Nature and prevalence of non-additive toxic effects in industrially relevant mixtures
of organic chemicals
Shahid Parvez a, Chandra Venkataraman b, Suparna Mukherji a
a
b
Centre for Environmental Science and Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India
Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India
a b s t r a c t
Keywords:
Concentration addition
Independent action
Synergism
Factorial design
Risk assessment
The concentration addition (CA) and the independent action (IA) models are widely used for predicting
mixture toxicity based on its composition and individual component dose–response profiles. However,
the prediction based on these models may be inaccurate due to interaction among mixture components.
In this work, the nature and prevalence of non-additive effects were explored for binary, ternary and quaternary mixtures composed of hydrophobic organic compounds (HOCs). The toxicity of each individual
component and mixture was determined using the Vibrio fischeri bioluminescence inhibition assay. For
each combination of chemicals specified by the 2n factorial design, the percent deviation of the predicted
toxic effect from the measured value was used to characterize mixtures as synergistic (positive deviation)
and antagonistic (negative deviation). An arbitrary classification scheme was proposed based on the magnitude of deviation (d) as: additive (610%, class-I) and moderately (10 < d 6 30%, class-II), highly
(30 < d 6 50%, class-III) and very highly (>50%, class-IV) antagonistic/synergistic. Naphthalene, n-butanol,
o-xylene, catechol and p-cresol led to synergism in mixtures while 1, 2, 4-trimethylbenzene and 1, 3dimethylnaphthalene contributed to antagonism. Most of the mixtures depicted additive or antagonistic
effect. Synergism was prominent in some of the mixtures, such as, pulp and paper, textile dyes, and a
mixture composed of polynuclear aromatic hydrocarbons. The organic chemical industry mixture
depicted the highest abundance of antagonism and least synergism. Mixture toxicity was found to
depend on partition coefficient, molecular connectivity index and relative concentration of the
components.
1. Introduction
Determination of mixture toxicity alone is inadequate for characterizing mixtures based on their toxic potency. Interaction
among mixture components is a common phenomenon which
may lead to synergism or antagonism. Synergism is defined as
the interaction of two or more chemicals to produce an effect
greater than the additive effect while antagonism is defined as
the interaction of two or more chemicals to produce an effect lower than the additive effect. Hoffmann et al. (2003) studied the synergistic and antagonistic effects of chemical mixtures using the
Vibrio fischeri assay. Ten organic toxicants, commonly present in
effluents were selected, three component mixtures were formed
and their toxicity was determined. Apart from one mixture which
showed synergism, all the others depicted antagonism.
The potential for joint toxic action of chemicals has been recognized by diverse groups, i.e., scientists, toxicologists, health risk
assessors and environmentalists. Altenburger et al. (2003) summa-
rized the results of exposure of test organisms to chemical mixtures based on different types of joint action. Chen and Lu (2002)
studied the joint toxicity of 44-binary mixtures of organic chemicals on E. coli and proposed a set of generalized criteria for prediction of mixture toxicity. They proposed that a mixture will show
antagonism if it has components acting through a similar toxic
mechanism and vice versa. Understanding the mechanism of joint
action requires modeling approaches that make use of available
knowledge regarding the inherent toxicity of each component
(Backhaus et al., 2004). The two commonly used models for mixture toxicity prediction include the concentration addition (CA)
and independent action (IA) model. While the CA model assumes
all the components in a given mixture have similar mode of action,
the IA model assumes that all the components have dissimilar
mode of action. Dissimilar mode of action implies interaction of
toxicants with different molecular target sites to produce a common toxicological endpoint through a distinct chain of reactions
within an organism (Faust et al., 2001). These predictive models
rely on single component toxicity data. Many researchers have reported that the concentration addition model is more effective in
describing the mixture effects for a majority of industrial chemicals
1430
found in surface water while the IA model results in underestimation of mixture toxicity (Backhaus et al. 1999; Altenburger et al.,
2003).
These studies were specifically designed for identifying the
toxic chemicals that tend to produce non-additive response. Such
identification can facilitate possible substitution of potent toxicants used in industrial processes with alternative chemicals.
Increasing concern over exposure to multiple rather than single
chemicals motivated the present investigation into the joint effect
of chemical mixtures. This study was aimed at understanding mixture effects in industrial effluents so as to facilitate waste minimization and pollution prevention. Chemical mixtures representative
of organic chemical industry, textile dye industry, pulp and paper
industry and petroleum refinery were chosen since these industries have been found to involve the use of potent toxicants (Prakash et al., 1996). Binary, ternary and quaternary mixtures were
formulated based on the 2n full factorial design. However, while
such designs are typically applied for determining the main effects
and interaction effects of n-factors based on the entire set of 2n
runs, no attempt was made to perform these analyses. Parvez
et al. (2008a) reported that for a set of four-component mixture
of hydrophobic organic compounds (HOCs), the two-way and
three-way interactions were often significant, while the four-way
interactions were consistently insignificant for various mixtures.
In this study, the measured toxicity for each of the runs based on
2n full factorial design was compared with the CA and IA model
predictions. The nature and degree of deviation observed was used
to characterize and classify the various HOC mixtures.
2. Materials and methods
A total of six mixtures were formed using 14 chemicals. Four
mixtures were representative of effluent from various industries
and were formulated as discussed by Parvez et al. (2008a) while
the other two were hypothetical mixtures. The mixture representative of pulp and paper industry (mixture-A) contained p-cresol
(Cr), phenol (P), catechol (Ca) and acetaldehyde (Ac). The components in the organic chemical industry (mixture-B) were naphthalene (N), ethylbenzene (Eb), acetaldehyde (Ac) and o-xylene (X).
The mixture representative of textile dye industry (mixture-C) contained 1, 2, 4-trimethylbenzene (Tmb), naphthalene (N), n-butanol
(B) and o-xylene (X). The components in the mixture representative of petroleum refinery (mixture-D) included 1, 2, 4-trimethylbenzene (Tmb), naphthalene (N), phenol (P) and ethylbenzene
(Eb). One of the hypothetical mixture (mixture-E) contained naphthalene (N), aniline (A), phenol (P) and toluene while the other
hypothetical mixture (mixture-F) contained polycyclic aromatic
hydrocarbons (PAHs), i.e., naphthalene, 1-methylnaphthalene
(1MN), 2-methylnaphthalene (2MN) and 1, 3-dimethylnaphthalene (DMN).
