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Structure—Activity relationships for mono alkylated or halogenated phenols
Toxicology Letters, 121-130
121
Elsevier
TXL 01794
STRUCTURE-ACTIVITY RELATIONSHIPS HALOGENATED PHENOLS
FOR MONO ALKYLATED
OR
(Structure-activity; phenols; 1-octanol/water partition coefficient; Hammett sigma constant; ionization constant; Tetrahymena pyriformis; population growth; toxicity prediction)
T. WAYNE
SCHULTZ
and MARIELY
of Veterinary Medicine,
College
Knoxville,
TN 37901-1071
(Received
1 October
(Revision
received
(Accepted
4 March
CAJINA-QUEZADA
Environmental
Toxicology
Program,
The University of Tennessee,
(U.S.A.)
1986) 5 February
1987)
1987)
SUMMARY The quantitative tion growth,
structure-activity
and two molecular
the Hammett
sigma constant
para-substituted
alkylated
to be an excellent
sigma
as the estimator (log K,,)
log BR = 0.7845 has been developed
planar
toxicity
model
(log BR), monitored partition
as cell popula(log Kay) and meta- and
The equation:
= 0.897 s = 0.170
for these chemicals. electronic
(pKzJ + 2.1144;
coefficient
(pKa) for a series of 27 ortho-,
have been examined.
1.5538; 4
of ortho-position
- 0.3702
parameter
phenols
+ 1.2447 (0) -
has been found constant
between
the log I-octanol/water
(u) or the ionization
or halogenated
(log K,,)
log BR = 0.7998
relationships descriptors,
2
effects.
This model uses the para-position A similar
equation:
= 0.860 s = 0.199
using pK, in place of c.
INTRODUCTION
The ability to predict toxic effects from physio-chemical properties is becoming an accepted first step in evaluating organic chemicals for environmental hazards. The majority of industrial organic chemicals are non-ionic and non-reactive, and exhibit ‘narcosis’ [l], or general membrane perturbation, as the mode of action in aquatic organisms. Narcosis, as meant by Veith et al. [I], will be referred to as narcosis I in this article. This reversible mechanism is simply the retardation of 0378-4274/87/$
03.50 0
1987 Elsevier
Science
Publishers
B.V. (Biomedical
Division)
122
cytoplasmic activity due to the partitioning of the chemical into biological membranes and is considered baseline toxicity. Such activity is independent of molecular structure but linearly correlated to the log of the 1-octanol/water partition coefficient (log K,,). Recently, Schultz and co-workers have developed a log K,,-dependent linear model for mono-substituted phenols that differs from baseline narcosis I toxicity [2]. This model is the same as the polar narcosis or narcosis II model of Veith et al., who note that the predictive ability of such a model may be improved by the addition of an orthogonal electronic parameter [3]. Schultz et al. [4] show that, for a heterogeneous set of para-substituted phenols, the field electronic parameter (fl is the best second descriptor. These studies suggest that, while a hydrophobic parameter is commonly a highly significant descriptor of aquatic toxicity, here are instances where the addition of an electronic parameter may significantly aid in explaining such biological activity. An examination of the earlier literature further supports this premise. Hansch and Fujita [5], in their classic paper on the use of substituent constants for structure-activity constants, show how to improve the predictability of the toxic action of phenols by the addition of a second structural descriptor, the Hammett sigma constant (a), to a model using T, the substituent constant of log K,,. Fujita [6], using three different substituted phenol data sets, shows that physiological activity is related to ?r and further notes that the addition of ApK,, the pK, of the derivative minus the pK, of the unsubstituted phenol, as a second independent variable improves the predictability of each model. More recently, Saarikoski and Viluksela [7] have similarly shown that the predictability of fish toxicity is improved when A-pK, is added to log K,, as a second descriptor. The major drawback of these studies is the limited use of orthosubstituted derivatives in the data sets. While electronic parameters are easily determined for para- and meta-position substituents, they are difficult to evaluate for ortho-substituents because of steric interactions and the so-called ortho effect. Attempts have been made to understand the nature and composition of the ortho effect by quantitatively segregating components. Taft [8] separates the ortho effect into a polar and a steric component. Charton [9] separates it into an ‘inductive’, ‘mesomeric’ and steric component. Fujita and Nishioka [lo] divide the ortho effect into an ‘ordinary polar effect’, a ‘proximity polar effect’ and steric effects. They define the ordinary polar effect of orthoposition substituents as being equal to the para-position sigma value, “p [lo]. They [lo] further note that those portions of the proximity polar effects which overlap the ordinary polar effects are factored by F [II] and, therefore, u - F, i.e. R or the resonance electronic effect [l 11, equals the non-overlapped proximity polar effects. Steric effects are primarily due to a space-filling [lo]. In an effort to explore which electronic or ionization parameter best aids in predicting the toxicity of phenols, we determined the relative biological activity of 27 alkylated or halogenated phenols and examined correlations between the log K,,, electronic or ionization parameters, and toxicity.
