Parametrization of Electrophilicity for the Prediction of the Toxicity of

Liverpool, L3 3AF, England, and The University of Tennessee, College of Veterinary Medicine,. 2407 River Drive, Knoxville, Tennessee 37996-4500. Recei...
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Chem. Res. Toxicol. 2001, 14, 1498-1505

Parametrization of Electrophilicity for the Prediction of the Toxicity of Aromatic Compounds M. T. D. Cronin,*,† N. Manga,† J. R. Seward,‡ G. D. Sinks,‡ and T. W. Schultz‡ School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England, and The University of Tennessee, College of Veterinary Medicine, 2407 River Drive, Knoxville, Tennessee 37996-4500 Received March 16, 2001

The aim of this study was to determine which descriptor best parametrized the electrophilicity of aromatic compounds with regard to their acute toxicity. To achieve this, toxicity data for 203 substituted aromatic compounds containing a nitro- or cyano group were evaluated in the 40-h Tetrahymena pyriformis population growth impairment assay. Quantitative structureactivity relationships (QSARs) were developed relating toxic potency [log(IGC50-1)] with hydrophobicity quantified by the 1-octanol/water partition coefficient (log P) and electrophilic reactivity quantified by the molecular orbital parameters, either the energy of the lowest unoccupied molecular orbital (ELUMO) or maximum acceptor superdelocalizability (Amax) was developed. For the full data set, ELUMO and Amax were collinear (r ) 0.87). A comparison of the QSARs [log(IGC50-1) ) 0.40 log P - 0.94ELUMO - 1.27; n ) 203, r2 ) 0.60, s ) 0.49, F ) 151] and [log(IGC50-1) ) 0.37 log P + 13.1Amax - 4.30; n ) 203, r2 ) 0.70, s ) 0.42, F ) 237] reveals Amax to be the better electrophilic parameter for modeling these data. Analysis of outliers indicates a preponderance of 4-subsituted nitrophenols and nitroanilines. Smaller datasets (51 and 102 compounds) selected in order to reduce the collinearity between Amax and ELUMO were also evaluated. Results indicate Amax to be the superior descriptor of electrophilicity for the purpose of toxicological QSARs for aromatic compounds. Development of QSARs using partial least-squares yielded similar results.

Introduction The global prediction of toxicity using QSARs1 has been the goal of many workers who utilized a variety of approaches. This goal is alluring, but has yet to be achieved satisfactorily. There are a number of reasons for the absence of success. The deficiency of available toxicity data has clearly held back progress. This lack of success has been compounded in many studies by a poor appreciation of the insufficient heterogeneity, or chemical diversity, in the data set. Further, while some molecular properties (such as hydrophobicity) are well described, others, including electrophilic reactivity, ionization, and hydrogen bonding, are poorly parametrized. Last, mechanisms of toxic action are not fully understood or misinterpreted, or their relevance in the modeling of toxicity is ignored. Recent approaches by the authors have attempted to alleviate these problems by the creation of empirical models for toxicity based upon the “responsesurface”, or two-parameter QSAR technique (1-3). These models have been developed for limited sets of compounds known to be acting by mechanisms of toxic action other than nonpolar narcosis. The approach has been simplistic in that toxicity is modeled as a function of the ability of * To whom correspondence should be addressed. Phone: +44 151 231 2066. Fax: +44 151 231 2170. E-mail: [email protected]. † School of Pharmacy and Chemistry. ‡ The University of Tennessee. 1 Abbreviations: QSARs, quantitative structure-activity relationships; IGC50, 50% growth inhibitory concentration; log P, 1-octanol/ water partition coefficient; EHOMO, energy of the highest occupied molecular orbital; ELUMO, energy of the lowest unoccupied molecular orbital; Amax, maximum acceptor superdelocalizability; Aave, average acceptor superdelocalizability; PLS, partial least-squares.

