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Environmental Risk AssessmentEnvironmental Risk AssessmentEnvironmental Risk AssessmentEnvironmental Risk Assessmentof Antimicrobialsof Antimicrobialsof Antimicrobialsof Antimicrobials

Hans-Christian Holten Lützhøft

Thesis for the degree of Philosophiae Doctor.

The defence takes place Thursday 31 August 2000 at 14.00 in the Benzon lecture room,

The Royal Danish School of Pharmacy, Universitetsparken 2, DK-2100 Copenhagen Ø.

The Royal Danish School of Pharmacy

Department of Analytical and Pharmaceutical Chemistry

Copenhagen 2000

ISBN 87-988069-0-4

Hans-Christian Holten LützhøftSection of Environmental ChemistryDepartment of Analytical and Pharmaceutical ChemistryThe Royal Danish School of PharmacyUniversitetsparken 2DK-2100 Copenhagen ØTel: +45 35 30 60 00 direct: +45 35 30 64 59Fax: +45 35 30 60 13e-mail: [email protected]: www.dfh.dk

Til Rikke

Preface • v

Preface

This Ph.D. thesis was performed at Section of Environmental Chemistry, Department of

Analytical and Pharmaceutical Chemistry, The Royal Danish School of Pharmacy, in order to

obtain the pharmaceutical Ph.D. degree. The project was undertaken from October 1996 to

July 2000, with Professor D.Sc. Sven Erik Jørgensen and Assoc. Professor Ph.D., M.Sc.

(Pharm.) Bent Halling-Sørensen as supervisors.

In the thesis 4 articles are enclosed which have been published or submitted to international

peer-reviewed journals. A bold Roman numeral will follow reference to one of mentioned

articles:

I Holten Lützhøft HC, Halling-Sørensen B, Guardabassi L, Ingerslev F and

Tjørnelund J. Submitted. Oxolinic acid in freshwater sediment – extraction

method and occurrence due to fish farm activities.

II Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, and

Hermens JLM. 2000. 1-Octanol/water distribution coefficient of oxolinic

acid: Influence of pH and its relation to the interaction with dissolved

organic carbon. Chemosphere, 40(7), 711-714.

III Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, and

Hermens JLM. Accepted. The influence of pH and other modifying factors

on the distribution behaviour of 4-quinolones to solid phases and humic

acids studied by “negligible-depletion” SPME-HPLC. Environ Sci Technol.

IV Holten Lützhøft HC, Halling-Sørensen B, and Jørgensen SE. 1999. Algal

toxicity of antibacterial agents applied in Danish fish farming. Arch Environ

Contam Toxicol, 36(1), 1-6.

Copenhagen, July 2000.

Hans-Christian Holten Lützhøft

• Contentsvi

Contents

Preface v

Contents vi

Abbreviations ix

Figures x

Tables xi

1. General Introduction 1

1.1. Introduction 2

1.2. Selection of Chemicals 3

1.3. Objectives of This Ph.D. Thesis 4

1.4. Outline of This Ph.D. Thesis 7

2. Basic Data of Danish Fish Farms and Antimicrobials – Initial Environmental

Assessment 11

2.1. Fish Farm Characteristics 12

2.2. Chemicals 13

2.2.1. Antimicrobial Therapy in Fish Farming 13

2.2.2. Physical Chemical Properties 15

2.2.3. Environmental Distribution According to Fugacity 20

2.2.4. Pharmacology 22

2.2.4.1. Pharmacokinetics in Rainbow Trout 22

2.2.4.2. Pharmacodynamics 28

2.3. Initial Environmental Assessment 30

3. Environmental Occurrence of Antimicrobials 35

3.1. Introduction 36

3.2. Analytical procedures 38

3.2.1. Separation methods 38

3.2.2. Extraction Methods 43

3.2.2.1. Marine sediment 43

3.2.2.2. Freshwater sediment 43

3.3. Antimicrobials in Environmental Samples 49

Contents • vii

3.3.1. Marine occurrence 49

3.3.2. Freshwater Occurrence 50

4. Environmental Fate of Antimicrobials 57


4.2. Environmental Distribution 58

4.2.1. pH-dependent 1-octanol/water distribution 59

4.2.2. Experimental Distribution Coefficients 60

4.2.3. Complexation with metals 65

4.3. Degradability 67

4.3.1. Abiotic degradation 67

4.3.1.1. Hydrolysis 67

4.3.1.2. Photodegradation 68

4.3.2. Biotic degradation 71

4.3.2.1. Biodegradation 71

4.3.2.2. Enzymatic Degradation 73

4.4. Transport 73

5. Environmental Effects of Antimicrobials 75


5.2. Selection of Test Organisms 76

5.3. Toxicity on Various Trophic Levels 78

5.3.1. Micro-organisms – Bacteria and Micro-algae 80

5.3.2. Crustaceans 81

5.3.3. Fish 82

5.4. Factors Affecting Toxicity 82

6. Environmental Risk Assessment of Antimicrobials Applied in Danish Fish Farms 87


6.2. Predicted No Effect Concentrations 89

6.3. Exposure Scenarios 92

6.3.1. Main Settings 93

6.3.2. Definitions and Procedures to Derive Predicted Environmental Concentrations 94

6.3.2.1. Scenario 1 – Worst Case 94

• Contentsviii

6.3.2.2. Scenario 2 – Incorporation of Inevitable Processes 95

6.3.2.3. Scenario 3 – Incorporation of Inevitable Processes and Natural Dilution 96

6.4. Assessment of the Derived Risk Quotients 97

6.5. Risk Management Procedures 101

7. Conclusions 105

References 109

Summary 123

Resumé på dansk 127

Publications 130

Abstracts 131

Curriculum Vitae 132

Acknowledgements 133

Abbreviations • ix

Abbreviations

AMX AmoxicillinAUC Area under the plasma concentration-time curveDDOC Dissolved organic carbon/water distribution coefficient for an ionized

moleculeDF Dilution factorDNA Deoxyribonucleic acidDOC Dissolved organic carbon – a measure for humic acidsDOW 1-Octanol/water distribution coefficient for an ionized moleculeDSED Sediment/water distribution coefficient for an ionized moleculeEC50 The concentration that provokes effect in 50 % of the populationERA Environmental risk assessmentF BioavailabilityFLU FlumequineHPLC High performance liquid chromatographyi.a. Intra arterial administrationi.v. Intra venous administrationKCOM Metal complexation constantke Elimination rate constantKOC Organic carbon/water partition coefficient for a neutral moleculeKOW 1-Octanol/water partition coefficient for a neutral moleculeLC50 The concentration that provokes lethality in 50 % of the populationLLE Liquid liquid extractionMeOH MethanolMIC Minimum inhibitory concentrationnd Negligible depletionNOEC No observed effect concentrationOC Organic carbonOTC OxytetracyclineOXA Oxolinic acidp.o. Oral administrationPEC Predicted environmental concentrationPNEC Predicted no effect concentrationQSAR Quantitative structure activity relationshipRP HPLC Reversed phase high performance liquid chromatographyRQ Risk quotientSAF SarafloxacinSDZ SulphadiazineSPE Solid phase extractionSPME Solid phase microextractionTHF TetrahydrofuranTMP Trimethoprim

• Figuresx

Figures

Figure 1.1 – Conceptual diagram over the processes that an antimicrobial encounters in the

aquatic environment. 4

Figure 1.2 – Conceptual diagram of the tasks performed in this Ph.D. thesis. 8

Figure 2.1 – A schematic drawing of a typical Danish fish farm. 13

Figure 2.2 – Fractional composition and experimental 1-octanol/water distribution

coefficients vs. pH for the antimicrobials. 18

Figure 2.3 – pH corrected DOW profiles for the neutral species of OXA and TMP. 19

Figure 2.4 – Plasma concentration-time profiles for FLU in rainbow trout. 24

Figure 3.1 – Anticipated exposure routes of antimicrobials applied in fish farming. 36

Figure 3.2 – Column material effect on the chromatography of OXA. 38

Figure 3.3 – SPME-HPLC analysis of FLU, OXA and SAF. 39

Figure 3.4 – Procedure to extract OXA from freshwater sediment. 44

Figure 4.1 – Experimental 1-octanol/water distribution for OXA. 59

Figure 4.2 – Experimental distribution coefficients to DOC for FLU, OXA and SAF. 62

Figure 4.3 – Experimental distribution coefficients vs. log DOW. 64

Figure 5.1 – Simplified characteristics of micro-organisms. 77

Figure 6.1 – Illustration of the constant assessment factor approach. 90

Figure 6.2 – Interpretation of the risk quotient in the context of this thesis. 98

Figure 6.3 – Graphical representation of the antimicrobial RQs according to scenario 3. 100

Tables • xi

Tables

Table 2.1 – Antimicrobial consumption in Danish fish farming during 1994-1997, kg. 14

Table 2.2 – Chemical structures and selected physical chemical properties of the

investigated antimicrobials. 16

Table 2.3 – Environmental distribution of antimicrobials according to fugacity

principles. 21

Table 2.4 – Pharmacokinetics in rainbow trout. 26

Table 2.5 – Dosing regimes and environmental load in Danish fish farms. 30

Table 2.6 – Experimental 1-octanol/water distribution coefficients. 32

Table 3.1 – HPLC methods for the investigated quinolones. 40

Table 3.2 – Eluents evaluated for the freshwater sediment extraction. 45

Table 3.3 – Recovery study for the extraction of OXA from freshwater sediment. 46

Table 3.4 – Sediment extraction methods for the investigated quinolones. 47

Table 3.5 – Environmental occurrence of the investigated antimicrobials. 52

Table 4.1 – Estimated and experimental distribution coefficients. 60

Table 4.2 – Influence of cations on the antimicrobial activity. 66

Table 4.3 – Antimicrobial stability under illumination. 70

Table 4.4 – Antimicrobial biodegradability. 71

Table 5.1 – Acute toxicity of antimicrobials, LC50 (NOEC), mg/L. 79

Table 5.2 – Chronic toxicity of antimicrobials, EC50 (NOEC), mg/L. 79

Table 6.1 – Antimicrobial PNECs in the aquatic environment. 91

Table 6.2 – Overview of exposure scenarios. 97

Table 6.3 – Antimicrobial risk quotients in the freshwater environment. 99

Chapter 1

General Introduction

• General Introduction2

1.1. Introduction

Until a few years ago, limited attention was paid to the possible risk associated with

application of antimicrobials in various environmental contexts (Schneider, 1994; Henschel et

al., 1997; Halling-Sørensen et al., 1998; Jørgensen et al., 1998; Pors, 1998; Montforts et al.,

1999; Ternes, 1999; Jørgensen and Halling-Sørensen, 2000). One of several applications is in

fish farming, although it is not the most significant. In this situation, the exposure is directly

to the aquatic environment. Aquaculture is a comprehensive industry, with production of e.g.

shrimp and trout in Asia and salmon and trout in America as well as Europe. Production water

undergoes only a simple wastewater treatment. In most cases the water only passes a

sedimentation pond in order to settle suspended solids. Sediment/sludge from fish farming is

often applied as fertilizer on arable land. If antimicrobials have high affinity for solids e.g.

sediment or soil, arable land will thus be indirectly contaminated with antimicrobials if

fertilized with fish farm sediment/sludge.

Thorough investigations are needed for the registration of pharmaceuticals to obtain a

marketing license. Some of these data are covered by the patent, but some can be found in the

open literature. Basic physical chemical and toxicological data for the antimicrobials can

therefore be achieved. However, an environmental risk assessment (ERA) has not yet been

required for the registration. Thus, only limited knowledge of the environmental impact of

antimicrobials used in aquaculture exists.

Antimicrobials are chemicals with a specific mode of action, designed to control pathogenic

bacterial infections. A few investigations show that depending on environmental conditions

some antimicrobials, e.g. oxytetracycline and oxolinic acid are persistent in marine

environments (Jacobsen and Berglind, 1988; Samuelsen et al., 1992b; Hektoen et al., 1995;

Lunestad et al., 1995). In Denmark, the major quantity of antimicrobials is applied in

agriculture (Halling-Sørensen et al., 1998). A few tonnes are applied in Danish fish farming,

whereas the consumption in fish farming in other countries is both less and more. Therefore,

application of antimicrobials in aquaculture creates a possible risk for the environment.

The environmental risks associated with application of antimicrobials in aquaculture are e.g.

acute toxicity (Harras et al., 1985), genotoxicity (Mamber et al., 1993; Couturier and

Chapter 1 • 3

Melderen, 1998), mutagenicity (Mamber et al., 1993) and development of bacterial resistance

(Nygaard et al., 1992; Samuelsen et al., 1992b; Guardabassi et al., 2000b). Especially bacteria

and related organisms, e.g. cyanobacteria may be affected in the environment. Not only fish

pathogenic bacteria, but also natural occurring bacteria may acquire resistance towards certain

antimicrobials. Resistance genes may be transferred to other (pathogenic) bacteria, with a

high extent of irreversible effects on the environment including mankind (Toranzo et al.,

1984; Sandaa et al., 1992; Kruse and Sørum, 1994). Consequently, even long time after the

chemical’s disappearance side effects as resistance may persist.

1 January 1998, the European Community adopted an environmental assessment guideline for

veterinary pharmaceuticals (EMEA, 1998b). Consequently, an environmental exposure

assessment, the so-called phase I assessment, is required for the registration of new veterinary

pharmaceuticals. However, due to the direct entry into the aquatic environment antimicrobials

applied in aquaculture have to undergo a more thorough investigation, the so-called phase II

assessment, compared to other veterinary pharmaceuticals. This assessment considers both

fate and effects studies (EMEA, 1998b).

In this thesis I will focus on specific elements, i.e. occurrence, distribution and selection of

target organisms, enabling an assessment of the environmental risks of currently used

antimicrobials in Danish fish farming in the context of said guideline.

1.2. Selection of Chemicals

The investigated chemicals represent different antimicrobial groups, see Chapter 2 for a

detailed description. Oxolinic acid, 5-ethyl-5,8-dihydro-8-oxo-1,3-dioxolo[4,5-g]quinoline-7-

carboxylic acid, (OXA); sulphadiazine, 4-amino-N-2-pyrimidinylbenzenesulfonamide,

(SDZ); trimethoprim, 5-[(3,4,5-trimethoxyphenyl)methyl]-2,4-pyrimidinediamine, (TMP);

amoxicillin, [2S-[2α,5α,6β(S*)]]-6-[[amino[4-hydroxyphenyl)acetyl]amino]-3,3-dimethyl-7-

oxo-4-thia-1-azabicyclo[3.2.0]heptane-2-carboxylic acid, (AMX) and oxytetracycline, [4S-

(4α,4aα,5α,5aα,6β,12aα)]-4-(dimethylamino)-1,4,4a,5,5a,6,11,12a-octahydro-

3,5,6,10,12,12a-hexahydroxy-6-methyl-1,11-dioxo-2-naphthacenecarboxamide, (OTC) are

currently applied in Danish fish farming.

Flumequine, 9-fluoro-6,7-dihydro-5-methyl-1-oxo-1H,5H-benzo[i,j]quinolizine-2-carboxylic

acid, (FLU) and sarafloxacin, 6-fluoro-1-(4-fluorophenyl)-1,4-dihydro-4-oxo-7-(1-


piperazinyl)-3-quinolinecarboxylic acid, (SAF) are included, as FLU is used and SAF is

contemplated to be used in other countries.

This selection means that the physical chemical properties are limited to the mentioned

antimicrobials; thus, properties for all possible chemicals will not necessarily be covered.

1.3. Objectives of This Ph.D. Thesis

When entering the aquatic environment, antimicrobials, similar to other xenobiotics,

encounter various processes, e.g. binding and degradation, as conceptualised in Figure 1.1.

Figure 1.1 – Conceptual diagram over the processes that an antimicrobial encounters inthe aquatic environmenta.

a: OXA is shown as example. “Sun”: photodegradation, Ca2+: complexation with ions, T: temperature, :biodegradation, and DOC: dissolved organic carbon.

The aim of this thesis is therefore to answer the following questions connected with the fate

and effects of antimicrobials:

1. Antimicrobials are used in fish farming to treat infections among fish. Knowing that

effluents from fish farming are discharged to the local stream, the exposure is directly

into the aquatic environment. Can antimicrobial residues therefore after treatment be

found in the sediment near Danish fish farms?

2. Antimicrobials are hydrophilic chemicals often with many functional groups (see

Chapter 2) affecting the fate of said chemicals, e.g. distribution between solids/organic

Chapter 1 • 5

phases and water. Often these groups are weak (carboxylic) acids or weak bases, which

make the distribution pH dependent. Furthermore, these groups contribute to e.g.

hydrogen bonds, dipole-dipole and ionic interactions. Humic acids, in this thesis

designated dissolved organic carbon (DOC), contains both hydrophobic sites and

functional groups similar to the antimicrobials, which make interactions likely to take

place. Consequently, the distribution is not straightforward and may therefore be

difficult to predict. It raises the following questions:

a) does the antimicrobial distribution coefficient between 1-octanol and water (DOW)

follow the understanding of distribution of ionized species?

b) to what extent do antimicrobials distribute to DOC and sediment?

c) can the distribution to DOC or sediment be predicted from the DOW?

3. In conventional ERA three trophic levels – algae, crustaceans and fish – are normally

proposed used to evaluate the effects of chemicals in the aquatic environment (OECD,

1992; EMEA, 1998b). Guidelines, e.g. ISO (1989), often suggest Selenastrum

capricornutum as test organism to represent micro-algae. Is this model target organism

an appropriate selection as the lowest trophic level for evaluation of antimicrobials?

4. Knowledge about environmental exposure, fate and effects of antimicrobials enable the

risk assessment of the application in Danish fish farming. Based on mentioned

properties, is it possible to rank the expected risk? Hence, which antimicrobials can be

recommended and which can not be recommended?

Answers to the above-mentioned questions all improve the reliability of the ERA of

antimicrobials; thus making it more realistic. The following investigations were performed to

answer the outlined questions; for a detailed scheme of the experimental work, see Figure 1.2:

Re. 1. In co-operation with a fish farm in Jutland, Denmark, sediment samples were taken

before and after a treatment with OXA. Samples were taken from the inlet, the

medicated pond, at the outlet and 300 m downstream the outlet. A sediment extraction

method was developed and the samples were analysed for the occurrence of OXA by

means of high performance liquid chromatography (HPLC). This subject is covered in

Holten Lützhøft et al. (Submitted) I.


Re. 2. The focus for the fate experiments was directed towards the quinolone antimicrobials.

Using the three selected quinolones – FLU, OXA and SAF – made it possible to

predict trends in their fate due to physical chemical properties.

a) In order to evaluate the distribution of antimicrobials between organic phases and

water, the OXA distribution between 1-octanol and water was studied. In order to

study the distribution of ionized species the DOW was investigated in the pH range

3 to 11. The DOW was evaluated according to the Henderson-Hasselbalch

principles. The results are published in Holten Lützhøft et al. (2000) II.

b) A newly developed technique involving solid phase microextraction (SPME)

coupled to HPLC was used to investigate the distribution behaviour between DOC

and water. Aldrich humic acids was used as DOC source. Samples were allowed to

equilibrate and the distribution coefficients between DOC and water (DDOC) for

FLU, OXA and SAF were investigated between pH 3 and 8. These experiments

are shown in Holten Lützhøft et al. (Accepted) III. A simpler set-up was used for

the sediment experiments. Flasks containing sediment, water (pH=7) and the

investigated chemical were allowed to equilibrate. Samples were taken, filtered

and analysed by HPLC. The distribution coefficient between sediment and water

(DSED) for FLU, OXA, SAF and TMP was established.

c) Prediction of binding to organic matter is often based on the partition coefficient

between 1-octanol and water (KOW) for the chemical. However, this approach

requires that the hydrophobicity of the chemical reflects its affinity for organic

matter. For ionizeable chemicals, the affinity for organic matter is expected to

decrease when the chemical is ionized, unless electrostatic interactions contribute

to the binding. In Holten Lützhøft et al. (2000) II an experimental DOW value for

OXA was used to predict the binding to organic carbon. This value was compared

with the DDOC value.

Re. 3. Antimicrobials are chemicals designed to prevent the growth of or kill micro-

organisms, i.e. bacteria. There is a distinct difference between micro-algae and bacteria

viz. micro-algae are eucaryotic and bacteria are procaryotic. However, the blue-green

algae or cyanobacteria are organisms sharing the oxygen producing properties of algae

and the procaryotic structure of bacteria. The usefulness of the green alga S.

capricornutum as test alga for antimicrobials was therefore evaluated using the

Chapter 1 • 7

cyanobacteria Microcystis aeruginosa and the cryptophycean Rhodomonas salina. The

growth inhibiting effects of FLU, OXA, SAF, SDZ, TMP, AMX and OTC were

investigated towards the three algae. This enabled the comparison of the procaryotic

cyanobacteria with the eucaryotic algae and the evaluation of the standard test

organism. These results are published in Holten Lützhøft et al. (1999) IV.

Re. 4. An ERA was performed for antimicrobials applied in Danish fish farming. Including

the knowledge acquired from the afore-mentioned investigations, the assessment

became more realistic. A risk quotient (RQ) viz. the ratio between the predicted

environmental concentration (PEC) and the predicted no effect concentration (PNEC),

PEC/PNEC, was calculated for each antimicrobial. Considering both exposure and fate

a PEC was calculated. As many relevant organisms as possible were considered when

calculating the PNEC. The RQs include currently available data. Based on the RQs the

antimicrobials and their applications were assessed.

Despite its importance, the aspect of bacterial resistance was not a subject for this thesis. All

investigations were limited to parent compounds, thus no biotransformation and degradation

products were considered. Only sediment from a freshwater fish farm was studied. Though

interesting, it is beyond the scoop of this thesis to investigate the situation in other countries –

consequently the situation will only be assessed in Denmark.

1.4. Outline of This Ph.D. Thesis

The various tasks performed in this thesis are conceptualised in Figure 1.2.


Figure 1.2 – Conceptual diagram of the tasks performed in this Ph.D. thesis.

Chapter 2 serves as a chapter with basic information about fish farming in Denmark.

Moreover, knowledge of the basic physical chemical and pharmacological properties of the

investigated antimicrobials are given. Based on said information an initial environmental

assessment is performed.

Chapters 3 to 5 mainly serve to provide answers to objective 1 to 3. They address the

occurrence, fate and effects of antimicrobials in an environmental context, respectively.

Additionally, Chapter 3 addresses analysis methods and extraction methods. Besides

distribution processes to environmental constituents, the fate processes discussed in Chapter

4 also cover abiotic and biotic degradation. Chapter 5 is not limited to the discussion of

proper test organisms; also, the toxicity of the antimicrobials towards different trophic levels

is discussed.

Along with data from Chapter 2, Chapters 3 to 5 also serve as input to the ERA – the

answer to objective 4.

Chapter 6 thus synthesises the basic information of the antimicrobials with the environmental

properties in an ERA. The way to derive PNECs as suggested by EMEA (1998b) is discussed.

Three exposure scenarios are presented and discussed. Finally, RQs are derived and

recommendations for the use of antimicrobials in fish farming are given.

Chapter 2

Basic Data of Danish Fish Farms

and Antimicrobials – Initial

Environmental Assessment

• Basic Data of Danish Fish Farms and Antimicrobials – Initial Environmental Assessment12

2.1. Fish Farm Characteristics

At present 470 freshwater fish farms are in operation in Denmark – mainly in the middle and

northern part of Jutland. In total, they produce abt. 35,000 tonnes of fish, of which the vast

majority is rainbow trout (Oncorhynchus mykiss formerly known as Salmo gairdneri). An

average fish farm in Denmark has a yearly production of 74 tonnes. Small fish farms have ca

15 ponds and large farms have ca 90 ponds. A typical pond measures an area of 150-200 m2

with a depth of 0.7 m. In total the yearly water flow is abt. 4⋅1012L. Each pond contains up to

2,000 kg fish. A sediment layer of 5 cm is assumed.

The production of fry is abt. 610 tonnes, which takes place in either glass or concrete ponds.

The size of a fry pond is abt. 0.7 m in the width, abt. 6 m in the length, and 0.5 m in the depth.

The density of fry is 15-30 kg/m3, which results in abt. 50 kg in each pond.

When the fry are transferred to the ponds they measure 4-5 cm and weigh abt. 2.5 g. 75 % of

the fish weigh 200-350 g and measure 30 cm.

A schematic drawing of a typical Danish fish farm is exhibited in Figure 2.1. The water to the

fry section is supplied from a well, whereas the water to the ponds is supplied from the local

stream. Presently fish farmers are allowed to utilize the total water flow of the stream, but

current legislation aims at reducing the utilization to half the median water flow by the year

2005. Water from the fry section and the ponds are led to a backchannel, which often also

contains fish. The water is further led to a sedimentation pond to allow particulate matter to

settle, but no active degradation procedures are undertaken. Finally, the water is often stripped

with air in order to increase the oxygen content and oxidize organic carbon before it is

discharged to the stream (Michelsen, Personal communication).

During the years 1993 to 1997 the average pH value and temperature of Danish streams, to

which fish farms effluents are discharged, were abt. 7.2 and 7.2ºC, respectively (Svendsen,

Personal communication).

Chapter 2 • 13

Figure 2.1 – A schematic drawing of a typical Danish fish farma.

a: arrows indicate water flow.

2.2. Chemicals

2.2.1. Antimicrobial Therapy in Fish Farming

In order to run an economically sound business the farmers themselves often seem compelled

to apply antimicrobials in the production (Dalsgaard and Bjerregaard, 1991). Factors that

cause diseases are e.g. intensive farming, poor water quality, inadequate feeding regimes

(McCracken et al., 1976; Dalsgaard and Bjerregaard, 1991). This can be optimized and

although vaccination programmes are used, antimicrobial treatment may be required

(Dalsgaard and Bjerregaard, 1991). Antimicrobials are either applied prophylactically or

therapeutically, however the only legal way of application is through a prescription from the

veterinarian (Tørnæs, 1990; Danmarks Apotekerforening, 1996; Andersen, 1999).

Bacterial diseases that usually occur in production-fish in Danish fish farming are enteric red

mouth disease (Yersinia ruckeri), furunculosis (Aeromonas salmonicida), and vibriosis

(Vibrio anguillarum). They are treated with either OXA or a combination of SDZ/TMP.

Under special circumstances, e.g. failure of previous treatment, AMX or OTC may be

prescribed. However, AMX and OTC are mainly used to treat fry mortality syndrome


(Cytophaga psychrophila). These two chemicals are only prescribed on dispensation from the

Danish Veterinary Directorate.

Antimicrobials are mainly administered as medicated food pellets, i.e. incorporated in the feed

pellets (Dalsgaard and Bjerregaard, 1991; Elema, 1995; Kilsgaard, 1996). However, it is

known that the food consumption is reduced among sick fish (Poppe, 1990; Samuelsen et al.,

1992a; Burka et al., 1997) and that medicated feed reduces feed intake (Hustvedt et al.,

1991b). Since the antimicrobials are administered directly to the water of the pond, a risk for

exposing the receiving stream exists. This will happen if the antimicrobials are not degraded

or if overfeeding takes place to circumvent anorexia (Samuelsen et al., 1992a).

Table 2.1 shows data for the consumption of antimicrobials in Danish fish farming from 1994

to 1997. As for human medicines, no reporting system for medicines applied in fish farming

exists yet. However, some voluntarily reporting from fish farmers to local authorities

(counties) and from manufactures to national authorities (The Danish Plant Directorate),

occur. It is therefore assumed that the data presented in Table 2.1 is the minimum

consumption of antimicrobials in Danish fish farming.

Table 2.1 – Antimicrobial consumption in Danish fish farming during 1994-1997, kg.

Year OXAa SDZa TMPa AMXb OTCb Otherb Total

1994 700 1,000 200 6 94 132 2,132

1995 906 1,241 248 78 67 242 2,782

1996 511 845 169 141 27 177 1,870

1997 587 1,677 335 132 16 181 2,928a: Viuf (Personal communication), b: Data from 3 counties (Danske amter, Personal communication), Other:dimetridazole (antiprotozoal), florfenicol, metronidazole, sulfamerazine, and antimicrobials as such.

An annual quantity of 2-3 tonnes is applied resulting in a usage of 57-86 mg per produced kg

fish. For comparison, both in Norway and Scotland, the usage of antimicrobials per kg

produced fish has decreased during the last years, mainly due to vaccination and improved

husbandry (Baird et al., Personal communication; Markestad and Grave, 1997). In Norway

the usage decreased from 885 mg/kg in 1987 to 7 mg/kg in 1994. In Scotland, the usage was

40 mg/kg in 1994 decreasing to 8 mg/kg in 1997. On the other hand, antimicrobial

concentrations of 4 mg/L was predicted in the waste water from American fish farming due to

Chapter 2 • 15

an annual usage of ca 11,700 tonnes (Vicari et al., Personal communication). A similar

measure for Danish conditions predicts concentrations reaching 0.7 mg/L in the effluents.

2.2.2. Physical Chemical Properties

Table 2.2 shows structures and selected physical chemical properties of the investigated

antimicrobials. They are grouped with respect to their chemical classes. FLU, OXA and SAF

belong to the 4-quinolones of which FLU and SAF also are classified as fluoroquinolones.

SDZ and TMP are so-called folate inhibitors. AMX belongs to the group of β-lactams and

OTC belongs to the tetracyclines.

Table 2.2 – Chemical structures and selected physical chemical properties of the investigated antimicrobials.

Group 4-quinolones Folate Inhibitors β-lactams TetracyclinesAntimicrobial FLU OXA SAF SDZ TMP AMX OTC

StructureN

F

CH3

COOH

O

N

O

COOH

CH3

O

O

N

F

O

COOH

N

HN

F

N

N NHS

NH2

O O

OCH3

H3CO

H3CON

N NH2

NH2

HO

NH

O

NH2 N

S CH3

CH3

COOHO

H H

O OOH

OH OH

OH

OHHO CH3N

CH3

HH

NH2

O

H3C

CAS # 42835-25-6 14698-29-4 98105-99-8 68-35-9 738-70-5 26787-78-0 79-57-2

Molecular formula C14H12FNO3 C13H11NO5 C20H17F2N3O3 C10H10N4O2S C14H18N4O3 C16H19N3O5S C22H24N2O9

Mw, g/mole 261.25 261.23 385.37 250.28 290.32 365.41 460.44

mp, °C 253-255b 314-316 (dec)b 275 (dec)i 252-256a 199-203a 194 (dec)r 200 (dec)v

S, mg/L 71c 4.1c 100j 74l 400a 4,000s 241x

pKa 6.4d 6.9f 4.1k, 6.8k or 6.0i & 8.6i 2.0 & 6.5m 7.1p 2.7, 7.2 & 9.6t 3.3, 7.3 & 9.1y

Log KOW 1.7d 0.7g - -0.1n 0.8q -1.2u -0.9z

Log DOW, pH=7.4 1.1e 0.4h -1.2i -1.0o 0.6p -1.5u -1.2g

KH, atm·m3/molea 2.7⋅10-13 4.1⋅10-16 1.9⋅10-19 1.6⋅10-10 2.4⋅10-14 2.5⋅10-21 1.7⋅10-25

Mw: Molecular weight, mp: melting point, dec: decomposes, S: Aqueous solubility at pH 7, Log KOW: Distribution coefficient at the pH where the uncharged or most neutral speciesdominates (see fractional composition according to Figure 2.2), a: KH values estimated according to Howard and William (1992), b: Budavari (1996), c: Elema (1995), d: Takács-Novákand Avdeef /1996), e: Hirai et al. (1986) pH=7.2, f: Timmers and Sternglanz (1978), g: Takács-Novák et al. (1992), h: Bjørklund and Bylund (1991), i: Renau et al. (1995), j: Appearsmore soluble than FLU and OXA, k: Holten Lützhøft et al., (Accepted) III, l: Stober and DeWitte (1982), m: Koizumi et al. (1964), n: Morishita et al. (1973), o: Wang and Lien (1980),p: Seiler et al. (1982), q: Dietrich et al. (1980), r: Budavari (1996) β-naphthalenesulfonate trihydrate, s: Budavari (1996) trihydrate, t: Tsuji et al. (1978), u: Smyth et al. (1981), v:Chapman & Hall (1998), x: Parfitt (1999), y: Stephens et al. (1956), z: Schumacher and Linn (1978), -: no data found.

Chapter 2 • 17

The structures shown in Table 2.2 give the impression of complex chemicals with many

functional groups, e.g. carboxylic acids, amines, carbonyl- sulfur- and hydroxyl groups. These

groups are ionizeable and polar groups, which make the speciation dependent on various

conditions, e.g. complex forming ions as Mg2+ and Ca2+ and pH. According to the pKa values

the chemicals will to some extent be ionized at physiological and environmental relevant pH

values, viz. 5-8. When the chemicals get more ionized the likelihood of electrostatic

interactions increases. The combination of water-soluble chemicals, solubility (S) at neutral

pH ranges from 4 mg/L to several grams per litre, and hydrophilic chemicals, log KOW ranges

from –1.2 to 1.7, decreases the apparent risk for (bio)accumulation. The S and KOW data

therefore indicate that the chemicals have high affinity for the aquatic compartments. The

molecular weight makes absorption by passive diffusion possible. However, the absorption

rate will be affected as the chemicals are ionized due to changes in pH.

