ECOTOXICOLOGY

AND

Quantitative

ENVIRONMENTAL

SAFETY

22, 198-224 (199 I)

Structure-Activity Relationships for Chemical Toxicity to Environmental Bacteria DIANE J. W. BLUM*,’

*Diane

J. W. Blum. Pennsylvania

AND R. E. SPEEcEt

Ph.D., P.E., Environmental Engineering, 112 Clwyd 19004; and tDepartment of Civil and Environmental Vanderbilt University, Nashville, Tennessee 37235

Received

September

Road, Bala Cynwyd, Engineering,

28, 1990

Quantitative structure-activity relationships (QSARs) were developed for nonreactive chemical toxicity to each of four groups of bacteria of importance in environmental engineering: aerobic heterotrophs, methanogens, Nitrosomonas, and Microtox. The QSARs were based on chemicals covering a range of structures and including important environmental polhttants (i.e., chlorinated and other substituted benzenes, phenols, and aliphatic hydrocarbons). QSARs were developed for each chemical class and for combinations of chemical classes.Three QSAR methods (groups of chemical describing parameters) were evaluated for their accuracy and easeof use: log P, linear solvation energy relationships (LSER), and molecular connectivity. Successful QSARs were found for each group of bacteria and by each method, with correlation coefficients (adjusted r2) between 0.79 and 0.95. LSER QSARs incorporated the widest range of chemicals with the greatest accuracy. Log P and molecular connectivity QSARs are easier to use because their parameters are readily available. Outliers from the QSARs likely due to reactive toxicity included acryis, low pK, compounds, and aldehydes. Nitro compounds and chlorinated aliphatic hydrocarbons and alcohols showed enhanced toxicity to the methanogens only. Chemicals with low I&, concentrations (log I& rmol/liter < 1.5) were often outliexs for Nitrosomonas. QSARs were validated statistically and with literature data. A suggested method is provided for use of the QSARs. o 1991 Academic Press, Inc.

INTRODUCTION Knowing a chemical pollutant’s toxicity to bacteria is important for its relevance to ecosystems and biological wastewater treatment systems, and because of potential relationships with higher organisms such as fish. However, there are an overwhelming number of chemicals being introduced into the environment, and toxicity information can be costly and time consuming to obtain. Quantitative structure-activity relationships (QSARs) can provide correlations between the toxicity of a chemical and the physical and chemical descriptors of the chemical. They can aid the development of toxicity data by allowing estimations of toxicity to an organism to be made based on easily measured or calculated characteristics. In this study, toxicity data were collected for aerobic heterotrophs, Nitrosomonas, and methanogens, all key actors in the natural recycling of organic material in the environment and in wastewater treatment systems. Toxicity to Photobacterium phosphoreum, the bacterium used in the Microtox test (Microbics Corp.), was also measured or data were collected from the literature. Chemicals were selected to cover a range ’ To whom correspondence should be addressed. 0147-6513/91 $3.00 Copyright 0 1991 by Academic Press, Inc. All rights of reproduction in any form reserved.

198

QSARS

FOR

TOXICITY

TO

ENVIRONMENTAL

BACTERIA

199

of structures and include important environmental pollutants (e.g., chlorinated and other substituted benzenes, phenols, and aliphatic hydrocarbons). The data were used to develop QSARs for each bacterial group. The purpose was to apply QSAR methods (particularly those already found most useful in the field of aquatic toxicology) to chemical toxicity to bacteria of interest to environmental engineers. METHODS Detailed descriptions of the toxicity testing methods are found in Blum and Speece (199 1). All assays of the environmental bacteria (aerobic heterotrophs, Nitrosomonas, and methanogens) were conducted in sealed 125-ml serum bottles at near neutral pH for approximately 1 day. Seed bacteria were obtained from cultures maintained in the laboratory and provided with ample substrate and nutrients. The concentration of toxicant that inhibited the activities of a bacterial culture by 50% (IC,,) compared to uninhibited controls was identified. The activities of the aerobic heterotrophs, Vitrosornonas, and methanogens were measured as oxygen consumption, ammonia consumption, and gas production, respectively. Microtox tests were performed using standard methods recommended by Microbics Corp. Five-minute results were used. Microtox data for the chlorinated benzenes were taken from Kaiser and Ribo (1987). All data were corrected for gas/liquid partitioning and ionization, with only the unionized fraction presumed to contribute significantly to toxicity. Most of the chemicals tested were nonreactive toxicants and the QSARs were developed for nonreactive toxicity. Nonreactive toxicity is associated with the quantity of toxicant acting upon the cell (Albert, 1968), not with a specific reactive mechanism (such as a chemical reaction with an enzyme or inhibition of a metabolic pathway). The elements of structure most closely related to nonreactive toxicity are those that describe the partitioning of the chemical between the organism and the aqueous phase and thus involve solubility. Some known and suspected reactive toxicants were also included in the data to test the premise that reactive toxicants show enhanced toxicity above the “baseline” toxicity of the nonreactive toxicants (Lipnick, 1985). Three QSAR methods (groups of chemical describing parameters) that have been successful in previous studies of nonreactive toxicity in environmental toxicology were used: octanol/water partition coefficient (log P), solvatochromic parameters used in linear solvation energy relationships (LSER), and molecular connectivity indices. General descriptions of these three methods can be found in Blum and Speece (1990). Specific information used to apply each method will be given below followed by a description of the statistical techniques used for all three methods. OctanoZ/water partition coe@cient. The log P values used in this study (Table 1) were obtained from the CLOGP3 program (Leo and Weininger, 1984) and provided by Albert Leo. Due to uncertainty associated with crowding of halogens and values for quinones, measured rather than calculated values were preferred in these cases. Experimentally measured values of log P were taken from the CLOGP program and from the ChemFate file of the Syracuse Research Corp.‘s Environmental Fate Data Base (1987). Linear solvation energy relationships. The solvatochromic parameters (v,, r*, (Y, , &,,) (Table 1) were primarily obtained from literature published by Kamlet and coworkers (e.g., Kamlet et al., 1983, 1986a, 1986b, 1987). Some of these published values are experimentally determined, others are estimates based on closely related

200

BLUM

AND SPEECE TABLE

1

CHEMICALSANDQSARPARAMETERS Molecular connectivity

LSER Chemical Cyclohexane Octane Decane Undecane Dodecane Pentadecane Heptadecane Nonadecane Chloromethane Methylene chloride Chloroform Carbon tetrachloride I,1 -Dichloroethane 1,ZDichloroethane 1, 1,l -Trichloroethane 1,l ,f-Trichloroethane 1, 1,1,2-Tetrachloroethane 1, I ,2,2-Tetrachloroethane Pentachloroethane Hexachloroethane I-Chloropropane 2-Chloropropane 1,2-Dichloropropane I ,3-Dichloropropane 1,2,3-Trichloropropane 1-Chlorobutane 1,ZDichlorobutane 1,2,3,4-Tetrachlorobutane 1-Chloropentane 1,5-Dichloropentane I-Chlorohexane 1Chlorooctane 1-Chlorodecane 1,2-Dichloro-2-methylpropane I-Chloro-2,2-dimethylpropane Bromomethane Bromodichloromethane 1,1,2-Trichlorotrifluoroethane 1,1-Dichloroethylene 1,2-Dichloroethylene cis- 1,2-Dichloroethylene mm- 1,2-Dichloroetbylene Trichloroethylene Tetrachloroethylene I-Chloro-2-methylpropene 1,3-Dichloropropene 3-Chloropropyne Xhloro- 1-pentyne Methanol Ethanol

Log P

VJlOO

**

j3,

3.35 4.93 5.98 6.51

0.598 0.842 1.036 1.134 1.232 1.521 1.717 1.913 0.252 0.336 0.427 0.514 0.442 0.442 0.519 0.519 0.617 0.617 0.700 0.790 0.450 0.450 0.541 0.541 0.631 0.548 0.640

0.00 0.01 0.03 0.04 0.05 0.07 0.10 0.12 0.45 0.82 0.58 0.28 0.94 0.81 0.89 0.81 0.78 0.95 0.62 0.27 0.39

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

0.00 0.00 0.00 0.00 0.00 0.00 0.00** o.oo** o.oo** 0.25 0.35 0.00 0.00** 0.00 0.00 0.00** 0.00** 0.20** 0.20 o.oo** 0.00 o.oo** o.oo** o.oo** 0.00** 0.00 o.oo**

0.645 0.735 0.745 0.943 1.141 0.639 0.611

0.35

0.10 0.10 0.10 0.10 0.10 0.10 0.10

0.00 0.00** 0.00 o.oo** o.oo** o.oo** o.oo**

0.94 1.25 1.95 2.87 1.78 I .46 2.48 2.05 3.03 2.64 3.63 4.14* 1.99 1.99 1.99 1.71 1.98 2.52 2.52 2.50 3.05 2.77 3.58 4.64 5.70 3.03 2.79 1.08 2.09 3.29 1.86’ 2.09* 2.27 3.02 2.29 1.60 0.59 1.64 -0.76 -0.24

0.37

0.33

0.470

0.73

0.10

0.35**

0.406 0.406 0.406 0.406 0.492 0.578 0.513 0.541

0.72 0.95 0.44 0.53 0.28

0.05 0.05 0.05 0.05 0.05 0.05 0.10 0.05

0.00** 0.00 0.00** 0.00 0.00** o.oo** o.oo** o.oo**

0.205 0.305

0.40 0.40

0.42 0.45

0.35 0.33

1X

1XV

3.00 3.91 4.91 5.41 5.91 7.41 8.41 9.41 1.00 1.41 1.73 2.00 1.73 1.91 2.00 2.27 2.56 2.94 2.94 3.25 1.91 1.73 2.27 2.41 2.80 2.41 2.80 3.71 2.91 3.41 3.41 4.41 5.41 2.56 2.56 1.41 1.73

3.00 3.91 4.91 5.41 5.91 7.41 8.41 9.41 1.13 1.60 1.96 2.26 1.88 2.10 2.20 2.51 2.85 3.29 3.29 3.65 2.00 1.80 2.44 2.60 3.07 2.50 2.97 4.06 3.00 3.60 3.50 4.50 5.56 2.72 2.65 2.09 2.44 2.30 1.48 1.64 1.64 1.64 1.86 2.51 1.94 2.19 1.44 2.44 0.45 1.02

