Prediction for the mixture toxicity of six organophosphorus pesticides to the luminescent bacterium Q67

ARTICLE IN PRESS

Ecotoxicology and Environmental Safety 71 (2008) 880–888 www.elsevier.com/locate/ecoenv

Prediction for the mixture toxicity of six organophosphorus pesticides to the luminescent bacterium Q67 Ya-Hui Zhanga, Shu-Shen Liub,, Xiao-Qing Songc, Hui-Lin Geb a

State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210093, PR China b Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, PR China c Department of Material and Chemical Engineering, Guilin University of Technology, Guilin 541004, PR China Received 18 July 2007; received in revised form 8 January 2008; accepted 12 January 2008 Available online 4 March 2008

Abstract Organophosphorus (OP) pesticides are ubiquitous in the surface water as mixtures. To examine the mixture toxicity in the multicomponent space, the uniform design (UD) which can explore the concentration changes with few experimental efforts was employed to design the mixtures. On the other hand, the fixed concentration ratio ray was applied into six UD mixtures and two equivalent-effect concentration mixtures to build the whole concentration–response curves to overcome the demerit of the classical ‘‘point-to-point’’ method. The experimental toxicities of six pesticides and their mixtures to the luminescent bacterium Q67 were determined. The mixture toxicities were predicted by two models, concentration addition (CA) and independent action (IA). The results showed that all the mixture toxicities observed had no significant differences from the ones predicted by CA. However, the mixture toxicities were also well predicted by IA especially at the low-concentration section. r 2008 Elsevier Inc. All rights reserved. Keywords: Mixture toxicity; Uniform design; Organophosphorus pesticide; Concentration addition; Independent action; Q67

1. Introduction Since organophosphorus (OP) pesticides replaced organochlorine pesticides in the 1970s (Herna´ndez et al., 2000; Phillips et al., 2003), OP pesticides have been most widely used in agriculture and homes, and become ubiquitous contaminants in water bodies (Ballesteros and Parrado, 2004; Lambropoulou and Albanis, 2001; Tomkins and Ilgner, 2002; Zhang et al., 2002) primarily through surface run-off processes. The inhibitors of acetylcholinesterase (AChE) enzyme (Mileson et al., 1998) OP pesticides have raised concerns about their potentials to threaten nontarget organisms in the surface water. Burkepile et al. (2000) examined the effect of diazinon on five non-target organisms in the aquatic environment; Diamantino et al. (1998) determined the inhibition of Daphnia magna by chlorpyrifos in different conditions (static, semi-static, and Corresponding author. Fax: +86 21 65982767.

E-mail address: [email protected] (S.-S. Liu). 0147-6513/$ - see front matter r 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2008.01.014

flow-through); Rickwood and Galloway (2004) measured the impact of the blue mussel Mytilus edulis exposed to chlorfenvinphos using hemolymph AChE activity, cellular integrity, immunotoxicity, and physiological status as biomarkers; Reyes et al. (2002) studied the effect of diazinon, folidol, and gusathion on the oxygen consumption of the shrimps (Litopenaeus vannamei). In the study of the acute toxicity of 11 OP pesticides to two marine invertebrates Artemia sp. and Brachionus plicatilis, chlorpyrifos was considered as the most toxic insecticide to both species (Guzzella et al., 1997). Bailey et al. (1997) evaluated the joint toxicity of binary mixture of chlorpyrifos and diazinon at 50% lethal concentration (LC50) on Ceriodaphnia dubia in various waters (laboratory water, natural water, and urban storm water) and concluded that diazinon and chlorpyrifos exhibited additive toxicity in the aquatic environment. These studies on the toxicity of OP pesticides in the aquatic environment, however, have focused on single substances and binary mixtures, and the results were restricted to the median effect concentration of

