Individual and Mixture Toxicity of Pharmaceuticals and

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Individual and Mixture Toxicity of Pharmaceuticals and
Individual and Mixture Toxicity of
Pharmaceuticals and Phenols on Freshwater
Algae Chlorella vulgaris
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Engineering at the University of Applied Sciences
Technikum Wien - Degree Program Environmental Management and Ecotoxicology
By: DI (FH) Elisabeth Geiger
Student Number: 1210332015
Supervisor 1: Dr. Romana Hornek-Gausterer
Supervisor 2: Prof. Dr. Melek Türker Saçan
Vienna, 18 September 2014
Declaration
„I confirm that this thesis is entirely my own work. All sources and quotations have been
fully acknowledged in the appropriate places with adequate footnotes and citations.
Quotations have been properly acknowledged and marked with appropriate punctuation.
The works consulted are listed in the bibliography. This paper has not been submitted to
another examination panel in the same or a similar form, and has not been published. I
declare that the present paper is identical to the version uploaded."
Vienna, 18.09.2014
Place, Date
Signature
Kurzfassung
Aquatische Ökosysteme sind durch den Austritt von toxischen Substanzen stark bedroht.
Chemikalien, die vermehrt in Haushalt, Landwirtschaft und Industrie verwendet werden, z.B.
Phenole und Pharmazeutika, müssen auf potentielle Umweltgefährdung evaluiert werden, da
sie weltweit in Gewässern detektiert werden können. Pharmazeutika sind so konzipiert, dass
sie einen biologisch-therapeutischen Effekt in Menschen bewirken. Sie können jedoch auch
ähnliche Effekte in Nicht-Zielorganismen verursachen. Daher zählen pharmazeutische
Schadstoffe zu den zunehmend besorgniserregenden Substanzen. Die aktuelle
Chemikalien-Legislatur, angeführt von REACH und CLP, hat sich den Schutz von
menschlicher Gesundheit und Umwelt zum Ziel gesetzt. Diese basiert jedoch lediglich auf
der Evaluation und Risikobewertung von Einzelstoffen. Da Mensch und Umwelt einer großen
Vielzahl von Stoffen ausgesetzt ist, steigt die Besorgnis über potentielle nachteilige
Kombinationseffekte der Chemikalien. In dieser Studie wurden Toxizitätstests nach OECD
Nr. 201 Kriterien durchgeführt, welche auf Inhibition des Algenwachstums basieren. Einzelals auch binäre Mischungstoxizitätsexperimente von ausgewählten Pharmazeutika
(Ibuprofen und Ciprofloxacin HCl) und Phenolen (2,4-Dichlorophenol und 3-Chlorophenol)
wurden anhand der Süsswasseralge Chlorella vulgaris durchgeführt. Nominale
Konzentrationen der Testlösung wurden am Ende des Experiments mit analytischen
Methoden gemessen (HPLC, GC und Spektrophotometer). Als Testendpunkt wurde
Wachstumsinhibition herangezogen, ausgedrückt als mittlere spezifische Wachstumsrate als
auch Ertrag. Tägliche Messungen der optischen Dichte bei 680 nm während einer
Expositionsdauer von 96 h wurden durchgeführt. Alle Substanzen hatten einen signifikanten
Effekt auf die Algen-Populationsdichte und zeigten einen IC50 Wert von < 100 mg/L. Die
Reihenfolge der Toxizitäten der getesten Stoffe ergab 2,4-DCP > Ciprofloxacin HCl > 3-CP >
Ibuprofen gemäß Annex VI der Richtlinie 67/548/EEC. Binäre Mischungstests wurden
anhand von Proportionen der jeweiligen EC50s (=1 toxic unit (TU)) durchgeführt. Die
Konzentrations-Effektkurven der Mischungen wurden mit den zu erwartenden Effekten,
basierend auf den von der ECHA vorgeschlagenen Modellen der Concentration Addition
(CA) und Independent Action (IA), verglichen. Es konnte gezeigt werden, dass die
Mischungstoxizität von Pharmazeutika und Phenolen vorwiegend zu additiven Effekten führt,
ausgenommen die Mischung 3-CP und Ibuprofen zeigte einen antagonistischen Effekt. Das
CA Modell ist für die Vorhersage der Mischungstoxizität sehr gut geeignet, wogegen IA zur
Unterschätzung dieser tendiert. Pharmazeutika, die einen Einfluss auf aquatische
Organismen zeigen, könnten als neue Kandidaten in die EU Dringlichkeitsliste, gemäß der
Wasserrahmenrichtlinie
2000/60/EC,
aufgenommen
werden.
Weiters
müssen
Expositionsmodelle entwickelt werden, um die Exposition von Chemikalien, Metaboliten und
Transformationsprodukten
an
nachfolgenden
Generationen
in
verschiedenen
Umweltkompartimenten, besser bestimmen zu können.
Schlagwörter: Pharmazeutika, Phenole, Mixturen, aquatische Toxizität, Algen
Abstract
Aquatic ecosystems have been severely threatened by accidental or intentional discharges
of toxic compounds. Increasing chemical usage for industrial, agricultural and domestic
purposes, such as phenols and pharmaceuticals, need to be evaluated for potential threat,
as they can be detected in water bodies throughout the world. Pharmaceuticals are designed
to have a biological therapeutic effect on human bodies, but may also cause similar effects in
non-target organisms. Thus, pharmaceutical pollutants have become an emerging area of
concern. The current chemical legislation, spearheaded by REACH and CLP, aims to ensure
a high level of protection of human health and the environment, but is only based on the
evaluation and risk assessment of individual substances. Since human beings and their
environment are exposed to a wide variety of substances, there is an increasing concern
about the potential adverse combination effects of chemicals. In this study, the toxicity
experiments have been carried out based on the algal growth inhibition test OECD No. 201
criteria. Individual and binary mixture toxicity experiments of selected pharmaceuticals
(ibuprofen and ciprofloxacin HCl) and phenolic compounds (2.4-dichlorophenol and 3chlorophenol) have been performed with freshwater algae Chlorella vulgaris. Nominal
concentration of test solution of each chemical was measured at the end of the experiment
by instrumental analytic methods (HPLC, GC and spectrophotometer). Inhibition of growth
was used as the test endpoint, expressed as average specific growth rate and yield during
an exposure period of 96 hours determined by daily measurements of optical density at 680
nm. All substances tested had a significant effect on Chlorella vulgaris population density
and revealed IC50 values < 100 mg/L. The toxic ranking of these four compounds to Chlorella
vulgaris was 2,4-DCP > Ciprofloxacin HCl > 3-CP > Ibuprofen according to Annex VI of
Directive 67/548/EEC. Binary mixture tests were conducted using proportions of the
respective EC50s (=1 toxic unit (TU)). The mixture concentration-response curve was
compared to predicted effects based on both the concentration addition (CA) and the
independent action (IA) model as suggested in regulatory risk assessment provided by the
European Chemicals Agency (ECHA). It could be demonstrated that the combined toxicity of
pharmaceuticals and phenols can predominately lead to additive effects, except for 3-CP and
Ibuprofen in mixture the effect was antagonistic. The CA model is appropriate to estimate
mixture toxicity, while the IA model tends to underestimate the joint effect. Pharmaceuticals
with potential to have an impact on aquatic organisms could be included in the EU List of
Priority Substances relevant to the Water Framework Directive 2000/60/EC. Exposure
models still have to be further developed to ensure a better estimation of the exposure of the
chemicals, transformation products and metabolites in several environmental compartments
on several generations.
Keywords: Pharmaceuticals, Phenols, Mixtures, Aquatic Toxicity, Algae
2
Acknowledgements
I would like to express my gratitude to all those who gave me the possibility to complete this
thesis.
I am particularly grateful to my thesis advisor, Prof. Dr. Melek Türker Saçan, for inspirational
and fruitful discussions and her continuous guidance and support. She kindly invited me to
work in Istanbul and let me use her laboratory equipment to perform my experiments.
Without her help, success in this study would have never been possible.
I am also deeply indebted to my supervisor Dr. Romana Hornek-Gausterer, who assisted me
from Vienna and always provided me with useful hints and valuable comments. Her guidance
and assistance was of great help for me.
Both, Dr. Hornek-Gausterer and Prof. Dr. Saçan encouraged me to give an oral presentation
about my findings in this thesis at the 5th EuCheMS Chemistry Congress in Istanbul, which
was a great milestone in my scientific career.
Sincere thanks to Gülçin Tugcu for guiding and assisting me in the laboratory work and for
her support throughout the thesis.
I would like to offer my sincere gratitude to Prof. Dr. Ferhan Ceçen, who helped me with
administrative things during my exchange semester in Istanbul. Very special thanks to my
friends and colleagues in the Institute of Environmental Sciences.
Finally, I would like to thank my wonderful family, friends and university colleagues at home
for all their support. Special gratitude goes to Allieu Kamara. He encouraged me to go
abroad and he takes equally part of my success.
The financial support of Bogaziçi University Research Funds (project 8502) is very much
appreciated.
3
Table of Contents
1
Introduction ............................................................................................................ 6
1.1
2
Aim of this study ..................................................................................................... 8
Theoretical Background.......................................................................................... 9
2.1
2.1.1
2.2
Algae toxicity testing ............................................................................................... 9
Chlorella vulgaris .................................................................................................. 11
Toxicity Testing .................................................................................................... 12
2.2.1
Single Toxicity Testing.......................................................................................... 12
2.2.2
Mixture toxicity testing .......................................................................................... 13
2.3
Pharmaceuticals ................................................................................................... 15
2.3.1
Ibuprofen .............................................................................................................. 17
2.3.2
Ciprofloxacin HCl .................................................................................................. 18
2.4
Phenols ................................................................................................................ 20
2.4.1
2,4-Dichlorophenol ............................................................................................... 21
2.4.2
3-Chlorophenol ..................................................................................................... 22
3
Materials and Methods ......................................................................................... 24
3.1
Material ................................................................................................................ 24
3.1.1
Chlorella vulgaris .................................................................................................. 24
3.1.2
Test chemicals ..................................................................................................... 24
3.1.3
Reagents .............................................................................................................. 25
3.1.4
Instruments and consumable materials ................................................................ 27
3.2
Experimental methods .......................................................................................... 29
3.2.1
Analytical methods ............................................................................................... 29
3.2.2
Algal growth inhibition assay using Chlorella vulgaris ........................................... 29
3.2.3
Measurement and calculation of algal growth ....................................................... 33
3.2.4
Statistic analysis of single and mixture toxicity...................................................... 34
4
Results ................................................................................................................. 38
4.1
Specific growth curve Chlorella vulgaris ............................................................... 38
4.2
Single toxicity tests ............................................................................................... 39
4.3
Mixture toxicity tests ............................................................................................. 45
4.3.1
Single toxicity tests versus mixture toxicity tests ................................................... 46
4.3.2
CA and IA approach versus observed effect ......................................................... 48
4.3.3
Additive Index ....................................................................................................... 55
4
5
Discussion ............................................................................................................ 57
5.1
5.1.1
Ibuprofen .............................................................................................................. 58
5.1.2
Ciprofloxacin HCl .................................................................................................. 59
5.1.3
2,4-Dichlorophenol ............................................................................................... 59
5.1.4
3-Chlorophenol ..................................................................................................... 60
5.2
Mixture toxicity tests ............................................................................................. 60
5.2.1
Toxic unit and additive index ................................................................................ 61
5.2.2
CA versus IA ........................................................................................................ 61
5.3
Risk assessment of mixtures ................................................................................ 64
5.3.1
Options for regulatory mixture effect assessment ................................................. 64
5.3.2
Environmental exposure assessment ................................................................... 66
5.4
6
Single toxicity tests ............................................................................................... 57
Environmental impact ........................................................................................... 67
5.4.1
EC50 versus environmental concentration ............................................................. 70
5.4.2
Fate and transport of test chemicals ..................................................................... 71
Conclusion ........................................................................................................... 75
5
1 Introduction
The industrial, agricultural and domestic usage of chemicals is increasing worldwide and
therefore evaluation and characterization is required in order to estimate the potential
adverse effect on human health and environment. Aquatic ecosystems have been severely
threatened by intentional or accidental discharges of toxic compounds. As a consequence,
pharmaceuticals and industrial chemicals can be detected in water bodies throughout the
world.
According to the European system for the Registration, Evaluation, Authorization and
Restrictions of Chemicals (REACH), all substances manufactured or imported in quantities
greater than 1 tonne per annum (tpa), have to be evaluated for their adverse effects on the
environment. The European Parliament and the European Council implemented REACH
on 18 December 2006 through the Regulation Directive EC 1907/2006 (EC, 2006). The
European Chemicals Agency (ECHA) reported a number of approximately 150000
preregistered chemicals between June 1st and December 1st 2008 (ECHA, 2008). All those
substances have the potential to be distributed to air, soil and water and pose the threat to
finally end up in food, as a result of intentional or accidental discharges or during the
normal life cycle of the chemical substance. Besides REACH, an international standard for
classification, labeling and safety data sheets called GHS (Globally Harmonized System)
have been issued by UN organizations. The GHS was adopted by the European law in
2009 through the Regulation Directive EC 1272/2008 (EC, 2008) on Classification,
Labeling and Packaging (CLP) of substances and mixtures. Detailed guidance on
registration of chemical substances and their risk assessment for human health and the
environment are provided and published by the European Chemicals Agency (ECHA).
Pharmaceuticals and Personal Care Products (PPCPs) have become an emerging area of
concern and are now viewed as a new class of priority pollutants in the field of
ecotoxicology (Zuccato et al., 2000). The use of pharmaceuticals is rapidly increasing.
Between 1999 and 2009, an estimated rise from 2 billion to 3.9 billion annual prescriptions
have been reported in the United States alone (Tong et al., 2011). Pharmaceuticals are
designed to have a biological effect and therefore these substances may cause similar
adverse effects in non-target organisms, once they are released into the environment
(Henschel et al., 1997). Potential toxic effects of pharmaceuticals have not been properly
investigated and evaluated, even though these substances are widely discharged into
aquatic ecosystems. Research has focused mostly on the effects of herbicides on algae.
Less than 1 % of the ecotoxicological data concerns pharmaceuticals (Sanderson, 2004).
6
The chemical legislation, spearheaded by REACH and CLP aims to ensure a high level of
protection of human health and the environment, but it is rarely based on the assessment
of combination effects of chemicals. The current used regulatory approaches are based
predominantly on the evaluation and risk assessment of individual chemicals. Since human
beings and their environment are exposed to a wide variety of substances, there is an
increasing concern in the general public about potential adverse combination effects of
chemical compounds when present simultaneously in a mixture (SCHER, 2011). In natural
ecosystems, the toxicity does not result from exposure of single contaminants, but is rather
a result of exposure to chemical mixtures (Altenburger et al., 1996; Gardner et al., 1998).
Complex exposure situations whereas several compounds can be found simultaneously
during the chemical analysis of human tissues or in environmental compartments, are likely
to happen. As an example, a campaign of the World Wildlife Fund (WWF) raised
awareness of the continuous long-term exposure of European citizen to a complex mixture
of persistent, bioaccumulative and toxic chemicals. 101 substances belonging to different
chemical classes were analyzed in blood samples from 47 volunteers from 17 different
European countries. Tested chemicals included 45 polychlorinated biphenyls (PCBs), 12
organochlorine pesticides, 23 polybrominated diphenyl ether (PBDE) and other brominated
flame retardants, 13 perfluorinated chemicals and 8 phthalates. It could be demonstrated
that the human body of every volunteer examined was contaminated by each of the five
chemical groups tested. A 54 year old person revealed the highest number of detected
chemicals with a median number of 41. Thirteen chemicals were found in every single
person tested in this study. Such findings confirm the increasing concern of potential
cumulative long-term effects of chemical mixtures (Commission of European Communities,
2003).
In most cases the toxicity effect of combined toxicants is additive, meaning the chemicals
exhibit the sum of their individual or single effects. Marking (1977) reported that chemicals
in mixtures can also elicit antagonistic (less than additive) or synergistic (greater than
additive) effects. Generally, the biochemical mode of action of the contaminants
determines the basic concepts of mixture toxicity. Chemical mixtures can be based on
similar or dissimilar mode of actions. Moreover, the compounds can interact with each
other, and therefore have an impact on the respective mode of action of each chemical, or
work in a non-interactive way and do not influence each other´s mode of action. Empirical
models are used to determine whether a given mixture elicits antagonistic, additive or
synergistic effects. Basically, two different concepts are available for that purpose, and are
termed concentration addition (CA) and independent action (IA) (EIFAC, 1987; Boedeker
et al., 1992). Both, the CA and IA concepts, have been suggested as default models in
regulatory risk assessment in order to predict the toxicity of chemical mixtures.
7
The evaluation process for chemicals manufactured or imported in quantities greater than
1 tpa, requires basic ecotoxicological information including short-term toxicity data on
green algae (EC, 2006). Algae play a crucial role in the ecosystem as well as in the
regulatory risk assessment, as they provide food for higher trophic levels and thus,
represent the base of food webs. Despite their ubiquitous distribution in aquatic
ecosystems and advantages for laboratory testing, reliable algal toxicity data are limited
(Cronin et al., 2004; Netzeva et al., 2008). As pharmaceuticals can cause adverse effects
in non-target organisms, determination of the toxicity to non-target species such as algae is
beneficial to understand the impact of these substances to ecosystems. In this thesis,
single and mixture exposure experiments were conducted to fill the gap on data available
for algae in order to assess the environmental risk of pharmaceutical compounds within the
REACH framework. Pharmaceuticals and phenols were chosen for toxicological
assessment considering their widespread use and environmental significance.
1.1 Aim of this study
The purpose of this present study was to investigate
-
-
The toxicity of single contaminants belonging to different therapeutic and chemical
classes (Ibuprofen, Ciprofloxacin HCl, 3-Chlorophenol and 2,4-Dichlorophenol)
according to the standardized algal growth inhibition test OECD No. 201 (OECD,
2006) prepared by the Organization for Economic Cooperation and Development
using Chlorella vulgaris as test organism
Whether binary mixtures of all possible combinations of the compounds listed
above elicits antagonistic, additive or synergistic effects
The predictability of the mixture toxicity effects according to the concepts of
concentration addition (CA) and independent action (IA)
The generated toxicity data compatible with the requirements of REACH will help to fill the
data gap for environmental risk assessment on active pharmaceutical compounds and
phenols.
8
2 Theoretical Background
Aquatic ecosystems have been severely threatened by intentional and accidental
discharges of toxic compounds. According to Saçan and Balciolglu (2006), parameters
such as chemical or biological oxygen demand are not sufficient to provide necessary
information on the potential adverse effects of chemicals to the aquatic environment for risk
assessment purpose. Living organisms respond quickly to habitat disruptions. Therefore,
biological assays have become a very important tool to assess the environmental impact of
chemicals. Bioassays play a decisive role in the development of strategies for risk
assessment and environmental management.
According to Tarazona (2014), OECD guidelines have been extensively used for aquatic
studies submitted within the framework of the REACH regulation, followed at a lower extent
by ISO, US EPA and German DIN guidelines. Most algae studies have been conducted on
two Chlorophyceae: Desmodesmus subspicatus and Pseudokirchneriella subcapitata,
synonym Raphidocelis subcapitata. In this thesis, single and mixture toxicity experiments
have been carried out according to to the standardized algal growth inhibition test OECD
No. 201 (OECD, 2006) prepared by the Organization for Economic Cooperation and
Development using the freshwater algae Chlorella vulgaris as test organism. Cronin et al.
(2004) reported that toxicity data for primary producers (e.g. algae) is limited, while there
are relatively large databases for fish and crustaceans, which represent higher trophic
levels.
2.1 Algae toxicity testing
Evaluation of data using microalgae toxicity tests is an integral part of environmental risk
assessment (Christensen et al., 2009). From an ecological point of view, toxicants may
affect and alter the composition of phytoplankton communities which in turn might have a
negative impact on the functioning and structure of whole ecosystems. In addition,
particularly low concentrations of pollutants might possibly lead to a better expression in
algae, which makes microalgae toxicity tests indispensible for the environmental risk
assessment (Nyholm and Källqvist, 1989).
The purpose and aim of microalgae toxicity tests is to determine the effects of a substance
on algal growth (OECD, 2006). Cells from a single algal clone are applied in great
numbers, which provides the benefit that this test is not influenced by the individual
tolerance of test organisms and thus, results in a response with a continuous parameter
(Christensen et al., 2009). The basic concept of this algal test is to expose exponentially
9
growing microalgae to the chemical in batch cultures over a prescribed test period (usually
48 to 96 hours). The major advantage is the brief test duration which allows assessment of
toxicity effects over several generations. The response is the reduction of growth of algal
cultures exposed to increasing concentration of a test chemical. The algal growth is
calculated from biomass measurements as a function of time. The average specific growth
rate of unexposed algal control cultures is compared with exposure concentration of
chemical replicates, which form the base of the response evaluation. Algae cultures are
allowed to unrestricted exponential growth under continuous light and nutrient sufficient
conditions to measure reduction of the specific growth rate to fully express the system
response to toxic effects (optimal sensitivity). Due to the difficulties in determining the dry
weight per volume of the algal biomass, surrogate parameters are used which include cell
counts, fluorescence, optical density etc., to quantify algal biomass. The inhibition of
growth during the exposure period is used as test endpoint. The growth inhibition is
expressed as the logarithmic increase in biomass (termed as average specific growth rate)
during the exposure time. The concentration leading to a specified x% inhibition of growth
rate (e.g. 50%) from an increasing concentration of test solutions is determined (OECD,
2006).
An additional response variable used in the most recent guideline prepared by the OECD