2.1. Full factorial design and mixtures toxicity measurement
Mixtures were designed using the 2n full factorial statistical design approach where, ‘n’ refers to the number of components in a
mixture (Berthouex and Brown, 2002). Each mixture containing
four components was subdivided to form 2-component (binary),
3-component (ternary) and 4-component (quaternary) sub-mixtures. For full factorial design, a binary sub-mixture required
22 = 4 combinations, a ternary sub-mixture required 23 = 8 combinations, and a quaternary sub-mixture required 24 = 16 combinations. Each combination was characterized by a unique
concentration ratio of the various components (Berthouex and
Brown, 2002; Parvez, 2008). The 11 sub-mixtures, i.e., 6-binary,
4-ternary and 1-quaternary sub-mixture, resulted in a total of 72
combinations (R (number of sub-mixtures number of combination in each sub-mixture) = 6 4 + 4 8 + 1 16) for each mix-
Table 1
Mixture composition and fixed dose levels employed in the 2n full factorial design.
Mixtures
A
B
C
D
E
F
Components
Low ()EC10(mg L1)
High (+) EC40(mg L1)
Pulp-paper industry
p-Cresol (Cr)
Phenol (P)
Catechol (Ca)
Acetaldehyde (Ac)
9.50
7.26
9.90
120
75.0
28.0
46.6
474
Organic chemical industry
Naphthalene (N)
Ethylbenzene (Eb)
Acetaldehyde (Ac)
o-Xylene (X)
0.68
1.06
120
0.88
2.36
4.28
474
7.80
Textile-dyes industry
1,2,4-Trimethylbenzene (Tmb)
Naphthalene (N)
n-Butanol (B)
o-Xylene (X)
0.25
0.68
2625
0.88
1.32
2.36
5375
7.80
Petroleum refinery industry
1,2,4-Trimethylbenzene (Tmb)
Naphthalene (N)
Phenol (P)
Ethylbenzene (Eb)
0.25
0.68
7.26
1.06
1.32
2.36
28.0
4.28
0.68
41.8
7.27
8.0
2.36
171.2
28.0
57.0
0.68
0.16
0.24
0.11
2.38
0.84
0.86
0.54
Hypothetical mixtures
Naphthalene (N)
Aniline (A)
Phenol (P)
Toluene (T)
Naphthalene (N)
1-Methylnaphthalene (1MN)
2-Methylnaphthalene (2MN)
1,3-Dimethylnaphthalene (DMN)
Note: The concentration for each component is reported in mg L1 For components existing as liquid in their pure state, the published density values were used to express
concentration in units of mg L1.
Table 2
Summary of measured (Im) and CA model predicted (Ip) bioluminescence inhibition for mixtures.
Combination
Concentration ratio (mg L1)
(C1)
(C2)
(C3)
Mixture (A)
(C4)
Im
Mixture (B)
Ip
Im
Mixture (C)
Ip
Im
23
46
42
54
N + Eb
18 ± 1
47 ± 1
30 ± 1
51 ± 1
Mixture (D)
Ip
Im
24
44
48
59
Tmb + N
24 ± 1
13 ± 1
52 ± 1
47 ± 0
Mixture (E)
Ip
Im
21
45
44
56
Tmb + N
26 ± 2
34 ± 0
45 ± 3
53 ± 6
Mixture (F)
Ip
Im
Ip
40
40
44
56
N+A
26 ± 2
54 ± 1
53 ± 1
64 ± 1
21
45
47
N + 1MN
30 ± 0
59 ± 0
65 ± 0
76 ± 0
22
46
50
61
–
+
–
+
–
–
+
+
Cr + P
05 ± 1
53 ± 2
16 ± 1
53 ± 3
1
2
3
4
–
+
–
+
–
–
+
+
Cr + Ca
02 ± 0
42 ± 1
41 ± 2
64 ± 1
22
44
42
56
N + Ac
07 ± 0
44 ± 2
21 ± 2
54 ± 2
25
46
48
58
Tmb + B
17 ± 2
33 ± 1
51 ± 1
73 ± 0
25
52
50
Tmb + P
00 ± 3
19 ± 2
28 ± 2
42 ± 3
22
44
44
53
N+P
25 ± 2
57 ± 1
35 ± 1
60 ± 1
26
47
47
57
N + 2MN
48 ± 2
66 ± 0
56 ± 0
72 ± 0
20
46
46
56
1
2
3
4
–
+
–
+
–
–
+
+
Cr + Ac
00 ± 2
37 ± 1
04 ± 1
39 ±
24
46
44
57
N+X
24 ± 2
47 ± 3
21 ± 1
55 ± 1
18
42
46
54
Tmb + X
15 ± 1
04 ± 1
42 ± 1
31 ± 1
18
43
45
54
Tmb + Eb
00 ± 3
30 ± 1
32 ± 3
44 ± 2
21
44
45
56
N+T
29 ± 1
58 ± 1
65 ± 0
68 ± 1
21
43
46
N + DMN
29 ± 0
62 ± 0
45 ± 0
65 ± 0
20
45
47
56
1
2
3
4
–
+
–
+
–
–
+
+
P + Ca
20 ± 0
41 ± 1
67 ± 0
75 ± 1
24
45
46
56
Eb + Ac
01 ± 0
22 ± 1
14 ± 2
36 ± 2
37
24
47
60
N+B
34 ± 1
46 ± 0
74 ± 1
76 ± 1
28
49
54
N+P
36 ± 2
47 ± 0
41 ± 1
58 ± 2
24
32
48
56
A+P
19 ± 2
50 ± 1
38 ± 1
56 ± 1
21
46
46
56
1MN + 2MN
45 ± 0
67 ± 0
51 ± 0
67 ± 0
18
44
43
60
1
2
3
4
–
+
–
+
–
–
+
+
P + Ac
00 ± 1
25 ± 0
14 ± 1
36 ± 1
22
44
48
57
Eb + X
18 ± 1
32 ± 1
29 ± 2
38 ± 2
16
48
52
56
N+X
19 ± 1
52 ± 1
41 ± 1
59 ± 0
20
42
45
54
N + Eb
32 ± 2
52 ± 0
52 ± 2
60 ± 1
20
45
48
59
A+T