the toxicant to reach the active site and its ability to react covalently (or otherwise) with some biological macromolecule (4). These Hansch-type QSARs have distinct advantages over the use of class-based QSARs (i.e., single QSARs for particular mechanisms of toxic action such as nonpolar narcosis, polar narcosis, uncoupling, etc.). The most significant advantage is that, despite its simplicity, the approach obviates a requirement for a priori identification of toxic mechanism of action, as is required for the use of class-based QSARs. Such classification of mechanism of action from structure alone is a difficult, if not impossible, task (3). Response-surface, or two-parameter, QSARs are also universally applicable to acute toxicity across a variety of species and endpoints. For instance, they have been utilized successfully to model toxic effects at different trophic levels (3, 5). The passage of a compound through membranes is well modeled by molecular hydrophobicity. This can be described adequately by the logarithm of the octanol-water partition coefficient (log P). The authors previously have utilized two different descriptions of molecular electrophilicity (putatively considered to describe the ability of a compound to react covalently with biological macromolecules). These are the energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum acceptor superdelocalizability (Amax). As a result of the lack of molecular diversity in the data sets already analyzed in the response-surface approach, it has been difficult to establish whether either of these descriptors is more efficacious. The lack of molecular diversity in many previous studies has resulted in considerable collinearity between Amax and ELUMO.

10.1021/tx015502k CCC: $20.00 © 2001 American Chemical Society Published on Web 10/12/2001

Electrophilicity in Ecotoxicity

Chem. Res. Toxicol., Vol. 14, No. 11, 2001 1499

Table 1. Chemicals Evaluated, Chemical Abstract Service (CAS) Number, Toxicity Data, and Molecular Descriptors name

CAS no.

log (IGC50-1)

log Pa

ELUMO

Amax

4-cyanopyridine 2-cyanopyridine 3-cyanopyridine 2-nitrobenzamide 3-cyano-4,6-dimethyl-2-hydroxypyridine benzonitrile 2-cyanoaniline 2-hydroxy-4-methyl-3-nitropyridine 3-cyanoaniline 2,3-dicyanohydroquinone 2-amino-5-nitropyrimidine 4-cyanobenzamide 4-acetylbenzonitrile 1,2-dicyanobenzene 2-cyanobenzamide 4-fluorobenzonitrile 3-tolunitrile 2-tolunitrile 3-(hydroxymethyl)nitrobenzene 3-nitrobenzamide 2-hydroxy-4-methyl-5-nitropyridine 2-(hydroxymethyl)nitrobenzene 4-tolunitrile 3-chlorobenzonitrile methyl-4-cyanobenzoate 3-cyanophenol 3-cyanobenzaldehyde 2-amino-3-nitropyridine 2-methoxy-2-nitropyridine 4-chlorobenzonitrile 3-nitroaniline 2-cyanophenol 4-cyanobenzaldehyde 3-methoxybenzonitrile 2-methylnitrobenzene 3-methylnitrobenzene 8-nitroquinoline 4-methoxybenzonitrile 4-nitrobenzyl alcohol 4-nitrophenylacetonitrile 3-nitrobenzaldehyde nitrobenzene 2-nitrobenzaldehyde 4-methylnitrobenzene 4-nitrobenzamide 4-nitrobenzaldehyde 1-fluoro-3-nitrobenzene 2-amino-5-nitropyridine 1-fluoro-2-nitrobenzene 4-cyanoaniline 4-fluoronitrobenzene 4-fluoro-2-nitrotoluene 3-hydroxy-4-nitrobenzaldehyde 2-chlorobenzonitrile 2-chloro-4-methyl-3-nitropyridine 4-bromobenzonitrile 2,6-dimethylnitrobenzene 3-nitroacetophenone 5-hydroxy-2-nitrobenzaldehyde 2-fluoro-4-nitrotoluene 4-methyl-2-nitroaniline ethyl-4-cyanobenzoate 2-amino-4-methyl-5-nitropyridine 3,4-dinitrophenol 4-nitroanisole 4-bromonitrobenzene 5-nitroquinoline 3-hydroxy-6-methyl-2-nitropyridine methyl-4-nitrobenzoate 4-nitropyridine 2-chloro-4-methyl-5-nitropyridine 4-ethylnitrobenzene 4-chloronitrobenzene 2-amino-5-chlorobenzonitrile 3-nitrobenzonitrile 4,5-dimethyl-2-nitroaniline

