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- MATERIALS AND METHODS
The single-substance and mixture toxicity of five pharmaceuticals and personal care products (fluoxetine, propranolol, triclosan, zinc-pyrithione, and clotrimazole) to marine microalgal communities (periphyton) was investigated. All compounds proved to be toxic, with median effective concentration values (EC50s) between 1,800 nmol/L (triclosan) and 7.2 nmol/L (Zn-pyrithione). With an EC50 of 356 nmol/L, the toxicity of the mixture falls into this span, indicating the absence of strong synergisms or antagonisms. In fact, a comparison with mixture toxicity predictions by the classical mixture concepts of concentration addition and independent action showed a good predictability in the upper effect range. However, the mixture provoked stimulating effects (hormesis) in the lower effect range, hampering the application of either concept. An independent repetition of the mixture experiment resulted in a principally similar concentration–response curve, again with clear hormesis effects in the lower range of test concentrations. However, the curve was shifted toward higher effect concentrations (EC50 1,070 nmol/L), which likely is due to changes in the initial species composition. Clear mixture effects were observed even when all five components were present only at their individual no-observed-effect concentrations (NOECs). These results show that, even with respect to mixtures of chemically and functionally dissimilar compounds, such as the five pharmaceuticals and personal care products investigated, environmental quality standards must take possible mixture effects from low-effect concentrations of individual compounds into consideration. Environ. Toxicol. Chem. 2011;30:2030–2040. © 2011 SETAC
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- MATERIALS AND METHODS
Recent chemical–analytical studies demonstrate the ubiquitous presence of pharmaceuticals and personal care products (PCPs) in all major environmental compartments [1–4, and references therein. Aquatic systems are contaminated by effluents from sewage treatment plants, from runoff after the application of sewage sludge or manure on agricultural fields, and from poorly controlled production sites 5. However, chronic and community-level ecotoxicity data are still lacking to a large extent and, with few exceptions, no data for marine species are available 6.
In any given area, a whole range of pharmaceuticals and PCPs is used and emitted into the environment; hence the typical environmental exposure leans toward multicomponent mixtures of these chemically and functionally heterogeneous compounds. Empirical knowledge of the ecotoxicology of this mixture type is still limited. However, previous studies on the ecotoxicity of dissimilarly acting or chemically heterogeneous compounds have shown one consistent pattern: the mixture effect is always clearly higher than the individual toxicities of its components, and, additionally, the mixture is toxic even if all components are present only at concentrations at or even below their individual no-observed-effect concentrations (NOECs) 7–9. Hence, the setting of environmental quality standards or the ranking of pollution scenarios must take mixture effects into consideration. Analyzing the effects of individual pharmaceuticals provides vital ecotoxicological knowledge but is insufficient alone.
Current empirical knowledge unanimously shows that the toxicity of multicomponent mixtures composed of pharmaceuticals for which a similar mode or mechanism of action has been described in the test organisms can be predicted by concentration addition (CA) 9 (see also the detailed introduction to CA in Materials and Methods). Even though multicomponent mixtures of nonsimilar substances might be regarded as the more typical situation environmentally, systematic studies on the predictability of the joint effects of such mixtures have been conducted to a lesser extent. Studies involving pharmaceuticals 10, 11 or other toxicants (12, 13; http://ec.europa.eu/environment/chemicals/pdf/report_Mixture%20toxicity.pdf) suggest that the competing concept of independent action (IA; for details, see Materials and Methods) is rather powerful in predicting the toxicity of this type of mixture and that CA usually leads to a slight but distinct overestimation of mixture toxicity.
Previous studies have demonstrated the applicability of both CA and IA on the level of biological communities 14–19, indicating that the community level per se does not confound the predictability of mixture toxicities. However, so far, the studies are limited to either short-term analysis of physiological endpoints or mixtures of similarly acting substances. The applicability of the concepts has not yet been analyzed for a mixture of nonsimilarly acting substances on ecological time scales, allowing a succession of species to take place. For this purpose, the endpoint employed must be able to detect effects independent of the mode or mechanism of action; at the community level, only endpoints that reflect species composition or biomass are able to do so.