(2006) is yield, which is defined as the biomass at the end of the exposure period minus
the biomass at the start of the exposure period. The parameter biomass generally provides
a lower numerical value compared with the specific growth rate. Therefore, from an
ecotoxicological risk assessment point of view, it is preferred to use the EbC50 value (i.e.,
the concentration at which 50 % reduction of biomass is observed) rather than ErC50 (the
concentration at which a 50%inhibition of growth rate is observed) as the endpoint.
According to Bergtold and Dohmen (2010), the parameter growth rate is more appropriate
and robust against deviations in test conditions, permitting better interpretation and
comparison between studies. The study of Bergtold and Dohmen (2010) compared field
and laboratory data and concluded that using ErC50 values combined with the assessment
factor of 10 is sufficient to exclude significant risk in the aquatic environment.
Data obtained from algal toxicity tests form the base for the evaluation of chemicals. The
compounds are ranked according to their environmental toxicity for the use of
environmental hazard evaluations (Nyholm and Källqvist, 1989). For the toxicity tests to be
conducted in this thesis, a unicellular microalgae, representative of freshwater environment
(Chlorella vulgaris), was selected.
10
2.1.1 Chlorella vulgaris
The genus Chlorella comprises green freshwater algae which are unicellular, non-motile
and globular with an average diameter of 4-10 µm (Kuhl and Lorenzen, 1964). The small
spherical or elliptical coccoid green algae is lacking any special morphological features
such as bristles or spines. Since Beijernick (1890) named the algae Chlorella vulgaris more
than a hundred of species have been established. The scientific classification of Chlorella
vulgaris is provided in Table 1. Figure 1 presents a microscopic view of the freshwater
algae.
Table 1: Scientific classification of Chlorella vulgaris
Classification of freshwater algae Chlorella vulgaris
Domain
Kingdom
Division
Class
Order
Family
Genus
Species
Eukaryota
Plantae
Chlorophyta
Trebouxiophyceae
Chlorellales
Chlorellaceae
Chlorella
Chlorella vulgaris
Figure 1: Microscopic view of Chlorella vulgaris
(©Culture Collection of Algae and Protozoa – with permission)
11
Chlorella vulgaris were chosen as test organism in ecotoxicity testing for several reasons.
First of all, from an ecological point of view, algae form the base of food webs (i.e., primary
producers) and provide food for higher trophic levels. Additionally, algae produce oxygen
which is necessary and vital for the sustainability of aquatic organisms (Saçan et al, 2014).
Furthermore, algae have a strong impact on biochemical cycles, such as nitrogen and
carbon cycles (Boyce et al., 2010). Apart from the decisive role they play in the aquatic
ecosystem, their ubiquitous distribution throughout the globe, high surface area to volume
ratio, ease of collection and culturing as wells as rapid growth rate make them ideal for
laboratory testing (DeLorenzo, 2009). Chlorella vulgaris has been selected in several
toxicity studies (Scragg, 2006; Sahinkaya and Dilek, 2009; Cai et al., 2009; Murkovski and
Skórska, 2010), because of its widespread distribution and natural presence in freshwater
ecosystems (Ventura et al., 2010).
2.2 Toxicity Testing
2.2.1 Single Toxicity Testing
The concentration-response relationship, or exposure-response relationship, describes the
change in effect of an organism caused by increasing concentrations of a test chemical
after a certain exposure period. Developing concentration-response models is essential to
determine hazardous levels for drugs, potential pollutants, and other substances to which
humans or other organisms are exposed. Concentration-response relationships generally
depend on the exposure time and exposure route. Moreover, exposure point of time in
relation to the life span of an organism has an important impact on toxicity, e.g. juvenile
fish are eventually more prone to pollutants compared to adult fish. A typical and
commonly used concentration-response curve is the EC50 curve. EC50 represents the
concentration of a compound where 50% of its maximal effect is observed. It is also related
to IC50 which is a measure of a compound's inhibition (50% inhibition).
However, the concept of linear concentration-response relationship may not apply to nonlinear situations, e.g., endocrine disruptors or pharmaceutical compounds. Thus,
concentration-response curves are not linear or threshold, but result in a U- or inverted Ushaped concentration response (Calabrese and Baldwin., 2001). Hormesis is a
concentration-response relationship phenomenon and can be described by lowconcentration stimulation and high-concentration inhibition. Hormesis has been frequently
observed in properly designed studies and viewed as being independent of biological
model, chemical agent and test endpoint. In risk assessment, concepts of lowest observed
effect concentration (LOEC) or no observed effect concentration (NOEC) are applied. Over
the past years, it was demonstrated that there are several responses to chemical
12
exposures that occur below the traditional NOEC. Numerous studies revealed that
chemicals can act as antagonists at high concentrations, but may become partial agonists
at lower concentrations, thus following a hormetic concentration response curve. According
to Calabrese et al. (2003), hormetic-like biphasic concentration responses become more
recognized and will help to improve research strategies in risk assessment procedures,
ecotoxicology, drug development and chemotherapeutic methods.
Single algal toxicity of chemicals can be determined by statistical analysis using the
average specific growth rate or yield as the response variable. Percent inhibition relative to
the unexposed control growth rate is fitted against the test substance concentration and
the inhibitory concentration that reduces the response variable by 50 percent (IC50) and
calculated at the end of 48 h, 72 h and 96 h. After determination of the single toxicity (IC50
value), experiments can be performed to assess the effects of interactions of chemical
mixtures.
2.2.2 Mixture toxicity testing
Interaction between chemicals and mechanisms of action remain poorly understood and
therefore mixture toxicity evaluations from single substance testing are hard to determine
(Berenbaum 1985). Interactions between chemicals usually occur under the influence of a
receptor or during uptake and metabolism and may exhibit an effect greater (synergism) or
smaller (antagonism) than expected. An additive effect appears when the joint effect of
chemicals is equal to the sum of the effects of each single compound alone (Eaton and
Klaassen 2001).
Basically, two different models are available for the assessment of joint effects, and
generally they are termed concentration/dose addition (CA) and independent action (IA)
(EIFAC, 1987; Boedeker et al., 1992). Both, the CA and IA concept, have been suggested
as default models in regulatory risk assessment of chemical mixtures. Several studies have
demonstrated the predictive power of concentration addition and independent action with
regards to the estimated toxicity in mixtures (Faust et al., 2001, Belden et al., 2007,
Backhaus et al., 2004b, Cedergreen et al., 2008).
Concentration addition
Concentration addition (CA) assumes a similar mechanism of action of mixture
components were the toxicity is in proportion to the concentration of the compound
(Deneer, 2000; Rider & LeBlanc, 2005; Junghans et al., 2003 a & b). The equation of
13
concentration addition is defined by Berenbaum (1985) and provides prediction of effect
concentrations for mixtures. Equation (1):
(1)
In this equation, ci are the concentrations of the individual substances present in a mixture
with a total effect of x%. ECxi are the equivalent effect concentrations of the single
substances. Quotients ci/ECxi express the concentrations of mixture components as
fractions of equi-effective individual concentrations and have been termed toxic units
(Sprague, 1970). If the CA equation holds true, a mixture component can be replaced by
an equal fraction of an equi-effective concentration of another substance without changing
the overall mixture toxicity effect. In other words, the overall mixture effect remains
constant as long as the sum of the toxic units remains constant. Toxic units (TUs) is
frequently used and assessed in ecotoxicological settings. TU describes the ratio between
the concentration of a mixture component and its toxicological endpoint (e.g. acute LC50 or
chronic NOEC). The sum of TUs of individual compounds displays the toxic unit of a
mixture (TUm). CA is based on the fact that combination effects are increasing with the
concentration of the mixture components, the number of mixture components and the
steepness of the individual concentration-response curves (Boedeker et al., 1992)
Independent Action
The alternative concept to concentration addition is the independent action approach,
which was described by Bliss (1939). Independent action is based on dissimilar acting
mixture components. In this context dissimilar means that the chemical mixture
components have different molecular target sites and as a consequence are not affected
by the presence of other substances within the organism (Backhaus et al., 2003;
Cedergreen et al., 2006; Lydy et al., 2004).
For a multi-component mixture this situation is given by the equations (2 and 3):
(2)
or in general
(3)
14
In which ci and cmix are the concentrations of the individual substance and the total
concentrations of the mixture, respectively. E(ci) denotes the corresponding effects of the
individual compounds and E(cmix) the total effect of the mixture. Effects E are expressed as
fractions (x%) of a maximum possible effect.
2.3 Pharmaceuticals
Pharmaceuticals are designed to produce a biological and therapeutic effect on the human
body and are usually active at very low concentrations. Pharmaceuticals and personal care
products (PPCPs) and their active metabolites pose a threat to aquatic organism and may
enter the aquatic ecosystems through spray irrigation of treated wastewater, septic
systems, leachates from waste disposal sites, wastewater from sewage treatment plants,
and the use of sludges in agriculture (Henschel et al., 1997). The environmental impact of
active pharmaceutical compounds is poorly understood, however they can be detected in
water bodies throughout the world. Therapeutic substances have been found in surface
waters and occasionally in groundwater (Ternes 1998, Heberer et al. 2002, Zuccato et al.
2006). Several studies suggest that pharmaceuticals at concentrations detected in the
environment may have potential adverse effects on aquatic living organisms (Daughton
and Ternes 1999, Ferrari et al. 2003, Isidori et al. 2005b). Kümmerer (2001) described the
disturbance to the microbial life in surface waters caused by pharmaceuticals, while
Baguer et al. (2000) and Halling-Sorensen et al. (2000) examined the effects of therapeutic
substances on other organisms at low concentrations. PPCPs are consumed in large
quantities and continuously, which might result in a potential chronic exposure of aquatic
organisms to a mixture of compounds (Schwaiger et al., 2004). Humans are exposed to
pharmaceuticals that contaminate the aquatic environment through consumption of aquatic
organisms or drinking water. Aquatic organisms are more affected by the exposure to
pharmaceuticals than humans, and some substances such as acetylsalicylic acid,
ibuprofen, amoxicillin, paracetamol, mefenamic acid and oxytetracycline are thought to be
present in water at levels that are not negligible for water organisms (Christensen 1998;
Stuer-Lauridsen et al., 2000; Jones et al., 2002; Grung et al. 2008). This documented
evidence confirms that pharmaceuticals pose the potential risk to negatively impact the
aquatic ecosystem and therefore, active pharmaceutical compounds may be included in
the current or future revision on the EU List of Priority Substances relevant to the Water
Framework Directive 2000/60/EC (Bottoni et al., 2010).
Most of PPCPs remain in the effluents that are discharged as pollutants into the surface
and groundwater, because quantitative removal in waste water treatment plants is not
sufficient (Ternes, 1998; Möhle et al., 1999; Doll and Frimmel, 2003). Pharmaceuticals
15
remain active after being released into environment, so they can affect any water
organisms by influencing their biological systems as enzymes. The effect caused by drugs
varies according to the chemical structure (Wiegel et al. 2004). Lipophilic substances might
lead to an accumulation in sediments or soils while the mobility of watersoluble compounds
can contaminate surface and groundwater (Isidori et al. 2005a, Fent et al. 2006). Literature
data is lacking qualitative and quantitative information on the processes that determine the
fate and effects of bioactive compounds (Ternes 1998; Halling-Sorensen et al. 2000; Isidori
et al. 2005a) or their derivatives, which is the result of drug transformations. Derivates,
metabolites or transformation products in the environment may be more dangerous than
the original parent compound (Andreozzi et al. 2003; DellaGreca et al. 2003).
The presence of antibiotics, blood lipid regulators, painkillers, steroids, estrogens, antiinflammatories, antihypertensive drugs , antiseptics, antiepileptics, antineoplastic agents,
and other substances is well-documented in aquatic ecosystems (e.g. lakes, rivers,
drinking water, groundwater, sea coastal water, treatment plants and urban effluents)
(Daughton and Ternes 1999; Steger-Hartmann et al., 1997; Tixier et al. 2003; Stumpf et al.
1999; Sacher et al. 2001; Buser and Muller 1998; Reddersen et al., 2002; Andreozzi et al.,
2003; Atkinsons et al., 2003). A study conducted by Hernando (2006), demonstrated the
presence of 28 pharmaceutical compounds in surface waters, sewage treatment plant
effluents and sediment. The detected pharmaceuticals belonged to different therapeutic
classes including antibiotics, lipid regulators, analgesics and anti-inflammatories, steroid
hormones, beta-blockers and anti-epileptics. Most chemical concentrations were found at
low levels (ng/L), however, there are uncertainties about the levels at which toxicity occurs.
Moreover uncertainties remain whether bioaccumulation of these pharmaceutical
compounds are likely to happen.
Individual and mixture effects of selected PPCPs (simvastatin, clofibric acid, triclosan,
fluoxetine, diclofenac, and carbamazepine) has been performed with the marine algae
Dunaliella tertiolecta using a standard 96-hour static algal bioassay protocol (DeLorenzo
and Fleming, 2008). The chemicals used in this study were diverse in their therapeutic
purposes and mechanisms of action. All tested PPCPs resulted in reduced cell density and
additive mixture toxicity effects. Binary Mixture toxicity of selective serotonin reuptake
inhibitors (SSRIs) (citalopram, fluoxetine, and sertraline) was performed using the
freshwater algae Pseudokirchneriella subcapitata. In this study, it was demonstrated that
the combined toxicity of the tested SSRIs is predictable by the model of concentration
addition. No indications of synergism or antagonism were seen (Christensen et al, 2007). A
study on antibacterial agents revealed synergistic effects when ciprofloxacin and
norfloxacin (both belonging to the group of fluoroquinolones) were present simultaneously
in a binary mixture with the fresh water algae Pseudokirchneriella subcapitata (Yang et al,
2008). Another study was performed using four drugs, erythromycin, fluoxetine, naproxen
and gemfibrozil, all belonging to different therapeutic classes, to examine their toxicity to
16
plankton organisms from different trophic levels: algae (Chlorella vulgaris
and
Ankestrodesmus falcatus), protozoa (Paramecium caudatum), rotifera (Brachionus
calyciflorus) and cladocera (Daphnia longispina). LC50 values revealed that algae are the
most sensitive organisms when exposed to the selected pharmaceuticals even at low
concentration (El-Bassat et al, 2012).
2.3.1 Ibuprofen
Ibuprofen ((RS)-2-(4-(2-methylpropyl)phenyl)propanoic acid) is classified as a nonsteroidal
anti-inflammatory drug (NSAID), known for its anti-inflammatory, antipyretic and analgesic
properties. Other common drugs belonging to this class are naproxen, diclofenac and
acetylsalicylic acid. NSAIDs belong to one of the most important groups of pharmaceuticals
worldwide, with an estimated annual production of several kilotons (Cleuvers, 2004).
According to UBA (2011), it could be observed that ibuprofen consumption in Germany
increased by 116 %, corresponding to an increase of 419,424 kg within a time frame of 7
years (2002-2009). The total consumption of this anti-inflammatory drug in the year 2009
was 782,378 kg. Due to its analgesic, antipyretic and anti-inflammatory actions, it is used in
the treatment of inflammatory conditions such as fever, osteoarthritis, rheumathoid arthritis,
ankylosing spondyolitis, mild and moderate pain, dysmenorrhoea and vascular headache.
Ibuprofen were detected at concentrations up to 0.1µg/L in effluent samples from Sewage
Treatment Plants (STPs) in Berlin (Heberer, 2002). In US streams this anti-inflammatory
drug was found at median concentration of 0.2 µg/L (Kolpin et al, 2002). UBA (2011)
issued an alarming report revealing four cases of ibuprofen tested positive in drinking water
in Germany. Findings in the same report elicited maximal environment concentration of 2.4
µg/L found in German surface water. Acute aquatic toxicity for Ibuprofen on green algae
was performed only to Pseudokirchneriella subcapitata as test organisms, revealing an
IC25 value > 35 µg/L (Brun et al., 2006).
Table 2: Estimated chemical properties of Ibuprofen25 retrieved from EPISuite, version 4.11
Chemical properties of Ibuprofen25
Chemical class
nonsteroidal anti-inflammatory agent
CAS Nr.
Chemical Formula
15687-27-1
C13H18O2
Mechanism of Action
Structural Formula
Inhibitor of cyclooxygenase
17
Table 2: continued
Chemical properties of Ibuprofen25
Formula Weight
206.28 g/mol
Log KOW
3.97
Log KOC
Log KOA
BCF
pKa
2.35
9.18
Solubility
21 mg/L in water
412.1 g/100 ml DMSO
Vapor Pressure [Pa, 25°C]
0.0248
Removal in WW Treatment [%]
Amounts detected in environment
28.72
0.1µg/L detected in Berlin waterways (Heberer, 2002),
0.50
4.9
Median of 0.2µg/L in US streams (Kolpin et al, 2002)
29 µg/kg in sewage sludge in Germany (UBA, 2011)
2.4 µg/L max in surface water in Germany (UBA, 2011)
2.3.2 Ciprofloxacin HCl
Ciprofloxacin belongs to the group of fluoroquinolones, which form a major class of
antibiotics world-wide. This substance is used for human and veterinary medicine against
most strains of bacterial pathogens responsible for urinary tract, respiratory,
gastrointestinal and abdominal infections. Fluoroquinolones become an emerging area of
concern, as they are widely used and are not readily biodegradable by microorganisms (AlAhmad et al. 1999). According to UBA (2011), it could be demonstrated that ciprofloxacin
consumption in Germany increased by 92 % in the time period 2002 – 2009 resulting in
32,980 kg. 70 % of ciprofloxacin is excreted from the human body in an unconverted form,
while nearly 20 % of this antibiotic is released as metabolites of this drug
(desethylenciprofloxacin, sulfociprofloxacin, oxiciprofloxacin and formylciprofloxacin).
Among fluoroquinolones, ciprofloxacin (1-cyclopropyl-6-fluoro-4-oxo-7-(piperazin-1-yl)quinoline-3-carboxylic acid) is widely detected in the environment following its own use, or
as the main metabolite of enrofloxacin. Ciprofloxacin hydrochloride targets gyrases and
topoisomerases inhibiting DNA unwinding. It has been used as a plant fungicide and is
known to be effective at low concentrations (10 µg / mL) effectively eradicating
mycoplasms.
For many years, the antibiotic ciprofloxacin has been detected in aquatic and terrestrial
environments (Kemper, 2008). The antibiotic residues detected in some effluent waters
originating from hospitals can be very high. 0.7–124.5 µg/L of ciprofloxacin was found in
waste water of a Swiss hospital (Fink et al., 2012). This level even exceeds the lethal
concentration of a variety of water organisms determined in laboratory experiments (Boxall
18
et al., 2004). In US streams ciprofloxacin was found at median concentration of 0.02 µg/L
(Kolpin et al, 2002). 45-568 ng/L of this fluoroquinolone was detected in domestic sewage
in Switzerland, however the removal effiency for this drug in waste water treatment plant
(WWTP) was in the range of 79 % - 87 % (Fink et al., 2012; Golet et al., 2002).
Ciprofloxacin is a weak inhibitor of Chlorella vulgaris. There is no significant growth
inhibition reported at exposure times less than 48 hours. Compared to the control
treatment, concentrations of 2.0 and 31.25 mg/L resulted after a 96 hour exposure period
in a growth inhibition rate of 9.2 and 72.4% respectively (Nie et al., 2008). Aquatic toxicity
data for ciprofloxacin was also generated using the species Microcystis aeruginosa,
Pseudokirchneriella subcapitata and Lemna minor, resulting in EC50 values of 17, 18700
and 203 µg/L, respectively (Robinson et al, 2005). However, a different study reported an
EC50 value of 2.97 mg/L using the green algae P. subcapitata (Halling-Sorensen et al.,
2000).
Table 3: Estimated chemical properties of Ciprofloxacin HCl retrieved from EPISuite, version 4.11
Chemical properties of Ciprofloxacin HCl
Chemical class
fluoroquinolone antibiotic
CAS Nr.
Chemical Formula
86483-48-9, 93107-08-5, 86393-32-0
C17H18FN3O3 HCl
Mechanism of Action
Structural Formula
Inhibition of enzymes topoisomerase II & IV (DNA gyrase)
Formula Weight
Log KOW
367.8 g/mol
0.28
Log KOC
-0.004
Log KOA
BCF
16.96
0.50
pKa
Solubility in water [mg/L]
6.43
Soluble in water
Vapor Pressure [Pa, 25°C]
Removal in WW Treatment [%]
3.8E-011
79 – 87
Amounts detected in environment
Median of 0.02 µg/L in 26 % US streams (Kolpin et al., 2002)
0.7–124.5 µg/L in WW of Swiss hospital and
249-405 ng/L in Swiss sewage WWTP (Fink et al., 2012)
1–2.4 mg/kg in Swiss sewage sludge (Golet et al., 2001)
0.018 µg/L in Swiss surface water (McArdell et al., 2003)
0.06 µg/L max in surface water in Germany (UBA, 2011)
45-568 ng/L in Swiss sewage WWTP (Golet et al., 2002)
19
2.4 Phenols
Besides pharmaceuticals, phenols were selected in this thesis as test chemical because of
their environmental and toxicological importance. Hydroxybenzene, or phenol, is the parent
molecule for the class of chemicals named phenols which carry the structure of a benzene
ring with a hydroxyl group, as depicted in Figure 2.
Figure 2: The parent phenol molecule
Phenols have been used in the production of pesticides, perfumes, dyes, synthetic resins,
pharmaceuticals, synthetic tanning agents, lubricating oils and solvents since 1860 (Rayne
et al., 2009). Phenols have been detected in aquatic and terrestrial food chains (Jensen,
1996) and in environmental samples, particularly in those obtained from aquatic
ecosystems (WHO, 1987, 1989, 1994), due to their widespread use and persistence in the
environment. The largest use of phenol is an intermediate in the production of phenolic
resins, which are used in the construction, adhesive, plywood, automotive and appliance
industries. Owing to its anesthetic effects, phenols are also used in medicines such as
ointments, nose and ear drops, cold sore lotions, and sprays and antiseptic lotions
(USEPA, 2002a). Chlorophenols have the highest industrial value (Rayne et al., 2009). The
toxicity of chlorophenols towards Chlorella vulgaris was previously determined by Shigeoka
(1988) and Ertürk et al. (2013).
Mode of action (MOA) is defined as an exposure action of a chemical or drug with regards
to the type of response produced in an organism (Borgert et al., 2004). Target sites for
toxic effects include biological membranes, which are among the most important ones.
Hydrophobic substances partitioning into biological membranes cause disturbances in the
structure and functioning of the membranes and results in the so-called baseline toxicity or
narcosis, which constitutes the minimal toxicity of any hydrophobic pollutant. Narcosis (i.e.,
non-polar and polar narcosis) is the most important mode of toxic action in ecotoxicological
settings, as approximately 70% of all organic industrial chemicals are believed to act via
narcosis (Escher and Schwarzenbach, 2002).
20
2.4.1 2,4-Dichlorophenol
2,4-dichlorophenol (2,4-DCP) is a chlorinated derivative of phenol and an important
intermediate in the industrial manufacture of 2,4-dichloro-phenoxyacetic acid (2,4-D), the
well-known industrial commodity herbicide used in the control of broadleaf weeds. It is one
of the most widely used herbicides in the world and can be found in various formulations
under a wide variety of brand names (e.g. Weed B Gon MAX, PAR III, Trillion, Tri-Kil, Killex
or Weedaway Premium 3-Way XP Turf Herbicide). 2,4-D is a synthetic auxin (plant
hormone), and as such often used in laboratories for plant research and as a supplement
in plant cell culture media. It is also used in the manufacture of other pesticide products
and pharmaceuticals and formed as a byproduct during the manufacturing of various
chlorinated chemicals. Chlorination processes involves water treatment and wood pulp
bleaching. The main route of entry to the aquatic environment is likely to be a result of
discharges from manufacturing plants. According to the online Hazardous Substances
Data Bank (HSDB, 2014), the major source of 2,4-dichlorophenol in the environment is
degradation of 2,4-D in contaminated soil and water. Photolysis and, potentially,
volatilization are the main routes of non-biological degradation. Hydrolysis is not expected
to be an important fate process due to the lack of hydrolysable functional groups.
A study reported from the Environment Agency in the United Kingdom (EA UK, 2008)
revealed an EC50 value of 5.7 mg/L using the green algae P. subcapitata in a 72 h growth
inhibition test based on OECD guidelines. Another green algae study conducted by
Shigeoka et al. (1988) elicited for Chlorella vulgaris an EC50 of 9.62 mg/L and for
Selenastrum capricornutum EC50 of 14 mg/L over an exposure period of 96 hours.
According to ECHA, 2,4-DCP is listed in Annex VI of Regulation (EC) No 1272/2008 on
classification, labeling and packaging of substances and mixtures. 2,4-DCP is known to
cause serious eye damage or eye irritation and is classified as corrosive to the skin. Apart
from the negative effect to human health, this substance is particularly hazardous to the
aquatic environment on a long-term basis.
List of hazard statements for 2,4.DCP:

Acute toxicity – oral: Acute Tox. 4 H302: Harmful if swallowed.

Acute toxicity – dermal: Acute Tox. 3 H311: Toxic in contact with skin.

Skin corrosion / irritation: H314: Causes severe skin burns and eye damage.

Serious eye damage / eye irritation conclusive but not sufficient for classification

Aquatic Chronic 2 H411: Toxic to aquatic life with long lasting effects.
21
Table 4: Estimated chemical properties of 2,4-dichlorophenol retrieved from EPISuite, version 4.11
Chemical properties of 2,4-dichlorophenol
Chemical class
Phenol
CAS Nr.
Chemical Formula
120-83-2
Cl2C6H3OH
Mechanism of Action
Structural Formula
Polar narcotics
Formula Weight
163 g/mol
Log KOW
3.06
Log KOC
Log KOA
BCF
pKa
2.81
6.816
Solubility in water
Vapor Pressure [Pa, 25°C]
4.50 g/L
Removal in WW Treatment [%]
6.46
Amounts detected in environment
In water and soil in the ng/L - µg/L range through
degradation of 2,4-D and chlorination of waste water
(HSDB, 2014)
1.686
7.89
8.76
2.4.2 3-Chlorophenol
3-chlorophenol (3-CP) is a halophenol with antifungal activity and is commonly used as a
building block or intermediate in the preparation of variety of biologically active compounds.
This chlorophenol is also used to extract sulphur and nitrogen compounds from coal and
as an intermediate in organic synthesis of other chlorophenols and phenolic resins.
3-chlorophenol's production and use in organic synthesis may result in its release to the
environment through various waste streams. According to CLP legislation, this substance
is listed in Annex VI of Directive (EC) No 1272/2008.
List of hazard statements for 3-CP:

Acute toxicity – oral: Acute Tox. 4 H302: Harmful if swallowed.