1.35 1.48 1.48 1.48 1.86 2.25 1.86 2.02 1.34 2.34 1.00 1.41

QSARS FOR TOXICITY TABLE

TO ENVIRONMENTAL

l-Continued Molecular connectivity

LSER Chemical 1-Propanol I-Butanol 1-Pentanol 1-Hexanol 1-Q&an01 1-Decanol 1-Dodecanol 2,2-Dichloroethanol 2,2,2-Trichloroethanol 3-Chloro-1,2-propanediol Ethylether Isopropylether Acetone 2-Butanone 2-Hexanone 4-Methyl-2pentanone 1,4-Benzoquinone Ethyl trichloroacetate Ethyl acrylate Butyl acrylate Octyl acrylate 2-Chloropropionic acid Trichloroacetic acid Diethanolamine 1-Methylpyrrolidine Acetonitrile 2-Methylpropionitrile Actylonitrile NJ-Dimethyl acetamide Carbon disulfide Dimethyl sulfoxide Benzene Toluene Xylene Ethylbenzene Chlorobenzene 1,2-Dichlorobenzene 1,3-Dichlorobenzene 1,CDichlorobenzene 1,2,3-Trichlorobenzene 1,2,4-Trichlorobenzene 1,3,5Trichlorobenzene 1,2,3,4-Tetrachlorobenzene 1,2,3,5-Tetrachlorobenzene 1,2,4,5-Tetrachlorobenzene Hexachlorobenzene 2-Chlorotoluene 2-Chloro-p-xylene Benzyl alcohol Anisole 4-Chloroanisole 2-Furaldehyde

201

BACTERIA

WktP

FJlOO

?r*

8,

%

la

lxv

0.29 0.82 1.35 1.88 2.94 4.00 5.06 0.49 1.54 -1.00 0.87 1.49 -0.27 0.26 1.32 1.19 0.20* 2.14 1.28 2.33 4.45 0.91 1.64 -1.46 0.84 -0.39 0.44 0.23 -0.80 1.94 -1.38 2.14 2.64 3.14 3.17 2.85 3.57 3.57 3.57 4.28 4.28 4.28 4.99 4.99 4.99 6.42 3.35 3.85 1.10 2.06 2.91 0.67

0.402 0.499 0.593 0.690 0.882 1.080 1.278

0.40 0.40 0.40 0.40 0.40 0.40 0.40

0.45 0.45 0.45 0.45 0.45 0.45 0.45

0.33 0.33 0.33 0.33 0.33 0.33** 0.33**

0.505 0.699 0.380 0.477 0.670 0.904

0.27 0.27 0.71 0.67 0.63 0.65

0.47 0.47 0.48 0.48 0.48 0.48

0.00 0.00 0.04** 0.03 0.03 0.00

1.91 2.4 1 2.91 3.41 4.4 1 5.41 6.41 2.27 2.56 2.80 2.41 3.12 1.35 1.91 2.91 2.77

1.52 2.02 2.52 3.02 4.02 5.02 6.02 2.03 2.37 2.19 1.99 2.78 1.20 1.76 2.76 2.62

0.79 I 0.590 0.788 1.182 0.511

0.61 0.63 0.55 0.39 1.oo

0.25 0.38 0.38 0.38 0.35

0.00 0.00 o.oo** o.oo** 0.67**

3.66 2.61 3.11 2.29 2.19 3.41

3.35 2.10 2.81 5.10 1.94 2.22 2.33

0.27 1 0.474 0.344 0.543

0.75 0.70 0.80 0.88

0.31 0.31 0.28 0.76

0.15 o.oo** 0.00 0.00

0.79 1.73 0.98 2.29

0.72 1.66 0.92 1.82

0.466 0.49 1 0.592 0.671 0.687 0.581 0.67 1 0.67 I 0.67 1 0.761 0.761 0.761 0.851 0.851 0.851 1.031 0.679 0.761 0.634 0.630 0.720

1.oo 0.59 0.55 0.51 0.53 0.71 0.80 0.75 0.70 0.85 0.75 0.70 0.80 0.80 0.70 0.70 0.67 0.63 0.99 0.73 0.73

0.76 0.10 0.11 0.12 0.12 0.07 0.03 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.08 0.52 0.32 0.22

0.00 0.00 0.00 o.oo** 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00** 0.35 0.00 0.00

2.00 2.41 2.82 2.97 2.41 2.82 2.82 2.82 3.24 3.23 3.23 3.66 3.65 3.65

2.00 2.41 2.82 2.97 2.47 2.96 2.95 2.95 3.44 3.43 3.43 3.92 3.92 3.92 5.08 2.89 3.30 2.58 2.52 3.00

2.82 3.23 2.97 2.97 3.38

202

BLUM TABLE

AND

SPEECE

l-Continued Molecular connectivity

LSER Chemical

Benzonitrile m-Tolunitrile Nitrobenzene 2,6Dinitrotoluene Pentachloronitrohenzene 4-Nitroaniline I-Nitronapthalene Naphthalene Phenanthrene Benzidine Pyridine Quinoline Phenol mCreso1 pCreso1 2,4-Dimethylphenol 3-Ethylphenol 4-Ethylphenol 2-Chlorophenol 3Chlorophenol 4Chlorophenol 2,3-Dichlorophenol 2,4-Dichlorophenol 2,5Dichlorophenol 2,6-Dichlorophenol 3,CDichlorophenol 3,5-Dichlorophenol 2,3,4-Trichlorophenol 2,3,5-Trichlorophenol 2,3,6-Trichlorophenol 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol 2,3,5,6-Tetrachlorophenol Pentachlorophenol 2-Bromophenol 3-Bromophenol 4-Bromophenol 2,6-Dibromophenol 2,4,6-Tribromophenol Pentabromophenol Catechol Resorcinol Hydroquinone 2-Aminophenol 4-Aminophenol 2-Nitrophenol 3-Nitrophenol CNitrophenol 2,4-Dinitrophenol

mp

Vi/l00

**

8,

1.57 2.07 1.88 2.15 4.89 1.31 3.06 3.32 -1.43 1.58 0.66 2.05 1.47 1.97 1.97 2.47 2.50 2.50 2.20 2.49 2.49 3.07 3.07 3.07 2.79 3.35 3.35 3.85 3.85 3.57 3.85 3.57 4.32 5.06 2.36 2.63 2.63 3.09 4.02 4.69* 0.8 1 0.81 0.59* 0.65 0.25 1.85 1.85 1.85 1.91

0.590 0.688 0.631 0.869 1.081 0.702 0.893 0.753 1.015 1.062 0.470 0.734 0.536 0.634 0.634 0.734

0.90 0.86 1.01 1.02 1.01 1.25 1.12 0.70 0.80 1.46 0.87 0.92 0.72 0.68 0.68 0.64

0.37 0.38 0.30 0.56 0.05 0.48 0.30 0.15 0.20 1.00 0.44 0.64 0.33 0.34 0.34 0.35

0.47 0.00 0.00 0.00 0.32 0.00 0.00 0.61 0.58 0.58** 0.50**

0.734 0.626 0.626 0.626 0.716 0.716 0.716

0.66 0.82 0.77 0.72 0.87 0.77 0.17

0.35 0.28 0.23 0.23 0.18 0.18 0.13

0.58** 0.69** 0.69 0.69 0.78** 0.78** 0.78**

0.716 0.716 0.806 0.806 0.806 0.806 0.806 0.896

0.72 0.72 0.82 0.82 0.82 0.72 0.82 0.77

0.13 0.13 0.08 0.08 0.08 0.08 0.08 0.00

0.78** 0.78** 0.87** 0.87** 0.87** 0.87** 0.87** 0.96**

0.669 0.669 0.669

0.86 0.84 0.76

0.19 0.23 0.23

0.69** 0.69 0.69

0.72 0.72

0.37 0.37

1 .OO** 1 .OO**

0.676 0.676

1.05 1.15

0.33 0.32

0.83** 0.93**

CLoGP3

lxv

2.41 2.82 2.82 3.23 3.38 3.38 2.82 2.82 2.82 3.24 3.23 3.23 3.24 3.23 3.23 3.66 3.65 3.66 3.65 3.65

2.13 2.54 2.54 2.96 3.10 3.10 2.61 2.61 2.61 3.10 3.09 3.09 3.10 3.09 3.08 3.58 3.57 3.58 3.57 3.57

2.82 2.82 2.82 3.24 3.65

3.03 3.02 3.02 3.93 4.82

2.82 2.82 2.82 2.82 2.82

2.27 2.26 2.26 2.33 2.33

0.00 o.oo** 0.00 o.oo** o.oo**

0.607 0.607

* Experimental log P value; all other log P values from ** LSER parameters calculated for these chemicals.

lx

(Leo and Weininger,

1984).

QSARS

FOR

TOXICITY

TO

ENVIRONMENTAL

BACTERIA

203

chemicals and calculated according to estimation rules (Kamlet, 1989). In addition, some new parameters were calculated using these estimation rules. Molecular connectivity. Simple and valence connectivity indices up to third-order path and cluster levels were calculated using the CONEX program (Nirmalakhandan, 1988). The algorithm for their calculation is based on the methods described by Kier and Hall (1976, 198 1). (The methods differ in the simple 6 values assigned to atoms involved in double bonds. QSARs will be developed in a future study using indices calculated by the MOLCONN2 program of Hall (1987)) The parameter list also included atom and bond counts, sums and differences of valence and simple connectivity indices for each order, and the inverse and square of the first-order simple and valence indices. The first-order simple and valence indices are listed in Table 1. All statistical analyses were performed using SAS (SAS Institute, Inc., 1985). The RSQUARE procedure of SAS was used to identify the combinations of molecular connectivity indices resulting in the highest r* values for each correlation. In all correlations, the simple and valence indices and the sum and difference indices up to third order were provided. If adequate correlations were not found using these two groups, then the inverse and squared first-order indices and the atom and bond counts were considered. Typically, a few index combinations had equivalent Y* values. Preference was given to simple and valence indices over the sum and difference indices. Depending on the particular data set, some indices (identified by using the CORR option of the RSQUARE procedure) were nearly collinear with each other and could not be used. Therefore, although 20 indices were initially considered, high collinearity greatly reduced this number. Statistical

Methods

Multiple linear regression. For all three QSAR methods (log P, LSER, and molecular connectivity), the relationship between the chemical descriptors and toxicity was quantified by multiple linear regression (REG procedure of SAS). For each regression, the following descriptive information is provided here:

Number

of observations used in analysis (n),

Adjusted r2 = ((n - l)r* - (p - l))/n -p,

and

Root mean square error (s), where p is the number of parameters. F tests were used to ensure that the relationship was significant at the 95% level. Student’s t tests were used to ensure that each parameter used in the regressions was significant at the 95% level. Influence diagnostics. Plots of residuals versus toxicity were examined to ensure that the residuals had an approximate mean of zero and similar variance throughout the range of toxicity values. The plots also helped to identify classes of chemicals deviating from the regression. Individual outliers and highly influential individual data points were also identified by the influence statistics, DFFITS and DFBETAS. These are scaled measures of the changes in the predicted value and parameter estimates, respectively, for a given observation if that observation is deleted from the analysis. These statistics were computed and interpreted as recommended by Belsley et al. (1980). Highly influential or outlying classes of data were identified by performing repeated multiple linear regressions, eliminating one chemical class at a time. Such regressions