100-48-1 100-70-9 100-54-9 610-15-1 769-28-8 100-47-0 1885-29-6 21901-18-9 2237-30-1 4733-50-0 3073-77-6 3034-34-2 1443-80-7 91-15-6 17174-98-0 1194-02-1 620-22-4 529-19-1 619-25-0 645-09-0 21901-41-7 612-25-9 104-85-8 766-84-7 1129-35-7 873-62-1 24964-64-5 4214-75-9 5446-92-4 623-03-0 99-09-2 611-20-1 105-07-7 1527-89-5 88-72-2 99-08-1 607-35-2 874-90-8 619-73-8 555-21-5 99-61-6 98-95-3 552-89-6 99-99-0 619-80-7 555-16-8 402-67-5 4214-76-0 1493-27-2 873-74-5 1493-27-2 446-10-6 704-13-2 873-32-5 23056-39-5 623-00-7 81-20-9 121-89-1 42454-06-8 1427-07-2 89-62-3 7153-22-2 21901-40-6 577-71-9 100-17-4 585-78-7 607-34-1 15128-90-2 619-50-1 1122-61-8 23056-33-9 100-12-9 100-00-5 5922-60-1 619-24-9 6972-71-0

-0.82 -0.79 -0.74 -0.72 -0.70 -0.52 -0.50 -0.50 -0.47 -0.44 -0.43 -0.38 -0.37 -0.34 -0.32 -0.26 -0.25 -0.24 -0.22 -0.19 -0.17 -0.16 -0.10 -0.06 -0.06 -0.06 -0.02 -0.01 -0.01 0.00 0.03 0.04 0.04 0.05 0.05 0.05 0.08 0.10 0.12 0.13 0.14 0.14 0.17 0.17 0.18 0.20 0.20 0.22 0.23 0.24 0.25 0.25 0.27 0.28 0.29 0.29 0.30 0.32 0.33 0.33 0.37 0.37 0.37 0.37 0.38 0.38 0.39 0.39 0.40 0.41 0.42 0.43 0.43 0.44 0.45 0.45

0.47 0.40 0.23 -0.15 1.77C 1.56 1.40 1.34C 1.07 0.84C -0.55C 0.47 1.22 0.99 -0.08C 1.72C 2.07C 2.21 1.21 0.77 1.34C 1.24 2.07C 2.29C 1.54C 1.70 1.18 0.84C 1.55 2.29C 1.37 1.61 1.21C 1.82C 2.30 2.42 1.40 1.82C 1.26 1.37 1.47 1.85 1.74 2.37 0.82 1.56 2.02 0.30C 1.69 1.00 1.89 2.45C 1.47C 2.16C 1.48C 2.55C 2.95 1.42 1.75C 2.53C 2.30C 2.07C 0.71C 1.79C 2.03 2.55 1.86 1.42C 1.94 0.33 1.68C 3.03 2.39 1.79C 1.35C 2.75C

-0.3480 -0.6933 -0.7243 -1.0888 -0.5909 -0.3891 -0.3880 -1.4318 -0.4832 -1.1988 -1.2620 -1.1690 -1.1265 -1.0199 -0.8204 -0.6766 -0.3440 -0.3485 -0.9963 -1.3444 -1.2900 -1.0658 -0.3596 -0.6767 -1.1139 -0.5146 -0.9920 -1.0435 -1.2687 -0.7351 -0.9488 -0.5092 -1.1918 -0.4205 -1.0091 -1.0161 -1.3329 -0.3110 -1.0602 -0.5293 -1.4028 -1.0670 -1.4907 -1.0442 -1.5445 -1.6762 -1.3358 -0.9844 -1.3016 -0.1956 -1.3528 -1.2738 -1.7554 -0.6704 -1.2233 -0.7944 -0.8652 -1.3589 -1.4610 -1.3015 -0.7608 -1.0947 -0.9222 -1.8649 -0.9825 -1.4127 -1.4846 -1.2724 -1.6489 -1.3929 -1.6062 -1.0333 -1.3440 -0.7918 -1.9463 -0.7402