Periphyton are microbial biofilm communities that attach to surfaces in aquatic environments, comprising various taxonomic kingdoms and trophic levels, such as bacteria, fungi, algae, and microfauna, all embedded in an matrix of polysaccharides. They occur commonly in aquatic ecosystems and fulfill important roles in energy conversion and nutrient cycling 20, 21. Because of the short generation time of the organisms involved, toxicant-induced changes in biomass and species composition are comparatively easy to analyze in this community.
The aim of the present study was to investigate the joint ecotoxicity of a set of five dissimilar pharmaceuticals and personal care products to the photosynthetic component of natural marine periphyton. We were especially interested in the performance of the two classical mixture concepts, IA and CA, on a community level of biological complexity and under conditions of long-term exposure that cover ecological succession. Hence complete concentration–response curves of the mixture components were determined prior to the mixture experiments. Because pharmaceuticals and personal care products often occur only in low-effect concentrations in the aquatic environment, we also analyzed whether such concentrations might still contribute to the overall toxicity of the mixture.
Five human pharmaceuticals and personal care products were selected for the study (see Table 1 for structures, fields of application, and modes of action in the target organisms). All have previously been described in the literature as having considerable toxicity in standard single-species algal assays (EC50 values between 14 µmol/L and 1.7 nmol/L, with the bulk of data around 100–500 nmol/L). Triclosan is a broad-spectrum biocide with widespread use in household hygiene products, such as toothpastes, soaps, detergents, and disinfectants. Zinc-pyrithione, the second investigated personal care product, is also used as a broad-spectrum biocide, for example, in antidandruff shampoo and antifouling coatings. Propranolol is a nonselective blocker of β-adrenergic receptors employed in the treatment of high blood pressure in human medicine. Fluoxetine is the active ingredient of the antidepressant Prozac and acts by inhibiting the selective serotonin reuptake after its release in synapses. It should be mentioned here that, for propranolol as well as for fluoxetine, the precise molecular mechanism of action in algae is currently unknown, although both compounds are clearly more than only baseline toxicants in single-species algal assays; i.e., there are strong indications of a more specific mode of action 22, 23. The last compound of the mixture was clotrimazole, a topical antimycoticum that has found widespread application in human medicine. We have recently demonstrated that the sterol metabolism of natural marine microalgal communities is impacted by clotrimazole in the picomoles per liter range 24.
Table 1. Test compounds and their modes and mechanisms of action and their uses
All investigations were conducted with the so-called Swift periphyton test system 25, in which the effects of toxicants on natural periphyton communities are determined after an exposure of 96 h. For the tests, periphyton communities were sampled from the fjord by colonization on artificial substrata and then taken to the laboratory, where they were exposed to individual toxicants or mixtures under controlled conditions. Effects on periphyton community succession were determined through high-performance liquid chromatography (HPLC) analysis of photosynthetic pigments. We assessed effects on biomass increment (growth, determined as the total content of photosynthetic pigments in the community) and pigment composition (surrogate measure of community structure but also of physiological status).
A comparison of the experimentally observed effects of the mixture with the toxicity predictions by CA and IA indicated a good predictability in the upper effect range. However, the mixture provoked clear stimulating effects (hormesis) in the lower effect range, hampering the application of either concept. An independent repetition of the mixture experiment resulted in a principally similar concentration–response curve, again with clear hormesis effects in the lower range of test concentrations. But the curve was shifted toward higher effect concentrations, which likely is due to changes in the initial species composition of the natural algal communities. Clear mixture effects were observed even when all five components were present only at their individual NOECs. These results clearly show that, even with respect to mixtures of chemically and functionally dissimilar compounds, such as the five pharmaceuticals and personal care products investigated, environmental quality standards must take possible mixture effects from low-effect concentrations of individual compounds into consideration.
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- MATERIALS AND METHODS
All five compounds were toxic to the algae in the marine periphyton communities, and concentration–response curves for the inhibition of final biomass (pigment content) could be recorded for all of them (Fig. 1 and Table 2). In comparing the toxicity of triclosan (with an EC50 of 1.7 µmol/L) with the available data on its toxicity in single-species assays with algae and cyanobacteria (EC50s between 1.7 and 228 nmol/L; Table 3), it becomes evident that the compound shows only comparatively low toxicity to marine periphyton.