123
MATERIALS
AND METHODS
Tetrahymena pyriformis under static conditions was the test system used. This chronic assay, which uses population densities of axenic cultures as its end-point, has been described previously [ 121. Each phenol was tested in duplicate for a minimum of three replicates following range finding experiments. Each replicate was, at minimum, a 5-step graded concentration series using freshly prepared stock solutions. Cultures without phenols served as controls. Cell population levels were estimated spectrophotometrically as absorbance at 540 nm. Only replicates with control absorbance values of 0.6-0.9, equivalent to late log-growth-phase, were used in the analyses. The IGCso, 50% growth inhibitory concentration, and 95% fiducial limits were determined for each phenol using probit analysis of the Statistical Analysis System (SAS) software and an IBM 3081 computer. In these analyses, Y was the absorbance normalized to percent control and X was the concentration of tested phenol in mg/l. The phenols selected for testing are a series of 27 alkyl or halogen derivatives; all but one, 3-fluorophenol, are listed in the Toxic Substance Control Act Chemical Substance Inventory. They were purchased from commercial sources (Aldrich Chemical Co., Milwaukee, WI, U.S.A. or Pfaltz and Bauer, Waterbury, CT, U.S.A.) and not repurified. All were of purity of 95% or better except for 3-isopropylphenol, which had a purity of only 60%. Stock solutions of the individual phenols were prepared in dimethylsulfoxide (DMSO) at a concentration of 10, 25, or 50 g/l. The volume of stock solution added to the culture medium was limited so that DMSO did not exceed a final concentration of 0.75%, a level which has no effect on Tetrahymena growth [13]. For structure-activity analyses, log BR, i.e., the log of the inverse of the IGCso value in mmol/l, was used as the standard measurement of toxicity (Y) and the log K,, and electronic terms were selected as molecular descriptors (x). Log K,, values were computer calculated by the fragment method, with the CLOG P-3.2 program, or retrieved as measured values from the select list for comparison [4]. Values for the electronic parameters were secured from Appendix I of Hansch and Leo [14]. The pKa values were taken from Lange’s Handbook of Chemistry [ 161. The General Linear Model routine for regression analysis from SAS was used to generate the models with model adequacy measured by the coefficient of determination (?). RESULTS
Table I is a summary of the toxicity data for the 27 tested phenols. In all cases, the data fit the probit model extremely well with P 30. The log BR and molecular descriptor data for the tested
I
0.9974 0.9928 0.9887 0.9999 1.0000 0.9842 0.9716 0.9860 0.8896 0.9986 0.9996 0.9990
Y=7.8487-0.0169X Y= 8.8206-0.0502X Y= 9.4377-0.0969X Y=l.l422-0.1493X Y=8.6606-0.5190X Y= 7.0653-0.0192X Y= 7.2339-0.0634X Y=7.5923-0.0718X Y= 7.0683-0.0673X Y=7.7278-0.0233X Y= 7.5997-0.0360X Y = 6.3504-0.0403X
106-44-5
123-07-9
99-89-8
98-54-4
92-69-3
371-41-5
106-48-9
106-41-2
540-38-5
108-39-4
620-17-7
618-45-1
2 4-methyl
3 4-ethyl
4 4-isopropyl
5 4-(tert)-butyl
6 4-phenyl
7 4-fluoro
8 4-chloro
9 4-bromo
10 4-iodo
11 3-methyl
12 3-ethyl
13 3-isopropyl
-
P>x2
PHENOLS
0.9982
regression
OR HALOGENATED
Y= 7.0118-0.0079X
equation
Probit
OF ALKYLATED
108-95-2
CAS
TO TETRAHYMENA
1 Phenol
Compound
TOXICITY
TABLE
58
35
42
45
40
32
34
36
35
35
34
40
56
n
(21.57-44.68)
33.50
(54.19-98.90)
72.18
(97.39-147.48)
117.07
(21.64-37.61)
30.76
(28.52-43.84)
36.10
(26.23-47.05)
36.68
(42.67-133.75)
107.81
(5.70-8.26)
7.05
(14.08-22.20)
18.37
(39.88-52.82)
45.81
(63.49-88.17)
16.06
(140.84-224.53)
168.25
(204.99-330.17)
253.90
(mg/liter)
lGC50
IGso
0.246
0.591
1.083
0.140
0.209
0.285
0.962
0.042
0.122
0.336
0.623
1.556
2.698
Immol/liter)
585-34-2
580-51-8
372-20-3
108-43-o
591-20-S
626-02-s
95-48-7
90-00-6
88-69-7
88-18-6
90-43-7
367-12-4
95-57-S
95-56-7
14 3-@err)-butyl
15 3-phenyl
16 3-fluoro
17 3-chloro
18 3-bromo
19 3-iodo
20 2-methyl
21 2-ethyl
22 2-isopropyl
23 2-(tert)-butyl
24 2-phenyl
25 2-fluoro
26 2-chloro
27 2-bromo
1169X
Y=7.5923-0.0718X
Y=6.9421-0.0286X
Y=5.9731-0.0167X
Y = 6.6023-o.
