Toxicity and hazard of selective serotonin reuptake inhibitor antidepressants fluoxetine, fluvoxamine, and sertraline to algae

ARTICLE IN PRESS

Ecotoxicology and Environmental Safety 67 (2007) 128–139 www.elsevier.com/locate/ecoenv

Toxicity and hazard of selective serotonin reuptake inhibitor antidepressants fluoxetine, fluvoxamine, and sertraline to algae David J. Johnson, Hans Sanderson, Richard A. Brain, Christian J. Wilson, Keith R. Solomon Centre for Toxicology and Department of Environmental Biology, Bovey Administrative Building, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Received 19 August 2005; received in revised form 24 March 2006; accepted 30 March 2006 Available online 5 June 2006

Abstract The toxicity of SSRIs to algae/phytoplankton was investigated using the US EPA ECOSAR, acute single-species growth inhibition assays, species sensitivity distributions (SSDs), and an outdoor microcosm mixture experiment. Worst-case ECOSAR estimates of SSRI toxicity to algae ranged from 0.73 to 13.08 mg/L. Sertraline was the most toxic SSRI tested in single-species growth inhibition assays followed by fluoxetine and fluvoxamine with worst-case 96-h IC10s of 4.6, 31.3, and 1662 mg/L, respectively. HC5s of 2.4, 3.6, and 1100 mg/L were estimated, respectively, for sertraline, fluoxetine, and fluvoxamine toxicity to algae-using SSDs. Microcosm phytoplankton structural endpoints were more sensitive than functional endpoints in the short term. However, in the long term, structural endpoints were resilient and functional endpoints remained impacted even after a period of recovery. The worst-case EC10 determined from the outdoor microcosm mixture toxicity to phytoplankton communities was 15.2 nM. Although SSRIs are toxic to algae, hazard quotients using worst-case PECs represent a margin of safety of 20 to phytoplankton. Although SSRIs do not appear to pose a hazard to primary production, this assessment is not protective of higher aquatic organisms and further research into the chronic toxicity to low levels of SSRIs to higher-level aquatic species is recommended. r 2006 Elsevier Inc. All rights reserved. Keywords: Selective serotonin reuptake inhibitor(s); Pharmaceutical(s); Algae; ECOSAR; Species sensitivity distributions; Microcosm; Hazard assessment

1. Introduction Fluoxetine, a selective serotonin reuptake inhibitor (SSRI) antidepressant pharmaceutical, has been detected in surface waters and sewage treatment outflows (Kolpin et al., 2002; Metcalfe et al., 2003). There are currently five SSRIs available on the market: citalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline. Like other pharmaceuticals, antidepressants reach surface waters via excretion after clinical use or improper disposal of unused medications (Daughton and Ternes, 1999). A modeled exposure assessment of SSRIs suggested that these pharmaceuticals would require further assessment, including the generation of toxicity data, since predicted

Corresponding author. Fax: +1 519 837 3861.

E-mail address: [email protected] (D.J. Johnson). 0147-6513/$ - see front matter r 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2006.03.016

environmental concentrations (PECs) exceeded trigger values (Johnson et al., 2005). The effects and toxicity of serotonin and SSRIs to nontarget species have been reviewed (Brooks et al., 2003; Fong, 1998; Henry et al., 2004). Most of the bioassays and testing in the literature have focused on the toxicological properties of individual SSRIs, but the need for data relating to mixtures of pharmaceuticals and the possibility of additivity have been identified (Black et al., 2003; Brooks et al., 2003; Johnson et al., 2005). Since SSRIs share the same pharmacological mode of action, a concentration addition model may be expected (Cleuvers, 2003, 2004). US EPA’s ECOSAR (ecological structure– activity relationship (SAR)) was used to predict the toxicity of antidepressant pharmaceuticals to algae and they were ranked in the top 10 most toxic of 49 pharmaceutical classes warranting further toxicity testing of algae with SSRIs (Sanderson et al., 2004).

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The use of a tiered approach to hazard assessment of pharmaceuticals has been recommended (EMEA, 2003; Koschorreck et al., 2002; Straub, 2002; U.S.FDA, 1998). The first tier of hazard assessment usually involves the comparison of a measured environmental concentration (MEC) or PEC and simple predicted no effects concentration (PNEC) to produce a hazard quotient (HQ ¼ PEC/ PNEC). The PNEC value is either modeled with a SAR (e.g., ECOSAR) or based on toxicity values (e.g., an effective concentration affecting x% of an endpoint, ECx) for a limited number of standard test species to which a conservative uncertainty factor of anywhere from 10 to 106 (Montforts et al., 1999; Salvito et al., 2002), depending on the nature of the available data, is applied (PNEC ¼ ECx  UF). Higher tiers involve the addition of ecologically more relevant data, to which smaller UFs are applied as uncertainty is reduced and the PNEC is refined. The possible procedures in higher tier aquatic effects assessment do not consist of strict, clearly defined rules, but are tailormade, depending on the information and the degree of uncertainty remaining after lower tier tests have been performed. A suitable approach to reducing the uncertainty related to interspecies toxicity variability may be to perform additional toxicity tests to construct species sensitivity distributions (SSDs) (Posthuma et al., 2002) or to perform microcosm toxicity tests (Giddings et al., 2002). The magnitude of UFs used for extrapolation from lower tiers in hazard assessment (e.g., acute to chronic ratios) have not been evaluated for their application to environmental impacts of pharmaceutical compounds. This paper reports toxicological findings of the SSRIs fluoxetine, sertraline, and fluvoxamine to algae using a tiered toxicity assessment involving ECOSAR analysis (tier 1), 96-h laboratory growth inhibition assays with four species of algae; Pseudokirchneriella subcapitata, Chlorella vulgaris, Scenedesmus acutus, and S. quadricauda, (tier 2), SSDs (tier 3), and an outdoor microcosom mixture exposure scenario (tier 4). Effect concentrations from each tier were used to quantify data-driven UFs required to be protective of tier 4. Finally, an overall HQ was computed for all SSRIs for algal communities based on PNECs derived using data-driven UFs and previously determined PECs (Johnson et al., 2005). 2. Materials and methods

129

treatment plant (STP) removal, and modeled waste water stream dilution factors.

2.2. Structure–activity relationships (SARs): ECOSAR SARs can be used in the absence of experimental test data to assess the fate and effects of chemicals including pharmaceuticals. The most used and qualified SAR is the U.S.EPA’s ECOSAR. It has been used since 1981 to predict the aquatic toxicity of new industrial chemicals in the absence of test data. ECOSAR is available at no charge from the U.S.EPA (http:// www.epa.gov/opptintr/exposure/docs/episuite.htm) along with a suite of models in the EPIWIN v.3.12 program. ECOSAR focuses on acute toxicity of a chemical to fish (both fresh and salt water), water fleas (daphnids), and green algae (Meyland and Howard, 1998). Over 150 SARs have been developed for more than 50 chemical classes based on measured test data that have been submitted by industry. Chemical abstracts registry number (CASRN) and/or simplified molecular input line entry system (SMILES) input is used to identify the chemical structure of the pharmaceutical. Acute algal toxicity EC50s and chronic values for each of the SSRIs were determined by selecting the lowest acute EC50 and chronic value provided by the ECOSAR output.