LSER

h%P

Method

5.24 - 4.15 VJlOO + 3.71 8, - 0.41 a,

n = 52, s = 0.27, r2 = 0.92

Equation ( 13)

Equation (10) n = 14, s = 0.35, r2 = 0.75h 5.12 - 0.81 log P

Phenols

Equation (4) n = 5, s = 0.13, r2 = 0.98”s’ 5.37 - 0.71 log P

Too few data (n = 4)

All classes

Nitrosomonas

Equation ( 14) n = 35, s = 0.36, r2 = 0.799 4.31 - 2.24 VJIOO - 1.65 r* + 4.30 ,&, - 1.52 a,

Unsuccessful

n = 6, s =

0.15, r2 = 0.98 4.22 - 0.45 log P Unsuccessful

Equation (5)

Equation (1) Unsuccessful n = 53, s = 0.39, r2 = 0.82bve 5.12 - 0.76 log P Unsuccessful Unsuccessful

Aerobic heterotrophs

Benzenes

Chloroalkanes Alcohols

All classes

Chemical class

2

Methanogens

6.82 - 4.47 VJIOO - 1.21 7r* + 3.10 8, - 1.02 01,

n = 50, s = 0.29, r2 = 0.95’

Equation (15)

= 0.65

Equation ( 16) n = 50, s = 0.60, r2 = 0.85’ 6.78 - 6.01 VJlOO - 1.34 ?r* + 2.89 &,, - 0.93 a,

2.27 - 0.71 log P

= 0.45,r2

n = 22,s

5.21 - 0.92 log P

Equation (7) n = 9, s = 0.33, r2 = 0.98’ 5.35 - 1.58 log P + 0.1 l(log P)’ Equation (9) n = 10, s = 0.29, r2 = 0.77 7.27 - 2.68 log P Equation ( 12)

Equation (3) n = 71, s = 0.80, r2 = 0.68bsde 4.50 - 0.89 log P Unsuccessful

Microtox

n = 24, s = 0.34, r2 = 0.87h

Equation (6) n = 9, s = 0.24, r2 = 0.97h 5.66 - 0.74 log P Equation (8) n = 13, s = 0.25, r2 = 0.83* 5.62 - 0.74 log P Equation (11)

Equation (2) n = 57, s = 0.51, r2 = 0.85O 5.44 - 0.83 log P Unsuccessful

SUMMARY OF QSAR EQUATIONS

TABLE

” 6 R 62

Equation (24) n=5,s=0.11,r2=0.99’ 6.70 - 0.80 ‘X Too few data (n = 4)

All classes

Phenols

Benzenes

Equation (32) n = 46, s = 0.46, r= = 0.74 5.72 - 0.91 ‘Xv+ 1.27 ‘Y

Equation (30) n= 13,s=0.35,rZ=0.76 6.46 - 0.63’X”

0.43 3Xc

r2 = 0.75’

Alcohols

Chloroalkanes

r2 = 0.78”’ 2.29 ‘Y

Equation ( 17) n = 46, s = 0.43, 6.09 - 0.59 OX + + 0.37 ‘xc Equation (2 1) n = 17, s = 0.19, 4.68 - 0.54 ‘X +

All classes

3.98 - 0.41 ‘Xv + 2.09’Y

n = 33, s = 0.44, r2 = 0.73cK

Equation (33)

Unsuccessful

r2 = 0.75”.‘9 4.13 + 1.42 ‘Y - 0.44 ‘X Equation (22) n = 19, s = 0.33, r2 = 0.8@ 4.32 + 11.88 ‘Y - 6.94 3Yp - 3.09 SY, Equation (25) n = 6, s = 0.14, r2 = 0.98’ 5.03 - 0.48 ‘X Unsuccessful

n = 33, s = 0.44,

Equation ( 18)

Equation (34) n = 51, s = 0.69, r2 = 0.71” 5.97 - 1.00 ‘Xv+ 1.64 ‘Y

7.84 - 0.90 OX’

n = 24, s = 0.33, rz = 0.88

6.98 - 0.79 ‘X Equation (28) n= 13,s=0.24,r2=0.84 4.78 - 1.29 ‘X, Equation (3 1)

n = 9, s = 0.22, r2 = 0.98

Equation (26)

Unsuccessful

Equation (19) n = 51, s = 0.46, r2 = 0.87”+ 5.46 - 1.73 ‘X, + 1.55 ‘Y

Note. All r2 values are adjusted for degrees of freedom. “Y = “X - ‘Xv. Units of toxicity (IC,,) values are log(~mol/liter). ’ Except chloroaliphatic hydrocarbons and alcohols. b Except chloroaliphatic hydrocarbons, improves accuracy. ’ Except phenols. d Except phenols, improves accuracy. ’ Except alcohols, decreases accuracy. /Except carbon tetrachloride. t Log IC,, greater than 1.5 rmol/bter. ’ Compatible with all-class equation. ’ High parameter to observation ratio.

Molecular connectivity (uniform indices)

Molecular connectivity

5.78 - 1.13’X’+

1.86 ‘Y

n = 47, s = 0.60, r2 = 0.82E

Equation (35)

7.40 - 0.85 OX Equation (29) n = 10, s = 0.30, r2 = 0.75 -0.84 + 7.13 I/‘X” Unsuccessful

n = 7, s = 0.25, r2 = 0.98

Equation (27)

Equation (23) n = 17, s = 0.33, r2 = 0.90’ 5.87 - 1.03 ‘Xc + 1.00 ‘X

6.14 - 1.24 ‘Xv + 1.70 2Y

n = 45, s = 0.56, r2 = 0.83b*cs’

Equation (20)

g 2:

3

8

3 2

3

2

E CA

0

206

BLUM AND SPEECE TABLE 3 AEROBIC HETEROTROPH DATA

AND

QSAR

RESULTS

Log IC& (pmol/liter) Residuals

Chemical Cyclohexane Methylene chloride Chloroform Carbon tetrachloride 1,l -Dichloroethane I ,2-Dichloroethane 1, 1,I-Trichloroethane 1,l ,ZTrichloroethane 1, 1,1,2-Tetrachloroethane 1,1,2,2-Tetrachloroethane Pentachloroethane 1-Chloropropane 2Chloropropane 1,3-Dichloropropane 1,2,3-Trichloropropane 1-Chlorobutane I-Chloropentane I-Chlorohexane I -Chlorooctane 1-Chlorodecane truns- 1,2-Dichloroethylene Trichloroethylene Tetrachloroethylene 1,3-Dichloropropene %Chloro- 1-pentyne Methanol Ethanol 1-Propanol 1-Butanol 1-0ctanol 1-Dodecanol Ethylether Acetone 2-Butanone Butyl acrylate 2-Chloropropionic acid Acetonitrile Acrylonitrile Benzene Toluene Xylene Ethylbenzene Chlorobenzene 1,2-Dichlorobenzene 1,3-Dichlorobenzene 1,4-Dichlorobenzene 1,2,4-Trichlorobenzene Hexachlorobenzene Benzyl alcohol 4-Chloroanisole

Expefimend 2.53 3.57 3.73 2.93 3.80 3.68 3.53 3.26 3.14 2.88 2.87 3.95 3.75 3.27 3.30 3.40 2.81 2.84 2.55 2.36 4.24 2.99 4.06 3.05 2.93 5.19 5.71 5.20 4.73 3.18 3.05 5.35 5.43 5.20 3.57 0.22 5.26 2.99 3.83 3.07 3.98 3.08 3.44 3.79 3.69 3.35 4.63 3.09 4.29 3.80

Log P @I. UN

LSER 0%. (13))

-0.04 -0.60 0.09 -0.0 1 0.03 -0.33 0.30 -0.30 0.32 -0.24 0.5 1 0.35 0.14 -0.55 -0.32 0.20 0.00 0.44 0.95 1.57 0.71 -0.41 1.23 -0.85 -0.95 0.09 0.41 0.30 0.23 0.30 1.77 0.89 0.10 0.27 0.22 -4.2 1 -0.16 -1.95 0.33 -0.04 1.24 0.37 0.48 1.39 1.28 0.95 2.76 2.85 0.01 0.89

-0.22 -0.54 0.04 -0.55 0.02 -0.10 0.07 -0.20 0.09 -0.09 0.25 0.21 0.01 -0.09 0.31 0.07 -0.13 0.32 0.85 1.48 0.50 -0.40 1.03 -0.13 -0.01 0.20 0.10 0.02 0.07 1.58 0.46 0.00 0.17 0.19 -3.93 0.06 -1.86 0.25 -0.12 1.08 0.25 0.35 1.23 1.12 0.79 2.55 2.13 -0.10 0.73

Molecular Connectivity (Eq. (17)) -0.49 0.06 -0.65 -0.06 0.04 -0.19 0.09 0.20 0.56 0.55 0.08 -0.29 0.05 0.67 -0.05 -0.23 0.22 0.76 1.55 0.37 -0.8 1 1.33 -0.37 -0.54 -0.39 0.32 0.23 0.17 0.30 1.82 0.73 0.75 0.98 0.46 -4.04 0.14 -1.91 -0.22 -0.49 0.89 -0.05 0.01 1.04 0.90 0.57 2.50 0.27 0.28

Exclusions“

S

T T T S S z S S S

QSARS FOR TOXICITY

TO ENVIRONMENTAL

207

BACTERIA

TABLE 3-Continued Log ICsO(pmollliter) Residuals

Chemical Benzonitrile m-Tolunitrile Nitrobenzene 1-Nitronapthalene Naphthalene Phenol mCreso1 pCreso1 2-Chlorophenol 3-Chlorophenol 4-Chlorophenol 2,3-Dichlorophenol 2,5-Dichlorophenol 2,6-Dichlorophenol 2,3,4-Trichlorophenol 2,3,6-Trichlorophenol 2,4,5-Trichlorophenol 2,3,5,6-Tetrachlorophenol 4-Bromophenol 2-Aminophenol 2-Nitrophenol 4-Nitrophenol

Molecular Log P LSER Connectivity Experimental (Eq. (1)) (Eq. (13)) (Eq. (17)) Exclusions” 3.66 3.39 3.47 3.34 3.72 4.08 3.61 3.38 3.45 3.09 2.88 3.11 3.06 3.40 1.59 1.86 2.07 0.82 2.86 -0.42

-0.27 -0.15 -0.22 0.55 1.12 0.08 -0.02 -0.25 0.00 -0.14 -0.35 0.32 0.27 0.40 -0.60 -0.55 -0.12 -1.02 -0.26 -5.05

1.88

-1.83

3.07

-0.64

-0.50 -0.40 -0.26 0.69 1.05 0.09 -0.03 -0.26 0.05 -0.12 -0.33 0.49 0.62 -0.24 0.02 0.24 -0.3 1 -0.17 -4.11

S S

-0.13 -0.12 -0.35 -0.10 -0.48 -0.68 0.22 0.15 0.52 -0.66 -0.40 -0.20 T 0.23 -4.6 1

T

-0.17

a Exclusions: S, aqueous solubility; T, reactive toxicity.

were also performed for each highly influential or outlying data point (a “jackknife” test). Suspect data points were then assessed by checking for any irregularities in the experimental data. The possibility that reactive toxicity mechanisms could make the data inappropriate for an equation describing nonreactive toxicity was considered. Collinearity diagnostics. Collinearity was diagnosed using methods described by Belsley et al. (1980), based on eigenvalues of the variance-covariance matrix. Their suggested diagnostic procedure was followed: degrading collinearity exists if a component with a high condition index (square root of the largest eigenvalue to each individual eigenvalue), approximately greater than 30, contributes strongly, approximately greater than 0.5, to the variance of two or more variables. Validation studies. The QSARs were validated statistically and with literature data. In statistical validation studies, 20% of the data points were selected at random and excluded from the data sets. The regression equations were then recalculated using the reduced data sets. The new equations were checked to ensure that they were compatible with the original equations at the 95% level. The omitted data points were checked to confirm that they fell within the 95% confidence interval of their predicted values.

