Probabilistic hazard assessment of environmentally occurring pharmaceuticals toxicity to fish, daphnids and algae by ECOSAR screening

Probabilistic hazard assessment of environmentally occurringpharmaceuticals toxicity to fish, daphnids and algae by

ECOSAR screening

Hans Sanderson *, David J. Johnson, Christian J. Wilson, Richard A. Brain,Keith R. Solomon

Center for Toxicology, University of Guelph, Bovey Building, Gordon Street, Guelph, Ont., Canada N1G 2W1

Received 8 April 2003; received in revised form 30 May 2003; accepted 30 May 2003

Abstract

The risks associated with occurrence of pharmaceuticals in water resources are mostly unknown. In the absence of

extensive toxicological data, we scanned all the compounds observed in the environment for toxicological properties by

(Quantitative) Structure Activity Relationship ((Q)SAR). The results of the probabilistic distribution of environmental

and effect concentrations and hazard quotients (HQs) do not indicate significant acute risks prior to application of

assessment factors. Compared with measured effect concentrations SAR predictions were more ‘‘sensitive’’ 80% of the

time. The long-term effects of subtle and chronic changes, additive or synergistic effects and effects on other endpoints

e.g. reproduction, behavior, metabolism, bacterial resistance etc. are still uncertain. (Q)SAR’s can be important

prioritization tools for subsequent experimental risk assessment of pharmaceuticals in surface waters, due to the

prevalent lack of ecotoxicological data.

# 2003 Elsevier Ireland Ltd. All rights reserved.

Keywords: Pharmaceuticals; ECOSAR; EC50; Probability; Hazard quotients

1. Introduction

Recently medical and personal care products

have received increasing attention from environ-

mental and health agencies across the European

Union and in North America. Surveys and reports

on the occurrence of pharmaceuticals in the

environment (primarily surface waters) show that

medical compounds are ubiquitous (Daughton

and Ternes, 1999). Pharmaceuticals are created

with the intent of causing a biological effect, they

often have similar types of physio-chemical beha-

vior that are characteristic of harmful xenobiotics

e.g. they are able to pass membranes, and they are

relatively persistent in order to avoid being in-

activated before having their therapeutic effect.

* Corresponding author. Tel.: �/1-519-824-4120x54794; fax:

�/1-519-837-3861.

E-mail address: [email protected] (H. Sanderson).

Toxicology Letters 144 (2003) 383�/395

www.elsevier.com/locate/toxlet

0378-4274/03/$ - see front matter # 2003 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/S0378-4274(03)00257-1

These compounds are excreted through feces and

urine as a mixture of metabolites and unchanged

substances. They, therefore, predominately enter

the environment via wastewater effluent, aggra-

vated by the fact that, in practice the majority of

people flush unused drugs down the drain or

dispose of it with household garbage (Jones et

al., 2001). Other sources include, direct application

in aqua farming, manure run-off, as run-off from

the application of sewage sludge and manure on

farmland as fertilizers (Halling-Sørensen et al.,

1998), via hospital effluent (Kummerer, 2001) or,

finally, via landfill leaching (Richardson and

Bowron, 1985). Since the sophistication of analy-

tical methods has increased, so has the range of

detection of xenobiotics in the environment.

Hence, pharmaceuticals have been proven to occur

in surface water (Kolpin et al., 2002). Even if the

environmental half-life of the parent pharmaceu-

tical compounds may not be relatively great

compared, this is compensated, however, by con-

tinuous replacement of the compounds in the

environment, which serves to sustain perpetual

life-cycle exposure for aquatic organisms (Daugh-

ton and Ternes, 1999).

The quantities of several of pharmaceuticals

used throughout the world are comparable to

agrochemicals (Jones et al., 2001). Directives by

the US Food and Drug Administration (FDA)

since 1995 (CDER, 1995) and in the EU since 1993

for human and veterinary compounds (Straub,

2002) stipulating that an environmental risk as-

sessment should be part of the approval procedure

of new medical substances. Few new medical

substances have been subjected to a complete

risk assessment (Halling-Sørensen et al., 1998)

primarily due to the fact that in most instances

the calculated environmental concentrations lie

below the proposed cut-off values, making further

ecotoxicological studies unnecessary. However,

the importance of identifying emergent risks such

as pharmaceuticals in the environment is reflected

in the fact that pharmaceuticals are one of the top

five goals of the Strategic Plan 2000 for the US

Environmental Protection Agency’s Office of Re-

search and Development (Daughton and Ternes,

1999).