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single OPs or the combined effect at their 50% effect concentration. The combinations of multi-component OP mixtures in the water in various concentration ratios have been expected to be still further studied. To investigate the mixture toxicity the factorial design is a rational approach for designing binary or ternary mixtures. Schuler et al. (2005) employed a 4  5 complete factorial design to examine the interaction of binary combinations of triazine herbicide and two OPs, chlorpyrifos, and diazinon. However, the experimental effort of the factorial design exponentially increases with the numbers of compounds in the mixtures. The factorial design may not be feasible to analyze the toxicities of the multiplecomponent mixtures which the number of components is larger than three or four. Recently, the fixed concentration ratio design has been frequently used to investigate mixture toxicity (Meadows et al., 2002). Moser and Gennings (Gennings et al., 2004; Moser et al., 2005) employed the ‘‘ray design’’ to detect the mixture toxicity of five OP pesticides in the fixed concentration proportions of the relative dietary exposure estimated by the EPA dietary exposure evaluation model. The observed mixture toxicity using the cholinesterase activity and the behavioral measures of adult male rats as the biomarkers were greater than the value predicted by the additivity model. Many studies that estimate and predict the mixture effect by concentration addition (CA) and independent action (IA) employed the fixed concentration ratio design such as an equivalent-effect (EE) concentration (EC50) (Faust et al., 2001, 2003; Payne et al., 2000). The fixed concentration ratio design, however, only represents one or some defined combinations of chemicals and cannot cover the mixture toxicity at various mixture ratios. Uniform design (UD) is an effective experimental design method established by Fang (Fang, 2001; Fang et al., 1993). Application of the uniform experimental design to study the combined effect of multi-component mixture is necessary and more efficient when the numbers of chemicals (the factors in the UD) and the involved concentrations (the levels of the factors) are enlarged. In contrast to the factorial design, the experimental effort of the UD linearly increases with the number of the components or the concentration levels of the components in the mixtures. The UD is one of the optimal experimental designs, which can effectively examine various concentration combinations of the chemicals in the mixture with the experimental effort as little as possible. For the multicomponent mixture, the UD is especially useful due to the linear increment with the chemicals. For mixture toxicity prediction two concepts of CA and IA based on the concentration–response relationships of individual components has been applied in many studies (Belden et al., 2007; Deneer, 2000). CA is based on the idea that all mixture components share the same or similar modes and mechanisms of action (Berenbaum, 1985). The model states that every component contributes to the overall toxicity and one component can be replaced by

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another one totally or partly when the overall toxicities of two components in the mixture are equal. The CA model had been successfully used to predict the toxicities of many mixtures including the narcotic action chemicals (Hsieh et al., 2006; Nirmalakhandan et al., 1997), the quinolone antibiotics (Backhaus et al., 2000b), and the phenol derivatives (Altenburger et al., 2000) to the luminescent bacterium Vibrio fischeri, the s-triazine herbicides to the unicellular green alga Scenedesmus vacuolatus (Faust et al., 2001), polycyclic aromatic hydrocarbons (PAHs) to D. magna (Brian et al., 2005; Olmstead and LeBlanc, 2005), and five estrogenic chemicals to the male fathead minnows (Brian et al., 2005) in the aquatic environment. In contrast to the CA model, IA is based on the assumption of the mixture components with different or dissimilar mechanisms of action (Bliss, 1939). IA can be an instrument for predicting the effect of the mixture of substances with diverse action mode on the freshwater algae (Faust et al., 2003; Walter et al., 2002), the algae communities (Backhaus et al., 2004a), and the photobacterium V. fischeri (Backhaus et al., 2000a). Photobacterium bioassay for detecting the aquatic toxicity of OP pesticides in the environmental samples has been performed in the Microtoxs assay with V. fisheri (Amoros et al., 2000; Ga¨lli et al., 1994; Ruiz et al., 1997). Since V. fisheri is a marine species and has a requirement for high salinity (2–3% NaCl) in the test solution, it may be inconsistent with the properties of freshwater samples. A new species of freshwater photobacteria, Vibrio qinghaiensis sp. Nov., isolated from the body surface of an edible fish named as Cymnocypris przewalskii (Zhu et al., 1994) was employed to determine the aquatic toxicity. And the strain V. qinghaiensis sp. Nov-Q67 (Q67) is more sensitive to insecticides than V. fisheri (Ma et al., 1999). In our previous study the mixture toxicity of substituted anilines (Ge et al., 2006) and phenol derivatives (Mo et al., 2006) to Q67 have been determined using a VeritasTM 96-well microplate luminometer as the testing equipment. The microplate bioassays with Q67 have advantages of simplicity, sensitivity, and reproducibility in the evaluation of the pollutant toxicities in the aquatic environment (Liu et al., 2006). In the present study the predictability of the toxicity of the mixtures consisted of six OP pesticides, fenitrothion (FEN), malathion (MAL), dicapthon (DIC), chlormephos (CHL), methyl parathion, and famphur (FAM), from the concentration-response curves (CRCs) of six individual OPs was examined. The toxicities of individual OP pesticide and the OP mixture were expressed as the percent inhibition ratios of the luminescence of Q67 by the toxicant or the mixture. On the basis of the CRCs of the mixture components, the toxicities of various mixtures were predicted by the CA and IA models. To effectively analyze and examine the mixture toxicity varies with various concentration compositions in the multi-component space, the UD, which can explore the concentration changes in three-dimensional space with few experimental efforts was employed to design the test mixtures. On the other hand, to

