Final Version Faridi (12okt)

An overview of QSAR-studies for sorption and accumulation of anionic and non-ionic surfactants: Limitations and new perspectives

Utrecht University

Faridi Purperhart

October 2016

An overview of QSAR-studies for sorption and accumulation of anionic and non-ionic surfactants October 14, 2016

2 Title page | Utrecht University, IRAS

Title page

An overview of QSAR-studies for sorption and

accumulation of anionic and non-ionic surfactants: Limitations and new perspectives

Colophon

Institute

Utrecht University

Course

Writing assignment

Author

Faridi Purperhart

Student number

3429032

Supervisor

dr. J.L.M. (Joop) Hermens

Date

October 2016


3 Content | Utrecht University, IRAS

Content

1. Introduction 2. A short introduction into QSAR

a. Hydrophobicity and the octanol-water partition coefficient 3. Objectives of this study 4. Surfactants

a. Structure b. Critical micelle concentration (CMC)

5. Effects of chemical structure of surfactants on environmental properties or parameters and examples of QSARs

a. Environmental behaviour: biodegradation, sorption and bioaccumulation b. Surfactant toxicity

6. A critical evaluation and discussion of Quantitative structure-activity relationship a. Limitations of log P based QSARs for anionic and non-ionic surfactants b. Alternatives for log P or log P based QSARs for anionic and non-ionic surfactants c. Recent developments

7. Conclusion 8. References


4 Introduction | Utrecht University, IRAS

Figure 2: Surfactants tend to lower the surface tension at low concentration and form micelles at high concentration. (x=surfactant concentration; y= surface tension) (Illustration: self-constructed).

Introduction

1. Introduction

Surfactant, which is a blend of surface-active agent, is a compound that lowers the surface tension at different interfaces, for example between two liquids or between a liquid and a solid (Ivancovic and Hrenovic, 2009). A lower surface tension of a liquid leads to an increase in the surface area. Because of this property, surfactants are widely used as detergents and cleaning products (Ivancovic and Hrenovic, 2009; Burlatsky et al., 2013), e.g. in household cleaning detergents, and industrial products, such as soap, personal care products, oil recovery (Burlatsky et al., 2013), pharmaceuticals and pesticides (Rosen, 2004). Surfactant molecules, are amphiphilic, meaning that they exert hydrophobic and hydrophilic behaviour. They consist of a polar head group (hydrophilic surface- active portion) and a non-polar hydrocarbon tail (Rosen, 2004; Ivancovic and Hrenovic, 2009) (figure 1). The presence of a hydrophobic and a hydrophilic group influences the behaviour of surfactants. At low concentrations surfactants tend to adsorb onto the surfaces or interfaces of liquids, meaning that they diffuse to the surface, thereby lowering surface tension (figure 2). This results in a shift of free energies between surfaces or at interfaces. At high concentrations surfactants tend to form micelles (figure 2). A micelle is a colloidal sized cluster of surfactant molecules in solution (Rosen, 2004; Ying, 2006). Here, the hydrophobic tails cluster together while the hydrophilic heads come in contact with the environment. However, depending on their environment, it can be the heads that cluster together while the tails come in contact with the environment. The formation of micelles takes place at a specific concentration, called the critical micelle concentration (CMC). The CMC is the concentration of surfactant molecules in a liquid at which micelles start to form (Ying, 2006) and it establishes the detergency and the solubility of the surfactant (Jensen, 1999). There are different classes of surfactants, depending on the charge of their head group. The head group can either be charged or free of charge (table 1). The nature of the non-polar hydrocarbon tail of a surfactant, which can consist of eight to twenty hydrocarbons, affects surfactant properties in five distinct ways. These will be discussed in chapter 4. The use of surfactants experienced two shifts during the seventies. Industries shifted from the use of soap-based detergents to synthetic surfactants and solid domestic detergents (powder) made room

Figure 1: Sodium dodecyl sulphate (SDS), an example of a surfactant molecule with a hydrophobic tail and a hydrophilic head. Source: Learn Biochemistry, 2011.