The current US regulatory guidance requiresnew pharmaceuticals to undergo standard acute

toxicity tests (algae, Daphnia magna and fish) if

the predicted or measured environmental concen-

tration (PEC/MEC) of the active ingredient is �/1

mg l�1. In the EU the cut-off PEC value is 0.01 mg

l�1, and no environmental concerns are apparent

no further testing is deemed necessary. In the

second tier a crude predicted no-effect concentra-tion (PNEC) for the aquatic compartment is to be

extrapolated by dividing the lowest E(L)C50 from

standard tests by an assessment factor of up to

1000 in the EU. If the PEC/PNEC is B/1 no

further assessment is necessary. The third tier is a

case-by-case study. Regulations may result in

labeling or restricted use (e.g. in hospitals, in-

surgery, etc.) (Straub, 2002). Due to the scarcity ofecotoxicological data and the presence of pharma-

ceuticals in water, the primary question is whether

medical substances at low environmentally realis-

tic concentrations (parts per billions or trillions)

will have any effect at all on different trophic levels

and/or on ecosystems.

This analysis combines the findings of pharma-

ceuticals in surface waters in the US (Kolpin et al.,2002) with those found in the EU reported in

surveys and reviews by Richardson and Bowron

(1985), Halling-Sørensen et al. (1998), Daughton

and Ternes (1999), Ayscough et al. (2000), Jones et

al. (2001), Kummerer (2001), Halling-Sørensen et

al. (2002) and Sturer-Lauridsen et al. (2002). We

performed an ecotoxicological (Quantitative)

Structure Activity Relationship ((Q)SAR) screen-ing (ECOSAR) of all the compounds reported in

the aquatic environment in an attempt to frame

the above question. We include; MECs and the

effect concentration where 50% of the organisms

either die or in other ways are adversely impaired

(EC50). Covered are values for fish (96 h and 14

days), daphnids (48 h and 21 days) and algae (48

h), chronic effects values are included whenavailable (�/75%) from the ECOSAR for all

model species. Effect measures in the ECOSAR

are based on data reported by the industry to the

OECD or USEPA, and are all according to

USEPA toxicity test guidelines for algae (typically

growth inhibition of Selenastrum capricornutum ;

lethality and reproduction of D. magna, and fish

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395384

Fathead minnows (Pimephales promelas ) personalcommunication, Nabholz, 2003).

The exact toxic mode of action of the pharma-

ceuticals to non-target test organisms is not known

nor accounted for in the SARs. The specificity of

the pharmacodynamic activity and the ecotoxico-

logical mode of action of pharmaceuticals does not

easily translate into an ecotoxicological mode of

action of pharmaceuticals. The concentrations ofpharmaceuticals needed to elicit intended pharma-

codynamic responses will exceed environmental

concentrations by factors in the range of 104�/106

(Seiler, 2002). Furthermore, if the effect is driven

by receptors that may be lacking in non-target

organisms, concentrations needed to evoke any

effect may then be even higher: as, e.g. serotonin

reuptake inhibitors, beta-blockers etc. are notpresent in most plants and insects. However,

more basic mechanisms of cellular functions like

those connected with signal transduction or cell

division that are generally well conserved in

evolution and can thus be identified throughout

the living world from unicellular to mammal

organisms are targeted by more recently developed

pharmaceuticals (Seiler, 2002).Due to the data scarcity and unknown risks

associated with pharmaceuticals in the environ-

ment the European Commission Scientific Com-

mittee on Toxicity, Ecotoxicity and the

Environment (CSTEE) recognizes that a prioriti-

zation procedure needs to be developed for

pharmaceuticals and their environmental risk

assessment. To ensure harmonization, this shouldfollow the general scheme for chemicals as de-

scribed in the White Paper for future chemicals

strategy (EU, 2001a). The main tool for prioritiza-

tion stressed therein is the use of QSARs (EU,

2001b).

The most extensively validated and used QSAR

is the USEPA EPIWIN suit with ECOSAR.

ECOSAR predictions does not replace the needfor experimental assessment of the environmental

risks posed by pharmaceuticals, but can serve as

an initial prioritization tool to estimate potential

hazards of pharmaceuticals in the environment.

ECOSAR has previously been successfully (low

false negative rates) applied to screening pharma-

ceuticals (Jones et al., 2002) and other complex

compounds such as fragrance materials (Salvito etal., 2002).

2. Methods

2.1. ECOSAR

The SARs in the ECOSAR are used to predict

the aquatic toxicity of chemicals based on thesimilarity of structure to chemicals for which the

aquatic toxicity has been previously measured.

