Single and combined toxicity of pharmaceuticals and personal care products (PPCPs) on the rainbow trout liver cell line RTL-W1

Aquatic Toxicology 93 (2009) 244–252

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Single and combined toxicity of pharmaceuticals and personal care products (PPCPs) on the rainbow trout liver cell line RTL-W1 Sabine Schnell a , Niels C. Bols b , Carlos Barata a,∗ , Cinta Porte a,∗ a b

Environmental Chemistry Department, IIQAB-CSIC, C/Jordi Girona 18, 08034 Barcelona, Spain Department of Biology, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

a r t i c l e

i n f o

Article history: Received 11 February 2009 Received in revised form 29 April 2009 Accepted 10 May 2009 Keywords: Pharmaceuticals Synthetic musks Cytotoxicity RTL-W1 fish cell line Combined toxicity

a b s t r a c t The toxicological implications of the presence of pharmaceuticals and personal care products (PPCPs) in the aquatic environment remain largely unknown. Acute toxicity tests have generally failed to detect the subtle action elicited by those compounds at environmentally relevant concentrations and they have often overlooked the fact that toxicity can be influenced by additive and synergistic effects. The aim of this study was to further assess the cytotoxicity of different pharmaceuticals and synthetic musks as well as their mixtures on the rainbow trout liver cell line RTL-W1. Eleven pharmaceuticals from different therapeutic classes (anti-inflammatory drugs, serotonin re-uptake inhibitors and lipid regulators) and five synthetic musks from the two major groups (nitro- and polycyclic musks) were selected for the study. Two fluorescent dyes were used to monitor cell viability. Among the tested compounds, estimated EC50s (effective concentration causing 50% decline of cell viability) denoted that polycyclic musks (7–25 ␮M) followed by anti-depressives (7–50 ␮M) showed the highest potential to induce cytotoxicity, whereas lipid regulators (20–380 ␮M), anti-inflammatory drugs (160–260 ␮M) and nitromusks (100–240 ␮M) had the lowest toxicity. Within a given therapeutic class, combined toxicity of mixtures was additive, following in most cases the concentration addition concept. However, the combined toxicity was higher than additive for those mixtures that included one compound from each class (i.e. dissimilar mixtures). Overall, this study shows that in the aquatic environment, toxicity of PPCPs on non-target organisms may occur at concentrations lower than expected due to synergistic effects between the different toxicants. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Pharmaceuticals and personal care products (PPCPs) are an extraordinarily diverse group of chemicals used in veterinary medicine, agricultural practice, human health and cosmetic care. Large quantities of pharmaceuticals like beta-blockers, antiinflammatory drugs, contraceptives, antibiotics, lipid regulators, neuroactive compounds and many others are sold and consumed worldwide for treatment or diagnosis of diseases. Ibuprofen and other frequently used drugs (e.g. acetylsalicylic acid, paracetamol or the oral antidiabetic meformin) are consumed in the range of hundreds of tons per year in countries such as England, Germany or Australia (Fent et al., 2006). As a consequence, a variety of pharmaceutical compound residues have been detected in wastewater and surface water in the range of ng/l to ␮g/l (Halling-Sørensen et al., 1998; Daughton and Ternes, 1999; Kolpin et al., 2002).

∗ Corresponding authors. Tel.: +34 934006100; fax: +34 932045904. E-mail addresses: [email protected] (C. Barata), [email protected] (C. Porte). 0166-445X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2009.05.007

In addition to pharmaceuticals, large amounts of personal care products, such as fragrances, skin and hair products are produced and sold worldwide. Among them, one prominent group are synthetic musks. They are divided into two major categories, nitro- and polycyclic musks, and they are used as fragrance additives in detergents, cleaning agents, soaps, shampoos and deodorants. During the last two decades, synthetic musks have been detected in aquatic biota (fish and mussels), in marine, surface and sewage water as well as in human adipose tissue, breast milk and blood (Yamagishi et al., 1983; Gatermann et al., 1995, 2002; Rimkus and Wolf, 1995, 1996; Angerer and Käfferlein, 1997; Rimkus et al., 1999). However, as for many synthetic chemicals in everyday use, the toxicological implications of the presence of PPCPs in the aquatic environment remain largely unknown (Jones et al., 2001). Despite PPCPs having relatively short environmental half-lives, they may assume the quality of persistent pollutants as they are continuously introduced into the environment via sewage treatment facilities and water runoff (Daughton and Ternes, 1999). The possibility for continual but undetectable or unnoticed effects on aquatic organisms is particularly worrisome because effects could accumulate so slowly that major change goes undetected until cumulative effects finally lead to irreversible change. In fact, acute toxicity tests have