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relationally compare the toxicities predicted by CA and IA to the observed toxicity, the fixed concentration ratio ray (corresponding to a CRC curve) was used to build a whole CRC to overcome the demerit of the classical ‘‘point to point’’ method, which can only analyze the toxic action in a defined effect point (50%). Also, the ray method was applied in the EE concentration ratio mixtures. It had been found that the observed toxicities of six UD mixtures and two EE ones have no significant differences from the toxicities predicted by the CA model. However, the toxicities of six OP mixtures were proved to be almost in agreement with the values predicted by the IA model due to the little deviations between the CRC predicted by the CA and IA models. 2. Material and methods 2.1. Materials Six OP pesticides together with their structures and some physical properties are listed in Table 1. FEN, MAL, CHL, methyl parathion (MPA), and FAM were purchased from Sigma (St. Louis, MO, USA) and DIC from ChemService (West Chester, PA). OP pesticides stock solutions were prepared in dimethylsulfoxide (DMSO, analytical grade) and stored in the dark at 4 1C. Prior to testing toxicity, OP test solutions were prepared by dissolving a proper amount (always lower than the water solubility of an individual chemical) into the distilled water. All treatments, including the controls, contained 1% (v/v) DMSO.

2.2. Cell culture The freeze-dried luminescent bacterium Q67 was supplied by East China Normal University. The culture medium consists of 13.6 mg

KH2PO4, 35.8 mg Na2HPO4  12H2O, 0.25 g MgSO4  7H2O, 0.61 g MgCl2  6H2O, 33.0 mg CaCl2, 1.34 g NaHCO3, 1.54 g NaCl, 5.0 g yeast extract, 5.0 g tryptone, 3.0 g glycerin, and 1000 mL distilled water and adjusted to pH 8.570.5. Before each test, the bacteria were inoculated from a stock culture, which is maintained on Q67 culture medium agar at 4 1C, to a fresh agar plate and cultured at 2271 1C for 24 h. The cells were further grown in liquid culture medium by shaking (120 r/min) at 2271 1C for 16–18 h until the final relative light unit (RLU) reached about 2.0  107 for the toxicity tests (Liu et al., 2006; Ma et al., 1999).

2.3. Toxicity test Toxicity tests of both single OP pesticide and OP mixture were performed on the VeritasTM luminometer with a 96-well microplate (Turner BioSystems Inc., USA). For an individual OP or a pesticide mixture an appropriate dilution factor was selected after some preliminary experiments to make the response (inhibition) values equably locate in the range from the maximum inhibition to 1% inhibition. To construct the CRC of a chemical or a mixture, 12 different test concentrations in three parallels and 12 controls (1% DMSO) in a 96-well microplate were arranged and the microplate test was repeated three times. The procedure in detail was as follows: In 12 wells of the first row in a microplate, added 100 mL 1% (v/v) DMSO as 12 controls and in 12 wells of the second row, added, respectively, 12 different toxicant volumes derived by a dilution factor such as 0.618 and supplied 1% DMSO up to a total volume of 100 mL. In the same way as the second row, prepared various test solutions in 12 wells of the third, fourth, or fifth row. And then 100 mL bacterial suspension was added into each test well to make the final test volume be 200 mL. Then did two duplicated microplates again (Ge et al., 2006; Liu et al., 2007). The RLU measurements of Q67 in various wells in the test microplate were then determined using the VeritasTM luminometer after 15 min exposure to the toxicants at 2271 1C. The toxicity of each OP pesticide or mixture to Q67 was expressed as an inhibition ratio (E or x) as follows: E¼x¼

I0  I  100% I0

(1)

Table 1 Some physico-chemical properties of six organophosphorus pesticides OP

Substance

CAS RNa

S RO

Purity (%)

Molecular weight (amu)

97.5

277.24

98.0 98.4

330.36 297.66

99.2 99.8

234.71 263.21

98.3

325.35

(or S) OR' P

RO

R FEN

Fenitrothion

122-14-5

R0

CH3

CH3 NO2

MAL DIC

Malathion Dicapthon

121-75-5 2463-84-5

CH3 CH3

—CH(COOCH2CH3)CH2COOCH2CH3

NO2

CHL MPA

Chlormephos Methyl parathion

24934-91-6 298–00–0

CH2CH3 CH3

Cl —SCH2Cl

NO 2 FAM

Famphur

52–85–7

CH3

O S O

a

CAS RN is the chemical abstracts service register number.