208

BLUM AND SPEECE TABLE 4 NITROSOMONASDATAAND

QSAR RESULTS

Log I& (pmol/liter) Residuals

Chemical

Experimental

Cyclohexane Methylene chloride Chloroform Carbontetrachloride 1,I-Dichloroethane 1,2-Dichloroethane 1,1,l -Trichloroethane 1,1,2-Trichloroethane 1,1,1,2-Tetrachloroethane 1,1,2,2-Tetrachloroethane Pentachloroethane Hexachloroethane 1-Chloropropane 2-Chloropropane 1,2-Dichloropropane 1,3Dichloropropane 1,2,3-Trichloropropane 1-Chlorobutane 1-Chloropentane 1$Dichloropentane 1-Chlorohexane tram- 1,ZDichloroethylene Trichloroethylene Tetrachloroethylene 1,3-Dichloropropene 5-Chloro-1-pentyne Methanol Ethanol 1-Propanol 1-Pentanol 1-0ctanol 1-Dodecanol 2,2,2-Trichloroethanol Isopropylether Acetone 2-Butanone 4-Methyl-Zpentanone Ethyl acrylate Butyl acrylate 2-Chloropropionicacid Acetonitrile Benzene Toluene Xylene Ethylbenzene Chlorobenzene

3.06 1.16 0.60 2.52 0.97 2.47 1.80 1.16 1.71 0.93 1.59 2.13 3.19 3.14 2.58 1.63 2.31 3.10 2.97 1.96 2.85 2.92 0.79 2.83 0.78 0.76 4.44 4.92 4.21 3.77 2.71 1.98 1.13 3.77 4.33 4.04 4.03 2.67 2.47 -0.42 3.25 2.24 2.96 2.98 2.96 0.80

LSER 0%. (14)) 0.09 -1.09 -1.69 -0.6 1 -1.23 0.06 -0.3 1 -1.08 -0.36 -0.56 -0.25 -0.40 0.10

0.19 0.25 0.32 0.03 -1.76 0.06 -0.03 0.52 0.03 0.01 -0.40 -0.24 -0.55 0.04 -0.12 0.75 -0.91 -0.81 -2.42 -0.32 -0.43 0.41 0.49 0.55 -1.34

Molecular connectivity (Eq. ( 18)) 0.25 -1.88 -2.10 0.14 -1.95 -0.35 -0.79 -1.29 -0.40 -0.80 -0.14 0.74 0.13 0.00 -0.09 -0.99 0.10 0.25 0.36 -0.20 0.44 -0.11 -2.52 0.56 -2.02 -2.09 -0.29 0.46 -0.03 -0.03 -0.43 -0.28 -2.17 0.44 0.30 0.26 0.62 -1.39 -1.27 -4.72 -0.74 -1.01 -0.11 0.09 0.13 -2.04

Exclusions” X X

X X

X X X

X

T T X

X

QSARS FOR TOXICITY

TO ENVIRONMENTAL

209

BACTERIA

TABLE 4-Continued Log &

(pmol/liter) Residuals

Chemical 1,ZDichlorobenzene

1,2,4-Trichlorobenzene Benzyl alcohol Benzonitrile m-Tolunitrile Nitrohenzene 2,6-Dinitrotoluene Naphthalene Phenol m-Cresol p-Cresol 4-Ethylphenol 2Chlorophenol 3-Chlorophenol 4-Chlorophenol 2,3-Dichlorophenol 2,CDichlorophenol 2,5Dichlorophenol 2,6-Dichlorophenol 3,5-Dichlorophenol 2,3&TrichlorophenoI 2,3,6-Trlchlorophenol 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol 2,3,5,6-Tetrachlorophenol 2-Bromophenol 4-Bromophenol 2,4,6-Tribromophenol Pentabromophenol Resorcinol 4-Aminophenol 2-Nitrophenol 4-Nitrophenol

Experimental 2.50 3.06 3.56 2.49 0.88 0.87 3.00 2.36 2.35 0.86 2.40 2.07 1.33 0.19 0.76 0.41 0.69 0.57 1.70 1.27 1.30 0.33 1.29 1.60 0.75 0.30 0.68 1.37 -0.26 1.85 -0.19 1.91 1.27

LSER 0%. (14)) 0.89 1.69 0.60 -0.60 -2.11 -1.65 -0.09 0.24 -0.07 -1.49 0.05 -0.13 -0.38 -1.39 -0.90 -0.45 -0.34 -0.24 0.37 1.12 0.16 0.96 1.43 1.18 -0.86 -0.82

-2.02 0.41

Molecular connectivity (Eq. ( 18)) 0.06

1.01

Exclusions”

X X

-0.22

X X -1.63 -2.93 -1.40 -1.47 -2.24 -3.38 -2.81 -2.77 -2.49 -2.61 -1.48 -1.92 -1.48 -2.44 -1.48 -1.17 -1.90 -1.53 2.68 -2.85 -4.67

X X X X X X X X X X X X X X X X X X X

Note. Phenols excluded from molecular connectivity QSAR (Eq. ( 18)). ’ Exclusions: X, log(ICsO pmol/liter) < 1S; T, reactive toxicity.

Limited work was also done to validate the QSARs with data found in the literature. Literature IC5evalues were compared with those predicted by the QSAR equations to seeif they were within the 95% confidence intervals. Chemicals were used for which QSAR parameters had been collected in this study. To validate the aerobic heterotroph QSARs, respiration inhibition data obtained using the OECD activated sludge test were taken from KIecka and Landi (1985). The validation of methanogen QSARs used data obtained with three different anaerobic cultures by Blum et al. (1986); the median of the range of toxicity values for the three cultures was used. For validation

210

BLUM AND SPEECE TABLE 5 METHANOGEN DATA AND QSAR RESULTS Log IGo (ccmol/liter) Residuals

Chemical Cyclohexane Octane Decane Undecane Dodecane Pentadecane Heptadecane Nonadecane Chloromethane Methylene chloride Chloroform Carbontetrachloride 1,l -Dichloroethane 1,ZDichloroethane 1,1,l -Trichloroethane I,1 ,ZTrichloroethane 1,1,l ,ZTetrachloroethane 1,1,2,2-Tetrachloroethane Pentachloroethane Hexachloroethane 1-Chloropropane 2-Chloropropane 1,ZDichloropropane 1,3-Dichloropropane 1,2,3-Trichloropropane I-Chlorobutane 1-Chloropentane 1,5-Dichloropentane 1-Chlorohexane 1Chlorooctane I-Chlorodecane 1-Chloro-2,2-dimethylpropane Bromomethane Bromodichloromethane 1,1,2-Trichlorotrifluoroethane 1,l -Dichloroethylene 1,2-Dichloroethylene tram- 1,2-Dichloroethylene Trichloroethylene Tetrachloroethylene 1,3-Dichloropropene 5-Chloro-1-pentyne Methanol Ethanol 1-Propanol I-Butanol

Molecular LSER connectivity Logp Experimental (Eq. (2)) (Eq. (15)) (Eq. (19)) Exclusions” 3.25 1.25 0.39 0.59 0.12 -0.36 -0.89 -1.49 2.89 1.93 0.88 1.62 1.80 2.41 0.59 1.03 1.oo 1.39 1.72 1.98 2.88 3.90 3.20 2.20 0.63 3.07 3.15 2.74 2.42 2.29 2.59 1.74 1.62 1.00 1.30 1.90 2.30 2.69 2.00 2.13 0.71 2.64 5.83 5.97 5.76 5.16

0.59 -0.10 -0.09 0.56

-1.77 -2.47 -2.95 -1.44 -2.16 -1.82 -2.79 -2.71 -1.92 -1.86 -0.71 -0.03 -0.9 1 0.11 -0.58 -1.82 -3.16 -0.28 0.24 -0.40 -0.05 0.70 1.88 -1.38 -2.93 -2.7 1 -1.41

-0.90 -1.79 -1.76 -1.11 -1.13 -0.30 0.09 0.40 -2.54 -2.40 -3.25 -2.86 -2.16 -1.72 -3.09 -2.75 -2.38 -1.58 -1.29 -1.28 -1.74

-1.14 -0.65 -0.96

-2.75 -2.35

-1.01 -1.56 -0.80 -3.40 -1.44 -0.24 0.33 0.56 0.40

-1.91 -2.10 -1.90 -0.5 1 -0.04 0.18 0.02

0.38 -1.70 -1.70 -1.04 -1.08 -0.27 0.07 0.34 -2.37 -3.24 -4.23 -3.44 -3.43 -1.89 -4.56 -2.64 -2.18 -0.54 -0.2 1 1.03 -1.57 -1.46 -0.58 -1.75 -2.00 -1.03 -0.52 -0.35 -0.83 -0.09 1.18 -1.75 -2.79 -3.36 -3.36 -2.34 -1.94 -2.46 -1.20 -3.67 -1.50 -0.48 -0.10 0.56 0.31