Figure 1. Individual and mixture toxicity of the five selected pharmaceuticals and personal care products to periphyton total pigment content. (A) Dashed line = fit to the experimental mixture data; solid line = prediction according to concentration addition; dashed-dotted line = prediction according to independent action. (B–F) Dashed line = fit to the experimental mixture data (the plot refers to the upper concentration axis); solid line = fit to the experimental single-substance data with the approximate 95% confidence interval (the plot refers to the lower concentration axis); solid symbols = treated samples; open symbols = untreated controls. For details on the models used and the parameter estimates see Table 2.
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Table 2. Concentration–response models and parameter estimates for the inhibition of total pigment content (TPC), respectively the content of specific pigments, and median effective concentration values (EC50) with approximate 95% confidence interval for the five pharmaceuticals and personal care products and their mixturea
|Clotrimazole||TPC||P||−6.412||2.425|| || ||441.2 (350.9–535.9)|
|Clotrimazole||Chlorophyll a||G||30.442||0.896||7.352|| ||91.10 (81.39–100.4)|
|Clotrimazole||Diadinoxanthin||G||217.49||0.978||−1.019|| ||198.7 (188.9–207.3)|
|Clotrimazole||Diatoxanthin||G||13.614||0.938||5.218|| ||46.23 (44.53–52.42)|
|Clotrimazole||Fucoxanthin||G||35.292||0.871||9.430|| ||124.3 (27.77–108.1)|
|Clotrimazole||Prasinoxanthin||G||41.895||0.955||4.794|| ||81.45 (73.18–83.20)|
|Clotrimazole||Zeaxanthin||G||1.659||0.966||10.970|| ||115.7 (112.8–127.1)|
|Clotrimazole||β-Carotene||G||20.659||0.885||8.663|| ||93.79 (7.615–83.78)|
|Triclosan||TPC||P||−18.301||5.969|| || ||1,166 (850.7–1,481)|
|Triclosan||Chlorophyll a||W||−53.704||16.329|| || ||1,846 (1,822–1,860)|
|Triclosan||Diadinoxanthin||W||−103.06||31.583|| || ||1,784 (1,729–2,015)|
|Triclosan||Diatoxanthin||W||−865.88||272.54|| || ||1,498 (1,348–1,663)|
|Triclosan||Fucoxanthin||W||−796.86||250.41|| || ||1,516 (1,330–1,661)|
|Triclosan||Prasinoxanthin||W||−124.83||37.789|| || ||1,966 (1,841–2,110)|
|Triclosan||Zeaxanthin||W||−34.591||10.849|| || ||1,428 (1,422–1,634)|
|Triclosan||β-Carotene||W||−56.233||17.0744|| || ||1,870 (1,864–1,970)|
|Zn-pyrithione||TPC||GL1||−8.276||6.997||0.294|| ||7.245 (6.547–7.876)|
|Zn-pyrithione||Chlorophyll a||W||−2.41||2.533|| || ||6.408 (5.540–7.228)|
|Zn-pyrithione||Diadinoxanthin||GL2||−53.147||56.383||0.083|| ||12.33 (10.50–13.06)|
|Zn-pyrithione||Diatoxanthin||W||−3.06||3.22|| || ||6.863 (6.214–7.550)|
|Zn-pyrithione||Fucoxanthin||GL1||−5.821||5.655||0.556|| ||7.398 (6.417–8.017)|
|Zn-pyrithione||Prasinoxanthin||MMF||4.39||4.843|| || ||8.062 (7.154–8.266)|
|Zn-pyrithione||Zeaxanthin||MMF||4.83||5.802|| || ||6.799 (6.639–7.126)|
|Zn-pyrithione||β-Carotene||P||−2.5||2.948|| || ||7.047 (6.560–7.118)|
|Fluoxetine||TPC||MMF||8.047||3.930|| || ||111.6 (93.30–130.9)|
|Fluoxetine||Chlorophyll a||P||−5.703||2.728||.|| ||123.0 (114.5–123.5)|
|Fluoxetine||Diadinoxanthin||P||−12.864||5.502||.|| ||217.7 (189.7–227.7)|
|Fluoxetine||Diatoxanthin||P||−5.409||3.069||.|| ||57.82 (54.87–61.52)|
|Fluoxetine||Fucoxanthin||P||−5.615||2.716||.|| ||116.7 (109.5–132.8)|
|Fluoxetine||Prasinoxanthin||MMF||13.470||6.289||.|| ||138.6 (124.4–139.4)|
|Fluoxetine||Zeaxanthin||GL2||−114.19||97.320||0.015|| ||43.43 (42.01–49.19)|