N

CH3 CH 3

ARTICLE IN PRESS Y.-H. Zhang et al. / Ecotoxicology and Environmental Safety 71 (2008) 880–888 where I0 was an average of the RLU of Q67 exposed to the controls (12 parallels) and I an average of the RLU to the test toxicant or mixture (three parallels) in one microplate.

2.4. Concentration–response curve fitting To quantitatively describe various effect-concentrations (ECx), especially at low effect, the observed concentration-effect data were fitted to two non-linear functions, Logit and Weibull (Scholze et al., 2001), and the best-fitted function was then used to estimate the ECx. The best-fitted function was selected using the relationship coefficient (R) and the root mean square error (RMSE) as a criterion. The higher the R or lower RMSE, the better the fitting. The formula of Logit (2) and Weibull (3) functions are as follows: E ¼ 1=ð1 þ expða  b log10 ðcÞÞÞ

(2)

E ¼ 1  expðexpða þ b log10 ðcÞÞÞ

(3)

where a, b are the parameters of the models; E is the luminescence inhibition to Q67; c is the test concentration of single OP or the mixture.

2.5. Mixture toxicity prediction The mixture toxicity can be predicted by the CA and IA models on the basis of the concentration–response relationship of single substances. CA is expressed mathematically as !1 n X pi (4) ECx;mix ¼ ECx;i i¼1 (Berenbaum, 1985) where ECx,mix is the effect concentration of the mixture eliciting x% effect, ECx,i denotes the concentration of the ith component when exists individually and elicits the same effect (x%) as the mixture, pi is the molar concentration ratio of the ith component in the mixture. One component can be replaced by another one in mixtures when the toxic units (ci =ECx;i ) of two components in mixtures are equal where ci is the individual concentrations of the single components in the mixture. The alternative model IA (or response addition) is a common approach for the prediction of the mixture toxicity of substances with the different or dissimilar mechanisms of action. IA is commonly defined as x% ¼ 1 

n Y ð1  F i ðpi ðECx;mix ÞÞÞ

(5)

i¼1

(Bliss, 1939) where x% is the overall effect caused by the total concentration cmix of a mixture; Fi is the function that depicts the concentration–response curve of the ith component. All the fitted and predicted tasks above Sections 2.4 and 2.5 were performed on the APTox (Assessment and Prediction for the Toxicity of

883

chemical mixtures) program developed by professor Liu in our laboratory. APTox was a program used to perform the fit of the CRCs, to predict the CRC by the CA and IA models, and to design the microplate toxicity experiment. The results obtained by APTox had been testified and received a software copyright registration certificates (No. 062731) authorized by National Copyright Administration of China (NCAC).

3. Results 3.1. Toxicity of single OP pesticide to Q67 The concentration–response data of single OP were fitted to Logit or Weibull model and the corresponding fitted parameters (a and b) and some statistics (the RMSE and the correlation coefficient R) were given in Table 2. From the values of RMSE and R in Table 2 both Logit and Weibull functions could well describe the relationships between the exposed concentration of single OPs and the toxicity (inhibition %) observed. The best concentration– response model was the Logit function for three OPs such as FEN, DIC, and FAM, and the Weibull equation for the other three OPs such as MAL, CHL, and MPA. The observed concentration–response scattered points of six OPs and the optimal fitted CRCs were demonstrated in Fig. 1. Using the fitted parameters (a and b) in Table 2, various ECx such as EC50 and EC5 of a pesticide can be easily computed from the optimal fitted model. The negative logarithm values of EC50 and EC5, pEC50 and pEC5, of single OPs together with the concentrations of various OP stock solutions were also listed in Table 2. It should be pointed out that the EC50 values for two pesticides, CHL and DIC, were extrapolated from the fitted CRCs due to their low water solubility. However, the EC50 values will not affect the mixture toxicity analysis because the concentration levels of various individual pesticides in a test mixture are in general lower than EC50. The toxicity order of the test pesticides was FAM 4FEN 4MPA 4DIC 4MAL 4CHL if the pEC50 value was selected as a toxicity criterion. The EC50 value of single OPs ranged over two orders of magnitude from 2.86  104 mol/L (pEC50 ¼ 3.55) for CHL to 2.37  106 mol/L