S. Schnell et al. / Aquatic Toxicology 93 (2009) 244–252

generally failed to detect the subtle action elicited by those compounds at environmental concentrations (Daughton and Ternes, 1999). However, some studies have shown that toxicity can be influenced by additive and synergistic effects. Thus, Pomati et al. (2006) indicated that a mixture of 13 drugs, merged to mimic both the association and low concentration (ng/l) profiles detected in the environment, inhibited cell proliferation of human embryonic cells and affected their physiology and morphology; the study suggested that water-borne pharmaceuticals can be potential effectors on aquatic life. Despite the mode of action of most PPCPs (mainly pharmaceuticals) being well known in humans and mammals, their toxicological mechanisms of action on non-target organisms is rather unknown. Current data on PPCPs residues in aquatic systems indicate that they are unlikely to pose a risk in terms of acute toxicity, but data regarding chronic toxicity in aquatic organisms, in particular in fish, is lacking (Fent et al., 2006), and only few studies have provided data on mixture toxicity and mechanisms of action of PPCPs in aquatic organisms (Cleuvers, 2003, 2004). In vitro assays using fish cells have the advantage of minimising animal use, allowing the testing of a wide range of chemicals and concentrations (Eisenbrand et al., 2002). Caminada et al. (2006) evaluated the in vitro cytotoxicity of 34 pharmaceuticals from different classes in two fish cell lines, PLHC-1 and RTG-2; cytotoxicity was found for 21 pharmaceuticals with EC50 values ranging from 2.1 ␮M (1.14 mg/l) (doxorubicin) to 8.66 mM (1200 mg/l) (salicylic acid); the authors concluded that fish cell lines could be suited for the first screening of acute toxicity of pharmaceuticals. Together with the high reproducibility of the obtained results, these in vitro assays also provide a quick and cost-effective option that has high positive correlations with in vivo results (Laville et al., 2004; Caminada et al., 2006). Comparatively, only few studies are available regarding the toxicological impact of synthetic musks and their metabolites on the aquatic environment. Luckenbach and Epel (2005) showed that synthetic musks have the potential to inhibit the activity of multidrug efflux transporters responsible for multixenobiotic resistance (MXR) in gills of the marine mussel Mytilus californianus. The amino-metabolites of musk ketone and musk xylene have been reported to bind to the estrogen receptor of both rainbow trout (Oncorhynchus mykiss) and South African frog (Xenopus laevis) (Chou and Dietrich, 1999), and musk ketone exposure lead to reproductive toxicity in early life stages of the zebrafish (Danio rerio) (Carlsson et al., 2000). Within this context, the aim of this study was to comparatively assess the cytotoxicity and mode of action of widely used pharmaceuticals and synthetic musks on the rainbow trout liver cell line RTL-W1. Special emphasis was placed on the assessment of combined toxicity, as these compounds are mainly present as mixtures in the aquatic environment. Eleven pharmaceuticals from different therapeutic classes, anti-inflammatory drugs (ibuprofen, naproxen, ketoprofen, diclofenac), serotonin re-uptake inhibitors (fluoxetine, paroxetine, fluvoxamine), lipid regulators (fenofibrate, clofibrate, bezafibrate, gemfibrozil) as well as five synthetic musks from the two major groups, nitromusks (musk ketone –MK–, musk xylene –MX–) and polycyclic musks (galoxolide, tonalide, celestolide) were selected for the study. Cytotoxicity was assessed by applying the Alamar Blue (AB) assay for changes in energy metabolism and 5 -carboxyfluorescein diacetate acetoxymethylester (CFDAAM) assay for evaluating membrane integrity (Schirmer et al., 1997). Combined cytotoxicity was firstly investigated by mixing pharmaceuticals from the same therapeutic class and musks from the same chemical class (five mixtures of ‘similar’ compounds). Thereafter, the toxicity of mixtures composed by selected compounds from each chemical class was assessed (two mixtures of ‘dissimilar’ compounds). Combined toxicity was modelled by using the concentration addition (CA) and independent action (IA) concepts.

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These models allow the calculation of the toxicity of a mixture on the basis of known toxicities of the mixture’s individual components (Boedeker et al., 1992). Briefly, the CA model is based on the assumption that each component of the mixture possesses a similar pharmacological mode of action, and thus is most applicable for toxic substances that have the same molecular target site (Pöch, 1993). Alternatively, the IA model assumes that mixture components possess dissimilar modes of action and interact with different target sites, leading to a common toxicological endpoint via distinct mechanisms or reactions within an organism. Under these assumptions, the effects of individual constituents are expected to be independent in a strictly probabilistic sense (Altenburger et al., 2000; Backhaus et al., 2000; Barata et al., 2007).

2. Material and methods 2.1. Chemicals Pharmaceuticals and synthetic musks were purchased from Sigma–Aldrich (Steinheim, Germany) except for paroxetine, celestolide and tonalide which were obtained from Tocris (Bristol, UK), LGC Promochem Gmbh (Wesel, Germany) and Promochem Iberia (Barcelona, Spain), respectively. Stocks and serially diluted test solutions were prepared in dimethyl sulfoxide (DMSO, Sigma–Aldrich, Steinheim, Germany).