|Fluoxetine||β-Carotene||P||−6.063||2.765||.|| ||155.7 (140.5–158.2)|
|Propranolol||TPC||BCW||−18.008||5.714||−0.246|| ||323.8 (289.4–353.6)|
|Propranolol||Chlorophyll a||GL1||−17.716||6.524||0.505|| ||355.0 (327.2–357.8)|
|Propranolol||Diadinoxanthin||W||−5.413||2.035||.|| ||302.1 (271.0–346.9)|
|Propranolol||Diatoxanthin||GL2||−11.826||4.833||1.175|| ||252.3 (236.3–268.2)|
|Propranolol||Fucoxanthin||GL1||−20.916||7.447||0.288|| ||314.9 (285.8–344.8)|
|Propranolol||Prasinoxanthin||GL1||−15.080||6.260||4.864|| ||511.1 (471.7–563.6)|
|Propranolol||Zeaxanthin||PBC||−99.551||70.164||−0.692|| ||316.6 (295.7–326.0)|
|Propranolol||β-Carotene||GL1||−20.630||7.228||0.345|| ||394.9 (372.3–451.0)|
|Mixture (exp. 1)||TPC||BQ||−0.0168||5.79E-07||0.0386||5.86E-07||355.6 (330.0–429.2)|
|Mixture (exp. 1)||Chlorophyll a||BQ||−0.0154||4.41E-07||0.0285||4.46E-07||368.8 (322.4–372.1)|
|Mixture (exp. 1)||Diadinoxanthin||BQ||−0.0659||2.27E-06||0.248||2.24E-06||406.2 (397.3–414.9)|
|Mixture (exp. 1)||Diatoxanthin||BQ||−0.0129||6.989E-07||0.0250||7.34E-07||276.7 (249.4–276.9)|
|Mixture (exp. 1)||Fucoxanthin||BQ||−0.0172||6.295E-07||0.0369||6.37E-07||338.9 (308.8–339.6)|
|Mixture (exp. 1)||Prasinoxanthin||BQ||−0.156||3.59E-06||0.2327||3.56E-06||387.3 (360.5–394.9)|
|Mixture (exp. 1)||Zeaxanthin||GL2||−5.132||1.508||3.711|| ||225.2 (222.8–249.1)|
|Mixture (exp. 1)||β-Carotene||BQ||−0.0219||6.28E-07||0.0823||6.36E-07||451.0 (437.8–509.9)|
|Mixture (exp. 2)||TPC||BQ||−3.52E-04||1.67E-09||1.27E-03||1.63E-09||1,071 (943.0–1,226)|
Table 3. Toxicity data from previously published studies on the five selected pharmaceuticals and personal care products to algal and cyanobacterial growth
|Species||Exposure duration/endpoint||Concentration (nmol/L)||Reference|
| Pseudokirchneriella subcapitata (limnic green alga)||96 hr/EC50||15.4||43|
| Scenedesmus subspicatus (limnic green alga)||72 hr/NOEC||1.7||43|
| Scenedesmus subspicatus||72 hr/NOEC||1.7||43|
| Skeletonema costatum (marine diatom)||96 hr/EC50||228||43|
| Anabaena flos-aquae (limnic cyanobacterium)||96 hr/EC50||3.4||43|
| Navicula pelliculosa (limnic diatom)||96 hr/EC50||66||43|
| Chaetoceros gracilis (marine diatom)||72 hr/EC50||10||44|
| Emiliania huxleyi (marine Coccolithophore)||72 hr/EC50||1.7||45|
| Synechococcus sp. (limnic cyanobacterium)||72 hr/EC50||>3||45|
| Pseudokirchneriella subcapitata||96 hr/EC50||145||46|
| Scenedesmus acutus (limnic green algae)||96 hr/EC50||295||46|
| Scenedesmus quadricauda (limnic green algae)||96 hr/EC50||689||46|
| Chlorella vulgaris (limnic green algae)||96 hr/EC50||14,028||46|
| Pseudokirchneriella subcapitata||u/EC50||78||47|
| Pseudokirchneriella subcapitata||u/EC50||126||48|
| Pseudokirchneriella subcapitata||48 hr/EC50||97||49|
| Desmodesmus subspicatusa (limnic green alga)||2 hr/EC50||2,455||50|
| Desmodesmus subspicatus||u/EC50||2,720||51|
The main reason for this pattern might be the dominance of diatoms in the marine microalgal communities 24, 25, because this group seems to be least sensitive to triclosan (Table 3). Furthermore, with a log KOW of approximately 4.8, triclosan is also rather lipophilic and should therefore readily sorb to colloidal organic matter in the natural seawater that forms the basis of the test medium in Swift. This would then lead to a lower bioavailability of the compound compared with the defined growth media of the bioassays that are listed in Table 3.