Judging the bare physical chemical properties the antimicrobials will be assessed to reach the

aquatic environment when discharged.

The pH effect mentioned above is for instance seen in the KOW/DOW values. Instead of the

partition coefficient to 1-octanol, KOW, the term distribution coefficient, DOW, should be used.

KOW is used for neutral molecules, whereas DOW is used for ionized molecules.

Table 2.2 gives both the true partition coefficient and the distribution coefficient at pH≈7. As

antimicrobials are ionizeable chemicals, their hydrophobicity decreases when they become

ionized. Whether they are weak (carboxylic) acids or weak bases, this happens at high and

low pH values, respectively.

Figure 2.2 represents the fractional composition and experimental DOW values as a function of

pH. For most of the chemicals the DOW is reflected in the degree of ionization – however, this

is not clear for SAF, which may be due to the few experimental data. The antimicrobial

activity is also reflected in the degree of ionization. OTC shows the highest activity between

pH 5.5 and 6 (Colaizzi and Klink, 1969), and the effect of FLU and OXA decreases abt. 10-15

times from pH 6 to pH 8 (Palmer et al., 1992). Figure 2.2 therefore shows that the

environmental pH is very important for the chemical speciation, accumulation and activity.


Figure 2.2 – Fractional composition and experimental 1-octanol/water distributioncoefficients vs. pH for the antimicrobialsa.

FLU

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

05101520253035404550

pH

Fra

ctio

nal

com

posi

tio

n Distrib

utio

n coefficient

SDZ

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

pH

Fra

ctio

nal

com

posi

tio

n Distribu

tion

coefficient

OXA

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

pH

Fra

ctio

nal

com

posi

tio

n Distribu

tion

coefficient

TMP

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0

1

2

3

4

5

6

pH

Fra

ctio

nal

com

posi

tio

n Distrib

utio

n coefficient

SAF

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0.00

0.05

0.10

0.15

0.20

pH

Fra

ctio

nal

com

posi

tio

n Distrib

utio

n coefficient

AMX

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0.00

0.01

0.02

0.03

0.04

0.05

0.06

pH

Fra

ctio

nal

com

posi

tio

n Distrib

utio

n coefficient

OTC

0 2 4 6 8 10 12 140.0

0.2

0.4

0.6

0.8

1.0

0.00

0.05

0.10

pH

Fra

ctio

nal

com

posi

tio

n Distrib

utio

n coefficient

a: Fractional composition for neutral and charged species are represented by full and dashed lines, respectively.Experimental 1-octanol/water distribution coefficients are represented by symbols. For each antimicrobialindividual symbols refer to different references, see Table 2.6.

Chapter 2 • 19

According to the Henderson-Hasselbalch principles, the hydrophobicity can be corrected for

pH effects. Using the KOW and the pKa values, while assuming no distribution of the ionized

species, the DOW profile for a monovalent weak (carboxylic) acid is expressed as:

apKpH

OWOW

101

KD

−+= Equation 2.1

and for a monovalent weak base as:

pHpK

OWOW

a101

KD

−+= Equation 2.2

These equations are valid for e.g. FLU, OXA and TMP while for SAF, SDZ, AMX and OTC

one can imagine that more complex equations are required due to the polyvalent structures. In

order to make similar distribution profiles the distribution coefficient for each species is

therefore needed (Winiwarter et al., 1998).

Figure 2.3 shows theoretical DOW profiles, i.e. distribution of the neutral species, for the weak

carboxylic acid OXA and the weak base TMP based on their KOW and pKa values, as

presented in Table 2.2.

Figure 2.3 – pH corrected DOW profiles for the neutral species of OXA and TMPa.

4 5 6 7 8 9 100

1

2

3

4

5

6

7

pH

DO

W

a: Calculated according to Equation 2.1 (OXA – full line) and Equation 2.2 (TMP – dashed line) using pKa andKOW from Table 2.2.

The DOW profiles shown in Figure 2.3, can be affected by the presence of complex forming

ions. For phenolate species, a relative increase in DOW was observed in the presence of cations


(K+ or Na+) due to formation of ion pairs (Escher and Schwarzenbach, 1996). When the

phenolate species were allowed to distribute to liposomes, the relative increase was much less

outspoken. However, antibacterial activity was shown to decrease in presence of Mg2+, see

Chapter 4, indicating a decreased uptake. The last mentioned is known from bioavailability of

OTC in humans; the presence of e.g. Ca2+ or Mg2+ decreases the bioavailability of OTC

(Jensen, 1993).

2.2.3. Environmental Distribution According to Fugacity

Mackay and Paterson (1981) presented an approach based on basic physical chemical

properties of chemicals in order to predict chemical behaviour in the environment. This

approach considers the chemical equilibrium distribution in the environment, by calculating

the fugacity, i.e. the chemical’s escaping tendency. Equilibrium is obtained when the fugacity

in one phase equals that from another. The fugacity approach assumes a well-mixed

environment in a steady state.

This approach enables the calculation of chemical distribution among several environmental

compartments, e.g. water, soil and air. The input data are: environmental temperature,

molecular weight, vapour pressure, solubility and log KOW. Assuming that hydrophobicity

reflects the (bio)accumulation KOW is used to calculate the distribution to soil (Ksoil =

0.02·KOC = 0.02·0.6·KOW), sediment (Ksediment = 0.04·KOC = 0.04·0.6·KOW) and biota (log Kbiota

= 0.85·log KOW – 0.70). Knowing that KOW is affected by pH the DOW at pH≈7 (see Table 2.2)

was used in this particular case.

However, one has to keep in mind that the fugacity approach uses a single DOW value to

estimate the chemical distribution to soil, sediment and biota. This single value is neither

corrected for effects due to pH or influence of complex forming ions. Nevertheless, if the

value for the neutral species is used, it will lead to overestimation of the (bio)accumulation,

which in fact accords to the precautionary principle. Still, under the assumption that

hydrophobicity reflects the (bio)accumulation potential.

As shown in figure 2.2 and figure 2.3 DOW for chemicals as the selected antimicrobials only

changes within less than two orders of magnitude. This is due to their inherent low KOW

Chapter 2 • 21

values and that distribution of the ionized species hardly takes place. This means that pH only

will have a minor effect on the estimated (bio)accumulation factors for said chemicals.

According to the fugacity approach (Mackay and Paterson, 1981), an exposure assessment

was performed using a computer programme (Mackay, 1991). The input data needed for the

program were taken from Table 2.2. An environmental temperature of 7°C was used. The

estimated values for Henrys constant, used to calculate the vapour pressure, are believed to be

acceptable, since the antimicrobials show high melting points and are therefore not expected

to be volatile.

Based on mole fractions, the results showed that the antimicrobial distribution to the aquatic

environment was ≥99.8% for each chemical, see Table 2.3. The computer programme can

only handle positive log DOW values. In case of negative log DOW, 10-10 was used instead. This

approximation seems to be acceptable, since the negative log DOW would only favour

distribution to the water, which already is ≥99.8%.

Table 2.3 – Environmental distribution of antimicrobials according to fugacityprinciplesa.


Air 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Water 99.80 99.96 99.98 99.98 99.93 99.98 99.98

Soil 0.10 0.02 0.01 0.01 0.03 0.01 0.01

Bottomsediment

0.10 0.02 0.01 0.01 0.03 0.01 0.01

Suspendedaqueous matter

0.00 0.00 0.00 0.00 0.00 0.00 0.00

Biota 0.00 0.00 0.00 0.00 0.00 0.00 0.00a: Calculated according to Mackay (1991). Figures represent percentage distribution to the variouscompartments. Input parameters needed for the fugacity calculation: Mw, S, and DOW according to Table 2.2.The vapour pressure was calculated from S and KH, see Table 2.2 (Berg et al., 1995). The environmentaltemperature was set to 7ºC. Since the fugacity programme would not accept negative values, log DOW for SAF,SDZ, AMX and OTC was set to 10-10.

This confirms the apparent assumption made from the physical chemical properties, that these

antimicrobials probably will distribute to the aquatic environment and not (bio)accumulate.

Due to their high affinity for the aquatic environment, antimicrobials applied in Danish fish


farming will distribute widely to surface waters. Furthermore, the results would not change

significantly whether the KOW was corrected for pH.

2.2.4. Pharmacology

In order to ensure an optimal treatment and minimal environmental impact knowledge of the

pharmacokinetics and –dynamics of antimicrobials are required (Horsberg et al., 1995).

However, dosing regimes are often based on field results (Soltani et al., 1995).

During the last 20 years several authors have stated a lack of knowledge on pharmacokinetics

of antimicrobials in fish and expressed a need for thorough investigations (Bergsjø and

Søgnen, 1980; Sohlberg et al., 1990; Bjørklund, 1991; Ishida, 1992; Nouws et al., 1992;

Hustvedt, 1992; Luzzana et al., 1994; Martinsen et al., 1994; Tan and Wall, 1995). Table 2.4

and Table 2.5 address the pharmacokinetics in rainbow trout, the dosing regimes and the

environmental load of the investigated antimicrobials.

Data for other organisms have to be considered with care, since the pharmacokinetics seem to

vary from organism to organism (Grondel et al., 1989; Martinsen et al., 1994; Tan and Wall,

1995; Horsberg et al., 1995) and from mammals to fish (Tan and Wall, 1995).

2.2.4.1. Pharmacokinetics in Rainbow Trout

As seen from Table 2.4 not all requested data have been reported. Data from other organisms

could have been included, but due to the mentioned organism sensitivity, this was not done.

Especially data for AMX and to a certain extent SAF are lacking. For the widely used SDZ, it

is surprising that, to the best knowledge of the author, the latest relevant data was published in

the late seventies. However, studies can be found for related structures and other organisms,

e.g. Uno et al. (1993) and Samuelsen et al. (1995).

Qualitative and quantitative distribution data indicate that the investigated antimicrobials are

widely distributed in the fish body, see Table 2.4. This is based on measured concentrations in

various tissues, autoradiographic studies, viz. the study of a radioactive labelled chemical’s

distribution in a body, and volume of distribution at steady state. Despite the effective

distribution, the oral bioavailability is often poor, ranging from a few percent for OTC to as

much as 72 % for FLU.

Chapter 2 • 23

However the oral bioavailability is affected by external factors, e.g. OXA is dose dependent

(Cravedi et al., 1987), OTC is affected by way of administration and FLU is temperature

dependent (Sohlberg et al., 1990; Sohlberg et al., 1994). Plasma concentrations and sampling

times after both intra arterial (i.a.) and oral (p.o.) administration of FLU at 3ºC and 13ºC were

presented by Sohlberg et al. (1994).

The bioavailability (F) is defined as follows:

( )( ) .a.i

.o.p

DAUC

DAUCF = Equation 2.3

AUC is the area under the plasma concentration-time curve and D is the dose for p.o. and i.a.

administration, respectively.

For i.a. administration AUC is defined as:

e

0p

k

CAUC

0=∞ Equation 2.4

∞0AUC is the area under the curve from time zero to infinite time. 0

pC is the plasma

concentration at time zero viz. at the moment of injection and calculated from the y-intercept

from linear regression of the first sampling points. ke is the elimination rate constant and

obtained as the slope in the linear regression of the curve which represents the elimination

phase.

For p.o. administration the AUC is defined as:

∞∞ += TT00 AUCAUCAUC Equation 2.5

T0AUC defines the area under the curve from time zero to the last sampling time, T. This part

is calculated by the trapezoidal rule. The residual area is then calculated as follows:

e

Tp

Tk

CAUC =∞ Equation 2.6


TpC is the plasma concentration at the last sampling time. It is recommended that the residual

area represents less than 10 % of the total area, since error in ke due to misinterpreted

distribution/elimination phase will underestimate the AUC.

The bioavailability for FLU presented in Table 2.4 was calculated from the above equations

and the plasma concentrations and sampling times given in Sohlberg et al. (1994). The plasma

concentration-time profiles are shown in Figure 2.4. It has to be mentioned that the residual

area represents more than 50 % of the total AUCp.o., which means that the bioavailability may

be underestimated.

Figure 2.4 – Plasma concentration-time profiles for FLU in rainbow trouta.

0 250 500 750 1000 1250 1500-2

-1

0

1

2

α=ke

α=ke

Cp0 3°°°°C

Time, h

Ln C

p,

mg

/L

0 200 400 600-3

-2

-1

0

1

2

α=ke

α=ke

Cp0

13°°°°C

Time, h

Ln C

p,

mg

/L

a: Data from (Sohlberg et al., 1994). The solid symbols represent i.a. administration and the transparent symbolsrepresent p.o. administration. Lines represent the linear regression of the initial and terminal phases. α representsthe slope for the respective parts of the curves. Full lines: i.a. administration and dashed lines: p.o.administration.

The fraction of the antimicrobial that is absorbed in the fish may be exposed to

biotransformation. The antimicrobial may e.g. be hydrolysed or oxidized (phase I reactions)

or conjugated by e.g. glucuronidation or acetylation (phase II reactions). The quinolones are

mainly phase II biotransformed; this is most pronounced for OXA. Glucuronidation leads to

e.g. amide or ester bonds, which are hydrolytically unstable chemicals. Thus, some conjugates

may be cleaved when entering the aquatic compartment and thereby liberate the parent

compound. Moreover, conjugated antimicrobials were cleaved in manure by bacteria (Berger

et al., 1986). Only OTC is not biotransformed.

The literature suggests that most of the antimicrobials be excreted both in urine and bile.

Depending on environmental temperature, dose and way of administration, the elimination

half-lives presented in Table 2.4 differ as much as several hundred hours.

Chapter 2 • 25

The withdrawal times have therefore been differentiated with respect to temperature. The

wide distribution and long elimination half-lives therefore result in correspondingly long

withdrawal times.

See footnotes to Table 2.4 for specific information of the individual antimicrobials and

pharmacokinetic parameters.

The bioavailability of oral administered antimicrobials is low, meaning that a high fraction

passes the fish unabsorbed. Moreover, the absorbed fraction is hardly biotransformed;

resulting in excretion mainly as a parent compound. Thus, the pharmacokinetics in rainbow

trout predict that antimicrobials are expected to reach the aquatic environment as parent

compounds.

Table 2.4 – Pharmacokinetics in rainbow trout.


Bioavailability, % 36a1, 72a2 14e1, 14f1, 38f2 incompletej - - - 1.3m2, 30n1, 5.6e6,8.6f1, 7.1f2

Distribution wellb widelyg, goode2, well (>90% outside plasma)h1, wellb

wellb,j wellk wellk, fastand widelyl1

- goode2,o1

Vd,ss, L/kg 3.2a3, 3.6a4 1.9e3, 2.6g, 2.9h2 - - 6.0l2 - 4.0m3, 2.7m4,2.1p1, 0.9n2, 1.2e7

Biotransformationproducts

noc

≤ 6 % -OHd

≤ 12.5 % -GLUd

66 %i1 - - probablyl3 - nop2

Urinary excretion probablya5 - most likelyj mostlikelyk

most likelyk,14.4l4

- -

Biliary excretion probablya5 29 %i2 most likelyj majorexcretorypathwayk

majorexcretorypathwayk,possiblel1

- yeso2

Elimination t½, h 569a6, 137a7,736a8, 285a9

20-43g, 42.8e4, 69.7e5,52.6h2

- - 8m1, 36.1l2 - 130m5, 76m6,150m7, 89.5p1,

94.2n2, 479.4n3,60.3e8, 74.9e9

Withdrawal timeq ≥10ºC: 40 d

<10ºC: 80 d

≥10ºC: 30 d

<10ºC: 60 d

≥10ºC: 40 d

<10ºC: 80 d

≥10ºC: 40 d

<10ºC: 80 d

≥10ºC: 40 d

<10ºC: 80 d

≥10ºC: 60 d

<10ºC: 120 d

Vd,ss: distribution volume at steady state,a1: Sohlberg et al. (1994) 3ºC, 5 mg/kg p.o. and i.a., see text for details, a2: Sohlberg et al. (1994) 13ºC, 5 mg/kg p.o. and i.a., see text for details, a3: Sohlberg et al. (1994) 13ºC, 5mg/kg i.a., a4: Sohlberg et al. (1994) 3ºC, 5 mg/kg i.a., a5: Sohlberg et al. (1994) due to rapid decrease form plasma, a6: Sohlberg et al. (1994) 3°C, 5 mg/kg i.a., a7: Sohlberg et al.(1994) 13°C, 5 mg/kg i.a., a8: Sohlberg et al. (1994) 3°C, 5 mg/kg p.o., a9: Sohlberg et al. (1994) 13°C, 5 mg/kg p.o.,b: Steffenak et al. (1991),c: Sohlberg et al. (1990),d: EMEA (1996) OH: hydroxy-FLU, GLU: FLU-glucuronide,e1: Bjørklund and Bylund (1991) 16ºC, 75 mg/kg p.o., 10 mg/kg i.v., e2: Bjørklund and Bylund (1991), e3: Bjørklund and Bylund (1991) 16ºC, 10 mg/kg i.v., e4: Bjørklund and Bylund(1991) 16ºC, 75 mg/kg p.o., e5: Bjørklund and Bylund (1991) 16ºC, 10 mg/kg i.v., e6: Bjørklund and Bylund (1991) 16ºC 75 mg/kg p.o., 20 mg/kg i.v., e7: Bjørklund and Bylund(1991) 16ºC, 20 mg/kg i.v., e8: Bjørklund and Bylund (1991) 16ºC, 20 mg/kg i.v., e9: Bjørklund and Bylund (1991) 16ºC, 75 mg/kg p.o.,f1: Cravedi et al. (1987) 14ºC, 100 mg/kg p.o., calculated from recovery from faeces, f2: Cravedi et al. (1987)14ºC, 20 mg/kg p.o., calculated from recovery from faeces,g: Hustvedt (1992),h1: Hustvedt and Salte (1991a), h2: Hustvedt and Salte (1991a) 8.5ºC, 10 mg/kg i.v.,i1: Ishida (1992) three glucuronides, 66 % present in the bile as OXA-glucuronide 24 hours after administration, i2: Ishida (1992) 29 % present in the bile as OXA 24 hours afteradministration,j: Martinsen et al. (1994) based on autoradiography studies,k: Bergsjø et al. (1979) based on autoradiography studies,l1: Tan and Wall (1995), l2: Tan and Wall (1995) 10ºC, 10 mg/kg i.a., l3: Tan and Wall (1995) high biotransformation anticipated, l4: Tan and Wall (1995) percentage parentcompound recovered in urine,m1: Nouws et al. (1992) 12ºC, 10 mg/kg intra muscular, m2: Nouws et al. (1992) 60 mg/kg p.o. and i.v., m3: Nouws et al. (1992) 10ºC, 60 mg/kg i.v., m4: Nouws et al. (1992) 19ºC, 60mg/kg i.v., m5: Nouws et al. (1992) 10ºC, 60 mg/kg i.v., m6: Nouws et al. (1992) 19ºC, 60 mg/kg i.v., m7: Nouws et al. (1992) 10ºC, 60 mg/kg intra muscular,n1: Abedini et al. (1998) 11ºC, 50 mg/kg i.a. and p.o. dissolved in MeOH and administered in a capsule, n2: Abedini et al. (1998) 11ºC, 50 mg/kg i.a., n3: Abedini et al. (1998) 11ºC,p.o. 50 mg/kg dissolved in MeOH and administered in a capsule,o1: Rogstad et al. (1991), o2: Rogstad et al. (1991) can be expected from the high liver/plasma ratio,p1: Grondel et al. (1989)12ºC, 60 mg/kg i.v., p2: Grondel et al. (1989) OTC biotransformation in fish is presumed very small,q: Dalsgaard and Bjerregaard (1991),

-: no data available.


2.2.4.2. Pharmacodynamics

All antimicrobials investigated in this thesis interfere with the deoxyribonucleic acid (DNA)

or protein synthesis in bacteria (Lambert, 1992), thus preventing the bacterial disease from

developing further. Table 2.5 gives the dosing regimes, the environmental load and the

percentage of fish/fry treated with antimicrobials during one year.

The 4-quinolones FLU, OXA and SAF are broad-spectrum synthetic antimicrobials, acting

primarily on Gram negative bacteria. The chemicals block the chromosomal replication by

specific inhibition of DNA gyrase which catalyses supercoiling of DNA. The 4-quinolones

have much higher affinity for prokaryotic than human DNA gyrase. Cross-resistance is often

observed among quinolones (Smith, 1995). Resistance is rarely mediated by plasmids. Mostly

the enzyme that 4-quinolones block has altered polarity, or the production of the proteins that

mediate the transport of 4-quinolones has decreased (Franklin, 1992; Smith, 1995).

The combination of the folate antagonists SDZ and TMP gives a synthetic broad-spectrum

antimicrobial, acting by interfering with two steps in the folate synthesis by substrate

competition and enzyme inhibition respectively. The TMP affinity for the bacterial enzyme is

more than 104 times that for the human (Jensen, 1993; Rang and Dale, 1993). Applied as

single substances they only act bacteriostatically, whereas applied in combination they act

bactericidally. The co-administration also results in one tenth of the doses that would be

needed if the chemicals were applied as single chemicals (Dalsgaard and Bjerregaard, 1991;

Rang and Dale, 1993). The resistance mechanism is plasmid mediated. The plasmid genes

code for alterations in the target enzymes, either by favouring the natural substrate or by being

resistant to the inhibitor (Franklin, 1992).

The ββββ-lactam AMX is a broad-spectrum semisynthetic antimicrobial, belonging to the group

of penicillins, acting on Gram positive as well as Gram negative bacteria. AMX is interfering

with the biosynthesis of peptidoglycan of the bacterial cell. AMX binds covalently to the

enzyme that cross-links the peptidoglycan. The enzyme is inactivated because AMX simulate

the natural substrate. Resistance is mediated through production of an enzyme, β-lactamase,

which cleave the β-lactam ring of the chemical. Cross-resistance towards cephalosporins is

often seen (Franklin, 1992).

Chapter 2 • 29

The tetracycline OTC is a broad-spectrum antimicrobial that is actively taken up by the

bacterial cell. OTC mediates its bacteriostatic effect on the protein synthesis by inhibiting

bacterial ribosome function through binding to the 30S subunit (Lambert, 1992). The

resistance is mediated by plasmid genes encoding for proteins that promotes the efflux of

tetracyclines (Franklin, 1992).

Depending on the antimicrobial the environmental load results in fish farm effluents

concentrations in the range 0.5-19 mg/L assuming no dilution, biotransformation or

degradation and application of the treatment in one lot. This is a worst case scenario enabling

one to assess the maximal exposure to the environment.

Equation 2.7 is used to calculate the percentage of fish/fry treated with antimicrobials. The

numerator represents the actual consumption of the particular antimicrobial and the

denominator represents a single treatment of the total biomass.

%100kgmg10mTD

m% reated,Fish/fry t 6

Fish/fry

A ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅

==== Equation 2.7

ma is the consumption of the respective antimicrobials in kg, see Table 2.1. D is the dose in

mg/kg bw/d and T is the duration in days, see Table 2.5. mFish/fry is the mass of total produced

biota in kg, see Fish Farm Characteristics.

Roughly estimated 55% of production fish and 30-50% of fry undergo antimicrobial treatment

once a year! In other words, nearly all fish can be considered treated with antimicrobials once

in their life time!


Table 2.5 – Dosing regimes and environmental load in Danish fish farms.


Dose, mg/kg bw/d 15d 10e 10f 25e,g 5e,g 50-80h 80e

Duration, d 7d 10e 5f 5e,g 5e,g 10h 8e

Environmental load, ga

fish 210 200 100 250 50 1,000-1,600 1,280

fry - - - - - 25-40 32

Cmax in pond, mg/La, b

fish 2 1.9 1 2.4 0.48 9.5-15 12

fry - - - - - 12-19 15

Fish treated, %c - 17 - 38 0.5-0.8 0.1

Fry treated, %c - - - - 27-43 4bw: fish body weight, d: days, a: based on specifications in Fish Farm Characteristics, b: one treatment applied inone lot assuming no dilution, biotransformation or degradation, c: based on specifications in Fish FarmCharacteristics, Table 2.1 and Equation 2.7, d: Schneider (1994), e: Kilsgaard (1996), f: EMEA (1998a), g:Dalsgaard and Bjerregaard (1991), h: Alderman and Michel (1992), -: not applied in Denmark.

2.3. Initial Environmental Assessment

In the previous sections physical chemical and pharmacological properties from the literature

along with fugacity calculations of the antimicrobials were presented. Based on that

knowledge the following properties are attributed the antimicrobials:

• Low oral bioavailability and low biotransformation results mainly in excretion of a

parent compound.

• The principal exposure is to the aquatic environment, however the distribution is

widely pH dependent.

• Environmental exposure of biological active chemicals may affect non-target

organisms.

Due to the antimicrobials’ affinity for the aquatic compartment (high aqueous solubility and

low hydrophobicity) surface waters and maybe ground water will consequently be the natural

recipient.

Chapter 2 • 31

Generally, the antimicrobials suffer from low bioavailability; resulting in low uptake in the

fish and direct excretion of active parent compound to the environment. Furthermore, the part,

which is absorbed in the fish, is hardly biotransformed, e.g. OTC. If the antimicrobial is

biotransformed, e.g. OXA, mainly glucuronide conjugates are formed. Since glucuronide

conjugates often are hydrolytically unstable chemicals, parent compounds may be liberated

upon cleavage when entering the environment. However, the absorbed part of the

antimicrobial is widely distributed in the fish body and stay there for long periods. This means

that, if antimicrobials enter the aquatic environment, the wild fauna may be exposed to and

absorb the antimicrobials.

Since the antimicrobials are biologically active chemicals, especially bacteria and similar

organisms, e.g. micro-algae, may be affected. The biodiversity among micro-organisms may

therefore be affected. Bacteria may develop resistance towards the antimicrobials. This

property is to a certain extent considered as an irreversible effect, since it may persist after the

disappearance of the resistance-provoking chemical. Moreover, resistance may be transferred

from target organisms to human pathogenic bacteria and environmental bacteria.

Therefore, the application of antimicrobials in quantities of tonnes in fish farming has to be

considered with caution, since they are directly exposed to the aquatic environment. However,

specific environmental research regarding fate and effects of antimicrobials is required in

order to make a proper ERA of said chemicals.


Table 2.6 – Experimental 1-octanol/water distribution coefficients.

Chemical Log DOW pH Ionic strength, M Buffer, M Method Ref.

FLU 1.60 < 4.4 ns 0.04 acetate SF aFLU 1.72 < 4.4 0.1 and 0.15 - pH-m aFLU 1.11 7.2 ns 0.1 phosphate m-SF b

OXA 0.68 4.0 ns 0.04 acetate SF cOXA 0.35 7.2 ns 0.1 phosphate m-SF bOXA 0.38 7.4 ns 0.1 phosphate SF d

SAF -1.18 7.4 ns ns RML eSAF -0.71 7.4 ns phosphate SF f

SDZ -0.09 3.0-3.5 0.1 acetate ns gSDZ -0.29 5.5 ns ns SF hSDZ -0.69 6.4 ns phosphate ns iSDZ -0.27 6.9 ns H2O SF jSDZ -1.00 7.4 ns 0.01 phosphate SF jSDZ -1.00 7.4 ns 0.01 TRIS SF jSDZ -1.26 7.5 ns phosphate SF kSDZ -2.12 9.2 ns 0.01 bicarbonate SF j

TMP -1.55 1 ns 0.1 HCl SF lTMP 0.64 7.4 ns 0.05 phosphate SF mTMP 0.82 13 ns 0.1 NaOH SF l

AMX -1.70 2 ns 0.1 KCl-HCl SF nAMX -1.70 3 ns 0.1 citrate SF nAMX -1.22 6 ns 0.1 phosphate SF nAMX -1.52 7.4 ns 0.1 phosphate SF nAMX -1.52 8 ns 0.1 phosphate SF n

OTC -2.46 2.1 0.1 phosphate SF oOTC -1.74 3.0 0.1 phosphate SF oOTC -1.11 3.9 0.1 phosphate SF oOTC -0.89 5.5 0.15 phosphate ns pOTC -1.12 5.6 0.1 phosphate SF oOTC -1.06 6.6 0.1 phosphate SF oOTC -0.92 6.6 0.1 phosphate SF qOTC -1.60 7.0 ns phosphate SF rOTC -1.22 7.4 ns 0.1 phosphate SF dOTC -1.60 7.5 0.1 phosphate SF oOTC -1.60 7.5 ns ns ns sOTC -2.07 8.5 0.1 phosphate SF ons: not specified, TRIS: tris(hydroxymethyl)aminomethan, SF: shake flask principle, pH-m: pH-metric, m-SF:modified version of shake flask, RML: Robertson Microlit Laboratories, a: Takács-Novák and Avdeef (1996), b:Hirai et al. (1986), c: Takács-Novák et al. (1992), d: Bjørklund and Bylund (1991), e: Renau et al. (1995), f:Jürgens et al. (1996), g: Morishita et al. (1973), h: Ehlert et al. (1998), i: Yamazaki et al. (1970), j: Wang andLien (1980), k: Abel et al. (1975), l: Dietrich et al. (1980), m: Seiler et al. (1982), n: Smyth et al. (1981), o:Colaizzi and Klink (1969), p: Schumacher and Linn (1978), q: Miller et al. (1977), r: Kellaway and Marriott(1978), s: Toon and Rowland (1979).

Chapter 3

Environmental Occurrence of

Antimicrobials

• Environmental Occurrence of Antimicrobials36

3.1. Introduction

Due to human activities antimicrobials are found in various environmental matrices, e.g. SDZ

in groundwater (Holm et al., 1995), TMP in river and surface waters (Hirsch et al., 1998) and

sewage treatment plant effluents (Hirsch et al., 1999) and ciprofloxacin (a fluoroquinolone) in

hospital waste water (Hartmann et al., 1999). Detailed discussions of the environmental

exposure routes of pharmaceuticals in general are found in Halling-Sørensen et al. (1998) and

Jørgensen and Halling-Sørensen (2000). However, only the environmental exposure and

occurrence of antimicrobials in connection to fish farm application is discussed in this

chapter.

Figure 3.1 represents a schematic drawing of the antimicrobial exposure routes due to

application in a traditional Danish fish farming.

Figure 3.1 – Anticipated exposure routes of antimicrobials applied in fish farminga.

a: Modified from Jørgensen and Halling-Sørensen (2000), full lines: flow, dashed lines: transport, dotted lines:interaction.

Chapter 3 • 37

Antimicrobials are applied as a consequence of intensive farming (Dalsgaard and Bjerregaard,

1991). They are conveniently applied directly to the water phase through medicated feed

pellets (Dalsgaard and Bjerregaard, 1991; Elema, 1995). The extent of distribution to the

sediment depends on the physical chemical properties of the antimicrobial. If the

antimicrobial has high affinity for the sediment it has the potential to accumulate in the

sediment, see Chapter 4. The sediment may be used as fertilizer on arable land if e.g.

cadmium content do not exceed 0.8 mg/kg dry weight (Holm Sørensen and Landsfeldt, 1997;

Jørgensen and Halling-Sørensen, 2000). In that case, there is a risk for indirect exposure of

soil organisms. However, if the sediment is not removed it will serve as an antimicrobial

reservoir. The antimicrobial then distributes to the water according to its particular

distribution coefficient, see Chapter 4. If the antimicrobial has high affinity for the water, it is

discharged to the stream and further to the ecosystem. In the latter case, there is a risk for

indirect exposure of aquatic organisms.

According to the pharmacokinetic data presented in Chapter 2, it is likely that antimicrobials

from treatment in fish are excreted to the environment. Furthermore, they are applied in a

restricted area and according to Figure 3.1, they may consequently be discharged either to the

terrestrial or the aquatic environment.

It is therefore expected that antimicrobial residues can be found in the sediment near fish

farms, whereas Hirsch et al. (1999) state that the antimicrobial application in non-human

treatment is of minor importance.

In order to quantitate antimicrobials in the environment, both separation and extraction

methods are required. In this thesis, the work was focused on quinolones, and therefore the

discussion of separation and extraction methods is limited to those chemicals. Moreover,

discussion of the extraction methods is limited to sediment. Following the analytical

procedures, the occurrence is discussed.

• Environmental Occurrence of Antimicrobials38

3.2. Analytical procedures

3.2.1. Separation methods

Table 3.1 gives a range of reversed phase (RP) HPLC methods for the analysis of FLU, OXA

and SAF. They are mainly applied to fish and sediment samples. All isocratic procedures

present an asymmetric, i.e. tailing peak shape of the quinolone. This is a well-known feature

of the chromatography of polar chemicals, e.g. amine and carboxylic acid containing analytes.

The problem is the interaction between the column and the ionizable group(s) of the analyte.