2.3. Algal growth inhibition assay 2.3.1. Algal cultures Axenic cultures of test organisms were obtained from the University of Toronto Culture Collection (UTCC): UTCC 37 P. subcapitata (formerly Selenastrum capricornutum), UTCC 266 C. vulgaris, UTCC 10 S. acutus, and UTCC 158 S. quadricauda. To prevent contamination, the algae species were only cultured one at a time using aseptic techniques in a laminar flow bench and 10–20 mL of the culture transferred to new media on a weekly basis. Occurrence of contamination with other algal species was assessed monthly using a Leica Leitz Laborlux compound microscope at 250  magnification; contaminated cultures were discarded. Halfstrength Bristol’s medium (Bold, 1949) was used for culturing and testing. Distilled deionized water was used in all tests. Media were autoclaved prior to use. 2.3.2. Test solutions and concentrations Test solutions were prepared by directly dissolving test compounds in half-strength Bristol’s growth medium (Bold, 1949) to produce stock solutions, making serial dilutions, and adding the appropriate aliquots to the test flasks. Nominal test concentrations were determined individually for each species of algae and SSRI combination by conducting rangefinding tests over several orders of magnitude and narrowing the range to cover a single order of magnitude as given in Table 1. Test solution pH was adjusted to 7.2570.15 and dissolved oxygen (DO) was 7.570.5 mg/L, measured with a Hach pH meter (Hach Company, Loveland, CO, USA) and a YSI Model 55 DO meter (YSI, Yellow Springs, OH, USA), respectively. Test compounds were purchased from the following companies: fluoxetine HCl (99.9%), Interchem, Paramus, New Jersey, USA, sertraline (100%), Ranbaxy Laboratories Limited, New Delhi, India, and fluvoxamine (99.58%), Brantford Chemicals, Brantford, Ontario, Canada.

2.1. Predicted environmental concentrations (PECs) The 99th percentile concentration from PEC distributions for the Canadian condition was used for the hazard assessment of SSRIs to algae are based on the US models (U.S.FDA, 1998)for the each SSRI presented in Johnson et al. (2005). The 99th percentile values represent a worst-case estimate of environmental exposure with values of 0.0295 mg/L (citalopram), 0.0191 mg/L (fluoxetine), 0.0297 mg/L (fluvoxamine), 0.0650 mg/L (paroxetine), and 0.122 mg/L (sertraline). Parameters included in the development of these PEC distributions were production/import values from Canada, the population of Canada, and US per capita water use (assumed to be the same as Canada), modeled estimates of sewage

2.3.3. Test procedure Test procedures were modified versions of the U.S.EPA 96-h static toxicity test using S. capricornutum (U.S.EPA, 1994). The only modification was growth medium used: Half-strength Bristol’s medium (Bold, 1949) was used for all tests because all algal species were able to attain the required exponential growth phase in this growth media and the recommended growth medium was inadequate for S. quadricauda. Test chambers were 250-mL Erlenmeyer flasks filled with 100 mL of growth medium and autoclaved. Algae, from a culture 5–7-day old (105–106 cells/ mL, enumerated by cell counts with a hemocytometer or Sedgewick– Rafter counting chamber), were then added to each cooled test flask to

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Table 1 Treatment concentrations used in acute algal growth inhibition assays for each species SSRI

Species

Test concentrations (mg/L)

Fluoxetine

P. subcapitata S. acutus S. quadricauda C. vulgaris P. subcapitata S. acutus S. quadricauda C. vulgaris P. subcapitata S. acutus S. quadricauda C. vulgaris

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

Fluvoxamine

Sertraline

10, 20, 40, 80, 100 50, 100, 200, 400, 800 50, 100, 200, 400, 800 1000, 2000, 4000, 8000, 10,000 500, 1000, 2000, 4000, 8000 500, 1000, 2000, 4000, 8000 500, 1000, 2000, 4000, 8000 5000, 10,000, 20,000, 40,000, 80,000 10, 20, 40, 80, 100 50, 100, 200, 400, 800 50, 100, 200, 400, 800 100, 200, 400, 800, 1000

give a cell count of 104 cells/mL. Four replicates were used for each concentration after an initial range-finding test was conducted. The tests were run in a growth chamber under continuous cool white fluorescent light at 43007200 lux at a temperature of 2571 1C with test vessels shaken and rerandomized twice daily by hand to minimize differences within the growth chamber. Algal cell counts were taken after 96 h. For assays with P. subcapitata, C. vulgaris, and S. acutus, test vessels were shaken to homogenize the cultures and then 2  10 mL samples were counted using a hemocytometer. For assays with S. quadricauda, test vessels were homogenized by shaking as above and 2  1 mL samples were counted using a Sedgewick–Rafter counting chamber. Cell densities for control response and coefficient of variation (CV) ¼ standard deviation/ mean  100% were evaluated. 2.3.4. Statistical analysis Significant changes in algal growth were evaluated using a one-way analysis of variance (ANOVA) allowing for subsamples using proc GLM in SAS v8.2 (SAS Institute, Cary, NC, USA) and a type I error rate of a ¼ 0:05. Algal cell count data were square-root transformed to meet ANOVA assumptions of residual normality and homogeneous variance that were assessed and confirmed using the Shapiro–Wilk test (proc univariate normal) and by graphical interpretation of residual plots. Inhibitory concentrations resulting in a decrease in growth by 10% and 50% (IC10 and IC50, respectively) were determined using linear and nonlinear regression performed with proc NLIN. These models (Table 2) were reparameterized to include the inhibitory concentration for an x% inhibition of growth, ICx, value, by methods described in Stephenson et al. (2000) and Haanstra et al.(1985). The best-fitting model was selected based on the mean corrected coefficient of determination ðR2c ¼ 1  ððerror sum of squaresÞ=ðcorrected sum of squaresÞÞÞ and by graphical interpretation of the model’s fit.