Y=6.3346-0.1539X
Y=6.1781-0.0549X
Y= 7.7883-0.0342X
Y=7.7026-0.0133X
Y=6.1760-0.0701X
Y=6.0615-0.1044X
Y=5.9015-0.0635x
Y= 5.8833-0.0234X
Y = 9.8696-0.6422X
Y = 6.6649-0.0596X
0.9545
0.9998
0.9803
0.9972
0.9994
0.9896
0.9524
0.9981
0.9960
0.9989
0.9999
0.9999
0.9925
0.9998
40
35
50
65
45
43
35
30
52
41
53
60
53
52 27.95
1.54)
(28.52-43.84)
36.10
(47.74-89.58)
67.97
(31.07-86.32)
58.24
(10.19-19.64)
13.70
(4.70-l
8.67
(11.73-30.79)
21.44
(64.19-100.73)
81.46
(157.76-262.86)
203.39
(8.88-22.92)
16.78
(0.00-14.52)
10.17
(2.65-20.21)
14.20
(0.00-53.69)
37.69
(6.87-8.26)
7.58
(20.23-37.14)
0.209
0.529
0.520
0.080
0.058
0.158
0.667
1.881
0.076
0.059
0.110
0.336
0.045
0.196
E
126
TABLE
II
BIOLOGICAL
RESPONSE
HALOGENATED
AND
MOLECULAR
DESCRIPTORS
OF MONO
ALKYLATED
Compound
log BR
log Kwa -____
ab
P
Rb
0.00
pK,c
1 phenol
- 0.43 1
1.46
2 4-methyl
-0.192
1.94
-0.17
10.28 10.00
9.99
3 4-ethyl
0.206
2.58
-0.15
4 4-isopropyl
0.473
3.05d
-0.15
10.25
5 4-(tert)-butyl
0.913
3.31
- 0.20
10.23
6 4-phenyl
1.383
3.36d
-0.01
I 4-fluoro
0.017
1.77
8 4-chloro
0.545
2.39
0.23
9 4-bromo
0.681
2.59
0.23
0.854
2.91
0.18
9.20
1.96
- 0.07
10.10
10 4-iodo 11 3-methyl
-0.062
9.55
0.06
9.89 9.43
_
9.34
12 3-ethyl
0.229
2.40
-0.07
10.07
13 3-isopropyl
0.609
3.05d
- 0.07
10.16
-0.10
10.25
14 3-(tert)-butyl
0.730
3.30
15 3-phenyl
1.351
3.23
0.06
16 3-fluoro
0.473
1.93
0.34
9.29
17 3-chloro
0.957
2.50
0.37
9.10
18 3-bromo
1.231
2.63
0.39
19 3-iodo
1.118
2.93
0.35
- 0.274
- 0.17
20 2-methyl
9.63
9.03
_
8.88
- 0.04
-0.13
10.33 10.20
21 2-ethyl
0.176
1.95 2.47
0.803
3.05d
-0.15 -0.15
- 0.05 - 0.05
-0.10
22 2-isopropyl
- 0.10
10.30
23 2-(tert)-butyl
1.239
3.46*
- 0.20
- 0.07
-0.13
10.28
24 2-phenyl
1.094
3.36d
- 0.01
0.08
- 0.09
9.55
25 2-fluoro
0.284
1.71
0.06
0.43
-0.37
8.73
26 2-chloro
0.277
2.15
0.23
0.41
-0.18
8.55
0.504
2.35 ___-
0.23
0.44
- 0.26
8.45
27 2-bromo a Measured
___values.
b From
Hansch
’ From
Lange’s Handbook of Chemistry, 13th edn. (1985), Table 5.8.
d CLOG
phenols log K,,
OR
PHENOLS
and Leo (1979).
P3.2 calculated.
are presented in Table II. Linear regression analysis (x) for all tested compounds yields the equation:
log BR = 0.7159 (log K,,) n = 27 2 = 0.679 s = 0.295
of log BR (Y) versus
1.2880 (1)
predictor of toxicity (P>F = 0.0001; df 1, 25). Log K,, :s ;I highiy significant Analysis of residual values reveals that their distribution is significantly different from nnrmal (W = 0.953; P< W = 0.346) mainly because of a high residual value for L1lL3 bromo derivative. Deletion of this derivative and subsequent reanalysis of residuals suggest that their distribution is not different from normal (W = 0.943; P< w = 0.220).