An RP column consists of silica substituted with alkyl chains, e.g. octadecylsilyl (C18) chains.

However, the substitution is rarely, if ever, complete. Not more than 50 % of the silanol

groups are substituted. The remaining free silanol groups are therefore active for interactions

with e.g. amine groups. The result of such an interaction is a broadening of the peak, which is

often seen as a tailing peak, Figure 3.2A. The results of tailing are decreased sensitivity,

efficiency and separation. However, many commercial columns are partly deactivated by

further substitution with shorter alkyl chains, so-called end capped columns (Smith, 1988).

Figure 3.2 – Column material effect on the chromatography of OXAa.

A

0 2 4 6 8 100.00

0.05

0.10

0.15

0.20

0.25

0.30

Time, min

UV

res

pon

se

B

0 2 4 6 8 10 12 14 16 18 200.00

0.05

0.10

0.15

0.20

0.25

0.30

Time, min

UV

res

pon

se

a: Analysis of a 20 mg/L (400 ng injected) solution of OXA. A: Conventional end capped C18-column, isocraticelutions; MeOH:2 mM H3PO4/KH2PO4 pH 2.9 70:30, 60:40 and 50:50, respectively. B: Discovery C18 endcapped column, isocratic elutions; MeOH:2 mM H3PO4/KH2PO4 pH 2.9 60:40, 50:50, 40:60 and 30:70,respectively. Detection: UV 260 nm. Flowrate: 2 mL/min. From Holten Lützhøft et al. (1999).

A gradient mobile phase can sometimes diminish tailing, however it is more a result of the

analyte’s increasing affinity for the mobile phase. To circumvent problems with tailing a

polymer column can be used or masking agents can be added the mobile phase, i.e. chemicals

that have similar properties as the analyte (Smith, 1988). It is actually seen from Table 3.1

Chapter 3 • 39

that often a polymer column is used or that most of the applied mobile phases are rather

complex, e.g. multiple organic modifiers and organic acids, although still providing tailing

peaks.

The problem is effectively eliminated by using a column that does not possess the ability to

interact in the above-mentioned way. This was demonstrated by Holten Lützhøft et al. (1999).

An RP Discovery C18 end capped column was used providing symmetric non-tailing peaks

although the quantity of analyte injected on the column was 400 ng, equal to a sample

concentration of 20 mg/L, Figure 3.2B.

Furthermore, a very simple mobile phase consisting of methanol (MeOH) and H3PO4/KH2PO4

pH 2.9 was used. Figure 3.2 represents a comparison of the chromatography of OXA on a

conventional end capped RP column and the RP Discovery C18 end capped column,

respectively. Using the Discovery column, complex mobile phases containing acetonitrile and

tetrahydrofuran (THF) can be avoided. Holten Lützhøft et al. (1999) applied the column in

connection with SPME-HPLC analysis. In these kinds of analyses it is especially required that

the analytes are chromatographed well, due to the nature of the SPME-HPLC application. In

this case, the column also served its purpose, see Figure 3.3.

Figure 3.3 – SPME-HPLC analysis of FLU, OXA and SAFa.

0 1 2 3 4 5 6 70.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

Time, min

UV

res

pons

e

a: Chromatographic conditions: Discovery C18 end capped column, gradient elution: MeOH:2 mMH3PO4/KH2PO4 pH 2.9 from 20:80 to 90:10 in 7 min. tR=3.6 min.: SAF, tR=4.9 min.: OXA and tR=6.0 min.:FLU. Dashed line: SPME injection. Full line: Autosampler injection. Detection: UV 260 nm. Flowrate: 2mL/min. From Holten Lützhøft et al. (1999).

Holten Lützhøft et al. (1999) recommended said column for future analysis of chemicals

containing problematic groups, e.g. the HPLC analysis of freshwater sediment extracts was

performed using mentioned column (Holten Lützhøft et al., Submitted I).

Table 3.1 – HPLC methods for the investigated quinolones.

Antimicrobial Stationary phase Mobile phase Detectiona tR, min Peak shape m, pgb Matrixc LOD, pg ref.FLU 5 µm PLRP-S polymer adsorbent

150×4.6 mm2mMH3PO4:MeCN:THF 65:20:15

F, 260, 380 8.4 asymmetric 5,000 fish tissues 200 A

FLU 3 µm ODS-Hypersil 100×5 mm 0.1 M pH 3.2C6H8O7:MeOH:MeCN:THF60:30:5:5

F, 324, 363 6.0 asymmetric 2,000 fish muscle 400 B

FLU 3 µm MOS-Hypersil (C8)150×4.6 mm

25 mM(COOH)2 pH3.2:MeCN:MeOH:THF

F, 325, 360 8.9 nc nc fish plasma 250 C

FLU 5 µm Ultrabase octacecyl250×4.6 mm

MeCN:2.7 mM(COOH)2 pH2.5 gradient

F, 252, 356 20 nc nc pig tissue 3,000 D

FLU 3 µm phenyl YMC/3-4-5cartridge columns 50×4 mm

MeCN:20 mMHCOOH pH2.75 gradient

MS 6.7 nc nc fish muscle 250 E

FLU 5 µm Supelco Discovery C18 endcapped column 150×4.6 mm

MeOH:2 mMH3PO4/KH2PO4

pH 2.9 gradient

UV, 260 6.0 nc nc buffer 162 F1

OXA 5 µm PLRP-S polymer adsorbent150×4.6 mm

2mMH3PO4:MeCN:THF 65:20:15

F, 260, 380 4.8 asymmetric 1,000 fish tissues 50 A

OXA 3 µm MOS-Hypersil (C8)150×4.6 mm

25 mM(COOH)2 pH3.2:MeCN:MeOH:THF

F, 325, 360 6.6 nc nc fish plasma 150 C

OXA 5 µm ISRP 150×4.6 mm MeCN:0.1 MKH2PO4 pH 2.010:90

UV, 254 9.4 asymmetric 10,000 sediment 5,000 G

OXA 5 µm LiChroSorp 100 RP-18E125×4.6 mm

20 mMH3PO4:MeCN76:24

UV, 262 6.6 asymmetric 25,000 sediment 1,000 H

OXA 5 µm LiChroSorp 100 RP-18E125×4.6 mm

20 mMH3PO4:MeCN76:24

UV, 262 - - - mudsandy mudsand

11,00010,00010,000

I

OXA 5 µm HISEP shieldedhydrophobic phase column150×4.6 mm

50 mMC6H8O7, 200mM Na2HPO4

pH 2.5 in 10mM (n-C4H9)4-NH4Br:MeCN85:15

UV, 265 13.5 asymmetric 100,000 fish serum 2,500 J

OXA 3 µm phenyl YMC/3-4-5cartridge columns 50×4 mm


MS 5.3 nc nc fish muscle 250 E

OXA 5 µm Supelco Discovery C18 endcapped column 150×4.6 mm


pH 2.9 gradient


OXA 5 µm Supelco Discovery C18 endcapped column 150×4.6 mm

60 % MeOH30 % MeOH

UV, 260UV, 260

2.715.4

symmetricsymmetric

400,000400,000

bufferbuffer

--

F2

SAF 5 µm PLRP-S polymer adsorbent150×4.6 mm

2 mMH3PO4:MeCN:MeOH 73:19:8

F, 278, 440 5.9 asymmetric 5,760 fish tissues 115 K

SAF 3 µm phenyl YMC/3-4-5cartridge columns 50×4 mm


MS 4.9 nc nc milk 20 E

SAF 5 µm Supelco Discovery C18 endcapped column 150×4.6 mm


pH 2.9 gradient


tR: retention time, LOD: limit of detection, nc: not comparable, -: not reported,a: detection: F: fluorescence, excitation wavelength, emission wavelength; MS: mass spectrometry; UV: ultraviolet, wavelength, b: injected quantity for the mentioned retention timeand peak shape, c: target analysis matrix,

MeCN: acetonitrile, THF: tetrahydrofuran, C6H8O7: citric acid, MeOH: methanol, (COOH)2: oxalic acid, HCOOH: formic acid, ISRP: internal surface reversed-phase,

A: Rogstad et al. (1989), B: Samuelsen (1989b), C: Samuelsen (1990) mobile phase: I: 25 mM (COOH)2 pH 3.2:MeCN:MeOH:THF (80:2.5:15:2.5), II: (COOH)2 pH3.2:MeCN:MeOH:THF (50:20:25:5); t0: I:II 100:0; t5: 0:100; 5 min isocratic and 5 min calibration, D: Guyonnet et al. (1996) mobile phase: I: MeCN, II: 2.7 mM (COOH)2 pH 2.5; t0:I:II 10:90; t20: I:II 70:30; 5 min isocratic and 5 min calibration, E: Volmer et al. (1997) mobile phase: I: MeCN, II: 20 mM HCOOH pH 2.75; t0: I:II 2:98; t10: I:II 57:43, F1: HoltenLützhøft et al. (1999) mobile phase: I: MeOH, II: 2mM H3PO4/KH2PO4 pH 2.9; t0: I:II 20:80; t8: I:II 100:0; 2 min isocratic and 8 min calibration, F2: Holten Lützhøft et al. (1999)mobile phase: accomplished by 2 mM H3PO4/KH2PO4 pH 2.9 to 100 %, G: Bjørklund (1990) and Bjørklund et al. (1991), H: Pouliquen et al. (1994b), I: Pouliquen et al. (1994a), J:Ueno and Aoki (1996), K: Hormazabal et al. (1991).

Chapter 3 • 43

3.2.2. Extraction Methods

Sediment extraction methods for quinolones, i.e. FLU and OXA that can be found in the

literature are presented in Table 3.4. Three methods are described for extraction from marine

sediment and one for freshwater sediment.

3.2.2.1. Marine sediment

In all three methods the chemical is extracted from the sediment into an aqueous phase – two

using NaOH and one KH2PO4 pH 7. Samuelsen et al. (1994) actually just analyse the NaOH

extract after centrifugation. This gives a recovery of 95.7 and 92.6 % for FLU and OXA,

respectively. Bjørklund et al. (1991) extract with KH2PO4 and concentrate the filtered extracts

on a C18 solid phase extraction (SPE) cartridge. The eluate is further reduced to 0.5 mL and

analysed. Pouliquen et al. (1994b) extract the sediment with NaOH, acidify and extract OXA

into an organic phase by liquid liquid extraction (LLE). After centrifugation, the organic

phase is evaporated to dryness, dissolved in 0.5 mL, and analysed. The latter two methods

both give recoveries of ca 70 %.

Pouliquen et al. (1994a) used the LLE to extract more specific sediment types. The result was

that OXA tends to be more easily extracted from sand/sandy sediment than from mud/muddy

sediment. Recoveries went from 60 % to 90 % when mud was replaced by sand.

Based on these results it is tempting to conclude that the sediment used by Samuelsen et al.

(1994) must have contained a large sand fraction! The method is essentially the same as the

method described and used by Pouliquen et al. (1994a) and Pouliquen et al. (1994b).

3.2.2.2. Freshwater sediment

The methods of Samuelsen et al. (1994) and Pouliquen et al. (1994b) were adopted for the

extraction of OXA from freshwater sediment. However, recoveries less than 50 % were

judged futile (Holten Lützhøft et al., Submitted I). Various attempts using water and NaOH

containing MeOH did not improve results. Instead an approach using sediment filled SPE

tubes was applied. Figure 3.4 outlines the steps in the extraction procedure.

44 • Environmental Occurrence of Antimicrobials

Figure 3.4 – Procedure to extract OXA from freshwater sedimenta.

a: Holten Lützhøft et al. (Submitted) I.

SPE tubes were filled with contaminated sediment. The strategy was to use an eluent that was

strong enough to extract OXA and at the same time was able to be removed in a simple way.

As was the case for chromatography on RP columns, polar interactions between OXA and the

sediment were expected. To circumvent this, eluents consisting of various agents were

evaluated.

Table 3.2 gives an overview of the variety in eluent composition evaluated for the freshwater

sediment extraction. MeOH or THF were used as the main organic modifier. These were in

some cases accomplished by either dimethylsulfoxide or triethylamine in order to enhance

extraction. Both acidic and alkaline buffers were used as aqueous phase. Furthermore, the

fraction of organic modifier was varied.

Chapter 3 • 45

Table 3.2 – Eluents evaluated for the freshwater sediment extractiona.

Agents v/v % No. of aliquotsb Volume of aliquot, mL Recovery, %c

MeOH 100 1 6 0

THF 100 0 6 0

THF:MeOH 80:20 0 6 0

THF:MeOH:DMSO 79:19:2 0 6 0

MeOH:NaHCO3 90:10 1 6 5

MeOH:H3PO4 90:10 5 6 24

THF:H3PO4:DMSO 70:20:10 2 10 31

THF:NaHCO3 90:10 5 6 38

THF:H3PO4 95:5 5 6 49

THF:H3PO4:Et3N 80:20:5mM 5 6 54

THF:H3PO4:DMSO 40:50:10 5 10 67

THF:H3PO4:DMSO 79:19:2 5 6 77

THF:H3PO4 80:20 4 10 95

THF:H3PO4 50:50 5 10 99v/v %: volume/volume %, THF: Tetrahydrofuran, MeOH: Methanol, DMSO: Dimethylsulfoxide, H3PO4: 10mM pH 2.5, Et3N: Triethylamine, NaHCO3: 10 mM pH 10,a: Holten Lützhøft et al. (Submitted) I, b: The figures represent the number of aliquots where detection of OXAwas possible. For the 6 mL aliquots, the sediment was always extracted five times. For the 10 mL aliquots, thesediment was always extracted seven times. c: Extractions were performed in duplicate.

THF was selected as the organic modifier, since eluents including THF showed the most

efficient extracting capacity. Furthermore, THF could easily be evaporated under N2 at

elevated temperature. 10 mM H3PO4 pH 2.5 was selected as the buffer, in a concentration of

20 % in THF, allowing a five time concentration step due to removal of THF. Three aliquots

of 10 mL were selected, since the fourth extraction did not improve the recovery significantly.

The following recovery study was performed, see Table 3.3 for details. 50 mL buffer pH 7

added 5 g dry sediment was spiked with OXA in three levels. The flasks were thoroughly

mixed and stored three days in the dark at 4°C. Sediment was extracted in triplicate using the

procedure described above. The aqueous phase was analysed in duplicate in order to establish

a mass balance. The obtained recovery was 98 % with a relative standard deviation of 36 %.

This was attributed to the heterogeneity of the sediment.


Table 3.3 – Recovery study for the extraction of OXA from freshwater sedimenta.

RecoveryVAQ, mL mS, g mOXA spiked, µg mOXA(AQ), µg mOXA(S), µg Totalb, % Sedimentc, %

50 5.45 25.34 5.05 18.46 93 91

50 5.43 50.67 9.55 30.24 79 74

50 5.85 101.34 23.40 99.81 122 128

98±28d 98±36d

a: Holten Lützhøft et al. (Submitted) I, VAQ: volume of the aqueous phase, mS: mass of sediment, mOXA spiked:mass of OXA spiked to the sediment sample of the former column, mOXA(AQ): mass of OXA in the aqueousphase, mOXA(S): mass of OXA in the sediment,

b: ( )

spikedOXA

OXA(S)AQOXA

m

mm100 recovery Total

+⋅= , c:

( )AQOXAspikedOXA

OXA(S)

mm

m100 recovery Sediment

−⋅= , d: Overall

recovery±relative standard deviation (n=9).

Table 3.4 – Sediment extraction methods for the investigated quinolones.

Antimicrobial Sediment mass, g Spike, µg/g Equilibration, h Extraction Concentration step Recovery±RSD ref.

FLU marine 1 25 - 4, 4 and 2 mL 0.1 M NaOHCentrifugationCombine supernatantsCentrifugation

no 95.7±2.8a A

OXA marine 5 - - 3×20 mL 0.1 M KH2PO4 pH 7HomogenizationCentrifugationFiltrationCombine filtrates

C18 SPEElute with 5 mLMeOH:1 M H3PO4

90:10Evaporation at 35ºCat reduced pressureto 0.5 mL

70.9±5.1b B

OXA marine 1 25 - 4, 4 and 2 mL 0.1 M NaOHCentrifugationCombine supernatantsCentrifugation

no 92.6±3.1a A

OXA marine 1 0.1-2.5 - 3×4 mL 0.2 M NaOHHomogenizationCentrifugationCombine supernatantsAdd 2.5 mL 1 M HClExtract with 4 mLEtOAc:CHCl3 1:1HomogenizationCentrifugation

Evaporation of theorganic phase underN2 at 35ºCDissolve in 0.5 mLmobile phase

68.1±1.7 (n=30) C

OXA mudsandy mud

sand

1 0.05-0.8 1 h do do 58.3±5.2 (n=15)70.9±4.7 (n=15)90.2±4.0 (n=15)

D

OXA freshwater 0.1 4,900-19,600 72 h Extract sediment filled SPEtubes with 3×10 mL 20 % 10mM H3PO4 pH 2.5 in THF

Reduction to 6 mLunder N2 at 36ºCFiltration

98±36 (n=9) E

RSD: relative standard deviation, SPE: solid phase extraction, MeOH: methanol, EtOAc: ethylacetate, THF: tetrahydrofuran, -: not reported,a: mean±standard deviation, b: mean±coefficient of variation

A: Samuelsen et al. (1994), B: Bjørklund (1990) and Bjørklund et al. (1991), C: Pouliquen et al. (1994b), D: Pouliquen et al. (1994a) calcium, magnesium, zinc, iron, aluminium,fraction < 63 µm, organic matter significant correlate negatively with recovery, E: Holten Lützhøft et al., (Submitted) I

Chapter 3 • 49

3.3. Antimicrobials in Environmental Samples

Environmental occurrence of antimicrobials has mainly been studied in Norway and Finland,

though studies in Italy and Ireland have also been conducted. An extensive simulation study

was performed in France. The occurrence has so far only been reported in the marine

environment. Table 3.5 lists the literature on the environmental occurrence of the

antimicrobials investigated in this thesis. With nine references, OTC has been in focus. Four

investigations report the finding of OXA and only two addresses the finding of FLU.

3.3.1. Marine occurrence

The vast majority of experiments have been undertaken with sediment, though a few studies

address the occurrence in wild fauna and one the experimental determination in water.

Near or around fish farms OTC sediment concentrations up to 10 µg/g are frequently reported

(Jacobsen and Berglind, 1988; Bjørklund et al., 1990a; Bjørklund et al., 1991; Pouliquen et

al., 1992; Coyne et al., 1994). However, one study reports up to 281 µg/g (Samuelsen et al.,

1992b). OTC is shown to distribute to deeper layers (Samuelsen et al., 1992b; Coyne et al.,

1994). Usually OTC persists for up to several weeks (Jacobsen and Berglind, 1988; Bjørklund

et al., 1990a; Coyne et al., 1994), but in the case of 281 µg/g still 15 µg/g was found 18

months after (Samuelsen et al., 1992b). A few studies also reported the half-life in sediment

(Bjørklund et al., 1990a; Pouliquen et al., 1992; Samuelsen et al., 1992b; Coyne et al., 1994).

It ranges from 9 to 419 days. It was concluded that the half-life was mainly affected by water

current, exposure, ratio that reach sediment, area and how deep OTC distributes in the

sediment (Bjørklund et al., 1990a; Coyne et al., 1994). Moreover, disappearance is mainly due

to leakage and out washing and to a minor extent degradation, since no biotransformation

products could be detected (Bjørklund et al., 1990a).

In a study performed in France OTC medication was simulated in a tank system. One tank

was medicated and the water flew to other tanks containing sediment and shellfish. Water

concentrations up to 250 µg/L was reported 14 days after medication (Pouliquen et al., 1993).

Sediment concentrations at the same time were up to 1.96 µg/g (Pouliquen et al., 1992). After

14 days, the shellfish still contained up to 0.7 µg/g, the highest concentration was reported to

1.42 µg/g (Le Bris et al., 1995).


A comparative study calculated the aqueous concentration 1 cm above the sediment to be 16

and 110 µg/L, one day after medication (Smith and Samuelsen, 1996). However, under the

marine circumstances the bioavailable concentrations would be diminished due to complex

formation and would not be harmful to the micro-fauna.

In one study, OTC has been reported in wild fish. At the last day of medication up to 1 µg/g

was found, however, 13 days after only traces could be found (Bjørklund et al., 1990a).

OXA has been reported in both wild fauna and sediment. In one case 0.12 µg/g was reported

in wild fish two days before medication of the farmed fish. In another case, up to 4.89 µg/g

was found in wild fish, post medication. The wild fish were sampled up to 50 m from the fish

farm (Ervik et al., 1994). Another investigation reported OXA concentrations up to 4.4 µg/g

in wild fish, 0.65 µg/g in blue mussels and 0.81 µg/g in crab sampled up to 400 m from the

medicated location (Samuelsen et al., 1992a). However, compared to OTC lower levels are

found in sediment. Concentrations up to 0.2 µg/g are reported and OXA seems to disappear

fast from sediment, since OXA could not be detected 6 days post medication. As for OTC,

OXA is suggested to leave the sediment rather than being degraded (Bjørklund et al., 1991).

Ervik et al. (1994) also found FLU concentrations of 0.95 µg/g in wild fish 1 day after

medication. At the outflow of a pond in Italy, sediment concentrations in ng/g were found,

whereas aqueous concentrations up to 49.82 µg/L were reported (Migliore et al., 1996).

As seen from Table 3.5 the quantity applied for a treatment with OTC is higher than for the

quinolones. Additionally, the bioavailability of OTC is much lower than for OXA. This may

explain that OTC concentrations found in sediment are accordingly higher.

3.3.2. Freshwater Occurrence

One investigation reports the environmental occurrence in a freshwater habitat (Holten

Lützhøft et al., Submitted I). In co-operation with a Danish fish farm in Jutland, sediment was

sampled before and after an OXA treatment. Samples were taken at the inlet, within the fish

farm, at the outlet and 300 m downstream the fish farm outlet. The analysis and extraction

methods described above were applied to the sediment samples.

Chapter 3 • 51

There was no clear correlation between sampling time/location and sediment concentration.

However, 21 days post treatment a sediment sample was taken 300 m downstream the fish

farm. An OXA concentration of 1.6 µg/g was found. From the results two conclusions was

drawn:

• It is very important that the sediment is sampled at the same location from time to

time, and that the depth of the sample is measured.

• After antimicrobial treatment OXA can be found in sediment samples nearby the fish

farm – even 300 m downstream the outlet.

This indicates that medication of Danish fish farms may result in antimicrobial residues in the

environment near the farm site.

The result, that OXA was present 21 days post treatment, is in contradiction to the findings in

the Baltic (Bjørklund et al., 1991). At that location, OXA could not be detected 6 days post

treatment. Approximately the same quantity was applied, but two differences are obvious:

sampling location and sediment type. In the study by Bjørklund et al. (1991) the sediment was

from the Baltic and therefore of marine origin, whereas the sediment in the study of Holten

Lützhøft et al. (Submitted) I was from a small freshwater stream. Due to the location, a much

higher dilution effect can be expected in the Baltic. According to Pouliquen et al. (1994a)

sediment of sandy character is better extracted than muddy sediment. However, neither

sediment was further described, but sediment from the Baltic appears to be more sandy than

freshwater sediment, and may therefore easier release OXA.

Table 3.5 – Environmental occurrence of the investigated antimicrobials.

Antimicrobial Total load, kg Compartmenta Concentration, µg/g or Lb Days after treatment Distance from fish farm ref.FLU 6 wild fishNO 0.95c1 1 up to 50 m AFLU - marine sedimentIT 0.00002-0.00618 - outflow from pond BFLU - salt waterIT 0.01-49.82 - do BOXA 34 wild fishNO 4.4d1

1.8d2

0.09d3

0.01d4

04713

up to 400 m C

OXA do blue musselsNO 0.65d5

0.05d6

0d7

047

do C

OXA do crabNO 0.81d8

0.45d9

0.08d10

0.03d11

04713

do C

OXA 9.84 wild fishNO 0.32d12

0.004d1307

up to 400 m C

OXA do crabNO 0.005d14

0.047d15

0.002d16

047

do C

OXA 0.36 marine sedimentFI 0.20

06

nearby D


06

do D


during treatment6

do D

OXA 34 wild fishNO 4.89c2 0 up to 50 m AOXA 10 wild fishNO 0.58c3 0 do AOXA 26 wild fishNO 2.41c4 1 do AOXA 20 wild fishNO 0.12c5

1.00c6-21

do A

OXA 1.4 wild fishNO 1.02c7 1 do AOXA 0.595 freshwater sedimentDK 1.6 21 300 m downstream EOTC - marine sedimentNO 0.13e 70 under cage FOTC - marine sedimentNO 4.19f 84 do FOTC - marine sedimentNO 0f

1.2g7 do F

OTC - marine sedimentNO 2.4 and 3.6f 28 do FOTC - marine sedimentNO 0.46 and 1.0g 28 do FOTC 1.6 wild fish – bleakNO 0.2-1.3

0.06h

traces

017

nearby G

OTC do marine sedimentFI 0.10

08

around treated pen G

OTC 1.78 wild fish – BleakFI

wild fish – RoachFItraces0.060.05

traces

11213

nearby G

OTC do marine sedimentFI 1-3.816i

1-4.4

18

308

around treated pen G

OTC 2.6 marine sedimentFI 6.42.6

712

nearby D


during treatment12

do D


112

do D

OTC 0.8625 marine sedimentNO 192j

1.7j10560

under cage H

OTC 1.6875 marine sedimentNO 281j

15j75560

do H

OTC not treated marine sedimentNO 30j

1.7j75560

do H

OTC 1.6875 salt waterNO 110k 1 do IOTC 0.315 marine sedimentFR 3.47-4.19

1.50-1.96014

tank systeml J

OTC do salt waterFR 2,000-2,500125-250

014

do K

OTC do Crassostrea gigasFR 1.420.7

014

do L

OTC do Ruditapes philippinarumFR 0.50.4

during treatment14

do L

OTC do Scrobicularia planaFR 1.00.62

during treatment14

do L

OTC 47 marine sedimentIR 9.9m

2.3m

1.6i,m

33266

under cage M

OTC 169.5 marine sedimentIR 10.9m

3.3m

1.6m

during treatment1933

do M

OTC do salt waterIR 16k 1 do N

a: NO: Norway, IT: Italy, FI: Finland, DK: Denmark, FR: France, IR: Ireland, b: g for fish and sediment. L for water, c: Percent positive samples of one or several of the following fish:Saithe, Cod, Ling, Ballan wrasse, Pollack, Haddock, Mackerel, Whiting, Flounder or Salmon, c1: 77 % of 31, c2: 100 % of 32, c3: 87 % of 15, c4: 88 % of 42, c5: 17 % of 24, c6: 69 % of39, c7: 77 % of 30, d: Percent positive muscle samples of several of the following fish: Coalfish, Ballan wrasse, Ling, Haddock, Salmon, Cod or Pollack unless otherwise stated. Atsampling location d1-d11 additionally 660 tonnes of fish were treated in the same area during the sampling. This may explain the relatively higher concentrations compared to samplinglocation d12-d16, d1: 100 % of 42, d2: 97 % of 32, d3: 93 % of 27, d4: 58 % of 33, d5: 100 % positive homogenate samples of 5 Blue mussels, d6: 60 % positive homogenate samples of 5Blue mussels, d7: 0 % positive homogenate samples of 3 Blue mussels, d8: 100 % of 5, d9: 85 % of 13, d10: 60 % of 10, d11: 33 % of 12, d12: 74 % of 19, d13: 20 % of 5, d14: 50 % of 4, d15:36 % of 22, d16: 9 % of 4, e: Water level 10 m, f: Water level 20 m, g: Water level 40 m, h: 1 sample out of 8, i: One sample, j: Not specified but most likely the upper 2 cm. Profileswere made and OTC was detected 12 cm down in the sediment 245 days after treatment. The profile indicated OTC to move downwards due to time. This may be do to furthersedimentation of particulate matter, e.g. faeces, k: In the water 1 cm above the sediment, l: Simulation of a fish farm. Water from a polluted tank flew to three other tanks from whichsamples were taken, m: In the upper 2 cm. Profiles were made and showed OTC concentrations in deeper layers to increase until 19 days after treatment, data from Smith andSamuelsen (1996),

-: not reported,

A: Ervik et al. (1994) Probably obtained by eating surplus medicated feed from the farm, B: Migliore et al. (1996), C: Samuelsen et al. (1992a), D: Bjørklund et al. (1991), E: HoltenLützhøft et al. (Submitted) I, F: Jacobsen and Berglind (1988), G: Bjørklund et al. (1990a), H: Samuelsen et al. (1992b), I: In Smith and Samuelsen (1996) calculated from Samuelsenet al. (1992b), J: Pouliquen et al. (1992), K: Pouliquen et al. (1993), L: Le Bris et al. (1995), M: Coyne et al. (1994), N: In Smith and Samuelsen (1996) calculated from Coyne et al.(1994).

Chapter 4

Environmental Fate of

Antimicrobials

58 • Environmental Fate of Antimicrobials

4.1. Introduction

The environmental fate of chemicals is an important factor in the ERA procedure (Berg et al.,

1995) and is required for veterinary pharmaceuticals that have direct entry into the aquatic

environment (EMEA, 1998b). The physical chemical properties of the chemicals determine

the distribution between solids/organic phases and the aqueous phase. Additionally the

inherent properties contribute to whether the chemicals will be degraded or not. The physical

chemical properties therefore affect both the occurrence and to what extent the chemicals will

show effects on organisms in the environment.

Since the majority of the work in this thesis has been carried out on the 4-quinolones, FLU,

OXA and SAF will be in focus, especially in the section describing the distribution properties.

The chapter will focus on the antimicrobial interaction with environmental constituents, e.g.

humic acids and sediment, and the likelihood of antimicrobial degradation in the environment,

e.g. biodegradation, hydrolysis and photolysis. A conceptual diagram of

mentioned/interfering processes was shown in Figure 1.1.

4.2. Environmental Distribution

KOW is often used to predict chemical behaviour e.g. Geyer et al. (1984), Di Toro (1985) and

Nendza and Hermens (1995). Quantitative structure activity relationships (QSARs) have been

developed to predict/estimate the interaction with solids, organic carbon (OC) or DOC using

the KOW for the chemical. Equation 4.1 and Equation 4.2 are examples of such QSARs.

Log KOC = 0.983·log KOW + 0.00028 Equation 4.1

Log KOC = 0.52·log KOW + 0.64 Equation 4.2

Equation 4.1 was developed for pesticides with log KOW values in the range 1 to 7 (Di Toro,

1985) and Equation 4.2 was developed for various chemicals with log KOW values in the

range –0.6 to 7.4 (Briggs, 1981).

This makes sense only if the chemicals' affinity for 1-octanol, i.e. its hydrophobicity, reflects

its affinity for DOC. The equations moreover assume a hydrophobic nature of the interaction

(Berg et al., 1995), which for several organic pollutants have shown to work (McCarthy and

Chapter 4 • 59

Jimenez, 1985; Day, 1991; Kukkonen and Oikari, 1991). However, it is advised not to apply

mentioned equations to ionizable chemicals due to more complex interactions, i.e. various

electrostatic interactions (Nendza and Hermens, 1995; EMEA, 1998b).

4.2.1. pH-dependent 1-octanol/water distribution

As discussed in Chapter 2, the hydrophobicity of antimicrobials is pH dependent and

decreases when they become ionized. To evaluate the distribution of ionized species the

influence of pH on DOW was investigated for OXA (Holten Lützhøft et al., 2000 II). The

experimental data appeared to fit Equation 2.1, which only uses the ionization constant and

the true partition coefficient. This means that only the neutral species distribute to 1-octanol,

and indicates that DOW may be a good predictor for DOC interaction, under the assumption

that only hydrophobic interactions govern the binding to DOC.

Figure 4.1 shows the pH-dependent DOW for OXA including data from literature.

Figure 4.1 – Experimental 1-octanol/water distribution for OXAa.

2 4 6 8 10 120

2

4

6

8

10

12

pH

DO

W

a: Holten Lützhøft et al., (2000) II, curve represents the fitting of individual measurements ( ) to Equation 2.1. data from Takács-Novák et al. (1992), data from Hirai et al. (1986), and data from Bjørklund and Bylund

(1991).

These results confirm that the hydrophobicity of antimicrobials decreases when the chemicals

become ionized and that the DOW for OXA follows the understanding of distribution of

ionized species. For weak (carboxylic) acids, this happens when pH increases and vice versa

for weak bases. It is therefore expected that the ionized species will not interact with DOC


and due to their inherent low KOW values, the interaction of the neutral form will furthermore

be negligible.

Using the QSARs outlined above to predict partition to OC (KOC) at neutral pH for the

investigated antimicrobials produces values of the same magnitude as their log KOW values,

see Table 4.1 and Table 2.2. When pH increases, weak carboxylic acids, e.g. FLU and OXA,

become increasingly ionized. In theory, when pH increases this should result in less and less

interaction with DOC, due to the decreasing part of the neutral molecule.