5 Introduction | Utrecht University, IRAS

for the use of liquids. These shifts resulted in an excess of non-biodegradable surfactants in, for example, wastewater (Scott and Jones, 2000), sewage systems (van Compernolle et al., 2006) and surface water (Tolls et al., 1997). Since the use of synthetic surfactants is increasing worldwide and chemical waste products can have devastating effects on the environment, it is important to determine the aquatic eco-toxicity of surfactants (Rosen, 1987). This has resulted in a change of legislation in many European countries, calling into existence the European law in 1973. The European Commission has directed that all new chemical products must be tested for their biodegradability. Compounds used as detergents must be degraded at least 80% within 28 days after disposal (Hallmann et al., 2013). This, in order to minimize the eco-toxic potential of chemical substances in the environment. There are different ways to determine the eco-toxicity of chemical substances. For surfactants, researchers have adopted the quantitative structure-activity relationship (QSAR) method. This is a computational method used to relate the physicochemical properties or descriptors (such as solubility, stability, form definition, partition coefficient and ionization constant) of chemicals to predict certain environmental parameters, such as their toxicity/bioactivity (Muller et al., 19991; Roberts, 2000). Such a relation can then be applied to predict the environmental parameter for untested compounds and this is the strength of the QSAR approach.


6 A short introduction into QSAR | Utrecht University, IRAS

A short introduction into QSAR

2. A short introduction into QSAR As previously mentioned, there are many ecological reasons to determine the toxicity of waste surfactants in the environment. For many risk assessment studies on the effects of surfactants on the environment and animals, QSAR modelling is often applied. QSAR modelling is based on the assumption that the molecular structure of molecules present characteristics which may explain their physical, chemical and biological properties. With such models, the biological activity of a chemical can be predicted when compared to similarly structured substances whose activities have already been experimentally determined (Gramatica, 2011). There are two approaches to QSAR modelling. Firstly, the toxicity is related to the structural parameters of the substance, and secondly, the physicochemical properties (descriptors) can be related to toxicity. It has been shown that the latter is the most relevant (Roberts, 2000). The most widely used physicochemical property is the log P (KOW), which indicates the hydrophobicity of a molecule (Versteeg et al., 1997; Muller et al., 19991; Uppgard et al., 2000; Roberts, 2000; Roberts and Castello, 2003; Haftka et al., 2015). Hydrophobicity plays a role in the uptake of compounds by organisms and in the sorption of the substance to dissolved and natural organic matter (NOM) (Muller et al., 19991). Therefore, methods focused on determining the hydrophobicity of chemicals are highly valuable in risk assessment studies. There are different methods to ascertain the degree of hydrophobicity/hydrophobic potential of chemicals. They can be determined in vivo, in vitro or using computational methods, such as the QSAR method. In QSARs the most widely used descriptor is the log P, or the octanol-water partition coefficient (Versteeg et al., 1997; Muller et al., 19991; Uppgard et al., 2000; Roberts, 2000; Roberts and Castello, 2003; Haftka et al., 2015). Hydrophobicity and the octanol-water partition coefficient The most frequently used descriptor is the octanol-water partition coefficient (KOW) of organic contaminants. This is an important parameter which describes the hydrophobicity of a substance. It is an indication of the solubility and predicts the bioaccumulation, toxicity and sorption to soil (natural organic matter; NOM) (van Compernolle et al., 2006). Even though KOW is a key descriptor, many scientists disapprove of its use in QSARs when predicting the eco-toxicity. Some scientists argue that using the KOW is a misleading or unreliable way to determine the eco-toxicity of surfactants. They argue that it is difficult to determine the KOW for surfactants, because of their tendency to adsorb to the surface and to accumulate at interfaces, which serves as a major limitation for QSAR modelling (Roberts, 2000; Haftka et al., 2015). The KOW parameter used in QSARs is often determined according to the Leo and Hansch method (or fragment method). According to this method, the components of a molecule contribute additively to its total KOW value. Thereby, KOW of the total molecule can be deduced from summing up the partial


7 A short introduction into QSAR | Utrecht University, IRAS

KOW values of each component (called f). This method also takes into account, the variation in how the different components are connected (called F) (Roberts, 2000). Experimentally, KOW is determined by the shake-flask method (Haftka et al., 2015, Muller et al., 19991) or HPLC on octadecane-coated silica particles (Muller et al., 19991; EOSCA, 2000).