For fluoxetine and Zn-pyrithione, no systematic differences were detected between the documented single-species toxicities and the toxicity in marine periphyton, whereas, for propranolol, the microalgal community was clearly more sensitive (Tables 2 and 3). Clotrimazole has been discussed as a priority marine pollutant (32; http://www.ospar.org/html_documents/ospar/html/04-12e_list_of_chemicals_for_priority_action.doc). However, it should be noted that, to our knowledge, no toxicity data for marine algal species can be found in the open scientific literature. A thorough discussion on the toxicity of clotrimazole in the Swift periphyton test can be found in Porsbring et al. 24.
Depending on their modes of action, different compounds have different impacts on the succession within biofilm communities, which leads to distinctly different species compositions at the end of the experiment and hence to different pigment patterns 25. This can also be seen in the pigment pattern changes caused by the pharmaceuticals and personal care products investigated here (Fig. 2). Already at their individual NOEC concentrations the different compounds lead to different (although, by virtue of the NOEC concentration, not statistically significantly different) pigment patterns. That is, the compounds lead to visibly different positions in the MDS plot.
Figure 2. Multidimensional scaling plot of the five tested pharmaceuticals and personal care products. Each compound was tested at its individual no-observed-effect concentration. Still, the exposed communities clearly position differently from the untreated controls and from each other. The latter is a consequence of the dissimilar impacts that the compounds have on the pigment pattern of the biofilm communities, which in turn indicates different modes of action and hence dissimilar impacts on the ecological succession.
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A concentration–response curve was recorded for a mixture of all five compounds (Fig. 1A and Table 2). For this purpose, a so-called fixed-ratio design was employed, and the overall concentration of the mixture was systematically varied between 10,000 nmol/L and 1 nmol/L (see Materials and Methods). With an EC50 of 356 nmol/L, the toxicity of the mixture falls into the span between the highest (Zn-pyrithione, 7.2 nmol/L) and lowest (triclosan, 1,166 nmol/L) individual toxicities, indicating the absence of strong synergistic or antagonistic effects. In fact, the toxicity of the mixture in the upper part of the curve is rather accurately predicted by the concept of IA (Fig. 1). The IA-predicted EC50 was 314 nmol/L, which is a mere factor of 1.13 to the observed EC50. Concentration addition predicted a slightly higher toxicity, but the estimated EC50 of 214 nmol/L is still only a factor of 1.7 away from the observed EC50. Clear hormesis effects were observed for the mixture in the low concentration range from approximately 200 nmol/L downward; i.e., in this region, the pigment content of the communities exposed to the mixture was higher than in the untreated controls. At a mixture concentration <3 nmol/L, the pigment content of the treated communities was again equal to the control level.