127
The addition of the electronic parameter, Hammett sigma substituent constant (a), as a second molecular descriptor is complicated by the fact that ortho-position electronic effects are difficult to quantitate. For this reason we modeled only the para- and meta-positioned derivatives. For these data the addition of this electronic term sharply enhances the predictability of the model and results in the equation: log BR = 0.7953 (log K,,) + 1.3967 (a) n = 19 3 = 0.923 s = 0.154
1.5694 (2)
In Equation 2, log K,, (P>F = 0.0001; df 2, 16) and g (P>F = 0.0001; df 2, 16) are highly significant descriptors. Again, residuals are probably not distributed differently from normal (IV = 0.934; P< W = 0.270). Regression analysis of log K,, versus c shows these two descriptors to be essentially orthoganol (n = 19, ? = 0.018). In an effort to include ortho-position derivatives in the model, three methods of estimating ortho-positional electronic effects were compared. These were to set ortho values equal to: (1) “p values, to account for the ordinary polar effect; (2) the field values, F, to account for the overlapped ordinary and proximity polar effect; and (3) the resonance values, R, to account for the non-overlapped proximity polar effect. In an effort to evaluate these three sets of estimators of ortho-position electronic effects, multiple linear regression of log BR versus log K,, and the individual electronic terms (see Table II) were analyzed. The results of these analyses are summarized in Table III. The “p constant is the best estimator of ortho-position electronic effects for these phenols. It is even so a slightly better descriptor of orthoposition electronic effects than is the F value. Using the “p values for the orthoderivatives in a combined regression of log BR (Y) versus log K,, and a(X) for all tested phenols yields the equation: log BR = 0.7998 (log Kc,,) + 1.2447 (a) n = 27 4 = 0.897 s = 0.170
1.5538 (3)
In Equation 3, log K,, (P>F = 0.0001; df 2, 24) and u (P>F = 0.0001; df 2, 24) are highly significant descriptors. Analysis of residual values reveals that their distribution is probably not significantly different from normal (W = 0.940; P< W
TABLE III EVALUATION
OF THE LEAST-SQUARES
(ELECTRONIC
TERM)
Electronic
REGRESSION MODELS, LOG BR =A (LOG K,,)
+ C, FOR MONO ALKYLATED
IJ
s
parameter
OR HALOGENATED
PHENOLS
Sum of squared
P> F electronic
residuals
parameter
“P
0.8973
0.1702
0.6951
0.0001
F
0.8967
0.1707
0.6992
O.oool
R
0.7974
0.2390
1.3706
0.0010
+ B
128
= 0.172). Substituting pKa values for u and subsequent reanalysis of log BR (Y) versus log K,, and pKa (X) for all 27 phenols yields the equation: log BR = 0.7845 (log K,,) - 0.3702 (pK,) + 2.1144 n = 27 2 = 0.860 s = 0.199
(4)
In Equation 4, log K,, (P>F = 0.0001; df 2, 24) and pK, (P>F = 0.0001; df 2, 24) are highly significant descriptors. However, analysis of residual values shows a distribution that is significantly different from normal (W = 0.982; P< W = 0.900). Further examination of the residuals shows that Equation 4 consistently underestimates the toxicity of the meta-substituted derivatives which have electronwithdrawing effects, i.e. phenyl and halogen derivatives. The pK, values as suspected are co-linear with u values (3 = 0.722). DISCUSSION
It is generally accepted that a separate structure-activity model can be generated for each mode of action. Recently Schultz and co-workers [2] have compared toxic responses of the fathead minnow (Pimephales promelas) and Tetrahymena to a heterogeneous series of phenols and describe two modes of toxic action for phenols. One, polar narcosis or narcosis II, is slightly more toxic than narcosis I. The second, uncoupling of oxidative phosphorylation, is so named because two classic uncoupling agents, 2,4-dinitrophenol and pentachlorophenol, model with this group [2]. Narcosis II is the suspected mode of action of all 27 phenols tested in this study. Veith and co-workers [3] remark that the log Kow-dependent model for polar narcosis may be improved with the addition of an orthogonal electronic descriptor. This study with alkylated or halogenated phenols suggests that the Hammett a parameter may be a useful electronic descriptor. While being a substituent constant limits its universality, its addition to the current data set, as seen with Equation 2, markedly improves the coefficient of determination with (T being a significant (P< 0.05) descriptor. Hammett [17] defined this parameter as u = log KX - log KH with log KH being the ionization constant for benzoic acid in water at 25°C and log KX being the ionization constant of the derivative X under identical conditions. Such electronic substituent constants can be used to model effects of substituents, e.g. -CH3 or -Cl, on other reaction centers, e.g. -OH, attached to aromatic systems. A positive u value represents electron withdrawal by the substituent from the aromatic system, while a negative u value represents electron release to the system. Sigma constants are dependent upon aromatic ring position. While para- and meta-substitution result in different u values, ortho-substitution effects, because of interactions, are difficult to measure. While ortho-sigma values have been tabulated, and Fujita and Nishioka [lo] have described a method for handling electronic effects of ortho-
129
positioned substituents, the approach we found most useful in predicting the toxicity of these phenols was to use the u value as an estimator of ortho-position electronic effects. While the use of pKa values as a second independent predictor does not result in as good a model as when u is used, it may be a more universal descriptor in the long term because it is not a substituent constant. Halogen substitution, while increasing the log K,, value, decreases the pK, value (see Table II). On the other hand the substitution of an alkyl group, while increasing log K,,, has little effect on the pKa (see Table II). The correlation of log K,, and an electronic or ionization term with the toxicological effects of phenols is difficult to dispute. However, analyses of larger and more heterogeneous data sets are needed before a more definitive statement can be made about parameter selection. ACKNOWLEDGEMENTS
This investigation was supported in part by U.S. Environmental Protection Agency (EPA) Grant R-81 3 190-01-o. The analyses and conclusions herein are those of the authors and do not necessarily reflect views of EPA. Therefore, EPA endorsement should not be inferred. Ms. Cajina-Quezada was supported by the University of Tennessee, College of Veterinary Medicine, Institute of Agriculture, Center of Excellence in Livestock Diseases and Human Health. Gratitude is expressed to David T. Lin who assisted in conducting some of the toxicity assays.