Table 2 The concentration–response models and some statistics of individual OPs as well as the stock concentrations OP FEN MAL DIC CHL MPA FAM

Model

a

b

RMSE

R

logEC50 (pEC50)

logEC5 (pEC5)

Stock (mol/L)

Logit Weibull Logit Weibull Logit Weibull Logit Weibull Logit Weibull Logit Weibull

15.59 12.12 8.30 6.52 13.35 11.18 4.99 4.24 10.39 8.00 11.70 7.70

3.51 2.82 2.18 1.81 3.13 2.70 1.44 1.30 2.39 1.93 2.08 1.45

0.0185 0.0257 0.0170 0.0163 0.0183 0.0213 0.0125 0.0104 0.0227 0.0178 0.0119 0.0273

0.998 0.995 0.996 0.996 0.996 0.994 0.993 0.995 0.995 0.997 0.999 0.997

4.44

5.28

1.07E-04

3.80 4.26

5.24 5.21

3.55

5.54

4.33 5.63

5.69 7.04

4.32E-04 9.79E-05 2.41E-04 1.60E-04 9.09E-05

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100 FEN

Table 3 The concentration compositions of eight mixtures designed by the uniform design and the equivalent-effect concentration method

MPA

80

% Inhibition

FAM 60 DHL 40

20

MAL CHL

Mixture

FEN

MAL

DIC

CHL

MPA

FAM

U1-Mix U2-Mix U3-Mix U4-Mix U5-Mix U6-Mix EC50-Mix EC5-Mix

EC5 EC10 EC20 EC30 EC40 EC50 EC50 EC5

EC10 EC30 EC50 EC5 EC20 EC40 EC50 EC5

EC20 EC50 EC10 EC40 EC5 EC30 EC50 EC5

EC30 EC5 EC40 EC10 EC50 EC20 EC50 EC5

EC40 EC20 EC5 EC50 EC30 EC10 EC50 EC5

EC50 EC40 EC30 EC20 EC10 EC5 EC50 EC5

0 1E-7

1E-6 1E-5 Concentration (mol/L)

1E-4

Fig. 1. The concentration-response relationships of six OP pesticides to Q67. Experimental data points of FEN (J), MAL (W), DIC (  ), CHL (B), MPA (&), and FAM (X); fitted curves () to the Logit for FEN, DIC, and FAM or the Weibull for MAL, CHL, and MPA.

(5.63) for FAM. For the pEC5, the range of the toxicity concentrations was from 6.23  106 mol/L (5.21) for DIC to 9.11  106 mol/L (7.04) for FAM.

Table 4 The percent concentration ratios (pi %) of the test mixtures Mixtures

FEN

MAL

DIC

CHL

MPA

FAM

U1-Mix U2-Mix U3-Mix U4-Mix U5-Mix U6-Mix EC50-Mix EC5-Mix

3.26 5.86 4.16 16.76 7.38 16.84 6.24 23.74

8.82 45.86 44.68 4.60 9.85 49.42 27.02 25.68

12.13 37.15 3.07 32.58 1.65 13.57 9.40 27.97

54.41 1.96 47.24 8.25 75.50 17.88 48.94 12.95

19.91 8.14 0.59 37.39 5.57 2.26 8.00 9.25

1.47 1.04 0.27 0.42 0.06 0.04 0.41 0.42

3.2. Toxicity of OP mixtures 3.2.1. Experimental design of OP mixtures To systematically investigate the toxicity changes of the OP mixtures with various concentration compositions in the whole concentration space, the test mixtures need to be rationally designed. Based on the ECx computed from the optimal Logit or Weibull function of single OP pesticides in Table 2, six sets of mixtures in which the concentration ratio between pairs of components maintains a constant were designed using the UD procedure. The various concentration ratios of the mixtures were selected from the former six lines in the UD table, U7 (76) with at best six factors (pesticides, superscript) and seven levels (concentrations) and seven experiments (mixtures, subscript). The reason why the last line in the uniform table is not selected to design a mixture is that all the concentration levels shown in the last line are the highest. In a uniform table each column denotes one mixture component described by the concentration level in the different mixtures and each line represents one mixture consisted of the concentration levels of various OP components. The effect concentrations of six OP pesticides in the mixtures designed according to the uniform table U7 (76) were listed in Table 3. For example, the mixture U3-Mix is composed of a FEN of EC20, MAL of EC50, DIC of EC10, CHL of EC40, MPA of EC5, and FAM of EC30. The concentration ratios of various OP pesticides in the mixtures, respectively, denoted as U1-, U2-, U3-, U4-, U5-, and U6-Mix, were listed in Table 4. To compare with the UD, the other two mixtures were designed using the equivalent-effect concentration ratio