Since 1981, the US Environmental Protection

Agency has used SARs to predict the aquatic

toxicity of new industrial chemicals in the absence

of test data. The acute toxicity of a chemical to fish

(both fresh and saltwater), water fleas (daphnids),

and green algae has been the focus of the devel-opment of SARs. These organisms are group

model-organisms and thus not specific species.

SARs are developed for chemical classes based

on measured test data that have been submitted by

industry or they are developed and structural

similarities. Using the measured aquatic toxicity

values and Kow values, regression equations (cur-

rently more than 150 for more than 50 chemicalclasses) can be developed for a class of chemicals.

Inserting the Kow into the regression equation and

correcting the resultant value for the molecular

weight of the compound may then calculate

toxicity values for new and similar yet non-

assessed chemicals (Nabholz, 2001). The ECOSAR

class program is a computerized version of the

ECOSAR analysis procedure as currently prac-ticed by the Office of Pollution Prevention and

Toxics (OPPT). It has been developed within the

regulatory constrain of the Toxic Substances

Control Act (TSCA) and is a pragmatic approach

to SAR as opposed to a theoretical approach

(Meyland and Howard, 1998). The ECOSAR

program can freely be downloaded via the USEPA

(http://www.epa.gov/oppt/exposure/docs/episui-tedl.htm). A validation assessment of ECOSAR

predictions has been performed and they indicate

an 87�/90% agreement between predictions and

measured data for more than 2000 different

chemicals and with B/3% false negatives (Nab-

holz, 2001).

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395 385

2.2. Probabilistic risk assessment

Probabilistic risk assessment (PRA) methods are

being assessed and considered for incorporation

into risk assessment procedures in a number of

regulatory jurisdictions. The method used in this

analysis has been implemented by the USEPA

(Hendly and Giddings, 1999). The use of PRA

recognizes that there are no absolutes in riskassessment. Instead there are continuums of po-

tential exposure and effect situations and a range

of certainty, which can be reported (Solomon et

al., 2000). This is the same in any risk assessment

ranging from economic, ecological, or nuclear

power plant safety. Implicit in the term risk (or

in positive terms ‘‘chance’’) dwells a distribution of

probabilities, which can be more or less thor-oughly elucidated (Bernstein, 1996). The use of

distribution curves for exposure (MECs) and

toxicity (in this case ECOSAR estimated EC50

values for fish, daphnids and algae) allows the

application of joint probability method to describe

the nature of risks posed by the MECs of

pharmaceuticals and the estimated effect concen-

trations. The straight-line transformations areconverted by probit transformation for the prob-

ability (ranked percentages) (1. axis) versus log-

transformation of the concentrations (2. axis). The

analysis was preformed in an EXCEL spreadsheet

designed to perform the double probability eco-

toxicology risk assessment procedures outlined in

Solomon et al. (2000). Overlap between the

measured concentrations and the estimated effectconcentrations are then indicative of the existence

of risk.

2.3. Hazard quotients

We also calculated the hazard quotient (HQ)

(MEC/EC50) for the compounds: Values B/1

indicate an insignificant risk and no need for

further risk assessments in a tiered procedure(Maund et al., 2001), depending on the assessment

factors that are being applied, whose values vary

between the US and the EU. A conservative

assessment factor of 106 has been proposed to

apply for ECOSAR predictions of fragrances by

Salvito et al. (2002), whereas the USEPA typically

applies an assessment factor of 2�/10 to ECOSARpredictions. The application of assessment factors

is a risk management decision outside the scope of

this paper, which focuses on the characterization

of environmental hazards associated with pharma-

ceuticals reported in surface waters; assessment

factors is thus not considered.

2.4. Conservative approach

The highest environmental concentration of

pharmaceuticals found in water, and the lowest

effect concentration from the ECOSAR estima-

tions, respectively, were used in the analysis, to

secure homogeneity and initially optimal conser-

vatism throughout the test. A few chemicals found

in the aquatic environment could not be estimatedin ECOSAR due to lack of SMILES notation for

the compounds (Meyland and Howard, 1998). The

precision of the ECOSAR predictions increases for

compounds where ECOSAR identifies an SAR

that allows assessment of excess toxicity beyond

the narcotic toxicity towards aquatic organisms

(personal communication, Nabholz, 2002). This

was the case for �/90% of the compoundsscanned. Most modern pharmaceuticals are opti-

mized for a specific pharmacodynamic modes of

action, which the ECOSAR will not identify,

pharmaceutical targets, e.g. membranes, enzymes,

or bacterial components, are not restricted to

mammalian physiology, as many of these are

ubiquitously present on many levels of biological

organization (Seiler, 2002). Furthermore, the avail-able experimental ecotoxicological data for human

and veterinary pharmaceuticals were compared

with the ECOSAR estimates. The chemicals White

Paper (EU, 2001b), stresses the foundation and

application of the precautionary principle, there-

fore, we used only the lowest EC50 predictions and

the highest MECs including inside sewage treat-

ment plant concentrations in this analysis.