0.2911 0.2851 0.2855 0.3311 0.2930 0.2674 0.2755 0.3311 0.2703 0.3047 0.3320 0.2938 0.2884 0.2975 0.2882 0.2927 0.2657 0.2682 0.2961 0.3119 0.3216 0.3013 0.2643 0.3072 0.2904 0.2768 0.2883 0.3172 0.3223 0.3146 0.2991 0.2894 0.2880 0.2779 0.2977 0.2986 0.3128 0.2844 0.2979 0.3147 0.3162 0.3011 0.3289 0.2965 0.3233 0.3281 0.3197 0.3132 0.3276 0.2745 0.3215 0.3155 0.3321 0.3176 0.3544 0.3109 0.2966 0.3108 0.3204 0.3140 0.3017 0.2896 0.3082 0.3823 0.3038 0.3400 0.3159 0.3277 0.3290 0.3320 0.3567 0.2966 0.3360 0.3087 0.3381 0.2988

1500

Chem. Res. Toxicol., Vol. 14, No. 11, 2001

Cronin et al.

Table 1. (Continued) name

CAS no.

log (IGC50-1)

log Pa

ELUMO

Amax

2,5-difluoronitrobenzene 6-nitroquinoline 2,3-dinitrophenol 2-amino-4-nitrophenol 2-methyl-4-nitroaniline 3-nitrophenol 4-amino-3,5-dinitrobenzamide 4-cyanophenol 4-nitrophenylene-1,2-diamine 3,5-dinitrobenzyl alcohol 2,4-dinitroaniline 2,6-dinitrophenol 2,3-dimethylnitrobenzene 4-nitrobenzonitrile 4-methyl-2-nitrophenol 1,2-dimethyl-4-nitrobenzene 2-chloro-5-nitrobenzaldehyde 4-hydroxy-3-nitrobenzaldehyde 2-nitroresorcinol 2-methyl-5-nitrophenol 2,6-dichloro-4-nitrophenol 2-nitrophenol 3-methoxynitrobenzene 4-nitrobenzaldoxime 2-chloronitrobenzene 2-nitroaniline 3-chloro-2-methylnitrobenzene 1-cyanonaphthalene ethyl-4-nitrobenzoate 4-methyl-3-nitrophenol 2-chloro-4-nitroaniline 4,5-difluoro-2-nitroaniline 2-chloromethyl-4-nitrophenol 4-ethoxy-2-nitroaniline 3-chloro-4-fluoronitrobenzene 2-chloro-5-nitropyridine methyl-4-chloro-2-nitrobenzoate 5-chloro-2-methylnitrobenzene 4-nitrophenetole 3-chloronitrobenzene 2,6-dinitroaniline 2-bromonitrobenzene 2,4,6-trimethylnitrobenzene 6-methyl-1,3-dinitrobenzene 3-hydroxy-2-nitropyridine 2-chloro-3-nitropyridine 1,3-dinitrobenzene 5-amino-6-nitroquinoline 3-fluoro-4-nitrophenol 3,5-dinitroaniline 2,5-dinitrophenol 4-amino-2-nitrophenol 2,4-dichloronitrobenzene 1-nitronaphthalene 2-methyl-1-nitronaphthalene 2,3-dichloronitrobenzene 2-bromo-5-nitropyridine 2-cyanonitrobenzene 1-fluoro-3-iodo-5-nitrobenzene 3,4-dinitrobenzyl alcohol 2,4-dinitrophenol 4-nitro-1-naphthylamine 5-fluoro-2-nitrophenol 2,5-dichloronitrobenzene 3,5-dichloronitrobenzene 3,4-dichloronitrobenzene 2-bromo-5-nitrotoluene 2-amino-4-chloro-5-nitrophenol 4-nitrobenzyl chloride 3,5-dinitrobenzonitrile 2-chloro-4,6-dinitroaniline 3-bromonitrobenzene 2,6-dinitromethylphenol 2-bromo-4,6-dinitroaniline 4-biphenylcarbonitrile 1,2-dinitrobenzene