method. The mixture with a ratio of EC50 values for all six OP components was named as EC50-Mix and the other one with a ratio of EC5 as EC5-Mix. The ratios of the concentrations of various OP components to the total concentration of the mixture were also listed in Table 4. 3.2.2. Joint toxicity of OP mixtures In the same way as single OP, the concentration– response (inhibition) data of six mixtures based on the UD (UD-Mix simply) and two mixtures on the equivalenteffect concentration ratio method (EE-Mix simply) were determined on the VeritasTM luminometer. Also, the observed concentration response data were fitted to Logit or Weibull model and the model parameters as well as some statistics listed in Table 5. The corresponding pEC50 values of all test mixtures ranged from the value of pEC50mix of 3.81 for U5-Mix to 4.27 for EC5-Mix. For all test OP mixtures the pEC50 values (pEC50mix) did not exceed the most active component (FAM) of 5.63 and were larger than the least toxic components (CHL) of 3.55 in comparison with the individual OPs. Similarly, the pEC5mix values did not exceed the most toxic component (FAM) of 7.04 and were larger than the least toxic components (DIC) of 5.21. As shown in Figs. 2 and 3, the observed concentration response scattered points of whether six UD-Mix mixtures or two EE-Mix ones were on the whole better described by the CA model than the IA model. Fig. 2 depicted the relationships between the observed concentration response data of six UD-Mix mixtures and the CRC predicted by

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Table 5 The concentration-response models and some statistics of eight OP mixtures Mixture

Model

a

b

RMSE

R

log EC50,mix (pEC50,mix)

log EC5,mix (pEC5,mix)

U1-Mix U2-Mix U3-Mix U4-Mix U5-Mix U6-Mix EC50-Mix EC5-Mix

Logit Logit Weibull Weibull Weibull Weibull Weibull Weibull

9.84 9.00 5.49 7.36 6.50 7.67 5.85 7.62

2.34 2.12 1.50 1.82 1.80 2.00 1.61 1.87

0.0259 0.0104 0.0302 0.0224 0.0161 0.0276 0.0197 0.0178

0.992 0.998 0.983 0.992 0.993 0.987 0.991 0.996

4.21 4.25 3.90 4.25 3.81 4.02 3.86 4.27

5.46 5.63 5.64 5.68 5.26 5.32 5.48 5.66

100

100

U2-Mix

U1-Mix 80

60

% Inhibition

% Inhibition

80

40 20

60 40 20

36 controls

0 0 -20 0 1E-7

1E-6 1E-5 1E-4 Concentration (mol/L)

0

100

100 U4-Mix

80

80

60

60

% Inhibition

% Inhibition

U3-Mix

40 20

40 36 controls 20

0

0 0 1E-7

1E-6 1E-5 1E-4 Concentration (mol/L)

1E-3

1E-6 1E-5 Concentration (mol/L)

0

100

1E-4

100 U5-Mix

U6-Mix

80

80

60

60

% Inhibition

% Inhibition

1E-6 1E-5 1E-4 Concentration (mol/L)

40

40

20

20

0

0 0 1E-7

1E-6 1E-5 1E-4 Concentration (mol/L)

1E-3

36 controls

0 1E-7

1E-6 1E-5 1E-4 Concentration (mol/L)

Fig. 2. The concentration–response relationships observed and predicted for six OP mixtures from the uniform design. Observed (K); controlled (J); predicted by the concentration addition (CA) (solid line); predicted by the independent action (IA) (dashed line).

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886

100

100

80

80 % Inhibition

% Inhibition

EC5-Mix

EC50-Mix

60 40

60 40

20

20

0

0 0

36 controls

0 1E-7

1E-7 1E-6 1E-5 1E-4 Concentration (mol/L)

1E-6 1E-5 1E-4 Concentration (mol/L)

Fig. 3. The concentration–response relationships observed and predicted for two OP mixtures from the equivalent-effect concentration ratio. Observed (K); controlled (J); predicted by the concentration addition (CA) (solid line); predicted by the independent action (IA) (dashed line).