2.4. Species sensitivity distributions The variation in sensitivity to a substance or mixture in different species can be described using a statistical distribution known as an SSD (Posthuma et al., 2002). Since the true distribution of toxicity endpoints is unknown, the SSD is estimated from a sample of toxicity data and visualized as a cumulative density function (CDF) that follows the distribution of the sensitivity data obtained from ecotoxicological testing, plotting effect concentrations from acute or chronic toxicity tests (e.g., ECx, ICx, PNEC). The data from an SSD can then be used in risk assessment to calculate the potentially affected fraction (PAF) of species at a given concentration of toxicant or for the development of environmental quality criteria to calculate the hazardous concentration that could affect x percentage of species (HCx). SSD analyses were conducted according to Aldenberg and Jaworska (2000) using the ETX-2000 computer program

Table 2 Reparameteritized linear and nonlinear models used to describe algal toxicity response relationships in order to determine inhibitory concentrations (ICx) or effective concentrations (ECx) Model

Formula

Linear Exponential

y ¼ a+((x*a*c)/ECx)

3-Parameter logistic 4-Parameter logistic

Ecx

0/ y ¼ a*(1+x)C y ¼ a/[1+((x)/1x)(C0/Ecx)b] y ¼ d+(a/(1+((C0/ECx)b)*((a/((1+x)*(d+a)d))1)))

Note: where ECx is the calculated concentration resulting in an x% (as a fraction) effect (e.g., for an increase of 10%, x ¼ 0:1, for a decrease of 10% x ¼ 0:1), C0 is the actual concentration (e.g. mg/L), y is the response; and a, b, and d are constants. When the effect in question is algal growth inhibition, ECx ¼ Icx.

(version 1.409) by fitting a lognormal model to the input data. Input data were IC10s, as a surrogate for the less useful NOEC (Crump, 1995; Suter, 1996) values calculated from acute toxicity tests with the four species of green algae exposed to each SSRI to assess interspecies extrapolation. The U.S.EPA and EU suggest a minimum of eight species from different families and a prescribed distribution across taxa (EU, 2003; Stephan, 2002; U.S.EPA, 1985). However, these methods are recommended for assessing ecosystem risk while, here, only algae are considered.

2.5. Microcosm study 2.5.1. Microcosm experimental design and treatment A 35-day microcosm experiment was performed at the University of Guelph Microcosm Facility located at the Guelph Turfgrass Institute (ON, Canada) and consists of thirty, 12,000 L outdoor artificial ponds or microcosms. To establish a natural system, 45 plastic trays (approximately 52  25  7 cm3 (length  width  depth), Plant Product, Brampton, ON, Canada) filled with amended sediment (Evergreen Sod, Waterdown, ON, Canada) were placed on the microcosm floor. The sediment consisted of a 1:1:1 mixture of sand, loam, and organic matter (mainly composted manure) by volume. These sediment trays covered approximately 50% of the total surface area of the floor of each microcosm. The water for the microcosms originated from an on-site irrigation pond (62  62  4 m3 deep) that is supplied by a 100-m-deep well. Water was circulated between the microcosm and the irrigation pond at an approximate rate of 12,000 L/day from 3 to 30 June 2003, at which time water flow was terminated 3 days prior to the addition of the SSRI test compounds on 3 July 2003. This circulation served to minimize inter-microcosm variability in zooplankton and algae populations and water chemistry parameters. At least four potted macrophytes (Myriophyllum spp.) were added to provide habitat for invertebrates. The microcosms were open to aerial colonization by insects, and the sides of the PVC liner allowed periphyton growth. One day prior to treatment, three species of fish were added to microcosms in cages: fathead minnows (Pimephales promelas, n ¼ 16/microcosm), pumpkinseed sunfish (Lepomis gibbosus, n ¼ 10/microcosm), and goldfish (Carassius auratus, n ¼ 10/microcosm). Fish were contained in 6-mm mesh enclosures with a total volume of approximately 400 L (o4% of the total microcosm volume). Experimental design, treatment, water chemistry, and analytical chemistry were previously described in Johnson et al. (2005). Briefly, 5 treatments consisting of mixtures of fluoxetine, fluvoxamine, and sertraline were randomly assigned to three separate microcosms (n ¼ 15 (3 replicate microcosms  (4 treatments+1 control)). The nominal treatment concentrations were 0, 12.5, 25, 50, and 100 mg/L for each of fluoxetine, fluvoxamine, and sertraline, constituting the control, 1  , 2  , 4  , and 8  treatments, respectively (i.e., the nominal low treatment consisted of 12.5 mg/L of fluoxetine, fluvoxamine, and sertraline) with time-weighted average concentrations (TWA, in mg/L and nM) presented in Table 3.

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Table 3 Measured time-weighted average (TWA) treatment concentrations for microcosms (n ¼ 3 per treatment) exposed to a mixture of fluoxetine, fluvoxamine and sertraline for 35 days SSRI

Fluoxetine Fluvoxamine Sertraline Total molarity

1  treatment

2  treatment

4  treatment

8  treatment

mg/L

nM

mg/L

nM

mg/L

nM

mg/L

nM

11.1 8.5 9.9

35.9 26.4 29.0 91.4

23.5 18.5 18.5

75.8 57.9 53.9 187.6

57.3 40.6 38.4

185.3 126.8 112.1 424.1

118.8 84.3 76.9

384.1 263.1 224.5 871.7

Note: nominal concentrations of each SSRI were 12.5 (1  ), 25 (2  ), 50 (4  ), and 100 mg/L (8  ) (Johnson et al., 2005).

2.5.2. Phytoplankton sampling Phytoplankton samples, including eukaryotic algae and prokaryotic cyanobacteria, were taken on days 1 (pretreatment), 1, 4, 7, 21, and 35 to evaluate structural endpoints of phytoplankton abundance and community richness as follows: A 4 L integrated sample (Solomon et al., 1982) was taken in treatment order from each replicate microcosm. From this sample, a subsample of 250 mL was taken and preserved with Lugol’s solution. Three 1 mL subsamples from the 250-mL preserved samples were allowed to settle for 25 min. Phytoplankton were enumerated and keyed out for taxonomic identification using a Sedgewick–Rafter counting chamber mounted on a Nikon inverted phase contrast microscope DIAPHOT-TMD using a magnification of 640  . Phytoplankton were keyed to the lowest taxonomic unit (most identified to the genus level, but some common species were also recorded) and enumerated into four classifications: Cyanobacteria, Chlorophyta, Bacillariophyta/Chrysophyta (Heterokonts), and Cryptophyta/Dinophyta. Abundance, a structural endpoint, was measured as the total number of cells/mL (non-filamentous phytoplankton) for each class and was also summed across classes for a measure of total abundance. The total number of individual taxa across all four classes was determined as a measure of community richness, a structural endpoint. Functional endpoints of net primary production and biomass production were evaluated. DO was used as a indication of the rate of net primary production and the dry weight of the standing crop of filamentous algae was used as an indication of biomass production and the rate of biomass production (Cairns et al., 1995). Temperature and DO measurements, determined on a YSI Model 55 meter (YSI, Yellow Springs, OH, USA), were taken in at the same position in all replicates 0.5 m from the microcosm edge at depths 25 and 55 cm from the surface and 10 cm from sediment. A total of nine subsample readings per replicate were taken on days 2, 1, 0, 2, 7, 14, 21, 28, and 35 between 04:00 and 06:00 h and again between 14:00 and 16:00 h. Filamentous algae were sampled on day 85, following SSRI dissipation measurements (Johnson et al., 2005), by harvesting the standing crop with large rakes. The dry mass of the standing crop of algae was measured as an indication of biomass production. The rate of biomass production was assumed linear over the test period with the slope (g/day) being evaluated by dividing the dry weight of the standing crop (g) by 85 days. 2.5.3. Statistical analysis Endpoints analyzed for days 7 and 35 included Cyanobacteria abundance, Chlorophyta abundance, Heterokont abundance, Cryptophyta/Dinophyta abundance, total abundance, total number of taxa, and DO and for day 85, biomass (filamentous algae dry mass). Day 7 was chosen to represent an acute response time point, while day 35 represents chronic exposure, allowing time for observation of community resiliency. All endpoints were analyzed using the linear and nonlinear regression models in Table 2 in SAS’s proc NLIN to determine EC10 and EC50 values. Abundance data were square-root transformed to meet regression assumptions of residual normality and homogeneous variance confirmed using the Shapiro–Wilk test (proc univariate normal) and by graphical