X X X S S S S S X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

QSARS FOR TOXICITY TO ENVIRONMENTAL

BACTERIA

211

TABLE S-Continued Log IC, (pmol/liter) Residuals

Chemical I -Pentanol 1-Hexanol 1-0ctanol 1-Decanol 1-Dodecanol 2,2-Dichloroethanol 2,2,2-Trichloroethanol 3-Chloro- 1,2-propanediol Isopropylether Acetone 2-Butanone 2-Hexanone 4-Methyl-2-pentanone 1+Benzoquinone Ethyl trichloroacetate Ethyl acrylate Butyl acrylate Octyl acrylate 2-Chloropropionic acid Trichloroacetic acid Diethanolamine 1-Methylpyrrolidine Acetonitrile 2-Methylpropionitrile Acrylonitrile Carbon disulfide Benzene Toluene Xylene Ethylbenzene Chlorobenzene 1,2-Dichlorobenzene 1,3-Dichlorobenzene 1,4-Dichlorobenzene 1.2,3-Trichlorobenzene 1,2,4-Trichlorobenzene 1,3,5Trichlorobenzene 1,2,3,4-Tetrachlorobenzene 2-Chlorotoluene 2-Chloro-pxylene Anisole 2-Furaldehyde Benzonitrile m-Tolunitrile Nitrobenzene 2,6-Dinitrotoluene Pentachloronitrobenzene 1-Nitronapthalene

Experimental 4.73 4.18 3.46 2.41 2.07 2.21 0.30 3.76 4.61 5.94 5.59 4.79 4.97 2.48 2.31 3.12 3.07 2.53 -1.12 -4.27 4.62 5.19 5.83 3.40 3.23 3.65 4.19 3.80 3.37 3.19 3.38 3.01 3.25 2.77 2.13 2.60 3.62 1.97 2.63 2.80 3.82 3.28 4.03 3.46 2.02 1.64 1.94 1.97

Molecular Log P LSER connectivity (Eq. (2)) (Eq. (15)) (Eq. (19)) 0.41 0.30 0.46 0.29 0.83 -2.83 -3.86 -2.51 0.41 0.27 0.37 0.44 0.51 -2.79 -1.36 -1.26 -0.44 0.78 -5.80 -8.35 -2.03 0.44 0.06 -1.67 -2.02 -0.18 0.52 0.55 0.53 0.38 0.30 0.54 0.78 0.29 0.24 0.71 1.73 0.68 -0.03 0.56 0.09 -1.61 -0.11 -0.26 -1.86 -2.02 0.56 -0.93

0.01 -0.11 0.03 -0.13 0.41

-0.20 0.27 0.30 0.30 1.52

0.32 0.19 0.33 0.17 0.69 -2.21 -3.62 -0.24 0.26 0.24 0.94 0.75 0.58

-0.98 -1.44 -0.7 1 0.31 -4.78

-0.85 -1.65 -0.45

0.36 -1.37 -1.91

0.27 -1.59 -2.13

0.00 -0.02 -0.18 -0.26 -0.16 0.11 0.29 -0.26 -0.21 0.13 1.09 -0.03 -0.56 -0.06 -0.24

-0.12 -0.04 -0.07 -0.11 -0.36 0.23 0.02 -0.40 0.21 0.32 0.82 0.93 -0.27 0.32 -0.17

-0.16 -0.37 -1.62 -1.74 1.08 -0.37

Exclusions”

X X X

-5.41 -8.62 -0.44 0 T

S

T T S T

212

BLUM AND SPEECE TABLE S-Continued Log KS0 (rmol/liter) Residuals

Chemical Benzidine Phenol m-Cresol p-Cresol 2,CDimethylphenol 4-Ethylphenol 2-Chlorophenol 3-Chlorophenol 4-Chlorophenol 2,3-Dichlorophenol 2,CDichlorophenol 2,5-Dichlorophenol 2,6-Dichlorophenol 3,4-Dichlorophenol 3,WXchlorophenol 2,3,4-Trichlorophenol 2,3$Trichlorophenol 2,3,6-Trichlorophenol 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol 2,3,5,6-Tetrachlorophenol Pentachlorophenol 2-Bromophenol 3-Bromophenol 4-Bromophenol 2,4,6-Tribromophenol Pentabromophenol Catechol Resorcinol Hydroquinone 2-Aminophenol 4-Aminophenol 2-Nitrophenol 3-Nitrophenol 4-Nitrophenol 2,4-Dinitrophenol

Experimental -0.47 4.35 3.92 2.92 2.76 3.29 3.10 3.25 3.32 2.55 2.59 2.51 2.96 2.45 1.94 1.59 0.96 1.76 1.37 1.91 -0.24 -0.85 2.78 2.90 3.31 1.35 -1.29 4.12 4.15 4.41 1.75 2.36 1.93 2.12 1.46 -1.42

Molecular LSER connectivity Log P (Eq. (2)) (Eq. (15)) (Eq. (19)) -4.60 0.13 0.11 -0.88 -0.63 -0.07 -0.5 1 -0.13 -0.05 -0.34 -0.30 -0.38 -0.16 -0.2 1 -0.72 -0.65 -1.28 -0.72 -0.88 -0.57 -2.10 -2.09 -0.70 -0.35 0.05 -0.75 -2.84 -0.65 -0.62 -0.55 -3.15 -2.87 -1.97 -1.79 -2.44 -5.27

-3.46 0.44 0.33 -0.66 -0.54 0.10

-0.04 0.19 0.21 0.27 0.18 0.26 0.14 -0.38 0.06 -0.57 0.22 -0.30 0.37 -1.10 0.16 0.13 0.44

Exclusions” T

0.08 0.05 -0.88 -0.24 0.03 -0.23 -0.52 -0.37 0.11

-0.22 -0.29 0.52 -0.36 -1.40 0.02 -1.04 0.19 -0.57 -0.10 T T 0.10 -0.22 0.25 1.29 T

-1.57 -0.96 -0.52 -0.92

0.26 -0.15 0.17 -2.0 1 -1.77

T T T T T T

a Exclusions: S, aqueous solubility; T, reactive toxicity; X, alkanes and chlorinated ahphatic hydrocarbons and alcohols; 0, suspected experimental error.

of the Microtox QSARs, the extensive data base of Microtox Kaiser and Ribo (1987) was used.

results compiled by

RESULTS Table 2 contains the best QSARs derived for each group of bacteria. Correlations are reported for all classesof chemicals simultaneously, and for each of four individual

QSARS

FOR

TOXICITY

TO

ENVIRONMENTAL

BACTERIA

213

chemical classes separately. In some cases, all chemical classes could not be included in the same QSAR. In other cases, specific inclusions decreased or increased the accuracy of the QSAR without changing the equation; these conditions are noted in the table. “All-class” QSARs contain the greatest number of chemical classes possible for the particular bacteria and QSAR method, raw data and residual (experimental minus predicted) ICsO values are given in Tables 3 through 6. All listed data for which QSAR parameters were available were included in QSAR development except those noted in the tables. The exclusions are discussed under Outliers and Reactive Toxicity Mechanisms. Statistical validation. Three statistical validations were done for each all-class equation. In every validation, the QSAR equation computed with 20% of the data omitted was compatible with the original QSAR equation. The average number of omitted data points for each trial that fell outside of the confidence interval was 5.2%, with a standard deviation of 10%. The mean percentage of outliers for each QSAR was in no case significantly different from 5%. Validation with literature data. For aerobic heterotroph QSARs (Nos. 1, 13, and 17) the literature data confirmed the QSAR predictions with residuals averaging about $ an order of magnitude. In the validation study of methanogen QSARs (Nos. 2, 15, and 19) ICso concentrations were well predicted. All values were within the 95% confidence intervals of the QSARs. The validation of Microtox QSARs (Nos. 3, 16, and 20) was also successful. For all of the QSARs, the number of random outliers (due to experimental error or for no apparent reason) was approximately the 5% anticipated statistically. Almost all of these outliers were suspected experimental anomalies; that is, they differed from other literature values for the same chemical. DISCUSSION Four topics will be discussed. First, an evaluation and comparison will be made of the three QSAR methods, based on defined criteria and the validation studies. Second, the reasons that some chemicals were outliers from the QSARs will be discussed. Third, observations will be made about the different chemical classes in relation to QSAR development. Finally, a suggested method will be given for using the QSARs. 1. Evaluation

and Comparison of QSARs

The different methods used in this study for QSAR development were evaluated in terms of their utility for prediction and for elucidating structural correlates of toxicity. While both goals are desirable, the engineer’s or applied scientist’s need for a practical method of estimating toxicity was emphasized. QSARs covering as many chemical classes as possible were preferred for their convenience. In all methods, two exclusions had to be made in the correlations: low log & values (less than 1.5 log pmol/liter) for Nitrosomonas and chlorinated aliphatic hydrocarbons and alcohols for methanogens (See under Outliers and Reactive Toxicity Mechanisms). For engineering purposes, a reasonable goal for the accuracy of a QSAR to predict toxicity to bacteria is about one order of magnitude (standard error of 4 order of magnitude) (Blum and Speece, 199 1). In the course of this study, a “good” correlation met this one-order-of-magnitude criterion (s = 0.4 to 0.5). An “excellent” correlation

214

BLUM AND SPEECE TABLE 6 MICROTOX

DATA AND QSAR RESULTS

Log IC50(pmol/liter) Residuals

Chemical

Molecular Log P LSER connectivity Experimental (Eq. (3)) (Pq (16)) (Eq. (20)) Exclusions”

Cyclohexane Octane 1, 1-Dichloroethane 1,2-Dichloroethane 1,1,2-Trichloroethane 1, 1,1,2-Tetrachloroethane 1,1,2,2-Tetrachloroethane Pentachloroethane Hexachloroethane 1-Chloropropane 2-Chloropropane 1,2-Dichloropropane 1,3-Dichloropropane 1,2,3-Trichloropropane 1-Chlorobutane 1,2-Dichlorobutane 1,2,3,4-Tetrachlorobutane 1Chloropentane 1,5-Dichloropentane I-Chlorohexane 1-Chlorooctane I-Chlorodecane 1,2-Dichloro-2-methylpropane cis-1,2-Dichloroethylene truns- 1,ZDichloroethylene Trichloroethylene Tetrachloroethylene 1-Chloro-2-methylpropene 1,3-Dichloropropene 3-Chloropropyne %Chloro-1-pentyne Methanol Ethanol 1-Propanol 1-Butanol 1-Pentanol 1-Hexanol 1-O&an01 1-Decanol 1-Dodecanol 2,2,2-Trichloroethanol 3-Chloro-1,ZPropanediol 2-Butanone 2-Chloropropionicacid 1-Methylpyrrolidine N,N-Dimethylacetamide Benzene Chlorobenzene