8 Objectives of this study | Utrecht University, IRAS

Objectives of this study

3. Objectives of this study In this study I will examine QSARs with the focus on Alcohol Ethoxylates (AE), representing non-ionic surfactants, and Linear Alkyl benzene Sulphates (LAS), representing anionic surfactants. The fate and adverse effects of residual AE and LAS in sewage are becoming a great interest for industry and regulators for many reasons, based on their use and production. For instance, the largest bulk of non-ionic surfactants produced are AE and they are especially applied in household detergents (Tolls et al., 1997). They are widely used in different fields of research and technology to strengthen the efficiency of the active ingredient in different formulations, ranging from pharmaceutical to biotechnological to cosmetics (Cserháti et al., 2002). While, LAS accounts for more than 40% of surfactants used with applications in industrial as well as household laundry detergents (Scott and Jones, 2000). Thus, the application of QSAR modelling has become a frequently used method to determine the eco-toxicity of such surfactants. However, there are many discussions on its validity for surfactants, due to its limitations. Therefore, in this study I will address the limitations of QSARs for non-ionic (neutral) and anionic surfactants and analyse how different scientists coped with these limitations. I will also discuss alternatives for this method.


9 Surfactants | Utrecht University, IRAS

Surfactants

4. Surfactants Surfactants generally consists of a polar head group and a non-polar hydrocarbon tail. The head region of a surfactant molecule is hydrophilic and can either be charged or uncharged. Consisting of both a hydrophilic and hydrophobic region, makes these chemicals unique amphiphilic compounds. Because of their amphiphilic nature they have a unique characteristic to alter surface and interfacial tension, they also possess the ability to self-associate into aggregates called micelles (Rosen, 2004; Hallmann et al., 2013). The most commonly used synthetic surfactants are LAS and AE and because they are largely discarded as waste after use, their behaviour and fate once they reach the environment has long been researched. Structure: The actions of surfactant molecules are highly influenced by their structures. A major factor in surfactant behaviour is the charge of their head group (Rosen, 2004). Surfactants can be divided in different classes based on the charge of their head group. The head group of surfactants can either be charged or free of charge. Table 1 illustrates the different classifications. Table 1: The classification of surfactants based on the charge of their head group.

Anionics, the most common type of surfactants, have a negatively charged head and are historically the oldest surfactants. Examples of anionics are linear alkylbenzene sulphonic acid (LAS), sodium dodecyl sulphate (SDS), alkyl sulphate (AS) and alkyl ethoxysulphate (AES). Cationic surfactants consist of a positive charged head and contain at least one hydrophobic hydrocarbon chain which is linked to a positively charged nitrogen atom. Examples of cationics are quaternary ammonium compound (QAC), Benzalkonium chloride (BAC) and hexadecyltrimethylammonium bromide (HDTMA). Amphoteric surfactants, the newest form of surfactants, consist of a both a positive and a negative charged head, making them capable of changing between charges depending on the pH. Examples of amphotheric surfactants are amide oxides (AOs) and sulfobetaine.