364-74-9 613-50-3 66-56-8 99-57-0 99-52-5 554-84-7 54321-79-8 767-00-0 99-56-9 71002-60-8 97-02-9 573-56-8 83-41-0 619-72-7 119-33-5 99-51-4 6361-21-3 3011-34-5 601-89-8 5428-54-6 618-80-4 88-75-5 555-03-3 1129-37-9 88-73-3 88-74-4 83-42-1 86-53-3 99-77-4 2042-14-0 121-89-9 78056-39-0 2973-19-5 616-86-4 350-30-1 4548-45-2 42087-80-9 89-59-8 100-29-8 121-73-3 606-22-4 577-19-5 603-71-4 121-14-2 15128-82-2 5470-18-8 99-65-0 35975-00-9 394-41-2 618-87-1 329-71-5 119-34-6 611-06-3 86-57-7 881-03-8 3209-22-1 4487-59-6 612-24-8 3819-88-3 79544-31-3 51-28-5 776-34-1 446-36-6 89-61-2 618-62-2 99-54-7 7149-70-4 02/07/6358 100-14-1 4110-35-4 3431-19-9 585-79-5 609-93-8 1817-73-8 2920-38-9 528-29-0

0.45 0.46 0.46 0.47 0.49 0.51 0.51 0.52 0.52 0.53 0.53 0.54 0.56 0.57 0.57 0.59 0.60 0.61 0.66 0.66 0.66 0.67 0.67 0.68 0.68 0.68 0.68 0.69 0.71 0.74 0.75 0.75 0.75 0.76 0.80 0.80 0.82 0.82 0.83 0.84 0.84 0.86 0.86 0.87 0.87 0.87 0.89 0.92 0.93 0.94 0.95 0.98 0.99 1.00 1.04 1.07 1.07 1.08 1.09 1.09 1.10 1.12 1.12 1.13 1.13 1.16 1.16 1.18 1.18 1.22 1.22 1.22 1.23 1.24 1.24 1.25

1.86 1.84 1.79C 1.53C 1.71C 2.00 1.23C 1.60 0.88 0.59C 1.72C 1.37 2.83 1.35C 2.37C 2.91 2.25C 1.47 1.56 2.35C 2.94 1.79 2.16 1.95 2.52 1.85 3.09 2.75C 2.33 2.27C 2.05C 2.19C 2.42C 2.39C 2.74C 1.26C 2.41C 3.05 2.53 2.47 1.79C 2.52 3.22C 1.98 0.92C 1.06C 1.49 1.98C 1.79C 1.89 1.75C 0.96 3.09 3.19 3.48C 3.05 1.29C 1.02 3.15C 0.59C 1.67C 1.43C 1.91 3.03 3.09 3.12 3.25C 1.80C 2.45C 1.06C 2.46C 2.64 2.29C 2.61C 3.46C 1.69

-1.5732 -1.4898 -1.9330 -0.9761 -0.6702 -1.1591 -1.8946 -0.4114 -0.8535 -1.8463 -1.4775 -1.9525 -0.9452 -2.1368 -0.9673 -0.9961 -1.6782 -1.3151 -0.9384 -1.1381 -1.4418 -1.0142 -1.0693 -1.4185 -1.0753 -0.7936 -1.2187 -0.8874 -1.6252 -1.1033 -0.9073 -1.3268 -1.1947 -0.8449 -1.5495 -1.6847 -1.5542 -1.2255 -0.9486 -1.2869 -1.7085 -0.9741 -0.8550 -1.8404 -0.9550 -1.4344 -1.9123 -1.2029 -1.2860 -1.2155 -2.1927 -0.9491 -1.3553 -1.3080 -1.1799 -1.2288 -1.7451 -1.5793 -1.5537 -1.7893 -1.8076 -1.0623 -1.2959 -1.2939 -1.4892 -1.5249 -1.3487 -0.9850 -1.3267 -2.2561 -1.6687 -1.3084 -1.8968 -1.6689 -0.7041 -1.8412

0.3414 0.3094 0.3768 0.2978 0.2951 0.3098 0.3907 0.2845 0.2930 0.3508 0.3587 0.3814 0.2959 0.3304 0.3089 0.2943 0.3704 0.3390 0.3152 0.3054 0.3421 0.3120 0.3049 0.3096 0.3370 0.3036 0.3157 0.2716 0.3058 0.3065 0.3121 0.3310 0.3203 0.2979 0.3393 0.3643 0.3427 0.3135 0.3023 0.3198 0.3630 0.3390 0.2925 0.3497 0.3255 0.3624 0.3520 0.3259 0.3367 0.3374 0.3768 0.2985 0.3518 0.3053 0.3026 0.3524 0.3686 0.3355 0.3378 0.3617 0.3748 0.3056 0.3288 0.3506 0.3332 0.3347 0.3356 0.3281 0.3109 0.3927 0.3782 0.3237 0.3762 0.3792 0.2685 0.3688

Electrophilicity in Ecotoxicity

Chem. Res. Toxicol., Vol. 14, No. 11, 2001 1501

Table 1. (Continued) name

CAS no.