3. Results

Table 1 illustrates the available concentration

data of pharmaceuticals in environmental water

samples from the EU and the US (�/2002) in the

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395386

Table 1

ECOSAR scan of environmentally occurring pharmaceuticals in Europe and North America

Compound CAS# Water concentration Fish Daphnid Algae log Kow HQ

Fish Daphnid Algae

EUAcetemineophen 103-90-2 /0:006 258.00 41.00 2549.00 0.27 2.33�/10�5 1.46�/10�4 2.40�/10�6

Acetylsalicylic acid 50-78-2 /0:0015 796.00 8858.00 61.00 1.13 1.88�/10�6 1.69�/10�7 2.46�/10�5

Betaxolo 63659-18-7 0.00019 20.00 1.50 4.40 2.98 9.50�/10�6 1.27�/10�4 4.32�/10�5

Bezafibrate 41859-67-0 /0:0046 5.30 25.00 18.00 /4:25 8.68�/10�4 1.84�/10�4 2.56�/10�4

Bisprolol 66722-44-9 /0:0029 113.00 8.00 14.00 1.84 2.57�/10�5 3.63�/10�4 2.07�/10�4

Carazolol 57775-29-8 0.00012 31.00 2.50 6.00 2.66 3.87�/10�6 4.80�/10�5 2.00�/10�5

Carbamazepine 298-46-4 /0:0063 101.00 111.00 70.00 2.25 6.24�/10�5 5.68�/10�5 9.00�/10�5

Clenbuterol 37148-27-9 0.00005 30.00 2.00 10.00 2.00 1.67�/10�6 2.50�/10�5 5.00�/10�6

Clofibrate 637-07-0 0.00004 5.00 6.50 0.50 /3:62 8.00�/10�6 6.15�/10�6 8.00�/10�5

Clofibric acid 882-09-7 /0:0016 53.00 293.00 192.00 2.84 3.02�/10�5 5.46�/10�6 8.33�/10�6

Cyclophosphamide 50-18-0 0.00002 70.00 1795.00 11.00 0.97 2.86�/10�7 1.11�/10�8 1.82�/10�6

Dextropropoxyphene 1639-60 /0:001 13.00 24.00 1.00 /3:20 7.69�/10�5 4.17�/10�5 1.00�/10�3

Diatrizoate 737-31-5 0.00023 6.14�/105 4.88�/105 2.52�/105 �/1.28 3.75�/10�10 4.71�/10�10 9.13�/10�10

Diazepam 439-14-5 0.00004 28.00 2.00 5.50 2.70 1.43�/10�6 2.00�/10�5 7.27�/10�6

Diclofenac 15307-79-6 /0:0012 532.00 5057.00 2911.00 0.57 2.26�/10�6 2.37�/10�7 4.12�/10�7

Dimethylaminophenazone 58-15-1 0.00034 3.70 8.30 1.30 0.60 9.19�/10�5 4.10�/10�5 2.62�/10�4

17a-Etinylestradiol 57-63-6 0.000043 2.10 2.10 2.00 /4:12 2.05�/10�5 2.05�/10�5 2.15�/10�5

Fenofibrate 49562-28-9 0.00003 0.80 0.35 0.10 /5:19 3.75�/10�5 8.57�/10�5 3.00�/10�4

Fenofibric acid 42017-89-0 0.00028 7.70 38.00 26.00 /4:00 3.64�/10�5 7.37�/10�6 1.08�/10�5

Fenoterol 13392-18-2 0.00006 20.00 17.50 25.00 1.22 3.00�/10�6 3.43�/10�6 2.40�/10�6

Gemfibrozil 25812-30-0 /0:0015 0.90 6.00 4.00 /4:77 1.67�/10�3 2.50�/10�4 3.75�/10�4

Ibuprofen 15687-27-1 0.00053 5.00 38.00 26.00 /3:79 1.06�/10�4 1.39�/10�5 2.04�/10�5

Ifosfamide 3778-73-2 /0:00191 140.00 1795.00 11.00 0.97 1.36�/10�5 1.06�/10�6 1.74�/10�4

Indomethacine 53-86-1 0.0002 3.90 26.00 18.00 /4:23 5.13�/10�5 7.69�/10�6 1.11�/10�5

Iopamidol 60166-93-0 /0:015 8.66�/105 7.35�/105 3.78�/105 �/1.38 1.73�/10�8 2.04�/10�8 3.97�/10�8