15 ± 1
45 ± 1
50 ± 1
58 ± 1
17
43
45
55
1MN + DMN
15 ± 0
55 ± 0
36 ± 1
56 ± 0
18
47
46
60
1
2
3
4
–
+
–
+
–
–
+
+
Ca + Ac
16 ± 1
54 ± 2
27 ± 1
55 ± 2
22
44
46
59
Ac + X
09 ± 0
20 ± 1
29 ± 1
37 ± 2
20
44
46
57
B+X
45 ± 1
74 ± 1
45 ± 1
89 ± 0
21
46
49
P + Eb
02 ± 1
35 ± 2
11 ± 2
43 ± 1
21
44
46
56
P+T
08 ± 1
27 ± 1
48 ± 1
52 ± 1
22
45
46
56
2MN + DMN
29 ± 1
34 ± 0
32 ± 0
38±0
17
44
48
56
Ternary
1
2
3
4
5
6
7
8
–
+
–
+
–
+
–
+
–
–
+
+
–
–
+
+
–
–
–
–
+
+
+
+
Cr + P + Ca
32 ± 2
58 ± 2
46 ± 1
64 ± 1
70 ± 0
81 ± 1
74 ± 1
81 ± 1
32
49
46
56
46
60
58
N + Eb + Ac
15 ± 2
34 ± 2
33 ± 0
47 ± 2
28 ± 2
51 ± 2
43 ± 0
58 ± 1
32
51
55
60
52
Tmb + N + B
36 ± 1
41 ± 1
51 ± 1
52 ± 0
79 ± 0
79 ± 0
80 ± 0
80 ± 1
38
57
58
56
Tmb + N + P
35 ± 2
44 ± 2
51 ± 2
55 ± 2
45 ± 3
54 ± 3
57 ± 0
57 ± 3
30
51
50
55
50
58
56
N+A+P
19 ± 1
50 ± 0
50 ± 1
63 ± 1
36 ± 1
56 ± 1
52 ± 1
64 ± 0
28
51
50
60
52
59
57
N + 1MN + 2MN
43 ± 0
61 ± 0
65 ± 0
73 ± 0
48 ± 0
66 ± 0
64 ± 0
72 ± 0
30
50
54
48
60
1
2
3
4
5
6
7
8
–
+
–
+
–
+
–
+
–
–
+
+
–
–
+
+
–
–
–
–
+
+
+
+
Cr + P + Ac
15 ± 1
51 ± 1
27 ± 2
51 ± 1
27 ± 1
56 ± 1
36 ± 0
62 ± 1
30
50
48
56
49
57
60
N + Eb + X
24 ± 1
48 ± 2
37 ± 1
53 ± 1
38 ± 1
53 ± 1
45 ± 0
56 ± 2
28
48
50
60
44
56
59
Tmb + N + X
20 ± 1
10 ± 2
47 ± 1
42 ± 1
38 ± 1
40 ± 1
60 ± 1
52 ± 1
25
48
45
56
48
56
56
Tmb + P + Eb
33 ± 3
41 ± 2
44 ± 3
53 ± 2
46 ± 3
47 ± 3
55 ± 1
59 ± 1
28
50
48
57
50
59
57
N+A+T
25 ± 1
53 ± 1
52 ± 1
62 ± 0
49 ± 2
63 ± 1
61 ± 1
69 ± 0
26
48
51
58
49
59
56
N + 1MN + DMN
31 ± 1
28
53 ± 0
50
60 ± 0
53
70 ± 1
42 ± 0
50
64 ± 0
60
58 ± 0
71 ± 0
1
2
–
+
–
–
–
–
Cr + Ca + Ac
33 ± 2
60 ± 1
29
52
N + Ac + X
14 ± 1
43 ± 1
28
48
Tmb + B + X
40 ± 1
37 ± 1
31
53
N + P + Eb
07 ± 2
17 ± 1
33
48
N+P+T
28 ± 1
55 ± 1
30
48
N + 2MN + DMN
43 ± 0
30
60 ± 0
50
(continued on next page)
1431
Binary
1
2
3
4
1432
Table 2 (continued)
Combination
Concentration ratio (mg L1)
(C1)
(C2)
(C3)
3
4
5
6
7
8
–
+
–
+
–
+
+
+
–
–
+
+
1
2
3
4
5
6
7
8
–
+
+
–
+
–
+
Quaternary
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
–
+
–
+
–
+
–
+
–
+
–
+
–
+
–
+
Mixture (A)
(C4)
Mixture (B)
Mixture (C)
Mixture (D)
Mixture (E)
Mixture (F)
Im
Ip
Im
Ip
Im
Ip
Im
Ip
Im
Ip
Im
Ip
–
–
+
+
+
+
63 ± 1
79 ± 0
44 ± 1
63 ± 1
65 ± 1
77 ± 1
46
48
60
22 ± 2
50 ± 2
28 ± 2
45 ± 0
37 ± 2
52 ± 1
50
60
48
60
59
78 ± 0
78 ± 0
46 ± 2
47 ± 0
79 ± 0
79 ± 0
54
52
18 ± 0
25 ± 2
08 ± 1
26 ± 1
14 ± 0
33 ± 1
51
49
60
57
42 ± 1
57 ± 1
57 ± 1
64 ± 1
59 ± 0
66 ± 0
50
57
50
58
57
50 ± 0
65 ± 0
49 ± 0
63 ± 0
44 ± 1
57 ± 0
50
60
51
60
60
–
–
+
+
–
–
+
+
–
–
–
–
+
+
+
+
P + Ca + Ac
25 ± 2
43 ± 1
58 ± 1
66 ± 1
23 ± 1
46 ± 1
61 ± 1
69 ± 1
30
49
51
56
50
58
Eb + Ac + X
02 ± 1
17 ± 2
15 ± 1
28 ± 0
15 ± 1
29 ± 2
27 ± 0
37 ± 2
27
51
50
49
58
59
N+B+X
40 ± 1
53 ± 0
75 ± 0
81 ± 0
50 ± 1
57 ± 1
77 ± 0
80 ± 0
34
54
56
55
N + Eb + Tmb
34 ± 1
48 ± 2
48 ± 1
55 ± 2
46 ± 1
55 ± 2
55 ± 1
61 ± 2
30
50
50
60
50
58
59
A+P+T
19 ± 1
43 ± 0
30 ± 1
50 ± 1
44 ± 1
55 ± 1
48 ± 0
59 ± 1
27
49
49
56
49
57
57
1MN + 2MN + DMN
39 ± 0
58 ± 1
44 ± 0
65 ± 0
22 ± 0
44 ± 1
32 ± 1
56 ± 0
24
52
50
50
58
–
–
+
+
–
–
+
+
–
–
+
+
–
–
+
+
–
–
–
–
+
+
+
+
–
–
–
–
+
+
+
+
Cr + P + Ca + Ac
17 ± 1
40 ± 1
28 ± 1
47 ± 0
53 ± 1
67 ± 1
60 ± 1
70 ± 2
25 ± 1
42 ± 2
34 ± 1
48 ± 2
57 ± 1
68 ± 1
61 ± 1
69 ± 1
35
50
51
58
52
53
N + Eb + Ac + X
11 ± 1
40 ± 2
26 ± 1
46 ± 1
27 ± 1
48 ± 1
33 ± 1
51 ± 1
24 ± 1
40 ± 1
34 ± 2
50 ± 1
34 ± 2
51 ± 2
42 ± 1
56 ± 1
36
51
55
55
53
Tmb + N + B + X
38 ± 3
43 ± 2
44 ± 2
51 ± 3
78 ± 3
82 ± 1
80 ± 2
81 ± 1
48 ± 3
51 ± 4
52 ± 3
58 ± 2
79 ± 3
83 ± 1
81 ± 1
84 ± 1
42
58
59
60
58
Tmb + N + P + Eb
35 ± 2
39
47 ± 1
53
52 ± 2
52
56 ± 2
60
50 ± 4
52
56 ± 2
59
59 ± 2
60
63 ± 2
53 ± 2
54
54 ± 3
58 ± 2
61 ± 1
56 ± 2
51 ± 6
61 ± 2
66 ± 1
N+A+P+T
25 ± 0
44 ± 0
47 ± 0
56 ± 0
39 ± 0
51 ± 0