2,4-dichloro-6-nitroaniline 4-chloro-3-nitrophenol 1,4-dinitrobenzene 2-phenylnitrobenzene 2-chloro-6-methoxy-3-nitropyridine 2,6-dibromo-4-nitrophenol 2-nitro-1-naphthol 2,5-dibromonitrobenzene 3,5-dinitrophenol 4-butoxynitrobenzene 2,4,6-trichloronitrobenzene 4-nitrophenol 2,3,5,6-tetrachloronitrobenzene 2,3,4-trichloronitrobenzene 3,4-dinitrotoluene 2,4,5-trichloronitrobenzene 3-phenylnitrobenzene 2-chloro-4-nitrophenol 3-methyl-4-nitrophenol 2,4-dibromo-6-nitroaniline 4-chloro-3-methyl-6-nitrophenol 3-trifluoromethyl-4-nitrophenol 4,5-dichloro-2-nitroaniline 4-chloro-2-nitrophenol 2,4-dinitro-5-fluoroaniline 2,4-dinitrofluorobenzene 4-chloro-3-nitrobenzonitrile 2-methyl-4,6-dinitrophenol 2,4-dichloro-6-nitrophenol 2,3,4,5-tetrachloronitrobenzene 4-tertbutyl-2,6-dinitrophenol 2,6-diiodo-4-nitrophenol 2,3,4,6-tetrafluoronitrobenzene 4-nitroaniline 1,2,3-trifluoro-4-nitrobenzene 4-nitrodiphenylamine 2,4-dinitronaphth-1-ol 4-chloro-1,3-dinitrobenzene 2,6-dichloronitropyrimidine 1,5-difluoro-2,4-dinitrobenzene 4,6-dichloro-5-nitropyrimidine 4-iodo-1,3-dinitrobenzene 2,4,6-trichloro-1,3-dinitrobenzene 1,2-dichloro-4,5-dinitrobenzene 4-bromo-1,3-dinitrobenzene 3,5-dichloro-1,2-dinitrobenzene pentafluoronitrobenzene 1,3-dinitro-2,4,5-trichlorobenzene 2-chloro-3,5-dinitropyridine 4-chloro-3,5-dinitrobenzonitrile 2,3,5,6-tetrachloro-1,4-dinitrobenzene a

log (IGC50-1)

log Pa

ELUMO

Amax

1.26 1.27 1.30 1.30 1.36 1.36 1.36 1.37 1.39 1.42 1.43 1.43 1.47 1.51 1.52 1.53 1.57 1.59 1.60 1.62 1.63 1.65 1.66 1.67 1.69 1.71 1.71 1.73 1.75 1.78 1.80 1.81 1.87 1.88 1.89 1.89 1.89 1.98 2.03 2.08 2.12 2.12 2.19 2.21 2.31 2.42 2.43 2.60 2.64 2.66 2.74

3.33C

-1.2250 -1.3407 -2.2103 -0.6880 -1.4300 -1.4514 -1.2624 -1.2720 -2.0161 -0.9425 -1.3404 -1.0664 -1.4192 -1.4777 -1.7890 -1.5435 -1.0683 -1.2623 -1.0050 -1.2443 -1.1938 -1.5807 -1.2367 -1.2296 -1.7068 -2.1520 -1.7138 -1.8231 -1.4327 -1.6539 -1.8331 -1.4204 -2.0745 -0.7039 -1.8493 -0.9149 -1.9522 -2.0613 -1.7643 -2.3828 -1.7643 -2.0731 -2.0382 -2.2399 -2.0970 -2.0925 -2.3360 -2.1277 -2.4456 -2.3008 -2.2138