To analyze the interrelation between the single-substance and the mixture toxicities, both concentration–response curves are given in one plot with two concentration axes (Fig. 1B–F). The upper x axis depicts the total mixture concentration, the lower x axis the concentration of the indicated compound. The axes are shifted in relation to each other proportional to the amount of the indicated compound in the mixture. Thus, on any vertical, the observed mixture effect and mixture effect concentration can be read off by referring to the dashed concentration–response curve of the mixture and the upper x axis. The amount of the indicated compound present in the mixture at that concentration is then given by the lower x axis, and the effect that the compound provoked at that concentration in the single substance analysis is given by the solid line, which is the fit to the single-substance data.
In the concentration range at which hormetic effects of the mixture were observed, no indications of any such pattern were observed in the single substance analyses of triclosan, Zn-pyrithione, or clotrimazole (Fig. 1B,C,F). Unfortunately, because of the experimental design that focused on providing sufficient data for modeling the standard part of the concentration–response curves and the limited experimental capacity, no data are available for fluoxetine and propranolol in those low-concentration regions (Fig. 1D,E).
Figure 1 also allows a visual estimation of the effect contribution of the individual compounds to the mixture toxicity. The components were mixed in relation to their NOECs, which were recorded in previous experiments. However, this does not imply that the components contribute equally to the observed mixture effects over the entire concentration range. It can be clearly seen that propranolol, fluoxetine, and Zn-pyrithione contribute with substantial effects over the whole effect region, whereas triclosan and clotrimazole contribute only in the high-effect region. The 50% effect observed at a total mixture concentration of 356 nmol/L is a result of 38.4% propranolol single-substance effect, 20.5% fluoxetine effect, and 20.0% Zn-pyrithion effect, but less than 1% effect is contributed by clotrimazole and triclosan. Independent action predicted 60.4% mixture effect and CA 72% mixture effect, i.e., rather close to the observed 50% mixture effect.
The mixture experiment was repeated two weeks after the first experiment (Fig. 3). Although the resulting concentration–response curve has the same principal shape (especially the hormesis effects were observed again), the curve as a whole shifted toward higher concentrations, and the EC50 increased from 356 to 2,260 nmol/L. This shift in sensitivity was correlated with a distinct shift in the pigment composition of the communities taken from the field and used for experimentation. The ratio of chlorophyll a to fucoxanthin was 0.7 in the first experiment but only 0.3 in the second experiment. This indicates major changes in the species composition of the test community. Fucoxanthin is a typical accessory pigment for Prymnesiophytes (Haptophyceae), Chrysophyceae (brown-golden algae), and Raphidophytes and Bacillariophyceae (diatoms) 33. The reduced chlorophyll a to fucoxanthin ratio thus indicates an increased dominance of those species in the starting communities used in the second experiment.
Figure 3. Comparison of the mixture toxicities of total pigment content in the exposed periphyton communities observed in three independent studies. Solid symbols = treated samples; open symbols = untreated controls. Each type of symbol denotes the data from one independent experiment. Dashed line = fit to the experimental data (for fit parameters and model description refer to the text and Table 2); dashed-dotted line = prediction according to independent action; solid line = prediction according to concentration addition.
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Mixture effects from low-effect concentrations
Such changes in the species composition of the source communities for the tests confound any analysis of the predictive power of IA and CA. In a separate experiment aimed specifically at analyzing the mixture effects from low, individually nonsignificantly toxic concentrations, mixture and single-substance experiments were therefore conducted in parallel. To allow statistical significance tests, each treatment was tested in triplicate (single compounds) or in nine replicates (mixture). This limited us to test only one concentration of each component and the resulting mixture concentration. This experimental setup does not allow calculating the expected mixture toxicity according to CA. However, it allows predicting the joint effect of the mixture according to IA, using the mean of the observed single-substance effects of the individual components as input values.
The results of this experiment are presented in Figure 4. At the individual NOEC concentrations, between 4.2 and 9.5% relative effect were observed. Although none of these effects was statistically significant (t test, α = 0.05), a highly significant effect of 28% (t test, α < 0.01) inhibition was observed for the mixture. Furthermore, IA accurately predicted the observed mixture effects. When the calculation was based on the single-substance effects that were recorded in parallel, IA predicted a mixture effect of 30%. If the calculation instead made use of the previously recorded single-substance concentration–response curves (Table 2), the IA-predicted mixture effect was 23%.