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Elsevier
TXL 01794
STRUCTURE-ACTIVITY RELATIONSHIPS HALOGENATED PHENOLS
FOR MONO ALKYLATED
OR
(Structure-activity; phenols; 1-octanol/water partition coefficient; Hammett sigma constant; ionization constant; Tetrahymena pyriformis; population growth; toxicity prediction)
T. WAYNE
SCHULTZ
and MARIELY
of Veterinary Medicine,
College
Knoxville,
TN 37901-1071
(Received
1 October
(Revision
received
(Accepted
4 March
CAJINA-QUEZADA
Environmental
Toxicology
Program,
The University of Tennessee,
(U.S.A.)
1986) 5 February
1987)
1987)
SUMMARY The quantitative tion growth,
structure-activity
and two molecular
the Hammett
sigma constant
para-substituted
alkylated
to be an excellent
sigma
as the estimator (log K,,)
log BR = 0.7845 has been developed
planar
toxicity
model
(log BR), monitored partition
as cell popula(log Kay) and meta- and
The equation:
= 0.897 s = 0.170
for these chemicals. electronic
(pKzJ + 2.1144;
coefficient
(pKa) for a series of 27 ortho-,
have been examined.
1.5538; 4
of ortho-position
- 0.3702
parameter
phenols
+ 1.2447 (0) -
has been found constant
between
the log I-octanol/water
(u) or the ionization
or halogenated
(log K,,)
log BR = 0.7998
relationships descriptors,
2
effects.
This model uses the para-position A similar
equation:
= 0.860 s = 0.199
using pK, in place of c.
INTRODUCTION
The ability to predict toxic effects from physio-chemical properties is becoming an accepted first step in evaluating organic chemicals for environmental hazards. The majority of industrial organic chemicals are non-ionic and non-reactive, and exhibit ‘narcosis’ [l], or general membrane perturbation, as the mode of action in aquatic organisms. Narcosis, as meant by Veith et al. [I], will be referred to as narcosis I in this article. This reversible mechanism is simply the retardation of 0378-4274/87/$
03.50 0
1987 Elsevier
Science
Publishers
B.V. (Biomedical
Division)
122
cytoplasmic activity due to the partitioning of the chemical into biological membranes and is considered baseline toxicity. Such activity is independent of molecular structure but linearly correlated to the log of the 1-octanol/water partition coefficient (log K,,). Recently, Schultz and co-workers have developed a log K,,-dependent linear model for mono-substituted phenols that differs from baseline narcosis I toxicity [2]. This model is the same as the polar narcosis or narcosis II model of Veith et al., who note that the predictive ability of such a model may be improved by the addition of an orthogonal electronic parameter [3]. Schultz et al. [4] show that, for a heterogeneous set of para-substituted phenols, the field electronic parameter (fl is the best second descriptor. These studies suggest that, while a hydrophobic parameter is commonly a highly significant descriptor of aquatic toxicity, here are instances where the addition of an electronic parameter may significantly aid in explaining such biological activity. An examination of the earlier literature further supports this premise. Hansch and Fujita [5], in their classic paper on the use of substituent constants for structure-activity constants, show how to improve the predictability of the toxic action of phenols by the addition of a second structural descriptor, the Hammett sigma constant (a), to a model using T, the substituent constant of log K,,. Fujita [6], using three different substituted phenol data sets, shows that physiological activity is related to ?r and further notes that the addition of ApK,, the pK, of the derivative minus the pK, of the unsubstituted phenol, as a second independent variable improves the predictability of each model. More recently, Saarikoski and Viluksela [7] have similarly shown that the predictability of fish toxicity is improved when A-pK, is added to log K,, as a second descriptor. The major drawback of these studies is the limited use of orthosubstituted derivatives in the data sets. While electronic parameters are easily determined for para- and meta-position substituents, they are difficult to evaluate for ortho-substituents because of steric interactions and the so-called ortho effect. Attempts have been made to understand the nature and composition of the ortho effect by quantitatively segregating components. Taft [8] separates the ortho effect into a polar and a steric component. Charton [9] separates it into an ‘inductive’, ‘mesomeric’ and steric component. Fujita and Nishioka [lo] divide the ortho effect into an ‘ordinary polar effect’, a ‘proximity polar effect’ and steric effects. They define the ordinary polar effect of orthoposition substituents as being equal to the para-position sigma value, “p [lo]. They [lo] further note that those portions of the proximity polar effects which overlap the ordinary polar effects are factored by F [II] and, therefore, u - F, i.e. R or the resonance electronic effect [l 11, equals the non-overlapped proximity polar effects. Steric effects are primarily due to a space-filling [lo]. In an effort to explore which electronic or ionization parameter best aids in predicting the toxicity of phenols, we determined the relative biological activity of 27 alkylated or halogenated phenols and examined correlations between the log K,,, electronic or ionization parameters, and toxicity.