0.3326 0.3271 0.3672 0.3066 0.3673 0.3491 0.3235 0.3579 0.3584 0.3021 0.3612 0.3080 0.3743 0.3658 0.3635 0.3633 0.2928 0.3254 0.3039 0.3375 0.3205 0.3458 0.3402 0.3249 0.3732 0.4008 0.3767 0.3760 0.3427 0.3774 0.3712 0.3473 0.3898 0.2961 0.3687 0.3016 0.3894 0.4094 0.4121 0.4243 0.4121 0.4060 0.4321 0.3989 0.4132 0.4044 0.4092 0.4343 0.4565 0.4659 0.4276

2683-43-3 610-78-6 100-25-4 86-00-0 38533-61-8 99-28-5 607-24-9 3460-18-2 586-11-8 7244-78-2 18708-70-8 100-02-7 117-18-0 17700-09-8 610-39-9 89-69-0 2113-58-8 619-08-9 2581-34-2 827-23-6 7147-89-9 88-30-2 6641-64-1 89-64-5 367-81-7 70-34-8 939-80-0 534-52-1 609-89-2 879-39-0 4097-49-8 305-85-1 314-41-0 100-01-6 771-69-7 836-30-6 605-69-6 97-00-7 16013-85-7 327-92-4 4316-93-2 709-49-9 Not known 6306-39-4 584-48-5 28689-08-9 880-78-4 2678-21-9 2578-45-2 1930-72-9 20098-38-8

2.46C 1.47 3.77C 1.74C 3.57 3.03C 3.41C 2.36 3.50 3.69 1.91 4.38 3.61 2.08 3.47 3.87 2.33C 2.47C 3.63C 2.93 2.77C 3.21C 2.47 1.59C 1.47C 1.83C 2.13 3.07C 3.93 3.61C 3.52C 1.86C 1.39 2.01C 3.74 2.96C 2.14C 1.73C 1.31C 0.44C 2.50C 2.97C 2.93C 2.29C 2.85C 2.00C 3.05C 0.84C 1.37C 3.44C

Measured value unless denoted by “C” indicating a calculated value.

Table 2. Relationship between ELUMO and Amax for the Full and Reduced Data Sets eq no.

eq

n

r2

s

F

1 2 3

ELUMO ) -10.6Amax + 2.21 ELUMO ) -10.7Amax + 2.26 ELUMO ) -10.9Amax + 2.35

203 102 51

0.77 0.67 0.56

0.23 0.32 0.40

664 209 64

The aim of this study was to investigate the parametrization of electrophilicity by Amax and ELUMO to ascertain whether one quantifies electrophilicity more favorably. This was achieved by the compilation of aromatic compounds from the TETRATOX database, with either a nitro or a nitrile substituent, for which toxicity data to Tetrahymena pyriformis were available. From this data set, a subset of compounds was chosen so to minimize the collinearity between Amax and ELUMO. A comparison was made of the response-surfaces for toxicity, which were developed for this subset, using log P with either Amax or ELUMO. These response-surfaces for the subset

were compared to those for the larger data set to ensure consistency and validation. Additionally, a consideration of the outliers was undertaken.

Materials and Methods Chemicals. A total of 203 monoaromatic homologues each containing either a nitro or a cyano moiety were compiled from the TETRATOX database and assessed. Caution: The following chemicals are hazardous and should be handled carefully. As reported here, several of these chemicals have significant acute toxicity as well as being potential mutagens and skin sensitizers (1). The chemicals evaluated, toxicity data, and molecular descriptors are given in Table 1. Compounds for toxicity testing were purchased from Aldrich Chemical Co. (Milwaukee, Wisconsin, USA) or MRM Research Chemicals Lancaster Synthesis Inc. (Windham, New Hampshire, USA). Chemicals had a purity of 95% or better and were not repurified prior to use. Stock solutions of each toxicant were prepared in dimethyl sulfoxide.