2.2. Fish cell line and exposure design RTL-W1, derived from rainbow trout liver (O. mykiss) (Lee et al., 1993), was routinely cultured in 75 cm2 culture flasks at 21 ◦ C in Leibovitz’s L-15 culture medium (Sigma–Aldrich, Steinheim, Germany) supplemented with 10% fetal bovine serum (FBS, Sigma–Aldrich, Steinheim, Germany) and 1% penicillin–streptomycin solution (10000 units/ml penicillin, 10 mg/ml streptomycin, Sigma–Aldrich, Steinheim, Germany). For exposure to pharmaceuticals and musks, confluent flasks were used to seed 96-well tissue culture plates (Nunc; Roskilde, Denmark) at a cell density of 40000 cells in each well in 200 ␮l of L-15 medium with 10% FBS. Cells were allowed to attach for 24 h prior to exposure. Two independent sets of experiments were performed. For single substances, 5–6 different concentrations were used to obtain accurate concentration dose–response curves that were used to define the studied combinations and to predict mixture toxicity response. In a second set of experiments, mixture toxicity analyses were carried out for each group of pharmaceuticals of the same therapeutic class or synthetic musks, grouped as nitro- and polycyclic musks (2–5-compound mixtures), followed by 5-compound mixtures of dissimilar chemicals that included the most (diclofenac, bezafibrate, fluvoxamine, musk ketone and galaxolide) and the less (ibuprofen, clofibrate, fluoxetine, musk xylene and celestolide) toxic compounds of each group. The toxicity of the mixtures was determined using a fixed ratio design. While keeping the mixture ratio constant, the total concentration was varied so that a complete concentration–response relationship of the mixture could be described experimentally. The concentration of the single compounds was based on their respective EC50s; five different mixture concentrations were assayed  corresponding to 0.1, 0.5, 1, 2 and 5toxic units (TU), where TU = ci /EC50i ; ci : concentration of the toxic substance (i); EC50i : effective concentration of substance (i) causing 50% decline of cell viability. This design allowed the study of equitoxic mixtures and permitted confrontation of observed and predicted responses following the CA and IA conceptual models (Altenburger et al., 2000; Barata et al., 2006).

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In most mixture toxicity studies, predicted mixture effects are based on previously determined concentration–response curves for the individual mixture components, but differences in sensitivity of the test organism are often ignored (Cedergreen et al., 2007). To account for this potential variation, exposures to single and PPCP mixtures were repeated three to four times in separated experiments. Within each experiment additions of the test substance or mixture were done in sextuplicates and the cells were incubated for 24 h at room temperature (19–21 ◦ C). Application of tested compounds to cell cultures was done directly; a small volume (1 ␮l) of stock solution was added directly to the culture wells. The final concentration of DMSO in culture wells was 0.5% (v/v). For each assay a control was performed by adding only the solvent (DMSO) to the cells. 2.3. Cell viability Two fluorescent dyes were used to monitor cell viability (Schirmer et al., 1997). Metabolic activity was monitored with Alamar Blue (resazurin) (BioSource International, Camarillo, CA) that was taken directly from the stock container. Cell membrane integrity was evaluated with 5 -carboxyfluorescein diacetate (CFDA-AM) (Molecular Probes, Eugene, OR, USA). CFDA-AM was diluted in DMSO to a final concentration of 4 ␮M. Both solutions were stored in the dark until needed. Cell viability in fish cell lines was measured after the medium was replaced by 100 ␮l of working solution of Alamar Blue/CFDAAM and an incubation period of 1 h using a fluorescence plate reader (Varioskan, Thermo Electron Corporation). The excitation and emission wavelengths used were 530 and 590 nm for AB, and 485 and 530 nm for CFDA-AM. Results were recorded as relative fluorescent units (RFUs). 2.4. Analysis of single and combined toxicity of mixtures and statistical analysis Predicted values for the studied individual components were estimated from concentration–response curves obtained, considering proportional RFU responses relative to control treatments (R) and by fitting observed responses to the non-linear allosteric decay regression model depicted in Eq. (1): R(ci ) =

1 1 + (cik /EC50k )

(1)

where R(ci ) is the proportional biological response at concentration ci relative to controls, ci the concentration of the toxic substance (i), EC50 the half saturation constant (i.e. the concentration that caused an inhibition of 50% in the biological process) and k the decay index. The allosteric decay model was selected to fit the data obtained since it can describe non-linear type responses (Allen et al., 1995; Barata et al., 2000, 2006). In Eq. (1), the EC50 and its 95% confidence intervals are regression parameters and hence can be calculated by the least square method (Barata et al., 2000). Model accuracy was assessed by using the adjusted coefficient of determination (r2 ) and by analysing residual distribution (Zar, 1996). Significance of the entire regression and regression coefficients were determined by analysis of variance (ANOVA) and Student’s t-tests, respectively (Zar, 1996). Secondly, predicted values for mixture combinations considering the CA and IA model were determined following a previous established procedure (Eqs. (2) and (3); Barata et al., 2006). Following the CA model and considering a mixture of n chemicals, where each chemical contributes to the overall toxicity proportionally to its EC50i (expressed as TUi ), expected responses of Eq. (1) can be