Iopromide 73334-07-3 /0:011 8.65�/106 7.66�/106 3.70�/106 �/2.49 1.27�/10�9 1.44�/10�9 2.97�/10�9

Ketoprofen 22071-15-4 0.00012 32.00 248.00 164.00 /3:00 3.75�/10�6 4.84�/10�7 7.32�/10�7

Methaqualone 72-44-6 /0:001 1.10 1.50 1.00 /4:33 9.09�/10�4 6.67�/10�4 1.00�/10�3

Methotrexate 59-05-2 0.00001 3.83�/105 651.00 192.00 �/1.28 2.61�/10�11 1.54�/10�8 5.21�/10�8

Morphine 57-27-2 0.0009 257.00 32.00 39.00 0.72 3.50�/10�6 2.81�/10�5 2.31�/10�5

Metoprolol 37350-58-6 /0:022 116.00 8.00 14.00 1.69 1.90�/10�4 2.75�/10�3 1.57�/10�3

Naproxen 22204-53-1 0.00039 34.00 15.00 22.00 /3:10 1.15�/10�5 2.60�/10�5 1.77�/10�5

Paracetamol 103-90-2 /0:014 40.00 41.00 2549.00 0.27 3.50�/10�4 3.41�/10�4 5.49�/10�6

Phenazone 60-80-0 0.00095 3.00 6.70 1.10 0.59 3.17�/10�4 1.42�/10�4 8.64�/10�4

Propranolo 525-66-6 0.00059 29.50 2.30 5.50 2.60 2.00�/10�5 2.57�/10�4 1.07�/10�4

Propyphenazone 479-92-5 /0:01 0.80 3.50 1.00 2.05 1.25�/10�2 2.86�/10�3 1.00�/10�2

Albuterol 18559-94-9 0.000035 38.00 30.00 36.00 0.64 9.21�/10�7 1.17�/10�6 9.72�/10�7

Salicylic acid 69-72-7 /0:041 1.28 59.00 48.00 2.24 3.20�/10�2 6.95�/10�4 8.54�/10�4

Terbutaline 23031-25-6 0.00012 1.05 27.00 32.00 0.67 1.14�/10�4 4.44�/10�6 3.75�/10�6

Theophylline 58-55-9 /0:001 1679.00 91.00 75.00 �/0.39 5.96�/10�7 1.10�/10�5 1.33�/10�5

3,4,5,6-Tetrabromo-o -cresol 576-55-6 /0:0001 0.01 0.70 0.13 /5:62 8.33�/10�4 1.43�/10�4 7.69�/10�4

Timolol 26839-75-8 0.00001 126.00 9.00 15.50 1.75 7.94�/10�8 1.11�/10�6 6.45�/10�7

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Table 1 (Continued )

Compound CAS# Water concentration Fish Daphnid Algae log Kow HQ

Fish Daphnid Algae

Tolfenamic acid 13710-19-5 /0:0016 0.40 1.70 1.30 /5:38 4.00�/10�3 9.41�/10�4 1.23�/10�3

USAChlorotetracycline 57-62-5 0.00069 1.39�/105 357.00 267.00 �/0.68 4.96�/10�9 1.93�/10�6 2.58�/10�6

Ciprofloxacin 85721-33-1 0.00003 2.46�/105 991.00 938.00 0.01 1.22�/10�10 3.03�/10�8 3.20�/10�8

Erythomycin 114-07-8 /0:017 61.00 7.80 4.30 2.48 2.79�/10�4 2.18�/10�3 3.95�/10�3

Norfloxacin 70458-96-7 0.00012 1.40�/104 1449.00 1232.00 �/0.31 8.55�/10�9 8.28�/10�8 9.74�/10�8

Lincomycin 154-21-2 0.00073 1391.00 82.00 86.00 0.29 5.25�/10�7 8.90�/10�6 8.49�/10�6

Oxytetracycline 79-57-2 0.00012 1.66�/105 2432.00 2294.00 �/2.87 7.23�/10�10 4.93�/10�8 5.23�/10�8

Roxithromycin 80214-83-1 0.00018 50.00 6.00 4.00 2.72 3.60�/10�6 3.00�/10�5 4.50�/10�5

Sulfadimethoxine 122-11-2 0.00006 226.00 3.50 24.00 1.17 2.65�/10�7 1.71�/10�5 2.50�/10�6

Sulfamethazine 57-68-1 0.00012 517.00 4.00 38.00 0.76 2.32�/10�7 3.00�/10�5 3.16�/10�6