50 ± 0
60 ± 1
46 ± 0
59 ± 1
57 ± 0
64 ± 0
52 ± 0
59 ± 0
59 ± 0
65 ± 0
36
52
52
60
54
60
58
52
60
N + 1MN + 2MN + DMN
24 ± 0
39
51 ± 1
53
47 ± 1
58
63 ± 0
43 ± 0
52
56 ± 1
59 ± 1
66 ± 1
40 ± 0
54
55 ± 0
60 ± 1
67 ± 0
23 ± 1
56 ± 0
58 ± 1
68 ± 0
–
–
–
–
–
–
–
–
+
+
+
+
+
+
+
+
Note: IA model based predictions remain fixed for binary, ternary and quaternary combinations due to ‘fixed effect’ factorial design. IA model predictions in: binary combinations are 19, 46, 46, 64 respectively for combination 1–4
in all mixtures; ternary combinations are 27, 51, 51, 68, 51, 68, 68, 78 respectively, for combination 1–8 in all mixtures; quaternary combinations are 34, 56, 56, 71, 56, 71, 71, 81, 56, 71, 71, 81, 71, 81, 81, 87, respectively, for
combinations 1–16 in all mixtures.
Symbol () and (+) represents the low (EC10) and high concentration (EC40) of components C1, C2, C3, C4 present in mixtures.
Symbol () indicates that in these mixtures Ip could not be calculated by applying the CA model.
1433
ture. All components were present at two fixed dose levels, EC10
and EC40. The negative sign () represents the low dose level while
the positive sign (+) represents the high dose level. EC10 and EC40
refer to the concentration of a mixture component that causes
10% and 40% inhibition in bioluminescence, respectively, when
present individually. The fixed-dose levels determined from the
individual component dose–response profile are shown Table 1.
The dose–response profile of each component and mixture toxicity
were obtained using the V. fischeri bioluminescence inhibition assay based on strain NRRL B-11177 with 30 min exposure time
(ISO, 1998; Parvez et al., 2008b). V. fischeri grown for 24 h was used
to prepare a reagent containing the harvested cells in a protective
medium which could be preserved at 20 °C. The reagent was
reconstituted using the reagent dilution medium and stabilized
at 15 °C for 45 min before conducting the toxicity measurements
using equal volumes (200 lL) of this reagent and test solution.
Exposure time of reagent with toxicant was fixed at 30 min and
bioluminescence was measured in relative light intensity (RLU)/s
using a Sirius luminometer (Berthold Detection Systems, Germany). All the bioluminescence inhibition measurements were
performed in triplicate and the percent inhibition (I,%) was
computed as per standard methods. All the stock solutions were
2.2. Mixture toxicity prediction based on concentration addition (CA)
model
The predictions based on concentration addition (CA) model
were obtained using the toxicity data for components derived
independently from the dose–response profile of each compound.
The profile for each compound needs to be defined for at least up
to 50% inhibition for obtaining prediction based on CA model.
The concentration of each component in the mixture (Ci) causing
different fixed values of effects ranging from 10% to 100% was selected and calculations were performed as illustrated in Eqs. (1)–
(4) (Backhaus et al., 2004). This model yields a mixture concentration (ECx,mix) having x% effects, and thus allows generation of the
full profile of effects ranging from 0% to 100% (or lower if the single
component profiles are not fully defined) for a mixture in which
the components are present at predetermined concentration ratios.
For all binary, ternary and quaternary sub-mixtures, ECx,mix was
predicted using this approach and thus, the % inhibition profile
was generated. Subsequently, the percent inhibition at the mixture
70
(b)
70
(a)
80
60
60
50
40
30
20
y = 1.358x - 13.321
R2 = 0.9705
10
IA model_I (%)
IA model_I (%)
prepared in 4.5% methanol and working solutions in 2% NaCl
(Parvez, 2008). The control in this study consisted of 2% Nacl.