calculated as: Rmix =

z =

1

1+ n

n

i=1

TUi

k 

  n  TU z where k =  ki i , and i=1

TUi

(2)

i=1

calculating k as the geometric mean of the ki obtained for each chemical of the mixture weighted by its relative toxicity (TUi ). This approach assumes that the response in a mixture of n chemicals is proportional to the addition of the equi-effective individual concentrations of its constituents. Thus by solving Eq. (2) it is possible to obtain expected mixture responses based on the CA model considering non-linear allosteric decay biological responses of the n mixture constituents. Alternatively, expected responses (Rmix ) of an n-compound mixture by the IA model can be obtained directly by Eq. (3), sensu Bliss (1939) and many others (Altenburger et al., 2000; Faust et al., 2003; Barata et al., 2006). Rmix =

n 

R(ci )

(3)

i=1

where ci is again the concentration of the ith component and R(ci ) is the proportional response relative to control treatments of that concentration if the compound is applied alone. Using Eqs. (2) and (3), then it is possible to plot predicted combined toxicity responses according to the CA and IA concepts, respectively, and hence to compare them with the observed responses. The adequacy of CA and IA models to predict combined toxicity of the studied mixtures was statistically tested by using t-tests. A good agreement between observed and predicted values was reflected by regression equations of intercept 0 and slope 1. Additionally, by testing the equality of slopes and elevations of the mixture curves obtained using the CA and IA concepts, it should Table 1 Summary of estimated EC50 (␮M) values for Alamar Blue and CFDA-AM assays in RTL-W1 cell line exposed to different pharmaceuticals and synthetic musks. Results are mean ± SD (n = 3). Compound

log Kow

Alamar Blue

Anti-inflammatory drugs Ibuprofen Diclofenac Naproxen Ketoprofen

CFDA-AM

3.72a 4.06a 3.00a 3.00b

255.8 257.4 218.9 201.8

± ± ± ±

17.5 4.2 17.1 14.0

207.4 266.8 231.3 157.7

± ± ± ±

27.3 39.8 51.7 18.8

Fibrates Clofibrate Bezafibrate Gemfibrozil Fenofibrate

3.62b 3.46a 4.39a 4.80a

21.8 275.7 102.7 28.2

± ± ± ±

1.1 11.7 4.7 0.7

24.4 266.0 90.8 21.1

± ± ± ±

5.1 53.2 6.2 1.3

Anti-depressive drugs Fluoxetine Paroxetine Fluvoxamine

4.65b 3.95c 3.90d

9.9 ± 0.7 8.4 ± 0.8 45.4 ± 2.0

7.1 ± 3.4 10.9 ± 6.7 49.9 ± 6.3

Nitromusks Musk ketone Musk xylene

4.30e 4.90e

263.7 ± 22.2 137.6 ± 9.7

210.1 ± 37.3 209.3 ± 7.1

Polycyclic musks Galaxolide Tonalide Celestolide

5.90f 6.35b 6.60g

25.6 ± 2.0 7.2 ± 0.3 7.7 ± 0.9

18.4 ± 0.03 7.8 ± 0.8 6.8 ± 1.5

log Kow obtained from: a SciFinder, b www.syrres.com, c MacLeod et al. (2007), d www.terrabase-inc.com, e Tas et al. (1997), f Balk and Ford (1999), g Paasivirta et al. (2002).

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be possible to compare both model predictions. Regression curves were obtained and analysed with the statistical analysis software (SPSS 11.2 Statistical Package 2001, Wacker Drive, Chicago, IL, USA) using the least square method. Wherever the inspection of residuals evidenced variance heterocedasticity, the estimates were corrected using the weighted-least square method, where the weight was proportional to the reciprocal of the variance. To estimate the mode of action of the studied PPCPs, EC50s were related with the reported Kow following the proposed activity structure regression models (QSAR) for class I or non-polar narcotic chemicals of Van Leeuwen and Hermens (1995) (Eq. (4)) log EC50 (mol/l) = a × log Kow + b; chemicals acting by non-polar narcosis or having a baseline toxicity should have a toxicity proportional to their Kow with slopes between −0.85 and −1. 3. Results Cytotoxicity was observed for all the compounds tested after 24 h incubation with the fish liver cell line RTL-W1. Dose–response curves of single exposures and effective concentrations causing a 50% decline in cell viability (EC50s) for CFDA-AM and Alamar Blue assays are summarized in Table 1 and Fig. 1. For clarity and due to the similar response curves obtained for CFDA-AM and AB, only AB response curves are depicted in Fig. 1. Dose–response (AB) curves of all the 16 PPCP compounds assayed were accurately predicted by the allosteric model of Eq. (1) (Fig. 1). All regression equa-