Charge of the head group Classification Example

Anionic Linear Alkyl benzene

Sulphonate

Sodium Dodecyl Sulphate

Alkyl Sulphate

Alcohol Ethoxy Sulphates

Cationic Quarternary Ammonium

Chloride

Benzalkonium Chloride

Hexadecyltrimethylammonium

Amphoteric Amine Oxide

Sulfobetaine

Non-ionic Alcohol Ethoxylate

Alkyl Phenol Ethoxylate

Fatty Alcohol Ethoxylate


10 Surfactants | Utrecht University, IRAS

Nonionic (neutral) surfactants lack a charged head preventing them from dissociating into ions in a water solution which makes them useful as emulsifiers, wetting agents and various biotechnological processes. Examples of nonionic surfactants are alkylphenol etoxylate (APE), alcohol ethoxilate (AE) and fatty acid ethoxilate (FAE) (Rosen, 1987). The head group does not only determine the class of surfactant, but the charge of the head also affects the sorption efficiency between surface and surfactant head-group and the adsorption of the surfactant on hydrophilic surfaces. Differently charged head and surface will attract one another, while similarly charged head and surface will act repellent (Yana et al., 2005) Critical micelle concentration (CMC): The critical micelle concentration, as mentioned before, is the concentration at which surfactants cluster to form micelles (Rosen, 2000). After reaching an aqueous system, surfactants will initially partition at the interface, where they lower the interfacial tension, thereby protecting the hydrophobic component of the surfactant from the aqueous environment. As the surfactant concentration increases, the surface tension decreases further and aggregation into micelles commences (Ying, 2006). When surfactant concentrations are above the CMC, the effectiveness of solubilising organic compounds is at its highest. Compounds are dissolved readily, more so then would be dissolved in water (Ying, 2006). This is known as the hydrophobic effect and leads to an increase of entropy in the encompassing water molecules (Rangel-Yagui et al., 2005). The efficiency with which surfactants solubilise water insoluble or poorly soluble substances is dependent on the sorbed compound, the environmental milieu in which it persists and the chemical nature of the surfactant itself (Ying, 2006).

Figure 3: The amount of material solubilized increases linearly with increasing surfactant concentration after CMC. Source: Ying, 2006.


11 Effects of chemical structure of surfactants | Utrecht University, IRAS

Effects of chemical structure of surfactants

5. Effects of chemical structure of surfactants on environmental properties or parameters and examples of QSARs.

A structural factor which influences surfactant behaviour is the hydrocarbon tail. The nature of the non-polar hydrocarbon tail of a surfactant, which can consist of eight to twenty hydrocarbons, affects surfactant properties in five distinct ways (Rosen, 2004):

1. A longer hydrophobic tail decreases the solubility of a surfactant in water, while it increases the solubility in organic materials; it may also result in closer packing of the molecules at the interface; increases the tendency of surfactants to form micelles; it increases the melting point of the surfactant and the absorbed film; it can also increase the sensitivity of an ionic surfactant to precipitate from water by counter ions.

2. Branching or unsaturated hydrophobic tails can decrease the solubility of surfactants in water or in organic materials; it decreases the melting points of the surfactant, and of the absorbed film as well; unsaturated fatty acids also causes looser packing of the surfactant molecules at the interface; a tail of this nature may also cause oxidation and colour formation in unsaturated compounds; it decreases the biodegradability in branched-chain compounds; and increases thermal instability.

3. Having an aromatic nucleus in the tail can increase the adsorption of surfactants onto polar surfaces; decrease its biodegradability; and cause looser packing of the molecules at the interface.

4. Polyoxypropylene units may increase the hydrophobic character of the surfactant, while polyoxyethylene decreases its hydrophobic properties.

5. Having either a perfluoroalkyl or polysiloxane group in the tail region, allows the surfactant to reduce the water surface tension to lower values than those obtained from hydrocarbon-based hydrophobic tails. Interestingly, perfluoroalkyl surfaces are both water- and hydrocarbon repellent.

Environmental behaviour: biodegradation, sorption and bioaccumulation After being discarded as waste, surfactants often end up in sewage treatment plants (WWTP: waste water treatment plants), where biodegradation plays an important role in removal of surfactants from the environment thus reducing their deleterious effects on biota (Jensen, 1999). For this reason, legislation has pushed for laws that compel manufacturers to determine the biodegradation rate of these chemicals, and to accept only those that are at least 80% degraded after 28 days (Hallmann et al., 2013). The biodegradation rate of surfactants can be influenced by many factors, such as chemical structure and the physical and chemical composition of the environmental media (Ivancovic and Hrenovic, 2009). Furthermore, surfactant class also affects its biodegradation rate. Table 2 shows the biodegradation rate of anionic surfactants LAS, and non-ionic surfactants AE (Ying, 2006).