Figure 4. Mixture toxicity on the total pigment content of the exposed periphyton communities from low-effect concentrations of the individual substances. Left side: Single-substance effects at the no-observed-effect concentration (NOEC), determined as the arithmetic mean of three samples; error bars denote the standard deviation. Right side: Open bar = observed mixture effect at a total concentration equaling the sum of all five individual NOECs (28.3%); hatched bars (predicted mixture effects) = independent action (IA; parallel data): IA-predicted mixture effect (30%), as calculated on the basis of the single substance data that were recorded in the same experiment as the mixture. IA (previous data) and concentration addition (CA; previous data): Independent action, respectively; CA-predicted mixture effects (20.3% vs. 30%) as calculated on the basis of the single-substance concentration–response curves from previous single-substance experiments (Table 2).
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Effects on individual pigments
In Figure 5, the concentration–response curves for the mixture are given together with the IA and CA predictions for the effects on each of seven individual pigments. As can be seen, the concentration–response curves of chlorophyll a and fucoxanthin are highly correlated; their shape and location are nearly identical and are also very similar to the concentration–response curve when using the total pigment content as the endpoint. This is a direct result of chlorophyll a being an essential photosynthetic pigment in all algal classes and fucoxanthin being the major accessory pigments in diatoms, which dominate the analyzed periphyton communities.
Figure 5. Effect of the five-component mixture on the content of seven selected algal pigments from the exposed periphyton communities. Solid symbols = treated samples; dashed line = fit to the experimental data (for fit parameters and model description refer to the text and Table 2); dash-dotted line = prediction according to independent action; solid line = prediction according to concentration addition.
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The hormesis effect is present for six of the seven pigments, although to different extents. Although the communities exposed to approximately 10 to 100 nmol/L of the mixture contain 60% more prasinoxanthin than the untreated control communities, the content of diadinoxanthin is elevated by only approximately 20%. However, the hormesis occurs in a similar concentration range for all analyzed pigments.
In comparison with all other pigments, zeaxanthin shows a distinctly different concentration–response relationship. Not only is the curve clearly flatter, the hormesis effect observed for all other pigments is also completely absent in the tested concentration range. Zeaxanthin is a marker pigment for Cyanophyta and Prochlorophyta 33. These organisms are prokaryotic primary producers, and hence their fundamentally different response to the pharmaceutical mixture can be considered a reflection of their biological dissimilarity.
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A good predictability by CA of the joint toxicity of mixtures that are composed of similarly acting chemicals is a common finding 8, 9, 13, 14, 17–19, independent of the specific bioassay and endpoint, selected set of test chemicals, and exposure duration. Deviations from the predictions usually occur only in binary mixtures, and even then the deviations are usually smaller than a factor of 2 to 3 on an EC50 level. For multicomponent mixtures, even those small deviations seem to be mostly absent.
In comparison with this body of evidence, our knowledge on the behavior of mixtures that are composed of nonsimilarly acting substances is scarce, especially for pharmaceuticals (see discussion by Backhaus et al. 9). As a first step toward analyzing the behavior of mixtures of dissimilarly acting substances on a community level of biological complexity, we previously published a study on a six-component mixture in a short-term community-metabolism assay 15. The study presented in here goes one step farther by analyzing a mixture of dissimilarly acting substances in a long-term, multigeneration assay using biomass and pigment patterns as more ecologically relevant endpoints.
The findings are largely in agreement with previous results on the toxicity of mixtures of dissimilarly acting substances. Again, IA provided a good prediction of the observed mixture effects, for example, on the EC50 level, and CA at the same time led to a minute overestimation of the mixture toxicity. Furthermore, we could show that even concentrations of the components at or below their individual NOECs provoke severe mixture effects that could be predicted by IA.
Independent action implies that only those components present at concentrations whose effects are greater than zero if they had been applied singly can contribute to the overall toxicity of a mixture (see Eqn. 10). Hence, at first glance, the result that even NOEC concentrations add up to a mixture that has a substantial overall effect seems somewhat contradictory to the good predictability of the mixture toxicity by IA. However, NOECs are not true no-effect levels. In fact, NOECs are the result of a statistical comparison of the data from treated samples with those from untreated controls. The highest tested concentration at which this comparison does not result in a statistically significant difference is denoted the NOEC. Such a failure to detect a statistically significant effect does not prove that there is no effect in reality. Hence the observed mixture effect together with the good predictive power of IA is simply an indication that there are actual effects of the individual components, despite their statistical nonsignificance.