123
MATERIALS
AND METHODS
Tetrahymena pyriformis under static conditions was the test system used. This chronic assay, which uses population densities of axenic cultures as its end-point, has been described previously [ 121. Each phenol was tested in duplicate for a minimum of three replicates following range finding experiments. Each replicate was, at minimum, a 5-step graded concentration series using freshly prepared stock solutions. Cultures without phenols served as controls. Cell population levels were estimated spectrophotometrically as absorbance at 540 nm. Only replicates with control absorbance values of 0.6-0.9, equivalent to late log-growth-phase, were used in the analyses. The IGCso, 50% growth inhibitory concentration, and 95% fiducial limits were determined for each phenol using probit analysis of the Statistical Analysis System (SAS) software and an IBM 3081 computer. In these analyses, Y was the absorbance normalized to percent control and X was the concentration of tested phenol in mg/l. The phenols selected for testing are a series of 27 alkyl or halogen derivatives; all but one, 3-fluorophenol, are listed in the Toxic Substance Control Act Chemical Substance Inventory. They were purchased from commercial sources (Aldrich Chemical Co., Milwaukee, WI, U.S.A. or Pfaltz and Bauer, Waterbury, CT, U.S.A.) and not repurified. All were of purity of 95% or better except for 3-isopropylphenol, which had a purity of only 60%. Stock solutions of the individual phenols were prepared in dimethylsulfoxide (DMSO) at a concentration of 10, 25, or 50 g/l. The volume of stock solution added to the culture medium was limited so that DMSO did not exceed a final concentration of 0.75%, a level which has no effect on Tetrahymena growth [13]. For structure-activity analyses, log BR, i.e., the log of the inverse of the IGCso value in mmol/l, was used as the standard measurement of toxicity (Y) and the log K,, and electronic terms were selected as molecular descriptors (x). Log K,, values were computer calculated by the fragment method, with the CLOG P-3.2 program, or retrieved as measured values from the select list for comparison [4]. Values for the electronic parameters were secured from Appendix I of Hansch and Leo [14]. The pKa values were taken from Lange’s Handbook of Chemistry [ 161. The General Linear Model routine for regression analysis from SAS was used to generate the models with model adequacy measured by the coefficient of determination (?). RESULTS
Table I is a summary of the toxicity data for the 27 tested phenols. In all cases, the data fit the probit model extremely well with P
I
0.9974 0.9928 0.9887 0.9999 1.0000 0.9842 0.9716 0.9860 0.8896 0.9986 0.9996 0.9990
Y=7.8487-0.0169X Y= 8.8206-0.0502X Y= 9.4377-0.0969X Y=l.l422-0.1493X Y=8.6606-0.5190X Y= 7.0653-0.0192X Y= 7.2339-0.0634X Y=7.5923-0.0718X Y= 7.0683-0.0673X Y=7.7278-0.0233X Y= 7.5997-0.0360X Y = 6.3504-0.0403X
106-44-5
123-07-9
99-89-8
98-54-4
92-69-3
371-41-5
106-48-9
106-41-2
540-38-5
108-39-4
620-17-7
618-45-1
2 4-methyl
3 4-ethyl
4 4-isopropyl
5 4-(tert)-butyl
6 4-phenyl
7 4-fluoro
8 4-chloro
9 4-bromo
10 4-iodo
11 3-methyl
12 3-ethyl
13 3-isopropyl
-
P>x2
PHENOLS
0.9982
regression
OR HALOGENATED
Y= 7.0118-0.0079X
equation
Probit
OF ALKYLATED
108-95-2
CAS
TO TETRAHYMENA
1 Phenol
Compound
TOXICITY
TABLE
58
35
42
45
40
32
34
36
35
35
34
40
56
n
(21.57-44.68)
33.50
(54.19-98.90)
72.18
(97.39-147.48)
117.07
(21.64-37.61)
30.76
(28.52-43.84)
36.10
(26.23-47.05)
36.68
(42.67-133.75)
107.81
(5.70-8.26)
7.05
(14.08-22.20)
18.37
(39.88-52.82)
45.81
(63.49-88.17)
16.06
(140.84-224.53)
168.25
(204.99-330.17)
253.90
(mg/liter)
lGC50
IGso
0.246
0.591
1.083
0.140
0.209
0.285
0.962
0.042
0.122
0.336
0.623
1.556
2.698
Immol/liter)
585-34-2
580-51-8
372-20-3
108-43-o
591-20-S
626-02-s
95-48-7
90-00-6
88-69-7
88-18-6
90-43-7
367-12-4
95-57-S
95-56-7
14 3-@err)-butyl
15 3-phenyl
16 3-fluoro
17 3-chloro
18 3-bromo
19 3-iodo
20 2-methyl
21 2-ethyl
22 2-isopropyl
23 2-(tert)-butyl
24 2-phenyl
25 2-fluoro
26 2-chloro
27 2-bromo
1169X
Y=7.5923-0.0718X
Y=6.9421-0.0286X
Y=5.9731-0.0167X
Y = 6.6023-o.
