc-tive). The forthcoming WHO guidelines for indoor airquality, currently under development in conjunctionwith the European Commission, would be an opportu-nity to incorporate thinking about risk assessment ofmixtures in a sectorial piece of legislation affectingalmost all the European population for more than 80%of the time. In this context, a Europe-wide data centeron human exposure to environmental chemicals couldprovide the necessary scientific support for effective riskassessment of chemical mixtures. For this purpose, theEuropean Commission’s Joint Research Centre in colla-boration with the European Environment Agency hasbeen working towards the development of this data cen-ter since 2008. So far, the basic concept for the datacenter has been developed. The available monitoringdata for cumulative exposure assessment are expected tobe pulled together by the EEA topical centres (on air,water, soil) in the course of 2011. In the food productsdomain, the European Food Safety Authority (EFSA) hasrecently initiated similar efforts, while the EuropeanChemicals Agency (ECHA) is in the process of populat-ing the IUCLID 5 database with physical/chemical andtoxicity data on industrial chemicals currently in theEuropean market. Setting up an interagency groupbringing together the efforts of all the European Unionorgans would be the most efficient way towards coordi-nated action to assess cumulative exposure assessmentto chemical mixtures.

A possible way forward to overcome the current gapsin knowledge that could act as obstacles to the defini-tion of a plausible regulatory approach to chemical mix-ture risk assessment would be to use a tiered approachas follows:(a) Use dose addition to calculate a hazard index tak-

ing into account interactions (eq. 16) as default optionfor hazard quantification and risk assessment. Thisapproach could be employed as default first-tierapproach for mixtures risk assessment. The formulationof the hazard index given in eq. 16 allows taking intoaccount the non-linear effects from the interaction ofmixture components if the necessary information isavailable, while simplifying down to a simple dose addi-tion if no interaction data exist. Such an approachwould be in line with the current practice across theAtlantic, as the interaction-based hazard index has beendeveloped and used extensively by the US Environmen-tal Protection Agency. Overall, it would give a reason-able approximation to the toxic potency of a mixture ifthe necessary data are available; if not, it would stillallow conservative assumptions about effects of com-bined exposure to multiple chemicals and interactionsamong the mixture components.In data-rich situations use more sophisticated tools,

including mechanistic, biology-based modeling thattakes into account the biologically effective dose of themixture components at the target tissues and incorpo-rates system-wide response data across the dose-response range using information derived from –omicstechnologies – the connectivity approach [42]. Thismore complex yet plausible methodology would be inline with the United States Academy of Science 2007report towards toxicity testing in the 21st century [43].The tiered approach outlined above ensures that it can

be readily applied in the current regulatory context andyet opens up the way to incorporating scientific state ofthe art in the near future. A key development that couldbe implemented readily in already existing pieces of leg-islation (during their regular review process) would beto demand that the collected toxicity data be amenablefor mixture toxicity assessment. This means essentiallythree things:i. that toxicity data are recorded and documented in a

coherent uniform way, independent of specific regula-tory context;ii. that emphasis should be given to the full range of

the dose-response function, and in particular below theNOAEL or DNEL (derived no effect level, as used inREACH); and,iii. that benchmark approaches should be given prefer-

ence over NOAELs or NOECs in setting up regulatorysafety limits for chemical substances.


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In addition to immediate regulatory change that couldfacilitate a rational assessment of chemical mixture toxi-city as described above, further research is needed tar-geting at least the following key issues:• development of dedicated exposure assessment stra-

tegies for environmental chemical mixtures (e.g. follow-ing the example of the US Geographic Survey collectionof water samples analyzing a large number of chemicals)• analysis of the determinants of synergistic action (i.e.

giving answers to the question ‘when is it likely that amixture acts synergistically?’)• identification and assessment of the chemicals that

contribute most to cumulative risk on the basis of inte-grated assessment under specific exposure scenarios ofrelevance to sectorial policies

ConclusionsThis paper gives a comprehensive overview of the cur-rently available methodological and computational toolsfor assessing the health effects of chemical mixtureswith a view towards applying them in chemical safetyregulation. The salient pitfalls in converting scientificknowledge on combination effects of co-exposure tomultiple chemicals are discussed and a tiered approachto mixture risk assessment is outlined for use by regula-tors and policy makers.An EU strategy for assessing and managing combina-

tion effects of chemicals in the environment could com-prise the following elements:– development of guidelines on general principles;

there is a need to explain why the combination effectsof chemicals have to be addressed effectively from con-certed policy and regulatory action based on robustscientific grounds; there is a need to bring out the pro-blems based on scientific evidence and to start assessingthese combination effects.– use of the methodological tools available as outlined

in this paper. For data poor situations, dose additioncould be used as default option in a tiered approachthat moves from dose addition to fully mechanistic ana-lysis of the possible interactions among the componentsin a chemical mixture [43].– resolution of the difficulties with dealing with the

different pieces of legislation related to the health effectsof environmental chemical mixtures and achievement ofa holistic approach. There is a clear need to address theissue in a coordinated fashion among the EuropeanCommission services responsible for legislation acrossdifferent media with a view to identifying the practicalsteps towards achieving this integrated approach.– further scientific research aiming at getting more

precise information on cumulative exposure to chemicalmixtures in the environment and using better the infor-mation collected through different pieces of legislation

in order to inform policy makers’ choices and enablethem to prioritize.

AcknowledgementsThe research that led to this article was co-funded by the EuropeanCommission in the frame of the 6th RTD framework program projectsHEIMTSA (contract no. GOCE-CT-2006-036913-2) and HENVINET(contract noGOCE-CT-2006-037019). The authors would like to thank Drs. Bo Larsen, SietoBosgra, Arno Gutleb and Peter van den Hazel for very useful comments onearlier versions of the manuscript.This article has been published as part of Environmental Health Volume 11Supplement 1, 2012: Approaching complexities in health and environment.The full contents of the supplement are available online at http://www.ehjournal.net/supplements/11/S1.

Author details1European Commission – Joint Research Centre, Institute for Health andConsumer Protection, Chemical Assessment and Testing, via E. Fermi 1,21027 (VA), Italy. 2Aristotle University of Thessaloniki, Chemical EngineeringDepartment, Environmental Engineering Laboratory, University Campus, Bldg.D, 50441 Thessaloniki, Greece.

Authors’ contributionsDAS supervised the study that led to this manuscript and wrote explicitlythe introduction, biological modeling, discussion and conclusions sections;he also takes the responsibility for drafting the overall manuscript as is. UHprovided the material for the sections reviewing the current methodologicalapproaches for cumulative risk assessment of chemical mixtures andcontributed to the discussion.

Competing interestsThe authors declare that they have no competing interests.

Published: 28 June 2012

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doi:10.1186/1476-069X-11-S1-S18Cite this article as: Sarigiannis and Hansen: Considering the cumulativerisk of mixtures of chemicals – A challenge for policy makers.Environmental Health 2012 11(Suppl 1):S18.

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Considering the cumulative risk of mixtures of chemicals – A challenge for policy makers