50
40
30
20
0
0
20
40
60
y = 1.2298x - 8.6993
R2 = 0.9201
10
0
80
0
20
CA m odel_I (%)
(c)
70
60
60
50
40
30
20
y = 1.0893x - 4.6541
R2 = 0.848
10
40
30
20
20
40
60
CA m odel_I (%)
80
0
(e)
80
60
60
IA model_I (%)
70
50
40
30
20
y = 1.3159x - 11.001
R2 = 0.9595
10
0
20
40
60
CA m odel_I (%)
80
20
40
60
CA m odel_I (%)
80
(f)
80
70
0
y = 1.3392x - 12.818
R2 = 0.9116
0
0
IA model_I (%)
50
10
0
80
(d)
80
70
IA model_I (%)
IA model_I (%)
80
40
60
CA m odel_I (%)
50
40
30
20
y = 1.1359x - 4.4233
R2 = 0.9588
10
0
0
20
40
60
80
CA m odel_I (%)
Fig. 1. Correlation between CA and IA model predicted values for (a) Pulp-paper mixture-A; (b) Organic chemical mixture-B; (c) Textile-dye mixture-C; (d) Petroleum refinery
mixture-D; (e) Hypothetical mixture-E; and (f) Hypothetical mixture-F.
1434
concentration corresponding to that of the experimental run was
predicted by CA model.
pi ¼
Ci
Ci
¼P
C mix
Ci
TU i ¼
X
ð1Þ
ð2Þ
p1
EC x;1
EC x;mix ¼ P
EðC mix Þ ¼ EðC i þ . . . þ C nÞ ¼ 1 n
Y
½1 EðC i Þ
ð5Þ
i¼1
pi
EC x;i
TU i ¼
caused by the mixture E(Cmix) based on the IA model was subsequently determined as shown in Eq. (5).
þ
p2
pn
þ ... þ
EC x;2
EC x;n
ð3Þ
where, E(Cmix) is the expected effect of the mixture composed of n
chemicals with an overall concentration of Cmix, where each component is present at a concentration Ci (i = 1, 2, 3 ,. . . , n); and E(Ci) is the
inhibition effect when the compound is applied individually at the
specified concentration.
2.4. Degree of synergism/antagonism in HOC mixtures
1
TU i
ð4Þ
where, ECx,i is the concentration of the ith mixture component that
causes x% inhibition in bioluminescence when applied individually,
Ci is the concentration of the respective component in the mixture,
Cmix is the total mixture concentration; pi is the relative concentration of the ith component in the mixture, TUi refers to the toxic unit
of the ith component, RTUi is the summation of the toxic units and
ECx,mix is the model predicted value of mixture toxicity, i.e., mixture
concentration causing x% effect.
2.3. Mixture toxicity prediction based on independent action (IA)
model
The full dose–response profile of each individual component
was first generated. Subsequently, instead of inputting values of
concentration causing x% effect as in the CA model, the effect
caused by the specified concentration of each component when
present individually (E(Ci), for all i) was determined. The effect
The percent deviation of the mixture predicted effect from the
measured effect was calculated for each mixture combination
and plotted with respect to the measured effect. A positive deviation indicates synergism and negative deviation indicates antagonism. Negligible deviation indicates additive behavior where the
interactions are insignificant. Based on the magnitude of percent
deviation (d), an arbitrary classification scheme is proposed to categorize mixtures. In this study we consider a deviation 610% as
additive (class-I). Deviation in the range of 10–30% (10 < d 6 30%)
was considered moderately antagonistic/synergistic (class-II)
while deviation in the range 30–50% (30 < d 6 50%) was considered
highly antagonistic/synergistic (class-III). Deviation above 50% was
considered very highly antagonistic/synergistic (class-IV). Based on
this approach, the mixtures were not only characterized as synergistic/antagonistic but were also classified on the basis of their degree of synergism/antagonism.
3. Results and discussion
3.1. Measured and model predicted toxicity for mixtures
(a)
(b)
Fig. 2. Deviation of (a) CA and (b) IA model predicted toxicity from the measured
values for pulp-paper industry mixture-A.
Prediction for all the combinations could not be obtained with
the CA model since complete dose–response profiles (up to 100%
inhibition) could not be generated for all the components due to
their low aqueous solubility values. However, in-spite of the
incomplete dose–response profile for some of the components,
prediction based on the IA model could be obtained for all the combinations. The measured values of bioluminescence inhibition (Im)
and the CA model predictions (Ip) are summarized in Table 2 for all
the six mixtures used in this study. Table 2 also highlights the concentration of different components in each mixture combination.
The IA model predictions for binary, ternary and quaternary combinations remain fixed in all the mixtures. This is the result of the
fixed effect factorial design used in this study. The IA model predictions for binary combinations are 19, 46, 46, 64, respectively, for
combinations 1–4 in all mixtures; for ternary combinations are
27, 51, 51, 68, 51, 68, 68, 78, respectively, for combinations 1-8
in all mixtures; and for the quaternary combinations are 34, 56,
56, 71, 56, 71, 71, 81, 56, 71, 71, 81, 71, 81, 81, 87, respectively
for combination 1–16 in all mixtures. In mixture-A, although a
good correlation (r2 = 0.97) was observed between the CA and IA
model predictions (Fig. 1), the correlation between predicted and
measured toxicity was poor (r2 < 0.6). Similar results were observed for the other mixtures. A good correlation was found to exist between the CA and IA model predicted values for all the six
mixtures (r2 > 0.8). In all the mixtures studied, the predictions
based on the IA model were mostly higher than the CA model predictions. In contrast, other researchers have typically reported that
the IA model underestimates mixture toxicity as was reported by
Backhaus et al. (1999) for joint toxicity of quinolines; Altenburger
et al. (2003) for a mixture of nitrobenzenes and Faust et al. (2001)
for a mixture of s-triazines.
1435
Table 3
Classification of mixture toxicity based on (a) CA and (b) IA model for pulp and paper industry mixture-A.
The concentration of all the compounds present in each combination is provided in Tables 1 and 2.
The colour coding highlights the nature of toxicity, i.e., synergism, antagonism, and additivity. The roman numbers I, II, III and IV highlight the degree of synergism,
antagonism and additivity.
3.2. Characterization of mixtures based on the degree of synergism/
antagonism
All the mixture combinations used in this study were classified
on the basis of the degree of deviation of the model predictions
from the measured values. For the pulp and paper industry mixture-A, the percent deviation of the predicted toxicity from the
measured toxicity data are demonstrated in Fig. 2 and the type
(synergism/antagonism/additivity) and degree of deviation observed for the various combinations are depicted in Table 3. A similar approach was applied to the other mixtures using the toxicity
data summarized in Table 2.