Furthermore, although dissimilar modes and mechanisms of action (Table 1) have been described for the five investigated pharmaceuticals, it should be noted that those are valid in a strict sense only for the investigated target organisms. For example, it is currently unclear why fluoxetine and propranolol are toxic to algae, even though the corresponding molecular targets are not present in those organisms.
These findings strongly imply that, even for mixtures of chemically heterogeneous, nonsimilarly acting compounds such as the investigated mixture of pharmaceuticals and personal care products, individual environmental quality targets are not sufficient. Even if all individual compounds are present below their individual NOECs, this does not safeguard against unwanted mixture effects. NOECs never describe an environmentally safe concentration, which is particularly evident from a mixture perspective. Whether certain fractions of individual NOECs, such as PNECs, which are based on NOECs divided by an assessment factor, are environmentally acceptable from a mixture perspective depends on the specific exposure situation, in particular on the number of involved components, their potency, and the environmental concentrations.
Fluoxetine and propranolol are known to interact with animal-specific pathways (serotonin reuptake at nerve synapses, blocking of β-adrenergic receptors), which recently have also been found in marine calanoid copepods 34, 35. Small copepods and other primary consumers are always present in the periphyton communities, and hence affecting them would relieve the algae from grazing pressure. As a consequence, this could lead to a higher algal biomass and an increased pigment content (hormesis) in the samples. Such hormesis effects raise new questions on the joint action of chemicals at an ecological level of biological complexity. From a scientific (ecological) point of view, a next step could be further investigating the underlying causality to unravel the connection between the effects of the individual compounds and the effects of the corresponding mixture. The hypothesis that fluoxetine and propranolol cause a top-down effect in the periphyton community by reducing grazing activity, which in turn leads to the observed microalgal biomass increase, provides a starting point for further experimentation.
From a chemical risk assessment perspective, the consideration of hormesis effects complicates the assessment, not least because two different NOECs now can be determined from the concentration–response relationship. Also, if hormesis is to be described experimentally, the demands for any concentration response study increase considerably. This demand is even increased in mixture studies if a strong correlation to the underlying single substance curves is to be drawn. Substantial amounts of experimental data are required for adequately covering the broadened concentration range for each and every mixture component. For example, in the present study, a five-component mixture was analyzed, and we were not able to fulfill this criterion completely, although approximately 150 treatments in total were analyzed. This effort is far too great for any routine assessment and is achievable only in very few bioassays that aim to analyze the effects of chemicals on an ecological level.
It should finally be pointed out that hormetic effects of a chemical or chemical mixture certainly cannot be considered the opposite of harmful, adverse ecological effects. Similarly to an inhibition of growth, they indicate a disturbance of the network and interactions within an ecological community. An assessment of their implications for risk assessment, however, is not possible without further detailed knowledge on the underlying causes. Such an understanding would in particular allow us to predict how widespread the phenomenon is and which conditions will provoke it.
The use of natural communities increases the ecological relevance of the observed responses. However, the use of natural starting materials for the tests (natural communities and natural waters that are necessary for their incubation in the laboratory) implies a principally lower reproducibility of the experimental data, because physiological status and species composition change between experiments. In addition, changes in water chemistry can lead to changes in the absorptive potential of the growth medium used, leading to corresponding changes in the bioavailability of the test chemicals. In our studies, a shift of the mixture EC50 from 356 to 2,560 nmol/L was observed. The mixture concentration–response experiment was only repeated once, so it is unclear whether such a shift might be considered average or extreme.
The preferred approach for providing a proof of principle of whether the toxicity of a given mixture is predictable in a certain setting is to record the effects of the individual mixture components and the mixture in one experiment. However, this is not possible when handling mixtures with too many components. In addition, for chemical risk assessment, which is often conducted on the basis of literature data, the question of how predictable mixture toxicities are when using existing data might actually be the more important question.