3.37 3.80 3.43 3.85 2.90 1.08 1.51 0.49 0.28 4.02 3.43 2.12 2.80 2.10 3.72 2.68 1.42 3.33 2.05 3.23 3.29 2.79 1.65 3.87 4.07 3.87 2.73 3.70 3.00 3.03 2.35 6.23 5.89 5.22 4.44 3.53 2.32 1.42 0.92 0.19 2.55 4.71 4.68 0.66 4.98 4.74 2.98 1.92

1.86 3.69 0.52 0.65 0.22 -0.72 -0.65 -0.78 -0.54 1.30 0.70 -0.01 -0.18 -0.63 1.46 0.43 -0.85 1.54 0.01 1.91 2.92 3.36 -0.15 1.03 1.43 1.39 0.92 1.24 -0.07 -0.95 -0.69 1.05 1.17 0.97 0.67 0.23 -0.50 -0.47 -0.02 0.20 -0.58 -0.68 0.41 -3.03 1.22 -0.47 0.38 -0.04

0.19 2.10 0.28 0.52 0.04 -1.23 -0.40 -1.35 -1.68 0.18

0.44 0.60 1.08

0.66 0.18 0.61 -0.34

0.33 0.48 0.39 0.20 -0.14 -0.77 -0.52 0.17 0.64 0.30 -2.10 0.21 -0.35 -0.62

0.95 2.51 0.17 0.53 0.43 -0.47 0.77 -0.24 0.45 0.47 -0.23 -0.09 0.11 0.14 0.80 0.50 0.83 1.03 0.59 1.53 2.85 3.71 -0.72 -0.08 0.12 0.03 0.40 0.03 -0.26 -1.25 -0.66 0.64 0.35 0.49 0.33 0.05 -0.55 -0.20 0.53 1.06 -0.02 0.21 0.28 -3.75 -0.14

-0.68 -1.02

S S

S

S S S

T

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6-Continued Log I&

(pmol/liter) Residuals

Molecular Chemical

1,2-Dichlorobenzene 1,3-Dichlorobenzene 1,CDichlorobenzene 1,2,3-Trichlorobenzene 1,2,4-Trichlorobenzene

1,3,5-Trichlorobenzene 1,2,3,4-Tetrachlorobenzene 1,2,3,STetrachlorobenzene 1,2,4,5-Tetrachlorobenzene Phenol 2-Chlorophenol 3-Chlorophenol 4-Chlorophenol 2,3-Dichlorophenol 2,4-Dichlorophenol 2,SDichlorophenol 2,6-Dichlorophenol 3,4-Dichlorophenol 3,SDichlorophenol 2,3,5-Trichlorophenol 2,3,6-Trichlorophenol 2,4,6-Trichlorophenol 2,3,5,6-Tetrachlorophenol 2-Bromophenol 3-Bromophenol 4-Bromophenol 2,6-Dibromophenol 2,4,6-Tribromophenol Pentabromophenol Catechol Resorcinol 2-Aminophenol 4-Aminophenol 2-Nitrophenol 4-Nitrophenol

mzp

Experimental

(Eq. (3))

1.27 1.32

-0.05 0.00 0.15 0.32 0.42 1.16 0.96 1.12 1.61 -0.9 1 -0.40 -0.6 1 -1.41 -0.42 -0.68 -0.14 -0.23 -1.03 0.13 -0.54 -0.29 -0.34 -1.34 -0.33 -0.81 -1.79 -0.41 -0.37 -2.50 -1.32 -0.25 -2.81 -4.25 -0.67 -1.19

1.47 1.01 1.11 1.85 1.02 1.18 1.67

2.29 2.14 1.67 0.87 1.34 1.09 1.63 1.79 0.48 1.65 0.53 1.03 0.98 -0.69 2.07 1.35 0.37 1.34 0.55 -2.17 2.46 3.53 1.12 0.03 2.18 1.67

LSER (Eq. (16))

connectivity (Eq. (20))

-0.49 -0.5 1 -0.43 -0.06 -0.09 0.58 0.43 0.59 0.94 -0.69 0.05 -0.33 -1.21 0.24 -0.15 0.53

-0.96 -0.89 -0.76 -0.5 1 -0.40 0.36 0.22 0.39 0.87

-0.68 0.49 0.27 0.77 0.72 -0.16 0.56 -0.3 1 -1.39

Exclusions“

S

T

T -1.19 -2.28

T T

0.43

Note. Phenols excluded from molecular connectivity QSAR (Eq. (20)). ’ Exclusions: S, aqueous solubility; T, reactive toxicity.

exceeded this accuracy and approached the standard deviation of the experimental methods (S = 0.2 to 0.4). A “fair” correlation waslessaccurate than the approximately one order of magnitude criteria (S = 0.5 to 0.7). Correlations of lesseraccuracy, while often highly significant statistically, were not considered accurate enough to be useful. Octanol/water partitioning. Groups of disparate chemicals were correlated with moderate successusing log P. Good all-classcorrelations were achieved for the aerobic

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heterotrophs (Eq. (1)) and methanogens (Eq. (2)). The all-class Microtox equation (Eq. (3)) is of questionable validity as the separate equations for subclasses (Eqs. (7), (9), and ( 12)) are significantly different. (Microtox correlations generally seemed least amenable to grouping diverse classes together.) No overall correlation was found for Nitrosomonas.

Much greater accuracy was achieved with this method by looking at toxicants by individual chemical class. Thus, good or excellent correlations were achieved for alcohols (Eqs. (4)-(7)), benzenes (Eqs. (8) and (9)), and phenols (Eqs. (lo)-(12)), for each bacteria except Nitrosomonas. Log P failed to correlate the toxicity of chlorinated alkanes for every group of bacteria. An important aspect of the toxicity of these compounds was not adequately modeled by partitioning between aqueous and lipid phases. The log P correlations did describe one specific structural aspect of toxicity not accounted for by the other methods: the lesser toxicity of phenols with ortho chlorine substitutions compared with meta or para chlorine substitutions. The lower pK, of the ortho-substituted isomers accounted for a large part of this difference. However, even after pK, correction, this trend was observed, particularly for the methanogens and Microtox bacteria. The log P equations have simple, logical interpretations. As partitioning into the lipid phase (represented by octanol) increases, toxicity increases. The octanol/water partitioning of a chemical is not a chemical attribute that one can easily discern by examining the chemical structure. Therefore, the equations cannot be applied empirically. However, the utility of this method is enhanced for the engineer by the availability of simple algorithms and computer programs for estimating log P values. Linear solvation energy relationships. The LSER method has the clear advantage over the other two QSAR methods in this study of incorporating the greatest variety of chemical structures into one equation with the greatest accuracy. Excellent correlations were achieved for the environmental bacteria (Eqs. ( 13)-( 15) s = 0.27 to 0.36) and a fair correlation for Microtox (Eq. (16) s = 0.60). The correlation equations determined here are similar in form to other toxicity correlations using LSERs (Kamlet et al., 1986a, 1987; Pa&no et al., 1988). As the intrinsic molar volume (V,) or the hydrogen bond donor acidity ((Y,) increases, aqueous solubility decreases and toxicity increases. As the hydrogen bond acceptor basicity (/3,) increases, aqueous solubility increases and toxicity decreases. The only parameter which runs counter to the trend expected on the basis of aqueous solubility is polarity/polarizability (r*). As polarity increases, aqueous solubility and toxicity increases. This is true for the correlations determined here as well as all previous toxicity correlations with the solvatochromic parameters. Kamlet et al. (1987) hypothesized that this effect may relate to the mechanism of nonreactive toxicity. In these as well as previous LSER toxicity correlations, the most influential terms are intrinsic molar volume ( V) and hydrogen bond acceptor basicity (@,), with polarity/ polarizability (r*) and hydrogen bond donor acidity (LY,) being relatively less important. The parameters used in LSER have clear chemical meanings. One can, for instance, predict the ramifications for toxicity if a modification in chemical structure changes the molar volume, polarity, or hydrogen bond donor characteristics. However, estimating such characteristics is often complicated and more in the domain of the chemist than the engineer or applied scientist. Hence, the utility of the LSER method is clearly limited by the availability of parameter values for chemicals of environmental interest.

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The most extensive compilation of parameters and parameter estimation rules is found in Kamlet (1989). Molecular connectivity. Diverse groups of toxicants were correlated with moderate success using molecular connectivity. Good correlations (Eqs. (17)-(20)) could be achieved for each bacteria. However, phenols did not fit into correlations for either Nitrosomonas (Eq. (18)) or Microtox (Eq. (20)). As with the log P correlations, improved molecular connectivity correlations were often achieved for toxicants by chemical class (Eqs. (21)-(31)). The two methods were similarly successful in accurately correlating toxicants by class; however, molecular connectivity QSARs (Eqs. (21)-(23)) were more successful than log P in correlating chlorinated alkanes. Each all-class equation (Eqs. ( 17)-(20)) and many of the individual class equations (Eqs. (21), (24)-(27), (30), and (31)) used a zero- or first-order index. The primary attribute modeled by this index is molecular volume (Kier and Hall, 1986; Dearden et al., 1988). First-order indices are found in numerous toxicity QSARs. In the QSAR equations derived here, the coefficients on the first- or zero-order indices were negative. In keeping with the nonreactive toxicity model based on simple partitioning, as molecular volume increases, toxicity increases. In many cases, where a second index added significantly to the correlation, it was a difference index (Eqs. (17)-(20)). These indices may reflect an electronic component (Kier and Hall, 1986) which decreases as toxicity increases. While the addition of this second index is necessary for accurate correlation, it sometimes strained the statistical requirement of 5 to 10 observations per parameter. The final index found to be important in the QSARs developed here, 3X,,, encodes information about three dimensions. Kier and Hall (1986) regard the index as describing the degree of flexibility or rigidity. The similarities in the types of indices used in the molecular connectivity QSARs beg the question as to whether a common set of indices could be identified that would correlate the toxicity to each of the bacteria. The set would include an index reflecting molecular volume and a difference index, the two types of indices that predominate in the QSARs. The best such uniform combination of indices identified was ‘X’ and (‘X - IX”). The resulting equations (Eqs. (32)-(35)) are found in Table 2. These “standardized’ equations are less accurate than the original equations. The loss in accuracy is relatively small for the aerobic heterotrophs, Nitrosomonas, and Microtox, but greater for the methanogens. A disadvantage in using molecular connectivity, particularly for diverse data sets, is that the choice of indices is based on the particular data. Some guidance in selecting indices is possible from past correlations of similar variables, but this is insufficient for an a priori selection of indices. While assigning physical meaning to the indices may be difficult, using the equations to determine the structural influences on toxicity is straightforward. The indices can all be calculated quickly and accurately from chemical structure, allowing the calculation of the predicted toxicological implication of any variation of structure. Validation and testing methods. The validation studies strongly support the QSARs tested. The statistical validation studies showed that the QSARs tested (Eqs. (l)-(3) and (13)-(20)) were not unduly influenced by the specific data points used in their derivation. Random groups of data could be excluded without changing the QSAR equations or their accuracy for predicting the excluded points.