Acute Tox. 4 H312: Harmful in contact with skin

Aquatic Chronic 2 H411: Toxic to aquatic life with long lasting effects.
22
Table 5: Estimated chemical properties of 3-chlorophenol retrieved from EPISuite, version 4.11
Chemical properties of 3-chlorophenol
Chemical class
Phenol
CAS Nr.
Chemical Formula
108-43-0
C6H5ClO
Mechanism of Action
Structural Formula
Polar narcotics
Formula Weight [g/mol]
128.56
Log KOW
Log KOC
2.5
2.475
Log KOA
BCF
7.351
1.317
pKa
Solubility in water [mg/L]
9.12
25 g/l
Vapor Pressure [Pa, 25°C]
9.18
Removal in WW Treatment [%]
Amounts detected in environment
3.12
Chlorinated sewage effluents have been found to contain
3-CP in the 0.5 µg/L range (HSDB, 2014)
23
3 Materials and Methods
3.1 Material
3.1.1 Chlorella vulgaris
Chlorella vulgaris strain (CCAP 211/11B) was obtained from Ecotoxicology and
Chemometrics Lab of Institute of Environmental Sciences, Bogazici University, Istanbul,
Turkey. This strain has been maintained in the laboratory conditions for many years and
was purchased from Culture Collection of Algae and Protozoa – (CCAP, The Scottish
Association for Marine Science, Scottish Marine Institute, Dunbeg, Argyll, UK).
3.1.2 Test chemicals
The pharmaceutical compounds used in this study were purchased from Fargem – a
distributor of pharmaceuticals in Turkey. Ibuprofen and Ciprofloxacin were selected for
single as well as mixture toxicological assessment. All phenols used in this thesis for
toxicological assessment were purchased from Sigma-Aldrich Co. The chemicals had
purity ≥98%, therefore, no further purification was undertaken. For the tests carried out
using freshwater algae, the stock solutions were prepared below the water solubility limits
of each compound using de-ionized water. Only the stock solution of ibuprofen was
prepared in dimethyl-sulfoxide (DMSO). For the thesis using this compound, an additional
solvent control containing the maximum DMSO concentration (0.1% v/v) was employed.
The inhibitory concentration of the chemicals was calculated taking the growth in solvent
controls into account. All stock solutions were sterile-filtered using 0.2 µm filters to remove
particles and impurities such as bacteria or fungal spores from the samples. All test
chemicals (Table 6) were of p.a. quality (high purity) and stored at room temperature in the
dark.
Table 6: Test chemicals used for toxicity testing
Product
CAS Nr.
Batch Nr./Expiry Date
Company
Ciprofloxacin HCl
93107-08-5
CF0891209
Matrix
Ciprofloxacin HCl
Ibuprofen 25
93107-08-5
15687-27-1
CFX-II/197/07/U-III
IB1T1575
Matrix
BASF
2,4-dichlorophenol pestanal
120-83-2
19.12.2014
Fluka / Sigma- Aldrich
3-chlorophenol pestanal
108-43-0
19.12.2014
Fluka / Sigma-Aldrich
24
3.1.3 Reagents
Table 7: Chemicals
Name and Manufacturer of used chemicals. Unless otherwise stated the
chemicals are of pro analysi (p.A.) quality.
Name
Manufacturer/Supplier
Calcium chlorid dihydrate
Cobalt(II) Chloride Hexahydrate
Cyanocobalamin (Vitamin B12)
Deionized water
Dichloromethan (methylene chloride)
Dimethyl sulfoxide for analysis EMSURE®
Di-potassium hydrogen phosphate
trihydrate
Disodium ethylenediamine tetraacetate
Ethanol, absolute for analysis EMSURE®
Iron (III) Chloride Hexahydrate
Magnesium sulfate heptahydrate
Manganese(II) chloride tetrahydrate
n-Hexane EMPLURA®
Nitric acid 64-66%
Potassium phosphate dibasic
Sodium chloride
Sodium molybdate dihydrate
Sodium nitrate, cryst., extra pure
Thiaminhydrochloride (Vitamin B1)
Zinc Chloride
Merck, Germany
Merck, Germany
Sigma-Aldrich, Germany
Self purified using Labconco Water pro
Sigma-Aldrich, Germany
Merck, Germany
Merck, Germany
Sigma-Aldrich, Germany
Merck, Germany
Merck, Germany
Sigma-Aldrich, Germany
Merck, Germany
Merck, Germany
Sigma-Aldrich, Germany
Sigma-Adrich, Germany
Merck, Germany
Sigma-Aldrich, Germany
Merck, Germany
Sigma-Aldrich, Germany
Merck, Germany
Table 8: Reagent-Formulation
Name and composition of used reagents
Name
Composition
Vitamin B1
0.12 g Thiaminhydrochloride in 100 ml deionized water
Filter sterile with 0.2 µm filter
25
Table 8: continued
Name
Composition
Vitamin B12
0.1 g Cyanocobalamin in 100 ml deionized water
Filter sterile with 0.2 µm filter
Stock solutions in g /
1000 ml water
75 g NaNO3
2.5 g CaCl2.2H2O
7.5 g MgSO4.7H2O
7.5 g K2HPO4.3H2O
17.5 g KH2PO4
2.5 g NaCl
Trace element solution
Add to 1000 ml of deionized water 0.75 g Na2EDTA and
minerals in exactly the following sequence:
97 mg FeCl3.6H2O
41 mg MnCl2.4H2O
5 mg ZnCl2
2 mg CoCl2.6H2O
4 mg Na2MoO4.2H2O
Bold basal medium with 3-fold
nitrogen and vitamins
10 ml NaNO3
10 ml CaCl2.2H2O
10 ml MgSO4.7H2O
10 ml K2HPO4.3H2O
10 ml KH2PO4
10 ml NaCl
6 ml Trace element
Make up to 1 liter with deionized water. Autoclave for 20
min at 121°C 2 atm, after solution cooled down add
sterile filtered vitamins:
1 ml Vitamin B1
1 ml Vitamin B12
Nitric acid 10 % (v/v)
50 ml nitric acid
450 ml deionized water
Ethanol 70% (v/v)
70 ml Ethanol absolute
30 ml deionized water
Unless otherwise stated, liquid solutions were sterile filtered or autoclaved. The water was
deionzed. All used chemicals were of p.a. quality.
26
3.1.4 Instruments and consumable materials
Table 9: Laboratory equipment
Name and manufacturer of used devices and materials
Instrument, type designation
Manufacturer/supplier
Analytical Scale, SBA31
Autoclave OT40L müve steam Art
Beaker 50 ml, 100 ml, 600 ml
Scaltec, USA
Nüve, Turkey
Simax, Czech Republic & Isolab,
Germany
Centrifuge Meditronic BL-S
P-Selecta, Spain
Cuvette, Quartz Suprasil 104-QS 10 mm
Hellma, Germany
Erlenmeyer flask Boro 3.3, 2 L
Simax, Czech Republic
Erlenmeyer flask Boro 3.3, 250 ml, 500 ml, 5 L Isolab, Germany
Fridge, 4°C incl. -20°C drawer
Arcelik, Turkey
Temperature controlled growth chamber
Equipment of Bogazici University
Gas chromatography Agilent 6890N
Agilent Technologies, USA
Heat Stir SB162 Stuart
Bibby Sterilin Ltd, UK
Hemocytometer Superior, Thoma Depth
Marienfeld, Germany
2
0.100mm 0.0025 mm
Hood
Equipment of Bogazici University
Labconco water pro purification system
Labconco, USA
Light Meter 8581
AZ Instrument, Taiwan
Magnetic stirrer
Sigma-Aldrich, Germany
Manual Pipettor Sealpette 100-1000µl
Jencons Scientific, USA
Microscope Olympus CX41RF
Olympus Corporation, Japan
Oven WiseVen Fuzzy Control System
Wisd Laboratory Instruments, Germany
pH Electrode Sen Tix HW
WTW, Germany
pH Meter WTW pH330i
WTW, Germany
Pipette 10 ml
Pobel, Spain
Pipette 100-1000 µl
Eppendorf, Germany
Pipette tips storage box
Eppendorf, Germany
Sample bottles 100 ml, GL-45, autoclavable
Isolab, Germany
Sample bottles 10 ml
Sigma-Aldrich, Germany
Spatula
Sigma-Aldrich, Germany
Sterile workbench
Equipment of Bogazici University
UV/VIS Spectrophotometer Double Beam,
Lasany International, India
Variable Band Width LI-2804
27
Table 9: continued
Instrument, type designation
Manufacturer/supplier
Volumetric flask 1 L, autoclavable
Simax, Czech Republic & Isolab,
Germany
Isolab, Germany
Volumetric flask 10 ml, 25 ml, 50 ml, 100 ml,
500 ml
Volumetric Pipette 10 ml
Volumetric Pipette 5 ml
Precicolor, Germany & Isolab, Germany
Opticolor, Germany
Table 10: Consumable materials
Name und manufacturer of consumable materials
Product
Manufacturer/Supplier
Aluminium foil
Filter Paper, 40 x 40 cm, 0.17 mm Thickness
Latex-Gloves, powder free, non sterile
Pipette tips 100-1000 µl
Syringe 10 ml, steril, disposable
Syringe Filter 0.2 µm PVDF Acrodisc LC
Available in every supermarket
Achem, USA
Aku-Med, Malaysia
Eppendorf, Germany
Hayat, Turkey
Pall Life Sciences, USA
Table 11: Software / Computer
Software- & Computerprogrammes and supplier
Software
Manufacturer/supplier
Digital Camera Canon Ixus 80 IS
Laptop R450 Intel Pentium Inside
Microsoft Office, Windows 7 for Intel-PC
Image processing programmes Paint 5.1
& Photo Editor 3.0.2.3
ToxCalcTM Software ver. 5.0.32
SPSS Software ver. 20.0.0
EpiSuite Software ver. 4.11
Literature Database:
Canon, Japan
LG, China
Microsoft, USA
Microsoft, USA
Tidepool Scientific, USA
SPSS, Inc., USA
Environmental Protection Agency, USA
TOXNET (http://toxnet.nlm.nih.gov/)
Epa ECOTOX
(http://cfpub.epa.gov/ecotox/quick_query.htm)
WikiPharma
(http://www.wikipharma.org/api_data.asp)
Elsevier (http://www.elsevier.com)
28
Table 11: continued
Software
Manufacturer/supplier
Santa Cruz (http://www.scbt.com/)
Chem Spider (http://www.chemspider.com/)
Bogazici Library
(http://www.library.boun.edu.tr/en/index.php)
SETAC (http://www.setac.org)
PubMed (NCBI) (http://www.ncbi.nlm.nih.gov/)
Science Direct (http://www.sciencedirect.com/)
RXList (http://www.rxlist.com/script/main/hp.asp)
Springer Link (http://www.springerlink.com)
The journal of biological chemistry
(http://www.jbc.org/)
Wiley InterScience
(http://www3.interscience.wiley.com/cgibin/home)
ECHA (http://echa.europa.eu/)
3.2 Experimental methods
3.2.1 Analytical methods
Nominal concentration of each test solution was measured at the end of the experiment by
instrumental analysis using High Performance Liquid Chromatography (HPLC) for
ibuprofen, Gas Chromatography (GC) for phenolic compounds and spectrophotometer for
ciprofloxacin HCl. Details can be found in Appendix A, B and C. Controls without algae
were analyzed at the end of the experiments to check if there is a significant chemical loss
due to volatilization, adsorption on the test vessel, etc. during the experiment. pH of the
growth medium containing the control cultures of each bioassay were measured with a pHmeter (WTW pH330i) using a special electrode (WTW pH-electrode Sen Tix HW).
3.2.2 Algal growth inhibition assay using Chlorella vulgaris
Algal growth inhibition tests were conducted in batch cultures according to the standard
procedures (OECD, 2006) using freshwater algae Chlorella vulgaris in exponential growth
phase. Parent cultures of this algae, Chlorella vulgaris strain (CCAP 211/11B) was
obtained from Ecotoxicology and Chemometrics Lab of Institute of Environmental
29
Sciences. All tests were performed in a laminar air flow cabinet reserved for microbiological
assays, which was pre-sterilized with ultraviolet light for at least an hour (Figure 3).
Figure 3: Algal inoculation in laminar air flow cabinet
Cultures were sterile-transferred as needed to maintain log phase growth. The test
conditions for the algal bioassay are listed in Table 12, the growth medium used in
experiments is provided in Table 7 and 8.
Table 12: Test conditions of the algal bioassay
Test conditions of the algal bioassay
Test type
Test organism
Starting inoculum
Temperature
Light quality
Light intensity
Photoperiod
Test chamber size
Test solution volume
Replicates
Agitation
Test concentration
Test duration
Endpoint
Growth medium
static non-renewal, batch test
Chlorella vulgaris
1 x 103 cells/ml
24 ± 0.5 °C
cool white fluorescent lighting
60µmol photons m2/s
continuous illumination
500 ml
100 ml
3
once daily by hand
five and a control
96 h
growth (optical density at 680 nm)
bold basal medium
30
Experiments were conducted using pre-sterilized equipment. The glassware was sterilized
in a temperature controlled oven (WiseVen Fuzzy Control System, Germany) at 180°C for
3 hours. The plastic equipment (pipette, magnetic stirrers, etc.) and algal growth medium
were autoclaved at 121°C under 2 atm for 20 minutes. All the glassware used during the
experiments were cleaned with diluted nitric acid (10% v/v) to remove possible precipitates
from the glassware and then washed three times with tap water. After, hexane was used to
remove possible organic content in the glassware (remnants of toxicants or chemicals).
Again the glassware was washed with tap water three times rigorously and finally rinsed
three more times with distilled water. After each use, the spectrophotometer cuvettes were
cleaned with hexane, washed three times with distilled water and left for drying for 1 hour.
The inoculums in the test medium were prepared with algae harvested from four days old
cultures in exponential growth phase. The initial biomass was chosen to be sufficiently low
to allow growth throughout the incubation period without risk of nutrient depletion. Each
milliliter of inoculums contained 1x103 cells. Experiments were carried out in the
temperature controlled growth chamber (24 ± 0.5°C) under continuous illumination (60µmol
photons m2/s).
Range finding assays were performed prior to final definitive tests in order to determine the
concentration range in which effects are likely to occur. Definitive experiments were carried
out in three replicates using five equally spaced concentrations of the test chemical. Stock
solutions were prepared by dissolving test compounds in deionized water or dimethyl
sulphoxide (DMSO), from which test solutions were prepared in addition to a solvent
control for each concentration. 100 ml test medium with algae including the test chemicals
was dispensed into sterile 500 ml borosilicate Erlenmeyer flasks. For solvent controls, 50
ml test medium with corresponding concentration of chemical was transferred into sterile
100 ml Erlenmeyer flasks. The test vessels were shaken daily by hand during all
experiments. In addition, the test flasks were repositioned within the environmental
chamber each day to minimize possible spatial differences in illumination and temperature
on growth rate (Figure 4).
31
Figure 4: Algal growth inhibition assay in growth chamber
To ensure generation of quality data, the acceptability of the bioassay was assessed based
on the algal growth inhibition test criteria prepared by the Organization for Economic
Cooperation and Development (OECD, 2006). The test guideline 201 (2006) recommends
that the algal biomass in the control cultures should increase exponentially by a factor of at
least 16 within the 72-hour test period (corresponding to a specific growth rate of
0.92 day-1). However, as stated in the guidelines, this criterion may not be met when
species are used that grow slower. For this purpose, the exposure period should be
prolonged to reach at least 16-fold growth in control cultures. Another validation criteria
recommended by the OECD for algal inhibition tests is the coefficient of variation of
average specific growth rates (SGR) during the entire exposure period in replicate control
cultures, which must not exceed 10%. Furthermore, mean coefficient of variation for
section-by-section specific growth rates (days 0-1, 1-2 and 2-3, for 72 hour exposure
period) in the control cultures must not exceed 35%. The increase in pH of the control
cultures during the test period should not exceed 1.5 units (and preferably should be within
0.5 units for compounds that partly ionize around the test pH). Apart from the test
acceptability criteria indicated above, the repeatability of tests was also assessed based on
the results obtained from the experiment using 3,5-dichlorophenol (3,5-DCP) as the
reference toxicant. This compound is recommended to be tested to ensure and prove the
viability of algal cells by the OECD (2006).
32
3.2.3 Measurement and calculation of algal growth
The growth response of Chlorella vulgaris exposed to each of the tested substances was
determined by daily measurements of optical density at 680 nm (OD680) with
spectrophotometer (Schmiadzu, UV/VIS) at the same time over 96 hours. Wavelength of
680 nm is indicated to correspond with maximum chlorophyll a absorption for Chlorella
vulgaris, therefore this wavelength was used to quantify the algal biomass. A linear
relationship between algal cell counts and optical density was observed. Therefore, optical
density was used as a surrogate parameter for the calculation of response variables for
Chlorella vulgaris to determine biomass increase during the test. The conversion from
optical density to cell counts was done using linear relationships for specific growth rate
calculations.
The cell counts were performed using 1 ml of cell suspension which was counted on a
haemocytometer (Thoma grid type) using Olympus CX41 light microscope (Olympus,
Japan). The rafter cell used for counting algae holds 100 m3 of liquid 1 mm deep over an
area of 25 x 25 mm. The base was divided into 1 mm squares. A cover glass trapped the
liquid to correct depth. For the determination of the number of algal cells, 5 grids were
counted and average of the counts was recorded. The plot of the linear relationship
between optical density at 680 nm and the cell counts for Chlorella vulgaris is provided in
the section Results. pH was measured in the control cultures at the beginning and at the
end of the test. The response variables, the average specific growth rate as well as yield,
were calculated as recommended in the OECD guideline (2006), which equations are
provided below.
Average specific growth rate: the logarithmic biomass increase during the whole
exposure period, determined per day and defined from the equation (4):
(4)
where:
µi-j
Xi
Xj
is the average specific growth rate during the entire exposure period (time i to j);
is the biomass at the beginning of the exposure period (time i);
is the biomass at the end of the exposure period (time j).
33
The percent inhibition of growth rate for each treatment replicate is calculated from
equation (5):
(5)
where:
%Ir
percent inhibition in average specific growth rate;
µC
mean value for average specific growth rate (µ) in the control group;
µT
average specific growth rate for the treatment replicate
Yield: this response variable is the biomass at the end of the exposure test minus the
biomass at the beginning of the exposure test (starting biomass). The percent inhibition in
yield (%Iy) is calculated for each treatment replicate as follows (6):
(6)
where:
% Iy
percent inhibition of yield;
YC:
mean value for yield in the control group;
YT:
value for yield for the treatment replicate.
3.2.4 Statistic analysis of single and mixture toxicity
Algal toxicity of each pharmaceutical compound was determined by statistical analysis of
the average specific growth rate and yield as the response variable. Percent inhibition
relative to the control growth rate was fitted against the test substance concentration in
order to obtain a concentration-response relationship. The inhibitory concentration that
reduces the response variable by 50 percent (IC50) and 20 percent (IC20) with associated
95% confidence intervals was calculated using methods in ToxCalcTM Software (ver.
5.0.32, Tidepool Scientific, CA, USA) at the end of 48 h, 72 h, and 96 h. Apart from linear
interpolation, IC values were also calculated using weibull and probit, to investigate if the
toxicity calculation model had a significant impact on the toxicity data.
The ToxCalcTM software offers a full range of statistical methods that meet United States
Environmental Protection Agency (USEPA) standards. A flow diagram of the appropriate
statistical methodology used is shown in Figure 5.
34
Figure 5: Flow diagram of USEPA approved statistical methods performed by ToxCalc TM 5.0.32 (©
Tidepool Scientific Software, USA)
If the generated data met the assumptions of normality and homogeneity of variance,
analysis could be employed to conduct hypothesis testing for statistically significant
differences between treatment and the control. Normality (Shapiro-Wilk´s test) and
homogeneity of variance (Bartlett`s test) were initially tested, since they are the underlying
assumptions of the Dunnett`s procedure. The lowest observable effect concentration
35
(LOEC) and no observed effect concentration (NOEC) values for growth were obtained
using this hypothesis test approach. The NOEC and LOEC of each compound were
calculated using Dunnett`s test in ToxCalc 5.0.32 (© Tidepool Scientific Software, CA,
USA). NOEC and LOEC are based on the choice of test concentrations used in the toxicity
tests, therefore caution must be given when using these values. Ideally NOEC and LOEC
are viewed in conjunction with another endpoint such as EC10. If the data do not meet the
assumption of normality, a non-parametric test, Wilcoxon Rank Sum test, was used to
calculate the data. If the data meet the assumption of normality, the F-test for equality of
variances was used to test the homogeneity of variance assumption.
After determination of the single toxicity for each pharmaceutical compound, tests were
performed to assess the effects of interactions between those substances in a binary
mixture of all possible combinations when present simultaneously. Binary mixture tests
was conducted using proportions of the respective EC50s (=1 toxic unit (TU)). Mixture
experiments were performed using the following concentrations: Σ 0.25 TU, Σ 0.5 TU, Σ 1
TU, Σ 2 TU and Σ 4 TU.
Compounds with similar mechanism of action in mixtures were predicted using CA, which
is defined by (Berenbaum, 1985) who established the equation: E(Cmix)=ci/ECxi, where ci
denotes concentration of individual constituents in mixture and ECxi effect concentration of
the single substances and E(Cmix) is the total effect of the mixture.
To assess potential mixture toxicity effects of chemicals, the Toxic Unit approach was used
(Marking, 1985), where the observed mixture toxicity response is compared to a predicted
response based on toxic units. The percent effect (based on the control values) of each
mixture treatment was calculated and graphed as a concentration-response curve. A 50%
growth inhibition of algae in the mixture is predicted to occur at 1 TU, which is the
treatment where the single compounds are present at one half of their individual EC50
values. The joint effect in this case is simply additive (concentration addition). When a 50%
effect occurs at less than 1 TU, the mixture represents potential synergism (more than
additive). When a 50% effect occurs at greater than 1 TU, the mixture is considered to be
less than additive, or antagonistic. This approach assumes that the mixture compounds
have similar modes of action (Faust et al., 2003).
The toxic interactions were also characterized by calculating the additive index (AI)
according to Marking (1977), based on the EC50 values obtained from the single toxicity
and mixture toxicity bioassays. The AI is calculated using the following equations (7 and 8):
S = (Am/Ai) + (Bm/Bi)
(7)
AI = (1/S) – 1 for S ≤ 1.0; AI = 1 – S for S ≥ 1.0
(8)
36
where S = sum of biological activity, Am = EC50 for compound A in mixture; Ai = EC50 for
compound A individually; Bm = EC50 for compound B in mixture; and Bi = EC50 for
compound B individually. S values will then be used to calculate an Additive Index.
An additive index value less than zero indicate antagonistic toxicity. An additive index value
greater than zero denote synergistic toxicity. An index with confidence limits overlapping
zero indicates that the mixture is simply additive.
If the individual compounds have completely different mechanism of action, then they
would be viewed to act independently in mixture. In this case, the independent action
model should be applied (Faust et al., 2003), which uses the equation
E(cmix) = 1 – (1-E(c1)(1-E(c2)), where E(c1) and E(c2) denote the percent effect caused by
the individual constituents c1 and c2, and E(cmix) is the total effect of the mixture.
Although the mechanisms of action are known for the pharmaceuticals tested in
vertebrates, the mechanism of toxicity remains unkown in non-target aquatic species. The
mixture concentration-response curves will therefore be compared to predicted effects
based on both the concentration addition approach and the independent action models.
37
4 Results
All bioassays conducted with freshwater algae Chlorella vulgaris concurred with the
validation and acceptability criteria recommended by the OECD (2006). At the end of 96
hours, the algal biomass increased by approximately 20-fold within 72 hours. The observed
growth rate for this exposure period was higher than the minimum specific growth rate (i.e.
0.92 d-1) recommended by the OECD. For all test durations, the coefficient of variation
within the controls was ≤10% throughout the tests. The pH in the beginning of the
bioassays was 5.8 (±0.2). The pH values recorded in the controls at the end of 96 hours
was 6.00 (±0.2). Microscopic examination of Chlorella vulgaris cultures revealed that the
algae were in good condition throughout all experiments conducted in this thesis. A
statistical comparison between 0.1 % DMSO controls and no-solvent controls revealed no
significant difference in algal growth (t-test p value > 0.05). For the thesis using these
compounds, an additional solvent control containing the maximum DMSO concentration
(0.1% v/v) was employed. The instrumental analysis revealed that none of the test