100

CA

CA

60

7.15 8.18

5.18 6.12

5.31 6.68

3.20 3.88

U5-Mix

U6-Mix

EC50-Mix

EC5-Mix

3.84 4.44

U4-Mix

U1-Mix

EC5-Mix

EC50-Mix

U6-Mix

U5-Mix

0 U4-Mix

0 U3-Mix

20

3.83 4.72

40

20

U2-Mix

IA

U3-Mix

40

% Inhibition

70.5 48.1 52.8

59.8

53.4 58.6

52.1 60.0

57.1

55.6 59.6

48.3

60.2

49.99 56.01

50.2

60

U1-Mix

% Inhibition

80

80

U2-Mix

IA

7.24 9.31 3.75 4.40

100

Fig. 4. The comparison of two classical effects observed and predicted by concentration addition (CA) and independent addition (IA) of eight OP mixtures in Table 5. (a) 50%; (b) 5%.

the CA model (solid lines) as well as the IA model (dotted line) while Fig. 3 showed the relationships of two EE-Mix mixtures. To compare with the results from the classical ‘‘point to point’’ method, the 50% effect values and the 5% effect values of eight mixtures predicted by the CA and IA models were shown in Fig. 4. On the condition of the 50% effect the excellent predictive power of CA became even more prominent for the mixtures of U1-Mix (50.15%) and U2-Mix (49.99%) and the relative deviations between the toxicities predicted by CA and observed were, respectively, 0.30% and 0.02% on the 50% effect as seen in Fig. 4a. The maximum deviation of 19.64% was the mixture EC50-Mix and the other’s derivations were o11.22%. The reason why the derivation was so large is that two EC50 values of DIC and CHL pesticides in the EE50-Mix were not got from the experiments but extrapolated from their fitted CRCs. On the 5% effect the results predicted by CA, especially for the mixtures such as U1-Mix, U5-Mix, and EC5-Mix, had a high relative derivation (430%) which might be contributed to a large uncertainty in the low effect

region. Nevertheless, from Fig. 4b the absolute deviation between the effects observed and predicted by CA was not too high and especially for the mixtures of U6-Mix (5.18%) and EC50-Mix (5.31%) the observed 5% effect was also in excellent agreement with the results predicted by the CA models. On the whole, the results calculated from the IA model were not better than CA although IA could also, especially at the low-concentration section, well describe the experimental toxicity of the mixtures as shown in Figs. 2 and 3. The maximum derivation from the IA predictions went up 40.99% of EC50-Mix and the minimum derivation was 5.66% of EC5-Mix on the 50% effect and ranged from 5.58% of U3-Mix to 86.13% of U1-Mix on the 5% effect (Fig. 4). It was well known that IA was commonly suitable to predict the mixture effects of substances with different or dissimilar modes of action. However, the difference between the toxicities predicted by the CA and IA models was not significant in our study, which was mainly from the CRC model parameter (b) of six OP pesticides and the true reason need to be further studied.

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4. Discussion Junghans et al. (2003) reported the joint toxicity of eight sulfonylurea herbicides to the green alga Scenedesmus vacuolatus and stated that the CA model can resulted in a relative precise prediction for the combined effect of the test chemicals which was differed a little from the results predicted by IA. In our present paper, the similar results were also concluded. Especially, the IA and CA models had an equally good prediction for the toxicities of some OP mixtures such as U4-Mix, U6-Mix, and EC5-Mix as shown in Figs. 2 and 3. This might be difficult to explain from the mechanistic point of view, but from the mathematical point of view. It was found that the slope of the CRCs of the mixture components was the most important factor to impact the quantitative relationships between CA and IA (Backhaus et al., 2004b). The slope of a CRC was difficult to be accurately defined and was replaced by a steepness defined as the ratio of EC5/EC50. In the paper, the average steepness of CRCs of six OPs (0.065) was close to that of the sulfonylurea herbicides (0.051). Backhaus et al. (2004b) carefully examined the toxicity changes of the mixtures consisting of 12 phenylurea herbicides and indicated that the slope parameter b of approximately 2.3 for the Weibull function (Eq. (3)) was a very important cause to result in the equal predictions of the IA model as CA. In our OP mixture system, the mean value of b of all individual OP pesticides was equal to 2.0 as shown in Table 2. The compliance of the observed mixture toxicity of the OP pesticides with the predicted results of both the CA and IA models might also be attributed to the slopes of the individual CRCs. Furthermore, Payne et al. (2000) employed an asymmetric Hill function to analyze the CRCs of four xenoestrogens including o, p0 -DDT, genistein, 4-nonylphenol, and 4-n-octylphenol in the YES assay and the results illustrated that the CA and IA models could be the tools to assess the overall toxicity of mixtures. The OP pesticides exert their acute effects by inhibiting AChE in the nervous system with subsequent accumulation of the toxic levels of acetylcholine. Although knowledge on the mechanisms of the toxicity action of the OPs to the luminescent bacterium Q67 was not sufficient, the joint toxicity of all the test OP mixtures could be precisely assessed by CA in our study. Junghans et al. (2006) also reported that the toxicity of a mixture of 25 pesticides at the realistic exposure levels could be well predicted by CA although the test mixture included single pesticides with dissimilar or inexplicit mechanisms of action, which suggested the CA model could predict the mixture toxicity of multiple components with undefined acting modes in the aquatic environment. 5. Conclusion We have shown that the overall toxicity of the multiplecomponent mixtures of six OP pesticides can be accurately predicted by the CA model on the basis of the CRCs of the