Fig. 2 illustrates that based on both CA and IA models, positive deviations are less abundant and of much lower magnitude
compared to the negative deviations. The highest negative devi-
ations are observed for low measured toxicity values. Based on
percent deviation of CA model prediction, 15 combinations depicted positive deviation and 29-combinations depicted negative
deviation >10% in magnitude and 11 combinations depicted
deviation 610% in magnitude. Hence, a total of 15 combinations
were synergistic, 29-combinations were antagonistic, 11-combinations were additive and 17-combinations could not be estimated (Table 3a). Similarly IA model based prediction of pulppaper industry mixture showed that 13 combinations were synergistic, 13 combinations were additive and 46 combinations
were antagonistic (Table 3b). Through such tables all the mixtures and their various combinations were characterized based
on the type and degree of deviation observed. The abundance
of synergism, additivity and antagonism observed across all the
six mixtures are summarized in Table 4. In Table 5, the CA
1436
Table 4
Relative abundance of synergism, additivity and antagonism across the six mixtures.
Mixture
Model
Synergism
B
Additivity
a
T
a
Q
a
a
Q
a
a
Q
a
RSyn
B
RAdd
B
CA
IA
5
4
10
9
0
0
15
13
4
3
6
8
1
2
11
13
15
17
9
15
5
14
29
46
17
0
B
CA
IA
3
1
0
0
0
0
3
1
5
5
2
1
0
0
7
6
16
18
23
31
5
16
44
65
18
0
C
CA
IA
7
10
5
12
1
5
13
27
6
6
5
7
0
4
11
17
8
8
9
13
4
7
21
28
27
0
D
CA
IA
4
5
3
3
0
0
7
8
5
3
14
3
6
3
25
9
15
16
10
26
2
13
27
55
13
0
E
CA
IA
7
8
2
0
0
0
9
8
9
8
17
12
1
0
27
20
6
8
9
20
8
16
23
44
13
0
F
CA
IA
16
14
8
10
0
0
24
24
3
4
9
14
1
1
13
19
5
6
5
8
4
15
14
29
21
0
a
T
a
A
b
T
Undefinedb
Antagonism
a
RAnt
Total binary (B), ternary (T) and quaternary (Q) combinations are 24, 32 and 16, respectively in each mixture.
Undefined implies model predictions could not be obtained.
model based deviations have been used to classify the mixture
combinations into classes I–IV.
3.2.1. Pulp-paper industry mixture-A
Five combinations in the binary sub-mixtures and ten combinations in the ternary sub-mixtures depicted synergism based on
CA model (Table 3a). Synergism was not observed in the quaternary sub-mixture. The synergistic combinations were typically in
the class-II category. Two combinations showed high (class III)
synergism, i.e., combination No. 3 in sub-mixture P + Ca and
combination No. 5 in sub-mixture Cr + P + Ca. Most binary and
ternary combinations with catechol and p-cresol showed synergism. The sub-mixtures and their combinations where p-cresol
or catechol were present at the EC40 concentration were more
synergistic than where they were present at EC10 concentration.
Based on the IA model, synergism was observed in the following
binary sub-mixtures: combination No. 2 in sub-mixture Cr + P;
combination Nos. 3 and 4 in sub-mixture P + Ca, combination
No. 2 in sub-mixture Ca + Ac (Table 3b). In ternary sub-mixtures,
the sub-mixtures Cr + P + Ca and Cr + Ca + Ac, showed synergism
in combination Nos. 1, 2, 5, 6 and 1, 2, 3, 4, respectively. ClassIII synergism was observed only for combination No. 3 in submixture P + Ca. No synergism was observed in the quaternary
sub-mixture. In the 46 antagonistic combinations, moderate to
very high antagonism (class II–IV) was observed. Three cases of
additivity were observed in binary combinations, eight in ternary
combinations and two in quaternary combinations (Table 3b).
Mostly synergism or additivity was observed for catechol and
p-cresol containing combinations at the EC40 dose level.
3.2.2. Organic chemical industry mixture-B
Synergism was very rare. Only three combinations in the binary sub-mixtures showed synergism (Table 4). Binary sub-mixture N + X, in combination Nos. 1, 2 and sub-mixture Eb + X, in
combination No. 1 showed class-II synergism (Table 5). No synergism was observed in the ternary and quaternary sub-mixtures. Most of the cases of synergism and additivity occurred
in the binary combinations. Additivity was observed for only
one ternary sub-mixture, N + Eb + X, in combination Nos. 2 and
6. Based on the IA model, there was only one binary combination, i.e., combination No. 1 in sub-mixture N + X, which showed
synergism. Additivity was not very prevalent. Mostly, only the
binary combinations yielded additive toxicity. Only one ternary
mixture, i.e., combination No. 2 in the sub-mixture N + Eb + X,
yielded additive effect. Antagonism was prevalent in this
mixture.
3.2.3. Textile-dye industry mixture-C
Synergism was quite prominent. In this mixture, seven binary
combinations, five ternary combinations and one quaternary combination, showed synergism. Most of the seven binary combinations showed class-II synergism except combinations Nos. 1 and
2 in the sub-mixture B + X. This binary mixture depicted class
III–IV synergism. Most of the five ternary combinations also
showed class-II synergism. In the quaternary sub-mixture, only
combination No. 5 showed class-II synergism. Of the twenty combinations showing antagonism, the entire range from class II to IV
was observed. In this mixture, six binary combinations and five ternary combinations showed additivity. Naphthalene and n-butanol
containing binary combinations showed synergism. The component 1, 3, 4-trimethylbenzene depicted a tendency to reduce synergism. Of the 27 combinations for which prediction were not
possible in the CA model, twelve showed synergism as per the IA
model predictions. Antagonism was uniformly distributed in all
types of sub-mixtures.
3.2.4. Petroleum refinery mixture-D
Four combinations in the binary sub-mixtures and three combinations in ternary sub-mixtures showed synergism based on the
CA model. Synergism was not seen in the quaternary sub-mixture.