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Validation of aerobic heterotroph QSARs (Eqs. (l), ( 13), and (17)) and methanogen QSARs (Eqs. (2), (15), and ( 19)) with literature data was likewise encouraging. Agreement between literature data and predicted toxicity was good. However, data used in these validation studies were limited and predominantly for phenols. Additional work with literature data might further support the QSARs developed here and test the potential for including additional classes of toxicants. The successful validation of the aerobic heterotroph QSARs using data obtained from the OECD activated sludge respiration tests also helped validate the serum bottle method used in this study for testing aerobic heterotrophs. Tang et al. ( 1990) confirmed the similarity of the results found from the two testing methods for nonvolatile chemicals. Validation of the Microtox QSARs (Eqs. (3), (16), and (20)) was conducted with the extensive data base compiled by Kaiser and Ribo (1987). The QSARs predicted the toxicity of these chemicals with expected statistical accuracy, even though the data were compiled from diverse sources. Toxicants known to act by reactive toxicity mechanisms were outliers as expected. The QSAR predictions also pointed out data that may be questionable experimentally. But generally, the highly consistent data confirms the utility of Microtox testing for obtaining reproducible toxicity data. 2. Outliers and Reactive Toxicity Mechanisms Developing QSARs for toxicity data can help identify unusual toxic behavior worthy of further investigation. Chemicals may be outliers from a nonreactive toxicity QSAR, either because they act by a reactive toxicity mechanism or because of experimental error. Experimental error may apply to a specific chemical for which anomalous results were obtained or there may be a methodical problem in the experimental technique affecting a group of chemicals. Chemicals known to be reactive toxicants and other groups showing methodical deviations from QSAR predictions were excluded from the QSARs. Each group is discussed below. Low pK,. Before QSARs were developed, nominal toxic concentrations were corrected for pK, under the assumption that the unionized portion of the chemical partitions most significantly into the lipid phase and contributes to toxicity. Correcting these compounds for pK, was necessary to develop accurate QSARs, providing some substantiation for the assumption. In particular, these corrections allowed QSARs to incorporate chlorinated phenols with the other classes. Scherrer and Howard (1979) described the phenomenon of nonreactive toxicity being related to the unionized portion of the toxicant. However, even when the toxic concentrations of chemicals with low pK, values were reduced to account for the ionized portion of the chemical, the lowest pK, chemicals still showed greater toxicity than expected; this was due to reactive toxicity. Scherrer and Howard ( 1979) illustrated this phenomenon with a number of examples showing that chemicals with low pK, values have an enhanced toxicity due to a specific electron effect. In one example, they showed that the more acidic a phenol, the more active it was as an uncoupler of oxidative phosphorylation. Schultz (1987) found that pK, could be used to distinguish between the toxic mechanisms of polar narcosis and the uncoupling of ox&dative phosphorylation by phenols for Tetrahymena pyriformis. For the 14 chemicals considered, he found that polar narcotics had pK, values greater than 8.0, while uncoupling agents had pK, values less than 6.5.

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The present study indicated an enhanced toxicity at a pK, value approximately less than 6.0. The chemical closest to this cutoff was 2,3,5,6-tetrachlorophenol, with a pK, of 5.2. This chemical was an outlier from the methanogen equations and was more toxic than predicted by the aerobic heterotroph equations and Microtox equations. Chemicals with the next highest pK, values, 2,4,5&ichlorophenol (pK, = 6.0) and 2,4,6-trichlorophenol (pK, = 6.2), did not exhibit enhanced toxicity. Chemicals with lower pK, values (ranging from 4.5 for pentachlorophenol to 0.5 for trichloroacetic acid) were clearly outliers whenever tested. These chemicals included 2,4dinitrophenol, which is often noted as a uncoupler of oxidative phosphorylation. While the cutoff at a pK, value of approximately 6.0 was reasonable for the present data, it should be recognized that it is not a distinct cutoff and represents a balance of effects between partitioning and uncoupling mechanisms. Acryl functional groups. Acrylates and acrylonitrile were excluded from the QSAR equations because they had been identified previously as reactive toxicants (Russom et al., 1988; Kamlet et al., 1987). In general, the present data confirmed the enhanced toxicity of these chemicals, although the results were not uniform. Acrylonitrile was much more toxic to the two bacteria tested than predicted by the nonreactive toxicity QSARs. Ethyl acrylate and butyl acrylate were more toxic to Nitrosomonas than predicted. Octyl acrylate was less toxic than predicted for the methanogens, but this was probably a result of its low aqueous solubility. There was one anomalous result: butyl acrylate tested for the aerobic heterotrophs was less toxic than predicted. Nitro functional groups. Aromatic chemicals with nitro functional groups showed enhanced toxicity to the methanogens. The only exception was pentachloronitrobenzene, which may have been limited by its aqueous solubility. Although testing of the nitro compounds for aerobic bacteria (aerobic heterotrophs, Nitrosomonas, and Microtox) was limited, no similar trend toward enhanced toxicity was observed. The enhanced toxicity of nitro compounds to methanogens requires further confirmation and exploration of potential mechanisms. The low oxidation-reduction potential of the methanogen culture may contribute to chemical reduction of the nitro group to a more toxic species. Bailey and Spanggord (1983) reported that the enzymatic reduction of the nitro group to reactive intermediates such as hydroxylamines is a likely mechanism to account for differences in toxicity between different nitroaromatic isomers. It is also commonly observed, although not well understood, that in a mixed bacterial culture in the presence of nitrate, methanogenesis will cease in favor of the more thermodynamically favorable reduction of nitrate. The mechanism for this inhibition of methanogenesis may be related to the reactive toxicity of the nitro functional groups indicated here or to the elevated oxidation-reduction potential caused by the presence of nitrate. Since only aromatic nitro compounds were tested, these conclusions cannot be generalized to aliphatic compounds. Aldehydes. Aldehydes have been identified as reactive toxicants due to their participation in Schiff-base formation with enzyme amine groups (Kamlet et al. 1986a, 1987). Results for the one aldehyde tested in the present study, 2-furaldehyde with methanogens, confirmed the enhanced toxicity of this compound. Validation studies with literature data for Microtox bacteria also confirmed the enhanced toxicity of benzaldehyde and acetaldehyde. Low aqueous solubility. Some compounds showed apparent toxicity concentrations in excess of their aqueous solubility. In many toxicity studies, aqueous solubility is first ascertained, and then the toxicity of the compounds is tested only up to their

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aqueous solubility. This was not done in the present study under the assumption that when the aqueous solubility limit was exceeded, toxicity would no longer increase with the amount of toxicant in the assay bottle, and an ICsO value would not be found. In some cases, this aqueous solubility limit was evident experimentally as expected. However, in many cases, the apparent toxicity exceeded the aqueous solubility; i.e. toxicity increased beyond the aqueous solubility. These compounds were eliminated from the QSAR development. The results in Tables 3 through 6 clearly show that in these cases, the predicted toxic concentration was much less than the nominal I&, indicated experimentally. Passino et al. (1988) made a similar observation for silanes when tested for toxicity to Daphnia pulex and Daphnia magna. The reason for this observed phenomenon is not known. There may be some potential for increased concentrations of the toxicant beyond aqueous solubility due to an adsorption mechanism or to a three-way partitioning among aqueous, organic, and biological phases. Chlorinated aliphatic hydrocarbons. The chlorinated aliphatic hydrocarbons and chlorinated alcohols were more toxic to the methanogens than predicted by nonreactive toxicity QSARs, no matter which QSAR method was used. In addition, the toxicity of the chlorinated alkyl compounds to the methanogens was not well correlated by any of the three QSAR methods. The reason for this enhanced toxicity is not known. Because the toxicity was not well correlated even within the class of chlorinated alkyls, there may be a reactive toxicity mechanism, not a simple partitioning phenomenon, involved. Some studies that have made conclusions about the greater susceptibility of methanogens to toxicants may have been highly influenced by toxicants from this class. For example, Koopman and Bitton ( 1986) compared the threshold toxicity as reported in the literature to activated sludge and anaerobic digestion and found that anaerobic digestion was more sensitive. Of the 17 compounds included in this comparison, 9 were chlorinated aliphatic hydrocarbons. This important anomaly also points out the importance of only applying QSARs to the classes of compounds for which they have been derived. There certainly may be other combinations of compounds and bacteria that would show unexpected enhanced toxicity. Toxicants with low ZC,, values. Inclusion of toxicants with low I&, values (less than 1.5 log pmol/liter) greatly decreased the accuracy of QSARs for Nitrosomonas. This appeared to result from decreased experimental accuracy at these low IGO values. Nitrosomonas had the lowest IC5a values of the three environmental bacteria tested in serum bottle studies. In comparison, the aerobic heterotrophs and methanogens had few toxicants with log I& values less than 1.5. While the absolute accuracy of the assays as measured by standard deviation was similar among bacteria, the coefficient of variation for Nitrosomonas data was twice that for the other bacteria. The low threshold toxicity concentration of acetone for Nitrosomonas also contributed to experimental error as it necessitated the use of small (less than 0.01 ml) injection volumes when acetone was used as a solvent. There did not appear to be a methodical error associated with the low toxicity values. In fact, the data were not consistently lower or higher than predicted by the QSAR equations. Miscellaneous outliers. A number of chemicals were outliers for particular QSARs. No relationships were evident between these chemicals, indicating that the most likely

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explanation for these outliers was experimental error. As these chemicals did not statistically alter the QSAR parameter coefficients, they were usually included in the QSARs. Perhaps a case could be made for the enhanced toxicity of nitrile compounds. Benzonitrile, 2-methylpropionitrile, and m-tolunitrile were more toxic than predicted. However, the results were usually within the 95% confidence intervals of the QSARs so that the effect, if real, was not strong. Any trend was further diminished by the good modeling of acetonitrile. 3. Observations on Chemical Classes