Table 2: The biodegradation rate of LAS and AE

Degradation of LAS is highly determined on its structure. LAS are composed of n-(p-sulfophenyl) alkanes (n-p-SPA), with the carbon chain lengths ranging from 10-13 carbons (Tolls et al., 1997; Hallmann et al., 2013). The position of the benzenesulfonate moiety could be attached to any internal carbon unit in the alkyl chain, creating homologues with 5-7 positional isomers (Tolls et al., 1997; Jensen, 1999; Hallmann et al., 2013). The N-oxidation of the alkyl chain and cleavage of the benzene ring are processes that require oxygen, thus, LAS can only be fully degraded under aerobic conditions with a half-life of 7-33 days (Jensen, 1999). In reality, however, LAS is not always completely degraded in treatment plants, and through sewage discharge they can reach the outside environment. In river water it can be completely degraded by the occupational natural microbial flora, but in marine environment such flora is absent, resulting in a slower degradation of LAS and its degradation products sulfophenyl carboxylates (SPC) (Jensen, 1999). Once on land, LAS are readily metabolised by residential aerobic bacteria in the soil and will not bioaccumulate further (Jensen, 1999). For AE, however, it is quite the opposite. They can undergo anaerobic as well as aerobic degradation (Ying, 2006), with the main difference being in the cleavage site of the molecule. The degradation rate can also differ according to the treatment surfactant-containing sludge in WWTPs receives. A continuous flow, for example, resulted in better degradation than a static state (Ying, 2006). Another process surfactants undergo after reaching the outside environment is sorption onto sediment or soil. This characteristic also determines the bioavailability in the environment and is different for every class of surfactant (van Compernolle et al., 2006). Research has shown that surfactants adsorb well to sludge and sediment and that non-ionic surfactants have a higher sorption to sediment than anionic surfactants (EOSCA, 2000). The sorption efficiency of ionic organic compounds depends on their molecular structure, the characteristics of the sediment and the specific ionic composition, such as the organic carbon content, temperature and pH, of the aqueous phase (EOSCA, 2000; Rico-Rico et al., 2010). Figure 4 shows the sorption isotherms for anionic and non-ionic surfactants (Ying, 2006). If concentrations are lower than 90 µg/mL, LAS show a linear isotherm, however, the amount of sorbed LAS increases exponentially at higher concentrations of LAS in

Surfactant Aerobic conditions Anaerobic conditions

LAS Degradable Persistent

AE Readily degradable degradable

Figure 4: A. Sorption isotherm for LAS. The amount of sorbed LAS increases exponentially at concentrations higher than 90 µg/mL. B. When the CMC is reached, there is a maximum of sorbed AE. Source: Ying, 2006.



solution (figure 4A). For AE, however, there is a maximum of sorbed AE on solid surface when the CMC of the surfactant is reached (figure 4B) (Ying, 2006). Besides the bioavailability, sorption may also affect the bioconcentration of surfactants in biota. Bioconcentration, noted as the bioconcentration factor or BCF, is the ratio between the concentration in biota (CB) and the concentration in water (CW). Thus, BCF= CB/ CW (Ying, 2006). Compounds with a higher bioaccumulation potential (high BCF) are mostly lipophilic, or hydrophobic, compounds (Ying, 2006). The hydrophobicity of a substance can be determined by the octanol-water partition coefficient (KOW) and has been considered the driving force for bioconcentration. Research has shown that the bioconcentration increases with the KOW (Versteeg et al., 1997; Ying, 2006).

Surfactant toxicity Toxicity can be derived from the sorption potential, the bioaccumulation potential, the bioconcentration of surfactants and their degradation rate, among other characteristics (Haftka et al., 2015). According to the CHARM model (chemical hazard risk assessment management system), the KOW is an essential input parameter for risk assessment and from here other factors are derived (figure 5) (EOSCA, 2000).

HPLC or shake flask

KOW KSW CPW

BCF

PECsediment

PECwater

PECbiota

PECsediment

PNECbenthic

PECbiota

PNECfoodchain

Figure 5: KOW as an essential input parameter for risk assessment. Pow = octanol/water partition coefficient Cpw = concentration in produced water Psw = sediment/water partition coefficient BCF = bioconcentration factor PEC = predicted environmental concentration PNEC = predicted no observed effect concentration