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tions were statistically significant (P < 0.05), with residuals evenly distributed and r2 ranging from 0.81 for celestolide to 0.97 for galaxolide. The polycyclic musks, celestolide and tonalide (6.8–7.8 ␮M) together with the anti-depressive drugs fluoxetine and paroxetine (7.1–10.9 ␮M) were the most toxic compounds, judged by the EC50 values (Table 1). On the contrary, the lipid regulator bezafibrate revealed the lowest potential for cytotoxic effects with an average EC50 of ∼276 ␮M. Nitromusks and anti-inflammatory drugs were among the less effective compounds for inducing cytotoxicity (EC50 values from 101 to 264 ␮M; Table 1). Anti-inflammatory drugs were the most homogenous group regarding toxic responses, with EC50s values from 158 to 267 ␮M (Fig. 1A); among them, diclofenac was the compound with the lowest toxic potential (EC50: ∼262 ␮M). In contrast, cytotoxicity varied over a wide range for fibrates: bezafibrate was the less toxic compound (EC50: ∼276 ␮M) whereas clofibrate and fenofibrate revealed the strongest cytotoxic potential with EC50s (22–28 ␮M) up to 13-fold lower than those calculated for bezafibrate (Fig. 1B). Among the anti-depressive drugs, fluoxetine and paroxetine were the most toxic compounds, whereas fluvoxamine had EC50 values 4–5-fold higher. Significant differences were observed among nitro- and polycyclic musk: the highest toxicity was observed for the polycyclic musks – celestolide and tonalide – and EC50s up to 31-fold higher were detected for musk xylene and musk ketone. The sensitivity of the toxicity assays used in this study, Alamar Blue and CFDA-AM, was similar for all tested compounds with the

Fig. 1. Cytotoxicity of different pharmaceuticals and synthetic musks in RTL-W1 cells with AB assay. (A) Anti-inflammatory drugs; (B) fibrates; (C) anti-depressive drugs; (D) synthetic musks. Each symbol corresponds to the mean values of one experiment. Standard deviation of mean (n = 6) is indicated.

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Fig. 2. Comparison of CFDA-AM and AB assays for the assessment of cytotoxicity. The linear regression curve and equation is depicted. Error bars correspond to SD.

exception of musk xylene, where AB was the more sensitive probe. Despite of this difference, a good correlation (r2 = 0.90; P < 0.01) was observed between the two cytotoxicity assays (Fig. 2). QSAR models relating cytotoxicity of pharmaceutical compounds with their octanol–water partition coefficient (Kow ) are depicted in Fig. 3. For the pharmaceutical compounds there was no relationship between cytotoxicity and Kow (P > 0.05, Fig. 3). In general, all compounds except naproxen, ketoprofen, fluvoxamine and fenofibrate revealed EC50s deviating from the expected for their Kow , particularly, the neuroactive compounds fluoxetine and paroxetine that exhibited lower EC50 values than expected. On the

Fig. 3. Relationships between log octanol–water partition coefficients (log Kow ) and log median effect concentrations (log EC50) in RTL-W1 cells (AB assay) of pharmaceuticals and synthetic musks. Estimated quantitative structure–activity regression (QSAR) curves and equations are depicted. The doted line is the QSAR model reported by Van Leeuwen and Hermens (1995) for baseline toxicity for guppy. Error bars are 95% confidence intervals.

contrary cytotoxicity of synthetic musks was significantly (P < 0.05) related with their Kow ; the QSAR model had a slope of −0.72, which was no significantly different to that obtained for LC50s of guppies exposed to chemicals acting by non-polar narcosis (Fig. 3).

Fig. 4. Combined toxicity analyses for each group of similar chemicals in RTL-W1 using the AB assay. (A) Anti-inflammatory dugs: ibuprofen, naproxen, ketoprofen, diclofenac; (B) fibrates: fenofibrate, clofibrate, bezafibrate, gemfibrozil; (C) anti-depressive drugs: fluoxetine, paroxetine, fluvoxamine; (D) nitromusks: MK, MX; (E) polycyclic musks: galoxolide, tonalide, celestolide. Each symbol corresponds to the mean values of one experiment. Standard deviation of mean (n = 4) is indicated. CA: concentration addition; IA: independent action.

S. Schnell et al. / Aquatic Toxicology 93 (2009) 244–252

249

Fig. 5. Mixture toxicity analyses of dissimilar acting chemicals in RTL-W1 cells using the AB assay. Two different mixture compositions of 5-dissimilar acting chemicals were assessed: (A) less toxic compounds: diclofenac, bezafibrate, fluvoxamine, musk ketone and galaxolide; (B) most toxic compounds: ibuprofen, clofibrate, fluoxetine, musk xylene and celestolide. Each symbol corresponds to the mean values of one experiment. Standard deviation of mean (n = 4) is indicated. CA: concentration addition; IA: independent action.