In the course of QSAR development, some interesting characteristics of different chemical classes became apparent. Alcohols. The alcohols produced high correlations in every situation. In many correlations covering diverse chemical classes (particularly those using log P and molecular connectivity), the correlation coefficient decreased if the alcohols were excluded from the correlations. The reason is that the alcohols cover a much wider range of toxicity values than any other class tested. In addition, their toxicity is related in a straightforward manner to their solubility properties. Thus, while chemicals in other classes were grouped together in one portion of the graphs of toxicity, the alcohols covered a wide range of toxicity values and seemed to act as a “backbone” for the correlations. Phenols. The phenols, particularly the chlorinated phenols, were the most toxic class tested. Their toxicity tended to increase with increasing halogen substitution. Ortho halogen substitutions were somewhat less toxic than meta or para substitutions, Phenols have often been put into separate QSAR equations from other nonreactive toxicants and designated polar narcotics. The possible need to separate the phenols was kept in mind in QSAR development but the separation proved to be necessary in only some cases. The LSER correlations incorporated the phenols with no difficulties. In log P and molecular connectivity correlations, including the phenols was more problematic. In some instances (Eqs. ( 18) and (20)) phenols could not be included in the all-class QSAR or correlated by themselves. In another case (Eq. (3)), phenols could be included in the all-class correlation but caused some decrease in accuracy. Finally, the phenols are noteworthy in having the lowest pK, values of the toxicants tested (see under Low p&). Benzenes.The benzenes were of intermediate toxicity among the compounds tested, with chlorinated benzenes being more toxic than alkyl substituted benzenes. The benzenes tended to fit well into the all-class QSARs. They were reasonably well correlated as a class in log P (Eqs. (8) and (9)) and molecular connectivity QSARs (Eqs. (28) and (29)) although the main differentiation made by these correlations was between chlorinated and nonchlorinated benzenes. The chlorinated benzenes were often not toxic at their aqueous solubility for the aerobic heterotrophs and methanogens. In many cases, particularly for the aerobic heterotrophs, the nominal toxic concentration apparent from the toxicity test was higher than the aqueous solubility (see under Low aqueous solubility). Chlorinated aliphatic hydrocarbons. The chlorinated aliphatic hydrocarbons were of intermediate toxicity among the toxicants tested, with the exception of their enhanced toxicity to the methanogens. For the methanogens, these chemicals were not correlated by any of the three QSAR methods. For the other bacteria, the correlation of the

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chlorinated aliphatic hydrocarbons was much more successful, but still problematic for log P and molecular connectivity QSARs. LSER QSARs (Eqs. ( 13), ( 14), and ( 16)) incorporated the chlorinated aliphatic hydrocarbons in the all-class QSARs with no difficulty. Log P correlations were not able to correlate chlorinated alkanes for any bacteria, but could incorporate them into the all class equations (Eqs. (1) and (3)) with some loss of accuracy to the correlation coefficients. This shows that although log P could place the chlorinated aliphatic hydrocarbons on the correct portion of the curve, it could not differentiate among the toxicities of the different chemicals in the class. The chlorinated alkanes were much better correlated by molecular connectivity QSARs (Eqs. (21)-(23)), although the parameter-to-observation ratios were high. Similar to the log P QSARs, the chlorinated aliphatic hydrocarbons were incorporated into the overall molecular connectivity equations (Eqs. (17)-(20)), but with some decrease in the correlation coefficient. An aspect of their toxicity is not differentiated by the overall molecular connectivity equations. The chlorinated aliphatic hydrocarbons had the highest gas/liquid partitioning. The use of Henry’s law constants to correct for the partitioning was important in being able to include these compounds in QSARs with the other classes.

4. Use of QSAR and Correlation Equations The QSARs developed here can be used to estimate toxicity for the bacteria and chemical classes for which they were developed. Any chemical that may act by a reactive toxicity mechanism should be excluded. In making toxicity estimates, all applicable QSAR equations should be identified from Table 2 (note limitations). The parameters required to apply the most promising QSARs should be calculated or sought from the literature. Finally, the predicted toxicity should be calculated by each applicable QSAR, and the results corrected for gas/liquid partitioning, ionization, and aqueous solubility as appropriate. It may be possible to calculate more than one predicted value from the multiple QSARs. Selecting a value should be guided by the accuracy with which the particular QSAR fits similar compounds and the accuracy of the parameters available for the method. CONCLUSION This research develops QSAR equations capable of estimating the nonreactive toxicity of a wide variety of chemicals to bacteria of importance to the environmental engineer. LSER QSARs are the most accurate and are able to incorporate the widest variety of chemical classes. Log P and molecular connectivity QSARs are often most accurate for individual chemical classes; they are simple QSARs to use because they are based on easily obtained parameters. Chemicals which are outliers from the nonreactive toxicity QSARs indicate unusual toxic or experimental behavior. ACKNOWLEDGMENT This research Bryan, Program

was funded Director.

by NSF Grant

ECE-86-

17 10 1 to Drexel

and Vanderbilt

Universities,

Dr. Edward

REFERENCES ALBERT, A. (1968). Selected Toxicity and Related Topics. Wiley, New York. BAILEY, H. C., AND SPANGGORD, R. J. (1983). The relationship between the toxicity and structure of nitroaromatic chemicals. In Aquatic Toxicology and Hazard Assessment: Sixth Symposium (W. E. Bishop,

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R. D. Cardwell, and B. B. Heidolph, Eds.), pp. 98-107. ASTM STP 802. American Society for Testing and Materials, Philadelphia. BELSLEY, D. A., KUH, E., AND WELSCH, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley, New York. BLUM, D. J. W., AND SPEECE,R. E. (1990). Determining chemical toxicity to aquatic species: The use of QSARs and surrogate organisms. Environ. Sci. Technol. 24,284-293. BLUM, D. J. W., AND SPEECE,R. E. (1991). A database of chemical toxicity to environmental bacteria and its use in interspecies comparisons and correlations. J. Water Polk Control Fed. 63, 198-207. BLUM, D. J. W., HERGENROEDER,R., PARKIN, G. F., AND SPEECE,R. E. (1986). Anaerobic treatment of coal conversion wastewater constituents. J. Water Poll&. Control Fed. 58, 122-l 3 1. DEARDEN,J. C., BRADBURNE,S. J. A., CRONIN, M. T. D., AND SOUNKI, P. (1988). The Physical Significance ofMolecular Connectivity Indices. Presented at “QSAR 88: The 3rd International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology,” Knoxville, TN. HALL, L. M. (1987). MOLCONNZ, Version 2.0, A Program for Molecular Topology Analysis. Hall Associates Consulting, 2 Davis Street, Quincy, MA. KAISER, K. L. E., AND J. M. RIBO (1987). Photobacterium Phosphoreurn toxicity bioassay. II. Toxicity data compilation. Toxic. Assess. 2. KAMLET, M. J. (1989). Solute-Solvent Interactions in Chemistry and Biology. Phase I Report, NIH Grant SSS-6(B)IR43GM38377-01. Advanced Technology and Research, Laurel, MD. KAMLET. M. J., ABBOUD, J. M., ABRAHAM, M. H., AND TAFT, R. W. (1983). Linear solvation energy relationships. 23. A comprehensive collection of the solvatochromic parameters, a*, 01,and p, and some methods for simplifying the generalized solvatochromic equation. J. Org. Chem. 46, 2877-2887. KAMLET, M. J., DOHERTY, R. M., VEITH, Cl. D., TAR, R. W., AND ABRAHAM, M. H. (1986a). Solubility properties in polymers and biological media. 7. An analysis of toxicant properties that influence inhibition of bioluminescence in Photobacterium phosphoreum (the Microtox test). Environ. Sci. Technol. 20,690695.

KAMLET. M. J., DOHERTY, R. M., ABBOUD, J. M., ABRAHAM, M. H., AND TAFT, R. W. (1986b). Linear solvation energy relationships. 36. Molecular properties governing solubilities of organic nonelectrolytes in water. J. Pharm. Sci. 75, 338-348. KAMLET. M. J., DOHERTY, R., TAFT, R. W., ABRAHAM, M. H., VEITH, G. D., AND ABRAHAM, D. J. (1987). Solubility properties in polymers and biological media. 8. An analysis of the factors that influence toxicities of organic nonelectrolytes to the golden orfe fish (Leuciscus idus melanotus). Environ. Sci. Technol. 21, 149-155. KIER, L. B., AND HALL, L. H. (1986). Molecular Connectivity in Structure-Activity Analysis. Research Studies Press, Wiley, New York. KIER, L. B., AND HALL, L. H. (1981). Derivation and significance of valence molecular connectivity. J. Pharm. Sci. 70,583-589. K~ER, L. B., AND HALL, L. H. (1976). Molecular Connectivity in Chemistry and Drug Research. Academic Press, New York. KING, E. F., AND PAINTER, H. A. (1986). Inhibition of respiration of activated sludge: Variability and reproducibility of results. Toxic. Assess. 1, 27-39. KLECKA, G. M., AND LANDI, L. P. (1985). Evaluation of the OECD activated sludge respiration inhibition test. Chemosphere 14, 1239-125 1. KOOPMAN, B., AND BITTON, G. ( 1986). Toxicant screening in wastewater systems.In Toxicity Testing Using Microorganisms, (G. Bitton and B. J. Dutka, Eds.), Vol. 2, pp. 101-132. CRC Press, Boca Raton, FL. LEO, A. J., AND WEININGER, D. (1984). CLOGP3. Medicinal Chemistry Project (with modifications as of June 1988). Pomona College, Claremont, CA. LIPNICK, R. L. (1985). A perspective on quantitative structure-activity relationships in ecotoxicology. Environ. Toxicol. Chem. 4, 255-257. NIRMALAKHANDAN, N. (1988). CONEX Civil and Environmental Engineering. Vanderbilt University, Nashville. PASSINO, D. R. M., HICKEY, J. P., AND FRANK, A. M. (1988). Linear Solvation Energy Relationships for Toxicity of Selected Organic Chemicals to Daphnia pulex and Daphnia magna. Presented at “QSAR 88: The 3rd International Workshop of Quantitative Structure-Activity Relationships in Environmental Toxicology,” Knoxville, TN. RUSSOM, C. L., DRUMMOND, R. A., AND HOFFMAN, A. D. (1988). Acute Toxicity and Behavioral Effects ofAcrylates and Methacrylates to Juvenile Fathead Minnows. Presented at “QSAR 88: The 3rd International

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Workshop of Quantitative Structure-Activity Relationships in Environmental Toxicology,” Knoxville, TN. SAS Institute, Inc. (1985). SAS Version 5.03. SAS Institute, Cary, NC. SCHERRER,R. A., AND HOWARD, S. M. (1979). The analysis of electronic factors in quantitative structureactivity relationships using distribution coefficients.In Computer Assisted Drug Design. American Chemical Society, Washington, DC. SCHULTZ, T. W. (1987). The use of ionization constant (pK,) in selecting models of toxicity in phenols. Ecotoxicol. Environ. Saj 14, 178183. Syracuse Research Corp. (1987). Environmental Fate Data Bases. Syracuse Research Corp., Syracuse, NY. TANG, N. H., BLUM, D. J. W., AND SPEECE,R. E. (1990). Comparison of a serum bottle activated sludge toxicity test with the OECD method. J. Environ. Eng. 116, 1076-1084.