In general there are two ways chemicals can exert their toxicity; there are the general narcotics and the polar narcotics. Toxicity of general narcotics can be determined with the Könemann equation (Uppgard et al., 2000; Roberts and Castello, 2003). Chemicals that adhere well to the general narcosis equation developed by Könemann are considered “unreactive” compounds. Such compounds do not interact specifically with receptors in organisms (Roberts, 1991; Uppgard et al., 2000; Roberts and Castello, 2003). Hydrocarbons, alcohols, ethers, ketones and non-ionic surfactants are considered such compounds and it was generally viewed that these are as toxic as their hydrophobic parts (Roberts and Castello, 2003). Chemicals that do not adhere to this equation, meaning that their predicted toxicity is often lower than their actual observed toxicity, are the phenols, aromatic amines and anionic surfactants, for example. These are referred to as the polar narcotics and their toxicity can be predicted by the polar QSAR developed by Saarikoski and Viluksela (Roberts and Castello, 2003): The main difference between both modes of action, general narcosis (Narcosis I) and polar narcosis (Narcosis II), is based on their interactions with membranes (Hodges et al., 2006). In polar narcosis the water-membrane partitioning takes place due to the interaction between the narcotic molecule and the head groups of the membrane lipids. For general narcosis, however, the narcotic molecule can move easily in all directions of the membrane (Hodges et al., 2006). The hydrophobicity of the chemical in this case plays an important role in membrane interactions. Hydrophobicity is an important characteristic of narcotic organic compounds; it influences their effects in aquatic systems (Hodges et al., 2006). For this reason the KOW has become an essential parameter in risk assessment for organic chemicals and can be determined according to the Leo and Hansch method (EOSCA, 2000; Hodges et al., 2006). The hydrophilic and hydrophobic parts of anionic surfactants have been shown to interact with the polar and non-polar components of, e.g. proteins and cellulose (Cserháti et al., 2002). Anionic

The general narcosis equation:

pLC50=0,87 log P + 1,13

(n=50, R2=0,976, s=0,24) (LC50 in mg/kg)

pLC50=0,63 log P + 2,25

(n=17, R2=0,964, s=0,16)



surfactants have also been shown to be more toxic than non-ionic surfactants. Longer alkyl chains of LAS result in increased acute toxicity in D. magna probably due to higher interactions (Ying, 2006). Most AE have been shown to possess a high bioaccumulation potential (log KOW >) and are known to interact with biological membranes (Muller et al., 19992). These surfactants are considered general narcotics (Muller et al., 19992; Roberts, 2000), and their toxicity towards aquatic organisms increases as the length of the alkyl chain increases paired with decreased branching (Muller et al., 19992, Muller et al., 19991). Once more confirming the link between surfactant behaviour and their chemical structure. Because of the aquatic and terrestrial toxicity surfactants may have, and their increased use and disposal into sewage, determining the toxicity of these chemicals beforehand is a major priority for manufacturers (Hallmann et al., 2013). For this purpose, researchers have developed a mathematical method, the QSAR, to predict the toxicity of chemical compounds.


16 A critical evaluation and discussion | Utrecht University, IRAS

A critical evaluation and discussion

6. A critical evaluation and discussion of Quantitative structure-activity relationship Even though the log P is the most dominant parameter in QSARs, there is some speculation on its predictive value for surfactant toxicity (Versteeg et al., 1997; Muller et al., 19991; EOSCA, 2000; Roberts and Castello, 2003; Haftka et al., 2015). KOW based QSARs have been predominantly applied to chemicals that are described as ‘unreactive compounds’. These chemicals do not interact specifically with receptors in organisms, and adhere well to the general narcosis equation developed by Könemann, when determining their toxicity (Roberts and Castello, 2003). Such chemicals are often non-polar organic compounds, which are devoid of specific interactions, such as hydrogen bonding. Which all results in a good correlation between KOW and their general toxicity (Haftka et al., 2015). This raises a problem for applying the KOW as a predictive parameter for surfactants, due to their amphiphilic nature and long carbon-chains (EOSCA, 2000; Haftka et al., 2015). Furthermore, it has been shown that for surface-active substances the shake-flask method creates emulsions, which can lead to experimental problems (Muller et al., 19991; EOSCA, 2000; Haftka et al., 2015) that cannot be avoided. Additionally, capacity factors used in the HPLC method when determining the KOW for AEs, are erroneously influenced by the length of the ethoxy chains (Muller et al., 19991). The difficulties with surface-active compounds, such as anionic and non-ionic surfactants, arise from their tendency to aggregate at interfaces, form micelles, and act as solubilising and emulsifying agents (Roberts, 2000). Furthermore, when determining the octanol-water partition coefficient for surfactants, they interact with each other and octanol, even at concentrations below their CMC (Muller et al., 19991). Because of their amphiphilic nature, they are distributed easily to both the octanol as well as the water phase (Versteeg et al., 1997). The main idea behind using the octanol-water partition coefficient to determine the hydrophobicity of chemicals, is to determine how well they react with biological compounds. Octanol serves as a surrogate for such biological compounds, e.g. NOM or biomembranes. However, no in vitro analysis can correctly mimic the exact interactions/reactions of complex compounds, such as surfactants, with NOM or biomembranes (Muller et al., 19991; Haftka et al., 2015). Limitations of log P based QSARs for anionic and non-ionic surfactants Since the physical and chemical properties of surfactants greatly influence their biological activity, log P based QSARs are often inadequate to predict their toxicity (Boeije et al., 2006; Ivancovic and Hrenovic, 2009). For AEs, the hydrophobicity is greatly influenced by alkyl chain length and EO number. Longer alkyl chains in combination with low EO numbers, show greater hydrophobic potential compared to higher EO numbers (Muller et al., 19991; Dyer et al., 2000; van Compernolle et al., 2006; Ivancovic