Table 4 Description of the predicted no-effects concentration (PNEC) used at each tier for calculation of hazard quotients (UFs were not applied to the endpoints in order to derive data-driven UFs) Tier

PNEC endpoint

1 2 3 4

ECOSAR chronic value IC50 for most sensitive species HC5 for SSD of IC10sa Lowest microcosm EC10a

a IC10s or EC10s are used as surrogates for no-observed-effects concentrations (NOECs).

interpretation of residual plots. The CV for n ¼ 3 control treatments was calculated for each endpoint. Temporal changes in total abundance, number of taxa, and DO, as well as the treatment-related differences in biomass (dry weight and rate of biomass production of filamentous algae), were analyzed using analysis of variance in SAS’s proc GLM. Significant differences from control were assessed with a Dunnett’s test for pairwise comparisons using a type I error rate of a ¼ 0:05.

2.6. Hazard assessment Hazard quotients (HQ ¼ PEC/PNEC ¼ PEC/(Endpoint  UF)) were used to assess the hazard of SSRIs to algae. In this fashion, HQs were derived for fluoxetine, fluvoxamine, and sertraline at each tier. The PEC value used represented a worst-case scenario by using the 99th percentile PEC. The PNEC used at each tier is outlined in Table 4. At each tier, the HQs for fluoxetine, fluvoxamine, and sertraline were summed, assuming concentration additions; to give an HQCUM in order to compare to the hazard estimates at each tier with the microcosm derived HQ. UFs were not applied to the endpoints to derive the PNEC. This was done in order to derive data-driven UFs between the tiers for SSRIs. Since microcosms are generally taken as being equivalent to ecosystems, and UF ¼ 1 is generally applied (EU, 2003; U.S.EPA, 2003), the microcosm HQ is compared to the PEC/endpoint values for the lower tiers in order to derive UFs. Finally, to estimate the hazard of all five SSRIs, HQSSRI, the UF for extrapolation from tier 1 (ECOSAR) to tier 4 was used to predict the added hazard of citalopram and paroxetine if they had been present in the mixture and add them to the tier 4 HQ, assuming concentration addition: HQSSRI ¼ HQmicrocosm þ ½PEC=Endpointtier 1:citalopram þ PEC=Endpointtier 1:paroxetine  UF:

ð1Þ

All HQs are then compared to 1 such that HQ41 represents a hazard to algal species tested or communities and an HQp1 represents no hazard.

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Table 5 ECOSAR estimates of acute and chronic toxicity of SSRIs to algae and estimates of acute-to-chronic ratios SSRI

CAS no.

ECOSAR algal acute EC50 (mg/L)

ECOSAR algal chronic value (mg/L)

Acute-to-chronic ratio

Citalopram Fluoxetine Fluvoxamine Paroxetine Sertraline

59729-33-8 59333-67-4 61718-82-9 78246-49-8 79559-97-0

2.19 0.84 5.10 148.18 117.93

0.73 0.35 1.40 15.70 13.09

3.0 2.4 3.7 9.4 9.0

Table 6 IC10 and IC50 values for the toxicity of the SSRIs fluoxetine, fluvoxamine, and sertraline to Pseudokirchneriella subcapitata, Scendesmus acutus, S. quadricauda, and Chlorella vulgaris in 96-h static algal growth inhibition assays at treatment concentrations given in Table 1 SSRI

Species

Control (CV) 103 cells/mL

IC107SEa mg/L

IC507SE mg/L

Model

cR2

Fluoxetine

P. subcapitata S. acutus S. quadricauda C. vulgaris P. subcapitata S. acutus S. quadricauda C. vulgaris P. subcapitata S. acutus S. quadricauda C. vulgaris

482 364 384 512 408 318 338 438 362 438 458 392

31.3471.93 55.6074.73 97.76713.54 2901.5771218.97 3987.387322.88 2503.657328.78 1662.917157.16 6162.867814.30 4.5770.66 54.5976.52 48.1973.27 152.7375.09

44.9971.76 91.2372.74 212.98716.13 4339.257446.09 4002.887142.52 3620.247134.96 3563.347118.94 10208.477379.24 12.1071.00 98.9276.74 317.02721.46 763.66725.42

Logistic 3 Logistic 4 Logistic 3 Logistic 4 Logistic 3 Logistic 4 Logistic 4 Logistic 4 Logistic 3 Logistic 3 Exponential Linear

0.979 0.986 0.967 0.925 0.969 0.977 0.981 0.973 0.975 0.972 0.954 0.958

Fluvoxamine

Sertraline

(13.9) (14.9) (7.7) (7.2) (11.9) (14.4) (8.8) (2.5) (7.1) (9.7) (4.3) (4.3)

b

Model constant parameters a ¼ 17:3536 b ¼ 4:1596 a ¼ 9:697 b ¼ 14:171 d ¼ 2:2454 a ¼ 14:5796 b ¼ 1:9303 a ¼ 7:9675 b ¼ 7:1317 d ¼ 4:1371 a ¼ 15:0365 b ¼ 978:2606 a ¼ 10:2325 b ¼ 6:4747 d ¼ 1:9554 a ¼ 11:1967 b ¼ 1009:8449 d ¼ 0:8608 a ¼ 14:1078 b ¼ 4:3477 d ¼ 0:0488 a ¼ 21:2415 b ¼ 1:5458 a ¼ 12:9007 b ¼ 2:5299 a ¼ 11:1748 a ¼ 13:8846

Note: The concentration response was modeled using nonlinear regression formulae and methods as outlined in Table 2. Control growth counts with coefficients of variation (CV), choice of nonlinear model, and associated parameters are summarized. a SE—standard error. b cR2—mean corrected coefficient of determination.