Table 4.1 – Estimated and experimental distribution coefficients.

QSAR estimated Log KOCa

Antimicrobial Equation 4.1 Equation 4.2 Log DDOCb Log DSED

c

FLU 1.1 1.2 4.2 2.3

OXA 0.4 0.8 4.5 2.7

SAF -1.2 0.0 4.7 2.7

SDZ -1.0 0.1 4.5d 2.7d

TMP 0.6 1.0 4.3d 2.3

AMX -1.5 -0.1 4.6d 2.8d

OTC -1.2 0.0 4.4-5.0e 2.7f

a: Estimated according to Equation 4.1 (Di Toro, 1985) and Equation 4.2 (Briggs, 1981) using log DOW fromTable 2.2, b: Holten Lützhøft et al. (Accepted) III pH 7, c: Holten Lützhøft (Unplublished) freshwater sedimentpH 7, d: based on linear regression (log DDOC or log DSED vs. log DOW from Table 2.2) of the other values of thatcolumn, see Figure 4.3, e: Rabølle and Spliid (2000) soil experiments pH 5.6-6.3, f: Lai et al. (1995) freshwatersediment pH 7.7.

Nevertheless, the use of KOW to predict interaction with DOC works for hydrophobic

chemicals but applying this estimation method to antimicrobials results in estimates deviating

from experimental data, see below.

4.2.2. Experimental Distribution Coefficients

The major parts of natural occurring DOC are fulvic and humic acids, of which humic acids

is the major constituent (Masini, 1993), and also possesses the highest binding capacities (De

Paolis and Kukkonen, 1997). These macromolecules contain hydrophobic cavities enabling

hydrophobic interactions. On the other hand, antimicrobials are chemicals with a variety of

functional groups enabling other interactions than just hydrophobic interactions, for chemical

structures see Table 2.2.

Chapter 4 • 61

Knowing that humic acids consist of carboxylic acids, amines, and phenolic groups (Masini,

1993; Arnold et al., 1998), not only hydrophobic interactions are possible. Interactions of

electrostatic character between DOC and the antimicrobials are likely as well.

In order to evaluate the extent of antimicrobial interaction with DOC a newly developed

technique, SPME-HPLC, was applied (Chen and Pawliszyn, 1995). Using Aldrich humic

acids as DOC source, DDOC for the 4-quinolones was studied (Holten Lützhøft et al., Accepted

III). The distribution coefficients were determined using negligible depletion (nd)-SPME-

HPLC (Vaes et al., 1996; Vaes et al., 1997; Urrestarazu Ramos et al., 1998). nd-SPME offers

the opportunity to measure freely dissolved concentrations in a matrix-containing solution

without disturbing existing equilibria (Vaes et al., 1996). Using this approach, extensive

sample preparations can be avoided.

Imagine the equilibrium between the chemical X and the matrix M, Scheme 4.1. The

distribution coefficient, DM, determines which ratio of X that is freely dissolved. The

introduction of another matrix, the SPME fibre, will interfere with mentioned equilibrium.

However, if the volumes of the aqueous and fibre phases are properly selected, the SPME

fibre will only extract a negligible quantity of the freely dissolved X, i.e. nd-SPME. Hereby

the free concentration of X can be determined without significant interference with other

equilibria.

Scheme 4.1 – Schematic representation of equilibria in SPME extractions.

( ) XMMaqXMD

⇔+

( ) XMMaqXFibreXFibreMFibre DD

⇔++⇔

X: a given chemical, M: matrix capable of binding X, XM: complex of X and M, DM: distribution coefficient forX between the matrix and the aqueous phase, XFibre: X bound to fibre, DFibre: distribution coefficient for Xbetween the fibre and the aqueous phase, Fibre: SPME fibre capable of binding X. Modified from Vaes et al.(1996).

In Table 4.1, DDOC for FLU, OXA and SAF at pH 7 is shown. Depending on chemical log

DDOC values of 4.2-4.7 were obtained. Values that are comparable to highly hydrophobic

chemicals like pentachlorobenzene, hexachlorobenzene and 1,1-di-(p-chlorophenyl) 2,2,2-

trichloroethane (Urrestarazu Ramos et al., 1998). This is a remarkably high degree of

interaction, since values corresponding to their log KOW values were expected, see estimates

in Table 4.1. This emphasizes the importance of using appropriate QSARs for prediction of


chemical behaviour, since the predicted values are 3-5 orders of magnitude lower than

experimental data.

In order to study the influence of ionization, DDOC was established from pH 3 to 8. pH-

dependent DDOC are presented in Figure 4.2. As discussed earlier, a decreased distribution is

expected when chemicals become ionized. However, the present experiments show that when

pH was increased log DDOC for FLU and OXA increased from 3.4 and 3.9, respectively to 4.4,

indicating higher degree of interaction at higher degree of ionization. The results for SAF also

show a maximum interaction, though it can not be directly attributed to ionization, cf. pKa and

fractional composition in Table 2.2 and Figure 2.2. From a log DDOC of 4.8 at pH 3, it

increases to 5.2 at pH 5 and then decreases to 4.5 at pH 8. This behaviour has not been

possible to describe from available inherent properties. However, SAF has more ionizable

groups than FLU and OXA and consequently interactions that are more complex can be

expected.

Figure 4.2 – Experimental distribution coefficients to DOC for FLU, OXA and SAFa.

2 3 4 5 6 7 83

4

5

6

pH

Log

DDOC

a: Holten Lützhøft et al. (Accepted) III. Symbols represent mean of 5 measurements ± standard error. FLU ( ),OXA ( ) & SAF (◊).

These results emphasize the complex interaction pattern of these chemicals due to their

chemical structures and inherent properties.

The interaction of other fluoroquinolones with natural occurring humic acids has been studied

(Schmitt-Kopplin et al., 1999). Log DDOC values between –4.0 and –2.7 was reported at pH

Chapter 4 • 63

9.2. Compared to the values reported by Holten Lützhøft et al. (Accepted) III a difference of

up to 9 orders of magnitude is observed. Part of this difference can be explained by humic

acid origin, pH value and the use of DOC or humic acid concentrations in the calculations.

However, this can only explain a minor part of the overall difference. Despite otherwise stated

in the article, log DDOC calculations were based on mg humic acid, instead of kg (Schmitt-

Kopplin, Personal communication). Considering all mentioned differences, identical

distribution coefficients were obtained in the two experiments. Other fluoroquinolones have

been studied, with respect to their distribution to soil and clay minerals (Noware et al., 1997).

Log KOC was found in the range 4.6 to 4.9, which tallies with above-mentioned results.

The pH-dependent organotin distribution to humic acids (Suwannee River and Aldrich) has

been studied in detail (Arnold et al., 1998). Although a relatively decreased distribution was

found when the chemicals became ionized higher experimental distribution coefficients were

found, compared to predicted distribution coefficient based on KOW. The results indicated that

not only hydrophobic but also electrostatic interactions account for the distribution.

The influence of pH on the distribution of two equally hydrophobic chemicals,

pentachlorophenol and benzo(a)pyrene, to OC was studied (De Paolis and Kukkonen, 1997).

It was found that the neutral benzo(a)pyrene distribution was not affected by pH, whereas the

distribution of the weak acid pentachlorophenol decreased to zero when pH was increased. A

similar result was obtained for the hydrophobic weak carboxylic acid dehydroabietic acid

(Kukkonen and Oikari, 1991).

Compared to the antimicrobials discussed above, the structures of these ionizable organic

chemicals are simpler, and a less complex interaction pattern can be expected.

A few investigations address the quantitative distribution of OTC to soil (Rabølle and Spliid,

2000), freshwater sediment (Lai et al., 1995) and marine sediment (Pouliquen and Le Bris,

1996). In the soil experiments, the distribution was normalised to OC content, resulting in log

DDOC values of 4.4 to 5.0 at various pH values, see Table 4.1. Based on data in Lai et al.

(1995) the freshwater log DSED was calculated to 2.7, however, it was not possible to

determine a distribution coefficient to marine sediment, since the aqueous concentrations

were below the limit of detection (Pouliquen and Le Bris, 1996). The distribution coefficient

of OXA to marine sediments (mud, sandy mud and sand) was also studied (Pouliquen and Le


Bris, 1996), resulting in logarithmic distribution coefficients decreasing from 2.1 to –0.5 for

the respective sediments.

The distribution between natural freshwater sediment from a Danish stream and buffer pH 7

was investigated for FLU, OXA, SAF and TMP (Holten Lützhøft, Unplublished). Including

OTC, log DSED values of 2.3 to 2.7 were obtained for mentioned five antimicrobials, see Table

4.1. The sediment distribution of said antimicrobials is not surprising, since Chapter 3

revealed that OXA and OTC are present in sediment after treatment in fish farming. However,

the magnitude of that distribution surprises, since the antimicrobial interaction with sediment

was not expected to that extent.

Figure 4.3 represents linear regression of the presented log DDOC and log DSED from Table 4.1

vs. the respective log DOW from Table 2.2. The aim of the regression was to predict the DDOC

for SDZ, TMP and AMX and DSED for SDZ and AMX, respectively, in order to improve their

environmental fate evaluation. Usually a relation of that kind should only be performed on

homologous chemicals (Nendza and Hermens, 1995), however similar structure elements, e.g.

carboxylic acids, amines, carbonyls and hydroxy groups, are recognized from the structures,

see Table 2.2. It therefore seems likely, that the predicted values are within acceptable

uncertainty, though it must be stressed that the statistical material is limited.

Figure 4.3 – Experimental distribution coefficients vs. log DOWa.

-2 -1 0 1 22

3

4

5

Log DOW

Log

DS

ED o

r L

og D

DO

C

a: Linear regressions are based on experimental values from Table 4.1 for log DSED ( ) and log DDOC ( ),respectively, vs. log DOW from Table 2.2. Solid symbols indicate predictions made from the respective regressionlines.

Chapter 4 • 65

Although an increase of pH decreased the pentachlorophenol interaction with DOC (De Paolis

and Kukkonen, 1997), other ionizable chemicals interact equally with DOC, as hydrophobic

chemicals do (Arnold et al., 1998; Schmitt-Kopplin et al., 1999). Moreover, in addition to the

unexpected high log DDOC values for FLU, OXA and SAF, FLU and OXA showed an

increasing interaction due to increased ionization (Holten Lützhøft et al., Accepted III). This

is in contrast to the DOW for OXA, which clearly showed to decrease due to increasing

ionization; thus, only the neutral molecule distributes to 1-octanol (Holten Lützhøft et al.,

2000 II). This means, that using DOW to predict DOC interaction avoids the electrostatic

contribution from the various ionizable groups.

The inherent properties of the chemicals may explain this difference. Several authors have

suggested electrostatic interactions to account for the extent of DOC interaction for ionizable

chemicals, e.g. 4-quinolones (Holten Lützhøft et al., Accepted III; Noware et al., 1997;

Arnold et al., 1998; Schmitt-Kopplin et al., 1999; Holten Lützhøft et al., 2000 II). Especially

the β-keto acid structure that all 4-quinolones possess was suggested to be the main

electrostatic interaction centre (Noware et al., 1997). This is based on a decrease from a log

KOC of 4.9 to 3.0 for a particular fluoroquinolone and its decarboxylated analogue,

respectively.

Noware et al. (1997) concluded that this pronounced interaction will prevent leaching into

ground and surface waters. Apparently, this interaction leads to decreased free concentrations,

however, contaminated sediment/DOC will continuously supply the aqueous phase with

contaminant, in order to maintain equilibrium.

4.2.3. Complexation with metals

It is known that the bioavailability of tetracyclines in the human body decreases when e.g.

Mg2+ or Ca2+ are present (Jensen, 1993). It can therefore be expected that OTC will form

complexes with mentioned ions present in the environment, consequently affecting its

environmental fate and effects.

A few studies have addressed the influence the presence of cations have on antimicrobial

activity, see Table 4.2. The effect on the minimum inhibitory concentration (MIC) was

studied for magnesium, calcium and sodium ions. None of the antimicrobial activities seem to

be affected by neither calcium nor sodium ions, but [Mg2+] of 26.2 to 54 mM reduced the


antimicrobial activity. The 4-quinolone efficiency was reduced by a factor 12-50, depending

on bacterial strain, pH and [Mg2+] (Palmer et al., 1992; Barnes et al., 1995; Pursell et al.,

1995). OTC efficiency was reduced 64 times (Barnes et al., 1995). However, AMX was not

affected by magnesium either (Barnes et al., 1995).

Although OTC forms complexes with both magnesium and calcium, see Table 4.2, the

reduced OTC efficiency was attributed to magnesium complexation (Lunestad and Goksøyr,

1990), by changes in molecular charge of the complex. This is based on the MIC of OTC,

since the presence of magnesium contrary to calcium increased the MIC 4 times, when

equimolar concentrations were used. The complexation constant (KCOM) for OTC-magnesium

is only 1.5 times higher than the OTC-calcium constant.

This difference is much more pronounced for OXA, where the constant for OXA-magnesium

is abt. 10 times higher than the constant for OXA-calcium (Timmers and Sternglanz, 1978).

This difference is in agreement with the findings of Pouliquen et al. (1994a). Due to the

structure similarities among 4-quinolones, especially the β-keto acid structure, KCOM values

for FLU and SAF are expected in the same order of magnitude as for OXA, see Table 4.2.

Table 4.2 – Influence of cations on the antimicrobial activity.

MIC in presence of Complexation constant KCOM, M-1

Antimicrobial Mg2+ Ca2+ Na+ Mg2+ Ca2+ f

FLU ↑a,b,c ÷b ÷a,b 1,000d 100d 0.71

OXA ↑a,c - ÷a 1,995e 251e 0.54

SAF ↑a - ÷a 1,000d 100d 0.71

SDZ - - - - - -

TMP - - - - - -

AMX ÷a - ÷a - - -

OTC ↑a - ÷a 294f 190f 0.78MIC: Minimum Inhibitory Concentration, f: Free fraction based on freshwater [Mg2+] = 0.3 mM and [Ca2+] = 1mM Stumm and Morgan (1996), ↑: MIC increases when cation is added, ÷: no effect of addition of cation, a:Barnes et al. (1995) [Mg2+] = 50 mM, [Na+] = 340 mM, pH 7.4 (tryptone soy agar medium), b: Pursell et al.(1995) [Mg2+] = 54 mM, [Ca2+] = 10 mM, [NaCl] = 25 ‰, pH adjusted to 7.8, c: Palmer et al. (1992) [Mg2+] =26.2 mM, pH adjusted to 6.0, 7.0 and 8.0, d: predictions based on similar properties due to similar substructuresas for OXA, e: Timmers and Sternglanz (1978) pH 7.5, 67 mM morpholinopropane-sulfonic acid, 100 mM KCl,f: Lunestad and Goksøyr (1990) pH 8.0, [NaCl] = 35 ‰, -: no data found.

Chapter 4 • 67

Contrary to the enhanced 1-octanol and liposome uptake of phenolate species (Escher and

Schwarzenbach, 1996), the bacterial uptake of antimicrobials is not affected by sodium ions,

see above. In addition, the presence of calcium ions does not seem to affect the uptake,

whereas magnesium ions strongly reduce the antimicrobial efficiency, indicating a reduced

uptake.

Thus, in an environmental context the consequence of complexation is that a part of the

antimicrobial becomes non-bioavailable resulting in decreased biodegradation. Furthermore,

the complex bound part of the antimicrobial may be protected from abiotic degradation, since

the complex may have altered the degradability of the chemical.

In a short time perspective, the free concentration is decreased, but the process may result in a

prolonged environmental presence of the antimicrobial, since disappearance is balanced by

the equilibrium.

4.3. Degradability

When antimicrobials occur in the environment, they encounter a wide range of degradation

processes. Dependent on the physical chemical properties of the antimicrobial, these

processes will influence its appearance. The abiotic as well as the biotic degradability of

chemicals in the environment are important parameters in the fate assessment of chemicals.

Application of fast degradable chemicals can be justified, although they would be undesired

in the environment, due to their toxicological effects.

In the following, a distinction will be made between abiotic degradation, e.g. hydrolysis and

photodegradation processes and biotic degradation, e.g. biodegradation and enzymatic

degradation.

4.3.1. Abiotic degradation

4.3.1.1. Hydrolysis

Tsuji et al. (1978) investigated the stability of β-lactam antimicrobials. AMX was found most

stable, at environmentally relevant pH values. At pH 5 to 7, a half-life of 29 days at 35ºC was

found, however the stability decreased to a half-life of 9.6 days at pH 8 and to 0.48 days at pH

9.5.

Vej-Hansen et al. (1978) investigated the hydrolytic stability of OTC at 60ºC. OTC was most

stable at pH 2 and slightly less stable in the pH range 3.5 to 10. At pH 4.6 OTC hydrolysis


was investigated at temperatures from 40 to 70ºC in order to predict stability at lower

temperatures. By plotting the logarithmic rate constants vs. the reciprocal temperature in

Kelvin, a frequency factor, A, of 2⋅1014 h-1 and an activation energy, Ea, of 94,500 j/mole

were obtained. The following relationship between the temperature and the hydrolysis rate

constant according to the Arrhenius relation was found:

( )

T

1372,1133

114

a

ek

T

1

moleKj8.31

molej94,500h102lnkln

T

1

R

EAlnkln

⋅−

−

=

⇒⋅⋅

−⋅=

⇒⋅−=

Equation 4.3

R is the gas constant, T is the absolute temperature in Kelvin and k is the hydrolysis rate

constant in hours. Accordingly half-lives of 1.4 and 57 days, were found at 35 and 7ºC,

respectively. Compared to AMX, OTC is less stable towards hydrolysis.

Solutions of TMP were refluxed at various pH values (Bergh et al., 1989). Several hydrolysis

products were formed, mainly the amino groups were replaced by hydroxy groups. Based on

chemical structures, SDZ can be expected to undergo hydrolysis due to the sulphonamide

substructure, which has been confirmed (Zajac, 1977). Regarding the 4-quinolones, no

immediate hydrolytic centres are found. Storage in the dark at 4ºC for up to 3 months did not

reveal any degradation products and the concentrations remained stable when analysed by

HPLC (Holten Lützhøft, Unpublished). However, it is not surprising that AMX and OTC

undergo hydrolysis, since several hydrolytic labile centres are found, e.g. amides.

Environmentally, the consequence of hydrolysis is that the antimicrobials can be divided in

three groups. AMX and OTC form the hydrolytic unstable group, SDZ and TMP form the

hydrolytic labile group and the 4-quinolones seem to be relatively stable towards hydrolysis.

4.3.1.2. Photodegradation

Both direct and indirect photodegradation can take place. A way to predict whether chemicals

will be sensitive to direct photodegradation is to examine their UV/Visible absorption spectra.

In order to undergo direct photodegradation, the chemical needs to absorb light of a given

wavelength (Berg et al., 1995). In aqueous systems, the light intensity is not only decreased

Chapter 4 • 69

due to dissolved and particulate matter (Berg et al., 1995). Additionally, the UV light intensity

(<300 nm) in subsurface water is found to be almost zero, although most pronounced in

seawater, due to its higher ion content (Wheaton, 1977). Therefore, if the chemical does not

absorb light in the range 800-300 nm, it is not likely that the chemical will undergo direct

photodegradation (Lunestad et al., 1995).

On the other hand, indirect photodegradation may take place if e.g. hydroxyl radicals are

formed from the irradiation of other chemicals, e.g. humic acids, organic or inorganic

chemicals (Berg et al., 1995; Stumm and Morgan, 1996).

Examining the maximum absorption wavelength (λmax) of the selected antimicrobials, only

OTC has a λmax exceeding 300 nm, see Table 4.3. Consequently, OTC is the only

antimicrobial that is expected to undergo direct photodegradation.

Lunestad et al. (1995) investigated the influence of two light intensities on the photostability

of various antimicrobials in seawater. Both seawater and vessels were sterilized, and controls

were kept in the dark. FLU, OXA and OTC were degraded when exposed to surface light

intensities, whereas SDZ was partly degraded and TMP was stable. When exposed to light

intensities corresponding to 1 m depth, OTC was still degraded, FLU partly degraded, but

OXA, SDZ and TMP were stable, see Table 4.3.

The effect of temperature and light intensities on OTC photodegradation was investigated in

seawater in the laboratory (Samuelsen, 1989a). The photodegradation was dependent on both

parameters.


Table 4.3 – Antimicrobial stability under illumination.

t½ in seawater, dbRemaining afterillumination, %

Antimicrobial λmax, nma sea level 1 m depth t½, d ÷ H2O2 + H2O2

FLU 231, 323c 3 127 - - -

OXA 260, 322c 3 ∞ - - -

SAF 281, 317c - - - - -

SDZ 240, 254d 47 ∞ - - -

TMP 214, 285e ∞ ∞ - < 0.06f 36f

AMX 229, 272g - - - 1.2h < 0.05h

OTC 249, 276, 353i 3 3 5.3-16.3j - -t½: half-life, a: Values in bold are global maxima, other values are local maxima, b: Lunestad et al. (1995)regression assuming 1. order degradation based on readings from graphs, ∞ means no degradation observedduring the 21 and 56 days of study, respectively, c: obtained in acetonitrile:H3PO4 (pH 2.9), d: Stober andDeWitte (1982) pH 7.5, e: Lunestad et al. (1995) seawater, f: Lunn et al. (1994) 1 hour illumination, g:Bhattacharyya and Cort (1978) acidic solution, h: Lunn et al. (1994) 2 hour illumination, i: Budavari (1996) pH4.5, j: Samuelsen (1989a) 4ºC in the dark and 15ºC in illumination by a 40 W fluorescent tube at 1.5 m distance,respectively, -: no data found.

Burhenne et al. (1997) and Schmitt-Kopplin et al. (1999) investigated the photodegradation of

fluoroquinolones and found that these chemicals are relatively sensitive to direct light

exposure. For a SAF-analogue the primary degradation occurred in the piperazine moiety.

Depending on the season of the year, environmental half-lives were calculated to be up to abt.

2 days (Burhenne et al., 1997), which was later confirmed by Schmitt-Kopplin et al. (1999).

Half-lives for the further degradation of the quinolone structure were calculated to be up to 36

days, also depending on the season of the year (Burhenne et al., 1997). The latter half-life is

actually as important as the half-life for the parent compound, since the antimicrobial activity

is situated in the quinolone structure.

Furthermore, the photodegradation decreased in the presence of DOC, as the half-life for the

SAF-analogue doubled due to interaction with DOC (Schmitt-Kopplin et al., 1999).

The photodegradation of AMX and TMP was studied under laboratory conditions by means

of a 200 W medium pressure mercury lamp (Lunn et al., 1994). AMX was degraded both in

the absence and presence of H2O2, whereas TMP was stabilised by H2O2. Since H2O2 is

contemplated as disinfecting agent in fish farming (Michelsen, Personal communication),

TMP would be photolytic stabilised.

Chapter 4 • 71

Since antimicrobials are applied in fish farming during daylight, it is obvious to contemplate

the possibility of photodegradation in the water. However, as expected from λmax values, OTC

is the only antimicrobial with pronounced photodegradation. Although OTC is photodegraded

it maintains its algal growth inhibition, see Chapter 5 and Holten Lützhøft et al. (1999) IV.

Nevertheless, antimicrobials applied in fish farming will not be substantially photodegraded,

since they often are administered as medicated feed pellets (Lunestad et al., 1995).

Additionally, when released to the water, the antimicrobial may be protected from light

exposure due to the high fish density or interaction with DOC/sediment.

4.3.2. Biotic degradation

4.3.2.1. Biodegradation

The biodegradability of antimicrobials in the environment has mainly been addressed in

Norwegian investigations, however some studies have been performed in France, Ireland,

Taiwan and America. Mainly OTC has been in focus, but a few studies included 4-quinolones

and folate inhibitors as well. Table 4.4 represents the obtained results.

Table 4.4 – Antimicrobial biodegradability.

t½, db t½, dd

Antimicrobial t½, da 0-1 cm 5-7 cm t½, dc marine freshwater

FLU ∞ 60 >300 - - -

OXA ∞ 151 >300 - - -

SAF - 151 >300 - - -

SDZ ∞ 50 100 - - -

TMP 30 75 100 - - -

AMX - - - - - -

OTC ∞ 151 >300 9-419 10.3; 4.4; 6.6 7.7; 2.4; 2.8

t½: half-life, a: Samuelsen et al. (1994) in artificial marine sediment, ∞ means no degradation observed during the180 days of study, 30 days means that TMP could not be detected after 60 days, b: Hektoen et al. (1995), inmarine sediment, however, leaching is suggested to be the main reason for antimicrobial disappearance, c: inmarine sediment after treatment in fish farm, nevertheless, it is suggested that the disappearance is due towashing out rather than biodegradation (Samuelsen, 1989a; Bjørklund et al., 1990a; Pouliquen et al., 1992;Samuelsen et al., 1992b and Coyne et al., 1994), d: Lai et al. (1995) in 10 % (w/v) aerobic original fish farmsediment slurries, t½ based on readings from graphs assuming 1. order degradation - abiotic t½ was calculated to47 days, t½ in anaerobic experiments were found equal to the abiotic t½, values corresponds to three consecutiveadditions, -: no data found.


The stability of FLU, OXA, SDZ, TMP and OTC was studied in artificial marine fish farm

sediment under laboratory conditions (Samuelsen et al., 1994). All but TMP remained stable

during the 180 days of study. TMP decreased to zero within 60 days. Antimicrobial activity

disappeared after 1 month for OTC and 3 months for TMP, whereas FLU, OXA and SDZ

maintained their activity throughout the study period.

In sediment from Oslo fjord, antimicrobial stability was investigated (Hektoen et al., 1995). In

the aerobic sediment layer (0-1 cm), half-lives for FLU, OXA, SAF, SDZ, TMP and OTC

were estimated from 50 to 151 days. In the anaerobic layer (5-7 cm), half-lives of more than

300 days were estimated for the 4-quinolones and OTC, whereas half-lives of 100 days was

estimated for the folate inhibitors. It was concluded that the antimicrobials mainly were

removed due to washing out and redistribution in the sediment.

After treatment in fish farms, OTC concentrations in the sediment have been followed. The

calculated half-lives range from 9-419 days (Samuelsen, 1989a; Bjørklund et al., 1990a;

Pouliquen et al., 1992; Samuelsen et al., 1992b; Coyne et al., 1994). However, the

disappearance was most likely due to washing out.

In a laboratory study, spiked sediment was covered with a 4 cm sediment layer, to obtain

anaerobic conditions. The resulting half-life increased from 32 to 64 days (Samuelsen,

1989a).

In one study from Taiwan, biodegradation of OTC was studied in both marine and freshwater

sediment slurries under aerobic and anaerobic conditions (Lai et al., 1995). The degradation in

the anaerobic investigation was found equal to the abiotic control. The non-degradability

under anaerobic conditions tallies with above-mentioned studies. In the aerobic studies,

biodegradation was 5-6 times faster than the abiotic control. Half-lives of 8 to 10 days were

obtained. Moreover, half-lives seemed to decrease due to acclimatisation and higher OTC

concentrations.

The mineralisation of 14C SAF was investigated in three soils (Marengo et al., 1997).

However, only 0.6 % was recovered as CO2, independent on soil. The low mineralisation was

attributed to the non-bioavailability of SAF, due to its interaction with soil.

Chapter 4 • 73

As discussed in the beginning of this chapter, antimicrobials interact widely with sediment

and DOC. This is reflected in the antimicrobial persistence in sediment. Since the chemicals

are non-bioavailable, they are not biodegradable. Except for AMX, for which no data were

found, neither of the antimicrobials seems to be biodegradable.

The environmental consequence is prolonged occurrence, and upon continuous exposure

accumulation in sediment may take place.

4.3.2.2. Enzymatic Degradation

The β-lactam antimicrobials are chemicals sensitive to β-lactamases, of which more than 100

have been characterised (Schito et al., 1994). β-Lactamases are produced by some bacteria as

a defence towards β-lactam antimicrobials. When a chemical like AMX is exposed to the

environment, it can be inactivated depending on the occurrence of β-lactamases. It is not

known whether the degradation process can take place also in the sediment or is restricted to

the aqueous phase, so if AMX is accumulating in the sediment AMX may be protected from

being degraded. However, in a study of the sorption of AMX to faecal substances, it was

concluded that the inactivation performed by β-lactamases was negligible compared to the

sorption process (Vries-Hospers et al., 1993).

4.4. Transport

Despite the relatively high water solubility (and low KOW), especially for OTC, the

antimicrobials are not very mobile in the environment. This is based on the relatively high

interaction with DOC and sediment. However, binding to DOC may cause transport in the

aqueous environment.

As illustrated in Figure 3.1, antimicrobials may unintentionally be transported to arable land

in case of utilization of sediment as fertilizer.

Chapter 5

Environmental Effects of

Antimicrobials

76 • Environmental Effects of Antimicrobials

5.1. Introduction

As shown in Chapter 3, antimicrobials are found in the aquatic environment; the occurrence is

probably due to fish farming activities, however, leaching from arable land may be the source

as well. This occurrence may provoke effects on organisms living in the habitats around fish

farms. However, these effects are influenced by the antimicrobials inherent properties in

connection with environmental conditions. Factors to this influence are discussed later in the

chapter.

In order to perform a proper assessment effect data on relevant organisms are needed

(Vermeire and Zandt, 1995). In context of the guideline adopted by the European Community

(EMEA, 1998b), indeed, effect data are required for the evaluation of antimicrobials applied

in fish farming.

In order to evaluate the environmental effects of a given chemical, either ecosystem functions

or biomass studies are performed. Ecosystem functions may be the study of

nitrification/denitrification and biomass may be studied by cell growth or death. Since one of

the aims in this thesis is to evaluate the environmental impact of antimicrobials in the aquatic

environment (see Chapter 6) only effects on aquatic organisms living in the habitats around

fish farms are presented in this chapter.

However, are the currently applied standard test organisms suitable for evaluation of

antimicrobials?

This chapter will especially discuss the suitability/selection of standard test organisms in the

evaluation of antimicrobials. Furthermore, current available effect data for the investigated

antimicrobials are presented.

5.2. Selection of Test Organisms

Due to the variety of organisms present in the environment, aspects of the test organisms shall

be considered. ERA is usually undertaken by evaluating the effects of the considered

chemical on different trophic levels. This is particularly done in order to detect effects

Chapter 5 • 77

restricted to certain taxa. However, it has been discussed whether the use of representative

organisms or functional important organisms is most suitable (OECD, 1993d). Functionality

is considered more relevant than representation of the taxa.

ERA of the aquatic environment is conventionally conducted by testing organisms on three

trophic levels represented by algae, crustaceans and fish (Leeuwen, 1995a). By doing so, the

aquatic ecosystem and a food chain are simulated; although strongly simplified.

Typical test organisms, for each of the mentioned trophic levels, are S. capricornutum,

Daphnia magna and Brachydanio rerio for which standard test protocols exist (ISO, 1989;

OECD, 1993a; OECD, 1993b; OECD, 1993c). Selection of mentioned organisms are due to

both practical and scientifical reasons (OECD, 1993d; Leeuwen, 1995a). Preferably, the

organisms should be easy to culture. Moreover, algae are selected as they are primary

producers and serve as food source for higher trophic levels/organisms. This may be

crustaceans, which graze on algae and serve as food for fish. Again fish graze on crustaceans

and serve as food for e.g. the human population.

Thus, if no direct effect is observed on e.g. fish an indirect effect may be observed if effects

are observed on e.g. algae.

Cyanobacteria are classified as algae but share morphology with bacteria (procaryotes) and

functionality with algae (eucaryotes) (Brock and Madigan, 1991). In the context of

antimicrobials, micro-organisms may be presented as illustrated in Figure 5.1.

Figure 5.1 – Simplified characteristics of micro-organismsa.

Bacteria Cyanobacteria AlgaeProcaryotic Procaryotic Eucaryotic

No nucleus No nucleus Nucleus

0.5-2.0 µm 0.5-60 µm 2-200 µm

No photosynthesis Photosynthesis Photosynthesis

a: Modified from Brock and Madigan (1991).

Since antimicrobials, by nature, exert effects on bacteria, cyanobacteria may be at risk when

antimicrobials are exposed to the environment.


The growth inhibiting effect of the antimicrobial streptomycin was tested on various algae

(Harras et al., 1985). Cyanobacteria, e.g. M. aeruginosa, was found most sensitive.

As S. capricornutum is a green alga, the effects of antimicrobials on phototrophic organisms

may be overlooked if conventional organisms are tested.

To confirm above-mentioned hypothesis/findings, Holten Lützhøft et al. (1999) IV

investigated several antimicrobials. S. capricornutum was selected due to its status as typical

standard test organism and to represent freshwater green algae (eucaryotes). R. salina was

selected to represent cryptophyceans (eucaryotes) as well as marine algae and M. aeruginosa

was selected to represent freshwater cyanobacteria (procaryotes).