and Hrenovic, 2009). The most common AEs used have alkyl chain lengths ranging from 12 to 18 carbons, with as many as 20 ethoxylate units ester-linked to each chain (Wong et al.,1997; Muller et al., 19991; Boeije et al., 2006; Eadsforth et al., 2006; van Compernolle et al., 2006). Many authors agree that, specifically for AEs, the assumption of additivity, meaning that every component adds to the overall toxicity of the molecule, applies (Boeije et al., 2006). Furthermore, for LAS it has been shown that the sorption potential increases two- to three-fold with every added carbon in the tail region (Jensen, 1999; Scott and Jones, 2000). Commercial LAS is built up of alkyl chain lengths ranging from 10 to 13 carbons and isomers that have different positions of the benzenesulfonate moiety on the alkyl chain (Tolls et al., 1997; Rico-Rico et al., 2010). Moreover, it has been shown that the isomers whose phenyl groups are on the outmost carbon atom have a higher sorption affinity, than those where the moiety is located more internally (Rico-Rico et al., 2010).

This effect of the structure on the hydrophobicity is problematic, because commercial LAS or AE are available in mixtures. Such complex mixtures could contain multiple structurally similar molecules and eco-toxicity data are readily available for the mixtures as a whole, but not for the individual molecules (Boeije et al., 2006; Rico-Rico et al., 2010). Since the toxicity is highly dependent on the distribution of multiple components of a chemical structure, the toxicity is not always linearly related to molecular descriptors. According to the theory of additivity, the eco-toxicity for surfactants shall increase logarithmically with increasing alkyl chain length (Boeije et al., 2006). An important fact to keep in mind, is that studies are often performed with commercially available complex mixtures of surfactants, and individual testing of the molecular components would be time consuming and expensive (EOSCA, 2000; Boeije et al., 2006). Consequently, in experimental data the more highly toxic components in a complex mixture will have a greater impact on the toxicity, which is not proportionate to their actual concentration. Complex mixtures are often represented by an average structure. So when using QSAR to derive the toxicity of a substance, it is derived from an average structure in a mixture. This may result in an overestimation of toxicity of multiple individual components in the mixture (Boeije et al., 2006). Alternatives for log P or log P based QSARs for anionic and non-ionic surfactants Over the years authors have proposed many alternatives to either the log P as a descriptor, or the traditional QSAR as a whole. Roberts (Roberts, 1991), for instance, proposed adjusting the Leo and Hansch method to also account for the branching positions of certain components in anionic as well as non-ionic surfactants, by extending the fragment method with a position-dependent branching factor (PDBF). Roberts was convinced that the Leo and Hansch method fit adequately for AE/non-ionic surfactants with just a slight adjustment, and that these compounds behaved as general narcotics (Roberts, 1991). The log P data that were collected by Roberts were compared to log KOW determined by the Syracuse Research