Sulfamethizole 144-82-1 0.00013 1113.00 5.00 60.00 0.41 1.17�/10�7 2.60�/10�5 2.17�/10�6

Sulfamethaxazole 723-46-6 /0:019 890.00 4.50 51.00 0.48 2.13�/10�5 4.22�/10�3 3.73�/10�4

Tetracycline 60-54-8 0.00011 16.00 550.00 475.00 �/1.33 6.88�/10�6 2.00�/10�7 2.32�/10�7

Trimethoprim 738-70-5 0.00071 795.00 4.80 2.60 0.73 8.93�/10�7 1.48�/10�4 2.73�/10�4

Tylosin 1401-69-0 0.00028 27.40 66.00 16.00 1.05 1.02�/10�5 4.24�/10�6 1.75�/10�5

Cimetidine 51481-61-9 0.00058 571.00 35.00 40.00 0.57 1.02�/10�6 1.66�/10�5 1.45�/10�5

Codeine 76-57-3 /0:001 238.00 16.00 23.00 1.28 4.20�/10�6 6.25�/10�5 4.35�/10�5

Diltiazem 42399-41-7 0.000049 23.00 2.90 1.90 2.79 2.13�/10�6 1.69�/10�5 2.58�/10��5

Enalaprilat 76420-72-9 0.000046 7.30�/104 3690.00 2523.00 �/0.94 6.30�/10�10 1.25�/10�8 1.82�/10�8

Fluoxetine 54910-89-3 0.000012 1.70 0.17 0.80 /4:65 7.06�/10�6 7.06�/10�5 1.50�/10�5

Gemfibrozil 25812-30-0 0.00079 0.90 6.00 4.00 /4:77 8.78�/10�4 1.32�/10�4 1.98�/10�4

Metformin 657-24-9 0.00015 3.32�/104 1345.00 511.00 �/2.64 4.52�/10�9 1.12�/10�7 2.94�/10�7

Ranitidine 66357-35-5 0.00001 1076.00 63.00 66.00 0.29 9.29�/10�9 1.59�/10�7 1.52�/10�7

Acetaminophen 103-90-2 /0:01 1.00 42.00 2549.00 0.27 1.00�/10�2 2.38�/10�4 3.92�/10�6

Caffeine 58-08-2 /0:006 805.00 46.00 46.00 0.16 7.45�/10�6 1.30�/10�4 1.30�/10�4

Cotinine 486-56-6 0.0009 4747.00 4535.00 2577.00 0.34 1.90�/10�7 1.98�/10�7 3.49�/10�7

1,7-Dimethylxanthine 611-59-6 0.00011 1679.00 91.00 75.00 �/0.39 6.55�/10�8 1.21�/10�6 1.47�/10�6

Ibuprofen 15687-27-1 /0:001 5.00 38.00 26.00 /3:79 2.00�/10�4 2.63�/10�5 3.85�/10�5

Acetophenone 98-86-2 0.00015 181.00 190.00 116.00 1.67 8.29�/10�7 7.89�/10�7 1.29�/10�6

17a-Etynyl-estradiol 57-63-6 0.000831 2.10 2.10 2.00 /4:12 3.96�/10�4 3.96�/10�4 4.16�/10�4

C�/1 (underlined indicate should be risk assessed by FDA (�/1 mg l�1�/�/0.001 mg l�1) all exceeded the EU cut off of 0.000001 mg l�1) and the estimated log Kow

(critical value for significant bioaccumulation potential �/3, underlined) the compound name is underlined if both criteria are fulfilled at the same time. The final row is

the HQ (MEC/EC50) for the three endpoints, ranked in Fig. 2.

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open literature (Richardson and Bowron, 1985;Halling-Sørensen et al., 1998; Daughton and

Ternes, 1999; Ayscough et al., 2000; Jones et al.,

2001; Kummerer, 2001; Halling-Sørensen et al.,

2002; Sturer-Lauridsen et al., 2002; Kolpin et al.,

2002; Schulman et al., 2002). The list depicts the

differences found in the EU and the US for

compounds of potential need for environmental

risk assessment and for significant bioaccumula-

tion. The differences detected for certain com-

pounds between the US and the EU surveys, could

either indicate significant differences in use pat-

terns, wastewater treatment, manure and sludge

management, environmental conditions or simply,and most likely, differences in analytical focus.

Twenty-three of the substances were in worst-

case scenarios detected at levels �/1 mg l�1, thus

fulfilling the FDA the requirements for an envir-

onmental risk assessment. All of them fulfilled the

EU criteria of 0.01 mg l�1, which is virtually the

detection limit. Thirteen had potential for signifi-

cant bioaccumulation (log Kow�/3). Bezafibrate,

Dextropropoxyphene, Gemfibrozil, Ibuprofen,

Methaqualone and Tolfenamic acid (or 8%) ful-

filled both risk characteristics.