5.3. Toxicity on Various Trophic Levels

The effect data presented in this chapter comprise both acute and chronic effect data, see

Table 5.1 and Table 5.2, respectively. Due to the nature of growth inhibition tests with algae,

they are short-term chronic tests (Leeuwen, 1995a). Therefore three fourth of the reported

data are from chronic studies.

OXA is the most studied antimicrobial with ten effect data of which six are chronic. A middle

group consisting of FLU, SDZ, TMP and OTC follows with five to six data of which three to

five are chronic. The least investigated antimicrobials are SAF and AMX with three data, all

from algal studies.

Since effect studies on fish are scarce, only OXA and TMP have been tested on the three

recommended trophic levels.

Among the published data, only a few are reported as no observed effect concentration

(NOEC), the majority is the concentration provoking effect in 50 % of the population (LC50 or

EC50).

Table 5.1 – Acute toxicity of antimicrobials, LC50 (NOEC), mg/La.

Organism Endpoint FLU OXA SAF SDZ TMP AMX OTC

Artemia sp. 48 h M 96.35b - - - - - -

A. tonsa 48 h M - (2.78)c - - 89d - -

D. magna 48 h M - 4.27e (0.78)c - 221f 123g - ≈ 1,000f

B. rerio 96 h M - (25)c - - (100)g - -a: For clarity, confidence limits have been omitted, b: Migliore et al. (1997), c: Andersen (1999), d: Andersen (Personal communication), e: Mean of 3.94 (Andersen,1999) and 4.60 (Wollenberger et al., 2000), f: Wollenberger et al. (2000), g: Halling-Sørensen et al. (In press), M: mortality, -: No data found.

Table 5.2 – Chronic toxicity of antimicrobials, EC50 (NOEC), mg/La.

Organism Endpoint FLU OXA SAF SDZ TMP AMX OTC

V. fischerib 24 h BI 0.01902 (0.00264) 0.02291 (0.000731) - - - - -

M. aeruginosac 7 d GI 0.159 0.18 0.015 0.135 112 0.0037 0.207

R. salinac 72 h GI 18 10 24 403 16 3,108 1.6

S. capricornutumc 72 h GI 5 16 16 7.8 130 (250) 4.5

D. magnad 21 d R - (0.38) - 13.7 - - 46.2a: For clarity, confidence limits have been omitted, b: Backhaus et al. (In press), c: Holten Lützhøft et al. (1999) IV, d: Wollenberger et al. (2000), BI: 24 hourbioluminescence inhibition, GI: growth inhibition, R: Reproduction, -: No data found.


5.3.1. Micro-organisms – Bacteria and Micro-algae

The bioluminescence inhibition of Vibrio fischeri was studied for several 4-quinolones

(Backhaus et al., In press). Both single substance and mixture toxicity was studied. For FLU

and OXA EC50 values of ca 20 µg/L were obtained. When the ten 4-quinolones were present

in concentrations corresponding to their single substance NOEC, the mixture caused an effect

of >99 %. Consequently, it was concluded that NOECs are insufficient estimates for no

toxicity. However, the simultaneous occurrence of ten 4-quinolones in the surroundings of

fish farms, is seldom. Usually one agent is used in the treatment.

Although, beyond the scoop of this thesis, uncritical environmental disposal of antimicrobials

may result in development of resistant bacterial strains, e.g. Nygaard et al. (1992) and

Guardabassi et al. (2000b).

The growth inhibiting effect towards algae was investigated for the antimicrobials selected in

this thesis (Holten Lützhøft et al., 1999 IV). The algae were selected to represent different

taxa and environmental habitats as mentioned above.

In the case of FLU, OXA, SAF and SDZ M. aeruginosa was 2 to 3 orders of magnitude more

sensitive than the two eucaryotic algae, but for OTC, the difference was only 1 order of

magnitude. An extreme difference of 5 to 6 orders of magnitude was found for AMX.

Generally EC50 values towards M. aeruginosa were found in the range 15-207 µg/L, whereas

the EC50 values towards the eucaryotic algae were found in the range 1.6-403 mg/L. However,

AMX deviated seriously from this pattern with an EC50 of 4 µg/L towards M. aeruginosa and

an EC50 of 3,108 mg/L and a NOEC of 250 mg/L towards to two eucaryotic algae. TMP was

found equally (non-) toxic towards the three algae, with EC50 values of 16-130 mg/L.

Regarding the eucaryotic organisms, except for OTC, the reported effect concentrations are

environmentally unrealistic. The nominal antimicrobial concentration reached in fish farms is

exceeded several times, even when the antimicrobial dose is applied in one lot, cf. Table 2.5.

Mentioned difference in sensitivity between the procaryotic cyanobacteria and the eucaryotic

algae has later been confirmed for antimicrobials structurally related to FLU/SAF (Halling-

Sørensen et al., In press) and AMX and OTC (Halling-Sørensen, 2000). The difference for

AMX was suggested to be related to its relative hydrolytic stability at the pH during the

Chapter 5 • 81

cyanobacteria test compared to the pH during the tests for the eucaryotic algae (Halling-

Sørensen, 2000), see below and Chapter 4.

As discussed in Holten Lützhøft et al. (1999) IV the inclusion of a procaryotic alga, e.g. M.

aeruginosa, in the ERA of antimicrobials seems relevant. Using test algae according to

standard protocols (ISO, 1989), e.g. S. capricornutum, does not detect the environmental

impact of said chemicals.

5.3.2. Crustaceans

Several investigations of antimicrobials have been performed on crustaceans, hardly showing

any toxicity e.g. Macrì et al. (1988) and Dojmi Di Delupis et al. (1992).

The acute toxicity (mortality) towards three crustaceans has been studied. An LC50 of 96

mg/L was found for FLU towards Artemia sp. (Migliore et al., 1997). A NOEC of 2.8 mg/L

was found for OXA towards Acartia tonsa (Andersen, 1999), who also found a NOEC and an

LC50 of 0.8 and 3.94 mg/L, respectively, towards D. magna. Last mentioned LC50 was

confirmed with 4.6 mg/L (Wollenberger et al., 2000), who also tested SDZ and OTC and

obtained LC50 of 221 and ≈ 1,000 mg/L, respectively. For TMP LC50 for A. tonsa was found

to 89 mg/L (Andersen, Personal communication) and 123 mg/L towards D. magna (Halling-

Sørensen et al., In press).

The chronic effect (reproduction) on D. magna was studied for OXA, SDZ and OTC

(Wollenberger et al., 2000). A NOEC of 0.38 mg/L was reported for OXA, whereas EC50 of

13.7 and 46.2 mg/L were obtained for SDZ and OTC, respectively.

Mentioned effect concentrations are relative high, and in most cases unrealistic in an

environmental perspective. However, the effect concentrations, especially the acute and

chronic NOECs for OXA indicate a possible risk, which was stressed by Wollenberger et al.

(2000). Thus, the chronic reproduction test with D. magna was suggested to be included in the

ERA of antimicrobials (Wollenberger et al., 2000).


Although a relatively high chronic effect concentration was observed for OTC towards D.

magna, it appears 10-220 times as high as the chronic effects towards algae. Hence, an

indirect effect is likely to occur for said chemical.

5.3.3. Fish

Since the antimicrobials in focus in the present thesis are used to treat infections among fish,

they are not expected to exert toxic effects towards said organisms. However, the literature is

scarce and only two investigations are found.

The acute toxicity towards zebra fish, B. rerio, was tested for OXA (Andersen, 1999) and

TMP (Halling-Sørensen et al., In press). Limit tests, conducted according to OECD (1993c),

indicated NOECs of 25 and 100 mg/L, corresponding to maximum solubility and maximum

test concentration, respectively.

This confirms the expected low toxicity

5.4. Factors Affecting Toxicity

As mentioned in chapter 4, the antimicrobials inherent properties affect the toxicity, although

often combined with environmental conditions. The environmental temperature has showed to

affect the pharmacokinetics in rainbow trout, e.g. Bjørklund and Bylund (1990b), Bjørklund

et al. (1992) and Sohlberg et al. (1994). For DOC interacting chemicals, their toxicity was

decreased in the presence of DOC, e.g. Kukkonen and Oikari (1987), Day (1991) and Cano et

al. (1996).

It is generally believed that only the neutral part of a molecule is able to penetrate

biomembranes unless a specific transport mechanism exists for the ionized species (Rang and

Dale, 1993). The hypothesis is therefore that neutral molecules show higher toxicity than

ionized species.

The toxicity of chlorophenols, weak acids, at various pHs has been studied towards both algae

(Smith et al., 1987) and fish (Könemann and Musch, 1981). It was found that the toxicity

decreased due to increasing pH, supporting the hypothesis.

Chapter 5 • 83

Algal growth inhibition tests conducted according to ISO (1989) aims at maintaining a pH of

8.3±0.2 with a maximal drift of 1.5 pH unit. In the tests performed with S. capricornutum and

R. salina, pH was initially 7.9. However, due to the high growth rate of mentioned organisms,

an inevitable increase in pH will be observed. Contrary, the pH in the tests with M.

aeruginosa was maintained at pH 7.9, as the growth rate of said organism is much lower.

As was shown in Figure 2.2, all antimicrobials investigated in this thesis are to some extent

ionized under the conditions in the algal experiments. It can therefore be expected that the

estimated growth inhibition may be underestimated compared to the average pH 7.2 in Danish

streams, see Chapter 2. Suggestions to circumvent the pH-drift are discussed in detail by

Halling-Sørensen et al. (1996), and the effect of ionization on toxicity is addressed in Holten

Lützhøft et al. (1999) IV.

pH will not only have influence on the environmental toxicity, but also the toxicity

determined in the laboratory. Thus, it is important to consider pH for a given test and realize

the possible effects on toxicity. However, pH does not only influence the degree of chemical

ionization, but as shown in Chapter 4, some antimicrobials are affected by hydrolysis too.

Regarding AMX, the hydrolysis half-life at 35°C decreases 20 fold from 9.6 days at pH 8 to

0.48 days at pH 9.5, see Chapter 4. As discussed in Halling-Sørensen (2000), this may explain

the difference in the effects observed for this antimicrobial, see Table 5.2.

Another explanation may be that the cell wall of cyanobacteria often contains peptidoglycan,

whereas eucaryotes do not (Brock and Madigan, 1991). Inhibition of the peptidoglycan

biosynthesis is exactly the target for antimicrobials as AMX, see Chapter 2.

As discussed in Chapter 4, substantial photodegradation takes place when OTC solutions are

illuminated. Due to the nature of the algal experimental set-up, test solutions are strongly

irradiated (ISO, 1989). Thus, OTC is expected to be photodegraded in such experiments.

Nevertheless, OTC still shows growth inhibiting effects towards the tested algae, meaning

that the degradation products are toxic (Holten Lützhøft et al., 1999 IV).


However, the results of algal batch test as described in e.g. ISO (1989) can only be used to

rank toxicity of chemicals. Test conditions are not according to field conditions.

It seems necessary to develop more suitable test systems for antimicrobials, due to their

specific modes of action, since current test systems are developed e.g. with narcotic chemicals

in mind.

Almost all the reported effect data have endpoints, e.g. growth inhibition or mortality, with

severe effects on the ecosystem. These endpoints are serious, but less dramatic endpoints may

also be important for the well being of organisms or ecosystems.

It is likely, that the conducted tests overlook important side effects occurring at much lower

concentrations.

Chapter 6

Environmental Risk Assessment

of Antimicrobials Applied in

Danish Fish Farms

88 • Environmental Risk Assessment of Antimicrobials Applied in Danish Fish Farms

6.1. Introduction

The ERA of antimicrobials has been addressed a few times in the literature. Jørgensen et al.

(1998) developed a model for ERA of antimicrobial growth promoters, Pors (1998) indicated

that application of antimicrobials in fish farming should be considered carefully, and

Montforts et al. (1999) applied a model to establish FLU and OTC concentrations in fish

farming effluents in The Netherlands. No attempt to quantify the risk associated with

application of antimicrobials in fish farming has yet been made.

According to Tas and Leeuwen (1995) risk assessment entails some or all of the following

elements: hazard identification, effects assessment, exposure assessment and risk

characterization. This chapter focuses on risk characterization in the aquatic environment.

Risk characterization is to relate occurrence to effects, which gives a mean to quantify the

likelihood of adverse effects. It can be expressed as the RQ as in Equation 6.1:

PNEC

PECRQ = Equation 6.1

When the RQ exceeds 1, the likelihood of adverse effects increases (Leeuwen, 1995b). The

approach is discussed in the next section.

The aim of ERA of antimicrobials applied in fish farming is not to ban the chemicals, but

based on relevant scientific knowledge to rank their environmental impact. If two

antimicrobials in a pharmacological sense are identical, why not choose the one having the

lowest environmental impact? Though FLU and SAF are not applied in Denmark, they will be

assessed anyway. This enables the individual ranking of the antimicrobials investigated in this

thesis.

The preceding chapters presented data on the occurrence, fate and effects of antimicrobials

applied in fish farming. The aim of this chapter is to incorporate that knowledge in the ERA

of said chemicals. According to OECD (1992), both freshwater and marine organisms have

been included in the ERA in order to obtain a wider data set for the effects.

Chapter 6 • 89

6.2. Predicted No Effect Concentrations

The PEC/PNEC relation, Equation 6.1, is an approach in which an appropriate assessment

factor is applied to the effect data (Leeuwen, 1995a). PNEC is the concentration of the

considered chemical that is estimated not to give adverse effects in the environment. EMEA

(1998b) recommends the recommendations from OECD (1992) in the ERA of antimicrobials

applied in fish farming. The aim is to represent the ecosystem by data from one organism at

three different trophic levels: algae, crustaceans and fish, as discussed in Chapter 5.

Additionally, the quality of these data should preferably be NOECs from chronic studies.

• If these requirements are fulfilled the lowest NOEC should be divided by an

assessment factor of 10 to obtain the corresponding PNEC.

• However, if the data do not comprise chronic values, the lowest EC50 should be

divided by another factor of 10.

• Finally, if data from less than three trophic levels exist, an additional assessment factor

of 10 is applied.

PNEC is thus derived according to Equation 6.2.

AF

ECor NOECLowest PNEC 50= Equation 6.2

AF is the assessment factor discussed above and depending on the quality of the effect data

the value is either 10, 100 (10×10) or 1,000 (10×10×10).

There is no scientific basis for this constant assessment factor approach, however among

different regulatory frameworks there is agreement of the magnitude (Leeuwen, 1995a). The

approach is illustrated in Figure 6.1.


Figure 6.1 – Illustration of the constant assessment factor approacha.

Three trophic levels

0

50

100

NOEC

Chronic Acute

NOEC

EC50 EC50

⇓AF=10

Log C

⇓AF=100

Effe

ct,

%

Less than three trophic levels

0

50

100

NOEC

EC50

Log C

⇓AF=1,000

Effe

ct,

%a: According to OECD (1992), Three trophic levels: algae, crustaceans and fish, Effect: e.g. growth inhibition,reproduction, mortality, AF: Assessment factor.

Mentioned relations assume that a factor of 10 corrects for the difference between a chronic

NOEC and an acute EC50. However, the same assessment factor of 10 was recommended for

the extrapolation from acute to chronic toxicity, since it was experimentally supported for

neutral organics (OECD, 1992). This is true, only if there is no difference between the EC50

and the NOEC! Meanwhile, correction from acute to chronic toxicity for anilines needs a

factor larger than 10!

Moreover, when data for less than three tropic levels are available a factor larger than 10 may

be applied, if the dose-response curve is shallow or if the data is obtained from an organism

known to be insensitive (OECD, 1992).

It is noteworthy that the effect data towards D. magna, as presented in Chapter 5, can be

compared with respect to EC50/NOEC and acute/chronic ratios. For OXA the acute EC50 must

be divided by a factor of 5 in order to obtain the acute NOEC. Furthermore, for OXA, SDZ

and OTC the acute toxicity needs to be divided by a factor of 2, 16 and 22, respectively, in

order to obtain the chronic toxicity, see Table 5.1 and Table 5.2.

Thus for OXA a factor of 10 was actually sufficient to calculate the chronic NOEC from the

acute EC50. On the other hand, factors of 16 and 22 were needed just to correct the difference

between acute and chronic EC50 for SDZ and OTC, respectively!

Chapter 6 • 91

Data, with larger variation in both antimicrobials and organisms, are needed in order to

evaluate the validity of this approach for antimicrobials and maybe even more generally for

pharmaceuticals.

Nevertheless, with the above-mentioned in mind, the constant assessment factor approach will

be applied to the effect data presented in Chapter 5.

The derived PNECs according to Equation 6.2 are presented in Table 6.1.

Table 6.1 – Antimicrobial PNECs in the aquatic environmenta.

Antimicrobial TLsb Organismc Qualityd EC, µg/Le AF PNECf, µg/L

FLU 2 V. fischeri chronic NOEC 2.64 1,000 0.00264

OXA 3 V. fischeri chronic NOEC 0.731 100 0.00731

SAF 1 M. aeruginosa chronic EC50 15 1,000 0.015

SDZ 2 M. aeruginosa chronic EC50 135 1,000 0.135

TMP 3 R. salina chronic EC50 16,000 100 160

AMX 1 M. aeruginosa chronic EC50 3.7 1,000 0.0037

OTC 2 M. aeruginosa chronic EC50 207 1,000 0.207a: Calculated according to OECD (1992 and EMEA (1998b), b: Number of trophic levels tested, c: Most sensitiveorganism, d: Quality of the effect data for the most sensitive organism, e: Value of the lowest NOEC or EC50, seeformer column, according to Table 5.2, f: According to Equation 6.2, AF: Assessment factor.

It appears from Table 6.1, that none of the antimicrobial data sets can live up to the strongest

requirement of chronic NOECs on three trophic levels, hence the lowest assessment factor

applied is 100 for OXA and TMP. The remainder are applied a factor of 1,000, due to lack of

data. TMP is the only antimicrobial which seems relatively harmless with a PNEC of 160

µg/L based on the alga R. salina. The remaining antimicrobials reveal PNECs in the ng/L

range (0.003-0.2 µg/L) based on chronic studies towards the bacteria V. fischeri and the

cyanobacteria M. aeruginosa. The latter concentrations are realistic in an environmental

context.

It shall be emphasized that SDZ and TMP will be assessed individually, although they are co-

administered, see Chapter 2. Pharmacologically, co-administration results in synergetic

effects at doses less than one-tenth of what would be needed if each antimicrobial were used

on its own (Rang and Dale, 1993). Hence, experimental studies with the relevant SDZ/TMP


ratio need to be performed to evaluate the mixture effect on relevant environmental

organisms.

6.3. Exposure Scenarios

In this section three exposure scenarios will be defined and used to evaluate the application of

antimicrobials in fish farming. The three scenarios are 1) a worst case scenario, 2) a scenario

including inevitable processes and 3) a scenario including both inevitable processes and

natural dilution possibilities. The definitions and the procedures to derive the PECs are

described in section 6.3.2. The three scenarios go from a simple scenario to a more complex

scenario, but at the same time the scenario also attempts to be more realistic. Thus, it is a

question of keeping the simplicity, in order to be able to interpret the result, but at the same

time to incorporate realism in the exposure scenario.

A worst case scenario is often used in the initial assessment of a chemical. In such a scenario

it is relevant to keep the conditions simple, thus degradation and biotransformation processes

may be omitted. However, a simple dilution of the effluents may be incorporated. This

renders the situation with the largest emission of chemical resulting in the highest possible

environmental concentration. A worst case scenario is a very conservative estimate for the

exposure. If the result of such an emission does not reveal likelihood of environmental risk

(RQ>1), the application of the focal chemical is most likely without risk; especially if various

fate processes happen to take place. Thus, the evaluation of a worst case scenario is a simple

and robust estimate for the exposure, and if no sign of risk is indicated, it is not necessary to

go further.

Nevertheless, if relevant data exist, e.g. degradation and distribution data, inclusion of such

data renders a more realistic result, but also a result that relies on a higher degree of

complexity. Such data may be included in an extended scenario, if the evaluation of the worst

case scenario indicates environmental risk. However, since the mentioned fate processes in

the present case, are not controlled by the fish farmer they will inevitably take place. Thus,

data of that type will be natural to include in an exposure scenario. By including such data, the

conditions for the focal chemical have been favoured. Hence, if the RQ still exceeds 1 more

information need to be included in the ERA or risk management has to be employed.

Chapter 6 • 93

A simple risk management process is the dilution of effluents. Although a dilution solution

does not seem to be sustainable in a long-term perspective, this process can, to a certain

extent, be controlled by the fish farmer. Furthermore, due to the natural water flow in the

stream, effluents will always be diluted, and dilution will also be natural to include.

Further risk management may be needed if the RQ still exceeds 1. Possible procedures to

reduce environmental impact of antimicrobials applied in fish farming are discussed at the end

of this chapter.

In the environmental assessment guideline adopted by the European Community (EMEA,

1998b) recommendations to calculate PECs are given. The following should be considered:

• the mass of produced fish,

• the volume of produced waste water,

• dosing regime and pharmacokinetics and

• dilution upon entry to the receiving stream; usually a dilution of one-third to two-third.

The worst case scenario described in this thesis is comparable to the mentioned

recommendations, although the two approaches differ slightly, which will be discussed later.

All the mentioned scenarios, including the scenario described in the guideline (EMEA,

1998b), are static exposure scenarios. Since the fish farm system by nature is dynamic, it

would be obvious to develop a model describing the environmental exposure caused by

antimicrobial treatment of a pond in a fish farm. Nevertheless, due to lack of data this is not

possible. Development of a dynamic exposure model requires knowledge of e.g. the

hydrology of the specific stream, the antimicrobial movement in the water compartment and

the sediment sorption and desorption kinetics for the individual antimicrobials. Unless such

data are included, only relatively simple, static, exposure scenarios can be assessed.

In the following paragraphs, the three above-mentioned exposure scenarios will be described.

6.3.1. Main Settings

The environment within the fish farm is not assessed, since the ponds inside the fish farm are

regarded as production sites, and thus some risk has to be accepted therein. The antimicrobial

is assumed to move through the fish farm to the receiving stream, where the ERA is


performed. The part of the stream under evaluation is 300 m downstream the fish farm. At

that site only a minimal risk should be accepted. 4.5 m and 0.6 m are taken as typical width

and depth, respectively. According to EMEA (1998b), a sediment layer of 5 cm with a density

of 1.5 kg/L can be assumed. This results in a total water body of 810,000 L with a sediment

concentration of 0.125 kg/L, given the phases are homogeneously mixed.

The environmental settings are furthermore based on the pond facts in Chapter 2. A pond of

122,500 L contains up to 2,000 kg fish, whereas a fry pond of 2,100 L contains abt. 50 kg fry.

The dosing regimes described in Table 2.5 are used to calculate the emission of

antimicrobials. Regarding AMX and OTC, medications of both fry and fish are assessed.

As shown in Chapter 3 a single experimental value of the occurrence of antimicrobials in

freshwater habitats is found. Thus, the experimentally determined concentration of OXA is

included and compared to PECs of the current ERA.

6.3.2. Definitions and Procedures to Derive Predicted

Environmental Concentrations

6.3.2.1. Scenario 1 – Worst Case

The worst case scenario is defined as a situation where a pond is under continuous treatment.

That situation results in a daily constant emission to the receiving stream. The following

accomplish the main settings mentioned above:

• The emission corresponds to one day's dose.

• Biotransformation, degradation and environmental distribution are neglected.

• One pond volume is used to calculate the concentration in the effluents.

• Since the current policy aims at a maximum utilization of the median water flow of 50

% (Michelsen, Personal communication), the concentration in the effluents is diluted

twice.

The calculated PEC is assumed not to be affected further by intramedia dispersion in the

stream. However, in this continuous exposure situation a steady state would be achieved at a

certain point.

Chapter 6 • 95

As mentioned above, there is a difference between this worst case scenario and the scenario

proposed in the guideline adopted by the European Community (EMEA, 1998b). The

guideline suggests to use the annual fish and waste water production and then to use e.g. the

annual antimicrobial consumption. This procedure was not followed, since it has not been

possible to derive the consumption of antimicrobials for the individual fish farms.

6.3.2.2. Scenario 2 – Incorporation of Inevitable Processes

This scenario considers all the factors, which the fish farmer does not control after having

initiated the treatment. Since these processes are consequences of the antimicrobials inherent

properties they will inevitable take place and contribute to the resulting PEC. The following

factors are not under the fish farmers control:

1. Excretion of unabsorbed and unchanged parent compound, see Table 2.4.

2. Distribution to environmental constituents, see Table 4.1 and Table 4.2.

3. Environmental degradation processes, see Chapter 4.

Re 1. Due to incomplete bioavailability and low biotransformation the full dose is assumed

exposed to the environment.

Re 2. Experimental DSED values were reported for FLU, OXA, SAF, TMP and OTC.

However, substructures suggest that also SDZ and AMX would distribute to sediment.

OXA and OTC were also shown to form complexes with magnesium and calcium.

Since FLU and SAF are structural analogues of OXA, KCOM values of similar

magnitude were suggested. Since it was shown that antimicrobial activity decreased in

the presence of mentioned cations, it is anticipated that the complexed ratio is non-

toxic/non-bioavailable. Mentioned DSED values and KCOM values are applied assuming

a homogeneous system.

Re 3. Although both AMX and OTC are hydrolytic unstable, the obtained half-lives are of

such a magnitude that they will be ignored. Concerning photodegradation, only OTC

was degraded at a depth of 1 m. Whether degraded or not the mentioned antimicrobials

still appear toxic in experiments with algae, see Chapter 5. Thus, the abiotic

degradability is neglected. Additionally, biodegradation experiments revealed half-

lives in aerobic marine sediment of more than 50 days. Only for OTC, a half-life of up

to 8 days was reported in freshwater sediment slurries.


In a short time perspective, half-lives of more than 14 days, which may result in

bioaccumulation and persistence, may be ignored in the PEC calculations (EMEA, 1998b).

Thus, in the present scenario, PECs were derived in the following manner:

• A quantity corresponding to a full treatment is exposed to the evaluated part of the

stream, see above.

• The fraction of antimicrobial which interacts with environmental constituents, i.e.

cations and sediment, is assumed lost. Only for OTC, a biodegradation half-life of 8

days during a study period of 21 days is considered.

• The free concentration is calculated from the remaining quantity, only using the water

body of the evaluated part of the stream.

6.3.2.3. Scenario 3 – Incorporation of Inevitable Processes and Natural

Dilution

Although it is difficult to judge the extent of dilution, some dilution of the effluents will

always take place. Depending on the season of the year the water flow in the receiving stream

varies. This flow is also influenced by the actual production stage, since an intensive

production may deplete the natural water flow in the stream, although current legislation aims

at a maximum water utilization of 50 % (Michelsen, Personal communication). An attempt to

incorporate the natural dilution factor (DF) is done in this scenario.

Equation 6.3 is used to calculate the RQ when a fixed dilution is applied.

DFPNEC

PECRQ

⋅= Equation 6.3

PECs are taken from scenario 2 and PNECs from Table 6.1. DF is calculated as how many

times the water body of the evaluated part of the stream is exchanged during a period of 21

days. Using the water flow of the pond, see Chapter 2, the water body of the stream is

calculated to be exchanged 1.6 times per day. Thus the water body is exchanged 34 times

during 21 days. This factor is used in Equation 6.3 as the DF.

The three scenarios are summarized in Table 6.2.

Chapter 6 • 97

Table 6.2 – Overview of exposure scenariosa.

Scenario 1 Scenario 2 Scenario 3

Emissionb One day's dose Full treatment Full treatment

Exposed to Pond Stream Stream

Inevitableprocessesc

No Sediment distribution,

Biodegradation (OTC)

Sediment distribution,

Biodegradation (OTC)

Dilution Pond effluentsdiluted twice

Diluted in the evaluatedpart of the stream

Diluted in the water that flows in theevaluated part of the stream during21 days (34 times scenario 2)

a: See text for details, b: According to Table 2.5, c: According to Chapter 4.

6.4. Assessment of the Derived Risk Quotients

The ERA of pharmaceuticals is slightly different from ERA of industrial chemicals.

Concerning (human) pharmaceuticals it is not possible to use the same precautionary

principles as for industrial chemicals, because an indispensable pharmaceutical will probably

not be banned from use because of too high environmental impact (Jørgensen and Halling-

Sørensen, 2000). However, to select a proper treatment not only the pharmacological issues,

but whenever possible also the environmental impact should be considered. In other words,

when more than one antibacterial agent can be used for the same indication, the one with the

lowest environmental impact should be preferred. Some times a specific antimicrobial is

required towards a specific infection thus it is not applicable to include environmental issues

in the selection of the treatment.

Nevertheless, the quantity of antimicrobials applied in fish farming can be and should be

reduced, if the environmental consequences exceed quality criteria, e.g. RQs exceeding 1.

The performed emission calculations only consider treatment of one pond. However, often

several ponds are under treatment, but this will not be discussed here. According to Equation

6.1 the likelihood of environmental effects increases when the RQ exceeds 1. Thus it is

apparently just a question of excluding the antimicrobials with RQs above 1, and recommend

the antimicrobials with RQs lower than 1.

However, the quality of the RQ is defined by the quality of the PEC and PNEC. With respect

to the PNEC the assessment factor accounts for the uncertainty in the effect data set. For the

PEC it is a question of selecting a proper/realistic exposure scenario. As discussed above, the


selected scenarios in this thesis are simple, although they include relevant knowledge

regarding the fate of the antimicrobials. But as explained, the application of a dynamic model

to describe the processes of antimicrobial interaction with the water flow and the sediment

would increase the quality of the prediction! Hence, there is a degree of uncertainty in the

PECs used to derive the RQs.

Nevertheless, since the same uncertainty is applied to all the calculations, the RQs can be used

to rank the individual antimicrobials with respect to their environmental impact. The derived

RQs are interpreted as follows: By convention RQs<1 are interpreted as “harmless under the

studied scenarios” and RQs>1 are interpreted as harmful. However, due to the explanations

mentioned above, the RQ may change if further studies are included in the calculation. Hence,

the RQs in this context are interpreted as “more studies are required” if RQ>1 and as

“harmful” if RQ>>1. This can be illustrated as in Figure 6.2.

Figure 6.2 – Interpretation of the risk quotient in the context of this thesis.

Harmless understudied scenarios

More studiesrequired RQHarmful

10-3 10-2 10-1 1 101 102 103

Table 6.3 represents the antimicrobial RQs derived for the three exposure scenarios discussed

in the former section. Scenario 1 is a worst case situation. Indeed very high RQs are derived,

although the application of TMP at this stage appears to be without risk. Meanwhile TMP and

SDZ are often co-administered, hence the RQ of SDZ needs to be considered before TMP can

be assessed. Recently the RQ for TMP was also calculated to be less than 1 for application in

a human scenario (Halling-Sørensen et al., In press). For the remaining antimicrobials

including SDZ RQs of more than 103 are obtained. As explained above this scenario can be

compared to the exposure scenario proposed in the environmental assessment guideline

(EMEA, 1998b). If the RQ exceeds 1 in that case, further studies are required, e.g. sediment

distribution, degradation and extended toxicity studies. Thus further evaluation is required.

Chapter 6 • 99

Table 6.3 – Antimicrobial risk quotients in the freshwater environmenta.

Scenario 1 Scenario 2 Scenario 3Antimicrobialb PEC, µg/L RQ PEC, µg/L RQ PEC, µg/L RQ

FLU 122 >104 9.8 >103 0.290 >102

OXA 82 >104 3.8 >102 0.113 15

OXAexp - - - - 3.2 >102

SAF 82 >103 1.9 >102 0.057 3.8

SDZ 204 >103 4.8 36 0.143 1.1

TMP 41 0.3 2.4 10-2 0.070 <10-3

AMX 531 >105 20 >103 0.591 >102

OTC 653 >103 3.7 18 0.110 0.5

AMXfry 774 >105 0.5 >102 0.015 4.0

OTCfry 952 >103 0.1 0.5 0.003 10-2

a: PECs are derived according to the three scenarios explained in the text, RQs are calculated according toEquation 6.1 and Equation 6.3 using PNECs from Table 6.1, b: subscript exp for OXA: PEC is determined fromthe sediment concentration, see Chapter 3, and the DSED, see Chapter 4, subscript fry for AMX and OTC:treatment of a fry pond, -: no data.

In scenario 2 the inevitable processes, e.g. sediment distribution are considered, which

corresponds to the studies requested in the guideline for the next evaluation step. RQs in this

scenario range from 10-2 for TMP up to more than 103 for FLU and AMX. Hence TMP is still

the only antimicrobial that does not require further investigation, but as before TMP and SDZ

are co-administered. Thus for the majority of the antimicrobials further studies are required.

In the guideline studies like dispersion in the aquatic environment, microcosms and

mesocosms are proposed.