In binary sub-mixtures class-II and class-III synergism was observed (Table 5). Combination No. 1 in the sub-mixtures
Tmb + N + P, Tmb + P + Eb and N + Eb + Tmb showed class-II synergism. Antagonism was abundant in this mixture, such that, fifteen
binary combinations, ten ternary combinations and two quaternary combinations, showed antagonism. Antagonism spread over
the entire range from class-II–IV was observed. Based on the IA
model, five synergistic combinations were observed in the binary
sub-mixtures. In ternary sub-mixtures, combination No. 1 in submixtures Tmb + N + P, Tmb + P + Eb and N + Eb + Tmb showed synergism. No synergism was observed in the quaternary sub-mixture.
Moreover, the combinations which could not be predicted based
on CA model were all antagonistic based on the IA model. Mostly
naphthalene containing combinations resulted in synergistic or
additive effects.
3.2.5. Hypothetical mixture-E
Percent deviation of CA model predicted toxicity data from the
measured toxicity data indicated that a total of 9-combinations
were synergistic, 27-combinations were additive, 23-combinations
were antagonistic and 13 combinations could not be predicted
(Table 4). In binary sub-mixtures, combination Nos. 1, 2, 3 in
N + A, N + T, and combination No. 2 in sub-mixture N + P showed
Table 5
Classification of mixture combinations based on the CA model across all the six mixtures.
Toxicity
class
Pulp paper mixture (A)
Organic chemicals mixture (B)
Textile-dye mixture (C)
Petroleum refinery mixture (D)
Hypothetical mixture (E)
Hypothetical mixture (F)
I
Ad
Cr + P (4); Cr + Ca (2, 3);
Ca + Ac (4); Cr + P + Ca (1, 3);
Cr + P + Ac (2, 6);
Cr + Ca + Ac (5, 6);
Cr + P + Ca + Ac (5)
N + Eb (2); N + Ac (2, 4); N + X (4);
Eb + Ac (2); N + Eb + X (2, 6)
Tmb + B (3); Tmb + X
(3); N + B (2); N + X
(1, 4); B + X (3);
Tmb + N + B (1);
Tmb + N + X (3, 7);
N + B + X (2, 5)
Tmb + N (3, 4); N + P (4); N + Eb
(3, 4); Tmb + N + P (3, 4, 6, 7);
Tmb + P + Eb (3, 4, 5, 7);
N + Eb + Tmb (2, 3, 4, 5, 6, 7);
Tmb + N + P + Eb (3, 4, 5, 6, 7, 9)
N + P (1, 4); A + P (2, 4); A + T
(2, 3, 4); P + T (3, 4); N + A + P
(2, 3, 4, 6, 7); N + A + T
(1, 2, 3, 4, 5, 6, 7); N + P + T
(1, 4, 6, 7); A + P + T (6);
N + A + P + T (4)
N + DMN (3); 1MN + DMN (4);
1MN + 2MN (4); N + 1MN + 2MN (5, 6);
N + 1MN + DMN (1, 2, 6);
N + 2MN + DMN (3, 4, 5, 6);
N + 1MN + 2MN + DMN (2)
II
Sy
Cr + P (2), Cr + Ca (4); P + Ca
(4); Ca + Ac (2); Cr + P + Ca
(2, 4, 6, 7); Cr + Ca + Ac
(1, 2, 3); P + Ca + Ac (3, 4)
N + X (1, 2); Eb + X (1)
N + Eb (2); Tmb + N + P (1);
Tmb + P + Eb (1); N + Eb + Tmb
(1)
N + A (1, 2, 3); N + P (2); N + T
(1, 2, 3); N + P + T (2, 5)
N + 1MN (1, 2, 3, 4); N + 2MN (3, 4);
N + DMN (2, 4); 1MN + 2MN (3);
1MN + DMN (2); N + 1MN + 2MN (2, 3);
N + 1MN + DMN (3); N + 2MN + DMN
(2); 1MN + 2MN + DMN (2)
An
Cr + Ac (2); P + Ca (1, 2);
Cr + P + Ac (4); P + Ca + Ac
(1, 2, 6); Cr + P + Ca + Ac
(2, 4)
N + Eb (4); N + Eb + Ac (4); N + Eb + X
(1, 4, 5); N + Ac + X (2, 4);
N + Eb + Ac + X (2)
Tmb + N(1, 3); N + B
(1, 3); N + X (2);
Tmb + N + B (5);
Tmb + B + X (1);
N + B + X (1, 3);
Tmb + N + B + X (5)
Tmb + N (4); Tmb + X
(1); N + X (3);
Tmb + N + B (3);
Tmb + N + X (1, 5);
Tmb + B + X (5);
Tmb + N + B + X (1, 9)
Tmb + N (2); Tmb + P (4);
Tmb + Eb (4); N + P (3); P + Eb
(2); Tmb + N + P (2, 5);
Tmb + P + Eb (2, 6);
Tmb + N + P + Eb (1, 2)
A + P (1,3); A + T (1); N + P + T
(3); A + P + T (2,4,5,7);
N + A + P + T (2, 3, 6, 7, 9, 12)
1MN + DMN (1, 3); N + 1MN + DMN
(5); 1MN + 2MN + DMN (3);
N + 1MN + 2MN + DMN (3, 5)
Sy
P + Ca (3); Cr + P + Ca (5)
–
B + X (2); Tmb + B + X (3)
N + P (1, 2); N + Eb (1)
–
An
Cr + Ac (4); Ca + Ac (1)
N + Eb (1); Eb + X (4); N + Eb + X (3, 7);
N + Ac + X (6)
Tmb + B (1); Tmb + N + B
(2); Tmb + N + X (4, 6);
Tmb + B + X (2);
Tmb + N + B + X (2, 3)
Tmb + Eb (2, 3); P + Eb (4)
N + P (3); N + A + P (1, 5);
A + P + T (1); N + A + P + T (1,5)
N + 2MN (2); N + DMN (1);
1MN + 2MN (2); 2MN + DMN (1);
N + 1MN + 2MN (1); N + 2MN + DMN
(1); 1MN + 2MN + DMN (1)
2MN + DMN (2, 3, 4); N + 2MN + DMN
(7); N + 1MN + + 2MN + DMN (9)
Sy
An
–
Cr + P (1, 3); Cr + Ca (1);
Cr + Ac (1, 3); P + Ac
(1, 2, 3, 4); Ca + Ac (3);
Cr + P + Ac (1, 3, 5, 7);
P + Ca + Ac (5);
Cr + P + Ca + Ac (1, 3, 9)
–
N + Eb (3); N + Ac (1, 3); N + X (3);
Eb + Ac (1, 3, 4); Eb + X (2, 3); Ac + X
(1, 2, 3, 4); N + Eb + Ac (1, 2, 3, 5);
N + Ac + X (1, 3, 5, 7); Eb + Ac + X
(1, 2, 3, 5, 6, 7); N + Eb + Ac + X (1, 3, 5, 9)
B + X (1)
Tmb + N (2); Tmb + B
(2); Tmb + X (2, 4);
Tmb + N + X (2)
–
Tmb + N (1); Tmb + P (1, 2, 3);
(Tmb + Eb (1); P + Eb (1, 3);
N + P + Eb (1, 2, 3, 5, 6, 7)
–
P + T (1, 2); A + P + T (3)
III
IV
N + 2MN (1); 1MN + 2MN (1)
1MN + 2MN + DMN (5, 7);
N + 1MN + 2MN + DMN (1)
Note: For each sub-mixture, the set of numbers within parenthesis indicate the various combination numbers based on factorial design.