3. Results 3.1. ECOSAR ECOSAR estimates of the acute (EC50) and chronic toxicity (mg/L) of the five SSRIs to algae are presented in Table 5. Fluoxetine was predicted to be the most toxic, followed by citalopram, fluvoxamine, sertraline, and paroxetine, for both the acute and chronic endpoints. The predicted ECOSAR classes for the SSRIs were neutral organics for sertraline and paroxetine and aliphatic amines for citalopram, fluoxetine, and fluvoxamine. The ratios between the acute and chronic toxicity values for the five SSRI are given in Table 5. 3.2. Algal growth inhibition assay Regression evaluation of algal growth inhibition assays for fluoxetine, fluvoxamine, and sertraline showed significant concentration responses. The IC10 and IC50 for the toxicity of each of these SSRIs to P. subcapitata, S. acutus, S. quadricauda, and C. vulgaris are shown in Table 6. Sertraline was found to be the most toxic, followed by fluoxetine and fluvoxamine. P. subcapitata was the most sensitive species when exposed to fluoxetine and sertraline,

with IC10s of 31 and 4.6 mg/L, respectively. S. quadricauda was the most sensitive species when exposed to fluvoxamine, with an IC10 of 1700 mg/L. C. vulgaris was found to be the least sensitive species to the three SSRIs with IC10s of 98, 6200, and 150 mg/L for fluoxetine, fluvoxamine, and sertraline, respectively. 3.3. Species sensitivity distributions (SSDs) Fig. 1 shows the SSDs for fluoxetine (A), fluvoxamine (B), and sertraline (C), with HC5s of 3.6, 1100, and 2.4 mg/L, respectively. The SSDs show that the sensitivity of the four species tested ranges over three orders of magnitude for fluoxetine and sertraline and one order of magnitude for fluvoxamine. The HC5s were 9, 1.5, and 2 times lower than IC10 for the most sensitive algal species tested for fluoxetine, fluvoxamine, and sertraline, respectively. 3.4. Microcosm Fig. 2 shows the change in the total abundance (A), the change in the total number of taxa (B), and the change in DO (C) over the 35-day exposure period as percent of control. Day 7 and 35 EC10 and EC50 values for all

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Fig. 1. Lognormal species sensitivity distributions of IC10 values (as surrogates for NOECs) for growth inhibition assays of four species of green algae exposed to (A) fluoxetine, (B) fluvoxamine, and (C) sertraline. SSDs were constructed according to Aldenberg and Jaworska (2000).

Fig. 2. (A) Total phytoplankton abundance, (B) number of taxa (community richness), and (C) dissolved oxygen (DO, net primary production), all as percent of control, over the 35-day microcosm SSRI mixture exposure period. (A) and (B) are measures of community structure, while (C) is a measure of community function.

endpoints are listed in Table 7. Phytoplankton abundance in treated microcosms showed a statistically significant concentration-dependent decrease compared to controls at the 4  (P ¼ 0:008) and 8  (P ¼ 0:0134) treatments by day 4 and at 1  (P ¼ 0:0155), 2  (P ¼ 0:0042), 4 

(P ¼ 0:0004), and 8  (Po0:0001) by day 7, with an EC10 of 18.7 nM. However, the population appeared to show resiliency by day 21, and by day 35, the 2  , 4  and 8  treatments had measures of phytoplankton abundance that were significantly (statistically) increased relative to

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Table 7 Days 7 and 35 EC10 and EC50 values for the toxicity of the SSRI mixture of fluoxetine, fluvoxamine, and sertraline described in Table 3 to microcosm phytoplankton endpoints Day Endpoint

Direction of response Control (CV)a EC107SE (nM) EC507SE (nM) Modelb

cR2

Nonlinear model parameters

7 7 7 7 7 7 7 35 35 35 35 35 35 35 85

Decrease Decrease Decrease Decrease Decrease Decrease Decrease Increase Increase Increase Increase Increase NSRb Decrease Decrease

0.738 0.743 0.857 0.836 0.849 0.517 0.606 0.876 0.83 0.745 0.567 0.958 NAb 0.732 0.963

a ¼ 1036:9923 b ¼ 0:5062 a ¼ 988:2606 b ¼ 0:6307 a ¼ 98:4991 b ¼ 2:408 c ¼ 98:4851 a ¼ 42:1154 a ¼ 2256:8273 b ¼ 0:4334 a ¼ 25:4544 a ¼ 4:7484 b ¼ 3:8605 c ¼ 5:9269 a ¼ 879:8778 b ¼ 1:3774 c ¼ 1830:303 a ¼ 886:3822 a ¼ 103:0378 a ¼ 7:0706 a ¼ 1945:8715 NAb a ¼ 12:6968 b ¼ 1:4862 a ¼ 41:9502 b ¼ 2:1333

Cyanobacteria Chlorophyta Heterokonts Cryptophtya/Dinophtya Total abundance Number of taxa Dissolved oxygen Cyanobacteria Chlorophyta Heterokonts Cryptophtya/Dinophtya Total abundance Number of taxa Dissolved oxygen Biomass

346 327 64 14 752 30 11 324 308 35 2 669 19 13 1748

(6.9) (8.0) (4.9) (5.3) (6.5) (15.3) (12.3) (5.1) (0.3) (13.6) (31.2) (1.8) (15.5) (7.9) (15.3)

53.3749.8 49.4740.3 51.4740.4 15.272.9 18.7716.5 211.67454.0 127.3769.6 198.8783.6 274.9731.8 316.6747.8 156.2734.0 248.6713.4 NAb 89.7754.1 120.7721.2

1040.17427.5 536.67169.2 3614.87NDb 99.6718.8 601.07186.7 1058.37226.6 3729.27NDb 957.97161.0 1169.47135.0 1346.37200.9 664.47144.2 1057.8757.0 NAb 246.9779.9 244.4724.2

Logistic 3 Logistic 3 Logistic 4 Exponential Logistic 3 Linear Logistic 3 Logistic 4 Exponential Exponential Exponential Exponential NAb Logistic 3 Logistic 3

The concentration response was modeled using nonlinear regression formulae and methods as outlined in Table 2. The direction of the response, either increase or decrease, control response with coefficients of variation (CV), choice of nonlinear model, and associated parameters are summarized for each endpoint. a Units are cells/mL for all endpoints except number of taxa, dissolved oxygen (mg/L), and biomass (g). b ND—not determined, NSR—no statistically significant response, NA—not applicable, cR2—corrected coefficient of determination, Logistic 3—threeparameter logistic model, Logistic 4—four-parameter logistic model.