Since the mentioned studies were beyond the scoop of this thesis a measure for the natural

dilution in the stream was applied in scenario 3. As mentioned above, a dilution factor of 34

was used, which corresponds to the total water exchange during the studied period. However,

a crucial factor is the distribution to the sediment. When the antimicrobials initially are bound

to the sediment, the respective DSEDs will attempt to maintain equilibrium between the

sediment and the aqueous phases. The water flow (in this scenario simulated by dilution)

contributes to a continuous removal of the part of the antimicrobial, which resides in the

water. Thus redistribution to the aqueous phase occurs resulting in a prolonged maintenance

of the aqueous concentration. The redistribution is not simulated in this scenario. Whether the

water flow or the redistribution from the sediment is the stronger process is currently


unknown. Without a dynamic model, it is difficult to simulate such a system, hence it is of

utmost importance to attain experimental knowledge of the antimicrobial sorption and

desorption kinetics between the sediment and aqueous phases.

Due to the dilution effects the magnitude of the RQs is lowered. Not surprising TMP is again

the least harmful with a RQ < 10-3, but also OTC for the treatment of fry shows a low RQ of

10-2. The highest are still FLU and AMX with RQs > 102. A middle group is formed with RQs

from 0.5-15. This can graphically be illustrated as in Figure 6.3.

Figure 6.3 – Graphical representation of the antimicrobial RQs according to scenario 3.


More studiesrequired

TMP OTCfry OTC SDZ SAF AM Xfry OXA FLU AMX OXAexp

RQHarmful

10-3 10-2 10-1 1 101 102 103

For OXA it is possible to compare the PEC with a sample from the real environment, see

Chapter 3. Assuming equilibrium and homogeneously mixed phases in the evaluated part of

the stream, this sediment sample was used to predict a concentration in the above water phase.

Compared to the RQ calculated from the PEC the RQ based on experimental data is 30 times

higher. Several reasons may explain this difference. Firstly, the quantity applied in the

treatment may have been higher than the quantity used in the emission scenarios. Secondly, as

mentioned before the evaluated scenario is simple, thus all processes may not be described in

full. Thirdly, although OXA had not been used on the fish farm 6 months in advance, OXA

from a former treatment could have been present in the sediment, due to the low

degradability, see Chapter 4.

Based on the evaluated scenarios most of the antimicrobial applications need to be

reconsidered. As illustrated in Figure 6.3, two-thirds of the applications result in RQ

exceeding 1; giving rise to further investigation or application of risk management

procedures.

The ranking as presented in Figure 6.3 only allows the differentiation based on environmental

impact. There is no basis for the differentiation on a pharmacological level. Thus in the

Chapter 6 • 101

specific case one needs to consider the bacterial infection. If more than one antibacterial agent

is applicable, viz. the antimicrobials have the same pharmacological profile, the

environmental impact can be included in the selection of treatment. Otherwise, if only one

antimicrobial is applicable, the pharmacological importance surpasses the environmental

impact. In a recent study concerning development of bacterial resistance towards

antimicrobials used in fish farming, OXA was suggested to be used with care (Guardabassi,

2000a). Among other agents, SDZ and TMP were suggested in the antimicrobial treatment of

fish in intensive farming.

The present ERA was performed for the aquatic environment. However, since the

antimicrobials distribute to the sediment, an ERA for sediment living organisms would have

been appropriate too. Meanwhile, to the knowledge of the author, no effect data for the

investigated antimicrobials towards sediment living organisms can be found in the literature.

6.5. Risk Management Procedures

The dilution as considered in scenario 3 is actually a natural consequence of the water flow;

though, to a certain limit controlled by the fish farmer. Besides the dilution, other approaches

can be made in order to reduce the risk associated with application of antimicrobials in fish

farming.

In the following, four risk management procedures are discussed:

1. Improved husbandry

2. Vaccination programmes

3. Solid phase extraction or isolation of effluents

4. Extended dilution

The first two methods are preventive, whereas the latter two seek to either reduce the

concentration or control the risk associated with application of antimicrobials.

Re. 1 The reason for infectious disease outbreaks may be due to non-optimal maintenance.

Factors as intensive farming, poor water quality, inadequate feeding regimes have

influence on the disease frequencies (McCracken et al., 1976; Dalsgaard and


Bjerregaard, 1991). If the fish density is considerably reduced, infection frequency

may decline. Thus, if production procedures are changed the use of antimicrobials may

be reduced and limited to a minimum.

Re. 2 The Norwegian experience with employing vaccination programmes to avoid disease

outbreaks was good (Markestad and Grave, 1997). Antimicrobial consumption was

decimated in a few years chiefly due to vaccination, but also due to improved

husbandry. In Norway, the main infection is furunculosis due to marine fish farming

activities. In Denmark, mainly enteric red mouth disease is the problem, and no

vaccination exists against the last mentioned infection (Larsen and Pedersen, 1997). It

is expensive to develop vaccines and often fish farmers doubt the efficiency of

vaccination, and consequently the motivation for companies to invest money in

development is limited (Larsen and Pedersen, 1997).

Re. 3 The inherent properties of the antimicrobials offer some opportunities to purify fish

farm effluents. As shown in Chapter 4, AMX and TMP could be extracted by a

polymer solid phase. Thus if applicable a high efficient SPE unit should be attached to

the effluent pipe of the individual ponds during treatment. This would significantly

reduce the antimicrobial concentration in the effluents. Furthermore, effluents from

treated ponds could be isolated in a special pond with a high density of sediment. FLU,

OXA, SAF, TMP and OTC would in this special pond be trapped due to their high

degree of interaction with sediment. However, this would disqualify the sediment to be

used as fertilizer on arable land.

Re. 4 The extended dilution approach is similar to Scenario 3, but in this case, a substantial

higher dilution should take place when effluents enter the stream. This can only be

achieved if a small fraction of the natural water flow of the stream is utilized in the fish

farm. Again, solutions of the dilution type are not sustainable. However, the extended

dilution will mainly influence the aqueous concentration. Since the antimicrobials

distribute to the sediment, it is again a question of which process being the stronger –

removal by water flow or redistribution from the sediment.

The mentioned procedures are listed according to their importance. If husbandry is badly

controlled bacterial infections may persist, and thus call for repeated antimicrobial treatments.

Chapter 6 • 103

Furthermore, if effluents can not be purified, concentrations in effluents may be reduced to a

minimum.

Although the first two mentioned procedures can reduce antimicrobial consumption,

application will occasionally be necessary. In case of antimicrobial treatment, the latter two

procedures may be considered in order to minimize antimicrobial concentration in the

effluents and in the stream, respectively.

Chapter 7

Conclusions

106 • Conclusions

According to the questions outlined in Chapter 1 the following conclusions were drawn:

1. Several studies from other countries have shown the occurrence of antimicrobials near

fish farms. A single investigation showed that OXA was found in the sediment near a

Danish fish farm. 300 m downstream the fish farm outlet a concentration of 1.6 µg/g

was found in the sediment. Thus, it is possible to find antimicrobial residues in the

sediment near Danish fish farms after antimicrobial treatment.

2a. It was shown that the distribution of OXA between 1-octanol and water followed the

Henderson-Hasselbalch equation, hence the OXA DOW follows the understanding of

distribution of ionized species. The DOW for the neutral species was found to 9.5.

When pH was increased DOW approached zero.

2b. Antimicrobials like FLU, OXA and SAF were found to have remarkably high log

DDOC values (3.4-5.2), despite their inherent ionic character and expected low

distribution. This was further confirmed in experiments with sediment. DSED values for

FLU, OXA, SAF, TMP, OTC were found to 2.3-2.7.

2c. Using traditional QSARs, based on hydrophobic interactions, it is not possible to

predict the antimicrobial distribution to DOC or sediment from their respective DOW

values. However, it may be possible to relate experimental DDOC/DSED to DOW for

chemicals with similar structures and properties, but further experimental/descriptive

studies are needed.

3. The standard test alga, S. capricornutum, was shown to be relatively insensitive

towards antimicrobial exposure of environmentally relevant concentrations, most

likely due to its eucaryotic nature. However, the procaryotic M. aeruginosa revealed

effect concentrations up to 6 orders of magnitude lower than the former mentioned.

EC50 were mainly in the lower µg/L range, with 3.7 µg/L as the lowest for AMX. TMP

appeared to be almost harmless, with EC50 of 16-130 mg/L, depending on the algae.

Consequently, a cyanobacteria was suggested to be included in the ERA of

antimicrobials. In addition, reproduction tests performed with D. magna suggested said

test to be required in ERA of antimicrobials.

Chapter 7 • 107

4. Applying the constant assessment factor approach, PNECs for the antimicrobials were

established. All antimicrobials revealed PNECs in the ng/L range (0.003-0.2 µg/L),

except TMP for which the value was 160 µg/L. Simple emission scenarios were

defined and accordingly PECs were derived. The worst case scenario revealed PECs in

the range of 41-952 µg/L, depending on antimicrobial and its application. For the most

realistic scenario, values of 0.003-0.591 µg/L were obtained. For OXA an

experimental value of 3.2 µg/L was established – a value 30 times higher than the PEC

from the most realistic scenario. The PECs were related to the PNECs using the risk

quotient approach. RQs for the different scenarios and the individual antimicrobials

were calculated. Based on the most realistic scenario an environmental ranking of the

application of antimicrobials in fish farming was performed. This is illustrated in the

following figure from Chapter 6:

Figure 6.3 – Graphical representation of the antimicrobial RQs according to scenario 3.


More studiesrequired

TMP OTCfry OTC SDZ SAF AM Xfry OXA FLU AMX OXAexp

RQHarmful

10-3 10-2 10-1 1 101 102 103

Thus, based on this environmental ranking it is possible to include environmental

issues in the selection of the proper antimicrobial treatment. However, this is only

feasible for antimicrobials with identical pharmacological profiles.

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Summary • 123

SummaryThe objective of this Ph.D. thesis is to assess the environmental impact due to application of

antimicrobials in Danish fish farming.

Can antimicrobial residues therefore after treatment be found in the sediment near Danish fish

farms? To what extent do antimicrobials distribute to humic acids (DDOC) and sediment

(DSED)? and can this distribution be predicted from 1-octanol/water distribution (DOW)? Is the

currently used model target organism an appropriate selection for the lowest trophic level for

antimicrobial evaluation? Answers to mentioned questions are incorporated in an

environmental risk assessment (ERA), which is used to rank the antimicrobials. Hence, which

antimicrobials can be recommended and which can not be recommended?

Antimicrobials are environmentally applied in several contexts; fish farming is one of the

important areas (Halling-Sørensen et al., 1998). In Denmark antimicrobials as oxolinic acid

(OXA), sulphadiazine (SDZ), trimethoprim (TMP), amoxicillin (AMX) and oxytetracycline

(OTC) are regularly used to control infections among the fish (Dalsgaard and Bjerregaard,

1991). In other countries flumequine (FLU) is used and the use of sarafloxacin (SAF) is under

consideration (Hektoen et al., 1995). The mentioned antimicrobials have been selected for

further investigation in this Ph.D. thesis.

Basic physical chemical and pharmacological properties of the antimicrobials showed that

low oral bioavailability, e.g. Bjørklund and Bylund (1991), and low biotransformation, e.g.

EMEA (1996), mainly resulted in excretion of parent compound. The principal exposure is to

the aquatic environment, although the distribution is widely pH dependent. This exposure of

biological active chemicals may affect non-target organisms.

Methods to extract antimicrobials from sediment could only be found for FLU and OXA in

marine sediment, e.g. Samuelsen et al. (1994). Recoveries between 60 and 95 % were found.

This could not be repeated on freshwater sediment; thus, a new method was applied. Although

a recovery of 98 % was obtained, a relative standard deviation of 36 % was found. This was

attributed to the heterogeneity of the sediment (Holten Lützhøft et al., Submitted I).

124 • Summary

Applying this method, a sediment concentration 3 weeks after treatment of 1.6 µg/g was

found for OXA 300 m downstream the outlet of a Danish fish farm. Moreover, several studies

have shown the occurrence of OXA and OTC in the sediment near fish farm activities in e.g.

Finland (Bjørklund et al., 1991) and Norway (Samuelsen et al., 1992b).

The experimental distribution to humic acids (Holten Lützhøft et al., Accepted III) and

sediment (Holten Lützhøft, Unplublished) showed log DDOC and log DSED values of abt. 4.5

and abt. 2.5, respectively. These are remarkably high values and comparable to highly

hydrophobic chemicals like hexachlorobenzene. Since the antimicrobials's log DOW values are

in the range -1.2 to 1.7, log KOC values of the same order of magnitude were expected.

However, the use of these conventional estimation methods results in values 3-5 orders of

magnitude lower than experimental data, thus emphasizing the importance of using

appropriate estimation methods.

The antimicrobial efficiency of FLU, OXA, SAF and OTC was decreased in the presence of

magnesium ions, indicating a lower bioavailability when the chemicals are complex bound,

e.g. Barnes et al. (1995).

Additional to distribution, antimicrobials also encounter degradation processes when entering

the aquatic environment. However, the degradability is low.

Concerning hydrolysis, OTC is the least stable with a half-life (t½) of 57 days at pH 4.6 and

7ºC (Vej-Hansen et al., 1978). Furthermore, OTC is also the least stable regarding

photodegradation. An experiment conducted in transparent containers at 1 m depths in the sea,

revealed a t½ for OTC of 3 days, whereas the other antimicrobials hardly were photodegraded

under the same conditions (Lunestad et al., 1995). Nevertheless, photodegradation under fish

farming conditions can be neglected, either due to the high fish density that will prevent light

to reach subsurface levels, or because the antimicrobials may bind to sediment.

The biodegradability has been studied in marine sediment. Half-lives in the deeper layers of

100 days were reported for SDZ and TMP, whereas more than 300 days were reported for

FLU, OXA, SAF and OTC (Hektoen et al., 1995). The disappearance in the top layer was

shorter, but was attributed to leaching rather than degradation. Furthermore, half-lives for

OTC have been reported in the range 9-419 days, depending on e.g. sediment type and

Summary • 125

location. In marine and freshwater slurries, t½ for OTC was found in the range 3-10 days (Lai

et al., 1995).

In the environmental effect assessment, normally tests on three trophic levels – algae,

crustaceans and fish – are proposed (OECD, 1992; EMEA, 1998b). To represent algae, the

standard eucaryotic test organism Selenastrum capricornutum is often used, e.g. ISO (1989).

However, in the test with the antimicrobials it was found that the procaryotic cyanobacteria

Microcystis aeruginosa was up to 6 orders of magnitude more sensitive than both S.

capricornutum and Rhodomonas salina (both eucaryotic). Thus, use of the standard test

organism underestimates the environmental effects of antimicrobials. For M. aeruginosa EC50

values were mainly obtained in the lower µg/L range, with 3.7 µg/L for AMX as the lowest.

For the eucaryotic algae, the majority of EC50 values was in the mg/L range, with AMX as the

least toxic. TMP appeared to be almost harmless with EC50 values of 16-130 mg/L, depending

on algae (Holten Lützhøft et al., 1999 IV).

In acute tests with crustaceans and fish, almost no toxicity was observed. However, in the

reproduction test with Daphnia magna OXA showed a no observed effect concentration of

380 µg/L (Wollenberger et al., 2000).

Thus, inclusion of chronic tests with cyanobacteria and crustaceans seems to be required in

the effect assessment of antimicrobials.

The ERA was performed defining a worst case scenario and defining a refined scenario by

incorporation of the above-mentioned fate data, which inevitably will take place. This

produced predicted environmental concentrations (PECs) under different conditions. The

effect concentrations were evaluated applying the constant assessment factor approach giving

predicted no effect concentrations (PNECs). By dividing the PECs with the PNECs risk

quotients were established. The antimicrobials were ranked according to their environmental

impact.

Hans-Christian Holten Lützhøft, July 2000.

Resumé på dansk • 127

Resumé på danskFormålet med denne ph.d. afhandling er at vurdere de miljømæssige konsekvenser i

forbindelse med brugen af antibiotika i danske dambrug.

Kan antibiotikarester efter medicinering derfor findes i sedimentet omkring danske dambrug?

I hvilken udstrækning fordeler antibiotika sig til humus (DDOC) og sediment (DSED)? og kan

fordelingen forudsiges ud fra 1-octanol/vand fordelingen (DOW)? Er den testorganisme der

anvendes på nuværende tidspunkt, det rette valg for det laveste trofiske niveau i evalueringen

af antibiotika? Svarene til de nævnte spørgsmål er indarbejdet i en miljørisikovurdering

(ERA), der bruges til at rangordne antibiotikaene. Hvilke antibiotika kan derfor anbefales, og

hvilke kan ikke?

Miljømæssigt anvendes antibiotika i flere sammenhænge, hvoraf dambrug er et af de vigtige

områder (Halling-Sørensen et al., 1998). Antibiotika som oxolinsyre (OXA), sulfadiazin

(SDZ), trimethoprim (TMP), amoxicillin (AMX) og oxytetracyklin (OTC) anvendes

regelmæssigt i Danmark i behandlingen af infektioner hos fisk (Dalsgaard and Bjerregaard,

1991). I andre lande anvendes flumequin (FLU) og sarafloxacin (SAF) påtænkes at blive

anvendt (Hektoen et al., 1995). De nævnte antibiotika er udvalgt for yderligere undersøgelse i

denne ph.d. afhandling.

Grundlæggende fysisk-kemiske og farmakologiske egenskaber for antibiotikaene viste lav

peroral biotilgængelighed, eksempelvis Bjørklund and Bylund (1991), og lav

biotransformation, eksempelvis EMEA (1996), hovedsageligt resulterende i udskillelse af

moderstof. Den primære eksponering er til vandmiljøet med en stærk pH-afhængig fordeling.

Udledningen af biologisk aktive kemikalier kan påvirke organismer, der er uden for

målgruppen.

Metoder til at ekstrahere antibiotika fra sediment kunne kun blive fundet for FLU og OXA i

marint sediment, e.g. Samuelsen et al. (1994). Genfindingen var mellem 60 og 95 %. Det

kunne ikke reproduceres for ferskvandssediment, hvorfor en ny metode blev anvendt. På trods

128 • Resumé på dansk

af, at genfindingen var 98 %, var den relative standardafvigelse 36 %. Årsagen hertil blev

tillagt det heterogene sediment (Holten Lützhøft et al., Submitted I).

Ved at bruge denne metode blev en sedimentkoncentration 300 m nedstrøms et dansk

dambrug på 1,6 µg/g fundet 3 uger efter medicinering. Derudover har flere undersøgelser vist

tilstedeværelsen af OXA og OTC i sedimentet nær dambrug i eksempelvis Finland (Bjørklund

et al., 1991) og Norge (Samuelsen et al., 1992b).

Eksperimentelt har fordelingen til henholdsvis humus (Holten Lützhøft et al., Accepted III)

og sediment (Holten Lützhøft, Unplublished) givet log DDOC og log DSED værdier på omkring

4,5 og 2,5. Størrelsesordenen er bemærkelsesværdig høj og sammenlignelig med meget

hydrofobe kemikalier som hexachlorobenzen. Idet antibiotikaenes log DOW værdier er i

størrelsesordenen -1,2 til 1,7, forventedes den samme størrelsesorden for log KOC. På den

anden side resulterer brugen af konventionelle estimationsmetoder i værdier, der er 3-5

størrelsesordner lavere end eksperimentelle værdier. Således understreges vigtigheden af at

anvende passende estimationsmetoder.

Den antimikrobielle effektivitet af FLU, OXA, SAF og OTC var nedsat i tilstedeværelsen af

magnesiumioner. Herved indikeres en lavere biotilgængelighed, når kemikalierne er kompleks

bundet, eksempelvis Barnes et al. (1995).

Udover fordeling undergår antibiotika også nedbrydningsprocesser, når de udledes til

vandmiljøet, skønt nedbrydningen er lav.

Hvad angår hydrolyse, er OTC det mindst stabile med en halveringstid (t½) på 57 dage ved pH

4,6 og 7°C (Vej-Hansen et al., 1978). Derudover er OTC også det mindst stabile overfor

fotolyse. Et forsøg udført i gennemsigtige beholdere på 1 m dybde i havvand resulterede i en

t½ på 3 dage for OTC, hvorimod andre antibiotika stort set ikke undergik fotolyse under de

samme forhold (Lunestad et al., 1995). Således kan fotolyse ignoreres under forhold i

dambrug, enten på grund af den tæthed fiskene dyrkes under, idet det vil forhindre lys i at nå

under overfladen, eller fordi antibiotikaene binder til sediment.

Bionedbrydeligheden har været undersøgt i marint sediment. Halveringstider i dybere lag blev

for SDZ og TMP rapporteret til 100 dage, hvorimod mere end 300 dage blev rapporteret for

FLU, OXA, SAF og OTC (Hektoen et al., 1995). Antibiotikaene forsvandt hurtigere fra det

øverste lag, men det blev mere tillagt nedsivning end nedbrydning. Derudover er

Resumé på dansk • 129

halveringstider for OTC blevet rapporteret i området 9-419 dage afhængig af eksempelvis

typen af sediment og lokaliseringen. I marine og ferskvandssedimentopslemninger blev t½ for

OTC fundet i området 3-10 dage (Lai et al., 1995).

Indenfor miljømæssig effektvurdering foreslås normalt test på tre trofiske niveauer – alger

krebsdyr og fisk (OECD, 1992; EMEA, 1998b). Ofte anvendes den eukaryote

standardtestorganisme Selenastrum capricornutum som repræsentant for alger, eksempelvis

ISO (1989). I test med antibiotika blev det fundet, at den prokaryote cyanobakterie

Microcystis aeruginosa var op til 6 størrelsesordner mere følsom end både S. capricornutum

og Rhodomonas salina (begge eukaryote). Derfor underestimerer brugen af

standardtestorganismen de miljømæssige effekter af antibiotika. For M. aeruginosa blev EC50

værdier hovedsageligt fundet i det lave µg/L område med 3,7 µg/L for AMX som den laveste.

For de eukaryote alger blev størsteparten af EC50 værdierne fundet i mg/L området med AMX

som den mindst toksiske. TMP viste sig nærmest uskadelig med EC50 værdier mellem 16 og

130 mg/L afhængig af algen (Holten Lützhøft et al., 1999 IV).

I akutte test med krebsdyr og fisk observeres næsten ingen toksicitet. I modsætning hertil

viste reproduktionstest med Daphnia magna, at OXA koncentrationen, der ikke gav effekt,

var 380 µg/L (Wollenberger et al., 2000).

Derfor lader det til at være påkrævet at inkludere kroniske test med cyanobakterier og

krebsdyr i effektvurderingen af antibiotika.

Ud fra et defineret worstcasescenario og forfinede scenarier, defineret ved at inkludere

ovenfor nævnte skæbne data, der uundgåeligt vil finde sted, blev en ERA foretaget. Heraf

fremkom estimerede miljøkoncentrationer (PEC) under forskellige forhold.

Effektkoncentrationerne blev vurderet ved at anvende fremgangsmåden med en konstant

vurderingsfaktor, der resulterede i estimerede koncentrationer uden effekt (PNECs). Ved at

dividere PECs med PNECs frembringes risikokvotienter. Antibiotikaene blev rangordnet i

henhold til deres miljømæssige konsekvenser.

Hans-Christian Holten Lützhøft, juli 2000.

130 • Publications

Publications

Halling-Sørensen B, Nors Nielsen S, Lanzky PF, Ingerslev F, Holten Lützhøft HC,

Jørgensen SE. 1998. Occurrence, fate and effects of pharmaceutical substances in the

environment – A review. Chemosphere, 36, 357-393.

Jørgensen SE, Holten Lützhøft HC, Halling-Sørensen B. 1998. Development of a model for

environmental risk assessment of growth promoters. Ecol Modell, 107, 63-72.

Holten Lützhøft HC, Halling-Sørensen B, Jørgensen SE. 1999. Algal toxicity of antibacterial

agents applied in Danish fish farming. Arch Environ Contam Toxicol, 36, 1-6.

Holten Lützhøft HC, Vaes WHJ, Hermens JLM. 1999. SPME-HPLC analysis of 4-

quinolones. The Reporter, Summer.

Ingerslev F, Holten Lützhøft HC, Halling-Sørensen B. 1999. Humant anvendte lægemidlers

vej til miljøet er gennem rensningsanlægget (In Danish). Dansk Kemi, 80, 22-25.

Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, Hermens JLM. 2000. 1-

octanol/water distribution coefficient of oxolinic acid: Influence of pH and its relation to the

interaction with dissolved organic carbon. Chemosphere, 40, 711-714.

Stuer-Lauridsen F, Birkved M, Hansen LP, Holten Lützhøft HC, Halling-Sørensen B. 2000.

Environmental risk assessment of human pharmaceuticals in Denmark after normal

therapeutic use. Chemosphere, 40, 783-793.

Anastácio PM, Holten Lützhøft HC, Halling-Sørensen B, Marques JC. 2000. Surfactant

(Genapol OX-80) toxicity to Selenastrum capricornutum. Chemosphere, 40, 835-838.

Madsen U, Sløk FA, Stensbøl TB, Bräuner-Osborne H, Holten Lützhøft HC, Poulsen MV,

Eriksen L, Krogsgaard-Larsen P. 2000. Ionotropic excitatory amino acid receptor ligands.

Synthesis and pharmacology of a new amino acid AMPA antagonist. Eur J Med Chem, 35,

69-76.

Halling-Sørensen B, Holten Lützhøft HC, Andersen HR, Ingerslev F. In press.

Environmental hazard assessment of anitbiotics; Comparison of mecillinam, trimethoprim and

ciprofloxacin. J Antimicrob Chemother.

Abstracts • 131

Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, Hermens JLM. Accepted.

The influence of pH and other modifying factors on the distribution behaviour of 4-

quinolones to solid phases and humic acids studied by SPME-HPLC. Environ Sci Technol.

Holten Lützhøft HC, Halling-Sørensen B, Guardabassi L, Ingerslev F, Tjørnelund J.

Submitted. Oxolinic acid in freshwater sediment – Extraction method and occurrence due to

fish farm activities.

Abstracts


agents applied in Danish fish farming. Oral presentation at the 8th SETAC-Europe meeting,

Bordeaux, France.

Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, Hermens JLM. 1998.

Distribution behaviour of 4-quinolones using SPME-HPLC analysis. Oral presentation at

Danmarks Farmaceutiske Højskoles 4. forskningens dag (In Danish), Copenhagen, Denmark.


agents applied in Danish fish farming. Poster presentation at Danmarks Farmaceutiske

Højskoles 4. forskningens dag (In Danish), Copenhagen, Denmark.


agents applied in Danish fish farming. Poster presentation at the 19th SETAC meeting,

Charlotte, NC, USA.

Holten Lützhøft HC, Vaes WHJ, Freidig AP, Halling-Sørensen B, Hermens JLM. 1999.

Distribution behaviour of 4-quinolones. Oral presentation at the 9th SETAC-Europe meeting,

Leipzig, Germany. Competed in the competition for the Award for best presentation

performed by a scientist under 30 years of age and became 2nd best.


agents applied in Danish fish farming. Poster presentation at the 3rd Ulla Summer School,

Copenhagen, Denmark.

Holten Lützhøft HC, Halling-Sørensen B, Jørgensen SE. 2000. Environmental risk

assessment of antimicrobials applied in Danish fish farming. Oral presentation at the 3rd

SETAC World congress, Brighton, United Kingdom.

132 • Curriculum Vitae

Curriculum Vitae

Hans-Christian Holten Lützhøft was born in Elsinore August 31 1972. After graduating from

the local gymnasium in 1991, he moved to Copenhagen to study pharmacy at The Royal

Danish School of Pharmacy. In the summer 1996 he became M.Sc. in Pharmacy with a thesis

on synthesis of AMPA receptor antagonists to be used in the treatment of e.g. Alzheimer's

Disease. In the fall later that year he initiated a Ph.D. study at The Royal Danish School of

Pharmacy in the field of ecotoxicological risk assessment of pharmaceuticals under the

supervision of Professor D.Sc. Sven Erik Jørgensen and Assoc. Professor Ph.D., M.Sc.

(Pharm.) Bent Halling-Sørensen. During 1998 he spent 7 months at the Research Institute of

Toxicology at the University of Utrecht, The Netherlands, in the group of Dr. Joop LM

Hermens, as a part of his Ph.D. study. This thesis is the final result of the Ph.D. study, which

will be defended in August 2000. During the fall 1999 he taught undergraduates at The Royal

Danish School of Pharmacy analytical chemistry and environmental chemistry. In the spring

2000 he assisted in the teaching of aquatic environmental chemistry for environmental

chemistry students at the University of Copenhagen. After the defence, he will take part in the

3 year EU project: “Environmental Risk Assessment of Veterinary Medicines in Slurries”,

which is a collaboration among Danish, Dutch, English and Spanish research teams.

Acknowledgements • 133

AcknowledgementsJeg vil gerne benytte lejligheden til at takke:DFH og især Sven Erik og Bent for at være en uvurderlig og meget motiverende hjælp.Flemming for at være én man kan holde ud at dele rum med i næsten tre år – også ved athuske mig i Holland med lange gode mails! Henrik der har været rar til at hjælpe mig i minemange trængsler og alligevel har haft lyst til at løbe træningsture med mig. Susanne for atvære god til at tackle mig og mine edderkopper. Søren for mange hyggelige samtaler. Louisefor at forsyne mig med en lind strøm af stroopwaffels. Jette for kvik og konstruktiv kritik.Hai Ping for at servere ægte kinesiske stegekartofler. Signe for hyggesnakken underopvasken. Trine for fortrøstningsfuldt at passe mine alger. Lars for altid at bidrage med godthumør. Christian og Claus at introducere mig for DFH’s alger og deres trivsel. Morten fordet underholdende samarbejde. Martin for at have lært mig, hvordan det er at være enskildpadde. Steen for opmuntrende gangsnak. Massimiliano for having a good time togethermodelling fish farms. Luca, Morten og Anja for hyggeligt feltarbejde i Jylland.

Also a thank to:RITOX, who received me with great hospitality – and whom I will always miss andremember as a different and cosy place, where I learnt to drink strange and tasty beers. Joopfor beeing a splendid tutor. Johannes for letting me feel at home, also in one of my shorterterms at RITOX. Wouter for beeing a good sport, colleague and a patient sailing instructor.Andreas for exalting talks and enjoyable food in his and Barbaras funny flat. Eric for lettingRikke and me experience a romantic Dutch wedding (and a furious cat). Dolores, Jean andKarin for pleasent talks during breaks. Eñaut for beeing a different and interesting friendshipwith a dangerous sport (I think of the hockey and my poor knees). Philipp for at være en godkammerat. Gert-Jan for beeing an amusing friend – Cheers! Agnes, Martine, Rik, Minne,Leon and Heather, it’s so nice to meet Dutch friends all around. Elsa for beeing sohospitable and funny. Aart, Frans and Theo for helping with problems they didn’t have tobut did anyway. Sandra for taking good care of both Rikke and me and letting us experiencepannekoeken and Dutch badminton. Hester for speaking of something else than HPLC.Janneke and Stephan for opening their nice home for Rikke and me and helping make ourstay much enjoyable.

Familien – der ind imellem har måttet lægge øre til mange og lange udredninger omkringalger* – og alligevel altid har troet på mig og givet mig god mad. Stine der er god til at snydeen computer og diverse DIKU-vagter og altid er med på en Lydolpher. Klavs for altid at væresuper god til at læse enorme mængder af udkast igennem på rekordtid. Min Far for at udredemine computerproblemer i en uendelighed. Rikke, for din hjælp og kærlighed gennem deforløbne år – og de kommende ……

*: alger har været dæknavn for alt arbejde de sidste tre-fire år.

Article IArticle IArticle IArticle IOxolinic acid in freshwater sediment – Extraction method andOxolinic acid in freshwater sediment – Extraction method andOxolinic acid in freshwater sediment – Extraction method andOxolinic acid in freshwater sediment – Extraction method and

occurrence due to fish farm activities.occurrence due to fish farm activities.occurrence due to fish farm activities.occurrence due to fish farm activities.In Prep.In Prep.In Prep.In Prep.

Co-workers:Co-workers:Co-workers:Co-workers:Bent Halling-Sørensen, Luca Bent Halling-Sørensen, Luca Bent Halling-Sørensen, Luca Bent Halling-Sørensen, Luca Guardabassi, Flemming Ingerslev andGuardabassi, Flemming Ingerslev andGuardabassi, Flemming Ingerslev andGuardabassi, Flemming Ingerslev and

Jette Jette Jette Jette TjørnelundTjørnelundTjørnelundTjørnelund

Oxolinic acid in freshwater sediment – Extraction methodand occurrence due to fish farm activities

Hans-Christian Holten Lützhøft1, Bent Halling-Sørensen1, Luca Guardabassi2, Flemming Ingerslev1 and JetteTjørnelund1

1Section of Environmental Chemistry, Department of Analytical and Pharmaceutical Chemistry, The RoyalDanish School of Pharmacy, Universitetsparken 2, DK-2100 Copenhagen Ø, Denmark.2Department of Veterinary Microbiology, The Royal Veterinary and Agricultural University, Stigbøjlen 4, DK-1870 Frederiksberg C, Denmark.