Fig. 1 illustrates that acute risks are probably

not significant, as there is no overlap between the

distribution of the MECs and the estimated EC50

values. There are 1�/2 orders of magnitude in

difference between the environmental concentra-

tions and the effect concentrations at the level

where 10% (10th centile) of the compounds effect

concentrations would be exceeded 5% (95th cen-

tile) of the time. The probability of the highest

MEC exceeding the 10th centile of EC50s for fish,

daphnids and algae are all 0.3%. In a theoretical

worst-case scenario there might be an overlap of

probabilities for the lowest fish EC50 and the least

frequent and highest environmental concentration.

3,4,5,6-Tetrabromo-o-cresol has the lowest EC50

for fish, 0.01 mg l�1 while its environmental

concentration is 0.0001 mg l�1 (see Table 1 and

Fig. 2), so the theoretical overlap is not a

consistent risk, before application of assessment

factor.

Fig. 1. Displays the percent rank distribution of the environmental concentrations from the literature and the effect concentrations in

mg l�1 derived from the ECOSAR screening.

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395 389

On average, the order of susceptibility among

the three endpoints was: algae]/daphnids�/fish.

Note that the cytostatic pharmaceuticals (Cyclo-

phosphamide and Ifosfamide) represent a special

high risk for mammals and potentially other

trophic levels in the environment, as these are

known to be carcinogenic, mutagenic and embry-

otoxic (Kummerer, 2001).

More than 99.9% of the ranked HQs (Maund et

al., 2001) in Fig. 2 are less than 1, before

application of an assessment factor. On average,

the MECs were five orders of magnitude smaller

than the related effect concentrations. This indi-

cates no significant environmental risks based on

the ECOSAR estimates and the available MECs.

For the exact HQs for each individual compound

and endpoint see Table 1.

Because most (Q)SAR models (including ECO-

SAR) uses lipophilicity, plus additional excess

toxicity due to structure to develop models to

predict toxicity these models should, when possible

be authenticated by comparing modeled versus

measured data (Nabholz, 2001). In this case it

maybe extra important as 82% of the compounds

were hydrophilic, thus we compared the modeled

predictions with data from the open literature.

Figs. 3�/5 is a graphically representation of mod-

eled versus experimental EC50 data for fish,

daphnids and algae (Halling-Sørensen et al.,

2002; Sturer-Lauridsen et al., 2002; Wilson et al.,

2002; Johnson et al., 2002) for 20 different

pharmaceuticals reported to occur in surface

waters that have experimental data for either

fish, daphnids and/or algae. In 80% of the cases

where both measured and modeled data were

available, the ECOSAR EC50 estimations were

the lower (or over-protective) than the measured

effect concentration. Cleuvers (2003) found that

for all endpoints and compounds he tested the

QSAR derived EC50 predictions were lower than

the measured EC50 values, even though only

Clofibrinic acid had a log Kow�/3. He concluded

that the compounds he worked with all acted

unspecifically by non-polar narcosis and that

Fig. 2. Graphical illustration of ranked HQs (MEC/EC50) for pharmaceuticals reported in the environment for fish, daphnids and

algae. More than 99.9% of the HQ’s were B/1 with an approximately median of 10�5, before application of an arbitrary assessment

factor.

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395390

toxicity thus may be associated with log Kow

rather than any specific toxic action in the non-

target organism (Cleuvers, 2003).

4. Discussion

The HQs were derived by comparing the highest

MECs from the literature with the lowest ECO-

SAR prediction, indicating low acute risk to

aquatic organisms (median HQ:/10�5). How-ever, if an assessment factor of 1000, as advised

in the EU, is applied to the (Q)SAR predictions

14% of the compound’s HQ would exceed 1 and

require further testing, which is consistent with

findings of 13% for high volume pharmaceuticals

exceeding 1 (EU, 2001a). Caution, due to uncer-

tainty connected to the regressions in the ECO-

SAR, has been raised by Kaiser et al. (1999). In

80% of the cases where both an experimental and

modeled effect concentration were available, the

estimated values were lower than the correspond-

ing lowest measured effect concentration (see Figs.