Y=6.3346-0.1539X
Y=6.1781-0.0549X
Y= 7.7883-0.0342X
Y=7.7026-0.0133X
Y=6.1760-0.0701X
Y=6.0615-0.1044X
Y=5.9015-0.0635x
Y= 5.8833-0.0234X
Y = 9.8696-0.6422X
Y = 6.6649-0.0596X
0.9545
0.9998
0.9803
0.9972
0.9994
0.9896
0.9524
0.9981
0.9960
0.9989
0.9999
0.9999
0.9925
0.9998
40
35
50
65
45
43
35
30
52
41
53
60
53
52 27.95
1.54)
(28.52-43.84)
36.10
(47.74-89.58)
67.97
(31.07-86.32)
58.24
(10.19-19.64)
13.70
(4.70-l
8.67
(11.73-30.79)
21.44
(64.19-100.73)
81.46
(157.76-262.86)
203.39
(8.88-22.92)
16.78
(0.00-14.52)
10.17
(2.65-20.21)
14.20
(0.00-53.69)
37.69
(6.87-8.26)
7.58
(20.23-37.14)
0.209
0.529
0.520
0.080
0.058
0.158
0.667
1.881
0.076
0.059
0.110
0.336
0.045
0.196
E
126
TABLE
II
BIOLOGICAL
RESPONSE
HALOGENATED
AND
MOLECULAR
DESCRIPTORS
OF MONO
ALKYLATED
Compound
log BR
log Kwa -____
ab
P
Rb
0.00
pK,c
1 phenol
- 0.43 1
1.46
2 4-methyl
-0.192
1.94
-0.17
10.28 10.00
9.99
3 4-ethyl
0.206
2.58
-0.15
4 4-isopropyl
0.473
3.05d
-0.15
10.25
5 4-(tert)-butyl
0.913
3.31
- 0.20
10.23
6 4-phenyl
1.383
3.36d
-0.01
I 4-fluoro
0.017
1.77
8 4-chloro
0.545
2.39
0.23
9 4-bromo
0.681
2.59
0.23
0.854
2.91
0.18
9.20
1.96
- 0.07
10.10
10 4-iodo 11 3-methyl
-0.062
9.55
0.06
9.89 9.43
_
9.34
12 3-ethyl
0.229
2.40
-0.07
10.07
13 3-isopropyl
0.609
3.05d
- 0.07
10.16
-0.10
10.25
14 3-(tert)-butyl
0.730
3.30
15 3-phenyl
1.351
3.23
0.06
16 3-fluoro
0.473
1.93
0.34
9.29
17 3-chloro
0.957
2.50
0.37
9.10
18 3-bromo
1.231
2.63
0.39
19 3-iodo
1.118
2.93
0.35
- 0.274
- 0.17
20 2-methyl
9.63
9.03
_
8.88
- 0.04
-0.13
10.33 10.20
21 2-ethyl
0.176
1.95 2.47
0.803
3.05d
-0.15 -0.15
- 0.05 - 0.05
-0.10
22 2-isopropyl
- 0.10
10.30
23 2-(tert)-butyl
1.239
3.46*
- 0.20
- 0.07
-0.13
10.28
24 2-phenyl
1.094
3.36d
- 0.01
0.08
- 0.09
9.55
25 2-fluoro
0.284
1.71
0.06
0.43
-0.37
8.73
26 2-chloro
0.277
2.15
0.23
0.41
-0.18
8.55
0.504
2.35 ___-
0.23
0.44
- 0.26
8.45
27 2-bromo a Measured
___values.
b From
Hansch
’ From
Lange’s Handbook of Chemistry, 13th edn. (1985), Table 5.8.
d CLOG
phenols log K,,
OR
PHENOLS
and Leo (1979).
P3.2 calculated.
are presented in Table II. Linear regression analysis (x) for all tested compounds yields the equation:
log BR = 0.7159 (log K,,) n = 27 2 = 0.679 s = 0.295
of log BR (Y) versus
1.2880 (1)
predictor of toxicity (P>F = 0.0001; df 1, 25). Log K,, :s ;I highiy significant Analysis of residual values reveals that their distribution is significantly different from nnrmal (W = 0.953; P< W = 0.346) mainly because of a high residual value for L1lL3 bromo derivative. Deletion of this derivative and subsequent reanalysis of residuals suggest that their distribution is not different from normal (W = 0.943; P< w = 0.220).