Corps. This comparison showed similar results between the two methods, but also significant differences for some part of the data (EOSCA, 2000). Since the main problem with QSARs has been the use of KOW as a descriptor for surfactants, Müller (Müller et al., 19991) suggested using the liposome-water partition coefficient (Klipw) as descriptor in QSARs for AEs. They argued that, first of all, the Klipw is readily determined experimentally, as long as concentrations are below CMC. Second of all, they find this value more superior to the KOW because its values are determined according to the fragment method tested on commonly available mixtures, while those of KOW are deduced from nonsurface-active compounds. And thirdly, they find that membrane-partitioning truly affirms bioaccumulation (Müller et al., 19991). Because of the distinctive mechanistic difference between polar and non-polar narcotics, this descriptor can, unfortunately only be applied for non-ionic surfactants (Roberts and Costello, 2003; Hodges et al., 2006). Boeije, on the other hand, suggested adjusting the traditional QSAR in such a way that the new QSAR would be suitable for complex substances, such as surfactant mixtures (Boeije et al., 2006). The new QSAR would determine toxicity with respect to the “ethoxymer” distribution, instead of the usual average structure. However, it is important to keep in mind that as the complexity of a molecule increases, the accuracy of certain methods decreases (EOSCA, 2000). With respect to this, instead of focusing on adjusting the QSAR method, which is still dependent on an existing incomplete and possibly mostly inaccurate database, it would seem logical to seek new analytical methods to determine parameters such as sorption or hydrophobicity of complex compounds as surfactants (EOSCA, 2000), as an alternative for the traditional octanol-water partitioning. Recent developments Because of the complex nature of surfactants, Haftka (Haftka et al., 2015) suggested that alternative methods, which focus on sorption to a hydrophobic phase, could best indicate the hydrophobicity. Besides the stationary phases used in liquid chromatography, solid-phase extraction devices and polymers used in passive sampling show a lot of promise (Haftka et al., 2015). Recently the focus has been on the latter option in determining certain surfactant characteristics. Using polyacrylate polymers, the SPME (solid-phase micro-extraction) method was developed. This method is based on the partition of a chemical between a certain matrix and a stationary phase coated on silica fibres (Aulakh and Malik, 2005). The SPME method is advantageous for surfactants, because no phase separation and purification steps are required (Rico-Rico et al., 2010; Haftka et al., 2015). The main idea is to place a fiber with a specific coat into a solution, and removing it after a certain amount of exposure time (Haftka et al., 2015). When the concentration of the sample chemical and the amount of sorbed sample chemical reaches an equilibrium (Aulakh and Malik, 2005), a polyacrylate-water partition coefficient (or fibre-water partition coefficient; Kfw) can be determined.



The SPME method has been mostly applied to analyse freely dissolved concentrations of organic compounds, however, there is only limited application known for ionic organic compounds (Rico-Rico et al., 2010). Research has shown, for anionic and non-ionic surfactants, that the measured Kfw correlates well with marine sediment sorption tests and BCF values (found in literature), suggesting that this may be used as an alternative parameter for the KOW (Rico-Rico et al., 2010; Haftka et al., 2015). It is, however, important to consider that the sorption mechanism of surfactants to sediment compared to the sorption mechanism of surfactants to fibers can differ. Different interactions take place to ensure the sorption distribution on a certain surface or other (Rico-Rico et al., 2010; Haftka et al., 2015).


20 Conclusion | Utrecht University, IRAS

Conclusion

7. Conclusion

In conclusion, although many scientists would agree that the octanol-water partition coefficient as a parameter to determine the hydrophobicity of surfactants with QSAR modelling is inefficient and inaccurate, there is still no universally accepted alternative method to determine the eco-toxicity of these compounds. A major obstruction in QSAR modelling is the scarce and often inaccurate data on the hydrophobicity of surfactants. Improvement and expansion of the main QSAR database could eventually lead to complete abandonment of animal testing, and accurate computational determination of the eco-toxicity, among other endpoints, of surfactants. Therefore, research focussed on new analytical methods to determine hydrophobicity and replace the octanol-water partition coefficient, is highly beneficial for surfactant risk assessment.


21 Conclusion | Utrecht University, IRAS


22 References | Utrecht University, IRAS

References

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