The experimental determination of the combined toxicity of mixtures of similar and dissimilar chemicals as well as the prediction made by the CA and IA concepts are depicted in Figs. 4 and 5. Statistical results testing for model accuracy predicting combined toxicity are shown in Table 2. Combined toxicity analyses consisted on 2- to 5-compound mixtures of either pharmaceuticals from the same therapeutic class or synthetic musks – grouped as nitroand polycyclic musks – (Fig. 4). Additionally, two different mixture compositions that included the least (diclofenac, bezafibrate, fluvoxamine, musk ketone and galaxolide) and most (ibuprofen, clofibrate, fluoxetine, musk xylene and celestolide) toxic compounds from each group were assessed (Fig. 5). In both cases, a fixed ratio design proportional to the EC50 of mixture constituents

was used. Combined dose–cytotoxicity responses (AB) were accurately and significantly (P < 0.05) predicted by the allosteric decay model of Eq. (1) showing r2 ranging from 0.82 (polycyclic musks) to 0.94 (dissimilar mixtures with least toxic compounds, Figs. 4E and 5A). Combined toxicity of similar chemical mixtures was additive in all cases since it could be accurately predicted by either CA (antiinflammatory drugs, fibrates, nitromusks), IA (anti-depressants) or both (polycyclic musks) models. Fig. 4 depicts graphically how close observed responses were from predictive models. Table 2 shows whether the elevations (a) and slopes (b) of predicted versus observed values deviate from additivity (a = 0 and b = 1) and whether CA and IA models are similar (ANCOVA results). Notice, however, that for musks predictions, both CA and IA models did not

Table 2 Linear regression models fitted to the predicted versus observed cell response relationships obtained for the mixtures following the concentration addition (CA) and independent action (IA) concepts. a, b are the intercepts and slopes of the regression models. N: sample size, r2 : coefficient of determination. Probability levels of t-tests (t) testing for departures of a = 0 and b = 1 are depicted. Within each mixture, different letters indicate significant differences among slopes and elevations between IA and CA concepts following ANCOVA and Tukey’s post-hoc multiple comparison tests (Zar, 1996); se: standard error; ns P ≥ 0.05; *0.01 < P < 0.05; **P < 0.01. Compound

a

se

t

b

se

t

N

r2

ANCOVA Slope

Elevation

0.55 0.84

A A

A B

48 48

0.78 0.88

A A

A B

ns *

54 54

0.89 0.76

A A

A B

0.06 0.09

ns ns

54 54

0.83 0.83

A A

A A

0.88 0.92

0.06 0.06

ns ns

51 51

0.79 0.79

A A

A A

* **

0.75 0.99

0.08 0.05

** ns

74 74

0.59 0.85

A A

A B

* **

1.05 1.08

0.05 0.05

ns ns

77 77

0.83 0.86

A A

A A

Anti-inflammatory drugs IA −0.1 CA −0.03

0.07 0.03

ns ns

0.78 0.92

0.1 0.05

* ns

54 54

Fibrates IA CA

−0.05 −0.01

0.05 0.03

ns ns

1.19 1.09

0.06 0.09

** ns

Anti-depressive drugs IA 0.08 CA 0.23

0.04 0.04

ns **

0.92 0.83

0.05 0.06

Nitromusks IA CA

0.17 0.06

0.04 0.04

** ns

1.03 1.17

Polycyclic musks IA CA

0.07 0.06

0.03 0.03

ns ns

Less toxic compounds IA −0.15 CA −0.09

0.05 0.03

Most toxic compounds IA −0.07 CA −0.12

0.03 0.03

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differ significantly: equality of slopes and elevations was observed (P < 0.05, ANCOVA from Table 2). For dissimilar compound mixtures, combined joint toxicity was greater than predicted by CA and IA models (Fig. 5 and Table 2). This was particularly evident for the mixture containing the less toxic compounds (diclofenac, bezafibrate, fluvoxamine, musk ketone, galaxolide; Fig. 5A). Notice that for the less toxic compounds, prediction of CA and IA models significantly differ; however, this was not the case for the most toxic compounds mixture.