controls (P ¼ 0:046, o0.0001, and o0.0001, respectively) with an EC10 of 248.6 nM. This same trend was seen with the cyanobacteria, chlorophyta, heterokonts, and cryptophyta/dinophyta. The total number of taxa present in the 1  (2370.9, P ¼ 0:0156), 2  (2070.6, P ¼ 0:0021), 4  (1970.9, P ¼ 0:0013), and 8  (1671.2, P ¼ 0:0002) treatments were significantly (statistically) lower than controls by day 7, when controls contained 3072.6 taxa with an EC10 of 211.6 nM. However, by day 35, there was no statistically significant difference in the number of taxa (P40:4278 for all treatments), as the number of taxa present in the controls had decreased to values similar to those for the treated systems, with taxa ranges from 19 to 24 species. These results indicate an initial acute (by day 4) impact on the phytoplankton community structure as both number of taxa (community richness) and total abundance are reduced compared to controls. However, the rebound in the phytoplankton abundance in the acutely affected systems prior to cessation of treatment suggests that the community is resilient or has adapted to the stressor. By day 2, DO concentrations showed a statistically significant concentration-dependent decrease from controls (DO ¼ 10.670.7 mg/L) at the 2  (62% of control, P ¼ 0:0100), 4  (49% of control, P ¼ 0:0014), and 8  (43% of control, P ¼ 0:0006) treatments. This decrease persisted for the duration of the 35-day exposure period (Fig. 2C). On average, the DO in the treatments were (mean for the 35-day exposure period7standard deviation) 12.572.6 (control), 11.371.7 (1  ), 9.571.7 (2  ), 7.271.4 (4  ), and 6.472.6 (8  ). The biomass of the standing crop of filamentous algae on day 0 was not measurable, as the water column was clear and no

filamentous algae could be harvested. Qualitative observation of the microcosms over the 35-day exposure period and SSRI dissipation period indicated a concentrationdependent decrease in growth of filamentous algae over the period of treatment. Following the dissipation period (50 days after the last treatment (Johnson et al., 2005)), the dry weight and rate of biomass production of the harvested standing crop of filamentous algae showed a concentration-dependent decrease compared to control (Table 8). On day 7, the most sensitive structural endpoint was the abundance of the Cryptophyta/Dinophyta group with a 7-day EC10 of 15.2 nM. This value was used as the microcosm PNEC. 3.5. Hazard assessment HQs for each SSRI and for fluoxetine, fluvoxamine, and sertraline (HQCUM) at each tier are shown in Table 9. Both the Tier 1 HQs and HQCUM were 4–6 orders of magnitude less than 1 without the application of uncertainty factors. Tier 2 and 3 HQ ratios were 2–5 orders of magnitude and the HQCUM was 1–2 orders of magnitude less than 1, without the application of uncertainty factors. The tier 4 HQ for the microcosm mixture was 1–2 orders of magnitude less than 1. The design of the tiered approach to risk assessment is such that lower tiers should be more conservative and therefore protective of higher tiers (the ultimate tier being the ecosystem). In the case of the data presented, the HQs for each tier should be protective of the highest tier, in this case, the microcosm mixture. In this way the magnitude of UFs can be derived such that lower tiers are protective of the microcosm tier. Using the HQs for fluoxetine,

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Table 8 Filamentous algae biomass (dry mass) and rate of biomass production (assuming linear over 85 days) in aquatic microcosms treated with mixtures of fluoxetine, fluvoxamine, and sertraline as outlined in Table 3 Treatment

Floss algae dry mass7SE (g)

Rate of biomass production7SE (g/day)

P value (Dunnett’s test)

Control (n ¼ 3) 1  (n ¼ 3) 2  (n ¼ 3) 4  (n ¼ 3) 8  (n ¼ 3)

17507154 1560757 1110798 240793 4078

21.071.80 18.070.67 13.071.20 2.8071.10 0.4470.09

— 0.4742 0.0027 o0.0001 o0.0001

Note: These results indicate a statistically significant treatment-related decrease in biomass and rate of biomass production.

Table 9 Hazard quotients (HQ ¼ PEC/PNEC) determined for individual SSRIs at each tier (Table 4) and for the mixture of SSRIs determined assuming concentration addition or in the microcosm exposure scenario Tier description

SSRI

Tier 1: ECOSAR

Citalopram Green algae chronic mg/L Fluoxetine Green algae chronic mg/L Fluvoxamine Green algae chronic mg/L Paroxetine Green algae chronic mg/L Sertraline Green algae chronic mg/L HQcum (Tier 1: fluoxetine, fluvoxamine, sertraline) Fluoxetine P.subcapitata IC50 mg/L Fluvoxamine S.quadricauda IC50 mg/L Sertraline P.subcapitata IC50 mg/L HQcum (Tier 2: fluoxetine, fluvoxamine, sertraline) Fluoxetine HC5 from SSD of IC10s mg/L Fluvoxamine HC5 from SSD of IC10s mg/L Sertraline HC5 from SSD of IC10s mg/L HQcum (Tier 3: fluoxetine, fluvoxamine, sertraline) Mixture Lowest microcosm EC10 nM

Tier 2: acute growth inhibition

Tier 3: species sensitivity distributions

Tier 4: microcosm mixture a

PNEC description

Units

PNEC

PECa

HQ

729 345 1395 15,699 13,086

2.95  102 1.91  102 2.97  102 6.50  102 1.22  101

45.0 3563.3 12.1

1.91  102 2.97  102 1.22  101

3.6 1125.9 2.4

1.91  102 2.97  102 1.22  101

18.7

5.10  101

4.05  105 5.53  105 2.13  105 4.14  106 9.32  106 8.59  105 4.24  104 8.33  106 1.01  102 1.05  102 5.27  103 2.64  105 4.99  102 5.53  102 2.73  102

Ninety-ninth percentile PEC (Johnson et al., 2005).

fluvoxamine, and sertraline for tier 1 (ECOSAR), tier 2 (acute laboratory toxicity), and tier 3 (SSDs) and the microcosm mixture at tier 4, the HQCUM (tier 1) ¼ 8.6  105, HQCUM (tier 2) ¼ 0.01, HQCUM (tier 3) ¼ 0.055, and HQCUM (tier 4) ¼ 0.027. The UF applied to tiers 1 and 2 to make the hazard estimate protective of tier 4 would be approximately 320 and 3, respectively. Since HQCUM for tier 3 is already4HQ for tier 4, a UF of 1 would be required. The UF for the extrapolation from ECOSAR to microcosm mixture could then be used to predict the added hazard of the other SSRIs, citalopram and paroxetine, if they had been present in the microcosm mixture and to calculate the HQ(SSRI): HQðSSRIÞ ¼ HQðtier 4Þ þ UF  ½HQðtier 1 : citalopramÞ þ HQðtier 1 : paroxetineÞ ¼ 2:7  102 þ 320  ½4:05  105 þ 4:14  106  ¼ 4:2  102 , which is o1, indicating that no hazard to algae/phytoplankton communities is expected at PECs.