127
The addition of the electronic parameter, Hammett sigma substituent constant (a), as a second molecular descriptor is complicated by the fact that ortho-position electronic effects are difficult to quantitate. For this reason we modeled only the para- and meta-positioned derivatives. For these data the addition of this electronic term sharply enhances the predictability of the model and results in the equation: log BR = 0.7953 (log K,,) + 1.3967 (a) n = 19 3 = 0.923 s = 0.154
1.5694 (2)
In Equation 2, log K,, (P>F = 0.0001; df 2, 16) and g (P>F = 0.0001; df 2, 16) are highly significant descriptors. Again, residuals are probably not distributed differently from normal (IV = 0.934; P< W = 0.270). Regression analysis of log K,, versus c shows these two descriptors to be essentially orthoganol (n = 19, ? = 0.018). In an effort to include ortho-position derivatives in the model, three methods of estimating ortho-positional electronic effects were compared. These were to set ortho values equal to: (1) “p values, to account for the ordinary polar effect; (2) the field values, F, to account for the overlapped ordinary and proximity polar effect; and (3) the resonance values, R, to account for the non-overlapped proximity polar effect. In an effort to evaluate these three sets of estimators of ortho-position electronic effects, multiple linear regression of log BR versus log K,, and the individual electronic terms (see Table II) were analyzed. The results of these analyses are summarized in Table III. The “p constant is the best estimator of ortho-position electronic effects for these phenols. It is even so a slightly better descriptor of orthoposition electronic effects than is the F value. Using the “p values for the orthoderivatives in a combined regression of log BR (Y) versus log K,, and a(X) for all tested phenols yields the equation: log BR = 0.7998 (log Kc,,) + 1.2447 (a) n = 27 4 = 0.897 s = 0.170
1.5538 (3)
In Equation 3, log K,, (P>F = 0.0001; df 2, 24) and u (P>F = 0.0001; df 2, 24) are highly significant descriptors. Analysis of residual values reveals that their distribution is probably not significantly different from normal (W = 0.940; P< W
TABLE III EVALUATION
OF THE LEAST-SQUARES
(ELECTRONIC
TERM)
Electronic
REGRESSION MODELS, LOG BR =A (LOG K,,)
+ C, FOR MONO ALKYLATED
IJ
s
parameter
OR HALOGENATED
PHENOLS
Sum of squared
P> F electronic
residuals
parameter
“P
0.8973
0.1702
0.6951
0.0001
F
0.8967
0.1707
0.6992
O.oool
R
0.7974
0.2390
1.3706
0.0010
+ B
128
= 0.172). Substituting pKa values for u and subsequent reanalysis of log BR (Y) versus log K,, and pKa (X) for all 27 phenols yields the equation: log BR = 0.7845 (log K,,) - 0.3702 (pK,) + 2.1144 n = 27 2 = 0.860 s = 0.199
(4)
In Equation 4, log K,, (P>F = 0.0001; df 2, 24) and pK, (P>F = 0.0001; df 2, 24) are highly significant descriptors. However, analysis of residual values shows a distribution that is significantly different from normal (W = 0.982; P< W = 0.900). Further examination of the residuals shows that Equation 4 consistently underestimates the toxicity of the meta-substituted derivatives which have electronwithdrawing effects, i.e. phenyl and halogen derivatives. The pK, values as suspected are co-linear with u values (3 = 0.722). DISCUSSION
It is generally accepted that a separate structure-activity model can be generated for each mode of action. Recently Schultz and co-workers [2] have compared toxic responses of the fathead minnow (Pimephales promelas) and Tetrahymena to a heterogeneous series of phenols and describe two modes of toxic action for phenols. One, polar narcosis or narcosis II, is slightly more toxic than narcosis I. The second, uncoupling of oxidative phosphorylation, is so named because two classic uncoupling agents, 2,4-dinitrophenol and pentachlorophenol, model with this group [2]. Narcosis II is the suspected mode of action of all 27 phenols tested in this study. Veith and co-workers [3] remark that the log Kow-dependent model for polar narcosis may be improved with the addition of an orthogonal electronic descriptor. This study with alkylated or halogenated phenols suggests that the Hammett a parameter may be a useful electronic descriptor. While being a substituent constant limits its universality, its addition to the current data set, as seen with Equation 2, markedly improves the coefficient of determination with (T being a significant (P< 0.05) descriptor. Hammett [17] defined this parameter as u = log KX - log KH with log KH being the ionization constant for benzoic acid in water at 25°C and log KX being the ionization constant of the derivative X under identical conditions. Such electronic substituent constants can be used to model effects of substituents, e.g. -CH3 or -Cl, on other reaction centers, e.g. -OH, attached to aromatic systems. A positive u value represents electron withdrawal by the substituent from the aromatic system, while a negative u value represents electron release to the system. Sigma constants are dependent upon aromatic ring position. While para- and meta-substitution result in different u values, ortho-substitution effects, because of interactions, are difficult to measure. While ortho-sigma values have been tabulated, and Fujita and Nishioka [lo] have described a method for handling electronic effects of ortho-
129
positioned substituents, the approach we found most useful in predicting the toxicity of these phenols was to use the u value as an estimator of ortho-position electronic effects. While the use of pKa values as a second independent predictor does not result in as good a model as when u is used, it may be a more universal descriptor in the long term because it is not a substituent constant. Halogen substitution, while increasing the log K,, value, decreases the pK, value (see Table II). On the other hand the substitution of an alkyl group, while increasing log K,,, has little effect on the pKa (see Table II). The correlation of log K,, and an electronic or ionization term with the toxicological effects of phenols is difficult to dispute. However, analyses of larger and more heterogeneous data sets are needed before a more definitive statement can be made about parameter selection. ACKNOWLEDGEMENTS
This investigation was supported in part by U.S. Environmental Protection Agency (EPA) Grant R-81 3 190-01-o. The analyses and conclusions herein are those of the authors and do not necessarily reflect views of EPA. Therefore, EPA endorsement should not be inferred. Ms. Cajina-Quezada was supported by the University of Tennessee, College of Veterinary Medicine, Institute of Agriculture, Center of Excellence in Livestock Diseases and Human Health. Gratitude is expressed to David T. Lin who assisted in conducting some of the toxicity assays.
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