concentration changed more than 20%.
4.1 Specific growth curve Chlorella vulgaris
A linear relationship between algal cell counts and optical density at 680 nm was observed
and used for measurement in order to calculate response variables for C.vulgaris to
express biomass increase during the test period. The cell counts were performed using 1
ml of cell suspension which was counted on a haemocytometer (Thoma grid type) using
Olympus CX41 light microscope (Olympus, Japan). The graph of the linear relationship
between optical density and the cell counts (specific growth curve) for C.vulgaris is
provided in Figure 6.
38
Figure 6: Absorbance versus number of algal cells (specific growth curve) for Chlorella vulgaris
4.2 Single toxicity tests
Each chemical exposure test included a control and five equally spaced concentrations
(based on range-finding assays). The concentrations tested were as follows: 2.4-DCP (0.8
mg/L, 1.6 mg/L, 3.2 mg/L, 6.4 mg/L, and 12.8 mg/L), 3-CP (7.5 mg/L, 15 mg/L, 30 mg/L, 60
mg/L, and 120 mg/L), Ciprofloxacin HCl (20 mg/L, 40 mg/L, 80 mg/L, 160 mg/L, and 320
mg/L) and Ibuprofen (35 mg/L, 70 mg/L, 140 mg/L, 280 mg/L, and 320 mg/L). Chlorella
vulgaris revealed concentration-dependent responses to the chemicals tested in this study.
All stock solutions were sterile-filtered using 0.2 µm filters to remove particles and
impurities. It should be noted that Ciprofloxacin HCl was tested twice, filtered and
unfiltered, as this antibiotic is designed to inhibit bacterial growth. The result of this
experiment revealed a great difference in toxicity. Unfiltered Ciprofloxacin HCl elicited an
IC50 value of 29.09 mg/L, while the filtered antibiotic resulted in an IC50 value of 94.35
mg/L. However, both tests followed the same pattern of concentration-response curve
functions. For further analysis, the result of unfiltered Ciprofloxacin HCl was taken into
account.
39
Concentration-response functions were determined for all test chemicals individually.
% growth inhibition versus concentration (mg/L) for all the chemicals is provided in Figure
7. Figure 8 shows the concentration response function for all chemicals over an exposure
period from 48 hours to 96 hours. IC values including 95% confidence intervals based on
specific growth rate and yield as response variable based on linear interpolation (ICp),
Weibull and Probit calculations are depicted in Table 13. The results show that apart
from the reference toxicant 3,5-DCP, ibuprofen with an IC50 of 89.65 mg/L had the lowest
toxic effect on Chlorella vulgaris, whilst 2,4-DCP had the highest toxic effect with an IC50
value of 10.76 mg/L based on specific growth rate and linear interpolation calculations.
The concentrations response curves for both phenols as shown in Figure 6, indicate
parallel toxicity functions, thus are likely to follow the same mode of action. As a
consequence the concentration addition approach can be applied for 2,4-DCP and 3-CP
when present in mixture simultaneously. CA assumes that the mixture components only
differ in the concentrations needed to elicit a toxic effect.
The concentration-response curve of Ciprofloxacin HCl differed compared to the other
chemicals tested in this study and therefore revealed dissimilar mode of action. As
suggested by the regulatory risk assessment, the independent action approach is more
likely to predict the joint toxicity effects of Ciprofloxacin HCl in mixture.
Figure 7: Concentration-response relationship curve for Chlorella vulgaris toxicity from single
compound toxicity tests of 2,4-dichlorophenol, 3-chlorophenol, Ciprofloxacin HCl and Ibuprofen
respectively. Response endpoint is reduction in growth (% Inhibition) after 96 h using specific
growth rate calculation and ICp method executed in Toxcalc software.
40
Figure 8 shows the concentration response function for all tested chemicals over a
exposure period from 48 hours to 96 hours. Apart from ciprofloxacin HCl, all tested
chemicals revealed the same toxicity pattern for all concentrations during the entire
exposure time. Only for the 48 h toxicity data of ciprofloxacin HCl, a difference in
concentration-response relationship compared to 72 h and 96 h could be determined. The
test concentration of 40 mg/L at 48 h elicited a growth inhibition in Chlorella vulgaris
population of 53.42 %, while a 72 h and 96 h exposure period revealed 41.14 % and 34.53
%, respectively.
For 3-CP, there was no significant difference (analysis of variance, Dunnett´s test) from the
control at 7.5 mg/L, but at the concentration of 15 mg/L tested there was a 24.2 %
decrease relative to the control based on 96 h exposure period (Figure 8). Only the highest
2,4-DCP concentration tested (11.7 mg/L) yielded a significant difference of more than
50% reduction in cell density. Among the pharmaceutical compounds, ibuprofen had a
significant effect on Chlorella vulgaris cell density at concentrations of 70 mg/L resulting in
30.49 % growth inhibition relative to the control. Ibuprofen at 140 mg/L and above resulted
in 100 % decrease in growth during the entire exposure period. Ciprofloxacin HCl elicited a
significant effect at concentrations of 20 mg/L and above.
Figure 8: Concentration-response relationship curve from single compound toxicity tests of 2,4dichlorophenol, 3-chlorophenol, Ciprofloxacin HCl and Ibuprofen respectively after 48h, 72h and 96
h using specific growth rate calculation and ICp method executed in Toxcalc software.
41
Table 13: 50% and 20% inhibitory concentrations (IC50 and IC20) calculated at the end of 48, 72 and 96 hours based on different methods executed in
ToxCalc software using yield and specific growth rate (SGR) calculations, no-observed effect concentration (NOEC), lowest-observed effect
concentration (LOEC), toxic class for C.vulgaris
Compound
Response
Variable
Method
ICp
SGR
3,5-DCP
(reference toxicant)
Sigma-Aldrich
Weibull
ICp
Yield
Weibull
ICp
SGR
Weibull
Probit
2,4-DCP
Sigma-Aldrich
ICp
Yield
Weibull
Probit
ICp
SGR
3-CP
Sigma-Aldrich
Weibull
Probit
ICp
Yield
Weibull
Probit
IC50
[mg/L]
48 h
IC20
[mg/L]
1.88
(1.7-2.0)a
1.69
(1.2-2.3)
1.36
(0.9-1.7)
1.25
(0.5-1.9)
11.04
(10.4-11.7)
11.16
(8.1-24.6)
11.17
(7.2-2623)
6.63
(5.2-7.8)
5.67
(3.0-10.4)
5.35
(2.4-25.7)
40.52
(36.4-45.6)
38.99
(30.4-47.6)
37.21
(28.7-49.8)
27.29
(23.2-32.32)
27.62
(20.7-36.6)
26.30
(18.5-37.2)
0.72
(0.5-1.0)
1.00
(0.4-1.3)
0.45
(0.3-0.6)
0.57
(0.0-0.9)
5.95
(4.9-6.8)
5.30
(0.9-7.5)
5.29
(0.0-8.0)
1.12
(0.1-4.6)
1.84
(0.2-3.3)
1.73
(0.0-3.4)
23.02
(19.2-27.4)
24.42
(13.5-31.1)
25.23
(12.4-31.7)
15.29
(10.8-19.2)
16.27
(5.6-21.4)
16.27
(5.8-21.8)
IC50
[mg/L]
72 h
IC20
[mg/L]
1.99
(1.9-2.1)
1.84
(1.5-2.4)
1.40
(1.2-1.6)
1.33
(0.7-1.9)
10.78
(10.2-11.5)
10.97
(7.7-26.7)
10.83
(7.2-97.5)
6.01
(4.5-7.6)
4.08
(1.4-9.8)
3.77
(0.9-71.3)
39.98
(36.4-44.5)
38.10
(32.5-45.2)
36.24
(31.2-51.7)
26.79
(22.5-31.9)
27.29
(20.2-35.2)
26.19
(19.0-35.5)
1.01
(0.8-1.2)
1.23
(0.7-1.5)
0.54
(0.4-0.8)
0.67
(0.0-1.0)
6.10
(4.9-6.7)
4.80
(0.7-7.0)
5.35
(0.2-8.2)
0.64
(0.2-1.6)
0.88
(0.0-2.1)
0.88
(0.0-2.2)
24.67
(20.2-30.7)
26.46
(19.2-31.2)
27.63
(18.0-32.1)
16.04
(10.1-20.1)
17.51
(4.1-22.3)
17.13
(6.6-22.2)
IC50
[mg/L]
96 h
IC20
[mg/L]
1.99
(1.9-2.0)
1.85
(1.5-2.5)
1.35
(1.2-1.4)
1.32
(0.7-1.9)
10.76
(10.1-11.6)
10.83
(8.0-19.9)
10.73
(7.2-94.3)
6.03
(5.3-6.6)
4.34
(1.8-9.2)
4.01
(1.3-32.3)
40.92
(36.2-44.6)
39.03
(33.7-45.7)
36.90
(32.2-52.1)
27.21
(22.6-32.3)
27.05
(20.1-35.8)
25.76
(18.2-36.2)
1.10
(0.9-1.2)
1.26
(0.7-1.5)
0.61
(0.4-0.9)
0.70
(0.1-1.0)
6.31
(6.0-6.7)
5.49
(1.0-7.6)
5.65
(0.1-8.1)
0.70
(0.3-1.4)
1.09
(0.0-2.3)
1.04
(0.0-2.4)
26.54
(20.9-32.3)
27.78
(21.1-32.4)
28.72
(21.0-33.0)
15.41
(2.8-20.9)
15.88
(5.3-21.0)
16.07
(5.9-21.4)
NOEC/
LOEC
[mg/L]b
Toxic
classc
<0.8/0.8
Toxic
<0.8/0.8
<0.73/0.73
Harmful
<0.73/0.73
15/30
Harmful
15/30
42
42
Table 13: continued
Compound
Ciprofloxacin HCl
Batch No. CFXII/197/07/U-III, Matrix
Dried with liquid
nitrogen
Response
Variable
Method
ICp
SGR
Weibull
Probit
Icp
Yield
Weibull
Probit
ICp
SGR
Ibuprofen25 in 0.1%
DMSO
Batch Nr. IB1T1575,
BASF
Weibull
Probit
ICp
Yield
Weibull
Probit
IC50
[mg/L]
48 h
IC20
[mg/L]
34.62
(3.6-93.3)
40.66
(0.0-92.9)
38.02
ND
15.14
(11.2-23.1)
6.61
ND
8.31
ND
82.25
(57.4-101.8)
77.39
(65.6-120.4)
75.70
ND
58.89
(45.9-75.0)
58.87
(40.3-74.6)
56.50
(40.9-76.3)
9.83
(5.9-18.8)
5.80
(0.0-22.3)
8.09
ND
6.06
(4.5-9.2)
0.37
ND
1.28
ND
48.20
(35.6-67.7)
52.94
(18.7-63.2)
58.11
ND
36.08
(8.3-50.4)
36.32
(7.3-47.9)
36.78
(14.6-47.9)
IC50
[mg/L]
72 h
IC20
[mg/L]
27.89
(8.9-40.6)
30.34
(0.0-69.9)
29.45
ND
13.62
(10.4-20.2)
3.57
ND
5.44
ND
86.43
(51.6-103.7)
80.72
(69.7-120.9)
77.19
ND
58.64
(43.0-84.5)
58.35
(39.4-75.5)
55.90
(39.8-76.8)
9.23
(5.7-19.7)
3.97
(0.0-17.8)
6.37
ND
5.45
(4.2-8.1)
0.20
ND
1.01
ND
51.05
(36.0-78.3)
56.57
(28.7-66.2)
62.56
ND
34.31
(9.2-56.2)
34.82
(6.6-46.7)
35.70
(13.2-47.0)
IC50
[mg/L]
96 h
IC20
[mg/L]
29.09
(8.36-40.7)
31.63
(0.0-70.8)
30.65
(0.0-75.3)
13.55
(10.0-21.1)
3.97
ND
6.03
ND
89.65
(71.3-103.5)
82.34
(71.2-112.9)
77.69
ND
59.34
(45.0-85.9)
59.18
(40.3-76.5)
56.73
(40.6-77.6)
9.67
(5.5-23.6)
4.46
(0.0-18.5)
7.03
(0.0-22.3)
5.42
(4.0-8.4)
0.29
ND
1.34
ND
54.84
(38.7-86.6)
57.80
(35.3-67.4)
65.42
ND
35.42
(8.2-57.9)
35.66
(6.7-47.5)
36.42
(13.7-47.8)
NOEC/
LOEC
[mg/L]b
Toxic
classc
<20/20
Harmful
<20/20
35/70
Harmful
35/70
a: 95 % confidence intervals [mg/L]
b: NOEC and LOEC values were calculated using 72 h toxicity data
c: toxic class based on 72 h ICp calculations
ND: not determined
43
43
The IC50 values obtained for 2,4-DCP corresponded well with the values compiled by
Ertürk et al., (2013) with 10.76 mg/L and 9.3 mg/L, respectively. Sigma Aldrich issued a
material data safety sheet (MSDS) for 2,4-DCP revealing an EC50 of 9.2 mg/L for Chlorella
vulgaris (Sigma, 2006). Ertürk et al., (2013) reported an IC50 value of 56.3 mg/L for 3-CP
on Chlorella vulgaris during 96 h exposure period, while this study elicited 40.92 mg/L.
Aruoja et al., (2011), determined a 3-CP concentration-response curve for
Pseudokirchneriella subcapitata (also known as Raphidocelis subcapitata or Selenastrum
capricornutum, a green mircoalgae) resulting in 11.5 mg/L at 50 % growth inhibition. The
same study revealed EC50 of 8.13 mg/L for 2,4-DCP on P. subcapitata. The big variation of
3-CP results might be due to the different species of green algae used in the experiment,
which in turn may lead to a different response to phenols. Sigma Aldrich reported in the 3CP MSDS a 96 h growth inhibition test with P. subcapitata of EC50 of 29 mg/L (Sigma,
2010).
Ibuprofen tested on Desmodesmus subspicatus (green algae) revealed IC50 values of
342.2 mg/L (Cleuvers et al., 2004). By contrast, ecotoxicological tests on
Pseudokirchneriella subcapitata (green algae) showed an IC50 value of 2.3 mg/L (Harada
et al., 2008). In this thesis, IC50 of 89.65 mg/L for Chlorella vulgaris could be determined
based on specific growth rate and linear interpolation calculations.
In this study, IC50 value for ciprofloxacin revealed 29.09 mg/L. The toxicity of ciprofloxacin
on Chlorella vulgaris growth was close to the one obtained by Nie et al. (2008) (EC50
96 h = 20.6 mg/L). Tests on another green algae (P. subcapitata) found in the literature
elicited various EC50 values of 2.97 mg/L, 4.83 mg/L and 18.7 mg/L by Halling-Sorensen et
al., (2000); Martins et al., ( 2012) and Robinson et al., (2005), respectively.
Based upon average specific growth rate, the IC50 and associated confidence intervals for
48 h and 96 h was found to overlap, which suggests that the toxicity of the tested phenols
and pharmaceuticals to Chlorella vulgaris did not change significantly between these
durations (Table 13). Based on the IC50 values, the least toxic compound was found to be
ibuprofen, while the most toxic compound was 2,4-DCP regardless of exposure duration or
response variable (Table 13). The toxicity of the chemicals based on the IC20 values also
followed the same toxicity pattern towards Chlorella vulgaris. As expected, either the IC50
or IC20 values based upon average specific growth rate were found to be higher than those
based upon yield due to the mathematical basis of the respective approaches (OECD,
2006). The ecotoxicological data obtained from the literature compared with the observed
data in this study, leads to the conclusion that Chlorella vulgaris is less sensitive to
pharmaceutical compounds than Pseudokirchneriella subcapitata.
44
The Classification, Labeling and Packaging (CLP) Regulation deals with the classification
and labeling of any substance or mixture/preparation manufactured or imported for the EU.
Currently there are more than 7000 hazardous substances listed in the Annex VI to the
CLP regulation. Annex VI of Directive 67/548/EEC classifies the toxicity of chemicals to
aquatic organisms according to the EC50 values (effective concentration that reduces the
measured endpoint by 50%; the endpoint comprises growth inhibition, lethality,
immobilization, etc.). Within this scheme, the toxicity of compounds is classified as
depicted in Table 14.
Table 14: Toxicity classification of chemicals according to Annex VI to GLP Directive 67/548/EEC
Toxicity range [mg/L]
Class
EC50 ≤ 1
1 < EC50 ≤ 10
10 < EC50 ≤ 100
EC50 > 100
Very toxic
Toxic
Harmful
Not toxic (not classified)
According to toxicity classification provided in Table 14, based on IC50 calculations
following the yield and SGR method, all test chemicals were classified as harmful. It
should be noted that environmental factors (e.g., presence of other toxicants, pH,
temperature and suspended matters) may enhance the acute or chronic toxicity of these
chemicals. As a consequence, the chemical release into the environment may cause
irreversible adverse effects on algal populations. Moreover, Saçan and Balcioglu (2006)
reported that if algal growth is affected, the biomass at higher tropic levels can be impacted
as well. Although the chlorophenol concentrations reported in the aquatic environment
(Czaplicaka, 2004) are not higher than the NOEC or IC20 values reported in the present
study, long-term effects might also have unexpected consequences on the ecosystem due
to the continuous low-level exposure to chemicals (Saçan and Balciogly, 2006).
4.3 Mixture toxicity tests
Mixture toxicity experiments were conducted using proportions of the respective EC50s
(=1 toxic unit (TU)) with following concentrations: Σ 0.25 TU, Σ 0.5 TU, Σ 1 TU, Σ 2 TU and
Σ 4 TU. Concentration-response curves from the single exposure tests of 2,4-DCP, 3-CP,
ciprofloxacin HCl and ibuprofen showed a significant decrease in EC values in mixed
exposure tests compared with the single exposure experiments.
45
4.3.1 Single toxicity tests versus mixture toxicity tests
Concentration-responses from the single toxicants (2,4-DCP, 3-CP, CiproHCl and
Ibuprofen) on the freshwater algae were compared with the concentration-response curves
obtained from the mixture toxicity tests (Figure 9-12).
The largest decrease in EC50 values between single and mixed exposures were found for
2,4-DCP and Ibuprofen when combined together, where the EC50 value changed from
10.76 to 5.16 mg/L and 89.65 mg/L to 43.0 mg/L, respectively. This implies a >52%
increase in toxicity for both chemicals when present in mixture simultaneously. Mixture
toxicity tests with 3-CP, Ibuprofen and Ciprofloxacin HCl reported an increase in toxicity at
low concentrations as well as high concentrations.
In contrast, 2,4-DCP in binary mixtures revealed a decrease in toxicity at very low inhibition
concentration. IC1 and IC5 values for 2,4-DCP individually reported slightly lower toxicity
concentrations compared to the mixture toxicity testing. Starting at IC10 the toxicity of 2,4DCP in mixture increased gradually in comparison to the results obtained from this
chemical when tested individually (Figure 9).
Ciprofloxacin HCl revealed the biggest increase in toxicity when applied in mixture at high
concentrations. IC95 showed an increase of >82% in toxicity when present with the other
selected chemicals. The result demonstrates that large differences in toxicity between
individual exposure tests and mixture toxicity tests occurred. Thus, it can be concluded that
test chemicals become more toxic when added together in a mixture.
Figure 9: Concentration-response curve of 2,4-DCP individually compared to mixed exposure tests.
46
Figure 10: Concentration-response curve of 3-CP individually compared to mixed exposure tests
Figure 11: Concentration-response curve of CiproHCl individually compared to mixed exposure
tests.
47
Figure 12: Concentration-response curve of Ibuprofen individually compared to mixed exposure
tests.
4.3.2 CA and IA approach versus observed effect
Concentration-response curves obtained from predicted mixture effects of concentration
addition (CA) and independent action (IA) were compared with the observed mixed
exposure effect (exp) from all binary mixture toxicity tests with Chlorella vulgaris. Equations
applied for the calculation of CA and IA is mentioned in section 2.2.2 Mixture toxicity
testing. It should be noted that joint effect calculations are based on the experimental data
obtained from single toxicity tests using the respective IC values derived from linear
interpolation method and specific growth rate as test endpoint. Only for 2,4-DCP the probit
method was used to estimate the mixture toxicity, due to the fact that linear interpolation
could not present IC values higher than 50 %. However, the obtained toxicity data of these
two statistical methods do not vary much, as depicted in Table 13.
Furthermore, to assess potential joint effects of chemicals, the Toxic Unit approach was
used (Marking, 1985), as described in section 3.2.4 Statistic analysis of single and mixture
toxicity. When a 50% effect occurs at 1 TU (± 0.2), the mixture is considered to be simply
additive (concentration addition). Potential synergism occurs at less than 0.8 TU, whereas
a 50% effect greater than 1.2 TU is supposed to be less than additive, or antagonistic (Lin
et al., 2004). This approach assumes that the mixture compounds have similar modes of
action.
48
2,4-DCP and 3-CP in mixture
The observed EC50 value of 2,4-DCP and 3-CP in mixture was 28.2 mg/L (20.5 – 35.6
mg/L confidence interval 95%). The predicted joint effects calculated according to
concentration addition predicted an EC50 value of 26.96 mg/L and therefore estimated the
toxicity more accurately than the independent action approach. Both chemicals are
supposed to follow the same mode of action, which gives the concentration addition model
preference in predicting the mixture toxicity. The EC50 value according to the independent
model was calculated as >37.36 mg/L, which shows that this approach clearly
underestimates the toxicity. The observed mixture at 50 % growth inhibition showed a sum
TU of 1.09 and confirms the additive model approach (Figure 13).
Exp
CA
IA
EC50
mixture
[mg/L]
28.20
26.96
>37.36
1 TU at 50% growth
inhibition (exp) = 1.09
additive effect
Figure 13: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of 2,4-DCP and 3-CP.
49
Ibuprofen and Ciprofloxacin HCl in mixture
Concentration addition predicted an EC50 value of 59.37 mg/L, while in the experiment the
concentration of 60.71 mg/L (33.88 – 96.34 mg/L confidence interval 95%) caused the
observed inhibition at 50%. Independent action predicted an EC50 value of >74.16 mg/L.
As shown in the graph, IA approach clearly underestimates the joint effect of ibuprofen and
ciprofloxacin HCl in mixture. In this case the CA approach should be given preference to
estimate the mixture toxicity, as the result is closer to the values obtained from the
experiment data. Despite of dissimilarly acting components in the mixture, CA is a better
predictor, although IA revealed parallel concentration-response curve as observed in the
experimental mixture. The Toxic Unit approach revealed 1.02 TU, therefore the mixture is
considered to be additive (Figure 14).
Exp
CA
IA
EC50
mixture
[mg/L]
60.71
59.37
>74.16
1 TU at 50% growth
inhibition (exp) = 1.02
additive effect
Figure 14: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of Ibuprofen and CiproHCl.
50
2,4-DCP and Ciprofloxacin HCl in mixture
The observed mixed exposure concentration-response curve for 2,4-DCP and
Ciprofloxacin HCl showed the same pattern compared to the curve for predicted mixture
effects according to concentration addition as well as independent action (Figure 14).
CA and IA approach revealed an EC50 value of 20.79 mg/L and >19.11 mg/L, respectively.
The EC50 value obtained from the experiment was 19.4 mg/L. Both models provided very
accurate estimates of the mixture toxicity at lower as well as higher range of growth
inhibition. Both were good predictors of 2,4-DCP and Ciprofloxacin HCl mixture toxicity,
with the actual observed concentration-response curve overlapping with the predicted
concentration-response curve. The toxic unit approach revealed 0.97 TU at 50% growth
inhibition of algae and is therefore considered to be additive (Figure 15).
Exp
CA
IA
EC50
mixture
[mg/L]
19.40
20.79
>19.11
1 TU at 50% growth
inhibition (exp) = 0.97
additive effect
Figure 15: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of 2,4-DCP and CiproHCl.
51
3-CP and Ciprofloxacin HCl in mixture
The observed EC50 value of 3-CP and CiproHCl in mixture was 38.88 mg/L (22.29 – 63.25
mg/L, confidence interval 95%). The predicted joint effects calculated according to
concentration addition predicted an EC50 value of 35.01 mg/L and therefore slightly
overestimated the mixture toxicity. The EC50 value according to independent model was
calculated as >42.43 mg/L. Both, the IA as well as CA predicted values were overlapping
the confidence interval. Hence, there could be no definite trend observed to predict the
joint effect of these compounds when present in mixture simultaneously. According to
Figure 16, CA estimated a slightly higher toxicity and therefore gives a worst case
scenario. The observed mixture at 50 % growth inhibition showed a sum TU of 1.11. The
mixture is supposed to be additive.
Exp
CA
IA
EC50
mixture
[mg/L]
38.88
35.01
>42.43
1 TU at 50% growth
inhibition (exp) = 1.11
additive effect
Figure 16: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of 3-CP and CiproHCl.
52
2,4-DCP and Ibuprofen in mixture
As shown in Figure 17, a large difference between the observed mixed exposure
concentration-response curve compared to the predicted mixture effect curve according to
independent action could be exhibited. The IA approach underestimated the toxicity by
more than 43 %, if compared with inhibition around 50%. Concentration addition, on the
other hand, provided accurate estimates of toxicity, with calculated EC50 values of 52.30
mg/L. The observed EC50 was 48.18 mg/L with confidence interval of 95 % between 39.53
and 59.93 mg/L. The mentioned confidence interval overlaps the data obtained from the
calculated concentration of the CA model. This result shows that concentration addition is
good at predicting the toxicity for 2,4-DCP and Ibuprofen in mixture. Due to the fact of
underestimation of IA approach, CA should be given preference to predict the joint effects
of these two compounds in mixture. The toxic unit approach revealed 0.96 TU at 50%
growth inhibition of algae and is therefore considered to be additive (Figure 17).
Exp
CA
IA
EC50
mixture
[mg/L]
48.18
52.39
>69.09
1 TU at 50% growth
inhibition (exp) = 0.96
additive effect
Figure 17: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of 2,4-DCP and Ibuprofen.
53
3-CP and Ibuprofen in mixture
The graph in Figure 18 shows the observed concentration-response curve for 3-CP and
Ibuprofen in mixture compared to the curve for predicated joint effects according to CA and
IA. Concentration addition overestimated toxicity by more than 22%, while independent
action underestimated the result by more than 10% when EC50 values are compared. Both
models could not provide accurate estimates of the mixture toxicity. Whether CA neither IA
approach was suitable to predict the joint toxicity accurately. Hence, there could be no
definite trend observed to predict the joint effect of these compounds when present in
mixture simultaneously. At higher range of inhibition (>50%) the predictions of both models
differed enormously compared to the observed concentration-response curve. All in all, the
CA model should be preferred approach as it generally predicts higher toxicity than