AbstractA method was developed for extraction of oxolinic acid (OXA) from freshwater sediment. Sediment sampleswere collected from the inlet, a medicated pond, the outlet and 300 m downstream of a Danish freshwater troutfarm undergoing treatment with OXA. Sediment packed solid phase extraction tubes were eluted with 20 % 10mM H3PO4 pH 2.5 in tetrahydrofuran and extracts were concentrated under N2. OXA was subsequentlyquantified using high performance liquid chromatography. OXA was detected in samples from the pond andfrom the outlet in concentrations up to 3.4 µg/g and were higher than OXA detected in samples from the inlet(up to 0.8 µg/g). Additionally, a sediment concentration of 1.6±0.9 µg/g was found 300 m downstream the outlet21 days after medication. In order to reduce the environmental impact of antimicrobial usage in land-based fishfarms, the ability of different effluent treatments to detain antimicrobials should be investigated.

Key words: Oxolinic acid, extraction, freshwater, sediment, fish farm.

IntroductionAntimicrobials applied in fish farming to treat or prevent infections may be exposed to the environment forexample as surplus medicated feed, as unabsorbed medication or as parent compound excreted with faeces orurine. The quinolone antimicrobial oxolinic acid (OXA) was found in sediment under marine fish farms inFinland, but the presence was shown to disappear soon after medication (Bjørklund et al., 1991). On the otherhand, oxytetracycline was detected in marine sediment under a Norwegian fish farm for up to 18 months aftermedication (Samuelsen et al., 1992). The distribution of quinolones to environmental constituents, e.g. humicacids, has been studied by several authors (Holten Lützhøft et al., Accepted; Noware et al., 1997; Schmitt-Kopplin et al., 1999). A remarkably high degree of interaction was found despite the low hydrophobicity of thechemicals. Correspondingly, a high distribution coefficient to freshwater sediment (DSED) of 102.7 L/kg wasfound for OXA (Holten Lützhøft, Submitted).The ecological impact associated with the presence of OXA and other antimicrobials in natural aquaticenvironments has recently been investigated. Various laboratory experiments have shown that lowconcentrations of antimicrobials inhibit the growth of non-target organisms like micro-algae (Harras et al., 1985;Holten Lützhøft et al., 1999; Halling-Sørensen, 2000). A prospective study performed in a freshwater fish farmshowed that treatment with OXA medicated feed was associated with a significant increase in the prevalence ofresistant bacteria at sites situated downstream from the fish farm (Guardabassi et al., 2000). These evidencessuggest that the occurrence, fate and effects of antimicrobials used in aquaculture should be considered carefully.Various methods have been described for the extraction of OXA from marine sediment (Bjørklund, 1990;Bjørklund et al., 1991; Samuelsen et al., 1994; Pouliquen et al., 1994a; Pouliquen et al., 1994b). In principle,OXA is extracted from the sediment into an aqueous phase using a neutral or an alkaline solution. One methodanalyzes the extract directly after centrifugation, whereas the two other methods concentrate the extract either bysolid phase extraction (SPE) or liquid liquid extraction before analysis. Depending on the method, recoveriesfrom 58 to 96 % were obtained. To the best knowledge of the authors the occurrence and fate of OXA has beenstudied in marine sediment only, and no method has been reported for the extraction of OXA from freshwatersediment.OXA is a widely used antimicrobial in aquaculture. In Denmark freshwater trout farming is the prevailingaquaculture activity, and OXA is one of three (OXA, sulphadiazine/trimethoprim and florfenicol) antimicrobialagents commercially available in the form of medicated feed for use in aquaculture. The objective of this

investigation was to develop a method for extraction of OXA from freshwater sediment. The method wassubsequently used for the analysis of sediment samples collected from a trout farm using OXA medicated feed.

Materials and methods

ChemicalsOxolinic acid (OXA) 5-ethyl-5,8-dihydro-8-oxo-1,3-dioxolol[4,5-g]quinoline-7-carboxylic acid (>98%) CAS #14698-29-4, was purchased from Unikem A/S (Copenhagen, Denmark). HPLC grade acetonitrile (MeCN) wasobtained from Labscan (Dublin, Ireland). Methanol purum > 99.8 % (MeOH) and analysis grade 85 % H3PO4

were obtained from KeboLab (Albertslund, Denmark). Spectrophotometric grade, inhibitor free >99.5 %tetrahydrofuran (THF) was obtained from Sigma-Aldrich (St.Louis, MI, USA). Analysis grade chloroform,dimethylsulfoxid (DMSO), ethylacetate, triethylamine (Et3N), NaOH and NaHCO3 were obtained from Merck(Darmstadt, Germany) and 36-38 % HCl was obtained from J.T. Baker (Deventer, Holland). OXA was dissolvedin 0.1 M NaOH and stock solutions were diluted to desired test concentrations in 10 mM H3PO4 and adjusted topH 7 with 5 M NaOH. All aqueous solutions were made in Milli-Q-water.

Chromatographic procedureThe high performance liquid chromatography (HPLC) system consisted of a Waters alliance system (WatersMilford, MA, USA), equipped with the Millenium32 software working on a Windows 95 platform. An integratedautosampler injected aliquots of 50 µL onto the Discovery C18 column (150×4.6 mm, 5 µm particle size,Supelco, Bellafonte, PA, USA) maintained at 40ºC. Analytes were eluted at a flow rate of 1.0 mL/min anddetected at 260 nm. The solvents used were MeCN (A) and 2 mM H3PO4, pH 2.9 (B). The composition of themobile phase A:B was 20:80 for 0-2 min, programmed to 80:20 within 6 min and maintained there for 2 minfollowed by an equilibration time of 3 min while the mobile phase returned to its original composition. Sampleswere stored at 4°C until analysis.

Sampling sites, times and methodsA Danish freshwater trout farm with a long history of OXA usage was selected for sampling. The trout farm wasstructured as a typical Danish earth pond system. Water was supplied by an unpolluted stream, which did notreceive water from other fish farms or recognized sources of antimicrobial contamination. The effluent treatmentconsisted of micro-sieves that separated sludge from water and a sedimentation pond in which water wascollected prior to release into the stream serving as water supply.Sampling was performed in co-operation with the fish farmer in March and April 1998 in connection with anoutbreak of enteric red mouth disease caused by Yersinia ruckeri. At the time of sampling the last medicationwith OXA was dated back to August 1997. The studied pond contained 1600-1800 kg fish and was treated withOXA medicated feed pellets according to the manufacturers recommendation. About 8.5 kg feed pellets with anOXA content of 0.25 % were administered daily in seven days, corresponding to a total usage of about 150 gOXA.Grab samples were collected before the medication period (day 0), and 1, 14 and 21 days after medication fromthe inlet, the outlet and the medicated pond. 21 days after medication an additional sample was taken 300 mdownstream the outlet. Residues of feed pellets and fish excreta were visible in the sediment samples collectedfrom the medicated pond. During the sampling period the temperature and pH of the water was 9-10°C and 6.2-6.4, respectively. The oxygen contents in the inlet and outlet water were 75-80 % and 65-70 %, respectively.

Sediment extraction proceduresThe methods for extraction of OXA from marine sediment described by Samuelsen et al. (1994) and Pouliquenet al., (1994b) were applied to the freshwater sediment used in this investigation. Unsatisfactory recoveries,determined as described below, of 20-50 % were obtained and a new approach using 3 mL empty SPE tubes(Supelco, Vallensbæk Strand, Denmark) packed with sediment was developed. A schematic illustration of thisextraction procedure is shown in Figure 1.

Figure 1 – Procedure to extract OXA from freshwater sediment.

Various eluent compositions were evaluated to optimize extraction efficiency, see Table 1. As main organicmodifier MeOH and THF were selected. In HPLC Et3N is often used to minimize the interaction between thecolumn and chemicals containing amino groups. Organic chemicals are in general well soluble in DMSO.Therefore, Et3N and DMSO were selected to accomplish MeOH and THF in order to enhance the extraction.Furthermore, due to the ionizable nature of OXA, aqueous phases of both alkaline and acidic pH were evaluated.

Table 1 – Eluents evaluated for the freshwater sediment extraction.

Agents v/v % No. of aliquotsa Volume of aliquot, mL Recovery, %b

MeOH 100 1 6 0THF 100 0 6 0THF:MeOH 80:20 0 6 0THF:MeOH:DMSO 79:19:2 0 6 0MeOH:NaHCO3 90:10 1 6 5MeOH:H3PO4 90:10 5 6 24THF:H3PO4:DMSO 70:20:10 2 10 31THF:NaHCO3 90:10 5 6 38THF:H3PO4 95:5 5 6 49THF:H3PO4:Et3N 80:20:5mM 5 6 54THF:H3PO4:DMSO 40:50:10 5 10 67THF:H3PO4:DMSO 79:19:2 5 6 77THF:H3PO4 80:20 4 10 95THF:H3PO4 50:50 5 10 99v/v %: volume/volume %, THF: Tetrahydrofuran, MeOH: Methanol, DMSO: Dimethylsulfoxide, H3PO4: 10 mM pH 2.5,Et3N: Triethylamine, NaHCO3: 10 mM pH 10, a: The figure represents aliquots containing OXA. For the 6-mL aliquots, thesediment was always extracted five times. For the 10-mL aliquots, the sediment was always extracted seven times. b:Extractions were performed in duplicate and recovery calculated according to the method described in Materials andmethods.

In the final experiment sediment samples were initially placed on a filter paper to remove pore water. Remainingpore water was removed at 37ºC for 24 hours and the dry sediment was homogenized in a mortar. 100 mgsediment sample was extracted/eluted with 3×10 mL 20 % 10 mM H3PO4 pH 2.5 in THF. The combined extractswere concentrated to approximately 6 mL under a gentle stream of N2 at 36±1ºC to remove THF from theextract. The resulting phosphoric acid phase was filtered through a 0.2 µm Minisart® RC 25 syringe filter(Sartorius AG 37070, Göttingen, Germany), analysed by means of HPLC and calibrated towards standards in 10mM H3PO4.

Recovery50 mL phosphate buffer pH 7 was added 5 g dry sediment and spiked with OXA at total concentrations of 507,1013 and 2027 µg/L, respectively. These concentrations correspond to sediment concentrations of 5, 10 and 20

µg/g based on the OXA DSED of 102.7 L/kg (Holten Lützhøft, Submitted). The containers were thoroughly mixedand stored three days in the dark at 4ºC in order to simulate natural exposure conditions. Aqueous phases werefiltered (0.2 µm Minisart® RC 25 syringe filter) and analyzed (n=2). Sediment samples (n=3) were extracted asdescribed above.Total and sediment recoveries were calculated from Equation 1 and Equation 2, respectively.

( ) ( )

spikedOXA

SOXAAQOXA

m

mm100 recovery Total

+⋅= Equation 1

( )

( )AQOXAspikedOXA

SOXA

m-m

m100 recovery Sediment ⋅= Equation 2

mOXA(AQ) and mOXA(S) is the quantities of OXA determined in the aqueous and sediment phases, respectively andmOXA spiked is the quantity of OXA spiked to the system.

ResultsA procedure for extraction of OXA from freshwater sediment was developed, see Figure 1. The extractionefficiency of various eluent compositions was tested, see Table 1. MeOH and THF were used as the mainorganic modifier in some cases accomplished by DMSO or Et3N. Both alkaline and acidic buffers were used asthe aqueous phase. 20 % 10 mM H3PO4 pH 2.5 in THF gave the best extraction with a recovery of 98 % with arelative standard deviation (RSD) of 36 % (n=9), see Table 2. Using this eluent, recoveries at individual spikinglevels were compared using one way analysis of variance on a 5% significance level. The null hypothesis wasnot rejected (P=0.1406), which means that the individual recoveries are not statistically significant.The calibration curve showed linearity in the concentration range 2.6-104.5 µg/L (r2=0.999, n=38). The limit ofquantification was determined to 0.16 µg/g with an RSD of 12% (n=4), since 100 mg sediment of 0.16 µg/gextracted to 6 mL results in an extract concentration of 2.6 µg/L.

Table 2 – Recovery study for the extraction of OXA from freshwater sediment.

RecoveryVAQ, mL mS, g mOXA spiked, µg mOXA(AQ), µg mOXA(S), µg Totala, % Sedimentb, %

50 5.45 25.34 5.05 18.46 93 9150 5.43 50.67 9.55 30.24 79 7450 5.85 101.34 23.40 99.81 122 128

98±28c 98±36c

VAQ: volume of the aqueous phase, mS: mass of sediment, mOXA(AQ): mass of OXA in the aqueous phase, mOXA(S): mass ofOXA in the sediment, a: calculated according to Equation 1,b: calculated according to Equation 2, c: Overallrecovery±relative standard deviation (n=9).

Application of this method allowed detection of OXA in sediment samples from the studied trout farm. Table 3shows the OXA sediment concentration at each sampling site and time. Depending on sampling time OXAconcentrations of 0.4 and 0.8 µg/g were observed for the inlet samples. Additionally, before medicationconcentrations of 1.2 and 4.2 µg/g were found in samples from the studied pond and from the outlet,respectively. In samples taken after medication from the studied pond and from the outlet concentrations wereequal to or higher than concentrations measured in inlet samples. OXA was found in the concentration range 0.5-3.4 µg/g, see Table 3 for detailed information. 300 m downstream the outlet an OXA concentration of 1.6±0.9µg/g was found 21 days after medication.No correlation was observed between treatment and occurrence of OXA in the sediment.

Table 3 – OXA concentration in sediment samples from the trout farm (µg/g)a.

Before medication Days after medicationSampling site 0 1 14 21Inlet 0.8b 0.8±0.2 0.4±0.1 0.4±0.2Medicated pond 1.2c 3.4±0.4 0.9±0.2 2.3c

Outlet 4.2b 1.4±0.1 0.5±0.2 2.5±0.5300 m downstream outlet - - - 1.6±0.9a: mean±standard deviation (n=3), b: n=1, c: n=2, -: no samples taken.

Discussion

Development and validation of the analytical methodSince existing methods for extraction of OXA from marine sediment could not be applied to freshwater sedimentwith satisfactory recoveries (20-50%), an appropriate method was developed.The extraction efficiency of various eluent compositions was tested, but apparently neither Et3N nor DMSOenhanced the extraction efficiency, see Table 1. It was found that it was a prerequisite that the eluent containedan aqueous solvent, since none of the pure organic eluents were able to extract OXA from the sediment.However, an acidic aqueous phase was better than an alkaline. The best recovery was thus obtained with 20 %10 mM H3PO4 pH 2.5 in THF.The overall mean recovery of 98 % showed a relatively high variation (RSD: 36 %), which may be explained bythe heterogeneous sediment. Based on visual examination the sediment consisted of sand, clay and silt. Thusdepending on to what extent OXA binds to the individual components and in which ratio those components arepresent, the quantity of recovered OXA will vary. This problem may be circumvented by homogenization of thesediment or by use of larger sediment samples.

Occurrence of OXA in sediment samples from the trout farmOXA outlet concentrations were higher compared with inlet concentrations throughout the period of study. Thisindicates that the use of OXA medicated feed at the trout farm contributed to the occurrence of OXAdownstream the farm outlet. However, OXA occurrence in the inlet samples may be due to either unknownsources of OXA contamination or interference from a sediment component eluting at the same time in the HPLCsystem and showing the same UV absorption properties as OXA. Occurrence of OXA in samples from the pondand from the outlet before medication was not surprising, as the farm had a long history of OXA usage.Several reasons may explain the variability observed within individual samples and among samples. Limited tosamples from the pond, the presence of feed residues and fish excreta may have influenced the sediment contentsof OXA. Additionally replicates of batch samples may vary in their contents of upper and lower sediment layers,which are exposed to various degrees of contamination. Furthermore, spatial differences among samples taken atdifferent times may contribute to the observed variability.Since batch sampling was performed instead of a sediment profile, it was not possible to generate a relationshipbetween depth and concentration.The fact that high concentrations of OXA were detected in sediment from the outlet and even 300 m downstreamthe outlet 21 days after medication indicates that OXA may persist for a long time in freshwater sediment. Thisis inconsistent with the findings reported by Bjørklund et al. (1991), where the OXA sediment concentrationbeneath the marine fish farm in the Baltic declined below the limit of detection (0.05 µg/g) 6 days aftermedication. The disappearance was proposed to be due to diffusion from the top sediment layer or tocomplexation. The longer persistence of OXA observed in the present study may be linked to the different natureof the sediment or to the fact that inland farms are more closed systems in comparison with marine farms. Thus,a higher water exchange/dilution rate may be expected in the marine farms.

Environmental significance

High OXA concentrations (up to 3.4±0.4 µg/g) were found in the samples from the studied pond.Sediment/sludge from fish farms is often used as fertilizer on arable land. Therefore, it is likely that OXA istransported to arable land because DSED of OXA is relatively high (Holten Lützhøft, Submitted). The use of OXAin fish farms may thus indirectly result in exposure of soil organisms.

A relative high OXA concentration (1.6±0.9 µg/g) was detected 300 m downstream the outlet of the farm,indicating that the effluent treatment was not able to detain OXA. Hence, the use of OXA and otherantimicrobials in freshwater fish farming has the potential to affect aquatic habitats situated downstream thefarm, with possible toxic effects on the indigenous micro-flora. Apart from the ecological implications, theoccurrence of antimicrobials in the environment is likely to enhance the spread of antimicrobial resistanceamong bacteria, with potential implications for human health (Toranzo et al., 1984; Sandaa et al., 1992; Kruseand Sørum, 1994). Low OXA concentrations have been demonstrated to select for bacterial resistance (Smith,1995). Effects on the selection of resistant bacteria have recently been demonstrated in a stream receiving waterfrom a Danish trout farm using OXA medicated feed (Guardabassi et al., 2000). It is therefore important toinvestigate the relationship between sediment concentration and development of bacterial resistance.Based on above-mentioned considerations, the ability of various effluent treatments to detain antimicrobialsshould be investigated in the future in order to reduce the environmental impact of antimicrobial usage in land-based fish farms.

ConclusionA method to extract OXA from freshwater sediment was developed using sediment filled SPE tubes. The methodshowed an average recovery of 98 %, although encumbered with a relatively high degree of variance.Analysis of samples from a trout farm using OXA medicated feed revealed higher OXA concentrations in thesediment from the medicated pond (up to 3.4 µg/g) and from the outlet (up to 2.5 µg/g) compared to thesediment from the inlet (up to 0.8 µg/g). Additionally, an OXA sediment concentration of 1.6±0.9 µg/g wasfound 300 m downstream the outlet.Thus, currently used effluent treatments are not sufficient to retain antimicrobials applied in fish farming andshould therefore be investigated in order to optimize the efficiency.

AcknowledgementsThe grammatical and technical help and assistance provided by Klavs Mulvad and Susanne Hermansen isgratefully acknowledged. Wouter HJ Vaes, Andreas P Freidig and Johannes Tolls are appreciated for theirscientific discussions. This investigation was partly funded by a grant from the Danish Centre for SustainableLand Use and Management of Contaminants, Carbon and Nitrogen under the Danish Strategic EnvironmentalResearch Programme, Part 2, 1997-2000, a grant from the European Community (FAIR GT95 2434) and theDanish Environmental Protection Agency, Ministry of Environment and Energy.

ReferencesBjørklund H., (1990). Analysis of oxolinic acid in fish by high-performance liquid chromatography. J.

Chromatogr., Biomed. Appl. 530, 75-82.Bjørklund H., Råbergh C.M.I., and Bylund G., (1991). Residues of oxolinic acid and oxytetracycline in fish and

sediment from fish farms. Aquaculture 97, 85-96.Guardabassi L., Dalsgaard A., Raffatellu M., and Olsen J.E., (2000). Increase in the prevalence of oxolinic acid

resistant Acinetobacter spp. observed in a stream receiving the effluent from a freshwater trout farmfollowing the treatment with oxolinic acid-medicated feed. Aquaculture 188, 205-218.

Halling-Sørensen B., (2000). Algal toxicity of antibacterial agents used in intensive farming. Chemosphere 40,775-781.

Harras M.C., Kindig A.C., and Taub F.B., (1985). Responses of blue-green and green algae to streptomycin inunialgal and paired culture. Aquat. Toxicol. 6, 1-11.

Holten Lützhøft H.C., (Submitted) Environmental risk assessment of antimicrobials. Section of EnvironmentalChemistry, Department of Analytical and Pharmaceutical Chemistry, The Royal Danish School ofPharmacey, Universitetsparken 2, DK-2100 Copenhagen Ø, Denmark.

Holten Lützhøft H.C., Halling-Sørensen B., and Jørgensen S.E., (1999). Algal toxicity of antibacterial agentsapplied in Danish fish farming. Arch. Environ. Contam. Toxicol. 36, 1-6.

Holten Lützhøft H.C., Vaes W.H.J., Freidig A.P., Halling-Sørensen B., and Hermens J.L.M., (Accepted) Theinfluence of pH and other modifying factors on the distribution behaviour of 4-quinolones to solid phases andhumic acids studied by SPME-HPLC. Environ. Sci. Technol.

Kruse H., Sørum H., (1994). Transfer of multiple drug resistance plasmids between bacteria of diverse origins innatural microenvironments. Appl. Environ. Microbiol. 60, 4015-4021.

Noware A., Burhenne J., and Spiteller M., (1997). Binding of fluoroquinolone carboxylic acid derivatives to clayminerals. J. Agric. Food Chem. 45, 1459-1463.

Pouliquen H., Le Bris H., and Pinault L., (1994a). HPLC determination of oxolinic acid and oxytetracycline inthree types of marine sediments: analytical validation. Quím. Anal. 13[suppl 1], S109-S113.

Pouliquen H., Pinault L., and Le Bris H., (1994b). Determination of oxolinic acid in seawater, marine sediment,and Japanese oyster (Crassostrea gigas) by high-performance liquid chromatography. J. Liq. Chromatogr.17, 929-945.

Samuelsen O.B., Lunestad B.T., Ervik A., and Fjelde S., (1994). Stability of antibacterial agents in an artificialmarine aquaculture sediment studied under laboratory conditions. Aquaculture 126, 283-290.

Samuelsen O.B., Torsvik V.L., and Ervik A., (1992). Long-range changes in oxytetracycline concentration andbacterial resistance towards oxytetracycline in fish farm sediment after medication. Sci. Total Environ. 114,25-36.

Sandaa R.-A., Torsvik V.L., and Goksoeyr J., (1992). Transferable drug resistance in bacteria from fish-farmsediments. Can. J. Microbiol. 38, 1061-1065.

Schmitt-Kopplin Ph., Burhenne J., Freitag D., Spiteller M., and Kettrup A., (1999). Development of capillaryelectrophoresis methods for the analysis of fluoroquinolones and application to the study of the influence ofhumic substances on their photodegradation in aqueous phase. J. Chromatogr., A 837, 253-265.

Smith J.T., (1995) In vitro and in vivo mutation frequencies to resistance - do they correlate in the long term? In"The 4-quinolones" (Crumplin G.C., Ed.), pp. 215-227.

Toranzo A.E., Combarro P., Lemos M.L., and Barja J.L., (1984). Plasmid coding for transferable drug resistancein bacteria isolated from cultured rainbow trout. Appl. Environ. Microbiol. 48, 872-877.

Article IIArticle IIArticle IIArticle IIReprinted from Reprinted from Reprinted from Reprinted from ChemosphereChemosphereChemosphereChemosphere, 40(7), Hans-Christian Holten, 40(7), Hans-Christian Holten, 40(7), Hans-Christian Holten, 40(7), Hans-Christian HoltenLützhøft, Wouter HJ Lützhøft, Wouter HJ Lützhøft, Wouter HJ Lützhøft, Wouter HJ Vaes, Andreas P Vaes, Andreas P Vaes, Andreas P Vaes, Andreas P Freidig, Bent Halling-Freidig, Bent Halling-Freidig, Bent Halling-Freidig, Bent Halling-

Sørensen and Joop LM Hermens, 1-Octanol/water distributionSørensen and Joop LM Hermens, 1-Octanol/water distributionSørensen and Joop LM Hermens, 1-Octanol/water distributionSørensen and Joop LM Hermens, 1-Octanol/water distributioncoefficient of oxolinic acid: Influence of pH and its relation to thecoefficient of oxolinic acid: Influence of pH and its relation to thecoefficient of oxolinic acid: Influence of pH and its relation to thecoefficient of oxolinic acid: Influence of pH and its relation to theinteraction with dissolved organic carbon, 711-714, Copyrightinteraction with dissolved organic carbon, 711-714, Copyrightinteraction with dissolved organic carbon, 711-714, Copyrightinteraction with dissolved organic carbon, 711-714, Copyright

(2000), with permission from (2000), with permission from (2000), with permission from (2000), with permission from Elsevier Science.Elsevier Science.Elsevier Science.Elsevier Science.

http://www.elsevier.com/locate/chemospherehttp://www.sciencedirect.com

Co-workers:Co-workers:Co-workers:Co-workers:Wouter HJ Wouter HJ Wouter HJ Wouter HJ Vaes, Andreas P Vaes, Andreas P Vaes, Andreas P Vaes, Andreas P Freidig, Bent Halling-Sørensen andFreidig, Bent Halling-Sørensen andFreidig, Bent Halling-Sørensen andFreidig, Bent Halling-Sørensen and

Joop LM HermensJoop LM HermensJoop LM HermensJoop LM Hermens

1-Octanol/water distribution coe�cient of oxolinic acid:in¯uence of pH and its relation to the interaction with

dissolved organic carbon

Hans-Christian Holten L�utzhùfta,b,*, Wouter H.J. Vaesb, Andreas P. Freidigb,Bent Halling-Sùrensena, Joop L.M. Hermensb

a Section of Environmental Chemistry, Department of Analytical and Pharmaceutical Chemistry, The Royal Danish School of Pharmacy,

Universitetsparken 2, DK-2100 Copenhagen é, Denmarkb Research Institute of Toxicology (RITOX), University of Utrecht, P.O. Box 80176, 3508 TD Utrecht, The Netherlands

Abstract

The distribution of oxolinic acid (OA) between 1-octanol and bu�ers at a broad range of pH values was studied and

found to decrease with increasing pH. The distribution coe�cient to dissolved organic carbon (DOC), log DDOC, was

estimated and compared with an experimentally derived log DDOC, showing the experimental value to be almost three

orders of magnitude higher. Because only the neutral molecule is assumed to distribute to 1-octanol, the interaction

with DOC is considered to be electrostatic in character. Ó 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Oxolinic acid; Dissolved organic carbon; 1-octanol; pH; Distribution coe�cient

1. Introduction

The pharmaceutical compound oxolinic acid (OA)

belongs to the group of 4-quinolone antibacterial agents

used to control bacterial infections. The chemical is ex-

posed to the environment by application in intensive ®sh

farming (Halling-Sùrensen et al., 1998). Recent studies

of the toxicity of antibacterial agents towards algal

species have shown growth inhibiting e�ects of OA in

the lg/l range (Holten L�utzhùft et al., 1999a).

The interaction of organic compounds with dissolved

organic carbon (DOC) is a parameter used in environ-

mental risk assessment of xenobiotics. Estimations of

various partition coe�cients such as the organic carbon

(OC) normalised partition coe�cient to sediment or

DOC (KOC) but also bioconcentration factors are often

based on the partitioning coe�cients to 1-octanol (KOW)

(Di Toro, 1985; Geyer et al., 1984; Nendza and Her-

mens, 1995). This is a reasonable assumption if the af-

®nity of the compound for 1-octanol re¯ects its a�nity

for DOC or ®sh lipids.

The objectives of this investigation were (1) to study

the in¯uence of pH on the distribution behaviour of the

4-quinolone OA, and (2) to discuss whether it is possible

to predict the interaction with DOC based on the dis-

tribution to 1-octanol. For this purpose the distribution

of OA between 1-octanol and bu�er solutions at as

broad a pH range as 3.6±11 was studied.

2. Theoretical considerations

The distribution of OA between the aqueous phase

(W) and the 1-octanol phase (O) can be analysed in

terms of ionisation states of the compound. Based on the

general equation:

Chemosphere 40 (2000) 711±714

* Corresponding author. Tel.: +45-306000; fax: +45-30-

6001.

E-mail address: [email protected] (H.-C. Holten LuÈtzhùft).

0045-6535/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved.

PII: S 0 0 4 5 - 6 5 3 5 ( 9 9 ) 0 0 4 4 1 - 5

AH ¡ Aÿ �H�; �1�where AH refers to the neutral compound and Aÿ refers

to the negatively charged compound, the following

equations can be derived:

Ka � �Aÿ�W � �H��W�AH�W

; �2�

DOW � �AH�O � �Aÿ�O�AH�W � �Aÿ�W

: �3�

Ka is the acid dissociation constant and DOW the ap-

parent distribution coe�cient. The following pH-de-

pendent distribution coe�cients can be derived;

analogous to the Henderson-Hasselbalch equations:

DOW � DAH

1� 10pHÿpKa; �4�

DOW � DAH � DAÿ � 10pH-pKa

1� 10pHÿpKa: �5�

DAH and DAÿ are the distribution coe�cients of the re-

spective species. Eq. (4) only considers distribution of

the neutral species, whereas Eq. (5) considers distribu-

tion of the neutral as well as the negatively charged

species. The above equations do not take distribution of

ion pairs into account, but only distribution of the

neutral and the charged species.

3. Materials and methods

3.1. Chemicals

OA 5-ethyl-5,8-dihydro-8-oxo-1,3-dioxolol[4,5-g]-

quinoline-7-carboxylic acid (>98%), was purchased from

Unikem A/S (Copenhagen, Denmark). Methanol

(MeOH) (HPLC grade, assay (GC) 99.9%) was obtained

from Labscan (Dublin, Ireland). 1-octanol (>99.5%) was

obtained from Fluka (Buchs, Switzerland). 2 mM bu�er

solutions were made of citric acid (pH < 6), KH2PO4

�66 pH < 8�, TRIZMA �86 pH < 10� and NaHCO3

�106 pH� and adjusted with 0.1 M NaOH to the desired

pH values. Depending on the volume of the bu�er so-

lution between 0.5 and 3 ml of NaOH were added, re-

sulting in an ionic strength between 1 and 5 mM sodium

ions. Bu�er substances and NaOH (analysis grade) were

purchased either from Merck (Darmstadt, Germany) or

Sigma-Aldrich (St. Louis, Missouri, USA). OA stock

solutions were made in 0.1 M NaOH. All aqueous so-

lutions were made from water puri®ed with a milli-pore

system. The chemical structure and some physical

chemical properties of OA are shown in Table 1.

3.2. HPLC-procedure

The HPLC-system consisted of a Varian 9012 Sol-

vent Delivery System (Varian, Walnut Creek, USA)

operated at a ¯ow rate of 2.0 ml/min; a Supelco Dis-

covery C18 column (Supelco, Bellafonte, USA),

150� 4:6 mm, 5 lm particle size stationary phase, and a

Merck Hitachi L-4000 UV-detector (Merck, Darmstadt,

Germany) operated at a wavelength of 260 nm. The

solvents used were MeOH (A) and 2 mm H3PO4/

KH2PO4, pH 2.9 (B). The composition of the mobile

phase A:B was programmed from 20:80 to 100:0 within

8 min and kept there for 2 min followed by an equili-

bration time of 8 min while the mobile phase returned to

its original composition. Aliquots of 20 ll were injected

by a Spark Holland Marathon autosampler (Spark

Holland, Emmen, The Netherlands). A calibration curve

for OA was established. All analyses were carried out at

ambient temperature.

3.3. pH-Dependent DOW-experiments

The experiments were carried out partly in accor-

dance with the slow stirring method described by

Brooke et al. (1986) and de Bruijn et al. (1989). Double-

walled vessels of approximately 250 ml were used. The

vessels were closed with a glass stopper, which allowed

samples to be taken directly from the 1-octanol phase

and samples to be taken from the aqueous phase by a

pasteur pipette through the 1-octanol phase. In each

experiment 200 ml of bu�er solution (pH range: 3.6±

11.0) was brought into the vessel together with a Te¯on-

Table 1

Chemical structure and some physical chemical properties of OA

Compound Chemical

structure

Molecular

formula

Molecular weight

(g/mole)

Aqueous solubility at

a pH of 7 (mg/l)

pKa Log KOW

Oxolinic acid

(OA)

C13H11NO5 261.23 4.1a 6.9b 0.68c

a Elema (1995).b Timmers and Sternglanz (1978).c Tak�acs-Nov�ak et al. (1992).

712 H.-C. Holten L�utzhùft et al. / Chemosphere 40 (2000) 711±714

coated magnetic stirring bar. On the top of the aqueous

phase, 50 ml of 1-octanol were carefully added with a

pipette to avoid mixing with the water phase. The stir-

ring rate (approximately 100 rpm) was adjusted so that a

vortex of approximately 1 cm was formed at the 1-oc-

tanol/bu�er interface. The two phases were allowed to

equilibrate during the night (Dearden and Bresnen,

1988). The next morning a certain quantity of OA stock

solution was applied to the bu�er solution with a pasteur

pipette through the 1-octanol phase resulting in con-

centrations well below aqueous solubility, and samples

were taken from both phases at time 0. The pasteur

pipettes were thoroughly cleaned by an acetone con-

taining tissue and the ®rst few lls were discarded. Pre-

liminary experiments showed that a steady state was

reached after approximately 24 h. 800 ll aqueous sample

acidi®ed with 200 ll 0.5 M H3PO4/KH2PO4, and 100 ll

1-octanol sample to which 900 ll MeOH were added

and taken, and they were both analysed by HPLC.