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mixture components. However, for three mixtures U4-Mix, U6-Mix, and EC5-Mix, the toxicities predicted by the IA model are insignificantly different from the CA results, which might be explained from the mathematical point of view. The small difference between the CA and IA predictions is because the slopes of near to 2.3 of the individual CRCs results in the nearly same predictions when the concentration composition changes. The CA and IA models provided useful tools for the assessment and prediction of toxicity of the mixtures consisted of OP pesticides in the aquatic environment. Acknowledgments Funding sources: The authors are especially grateful to the National Natural Science Foundation of China (20577023) and the Shanghai Basic Research Program (No. 06JC14067) and the Foundation for the Author of National Excellent Doctoral Dissertation of PR China (200355) for their financial supports. References Altenburger, R., Backhaus, T., Boedeker, W., Faust, M., Scholze, M., 2000. Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: mixtures composed of similarly acting chemicals. Environ. Toxicol. Chem. 19 (9), 2341–2347. Amoros, I., Connon, R., Garelick, H., Alonso, J.L., Carrasco, J.M., 2000. An assessment of the toxicity of some pesticides and their metabolites affecting a natural aquatic environment using the microtox system. Water Sci. Technol. 42, 19–24. Backhaus, T., Altenburger, R., Boedeker, W., Faust, M., Scholze, M., Grimme, L.H., 2000a. Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri. Environ. Toxicol. Chem. 19 (9), 2348–2356. Backhaus, T., Scholze, M., Grimme, L.H., 2000b. The single substance and mixture toxicity of quinolones to the bioluminescent bacterium Vibrio fischeri. Aquat. Toxicol. 49, 49–61. Backhaus, T., Arrhenius, A˚., Blanck, H., 2004a. Toxicity of a mixture of dissimilarly acting substances to natural algal communities: predictive power and limitations of independent action and concentration addition. Environ. Sci. Technol. 38, 6363–6370. Backhaus, T., Faust, M., Scholze, M., Gramatica, P., Vighi, M., Grimme, L.H., 2004b. Joint algal toxicity of phenylurea herbicides is equally predictable by concentration addition and independent action. Environ. Toxicol. Chem. 23, 258–264. Bailey, H.C., Miller, J.L., Miller, M.J., Wiborg, L.C., Deanovic, L., Shed, T., 1997. Joint acute toxicity of diazinon and chlorpyrifos to Ceriodaphnia dubia. Environ. Toxicol. Chem. 16, 2304–2308. Ballesteros, E., Parrado, M.J., 2004. Continuous solid-phase extraction and gas chromatographic determination of organophosphorus pesticides in natural and drinking waters. J. Chromatogr. A 1029, 267–273. Belden, J.B., Gilliom, R.J., Lydy, M.J., 2007. How well can we predict the toxicity of pesticide mixtures to aquatic life? Integr. Environ. Assess. Manag. 3, 364–372. Berenbaum, M.C., 1985. The expected effect of a combination of agents: the general solution. J. Theor. Biol. 114, 413–431. Bliss, C.I., 1939. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615. Brian, J.V., Harris, C.A., Scholze, M., Backhaus, T., Booy, P., Lamoree, M., Pojana, G., Jonkers, N., Runnalls, T., Bonfa`, A., Marcomini, A., Sumpter, J.P., 2005. Accurate prediction of the response of freshwater

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