independent action and can therefore be used as worst case scenario. The toxic unit
approach revealed 1.28 TU at 50% growth inhibition of algae and the mixture is therefore
considered to be antagonistic (Figure 18).
Exp
CA
IA
EC50
mixture
[mg/L]
83.87
65.28
>92.41
1 TU at 50% growth
inhibition (exp) = 1.28
antagonistic effect
Figure 18: Comparison of concentration-response curves obtained from predicted joint effects of
concentration addition (CA) and independent action (IA) with observed effect (exp) from the binary
mixture toxicity test of 3-CP and Ibuprofen.
54
4.3.3 Additive Index
Potential toxic interactions were characterized by calculating the additive index (AI), as
described previously by Marking (1977). The biological activity (S) can be calculated with
the equation: S = (Am/Ai) + (Bm/Bi). For AI the following equation can be applied: AI = (1/S)
– 1 for S ≤ 1.0; AI = 1 – S for S ≥ 1.0. An additive index value less than zero indicate
antagonistic toxicity. An additive index value greater than zero denote synergistic toxicity.
An index with confidence limits overlapping zero indicates that the mixture is simply
additive. Calculations were conducted with EC50 values based on SGR endpoint and ICp
method with an exposure period of 96 hours. Results are shown in Table 15 and are in
excellent agreement with joint effect estimates obtained from the toxic unit approach.
Table 15: 50% single and mixture effect concentrations at 96 hours, Additive Index and joint toxic
action for Chlorella vulgaris
Mixture
Component
EC50 [mg/L]
EC50 [mg/L]
Biological
Additive
individually
mixture
activity
Index
(Ai;Bi)
(Am; Bm)
(S)
(AI)
2,4-DCP
10.76
(10.1-11.6)a
5.87
(4.4-7.3)
3-CP
40.92
(36.2-44.6)
22.33
(17.1-27.4)
CiproHCl
29.09
(8.36-40.7)
14.87
(8.3-22.8)
Ibuprofen
89.65
(71.3-103.5)
45.83
(25.0-76.2)
2,4-DCP
10.76
(10.1-11.6)
5.24
(4.0-10.5)
CiproHCl
29.09
(8.36-40.7)
14.17
(10.5-26.8)
3-CP
40.92
(36.2-44.6)
22.72
(13.2-38.5)
CiproHCl
29.09
(8.36-40.7)
16.16
(9.2-24.6)
2,4-DCP
10.76
(10.1-11.6)
5.16
(4.4-6.4)
Ibuprofen
89.65
(71.3-103.5)
43.00
(35.3-57.6)
3-CP
40.92
(36.2-44.6)
26.28
(24.7-28.3)
Ibuprofen
89.65
(71.3-103.5)
57.57
(53.7-62.7)
1
2
3
4
5
6
Joint toxic action
1.091
-0.091
additive
1.022
-0.022
additive
0.974
0.027
additive
1.111
-0.111
additive
0.959
0.043
additive
1.284
-0.284
antagonistic
a: 95 % confidence intervals [mg/L]
55
All mixture combinations resulted in additive effects, except for 3-CP and ibuprofen in
mixture, which elicited an antagonistic effect. While the components in mixture revealed
stable EC50 values, Ibuprofen in mixture with 3-CP revealed higher EC50 value (57.57
mg/L) compared to the mixtures with ciprofloxacin HCl and 2,4-DCP with 45.83 mg/L and
43 mg/L, respectively. This observation cannot be readily explained now, but interactions
like antagonism usually occur at medium or high concentration levels. Metabolic,
toxicokinetic or toxicodynamic interactions are examples for interactions and considered to
result in antagonism or synergism (FEA, 2013). However, it should be noted, that the 95 %
confidence interval of mixture EC50 values for ibuprofen are overlapping and the
antagonistic effect can be viewed as very weak effect.
56
5 Discussion
For quality control reasons, it is recommended that a standard reference toxicant such as
3,5-DCP is tested at regular intervals (at least twice a year) on algal growth inhibition tests
in order to prove the validity of the test system as suggested by OECD TG 201 and ISO
8692. In a ring test conducted by the participation of 18 laboratories, the algal toxicity of 3,5
DCP to freshwater algae Pseudokirchneriella subcapitata was found to be 3.4±1.30 mg/L
(ISO, 2004). In this study, the toxicity of 3,5-DCP to another freshwater algae Chlorella
vulgaris was found to be 1.99 mg/L (95 % confidence interval 1.9 – 2.0 mg/L). The
obtained result is very close to the findings for P. subcapitata and small variation is likely to
occur due to different species of algae used for the experiments. All in all, it can be
confirmed that the results obtained in this study concur with international standards for
algal toxicity testing.
5.1 Single toxicity tests
It was observed that pharmaceuticals were less toxic than phenols towards Chlorella
vulgaris. Based on the IC50 values, the least toxic compound was found to be ibuprofen,
while the most toxic compound was 2,4-DCP regardless of exposure duration or response
variable (Table 13). The toxicity of the chemicals based on the IC20 values also followed
the same toxicity pattern towards Chlorella vulgaris. As expected, either the IC50 or IC20
values based upon average specific growth rate were found to be higher than those based
upon yield due to the mathematical basis of the respective approaches (OECD, 2006). The
toxic ranking of these four compounds to Chlorella vulgaris was 2,4-DCP > Ciprofloxacin
HCl > 3-CP > Ibuprofen according to Annex VI of Directive 67/548/EEC.
Based upon average specific growth rate, the IC50 and associated confidence intervals for
48 h and 96 h was found to overlap, which suggests that the toxicity of the tested phenols
and pharmaceuticals to Chlorella vulgaris did not change significantly between these
durations (Table 13).
The obtained ecotoxicity data of this study was compared with values found in the
literature. Differences in EC values from algal toxicity tests may be related to different
species or inter-laboratory variance. Results from different laboratories might differ, partly
because of variations among laboratories in the standardization operation (Netzeva et al.,
2008). Examples for variation factors include temperature, pH, nutrients, light, test
protocols or personal handling to name only a few.
57
Moreover, the sensitivity of different growth inhibition tests can be influenced by the choice
of mathematical calculation applied. As previously explained, using yield as test endpoint
usually results in a lower numerical value compared with the specific growth rate (Bergtold
and Dohmen, 2010). According to Bergtold and Dohmen (2010), the parameter growth rate
is more appropriate and robust against deviations in test conditions, permitting better
interpretation and comparison between studies. The study of Bergtold and Dohmen (2010)
compared field and laboratory data and concluded that using ErC50 values combined with
the assessment factor of 10 is sufficient to exclude significant risk in the aquatic
environment.
Weak acids such as chlorophenols tend to ionize at a pH greater than their acid
dissociation constants (pKa). Furthermore the degree of ionization enhances as the (pH –
pKa) differences increases. The decrease in toxicity of weak acids has been associated
with the fact that the unionized form of the molecule contributes to the toxicity more than
the ionized form because the neutral molecule is more bioavailable than the corresponding
charged molecule (Fahl et al., 1995; Escher and Schwarzenbach, 2002). As an example,
Fahl et al. (1995) measured the toxicity of sulfonylurea herbicides, which are weak acids
like chlorophenols, and found that a higher pH 6 led to a reduction in the toxicity to
freshwater Chlorella fusca, while pH 5 enhanced the toxicity. It is therefore, likely that the
pH increase caused by algal growth rendered the chlorophenols less toxic to Chlorella
vulgaris as exposure duration increased from 48 h to 96 h.
Another factor that can be related to the reduction in toxicity with increasing endpoint
duration might be the physiological adaption/acclimation of algae to the test compounds.
Observations were reported by Olivier et al. (2003) who stated that algae acclimated to
chlorophenol compounds and after a lag period the cultures began to grow rapidly. A
reasonable explanation as suggested by Scragg et al. (2003), could be some form of
detoxification which is required before the algae can resume growth. In this study, later
growth of Chlorella vulgaris cells could be observed after a lag phase in the presence of
relatively high concentrations of ibuprofen. In conclusion, together with the influence of pH
on toxicity as discussed above, algal acclimation to chemicals might play a role in
rendering these compounds less toxic at the end of 96 h exposure as compared to 48 h
exposure.
5.1.1 Ibuprofen
In this thesis, IC50 of 89.65 mg/L for Ibuprofen on Chlorella vulgaris could be determined
based on specific growth rate and linear interpolation calculations. Ibuprofen tested on
Desmodesmus subspicatus (green algae) revealed IC50 values of 342.2 mg/L (Cleuvers et
58
al., 2004). By contrast, tests on Pseudokirchneriella subcapitata (green algae) showed an
IC50 value of 2.3 mg/L (Harada et al., 2008). Literature reports regarding the toxicity of this
pharmaceutical compound to green algae suggest that the response to this chemical varies
considerably. The reason why the values for Ibuprofen deviated might be a result due to
different algal species used to determine the ecotoxicity of this pharmaceutical.
5.1.2 Ciprofloxacin HCl
In this study, IC50 value for ciprofloxacin revealed 29.09 mg/L. The toxicity of ciprofloxacin
on Chlorella vulgaris growth was very close to the one obtained by Nie et al. (2008) (EC50
96 h = 20.6 mg/L). Tests on another green algae (P. subcapitata) found in the literature
elicited various EC50 values of 2.97 mg/L, 4.83 mg/L and 18.7 mg/L (Halling-Sorensen et
al., 2000; Martins et al., 2012; Robinson et al., 2005). It can be concluded that
P.subcapitata tends to be more sensitive to ciprofloxacin compared to C. vulgaris. Toxicity
of ciprofloxacin to Chlorella vulgaris tended to decrease between test durations. This could
be associated with the increase in pH of the test medium because of the fixation of CO 2
during photosynthesis. This, in turn, affects the uptake, bioconcentration and toxicity of
phenolic compounds (Neuwoehner and Escher, 2001) and might have also an impact on
pharmaceuticals, such as CiproHCl.
Zhang et al. (2012) reported that co-contamination of ligand-like antibiotics (such as
ciprofloxacin) and heavy metals (e.g. copper, zinc, cadmium) prevails in the environment,
and thus the complexation between them is involved in the environmental risks of
antibiotics. Toxicity analysis indicated that antibiotics, metal and their complex acted
primarily as concentration addition. Therefore the complex was commonly highest toxic
and predominately correlated in toxicity to the mixture. Since the culture media for the
freshwater algae C.vulgaris contained zinc chloride, complexation with ciprofloxacin is
likely to occur, which may lead to secondary toxic effects and may explain differences in
ecotoxicity data for this compound? Environmental scenario analysis demonstrated that
ignoring complexation would improperly classify environmental risks of antibiotics (Zhang
et al., 2012).
5.1.3 2,4-Dichlorophenol
Ertürk et al. (2013) previously reported the toxicity of eight chlorophenols towards Chlorella
vulgaris in 96-h growth inhibition assays and the findings are consistent with those of the
study conducted in this thesis. The IC50 value obtained for 2,4-DCP corresponded well with
the values compiled by Ertürk et al. (2013) with 10.76 mg/L and 9.3 mg/L respectively.
59
5.1.4 3-Chlorophenol
3-CP on Chlorella vulgaris in this thesis revealed an IC50 value of 40.92 mg/L, while Ertürk
et al. (2013) reported an IC50 value of 56.3 mg/L for the same species. Studies for 3-CP on
a different green algae named P. subcapitata resulted in an EC50 of 11.5 mg/L (Aruoja et
al., 2011) and 29 mg/L (Sigma, 2010). Variations of these results might be due to different
species of green algae used in the experiments, which in turn may lead to different
responses of chlorophenols. The reasons why 3-CP from this study deviated from the
stated value from Ertürk et al., (2013) remain unclear as the experiments were performed
under the same conditions. The toxicity data of chlorphenols is abundant in the literature
and in general it was found to be very toxic to aquatic organisms. Interestingly, the
members of the genus Chlorella seem to be relatively tolerant to 3-CP compared to other
algae species.
5.2 Mixture toxicity tests
Generally, it can be concluded that EC values obtained from the mixture toxicity tests were
lower than the EC values obtained from the single toxicity tests for all chemicals tested in
this study. Empirical evidence on the ecotoxicity of chemical combinations show a common
pattern, regardless of the chemical composition of a particular mixture: the combined effect
of a chemical mixture is always higher than the individual toxic effect of the compound
present. It has been repeatedly observed that low toxic concentrations of individual
substances might result in a significant toxicity, if the substances are applied in a chemical
mixture (Faust et al., 2001; Altenburger and Greco, 2009; Backhaus et al., 2008).
Furthermore, a review by Kortenkamp et al. (2009), gives scientific evidence that mixtures
are more toxic than their individual components, independent of the chemical composition
of the mixture, the test organism or test endpoint selected. The toxic mixture effect of
chemicals is always higher than the individual effect of each mixture component. The same
pattern for all tested and combined substances could be observed in the experiments
conducted in this thesis.
Concentration-response curves from the single toxicity tests of all tested chemicals (2,4DCP, 3-CP, ciproHCl and ibuprofen) were compared with the concentration-response
curve obtained from the mixture toxicity exposure tests (Figure 9-12). EC50 values
obtained from the mixture toxicity tests were lower than the EC50 values obtained from
the single toxicity experiments for all four chemical tested. 2,4-DCP and ibuprofen showed
with > 52% the highest increase in toxicity at EC50 when combined together in the algal
growth inhibition test. Ciprofloxacin HCl revealed the biggest increase in toxicity when
60
applied in mixtures at high concentrations. IC95 showed an increase of >82% in toxicity
when present with the other selected chemicals.
5.2.1 Toxic unit and additive index
The toxic unit approach and additive index method were applied to calculate the mixture
toxicity of the selected compounds. The results of both methods revealed additive effects
for all mixtures, except for 3-CP and ibuprofen in mixture, which elicited antagonism. Both,
the additive index as well as toxic unit approach are reliable methods to calculate the
toxicity of chemical mixtures.
While each component in mixture revealed stable EC50 values (Table 15), Ibuprofen in
mixture with 3-CP revealed a slightly higher EC50 value (57.57 mg/L) compared to the
mixtures with Ciprofloxacin HCl and 2,4-DCP with 45.83 mg/L and 43 mg/L, respectively.
However, it should be noted, that the 95 % confidence intervals for Ibuprofen mixtures are
overlapping. Nevertheless, this result is surprising as 2,4-DCP and 3-CP revealed parallel
dose-response curves, only differing in their potency.
Further studies, especially for the antagonistic effect reported for 3-CP and ibuprofen in
mixture, are required in order to explain how the tested compounds interact with each
other.
5.2.2 CA versus IA
Basic concepts of mixture toxicity are based on the biochemical mode of action of the
toxicants. Mixtures are based on a similar or dissimilar mode of action. Moreover, the
compounds can interact with each other, and therefore have an impact on the respective
modes of actions, or work in a non-interactive way and do not influence each other`s mode
of action. Concentration addition (CA) and independent action (IA) are default approaches
in regulatory risk assessment of chemical mixtures in order to determine whether the given
mixture elicits antagonistic, additive or antagonistic effect.
Particularly CA has been proven to provide generally good estimation of expectable
mixture toxicities for a wide range of chemical mixtures. In most cases the toxicity of
chemicals in mixture is additive, meaning the chemicals exhibit the sum of their individual
effects. Synergistic mixture toxicities (considerably more than concentration-additive) seem
to be rare (KEMI, 2010).
61
A review of scientific literature revealed a surprisingly high power of CA to provide reliable
approximation of the toxicity of a broad range of mixtures including substances of different
chemical classes. Deviations from expected additivity in ecotoxicological studies were
found to be quite rare (Kortenkamp et al, 2009). It could be demonstrated that despite the
theoretical foundation of IA, the results of mixture toxicity of dissimilarly acting compounds
is also predictable by CA. Belden et al. (2007) concluded that these results indicate that
the CA model may be used as a conservative and widely applicable approach with a
relatively small likelihood of underestimating effects. As an example for CA, Junghans et
al. (2003a) tested eight similar acting herbicides, chloroacetanilides on Scenedesmus
vacuolatos and demonstrated that CA accurately estimated the toxicity of the herbicide
mixture. Except for 3-CP and Ibuprofen, for all binary mixture tests conducted in this study
the CA approach was more suitable to estimate the mixture toxicity. IA tended to
underestimate the toxicity in this study.
If two chemicals follow the same mode of action, as we could observe with the phenols
tested in this study, the CA approach can be applied to estimate the toxicity. CA model is
based on the fact that the mixture components only differ in the concentrations (relative
potency) needed to elicit a toxic effect. Chemicals that are similar or interchangeable are
assumed to follow the CA expectations. In other words, components can be replaced by an
equivalent concentration of another substance with similar mode of action without changing
the overall mixture toxicity. Figure 7 presents the concentration-response curve for 2,4DCP and 3-CP individually. From this graph the same pattern for these two phenolic
compounds could be observed, only differing in their potency. It can be concluded that
substances with similar modes of action exhibit combination effects that are larger than the
effects of each mixture component applied singly.
In contrast, the IA approach assumes that dissimilarly acting chemicals contribute to a
common biological endpoint, completely independent of other, simultaneously present,
agents. The combined effect can therefore be calculated from the effects caused by
individual mixture components by applying the IA equation (Bliss, 1939). It should be
pointed out that only rare cases have demonstrated that IA can be successfully used for
predicting the mixture effects of multi-component mixtures with different mode of actions.
The more independent and dissimilar the chemicals in a mixture act, the better the
observed mixture toxicity might be estimated by IA (Kortenkamp et al., 2009). One
example of successful application of IA was shown by Faust et al. (2003), giving
reasonable predictions for the toxicity of 16 dissimilar acting herbicides and fungicides on
the green algae Scenedesmus vacuolatos.
Both, CA and IA model, show some severe limitations in predicting the mixture toxicity.
Both approaches are only considering similarity or dissimilarity of toxic action of the mixture
components, but no assumption is made on the targeted biological system or any specific
62
properties of mixture components. The strength of the concepts is the ability to establish
general rules for mixture toxicity assessment, which are necessary to consider joint actions
of chemicals in regulatory guidelines. However, it cannot be assumed that the concepts
describe biological reality to its fullest extent, which results in a weakness of the concepts.
IA describes the extreme situation of completely independently acting chemicals, while CA
describes the opposite extreme of completely interchangeable or similarly acting
chemicals. The CA concept is based on the idea that the mixture components compete for
the same receptor site and that chemicals can therefore be replaced by another toxicant
with the same mode of action. Differences between CA and IA concepts and the observed
mixture toxicity may become visible with an adequate experimental resolution. The crucial
point is if the accuracy of a prediction is sufficient for a certain aim, but not if differences
between simple concepts and complex biological realities can be determined (KEMI,
2010). Chemical with and without the same mode of action are often found in the same
mixture. Moreover, components may toxicologically interact. Furthermore, interspecific
differences and possible interactions at the ecological levels are not satisfactorily
addressed by both, the CA and IA concept (KEMI, 2010).
Limitation factor for both models is the fact that uptake kinetics, transportation, metabolism
and excretion of the chemicals that may have potentially large effects on the mixture
toxicity, are not considered (Altenburger et al., 2003; Junghans et al., 2003a). Additionally,
in many cases information is missing on the modes of action of the chemicals in order to
divide them into groups of similar- and dissimilar action (Faust et al., 2001).
Studies have shown that CA and IA can equally well predict the same mixture toxicity. This
could be proved not only theoretically, but also experimental evidence has shown that
there are in fact examples were CA and IA models provide identical and accurate
predictions of mixture toxicities (Backhaus et al., 2002). This was demonstrated by Syberg
et al. (2008) who tested binary mixtures of similar- and dissimilar-acting chemicals on
Daphnia magna. The study conducted in this thesis, revealed that same phenomenon for
2,4-DCP and Ciprofloxacin HCl in mixture and 3-CP and Ciprofloxacin in mixture. Both, IA
as well as CA approaches were good and accurate predictors to estimate the toxicity.
3-CP and Ibuprofen in mixture elicited an antagonistic effect. Interactions, such as
antagonism or synergism, usually occur at medium or high concentration levels (relative to
the LOEC). Low concentration levels are supposed to be toxicologically insignificant or are
unlikely to occur. Interactions may be influenced by relative exposure levels, the routes,
timing and duration of exposure (including the biological persistence of the mixture
components) and the biological targets (KEMI, 2010). Metabolic, toxicokinetic or
toxicodynamic interactions are examples for interactions and considered to results in
antagonism or synergism (FEA, 2013). IA predicted the toxicity slightly more accurately at
EC50 for 3-CP and Ibuprofen in mixture, but the concentration addition approach should be
63
the preferred model as it generally predicts higher toxicity than independent action, and
therefore gives a worst case scenario.
According to SCHER (2011), it is recommended to prefer the CA method over the IA
approach, if no mode of action information is available. Prediction and explanation of
possible interactions requires in depth expertise and therefore needs to be evaluated on a
case-by-case base. CA would seem a reasonable worst case model for non interactive
combined effect prediction, as in most cases CA predicts higher mixture toxicity compared
to IA (FEA, 2013).
5.3 Risk assessment of mixtures
Risk assessment in the European Union mainly focuses on individual substances, except
“complex substances” falling under the REACH regulation, pesticides and biocidal
formulations as well as cosmetic products. Currently there are no generally accepted
criteria set for the methodology to conduct risk assessment for chemical mixtures. A
framework for the risk assessment of multi-component joint exposures has been proposed
by the WHO/IPCS (2009b). General support for this framework was given at an OECDWorkshop in 2011 (OECD, 2011). For risk assessment purpose the Predicted No Effect
Concentration (PNEC) is of importance and is calculated as followed: PNEC=NOEC/AF,
where NOEC is the No Observed Effect Concentration and AF stands for the Assessment
Factor. PNEC compared with the Predicted Environmental Concentration (PEC) is
essential to determine the risk in the environment. If PEC/PNEC results in >1, an
environmental risk is likely to occur, whereas <1 assumes no risk for the environment.
5.3.1 Options for regulatory mixture effect assessment
Generally, the evaluation of hazardous chemical mixtures can be assessed as a whole or
based on the single components of the mixture (KEMI, 2010).