1437
1438
class-II synergism (Table 5). In the ternary sub-mixture N + P + T,
combination Nos. 2 and 5 both showed class-II synergism. Mostly
naphthalene, aniline and toluene containing sub-mixtures showed
synergistic effect. As the concentration of these components increased from EC10 to EC40, the sub-mixtures turned more synergistic. Phenol depicted antagonistic effect. Combinations containing
phenol at EC40 were more antagonistic (e.g., combination No. 3 in
sub-mixture N + P and sub-mixture A + P and combination No. 2
in sub-mixture P + T). The IA model based predictions showed synergism only in the binary combinations. Antagonism was more
abundant in the ternary and quaternary combinations. Additivity
was observed in the binary and ternary combinations only. Mostly
synergism or additivity was observed for naphthalene, aniline and
toluene containing combinations. Based on the IA model predictions, a lower number of synergism and additivity were observed
compared to the CA model.
3.2.6. Hypothetical mixture-F
CA model predictions depicted that binary combination Nos. 1
and 2 in sub-mixture N + 2MN; combination No. 1 in sub-mixture
N + DMN; combination Nos. 1 and 2 in sub-mixture 1MN + 2MN;
and combination No. 1 in sub-mixture 2MN + DMN showed classIII–IV synergism. Antagonism was less abundant in this mixture.
Antagonism was observed in five binary combinations, five ternary combinations and four quaternary combinations. Additivity
was not very common in binary sub-mixtures. Some of the binary
combinations with naphthalene, 1-methyl naphthalene and 2methylnaphthalene showed class-IV synergism (e.g., combination
No. 1 in sub-mixture N + 2MN; combination 1 in sub-mixture
1MN + 2MN). In contrast, 1, 3-dimethylnaphthalene at EC40 level
showed greater antagonism than synergism. The IA model based
predictions demonstrated maximum number of synergism in the
binary combinations. In ternary sub-mixtures, ten combinations
yielded synergism while in the quaternary combination none
showed synergism. Compared to the CA model, more cases of
additive effects were observed in the binary and ternary
combinations.
There is sufficient literature correlating the toxicity due to single chemicals with their physicochemical properties (Parvez et al.,
2008b), however, literature on mechanisms pertaining to mixture
toxicity is lacking for mixtures of hydrophobic organic compounds. The interaction between mixture components is a complex phenomenon which can not be explained by any single
chemical property or structural information. Many factors together are responsible for mixture toxicity behavior such as octanol–water partition coefficient log(Kow), polarity of chemicals,
valence molecular connectivity index (1vv), and cell surface composition of the test organism. It has been observed that chemicals
with log(Kow) in the range of 2.0–4.0 are significantly toxic but
chemical with log(Kow) > 4.0 are less toxic (Lin et al., 2004). While
log(Kow) and (1vv) can explain the toxicity of single chemicals,
these properties have limited scope in explaining mixture behavior. Mixture behavior is also affected by other factors, such as, relative concentration ratio of mixture components. A component
with low log(Kow) but relatively higher concentration may depict
greater toxicity than others components. The significantly higher
concentration changes the response of the bacterial cell membrane to the other chemicals (Parvez et al., 2008a). For example,
chemicals such as acetaldehyde and butanol with low log(Kow),
depicted significantly higher toxicity in quaternary mixture B
and C, respectively. Similarly, in mixture-D, the component 1,2,4
trimethylbenzene with the highest log(Kow), was found to result
in lower toxicity in mixtures.
4. Conclusions
The CA and IA model based study on classification of mixtures
highlighted that though in most cases mixtures depict additive or
antagonistic effect, synergism is prominent in some of the mixtures, such as, pulp and paper (mixture-A), textile dyes (mixture-C), and the mixture composed of PAHs (mixture-F). Out of
the six mixtures studied, the highest abundance of synergism
was observed in mixture-C and F. Intermediate synergism was observed in mixture-A. Mixture B depicted the highest abundance of
antagonism and least synergism, among all the mixtures. Components, such as, naphthalene, n-butanol, o-xylene, catechol and pcresol led to synergism in mixture. Components, such as, 1, 2, 4trimethylbenzene and 1, 3-dimethylnaphthalene contributed to
antagonism (mixture-C, D and F). Phenol contributed to antagonism in most of the mixtures, except for mixture-E, where it
caused synergism. The behavior of a component in a mixture
can be affected by parameters, such as, log(Kow), 1vv and its relative concentration with respect to other components. The results
of this study have important implication on risk assessment and
policy formulation. If risk assessment is based on additivity it
would provide a conservative estimate of risk for most mixtures
containing HOCs, however, an appropriate safety factor needs to
be incorporated to account for synergism. Although an arbitrary
classification scheme is proposed for categorizing mixture toxicity, it may not be directly applicable for setting up regulatory
norms and guidelines. Further investigations may be conducted
to address the classification issue from a risk assessor viewpoint.
This is a complex issue and may depend on variables such as the
toxic nature of mixture components, their discharge, physicochemical properties, sensitivity of test model and mixture risk
to human health.
Acknowledgements
We gratefully acknowledge the doctoral fellowship award for
Shahid Parvez provided by the Council for Scientific and Industrial
Research (CSIR), New Delhi INDIA [CSIR-JRF/NET Award No. 9/
87(332)/2003-EMR-1].
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