4. Discussion In this study, the in vitro cytotoxicity of 11 pharmaceuticals and 5 synthetic musks has been assessed in the fish cell line RTL-W1 by applying Alamar Blue and CFDA-AM assays. Half-maximum cytotoxicity was determined for all 16 tested compounds. The cytotoxic endpoints were a diminishment in energy metabolism as evaluated by the capacity of cells to reduce resazurin (Alamar Blue) and a decrease in membrane integrity as measured by the ability to convert CFDA-AM to CF (5 -carboxyfluorescein). Both cytotoxicity assays yielded nearly to the same results, with a high correlation between assays (r2 = 0.90; P < 0.01), except for MX, where AB was the most sensitive probe. This might indicate an early disruption of mitochondrial activity by MX. Polycyclic musks and anti-depressive drugs showed the highest cytotoxicity whereas nitromusks and anti-inflammatory drugs were less toxic, with EC50 values of 1–2 orders of magnitude higher (Table 1). Laville et al. (2004) assessed the cytotoxicity of fenofibrate, clofibrate, fluoxetine and diclofenac in PLHC-1, a fish hepatocellular carcinoma cell line (Poeciliopsis lucida), and in primary cultures of rainbow trout hepatocytes (PRTH). Primary cells were less sensitive than PLHC-1 cells and this was related to a lower growth temperature and slower proliferation of the former together with a higher ability of PRTH to metabolise and inactivate drugs and to develop defence mechanisms against oxidants (Ferraris et al., 2002). Our results are in agreement with those obtained for fluoxetine and fenofibrate in PLHC-1 cells (Laville et al., 2004). However, EC50s for clofibrate and diclofenac were 10–13fold lower in the present study. Cytotoxicity data for 34 common pharmaceuticals from different classes has also been assessed on PLHC-1 and RTG-2 cell lines by Caminada et al. (2006). Interestingly, EC50s obtained for some of the compounds tested (ibuprofen, naproxen, bezafibrate, fenofibrate and gemfibrozil) were 8–200fold higher in PLHC-1 and RTG-2 cells than in the present work. Differences in the dosing procedure might have accounted for the observed differences in cytotoxicity. Caminada et al. (2006) performed a replacement of the culture medium of cells with new medium containing the test substance, whereas a direct dosing procedure was used in our work and the work by Laville et al. (2004), i.e. the test substance was directly added to the cells into the wells. We have earlier reported that the cytotoxicity of ibuprofen was dependent on the dosing protocol, being 10–30fold more toxic when directly dosed on RTL-W1 cells (Schnell et al., 2009). The most toxic pharmaceuticals found in this study were fluoxetine and paroxetine with EC50 values of 7.1 ␮М (2.5 ␮g/ml) to 10.9 ␮М (4.8 ␮g/ml). Significant cytotoxicity was observed at concentrations as low as 0.05 and 0.25 ␮g/ml for fluoxetine and paroxetine, respectively; which are about three orders of magnitude higher than concentrations reported in water samples (Kolpin et al., 2002; Metcalfe et al., 2003). As anti-depressants are highly lipohilic, parental compounds and their metabolites have been detected in the brain, liver and muscle of fish from effluent-dominated streams at concentrations in the range of 0.11–1.58 ng/g wet wt. (Brooks et al., 2005). Thus, concentrations reported in biota tissues are still

40–400-fold lower than concentrations eliciting a toxic response in RTL-W1 cells. Regarding synthetic musks, significant differences were observed between nitro- and polycyclic musks in terms of cytotoxicity: the polycyclic musks – celestolide and tonalide – were the most potent with EC50s up to 30-fold lower than the observed for musk xylene and musk ketone. Interestingly, the use volume of tonalide and galaxolide in Europe was of 358 and 1427 tonnes, respectively, in the year 2000, whereas the use of nitromusks (MK and MX) was only of 35–67 tonnes (OSPAR, 2004). Significant cytotoxicity for tonalide and galaxolide was observed at concentrations as low as 0.12 and 1.2 ␮M; thus, 0.03 and 0.30 ␮g/ml, respectively. These are concentrations 30–100-fold higher than those detected in different wastewater treatment effluents in Canada and Sweden (Ricking et al., 2003). Fish collected from areas all over Europe showed concentrations in the range of 0.1–1.5 ␮g/g wet wt. tonalide and galaxolide; an indication that significant toxicity may occur in certain polluted environments. Tonalide has been shown to cause acute liver damage in laboratory rodents (Steinberg et al., 1999). Musk ketone and musk xylene as well as the polycyclic musk tonalide affected negatively zebrafish embryos and larvae at concentrations found in sewage effluents and receiving waters (Carlsson and Norrgren, 2004), but so far, studies assessing the cytotoxicity of musks on aquatic organisms are scarce. Predicted environmental concentration (PEC) values, based on a conservative approach, are in the range of 11.6 nM (3.0 ␮g/l) for tonalide and 27.8 nM (7.2 ␮g/l) for galaxolide (Salvito et al., 2002). These concentrations are 10–40-fold lower than the concentrations leading to significant cytotoxicity in RTL-W1 cells. Considering that PEC values disregard additive and synergistic effects with other compounds released through sewage treatment plants, the results highlight the need to assess the toxicity of these polycyclic musks in aquatic organisms. Studies of quantitative structure–activity relationships (QSAR) in aquatic organisms and in vitro models have shown that the toxicity of many polluting substances is a function of their lipophilicity (Babich and Borenfreund, 1987; Saito et al., 1993; Veith et al., 1983; Verhaar et al., 1992). Such relationships between lipophilicity and toxicity are said to be narcotic, and are believed to be due to the impairment of normal physiological processes caused by the absorption of chemical compounds into biological membranes (reviewed by Van Wezel and Opperhuizen, 1995; Ren, 2002). In the present study, no significant relationship between the cytotoxicity of pharmaceuticals and their log Kow was observed (Fig. 3). In general, all the pharmaceuticals except naproxen, ketoprofen, fluvoxamine and fenofibrate revealed EC50s deviating from the expected ones. This contrasts with data by Caminada et al. (2006) who reported a correlation between the cytotoxicity of 18 pharmaceuticals and their octanol–water partition coefficient (log D) at physiological pH 7.0 in PLHC-1 cells. However, if our EC50 values are plotted versus log D, no improvement in the correlation was observed (log EC50 (mol/l) = −0.17 log D − 3.91; r2 = 0.07; P > 0.05). It might be worth to indicate that the cell systems used in both studies were slightly different. Thus, PLHC-1 cells (P. lucida hepatocellular carcinoma) were plated at a density of 6.0 × 105 cells/ml and cultivated at 30 ◦ C, whereas RTL-W1 was plated at a much lower density (2.0 × 105 cells/ml) and maintained at 18–20 ◦ C; both factors may affect membrane–water partition coefficients for the tested compounds. Interestingly, the cytotoxicity of musks was proportional to their Kow , with a QSAR slope similar to that reported for non-polar narcotic chemicals in fish but with a higher elevation (Fig. 3). Greater QSAR elevations may indicate less toxicity than expected or more probably, they may be related to the use of different biological systems (cells instead of whole fish). Interestingly, cytotoxicity of pharmaceuticals was poorly related to their Kow but was higher