4. Discussion The role of phytoplankton in the aquatic environment is that of primary production, in essence, the lowest trophic level on which food web interactions rely, as their energy input originates from the sun. According to EU directive 93/67/EEC (EU, 2003), which classifies substances according to their EC50 values (which could be an IC50 if the effect in question is growth inhibition) for aquatic organisms in different classes, fluoxetine and sertraline would be considered very toxic to algae and fluvoxamine toxic to algae. As a mixture, assuming concentration addition, SSRI antidepressants would be classified as very toxic to algae. However, the environmental hazard, based on the HQ developed for SSRI toxicity to algae, is low, as the HQ developed was 0.042, which represents a margin of safety of 20. This indicates that, while SSRIs are very toxic to algae, a conservative, worst-case scenario PEC used in this assessment is less than the concentration required to elicit toxicity to algae. Although SSRIs do not appear to pose a hazard to primary production, this assessment is not protective of higher aquatic organisms. Given the pharmacological activity of SSRIs, i.e., their central nervous system (CNS) effects, the size of uncertainty factors used to

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extrapolate from algal species, which lack a CNS, to fish and amphibians, for example, might be greater than 20 and as such would indicate potential hazard for these organisms. The toxicological effects of serotonin and antidepressant pharmaceuticals on other aquatic species have been reviewed. Fluoxetine, fluvoxamine, and paroxetine significantly (statistically) induced spawning in the male zebra mussel (Dreissena polymorpha) at 15.47, 0.32, and 370 mg/L, respectively (Fong, 1998). The aquatic ecotoxicology of fluoxetine was reviewed with EC50s for P. subcapitata (120-h acute), Ceriodaphnia dubia (48-h acute), Daphnia magna (48-h acute), P. promelas (48-h acute), and Chironomus tentans (10-day acute) of 24 mg/L (which is of the same magnitude of the findings of this study), 234, 820, 705, and 15.2 mg/g, respectively (Brooks et al., 2003). Forty-eight hour (48-h) LC50s for C. dubia ranged between 120 and 3900 mg/L, sertraline being the most toxic, followed by fluoxetine, fluvoxamine, paroxetine, and citalopram (Henry et al., 2004). This order of toxicity was also observed for algae in the present study. While these effect concentrations are at least an order of magnitude greater than the PEC for SSRIs, high acuteto-chronic ratios may be expected, since these chemicals have been designed to be biologically active. As such, further research into the chronic toxicity of low levels of SSRIs to higher-level aquatic species is recommended. The sensitivity of the different algal species tested in the single-species toxicity assays indicates that C. vulgaris is much less sensitive to these chemicals. These data indicate a difference in sensitivity of one to two orders of magnitude compared to the next most sensitive species. The specific mechanism of toxicity of SSRIs to algae has not been elucidated. C. vulgaris may be able to metabolically break down or transform SSRIs into less toxic metabolites or, if the toxic mechanism is receptor-mediated, perhaps C. vulgaris has an altered receptor. Further research related to the species sensitivity differences is needed. The microcosm evaluation of SSRI mixture toxicity to phytoplankton indicated that long-term structural endpoints (community richness and abundance) did not appear to be adversely affected, due to community resilience. However, the reductions in the short-term structural endpoints correlated with the reduction in the short-term functional endpoints (net primary production and biomass production). There was no long-term resiliency (net primary production) or recovery (biomass production) in the functional endpoints that resulted from the short-term structural perturbation. This implies that the short-term structural reductions directly impacted short-term functional endpoints, leading to long-term reductions in ecosystem function. The magnitude of these reductions may be considered ecologically significant. This is the rationale for the selection of the microcosm PNEC based on the most sensitive short-term structural endpoint. The general assumption in ecotoxicological risk assessment is that protecting community structure will be protective of

community function. These data support this assumption in the short term. However, in the long term, these data indicate that the protection of structural endpoints does not guarantee the protection of ecosystem functionality when the system has been perturbed in the short term. This has implications for remediation and a priori ecotoxicological risk assessment, especially in areas where functional redundancy in community structure may not be sufficient. Higher trophic organisms, namely zooplankton and fish, were present in the microcosms. It is possible that the treatment may have affected the top-down grazing pressure on phytoplankton from zooplankton. The response seen in the phytoplankton community would have then been an indirect effect of the treatment. This is one of the benefits of using microcosms for effects assessment that cannot be incorporated into acute single-species toxicity assays, or SSDs. It is unlikely that the fish would have had a topdown effect on phytoplankton or zooplankton, as they were confined in submersed cages that represented less than 4% of the entire volume of the microcosms. This would have limited them to passive feeding on micro-/macroinvertebrates and phytoplankton entering their cages rather than a direct predatory relationship. The evaluation of the rate of biomass production assumed a linear relationship; hence the units shown in Table 9 are g/day. Sampling of the standing crop of filamentous algae is destructive and as such only one sample could be taken from each microcosm (day 85), and the initial value was assumed to be approximately zero (as explained previously). Only two points were available; hence a linear relationship was assumed with the slope being the rate, dry mass (g)/time (day). While a logistic model often describes growth curves, because of lack of data points and for simplicity a linear model was used. SARs are recommended for use by the US Food and Drug Administration (U.S.FDA) for environmental fate assessments of pharmaceuticals (U.S.FDA, 1998), are recommended by the EU White Paper Strategy for a Future Chemicals Policy (EU, 2001), and are used by Environment Canada to predict persistence, bioaccumulation, and inherent toxicity of substances on the domestic substance list (CSTEE, 2001; Environment Canada, 2003). The most used and qualified SAR is the U.S.EPA’s ECOSAR, which has been used since 1981 to predict the aquatic toxicity of new industrial chemicals in the absence of test data. Over 150 SARs have been developed for more than 50 chemical classes based on measured test data that have been submitted by industry. Moore et al. (2003) compared the performance for six SAR models and ranked ECOSAR a close second behind a probabilistic neural network (which comes at a high cost). ECOSAR has been applied to the prioritization of aquatic toxicity of fragrance materials (Salvito et al., 2002) and pharmaceuticals (Sanderson et al., 2003, 2004). In the present study SAR SSRI toxicity estimates from ECOSAR for algae were greater than experimental values, possibly indicating a specific mode of toxic action. These findings may impact