3�/5). Intra- and inter-laboratory variability of

standard single species toxicity tests needs to be

taken into account when assessing the sensitivity

and quality of SAR estimates versus experimental

values. Personne and Janssen (1994) have found

that the average coefficient of variation (CV�/

S.D./mean�/100) in single species laboratory

bioassays exceeds 25% and can be as high as

50% in some cases. Furthermore, changes in

laboratory environmental factors such as tempera-

ture, light or pH can ‘‘modulate’’ the toxicity of

Fig. 3. Measured fish effect concentration from the literature vs. ECOSAR estimated fish effect concentration for 20 different

pharmaceuticals.

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395 391

compounds by up to two orders of magnitude

(Personne and Janssen, 1994).

The EU-Directive 93/67/EEC classifies com-

pounds according to their EC50 values: B/0.1

mg l�1�/extremely toxic to aquatic organisms;

0.1�/1 mg l�1�/very toxic to aquatic organisms;

1�/10 mg l�1�/toxic to aquatic organisms; 10�/100

mg l�1�/harmful to aquatic organisms; B/100 mg

l�1�/non-toxic to aquatic organisms. According

to EU-Directive 93/67/EEC, the anti-fungal

3,4,5,6-tetrabromo-o -cresol was extremely toxic;

12% of all the compounds found in surface waters

were toxic, 41% were harmful and 47% were non-

toxic. Thus despite low risks, more than half of

these pharmaceuticals may due to their intrinsic

toxicity cause unwanted harm in aquatic environ-

ments and are liable to be labeled N;R50/53

(Dangerous for the environment; very toxic to

aquatic organisms, may cause long-term adverse

effects in the aquatic environment) or R52/53

(Harmful to aquatic organisms, may cause long-

term adverse effects in the aquatic environment)

according to EU-Directive 93/67/EEC).

The current battery of ecotoxicological testing

of chemicals is not tailored for a risk assessment of

pharmaceuticals in terms of mechanistic knowl-

edge and statistical analysis in terms of replication

and statistical power (Weiss, 1998). As most

pharmaceuticals are designed to affect mammalian

physiology, it is not known what effects they could

have on other forms of life, e.g. aquatic fauna or

plants (Seiler, 2002). Knowledge of the availability

Fig. 4. Measured daphnids effect concentration from the literature vs. ECOSAR estimated daphnids effect concentration for 20

different pharmaceuticals.

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395392

of pharmaceuticals to cellular targets is required

for an effective risk assessment. Due to the

continued low exposure the effects of most interest

will be chronic and subtle effects on the organisms

function, reproduction, behavior, metabolism,

genotoxicity etc. (Jones et al., 2001). The current

protocols are primarily designed to suit other

chemicals such as pesticides, where acute effects

on algae, aquatic insects or fish are expected

because the ecotoxicological modes of action of

the compounds are better elucidated. Moreover,

the risk management and risk communication

process is also more complex for pharmaceuticals

than other chemicals as pharmaceuticals intui-

tively are perceived as ‘‘good’’, therapeutic com-

pounds, and the environmental risks are easily

outweighed, as pharmaceuticals’ benefits to hu-

mans are a greater priority (Henschel et al., 1997).

5. Conclusions

Due to the low MECs acute risks are not likely,

simple extrapolation of effects from higher con-

centrations does not necessarily have relevance at

lower concentrations. �/50% of the reportedpharmaceuticals were intrinsically toxic potentially

leading to the necessity for labeling in the EU. The

complicated issue of mixtures and additive, syner-

gistic or antagonistic effects need to be addressed

(Cleuvers, 2003) along with assessment of chronic,

population and ecosystem effects. Without these

Fig. 5. Measured algae effect concentration from the literature vs. ECOSAR estimated algae effect concentration for 20 different

pharmaceuticals.

H. Sanderson et al. / Toxicology Letters 144 (2003) 383�/395 393

analyses and careful consideration of the statisticalpower and detectability of the test (Sanderson and

Petersen, 2002), it would be unwise as well as

statistically and scientifically false to conclude that

pharmaceuticals are not causing effects in the

environment at all (Jones et al., 2001). The present

analyses indicate that the regulatory and risk

management context concerning pharmaceuticals

in the aquatic environment is more complicatedthan risk management of other chemicals. The

uncertainty concerning pharmaceutical mode of

action in environmentally relevant non-target

organisms, mixture interactions, degradation pro-

ducts, bioavaliability, low acute risks, but intrinsic

toxicity and bacterial resistance contributes to the

risk management challenges. Ultimately, the en-

vironmental risk assessment and managementframework for pharmaceuticals must balance these

uncertainties on a case-by-case basis against the

human health benefits of pharmaceuticals. QSARs

can be used as a prioritization tool for the risk

assessment and management of pharmaceuticals.

Acknowledgements

The authors greatly acknowledge The Canadian

Network of Toxicology Centers for supporting

this work.

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