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than that predicted for musks. Thus, these results strongly suggest that pharmaceuticals may impair cell viability by mechanisms other than non-polar narcosis. Unfortunately, to the best of our knowledge, little is known about such mechanisms in fish and fish cell lines. Regarding combined toxicity of PPCPs, cytotoxicity of chemicals belonging to the same therapeutic or structural class could be accurately predicted by the CA concept, with the exception of anti-depressives. This supports the hypothesis that within a pharmaceutical or musk class, the studied compounds act similarly on fish cells. Nonetheless, the combined toxicity of anti-depressive drugs was more accurately predicted by the IA model, which raises the question of whether the selected anti-depressives do act differently on fish cells. In fact, those anti-depressives differ strongly in terms of cytotoxic potential: cytotoxicity was observed at a concentration of 0.05 ␮g/ml of fluoxetine and 0.25 ␮g/ml of paroxetine, but much higher concentrations of fluvoxamine (5 ␮g/ml) were needed to elicit a toxic response in RTL-W1 cells. Additionally, increased cytotoxicity of fluoxetine and paroxetine over time was reported on PLHC-1 cells, whereas no such an increase was observed for fluvoxamine (Thibaut and Porte, 2008). Also exposure of cells to anti-depressants in the presence of 10 ␮М piperonyl butoxide (PBO), a general cytochrome P450 inhibitor, strongly increased the toxicity of fluoxetine (80%) and paroxetine (61%) while it had no effect on the toxicity of fluvoxamine. Thus, different cytochrome P450 isoenzymes might be involved in the metabolism and toxicity of the selected anti-depressants and to some extent, different mechanisms of toxicity can be anticipated in fish. However, for mixtures of compounds from different therapeutic or structural classes observed joint cytotoxicity was greater than expected by either the IA or CA concepts. These results highlight that different PPCP classes act differently in fish cell lines and that those synergistic effects are most likely to occur in the aquatic environment where fish are exposed to complex mixtures of PPCPs. Recent studies suggest that musks may enhance the toxic potential of other chemicals. Luckenbach and Epel (2005) demonstrated that different synthetic musks inhibited the activity of multidrug efflux transporters in gills of the marine mussel M. californianus at concentrations in the range of 0.74–2.56 ␮M. Inhibition of efflux transporters could result in unanticipated accumulation of xenobiotics in the cell and the consequent increase in toxicity. This may be one of the mechanisms behind the greater toxicity observed for dissimilar mixtures and that certainly deserves further investigation.

5. Conclusion A ranking of PPCPs from the least to the most cytotoxic to a fish liver cell line has been established: the two anti-depressants (fluoxetine and paroxetine) and the polycyclic musks (tonalide and celestolide) were the most potent. The effective concentrations for the former were about 1000-fold higher than those found in the environment and comparable results are described in the literature. However, effective concentrations for polycyclic musks were only 30–100-fold higher than reported environmental concentrations. Thus, among the tested compounds, polycyclic musks – tonalide and galaxolide – are of special environmental concern due to their increased use, increased detection in the aquatic environment, and relatively high toxicity on RTL-W1 cells. Additionally, the work indicates that mixtures of drugs sharing common mechanisms of action or similar chemical structure can be additive and can be modelled by the CA and IA concepts, whereas combined toxicity of dissimilar compounds on non-target organisms may occur at concentrations lower than expected due to synergistic effects between the different toxicants. Overall, the work shows that cytotoxicity assays with

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