Environ. Sci. Technol. 2010, 44, 4841–4842
Toxicological Mixture Models are Based on Inadequate Assumptions MARTINA G. VIJVER* Leiden University, The Netherlands WILLIE J. G. M. PEIJNENBURG Leiden University, The Netherlands National Institute for Public Health and the Environment, Bilthoven, The Netherlands GEERT R. DE SNOO Leiden University, The Netherlands
RHONDA SAUNDERS
Authors’ Viewpoint
mixtures is still based on these concepts, with addition forming the basis for all models, regardless of the sophistication of the mixture models employed or the number of additional interactions (e.g., with respect to exposure) defined (3). We believe the time is ripe for a new approach, for there is evidence that additivity is not in all cases as universal as has been postulated for more than 80 years. To determine whether classical mixture models are valid, we performed a metastudy on metal mixture responses compiled from the literature. Our metastudy was performed by assembling data of peer-reviewed articles published from 1981 to 2007 available via the ISI Web of Science database. The aim was not full coverage of all papers concerning mixtures, but to obtain a representative overview of papers on metal mixtures. The articles were a selection of binary or tertiary mixture toxicity studies on the effects of Cd, Cu, and/ or Zn. In total, 19 studies were included, giving 91 records on metal combinations: 67 binary mixture combinations and 24 tertiary mixture combinations. To properly deal with multiple modes of exposure, possibly leading to different interactions, we made a distinction between metal toxicity via oral uptake and via passive uptake from the environment. For this study, only organisms for which the latter constituted the primary route were considered. Studies on freshwater (41 records) and marine organisms (31 records) as well as on soil-dwelling organisms (19 records) were included, with the proviso for terrestrial studies that only soil-dwelling organisms exposed to pore water were considered. Fate of metals in the solutions was not explicitly accounted for because of lack of information on the physicochemical water properties determining metal speciation. More details on the records used in our study are available upon request. With regard to the toxic effects of mixtures, two reference models are available for the analysis of noninteractive joint action (1, 2). Joint action is classified as similar when the primary site of action is the same for both or all the compounds (eq 1), and dissimilar when the site of action differs (eq 2). n
Dose Addition ) E(cmix) )
ci
∑ ECx i)1
(1) i
n
The interactions of thousands of chemicals in the environment with millions of biological species ultimately determine whether a given mixture of chemicals has marginal or catastrophic consequences. The foundations for toxicological effect models of mixtures were laid by pharmacologists in the 1920s (1, 2). At the time, the problem of joint action was solved mathematically by simply adding doses and responses, on the assumption that compounds do not influence each other’s physiological action. In environmental risk assessment and human toxicology, our understanding of the toxicity of * Please address all correspondence regarding this Viewpoint to
[email protected]. 10.1021/es1001659
2010 American Chemical Society
Published on Web 06/29/2010
Response Addition ) E(cmix) ) 1 -
∏ (1 - E(c )) i
i)1
(2) Two deviations from the expected null interaction, i.e., simple addition, are possible: synergism and antagonism. Synergy occurs when the combination of two compounds has an effect greater than to be expected from summing the individual effects of each compound (4). Antagonism refers to a combination with less than the anticipated effect. For each of the 91 records, the evaluation of the significance of the deviation of additivity was taken from the study in question. The magnitude of the underlying interaction was assessed similarly. In this way, specific study conditions (like VOL. 44, NO. 13, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Distribution of types of joint effects in records found in the published literature. White bars represent the distribution of response types found in literature screened for all reference models (n ) 91), black bars the distribution of response types based on the DA model only (n ) 64). level of exposure, metal combination, metric of Effect Concentration) were explicitly taken into account as these conditions affect the ascription of the deviation of additivity (antagonism or synergism). The results revealed that additive responses occurred in only 13% of the cases when merging all available reference models. When focusing on only the dose addition reference model, 12.5% additive response was found. The predominant responses found in modeling the effects of metal mixtures were antagonism and synergism, irrespective of the organism and environmental compartment tested (Figure 1). We suggest that the predominant types of joint action found for metal mixturessantagonism and synergismsare systemic and not a biased result of simple additivity of responses or doses. After all, it is well-established that metals can induce physiological processes like protein induction (thus affording organisms some protection by reducing metal interactions with specific primers of toxicity), and “less harmful” cations may prevent toxic metal ions from interacting with biotic ligands. Likewise, certain mechanisms are known to increase the toxicity of metal mixtures, such as reduction of the transmembrane potential with increasing metal loadings. This indicates that noninteractively based mechanisms are an unsatisfactory basis for modeling metal mixture toxicity. These findings are surprising when compared with available results on mixtures of organic compounds. For
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these, the dose addition model appears to work very well and the underlying premise of additivity seems justified. Variations in the expression of effects can be adequately explained by the Funnel hypothesis (5) for organic compounds. Our results show that true additivity should not be taken as a basis for predicting metal mixture toxicity. To build new knowledge on joint effects regardless of species and specific field situations, we should determine the interactions of the compounds with specific toxicological end points and use these results to assemble appropriate models for these joint effects. We feel it is time to take a fundamentally new approach to analyzing the effects of mixtures by properly accounting for the specific mechanisms involved. Classical mixture models are formulated on the basis of noninteraction, with no models having been developed for predicting interactive joint action. We echo the sentient that scientists should move to develop models for metal mixtures based on complex interactions. For both researchers in the pharmacological and in the ecotoxicological field, the essential scientific challenge is to update the classical models for mixture data analysis in order to cover all relevant compounds. Today, these typically include novel manufactured substances such as the nanoparticles used in medical and technical products that are characterized by similar chemical but dissimilar physical properties. For policy making in the real but complex world, use of simple pharmacodynamic concepts is often unavoidable and sometimes justified. Nevertheless, we strongly recommend that policymakers should be aware of the fact that the term “Dose and Response Addition” is only a representation of the mathematical procedure involved, and not a reflection of the response types.
Literature Cited (1) Bliss, C. I. The toxicity of poisons applied jointly. Ann. Appl. Biology 1939, 26, 585–615. ˆ ber Kombinationswirkungen. Arch. (2) Loewe, S.; Muischneck, H. U Exp. Pathol. Pharmakol. 1926, 114, 313–326. (3) Posthuma, L. In Mixture Extrapolation Approaches; Solomon, K. R., Brock, T. C. M., Dyer, S. D., Posthuma, L., Richards, S., Sanderson, H., Sibley, P., van de Brink, P. J., Eds.; CRC Press: Boca Raton, FL, 2008. (4) Ashford, J. R. General models for the joint action of mixtures of drugs. Biometrics 1981, 37, 457–474. (5) Warne, M.St.J.; Hawker, D. W. The number of components in a mixture determines whether synergistic and antagonistic or additive toxicity predominate: the Funnel Hypothesis. Ecotoxicol. Environ. Saf. 1995, 31, 23–28.
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