Whole-mixture approach (WMA): direct experimental testing of the mixture itself,
same like single substance. The benefit is that unidentified materials in the mixture
as well as interactions among mixture components are taken into account (Boobis
et al., 2011). However, for this approach mixtures are restricted to a particular
composition without changing significantly, but it is the only reliable way to consider
synergistic or antagonistic interactions, which are unpredictable by CA or IA
method.
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
Component based approach (CBA): calculation of the predictable mixture toxicity
from data of individual mixture components. Information on the mode of action
should be used to assess the type of combined action (CA, IA) applicable. Both, the
concept of CA and IA, are based on the assumption that interactions do not occur
or are insignificant for the risk assessment. Limitation factors of this approach is
that knowledge might be missing about data on relevant mixture components and
their individual toxicities. IA requires much more data on the mixture components
than CA and bear a higher risk of underestimating the actual mixture toxicity.
Therefore, the usage of IA should be limited to situations where knowledge of mode
of actions and concentration-response relationships of mixture components are
available. For potential synergism, specific assessment factors may be
complemented for the CBA-based mixture toxicity prediction.

Grouping of mixture components based on structural similarities is recommended,
which can be conducted using tools such as the OECD (Q)SAR Application
Toolbox (OECD, 2009). Grouping can also be formed based on toxicological or
biological responses/effects.

Higher-tier assessment: Physiologically-based modelling may be useful for a
higher-tier assessment. This model can provide estimate of the concentration of the
compound at the target site for a toxicological effects. Such models require
intensive resources and expertise, and are therefore unlikely to be implemented in
routine settings.

Epidemiological studies has been proposed by Levy (2008). This study proposes
several criteria to provide quantitative concentration-response relationships within
the exposure levels for all key stressors with accounting interactions or other
combination effects. These criteria will almost never be fulfilled, as all key stressors
and factors will never be fully identified. However, the criteria may provide a basis
for the development of a framework allowing the best use of epidemiological data.