Calibration curves in bu�er and 1-octanol were made by

the same procedures. The pH was determined in the

aqueous phase at the end of the experiment and was in

agreement with the initial determined pH. The experi-

ments were thermostatically controlled at 26� 0:5�C.

The Non-linear regressions of Eqs. (4) and (5), as well

as linear regression were performed with the GraphPad

Prism, version 2.01, 1996, for Windows 95, software.

4. Results

Fig. 1 shows the pH dependent distribution coe�-

cient for OA between 1-octanol and bu�er. OA was al-

lowed to distribute until steady state at a broad range of

pH values. The data were ®tted to Eqs. (4) and (5) and

compared by means of an F-test, revealing that Eq. (5)

did not ®t signi®cantly better than Eq. (4),

F1;28 � 0:01; p � 0:9. The pKa and the DOW for the

neutral molecule with standard errors were estimated in

accordance with Eq. (4) to be 6:84� 0:05 and

9:51� 0:21, respectively.

The calibration curve �n � 16� for OA showed lin-

earity over the measured concentration range (24±3136

nm). The limit of detection was 44 nM, calculated as 3.3

times the standard deviation of the regression line di-

vided by the slope.

5. Discussion

The distribution of OA between 1-octanol and bu�er

can be described based on the pKa value and the DOW

value for the neutral molecule, in accordance with Eq.

(4), which is in agreement with the expected distribution

behaviour for an ionisable compound. Since the bu�er

concentration and the ionic strength was kept low (<7

mM in total) and only consisted of mono valent cations,

we assume no in¯uence on the distribution data due to

ion pair formation. The estimated pKa value of 6.8 is in

good accordance with the pKa value of 6.9 reported

earlier (Timmers and Sternglanz, 1978), whereas the

reported DOW value of 4.8 (Tak�acs-Nov�ak et al., 1992) is

a factor of 2 less than the DOW value of 9.5 obtained in

this study. An explanation might be that Tak�acs-Nov�ak

et al. (1992) only measured the disappearance of the

compound in the aqueous phase, and that the measured

partition coe�cient was carried out at room tempera-

ture, but they do not list exactly at which temperature

the experiment was performed. Furthermore, the ex-

periments were carried out in 40 mM bu�er at a pH of

4.00, and the true partition coe�cient was then calcu-

lated.

As mentioned in the introduction e.g. KOCÕs are often

estimated based on the KOW value. Estimation models

are normally derived from neutral organic chemicals.

But the same models are often also applied to other

types of compounds. The use of these procedures for OA

may produce results, which are not in agreement with

experimental data. In accordance with quantitative

structure activity relationships found in the literature,

log KOC for OA can be estimated to 1.0 (log KOC �0:983 � logKOW � 0:00028, (Di Toro, 1985)), based on

pesticides having log KOW in the range 1±7 and to 1.1

� logKOC � 0:52 � logKOW � 0:64, (Briggs, 1981)), based

on various chemicals having log KOW in the range ÿ0:6to 7.4. An experimental log DDOC value of 3:9� 0:1between bu�er at a pH of 3 and Aldrich humic acids has

been determined by Holten L�utzhùft et al. (1999b). The

experimental DDOC of OA is almost three orders of

magnitude higher than the estimated KOC. Since the

distribution to 1-octanol can be described in accordance

with Eq. (4), which only takes distribution of the neutral

Fig. 1. Distribution coe�cients for OA between 1-octanol and

bu�er. Symbols indicate single measurements and curve repre-

sents the ®tting to Eq. (4).

H.-C. Holten L�utzhùft et al. / Chemosphere 40 (2000) 711±714 713

species into account, we suggest that the interactions

with DOC also have some electrostatic character. Since

humic acids contain several polar groups, it seems likely

that it is the electrostatic interactions that account for

the higher interaction level seen for the experimental

value compared to the estimated values. These results

show that the risk assessment of ionic xenobiotics needs

another approach than for neutral compounds, and that

present models for KOC are not valid for such com-

pounds. Even at a pH of 3 where OA can be regarded

neutral, the polar groups of OA still may interact with

similar groups in the humic acids, and result in much

higher interaction levels than can be predicted from the

hydrophobicity of the chemical.

Acknowledgements

The grammatical and technical help and assistance

provided by Klavs Mulvad, Eric Verbruggen, Frans

Busser and Theo Sinnige is gratefully acknowledged.

The contribution concerning the equations in the part

`Theoretical considerations', performed by E~naut Ur-

restarazu Ramos, is appreciated by the authors. This

investigation was partly funded by a grant from the

Danish Centre for Sustainable Land Use and Manage-

ment of Contaminants, Carbon and Nitrogen under the

Danish Strategic Environmental Research Programme,

Part 2, 1997±2000.

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Article IIIArticle IIIArticle IIIArticle IIIInfluence of pH and Other Modifying Factors on the DistributionInfluence of pH and Other Modifying Factors on the DistributionInfluence of pH and Other Modifying Factors on the DistributionInfluence of pH and Other Modifying Factors on the Distribution

Behavior of 4-Quinolones to Solid Phases and Humic Acids StudiedBehavior of 4-Quinolones to Solid Phases and Humic Acids StudiedBehavior of 4-Quinolones to Solid Phases and Humic Acids StudiedBehavior of 4-Quinolones to Solid Phases and Humic Acids Studiedby “Negligible-Depletion” SPME-HPLC.by “Negligible-Depletion” SPME-HPLC.by “Negligible-Depletion” SPME-HPLC.by “Negligible-Depletion” SPME-HPLC.

Environmental Science and TechnologyEnvironmental Science and TechnologyEnvironmental Science and TechnologyEnvironmental Science and Technology, 2000, 34, 4989-4994., 2000, 34, 4989-4994., 2000, 34, 4989-4994., 2000, 34, 4989-4994.

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Joop LM HermensJoop LM HermensJoop LM HermensJoop LM Hermens

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Article IVArticle IVArticle IVArticle IVReprinted from Reprinted from Reprinted from Reprinted from Archives of Environmental Contamination andArchives of Environmental Contamination andArchives of Environmental Contamination andArchives of Environmental Contamination and

ToxicologyToxicologyToxicologyToxicology, 36(1), Hans-Christian Holten Lützhøft, Bent Halling-, 36(1), Hans-Christian Holten Lützhøft, Bent Halling-, 36(1), Hans-Christian Holten Lützhøft, Bent Halling-, 36(1), Hans-Christian Holten Lützhøft, Bent Halling-Sørensen and Sven Erik Sørensen and Sven Erik Sørensen and Sven Erik Sørensen and Sven Erik Jørgensen, Algal toxicity of antibacterialJørgensen, Algal toxicity of antibacterialJørgensen, Algal toxicity of antibacterialJørgensen, Algal toxicity of antibacterial

agents applied in Danish fish farming, 1-6, Copyright (1999), withagents applied in Danish fish farming, 1-6, Copyright (1999), withagents applied in Danish fish farming, 1-6, Copyright (1999), withagents applied in Danish fish farming, 1-6, Copyright (1999), withpermission from permission from permission from permission from Springer-Verlag.Springer-Verlag.Springer-Verlag.Springer-Verlag.

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Co-workers:Co-workers:Co-workers:Co-workers:Bent Halling-Sørensen and Sven Erik Bent Halling-Sørensen and Sven Erik Bent Halling-Sørensen and Sven Erik Bent Halling-Sørensen and Sven Erik JørgensenJørgensenJørgensenJørgensen

Algal Toxicity of Antibacterial Agents Applied in Danish Fish Farming

H.-C. Holten Lutzhøft, B. Halling-Sørensen, S. E. Jørgensen

Section of Environmental Chemistry, Department of Analytical and Pharmaceutical Chemistry, The Royal Danish School of Pharmacy,Universitetsparken 2, DK-2100 Copenhagen Ø, Denmark

Received: 24 February 1998/Accepted: 27 July 1998

Abstract. Algal toxicity of antibacterial agents applied in fishfarming was investigated. The growth-inhibiting effects ofamoxicillin (A), flumequine (F), oxolinic acid (OA), oxytetracy-cline hydrochloride (OT), sarafloxacin hydrochloride (SF),sulfadiazine (SD), and trimethoprim (T) were investigated by amodified test procedure based on the procedure described in theISO 8692 (1989) protocol on three algal species: the freshwatercyanobacteriaMicrocystis aeruginosa,the freshwater greenalga Selenastrum capricornutum,and the marine cryptophy-cean Rhodomonas salina.Algal growth was measured asincreased chlorophyll concentration by extraction with ethanolfollowed by measurement of fluorescence. Results were quanti-fied in terms of growth rates using the Weibull equation todescribe the concentration response relationship.M. aeruginosashowed higher sensitivity compared to bothR. salina andS. capricornutum,whereas the results for the latter two weremore or less identical. The toxicity (EC50 value, mg/L) indecreasing order were A (0.0037), SF (0.015), SD (0.135),F (0.159), OA (0.180), OT (0.207), and T (112) forM.aeruginosa;OT (1.6), OA (10), T (16), F (18), SF (24), SD(403), and A (3108) forR. salina;and OT (4.5), F (5.0), SD(7.8), OA (16), SF (16), T (130), and A (NOEC. 250) forS. capricornutum.Applying this test procedure the toxicity ofantibacterial agents, being mono- or polyprotic compounds,may be underestimated because of partitioning between ionizedand unionized forms.

More than 200 tons of antibacterial agents are used annually inDenmark for human as well as veterinary purposes (Halling-Sørensenet al.1998). Based on the consumption of amoxicillin,oxolinic acid and oxytetracycline in one Danish county in theyears 1994, 1995, and 1996 (Holm Sørensen and Landsfeldt1997) it is assumed that the application of antibacterial agentsin Danish fish farming is increasing. To treat bacterial infectionsin intensive fish farming, antibacterial agents are distributeddirectly to the water as feed additives. Biotic as well as abioticfactors affect absorption, disposition, metabolisation and elimi-nation of the antibacterial agents (Bjørklund and Bylund 1990;Bjørklundet al. 1992; Schneider 1994) in fish. The absorption

of antibacterial agents in fish varies from 10 to 80% for theindividual compounds, which, among other things, depend onthe environmental temperature (Cravediet al.1987; Hustvedtetal. 1991; Schneider 1994). The extent of metabolisation variesfrom 20 to 80% (Poppe 1990; Lunestadet al.1992; Miglioreetal. 1996) depending on the compound. Furthermore, sick fishdo often have a reduced consumption (Poppe 1990; Lunestadetal. 1992). Schneider (1994) states that about 70% of adminis-trated antibacterial agents applied in fish farming are releasedinto the environment. Antibacterial agents are often mono- orpolyprotic compounds. Schneider (1994) states that pKa valuesare important parameters to determine whether or not acompound will penetrate, persist, or be eliminated from theorganism. The bioavailability of the compounds in the environ-ment are therefore also pH-dependent. The application ofantibacterial agents in fish farming is consequently a directsource of exposure to the aquatic environment.

Jacobsen and Berglind (1988) and Miglioreet al. (1996)reported findings of flumequine and oxytetracycline, respec-tively, in water and sediment from the outflow of a breedingpond and below fish farms. From several investigations(Samuelsenet al. 1994; Hektoenet al. 1995; Marengoet al.1997) it is known that some antibacterial agents are relativelystable under environmental conditions, resulting in half-lives insediment over 100 days.

The toxic effect data of antibacterial agents on variousaquatic species found in the literature (Harraset al.1985; Macrı`et al.1988; Lanzky and Halling-Sørensen 1997; Miglioreet al.1997), show values in the mg/L range. Investigations haveprimarily been done on crustaceans and fish rather than algalspecies. The few tests done on algal species show on the otherhand that algae, especially cyanobacteria, may be sensitive toantibacterial agents (Harraset al. 1985; Lanzky and Halling-Sørensen 1997). Since algae take part in the photosynthesis, it isimportant to study whether certain compounds interact with thegrowth of these species or not.

The antibacterial agents in question in this investigation are:amoxicillin, A, (2)-6-[2-amino-2-(p-hydroxyphenyl)acet-amido]-3,3-dimethyl-7-oxo-4-thia-1-azabicyclo[3.2.0]heptane-2-carboxylic acid; flumequine, F, 9-fluoro-6,7-dihydro-5-methyl-1-oxo-1H,5H-benzo[ij]quinolizine-2-carboxylic acid; oxolinicacid, OA, 5-ethyl-5,8-dihydro-8-oxo-1,3-dioxolol[4,5-g]quino-line-7-carboxylic acid; oxytetracycline hydrochloride, OT,[4S-(4a,4aa,5a,5aa,6b,12aa)]-4-(dimethylamino)-1,4,4a,5,5a,6,-Correspondence to:H.-C. Holten Lutzhøft

Arch. Environ. Contam. Toxicol. 36, 1–6 (1999) A R C H I V E S O F

EnvironmentalContaminationa n d Toxicologyr 1999 Springer-Verlag New York Inc.

11,12a- octahydro-3,5,6,10,12,12a-hexahydroxy-6-methyl-1,11-dioxo-2-naphthacenecarboxamide; sarafloxacin hydrochloride,SF, 6-fluoro-1-(4-fluorophenyl)-1,4-dihydro-4-oxo-7-(1-pipera-zinyl)-3-quinolinecarboxylic acid; sulfadiazine, SD, 4-amino-N-2-pyrimidinylbenzenesulfonamide; and trimethoprim, T,5-[(3,4,5-trimethoxyphenyl)methyl]-2,4-pyrimidinediamine.Chemical structures are shown in Figure 1. Registered applica-tion does not reveal application of F and SF in Danish fishfarming. The compounds have meanwhile been or consideredto be (Hektoenet al.1995) introduced in other countries and assuch are tested in the present investigation. The compoundsrepresent different groups of antibacterial agents;b-lactams, A;4-quinolones, OA, F, and SF; tetracyclines, OT; sulphonamides,SD; and dihydrofolate reductase inhibitors, T. It is shown thatOT is actively taken up by the bacterial cell (Hugo and Russell1992). Table 1 represents some physicochemical properties ofthe antibacterial agents, which implies that exposure to theenvironment of the compounds will distribute to the aquaticenvironment.

The objective of this investigation was to establish data foralgal toxicity of antibacterial agents, applied in,e.g., Danishfish farming, on three different algal species:Microcystisaeruginosaas model organism for cyanobacteria in freshwater,Rhodomonas salinaas model organism for algae in saltwater,and Selenastrum capricornutum,commonly used in standardalgal toxicity tests (ISO 1989), as model organism for algae infreshwater.

Materials and Methods

Algal Species

Nonaxenic unicultures of the test organisms were obtained fromScandinavian Culture Centre for Algae & Protozoa, University ofCopenhagen, Denmark,M. aeruginosa;Laboratory of Marine Biology,Helsingør, Denmark,R. salina; and Norwegian Institute of WaterResearch culture collection, Oslo, Norway,S. capricornutum.All algalspecies are usually found in Danish water streams and oceans. Toprevent contamination, the two freshwater algae were only culturedone at a time. Occurrence of contamination with other algal species,was controlled monthly using an Olympus BHM microscope.

Test Media

The growth medium for the two freshwater algal species was preparedin accordance to ISO (1989). Testing the cyanobacteria 750.0 µg/L ofthioamine hydrochlorid, 10.0 µg/L of cyanocobalamine, and 7.5 µg/Lof biotin were added to the ISO medium. The growth medium for themarine alga was prepared in accordance to Hansen (1989) with smallmodifications: KNO3 was applied instead of NaNO3, Na2HPO4 insteadof NaH2PO4, and CuCl2 · 2H2O instead of CuSO4 · 5H2O. The concen-trations of thioamine hydrochlorid, cyanocobalamine, and biotin werethe same as mentioned above. The water used for the growth media waspurified with a Millipore system.

Chemical Substances

Test compounds (purity, %) were purchased from the followingcompanies: K2Cr2O7 (.99.8), Riedel-de Hae¨n, Seelze, Germany;amoxicillin (plant-cell-culture-tested), Duchefa, Haarlem, The Nether-lands; flumequine (.99.9), Sigma Chemical Co., St. Louis, MO;

oxolinic acid (.98), oxytetracycline hydrochloride (100.7), sulfadia-zine (99.8), and trimethoprim (100), Unikem A/S, Denmark. Sarafloxa-cin hydrochloride (88.5) was obtained from Abbott Laboratories, NorthChicago, IL. Chemicals used for growth medium were all of analyticalgrade and purchased from Merck, Denmark. K2Cr2O7 was used asreference chemical.

Test Solutions

All solutions were prepared from water purified with a Milliporesystem. A, OT, and T were dissolved in H2O. F, OA, SF, and SD weredissolved in 0.05 M NaOH, pH was adjusted with 0.1 M HCl anddiluted to the desired concentrations. The initial pH was 7.96 0.2,7.36 0.2, and 7.96 0.4 in the tests withM. aeruginosa, R. salina,andS. capricornutum,respectively, except for A and K2Cr2O7 in the testwith R. salina,which were 6.4–7.2 and 6.5–6.6, respectively. The pHincrease during the tests did not exceed 1.5 pH unit. The lowestincrease was observed for the cyanobacteria. The pH was measuredwith a PHM95 pH/ION METER, Radiometer Denmark A/S. Thecompounds were tested at three concentration levels without replicates,whereas the controls were grown in triplicates. Number of tests were$2. No analytical determination was done concerning the concentra-tions of antibacterial agents, why their nominal concentration was usedas exposure concentration in the calculations of EC50 values, see Tables2–4; Tested concentration levels.

Test Procedure

The applied test procedures were modified versions of the testprocedure described in the ISO (1989) protocol. The duration of thetests withM. aeruginosawas set to 7 days in order to obtain at least a16 doubling in the cell numbers, which is prescribed in the ISOprotocol. Inoculations were made with algal precultures set up 1–3days before the experiment and propagated under same test conditionsas the subsequent test. The algal concentrations in the preculture weredetermined on a Coulter Countert Model TaII Multichannel particlecounter, Coulter Electronics LTD (Coldharbour Lane, Harpenden,

Table 1. Physicochemical properties of the antibacterial agents tested

Solubility(mg/L)f pKa Log Kow

Amoxicillin 4 · 103,g — —Flumequine 71h 6.4a 1.72a

Oxolinic acid 4.1h 6.9j 0.68b

Oxytetracycline 106,g 3.3, 7.3, 9.1k 21.12c

Sarafloxacin Slightly solublei — 0.84d

Sulfadiazine 2 · 103 (37°C)g 2.0, 6.5l 0.12c

Trimethoprim 400g 7.3m or 6.6g 0.91e

— not founda Takacs-Novak and Avdeef (1996)b Takacs-Novak et al.(1992)c Herbert and Dorsey (1995)d CLog P for Windows (1995), solubility set to 10 mg/Le Rekkeret al.(1993)f Solubility in waterg Budavari (1996)h Elema (1995)i Abbott Laboratories, North Chicago, IL, USAj Timmers and Sternglanz (1978)k Stephenset al.(1956)l Koizumi et al.(1964)m Watson and Stewart (1986)

2 H.-C. Holten Lutzhøftet al.

Herts, England) and were in the order of 1–23 106 cells/ml. Bothcontrol and test flasks were inoculated with exponential growingalgae so an initial concentration ofM. aeruginosa, R. salina,andS. capricornutumwere 2 3 104, 1 3 104, and 1 3 104 cells/ml,respectively. All glassware used in the tests was rinsed in 1 M HCl forat least 1 h prior to use. Algal toxicity tests were performed in 250-mlErlenmeyer flasks containing 100 ml algal culture. The flasks werecovered with perforated laboratory film to avoid contamination andevaporation but to allow gas exchange. Algal preculture as well as testsolutions were grown on a shaking table, with a shaking rate of 93 rpm,under Philips TLM 40W/33rs and TLD 36W/84o white fluorescentlight. The light intensity in the tests withM. aeruginosa, R. salina,andS. capricornutumwas 3.16 0.2, 3.3 6 0.3, and 6.86 0.4 Klux,respectively. The tests were performed at 216 1, 21 6 1, and 2361°C, respectively. Samples were taken from control flasks at the start aswell as at the end of the tests, whereas samples from test flasks weretaken only at the end.

Chlorophyll Determination

Algal chlorophyll were quantified using a modified version of thewhole water extract fluorescence method described by Mayeret al.(1997). The algal chlorophyll was extracted using 67% ethanol. Startcontrol extraction samples were thoroughly shaken for 20 s before theywere stored in the dark at 4°C until the end of the tests. End controlextraction samples and test solution samples were placed on a shakingtable in the dark for at least 2 h at 216 1°C together with the storedcontrol samples before chlorophyll determination. In the tests withR. salinathe ethanolic extracts were filtered through a sterile 0.22-µmMillipore filter to remove flocculates before measuring the chlorophyllcontent. The chlorophyll was fluorometrically determined; excitationwavelength: 430 nm and emission wavelength: 671 nm, on a PerkinElmer Luminescence Spectrometer LS 50B at ambient temperature.The slit width was set to 10 nm and a flow-through cell of 750 µl wasused. The flow-through cell was rinsed with approximately 3 ml samplebefore measurement.

Statistic Treatment of Data

The results of the algal toxicity tests were quantified in terms of growthrates calculated from pooled measurements of chlorophyll content inthree subsequent tests. Growth inhibitions,I, were calculated from

relative growth rates asI i 5 1-m i 0m c, whereIi is the growth inhibitionfor test concentration i,µi is the growth rate for test concentration i, andmc is the growth rate for the control. EC50 values were determined byweighted nonlinear regression analysis directly on the data using theWeibull equation to describe the concentration response relationship(Nyholm et al.1992). A regression program that calculates confidenceintervals by proper inverse estimation and also takes into account thecovariance with the control response was used (Andersen 1994).

Results

Tables 2–4 exhibit the algal toxicity results of this investigationquantified as EC50 values. It is observed thatM. aeruginosaisapproximately two to three orders of magnitude more sensitivetoward the antibacterial agents than bothR. salina andS. capricornutum.The two latter algal species have almost thesame level of sensitivity.

As regardsM. aeruginosa,the antibacterial agents could bedivided into three groups corresponding to EC50 values over 1mg/L, between 0.1 mg/L and 1 mg/L, and below 0.1 mg/L. Tothe first group belongs only T with an EC50 of 112 mg/L. SD, F,OA, and OT belongs all to the second group with EC50’s of0.135, 0.159, 0.180, and 0.207 mg/L, respectively. Both A andSF belongs to the third group with EC50’s of 0.0037 and 0.015mg/L, respectively. On the other hand, A was not toxic towardeitherR. salinaandS. capricornutum.An estimated EC50 valueof 3,108 mg/L and a NOEC of 250 mg/L were obtained,respectively, both in total unrealistic environmental concentra-tions. EC50 values in mg/L of the remaining antibacterial agentsin increasing order forR. salinawere: SD, SF, F, T, OA, and OT,403, 24, 18, 16, 10, and 1.6, respectively, and forS. capricornu-tum: T, SF, OA, SD, F, and OT, 130, 16, 16, 7.8, 5.0, and 4.5,respectively. These observations are exhibited in Figure 2,which shows the decreasing growth inhibiting effects of theantibacterial agents ranged with respect to algal specie.

Discussion

The statistical treatments of the data were performed on resultsfrom three subsequent tests. This is not in conformity with the

Fig. 1. Chemical structures of the antibacterial agents tested

3Algal Toxicity of Antibacterial Agents

ISO8692 (1989) protocol, but was done in order to obtainestimates of EC50 values from the Weibull equation. The resultsexhibited in Tables 2–4 are therefore obtained from pooledtests.M. aeruginosagenerally seems to be two to three ordersof magnitude more sensitive than bothR. salina and S.capricornutum,which have almost equal sensitivity. Theseresults do not surprise, since Harraset al. (1985) showed thatM. aeruginosawas approximately a factor of 10 more sensitive

than S. capricornutumtoward streptomycin, an aminoglyco-side. It is impossible with the data presented in the presentinvestigation to generalise toxic relations among the antibacte-rial agents for any of the three algal species. Lanzky andHalling-Sørensen (1997) investigated the toxicity of metronida-zole to organisms at different trophic levels. They showed thatboth Chlorella sp.and S. capricornutumhad a much highersensitivity compared withAcartia tonsa(marine crustacean)and Brachydanio rerio (zebrafish). Miglioreet al. (1997)investigated the toxicity of bacitracin and F, among others,towardArtemia,and showed that both compounds were mostactive against nauplii and cysts. Macrı` et al.(1988) investigatedthe acute toxicity of furazolidone to different species and foundLC50 values toArtemia salina, Daphnia magna,and Culexpipiens larvae to be 250 mg/L, 60 mg/L, and 40 mg/L,respectively. Dojmi Di Delupiset al. (1992) investigated thetoxicity of different antibacterial agents towardD. magnaandshowed that only bacitracin had an EC50 value (48 h) below 100mg/L; furthermore, they showed that both bacitracin andlincomycin lowered the phototactic behavior at concentrationsof 10 and 5 mg/L, respectively, whereas aminosidine, atconcentrations of 10 mg/L, increased it. An overview of thetoxicity data, on algae, crustaceans, and fish, found in theliterature, are shown in Table 5. From toxicity data establishedin this investigation as well as data found in the literature, it isrealized that algae have a higher sensitivity toward antibacterialagents compared to crustaceans and fish. Furthermore, amongalgae, cyanobacteria have shown to be the most sensitive algalspecies, due to their structure being more like bacteria. Theseobservations indicate that effects on higher trophic levelsprimarily would be indirect. In order to perform a properenvironmental risk assessment of antibacterial agents, it wouldbe necessary to include a cyanobacteria as test organism in thetest battery.

In algal batch cultures the biomass density may quickly reacha level where the carbon demand by the growing algae exceedsthe transfer rate of CO2 from the gas phase to the liquid phase.In this situation, dissolved CO2 for algal growth will also bederived from medium bicarbonate, which results in an increaseof medium pH during the 3 days of test performance. Due to thetechnical problems of maintaining constant pH during an algalbatch toxicity test, a pH increase as large as up to 1.5 units isaccepted (ISO 1989). The antibacterial agents tested in thisinvestigation are all protic, with pKa values around 6–7; due toincreasing pH during test performance, increasing ionization ofthe compounds will occur. An increase in pH is of specialimportance in batch tests with protic substances. The pHincrease observed in this investigation were within the limits forboth R. salina and S. capricornutum.For the tests withM.aeruginosaalmost no pH increase was observed; all tests werewithin 0.2 units of increase, except F and T, which were 0.8 and20.2, respectively. The low pH increase is due to lower growthrate ofM. aeruginosacompared withR. salinaandS. capricor-nutum.Increasing pH will reduce the bioavailable concentra-tions of weak acids, which, for compounds with passivediffusion across the cell membranes, may lead to (fairly)underestimated results. The phenomenon of changed toxicitydue to different pH values is recognized for chlorinatedphenols. Koenemann and Musch (1981) found that a decreasein pH from 8 to 6 increased the toxicity of pentachlorophenolwith a factor of approximately 10. To avoid the influence of pHon the test results a test setup including a buffering capacity

Table 2. Results for the tests withM. aeruginosa

TestedConcentrationLevels(mg/L)

EC50

(mg/L)

95%ConfidenceInterval(mg/L) n

Amoxicillin 0.0009–0.0038 0.0037 — 2Flumequine 0.094–0.369 0.159 0.066–0.382 3Oxolinic acid 0.060–0.540 0.180 — 2Oxytetracycline 0.040–0.360 0.207 0.175–0.246 3Sarafloxacin 0.004–0.009 0.015OMR 0.009–0.023 3Sulfadiazine 0.020–0.180 0.135 0.082–0.223 3Trimethoprim 100–132 112 100–126 3K2Cr2O7 0.044–0.400 0.211 0.029–1.506 3µcontrol, d21 0.5–0.7

— not obtainableOMR Out of measured range

Table 3. Results for the tests withR. salina


EC50

(mg/L)


Amoxicillin 5.0–500 3,108OMR 320–30,199 2Flumequine 10–160 18 10–31 3Oxolinic acid 5.0–20 10 5.5–19 3Oxytetracycline 0.8–3.0 1.6 0.4–6.1 3Sarafloxacin 10–40 24 11–52 3Sulfadiazine 5.0–45 403OMR 146–1,113 3Trimethoprim 5.0–20 16 9.3–27 3K2Cr2O7 1.0–8.0 3.9 3.6–4.4 3µcontrol, d21 1.3–1.5

OMR Out of measured range

Table 4. Results for the tests withS. capricornutum


EC50

(mg/L)


Amoxicillin 2.5–250 250a — 2Flumequine 3.0–27 5.0 1.6–16 3Oxolinic acid 9.3–37 16 9.1–29 3Oxytetracycline 3.0–12 4.5 2.3–8.6 3Sarafloxacin 10–40 16 9.8–25 3Sulfadiazine 3.0–27 7.8 4.5–14 3Trimethoprim 30–270 130 81–211 3K2Cr2O7 0.3–2.7 0.6 0.5–0.7 3µcontrol, d21 1.9

— not obtainablea NOEC


should be applied. Several modifications (Halling-Sørensenetal. 1996) of the standard open flask test method have beensuggested to reduce the pH development in the test procedure ofthe ISO8692 (1989) protocol. Halling-Sørensenet al. (1996)calculated the quantity of [HCO32] in the medium needed tobuffer pH to different endpoint values. Initial algal densitycould be lowered to,e.g., 103 cells/ml. Test time could bereduced from the recommended 3 days in ISO (1989) standardtest withS. capricornutumto 1 or 2 days. If an initial pH of 7 isallowed a reduction of the test duration from 3 to 2 days wouldsignificantly reduce the problems of pH increase. Application of

a chemostat setup with buffered medium at pH 7 or even lowermight also increase the fraction of unionized compound.

Conclusion

In this investigationM. aeruginosais found to be about two tothree orders of magnitude more sensitive than eitherR. salinaand S. capricornutum.A and SF seem to be the most toxiccompounds towardM. aeruginosa,with EC50 values below 0.1mg/L; SD, F, OA, and OT seem to constitute a group with

Fig. 2. Decreasing EC50 values ranged withrespect to algal specie with corresponding95% confidence intervals

Table 5. Overview of different effect data

Compound Organism Effect Effect Value (mg/L)

Streptomycina M. aeruginosa Minimum inhibitory concentration 0.3S. capricornutum Minimum inhibitory concentration 2.1

Metronidazoleb Chlorella sp. Growth inhibition, EC50 38.8S. capricornutum Growth inhibition, EC50 39.1A. tonsa No effect level 100B. rerio No effect level 500

Bacitracinc Artemia Acute toxicity, EC50 (48 h) 21.8Artemianauplii 100% mortality 6.3Artemiacysts Hatching 25

Flumequinec Artemia Acute toxicity, EC50 (72 h) 96.4Artemianauplii 22% mortality 6.3Artemianauplii Transparency induced 1 ppm

Furazolidoned A. salina LC50 250D. magna LC50 60C. pipienslarvae LC50 40

Aminosidinee D. magna EC50 (48 h) 502.8Bacitracine D. magna EC50 (48 h) 30.5Erythromycine D. magna EC50 (48 h) 210.6Lincomycine D. magna EC50 (48 h) 379.4Aminosidinee D. magna Phototactic behavior, increased 10Bacitracine D. magna Phototactic behavior, decreased 10Erythromycine D. magna Phototactic behavior No effectLincomycine D. magna Phototactic behavior, decreased 5

a Harraset al.(1985)b Lanzky and Halling-Sørensen (1997)c Migliore et al.(1997)d Macri et al.(1988)e Dojmi Di Delupiset al.(1992)

5Algal Toxicity of Antibacterial Agents

EC50 value between 0.1 mg/L and 1 mg/L; and T seems to be theleast toxic compound with an EC50 value over 1 mg/L. Acyanobacteria has to be included in the test battery as testorganism, if an environmental risk assessment is to be per-formed for antibacterial agents. IfR. salinaandS. capricornu-tumare applied as test organisms, changes have to be made inthe standard batch test procedure, to avoid the difficultiesregarding pH increase.

Acknowledgments.The grammatical and technical help and assis-tance provided by Klavs Mulvad, Susanne Hermansen, Johan ChristianFriis, and Flemming Ingerslev is gratefully acknowledged. Thisinvestigation was partly funded by a grant from the Danish Centre forSustainable Land Use and Management of Contaminants, Carbon andNitrogen, under the Danish Strategic Environmental Research Pro-gramme, Part 2, 1997–2000. Sarafloxacin hydrochloride was gentlysupplied by Abbott Laboratories, North Chicago, Illinois, USA.

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