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the magnitude of UFs that are applied to SAR toxicity data generated for the environmental risk assessment of pharmaceuticals, especially since pharmaceuticals are biologically active; high-acute-to-chronic ratios may be expected. UFs were not applied to HQs, as these values vary depending on the jurisdiction in which the hazard assessment is being conducted. In the case of the Canadian Environmental Protection Act (Government of Canada, 1999), uncertainty factors are derived on a case-by-case basis depending on data quality. Factors of 1–10 account for intra and interspecies variation; additional factors of 1–100 are used for data inadequacies, such as in a key study or because of lack of chronic data. UFs can be in some cases, when multiplied out, as high as 10,000 (Chapman et al., 1998). In the EU, legislation specifically requires that UFs (referred to as assessment factors) be used in hazard assessment in the calculation of a PNEC. A UF of 1000 is applied to at least one short-term EC50 from each of fish, Daphnia, and algae (base-set toxicity data), 100 to one long-term NOEC (either fish or Daphnia), 50 to two longterm NOECs from species representing two trophic levels (fish and/or Daphnia and/or algae), 10 to long-term NOECs from at least three species (normally fish, Daphnia, and algae) representing three trophic levels, 5–1 applied to SSDs (on a case-by-case basis), and for field/model ecosystem data UFs are reviewed on a case-by-case basis. UFs are considered general factors that under certain circumstances may be changed and are designed to decrease as more certain data become available that reduce the uncertainties noted above (EU, 2003). The U.S.EPA Office for Pollution Prevention and Toxics (OPPT) calculates the PNEC (referred to as concentrations of concern) by identifying the most sensitive species and effect from the stressor–response profile and dividing by an appropriate UF. Unlike the EU, the base-set toxicity data can either be known from acute toxicity testing or predicted using SARs. A UF of 1000 is applied if only one acute value is available from the base set, 100 is applied to the most sensitive species in the base set, 10 applied to the lowest chronic value (ChV) for base-set data, and 1 applied to the ChV from a field study (e.g., pond) or from a cosm study (U.S.EPA, 1998). In practice, the only assessment factor used by the U.S.EPA OPPT is 10, since ECOSAR gives a toxicity profile that includes fish 96-h EC50, Daphnid 48-h EC50, green algae 96-h EC50, fish ChV, Daphnid ChV, and algal ChV (U.S.EPA, 2003). UFs are primarily policy-driven default factors that are often difficult to verify experimentally, while others should be considered data-driven adjustment factors that can be continually refined through the availability of additional data (Chapman et al., 1998; Wilkinson et al., 2000). The data-driven UF of 320 generated by comparing the ECOSAR estimate of SSRI toxicity to algae to the microcosm exposure scenario corresponds well with the UF of 1000 applied by OPPT, since the HQ generated for their purposes is to be protective of the ecosystem as

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opposed to only the phytoplankton community, to which the data-driven UF and corresponding HQ generated in this assessment refer. The UF of 3 generated from the comparison of HQ derived from acute algal growth inhibition assay IC50 endpoint to the microcosm exposure scenario indicates that the acute assay predicted microcosm findings quite well in this circumstance. This may be associated with the previously stated correlation that shortterm structural impacts resulted in longer-term functional impairment, which the microcosm PNEC was based on. Had the acute assay PNEC been based on IC10s, which may be thought of as a surrogate for a no-observed-effect concentration (NOEC), as opposed to IC50s, the datadriven uncertainty factor would have been closer to 1, as the IC10 values for the most sensitive species in the acute assays were approximately three times less than the IC50s. Given this, it is not surprising that a data-driven UF of 1 was derived using SSDs of IC10. While UFs are generally applied to hazard estimates to compensate for lack of definitive data pertaining to the effect of a chemical, uncertainty is addressed on the exposure side of the HQ by making conservative worstcase assumptions (Montforts et al., 1999). In many cases, the need for hazard assessment is established by first assessing the potential for exposure in the environment and is often limited to product identification and exposure assessment using trigger values and/or PECs. If trigger values are not exceeded, then the product is exempt from further testing. Environmental effects assessment and hazard characterization are carried out only if trigger values are exceeded. These trigger values are set at a PEC ¼ 0.01 mg/L in the EU (EMEA, 2003), environmental introduction concentration ¼ 1 mg/L in the US (U.S.FDA, 1998), and production/import volume o10 tonnes/year in Canada (Government of Canada, 1999). The possibility that the environmental hazard assessment may end at the level of the exposure assessment increases the demand for this part of the assessment to be thorough. Considering that the uncertainties in effects assessment are relatively well understood, and the concept of using UFs is becoming more refined, it should be realized that exposure assessment also has a relatively high level of uncertainty (Montforts et al., 1999). A comparison of the HQ for tiers 3 and 4 indicates that the SSD produced a more conservative assessment of hazard than the microcosm mixture. While these tiers give the same overall answer as far as the hazard of SSRIs to algae is concerned, the overall pictures these two methods portray are quite different. SSDs assemble data from several studies conducted using standard guidelines designed to have high statistical power and to reduce interlaboratory variability, which assess the intrinsic toxicity of a chemical to a theoretical community. However, SSDs cannot account for species interactions and therefore do not incorporate indirect and trophic-level effects. Microcosms represent quasi-natural long-term toxicity tests in which direct and indirect effects, structural

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and functional endpoints, community and trophic level interactions can be assessed. However, microcosms suffer from high natural/background variability and limited replication, thus requiring larger effect measures and environmentally unrealistic test concentrations to ensure statistical power (Sanderson, 2002). It thus appears that these two tiers complement each other and, when combined, may be able to significantly reduce the uncertainty in hazard assessment. 5. Conclusions The SSRIs fluoxetine, fluvoxamine, and sertraline were toxic to algae in 96-h acute growth inhibition assays, with IC10s ranging from 4.6 to 6100 mg/L, depending on the species of algae. ECOSAR toxicity estimates for SSRIs were greater than experimental values, possibly indicating a specific mode of toxic action. Microcosm phytoplankton structural endpoints (abundance and species richness) were more sensitive than functional endpoints (primary production and biomass production) in the short term. However, in the long term, structural endpoints were resilient, while functional endpoints remained impacted even after a recovery period. EC10 values in the microcosm experiment as low as 15 nM SSRIs. However, SSRIs do not represent a hazard to algae, with hazard quotients more than an order of magnitude below 1 when compared with realistic worstcase PECs. For lower tiers of risk assessment, the magnitudes of UFs required to be protective of algal semi-field microcosm endpoints were approximately 300 for ECOSAR, 3 for acute growth inhibition, and 1 for SSDs. Although SSRI do not appear to pose a hazard to primary production, this assessment is not protective of higher aquatic organisms. As such, further research into the chronic toxicity to low levels of SSRIs to higher-level aquatic species is recommended. References Aldenberg, T., Jaworska, J.S., 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1–18. Black, M., Armbrust, K., Henry, T.B., Kwon, J.-W., 2003. 2002 Progress Report: The Environmental Occurrence, Fate and Ecotoxicity of Selective Serotonin Reuptake Inhibitors (SSRIs) in Aquatic Environments. Environmental Protection Agency (EPA). http://cfpub.epa.gov/ ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/ 1755/report/2002 Bold, H.C., 1949. The morphology of Chlamydomonas chlamydogama, sp. nov. Bull. Torrey Botanl. Club 76, 101–108. Brooks, B.W., Foran, C.M., Richards, S.M., Weston, J., Turner, P.K., Stanley, J.K., Solomon, K.R., Slattery, M., La Point, T.W., 2003. Aquatic ecotoxicology of fluoxetine. Toxicol. Lett. 142, 169–183. Cairns Jr., J., Niederlehner, B.R., Smith, E.P., 1995. Ecosystem effects: functional end points. In: Rand, G.M. (Ed.), Fundamentals of Aquatic Toxicology. Taylor & Francis, Washington, DC, pp. 589–607. Chapman, P.M., Fairbrother, A., Brown, D., 1998. A critical evaluation of safety (uncertainty) factors for ecotoxicological risk assessment. Environ. Toxicol. Chem. 17, 99–108.

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