Specific aspects relating to ecological effect assessment The concept of CA and IA
are assumed to be the same for human and the environment. However, toxicology
and ecotoxicology show some substantial conceptual differences, which may affect
the application of CA and IA models. The most important difference is the objective
of the protection. Human toxicology aims to ensure a high level of protection of
individuals, while on the contrary, ecotoxicology aims to protect structure and
functions of biological communities and ecosystems. Endpoints may be different in
toxicology compared to ecotoxicology. The latter endpoints are relatively broad and
related to parameters such as reduction in fertility or massive mortality. Some
65
effects may be extremely important for individuals, but lead to a moderate effect on
population dynamics and are therefore negligible in ecotoxicological settings. In
comparison, human toxicology often focuses on endpoints to a specific target organ
that in turn are meaningless in ecotoxicology (SCHER, 2011; FEA 2013).
The assessment of chemical mixtures is particularly relevant for low or even very low
concentration exposures, as each single organism is exposed to huge number of a variety
of substances in the environment. The sensitivity of test organism can differ by several
orders of magnitude, even when exposed the chemicals with specific modes of actions.
Hence, the component selected for mixture toxicity assessment may differ for each species
as well as with time. The concepts of CA and IA at levels close to the no observed effect
level (NOEC) are applicable for individuals and species, but difficult to implement when
moving to population and community effects (FEA, 2013).
From an ecological point of view, there exist almost an infinite number of possible
combinations of chemicals to which humans and organism in the environment are exposed
to. In order to focus on mixtures which are of public concern due to their potential adverse
effects, some form of initial filter should be applied. At the present time, exposure
information and available number of chemicals with sufficient information on their mode of
action are limited. Currently, there is no defined set of criteria available that suggests how
to characterize or predict a mode of action for data-poor chemicals (SCHER, 2011).
5.3.2 Environmental exposure assessment
Water, sediment, air, soil and biota (food) are the main environmental compartments, the
latter only for chemicals with bioaccumulation and biomagnifications potential. The
environment is predominately exposed to a variety of mixtures and their compositions
change with time, hence must be estimated through transport and persistence patterns.
Pharmaceuticals are typical examples of industrial mixtures and formulations that often
contain several active components with different chemical structures and environmental
fate behaviour. The environmental fate (distribution and persistence) may be different for
individual mixture components even for substances released simultaneously. The
exposure risk assessment is much more complex as small difference in the behaviour of
each component may significantly affect the overall risk. Potential degradation (e.g.
photodegradation, hydrolysis), different physic-chemical properties and ecotoxicological
properties of individual components lead to difficulties in carrying out environmental risk
assessment for mixtures. Each mixture component will be subjected to different distribution
and fate processes once released to the environment. The use of QSARs for generation of
physico-chemical properties (e.g. log KOW, water solubility, melting point, vapour pressure)
66
and degradation rates is a reasonably well accepted method. Distribution in different
environmental compartments can be predicted by modelling (KEMI, 2010).
The presence of other mixture components can have a strong impact on the
biodegradation of chemicals. Biodegradation belongs to the major process, which can lead
to the disappearance of chemicals from aquatic and terrestrial environments. Interactions
of chemicals are expected to play a role in biodegradation rather than chemical or physical
patterns. Co-metabolism and enzyme induction also allow degrading complex mixtures
(SCHER, 2011; FEA 2013).
In addition to chemical mixture toxicity assessment, uncertainty analysis associated with
the individual chemicals as well as mixture itself need to be addressed. Examples for
uncertainties in the exposure assessment of mixtures include the level of accuracy with
which exposure to mixtures has been characterized or adequacy of the toxicological
database. Another factor is the mode of action of chemicals, which can differ for several
types of organisms (bacteria, plants, invertebrates, vertebrates) to name a few. REACH is
currently generating the largest database on chemicals in history, and data could be used
to reduce or eliminate some of the uncertainties (SCHER, 2011).
Hormetic-like biphasic concentration responses of substances become progressively more
recognized (Calabrese et al., 2003). Hormesis complicates the chemical risk assessment,
because two different NOECs can be determined from the concentration-response curve.
Hormesis in mixture toxicity studies can even increase the complexity if a strong correlation
to the single substance curve is to be drawn.
5.4 Environmental impact
Mixtures of toxic compounds that co-occur in an environmental compartment may
negatively impact organism, food and human body and thus, poses a substantial challenge
for the current risk assessment and management system of chemicals. Ecotoxicity
experiments are usually conducted at concentrations above 1 µg/L in order to assess
acute toxicity. In contrast, organisms in the environment are exposed continuously to low
concentrations of a variety of compounds simultaneously and thus, chronic effects are
likely to occur (FEA, 2013). Various studies suggest that pharmaceuticals at concentrations
found in the environment may have an impact on water organisms (Daughton and Ternes
1999, Ferrari et al. 2003, Isidori et al. 2005b). The continuous entry of drugs into the
aquatic environment, even at low concentration, may pose long-term potential risks to
aquatic and terrestrial organisms.
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As green algae, such as Chlorella vulgaris, form the base of the food web in the aquatic
ecosystem, it is of great concern that the effect on algal flora from toxicants released into
the environment will extend to the whole ecosystem. It is likely that agents showing toxic
activity to algae will cause effects on other organisms, such as zooplankton and insects.
The presence of PPCPs in the aquatic environment and impact on aquatic biota and on
human health has not yet been studied adequately, though it can be found in water bodies
throughout the world. Experimental evidence indicates that pharmaceuticals may cause
harmful effects, such as metabolic, morphological and sex alterations on water species,
induction of antibiotic resistance in pathogenic microorganisms, and disruption of
biodegradation activities in WWTPs. Especially the evaluation of chronic long-term toxicity
effects should be put as priority since simultaneous exposure to chemicals, metabolites
and transformation products of several different chemical classes are unkown.
Furthermore, probable effects on several subsequent generations in different
environmental compartments belonging to various species of different trophic levels should
be evaluated in order to gain reliable knowledge of contamination levels throughout the
world. Emerging pharmaceuticals should be integrated in the revision of EU List of Priority
Substances under the Water Framework Directive 2000/60/EC and a definition of adequate
environmental quality standards should be implemented. Moreover the question, to what
extent drugs can be transferred to humans through food-chain biomagnification, should be
addressed.
Although the mechanisms of action are known for the PPCPs tested in vertebrates, it is
unknown what the mechanism of action is in non-target water species. Some chemicals
(e.g. pesticides) have been developed with a specific activity and therefore the mode of
action is well known for the target organism, but toxicological mechanism of action for nontarget organisms is lacking. For example, pesticides affect certain metabolic function of the
target organism, but that is usually not common to all species present in a biological
ecosystem. Narcotic-type toxicity (baseline toxicity) is likely to occur in non-target
organisms exposed to the chemical. The Swedish Chemical Agency (KEMI, 2010)
mentioned in the report relationships between algal toxicity and octanol-water partition
coefficient (KOW) for some compounds belonging to different chemical groups with specific
and non-specific toxic effect on algae. It could be demonstrated that chemicals with
specific toxic effects (organophosphate and chlorinated insecticides) lead to baseline
toxicity on algae, while the toxicity of triazines (specific photosynthesis inhibitors) is orders
of magnitude higher. It is well known that non-specific toxicity of chemicals can be
described by two kinds of actions: non-polar narcosis (type I narcosis) and polar narcosis
(type II narcosis). Non-polar narcotic chemicals are considered baseline toxicants. It
means their toxicity is proportional to their concentrations at the site of action and is
caused by membrane perturbation (Escher and Schwarzenbach, 2002).
68
Ibuprofen
Rates of degradation of pharmaceuticals in waste water treatment plants vary enormously.
Ibuprofen has a very high elimination rate (> 90 %) and is rapidly degraded. For most
pharmaceuticals, the concentrations detected in the environment are at least an order of
magnitude lower than the levels shown to cause an effect. However, there are a few
exceptions, including ibuprofen, which have been detected in waste water treatment
effluents and surface waters at concentrations up to 2.4 µg/L. This is a concentration range
that has been reported to cause toxic effects to fish in the laboratory (Schwaiger et al.,
2004).
Ciprofloxacin HCl
Antibiotics are bioactive compounds and belong to pharmaceuticals of emerging concern
as they are considered to enhance antibiotic resistance among pathogenic bacteria,
rendering current antibiotics ineffective in the treatment of numerous diseases (Homem
and Santos, 2011). For many years, fluoroquinolones (ciprofloxacin) has been detected in
aquatic and terrestrial ecosystems (Kemper, 2008). The removal rate of this antibiotic is
approximately 85 % by conventional waste water treatment plants. However, due to the
high affinity to soil, the removed fraction is often accumulated in the sludge. Sludge is
sometimes used as fertilizer and thus, represents an additional environmental input route.
As a consequence antibiotics may be transferred to plants and will enter the human food
chain. For this reason, it is of paramount importance to develop effective treatments for the
destruction or inactivation of these pharmaceutical compounds. It is believed that only
advanced oxidation technologies are able to destroy these emerging contaminants
(Ikehata et al., 2006). Most conventional wastewater and drinking water treatments are
based on biological degradation, flocculation, coagulation, sedimentation and filtration –
processes shown to be insufficient to removing or destroy PPCPs including antibiotics.
Therefore, the development of new and more efficient process is recommended in order to
enhance the removal rate of pollutants of emerging concern (Hohem and Santos, 2011).
Antibiotics can also impact the endocrine system of fish and the potential long term health
effects attributed with chronic ingestion of antibiotic mixtures through drinking water remain
poorly understood (Ikehata et al., 2006; Fink et al., 2012). Until recently, PPCPs in the
environment have drawn very little attention, despite their presence in the effluents of
WWTPs. It was believed that pharmaceuticals were easily biodegradable in the
environment owing the fact that most drugs could be transformed and metabolized to some
extent in humans (Kümmerer et al., 2000; Ikehata et al., 2006). However, numerous recent
studies have confirmed the persistence of these pharmaceuticals in aquatic ecosystems
(Ikehata et al., 2006). Kümmerer (2009) has reported that Ciprofloxacin does not
69
biodegrade well under both, aerobic or anaerobic conditions, and therefore cannot be
classified as “readily biodegradable”.
Chlorophenols
Chlorophenols are common global pollutants in groundwater, surface water, waste water,
sludge products and drinking water due to their agricultural and industrial use (e.g. as
pesticides, wood preservatives etc) throughout the world. The widely used industrial
chlorophenols (polar narcotic chemicals) have gained significant attention due to the acute
as well as chronic toxicity to aquatic life, risk to ecological systems, potential to
bioaccumulation and resistance to degradation. 2,4-DCP is one of major contaminants of
phenolic compounds due to its ubiquitous occurrence and persistence, which pose health
risk to human. Adapted microflora is capable of biodegrading chlorophenols, hence
persistence of these compounds is low when adjusted plants are present. However,
persistence may become moderate to high depending on conditions in the environment.
5.4.1 EC50 versus environmental concentration
All compounds tested in this study have the potential to be harmful according to Annex VI
of Directive 67/548/EEC. Comparing the effect concentrations generated in this study to
maximum levels of the chemical compounds reported in environment, no tested chemical
has the potential to negatively impact phytoplankton in aquatic compartments. As the
highest concentrations found in the environment for all tested compounds did not exceed
the lowest observed effect levels, negative effects on Chlorella vulgaris are not expected.
Ibuprofen has been measured at maximum concentrations of 2.4 µg/L in surface water in
Germany (UBA, 2011) and is significantly less than the EC50 of 89.65 mg/L obtained in this
study with Chlorella vulgaris. The lowest observed effect concentration of 30 mg/L is 8000
times lower than the reported environmental concentration. This pharmaceutical had very
little effect on the freshwater algae and is unlikely to have a negative impact on natural
phytoplankton populations in surface waters.
The widely used antibiotic ciprofloxacin has been detected up to 124.5 µg/L in waste water
treatment plants near hospitals in Switzerland (Fink et al., 2012). EC50 value determined in
this study (29.09 mg/L) was well below than the highest reported concentration for this
antibiotic.
The EC50 values in mixtures decreased by more than 52 % for 2,4-DCP and Ibuprofen
when combined together, with EC50 values of approximately 5 mg/L and 43 mg/L,
70
respectively. Chorophenols detected in the environment are in the 0.5 µg/L range and
below the reported EC value of 2,4-DCP or 3-CP from this study, whether tested as single
compound or in mixture. Although the environmental risk increases for compounds in
mixtures, the mixture effect concentrations are still much higher than the expected
environmental concentrations, and a significant effect on Chlorella vulgaris population
would not be likely. The same conclusion can be made for Ibuprofen and Ciprofloxacin
HCl. Nevertheless, EC50 values for Ibuprofen, Ciprofloxacin and 3-CP are lower than the
LOEC values, thus it can be expected that these compounds have a potentially negative
effect on Chlorella vulgaris in surface waters when applied in mixture.
Ciprofloxacin HCl revealed the biggest increase in toxicity when applied in mixtures at high
concentrations. IC95 showed an increase of >82% in toxicity when present with the other
selected chemicals. This antibiotic revealed an IC95 of around 49 mg/L when combined
with Ibuprofen compared to the single toxicity IC95 of 278 mg/L. This value is still below the
highest reported environment concentrations, however, the rapid increase in mixture
toxicity raises concern due to the fact that the environment is not exposed to binary
mixtures but to a huge number of different substances simultaneously.
All in all, the obtained effect concentrations for the tested compounds were generally
above the levels detected in the aquatic system. However, the integration of exposure and
effect data in the Predicted Effect Concentration (PEC) / Predicted No Effect Concentration
(PNEC) ratios may pose risk for the other sensitive water species.
5.4.2 Fate and transport of test chemicals
The environmental fate and transport of chemicals are controlled by their chemical and
physical properties as well as environmental conditions. Among others, solubility, vapor
pressure, pKa and log Kow (octanol water partition coefficients) are important properties in
order to determine the transport and partitioning of chemicals.
A high vapor pressure of 3-CP and 2,4-DCP (> 8 Pa at 25 °C) indicates that the compound
will exist as vapor in the atmosphere when released to air, but is not expected to volatilize
from dry soil surfaces. High pKa values (> 7) of chlorophenols indicate that the compound
primarily exist in a non-dissociated form. The pKa value for the tested pharmaceuticals
(CiproHCl and Ibuprofen) are slightly lower, therefore this compound can exist in a nondissociated as well as ionized form in the aquatic environmental depending on the pH. If
released to soil, Ibuprofen, 3-CP and 2,4-DCP are expected to have moderate mobility
based upon a log Koc of around 2.5. Ciprofloxacin HCl with a log KOC value around zero,
has the potential to leach into surface and groundwater.
71
3-CP and 2,4-DCP is expected to biodegrade in both aerobic and anaerobic soils with
biodegradation half-lives ranging from 15 to 160 days. If released to the aquatic
environment, the tested phenolic compounds are considered to adsorb to suspended
solids and sediments. Based on the Henry´s Law constant, volatilization from water
surfaces is not expected to be a major removal process. The BCF between 1.3 and 1.6
suggests that bioconcentration in aquatic organisms is low. Hydrolysis is not expected to
play a crucial role. Photodegradation in surface waters is likely to have an impact in the
removal process of 2,4-DCP. This substance has been detected in rain waters, therefore
physical removal from air by means of wet deposition may have some influence in the fate
of this chemical.
Generally, it can be concluded that a chemical preferentially partition into organic matter if
its log Kow is >1. A low KOW reduces the affinity of the compound on soils, sediments,
minerals, and dissolved organic material leading to enhanced bioavailability of the
chemical in the environment (Jjemba, 2004). According to the chemical properties of
CiproHCl, this antibiotic demonstrates a very high level of bioavailability. Besides that, a
low KOW facilitates the transfer of the polar compounds into cells and enhances
bioaccumulation of the chemical. Log KOW for the studied chlorophenols are > 2, therefore
these compounds tend to partition and absorb into sediments. A low solubility and high log
KOW value usually indicates that a compound tend to dissipate from the water-phase and
absorb into organic matter and sediment. A high KOW is typical for hydrophobic chemicals
and therefore more soluble in octanol than in water. According to the chemical properties
of each tested chemical (Table 2 – 5), this is the case for ibuprofen. This widely used
painkiller might therefore be a potential threat to organisms living and feeding in the
sediment. There is also the tendency for ibuprofen to partition in lipids and to
bioaccumulate in organisms.
The other tested compounds (Ciprofloxacin HCl, 2,4-DCP and 3-CP) show rather high
solubility and low/moderate log Kow and are more likely to cause an effect to organisms
living in the aquatic environment. High solubilities and lower organic carbon coefficients
(KOC) for soils suggest that the lower chlorinated phenols may be susceptible to leach into
surface and ground waters. Chlorophenols are prone to photolysis and biodegradation.
The main route of removal for chlorophenols in deeper water and sediment, is aerobic and
anaerobic biodegradation, while photolysis is only expected near the surface of water
bodies. A low Henry´s Law Constant suggests that volatilization from surface waters is not
likely to be an important removal route for chlorophenols.
Studies have indicated that KOW may not always be a good descriptor of the behavior of
PPCPs in the environment (Boxall et al. 2004). When synthetic organic chemicals, such as
pesticides, pharmaceuticals, biocides and industrial chemicals, are released into the
72
environment, they are subject to various transformation processes. The environment is not
only exposed to mixtures of parent compounds but also to their corresponding metabolites
and transformation products. If metabolites are more persistent and mobile than their
parent compounds, they may be detected in even higher concentrations than their parent
compounds in the aquatic environment (Boxall et al., 2004).
Significant gaps still exist in the understanding of the interaction between metabolites,
residues and induction of resistance after excretion of pharmaceuticals, thus there is an
emerging concern in the general public about potential adverse effects on chemical
mixtures. The current EU legislation, spearheaded by REACH and CLP, requires only in a
few instances, the evaluation of joint risks from the exposure to multiple chemicals (e.g. for
pesticides when suitable methodology is available).
This study examined a very small subset of the thousands of prescribed drugs and
industrial relevant phenols with potential for entering the aquatic environment and causing
adverse effects in organisms. The real environmental concern might be the effects of these
complex chemical mixtures on aquatic organisms. Although most of the tested chemicals
did not affect Chlorella vulgaris at levels found in the environment, if multiple PPCPs or
other chemicals are present, lower than expected levels may lead to toxic effects. Most of
the mixture experiments in this study revealed additive effects. In other words, the toxicity
threshold for freshwater organisms decreased in proportion to the mixture response. If the
majority of substances interact in the same way, it may be feasible to predict the mixture
toxicity using individual toxicity data. However, further research studies need to be
conducted to understand the interactions on the tested compounds.
Mixture toxicity developed in a remarkable and productive way during the past ten years.
Owing to time and resource limitations, direct toxicological experiments or information will
never be available on all the possible mixtures to which humans or living organisms are
exposed to. The risk assessment of single toxicants is inefficient for the multiple
combinations of contaminants and different stressors existing in the environment.
Laboratory-based approaches cannot be the only answer address human health and
environment concerns. Exposure models still have to be further developed to better
estimate chemical exposure, as the currently used models have some severe limitations.
The effects of low concentration need to be sufficiently considered with accounting on the
sensitivities of the different species. In addition, statistically based methods may be
beneficial to support current approaches and to better assess uncertainties. Biomonitoring,
biomarkers, environmental monitoring, surveillance and population surveys can help to
ensure an accurate exposure assessment. The understanding of mechanism of actions of
emerging contaminants requires further development and progress. Nevertheless, the
assessment of interactions between chemicals and the environment remain very difficult.
Using natural ecosystems or communities, increases the ecological relevance of the
73
observed effects, however, it also leads to a lower reproducibility of the experimental data.
These data can be influenced by physiological aspects and species composition may not
remain constant between exposure experiments. The growth medium and its absorptive
behaviour might alter due to changes in water chemistry, leading to differences in the
bioavailability of the test chemicals (KEMI, 2010).
74
6 Conclusion
As demonstrated by several studies, humans and other organism in the environment are
exposed to a variety of substances and thus, causing an increasing concern in the general
public about potential adverse effects of interactions between those chemicals when
present in mixture. Aquatic ecosystems have been severely threatened by discharges of
toxic compounds. Pharmaceuticals are designed to have a biological therapeutic effect, but
may also cause similar effects in non-target organisms. The chemical legislation,
spearheaded by REACH and CLP, aims to ensure a high level of protection of human
health and the environment, but it is rarely based on the assessment of combination effects
of chemicals.
In this study, the toxicity experiments have been carried out based on the algal growth
inhibition test OECD No. 201 (OECD 2006) criteria prepared by the Organization for
Economic Cooperation and Development. Individual and binary mixture toxicity
experiments of selected pharmaceuticals (ibuprofen and ciprofloxacin) and phenolic
compounds (2.4-dichlorophenol and 3-chlorophenol) have been performed with freshwater
algae Chlorella vulgaris. All substances tested had a significant effect on Chlorella vulgaris
population density and revealed IC50 values < 100 mg/L. The toxic ranking of these four
compounds to Chlorella vulgaris was 2,4-DCP > Ciprofloxacin HCl > 3-CP > Ibuprofen
according to Annex VI of Directive 67/548/EEC. Binary mixture tests were conducted using
proportions of the respective EC50s (=1 toxic unit (TU)). The mixture concentrationresponse curve was compared to predicted effects based on both the concentration
addition and the independent action model as suggested in regulatory risk assessment
provided by the European Chemicals Agency (ECHA). The TU and Additive Index (AI)
approach could demonstrate that the combined toxicity of pharmaceuticals and phenols
mostly lead to additive mixture effects, except for 3-CP and Ibuprofen in mixture the effect
was antagonistic. The CA model is a better predictor to estimate toxicity, as the IA model
tends to underestimate the toxicity in most cases. The EC values obtained from the mixed
exposure tests were more than 52 % lower than the EC values obtained from the single
exposure experiments for all chemicals tested in this study. Further studies, especially for
the antagonistic effect reported for 3-CP and ibuprofen in mixture, are required in order to
explain how the tested compounds interact with each other.
The toxicity of chemical mixtures has to be adequately addressed in the regulatory risk
assessment. Pharmaceuticals with its potential impact on aquatic organisms could be
included in the EU List of Priority Substances relevant to the Water Framework Directive
2000/60/EC in the current or future revision. Approaches that directly address joint
exposure scenarios, as put forward in the WFD, might provide an adequate option to
75
further improve the protection of humans and the environment from chemical mixture risks
(KEMI, 2010).
For risk assessment purpose it is advisable to apply some form of initial filter (e.g. chemical
and physical properties, mode of action, etc.), as there exists almost an infinite number of
possible combinations of chemicals. Exposure models still have to be further developed to
better estimate chemical exposure, as currently used models have some severe
limitations. Especially the evaluation of chronic toxicity effects should be set out as priority
since simultaneous exposure to chemicals, transformation products and metabolites of
various chemical classes are unkown. Probable effects on several subsequent generations
in different environmental compartments should be assessed in order to gain reliable
knowledge of contamination levels throughout the world.
Moreover, the development of effective treatments for the destruction or inactivation of
pharmaceutical compounds and other chemicals of emerging concern is necessary, since
conventional waste water treatment plants based on biological degradation are shown to
be inefficient in the removal process.
Only further analysis will improve existing legislation in order to protect human, animals
and ecosystems from the threat posed by the presence of pharmaceuticals and other
industrial discharges in the environment.
76
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List of Figures
Figure 1: Microscopic view of Chlorella vulgaris ................................................................11
Figure 2: The parent phenol molecule ...............................................................................20
Figure 3: Algal inoculation in laminar air flow cabinet ........................................................30
Figure 4: Algal growth inhibition assay in growth chamber ................................................32
Figure 5: Flow diagram of USEPA approved statistical methods performed by ToxCalc TM
5.0.32 (© Tidepool Scientific Software, USA) ....................................................................35
Figure 6: Absorbance versus number of algal cells (specific growth curve) for Chlorella
vulgaris .............................................................................................................................39
Figure 7: Concentration-response relationship curve for Chlorella vulgaris toxicity from
single compound toxicity tests of 2,4-dichlorophenol, 3-chlorophenol, Ciprofloxacin HCl and
Ibuprofen respectively. Response endpoint is reduction in growth (% Inhibition) after 96 h
using specific growth rate calculation and ICp method executed in Toxcalc software. .......40
Figure 8: Concentration-response relationship curve from single compound toxicity tests of
2,4-dichlorophenol, 3-chlorophenol, Ciprofloxacin HCl and Ibuprofen respectively after 48h,
72h and 96 h using specific growth rate calculation and ICp method executed in Toxcalc
software. ...........................................................................................................................41
Figure 9: Concentration-response curve of 2,4-DCP individually compared to mixed
exposure tests...................................................................................................................46
Figure 10: Concentration-response curve of 3-CP individually compared to mixed exposure
tests ..................................................................................................................................47
Figure 11: Concentration-response curve of CiproHCl individually compared to mixed
exposure tests...................................................................................................................47
Figure 12: Concentration-response curve of Ibuprofen individually compared to mixed
exposure tests...................................................................................................................48
Figure 13: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of 2,4-DCP and 3-CP. .......................................49
Figure 14: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of Ibuprofen and CiproHCl. ...............................50
Figure 15: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of 2,4-DCP and CiproHCl..................................51
Figure 16: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of 3-CP and CiproHCl. ......................................52
90
Figure 17: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of 2,4-DCP and Ibuprofen. ................................53
Figure 18: Comparison of concentration-response curves obtained from predicted joint
effects of concentration addition (CA) and independent action (IA) with observed effect
(exp) from the binary mixture toxicity test of 3-CP and Ibuprofen. .....................................54
Figure 19: 2,4-dichlorophenol and 3-chlorophenol calibration curve for gas
chromatographic analysis..................................................................................................95
Figure 20: Ibuprofen chromatogram for HPLC chromatographic analysis ..........................96
Figure 21: Ciprofloxacin HCl spectrophotometric graph.....................................................97
91
List of Tables
Table 1: Scientific classification of Chlorella vulgaris .........................................................11
Table 2: Estimated chemical properties of Ibuprofen25 retrieved from EPISuite, version
4.11 ...................................................................................................................................17
Table 3: Estimated chemical properties of Ciprofloxacin HCl retrieved from EPISuite,
version 4.11 ......................................................................................................................19
Table 4: Estimated chemical properties of 2,4-dichlorophenol retrieved from EPISuite,
version 4.11 ......................................................................................................................22
Table 5: Estimated chemical properties of 3-chlorophenol retrieved from EPISuite, version
4.11 ...................................................................................................................................23
Table 6: Test chemicals used for toxicity testing ...............................................................24
Table 7: Chemicals ...........................................................................................................25
Table 8: Reagent-Formulation ...........................................................................................25
Table 9: Laboratory equipment..........................................................................................27
Table 10: Consumable materials .......................................................................................28
Table 11: Software / Computer..........................................................................................28
Table 12: Test conditions of the algal bioassay .................................................................30
Table 13: 50% and 20% inhibitory concentrations (IC50 and IC20) calculated at the end of
48, 72 and 96 hours based on different methods executed in ToxCalc software using yield
and specific growth rate (SGR) calculations, no-observed effect concentration (NOEC),
lowest-observed effect concentration (LOEC), toxic class for C.vulgaris ...........................42
Table 14: Toxicity classification of chemicals according to Annex VI to GLP Directive
67/548/EEC.......................................................................................................................45
Table 15: 50% single and mixture effect concentrations at 96 hours, Additive Index and
joint toxic action for Chlorella vulgaris ...............................................................................55
92
List of Abbreviations
Symbol
Explanation
AF
AI
BCF
CA
CAS
CBA
CLP
CP
Cv
DCP
DMSO
DNA
EC
EC50
Assessment Factor
Additive Index
Bioconcentration factor
Concentration Addition
Chemical Abstracts Service
Component Based Approach
Classification, Labeling and Packaging
Chlorophenol
Chlorella vulgaris
Dichlorphenol
Dimethyl sulfoxide
Desoxyribonucleic acid
European Commission
Concentration of a compound that causes 50% effect on
test organism relative to a control
European Chemicals Agency
ECOTOXicology database
European Inland Fisheries Advisory Commission
Environmental Protection Agency
European Union
Experimental
Gas Chromatography
Globally Harmonized System
High Performance Liquid Chromatography
Hazardous Substances Data Bank
Independent Action
Concentration that inhibits algal growth by 50%
Linear interpolation combined with bootstrapping
Concentration of a compound that causes 50% lethality
of the test organisms in a batch assay
Lowest Observed-Effective Concentration
Logarithm of n-octanol/air partition coefficient
Logarithm of organic carbon partition coefficient
Logarithm of n-octanol/water partition coefficient
Milimolar
Mode of Action
ECHA
ECOTOX
EIFAC
EPA
EU
Exp
GC
GHS
HPLC
HSDB
IA
IC50
ICp
LC50
LOEC
Log KOA
Log KOC
Log KOW
mM
MOA
Unit
mg/L
mg/L
mg/L
mg/L
93
MSDS
NOEC
NSAID
OECD
PBDE
PCB
PEC
pKa
PNEC
PPCP
QSAR
QSTR
REACH
SCHER
SGR
SSRI
STP
TU
UBA
USEPA
WFD
WHO/IPCS
WMA
WW
WWTP
Material Safety Data Sheet
No Observed-Effect Concentration
Nonsteroidal anti-inflammatory drug
Organization for Economic Cooperation and
Development
Polybrominated diphenyl ether
Polychlorinated biphenyl
Predicted Environment Concentration
Negative base 10 logarithm of the acid dissociation
constant
Predicted No Effect Concentration
Pharmaceutical and Personal Care Products
Quantitative Structure-Activity Relationship
Quantitative Structure-Toxicity Relationship
Registration, Evaluation, Authorization and Restrictions
of Chemicals
Committee on Health and Environmental Risks
Specific Growth Rate
Selective Serotonin Reuptake Inhibitor
Sewage Treatment Plant
Toxic Unit
Umweltbundesamt
United States of Environmental Protection Agency
Water Framework Directive
World Health Organization / International Program on
Chemical Safety
Whole Mixture Approach
Waste Water
Waste Water Treatment Plant
94
Appendix A: Calibration Curve for 2,4 Dichlorophenol
and 3-Chlorophenol
Method: GC Agilent 6890N equipped with an automatic sampler, split/splitless injection
port and flame ionization detector
Column: HP-5MS capillary, 0.25m, 30 m long, 0.25 mm inner diameter and 0.25 film
thickness
Flow rate: 33.3 cm/sec constant
Injector: splitless mode
Temperature: 40°C for 1 min, 140°C for 10 min, 260°C/min, injector temperature 250C,
detector temperature 300°C
Mobile Phase: Helium
Extraction: Methylene choride
Figure 19: 2,4-dichlorophenol and 3-chlorophenol calibration curve for gas chromatographic
analysis
95
Appendix B: HPLC Chromatogram for Ibuprofen
Method: HPLC Chromatographic System
Column: C-18, 5m, 4.6 x 150 mm, BDS
Detector : UV
Wavelength: 220 nm
Flow rate: 2 mL/min
Injection Volume: 100 L
Temperature: 25C
Mobile Phase: 0.01 M Orthophosphoric acid solution- Acetonitril (60:40)
Figure 20: Ibuprofen chromatogram for HPLC chromatographic analysis
96
Appendix C: Spectrophotometric graph Ciprofloxacin
HCl
Method: Spectrophotometer
Detector: UV
Wavelength: 276 nm
Figure 21: Ciprofloxacin HCl spectrophotometric graph

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