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Table 3. Response Surfaces Developed for the Full and Reduced Data Sets eq no.

a

eq

4 5 6

log(IGC50-1) log(IGC50-1) log(IGC50-1)

7 8 9

log(IGC50-1) ) 0.37(0.03) log P + 13.1(0.74)Amax - 4.30(0.25) log(IGC50-1) ) 0.30(0.05) log P + 14.4(0.94)Amax - 4.64(0.32) log(IGC50-1) ) 0.21(0.06) log P + 14.5(1.15)Amax - 4.55(0.39)

) log P - 0.94(0.07)ELUMO - 1.27(0.12) ) 0.37(0.06) log P - 0.91(0.09)ELUMO - 1.18(0.18) ) 0.31(0.09) log P - 0.76(0.12)ELUMO - 0.92(0.25) 0.40(0.04)a

n

r2

s

F

203 102 51

0.60 0.56 0.48

0.49 0.53 0.52

151 65 24

203 102 51

0.70 0.75 0.78

0.42 0.40 0.34

237 151 91

The figure supplied in parentheses are the standard deviations of the coefficients. Table 4. Outliers to Equations 4 and 7 and PLS Model in Table 5, and Potential Reasons for These Compounds Being Outliers outliers to eqs 4 and 7 and PLS model in Table 5 4-nitroaniline 2-hydroxy-4-methyl-3-nitropyridine outliers to eq 7 and PLS model in Table 5 3,4-dinitrophenol 3-methyl-4-nitrophenol 4-nitrophenol 4-amino-2-nitrophenol outlier to both eqs 4 and 7 4,6-dichloro-5-nitropyrimidine outliers to eq 4 alone 4-chloro-3,5-dinitrobenzonitrile 2-chloro-3,5-dinitropyridine outliers to eq 7 alone 3-cyano-4,6-dimethyl-2-hydroxypyridine 4-nitrodiphenylamine 4-nitro-1-naphthalamine

Toxicity Testing. Population growth impairment testing using the ubiquitous freshwater ciliate T. pyriformis (strain GLC) was performed following the protocol described by Schultz (6). The endpoint, population density, of this static 40-h assay was measured spectrophotometrically at 540 nm. Test conditions allow for eight to nine cell cycles in control cultures. Each compound was tested in a range finder prior to testing in duplicate for three additional replicates. Two controls, one without test chemical but inoculated with T. pyriformis and one with a blank with neither test chemical nor ciliates, were used to provide a measure of the test acceptability and as a basis for interpretation of treatment data. Each definitive test replicate consisted of six to eight different concentrations with duplicate flasks of each concentration. Only replicates with controlabsorbency values between 0.60 and 0.75 were used in the analyses. The 50% growth inhibition concentration, IGC50, which is taken to be a measure of toxicity, was determined for each toxicant using the Probit Analysis procedure of the Statistical Analysis System (SAS) software (7). All statistical analyses were performed on nominal concentrations, chemical analyses of concentrations were not performed. Absorbencies normalized to controls served as the Y values and toxicant concentration in milligrams per liter (mg/L) served as the X values in probit analyses. Molecular Descriptor Data. Molecular descriptors included log P and the molecular orbital, quantum chemical terms Amax and ELUMO. The log P values were secured as either a measured or a computer-estimated value from the ClogP for Windows software (BIOBYTE Corp., Claremont, CA); the measured value was used in preference to a calculated value. The ELUMO and Amax values were determined using the MOPAC6 program (8). Initially, individual SMILES (9) strings were constructed. These strings were entered into the TSAR molecular spreadsheet (Oxford Molecular Limited, Oxford, U.K.) and the CORINA software was used to convert them to threedimensional structures. Initial charges were assigned using the CHARGE-2 procedure and mechanical optimization was performed using the COSMIC force field (10, 11) default values. The COSMIC force field determines the potential energy of a conformer by accounting for the energy due to bond stretching,

potential reasons for outliers and reference abiotic transformation (16, 17) intramolecular hydrogen bonding, reducing reactivity of nitro group (15) potential for metabolism to redox cycler (19) potential for metabolism to redox cycler (19) abiotic transformation (16, 17) potential for metabolism to redox cycler (19) unique mechanism of action (25) unique mechanism of action (13) unique mechanism of action (15) hydrogen bonding, steric inhibition of cyano group (13, 15) potential for metabolism to redox cycler (19) potential for metabolism to redox cycler (19)

angle, and torsion, as well as van der Waals and electrostatic interactions. Compounds were saved as protein database (.pdb) files and converted into MOPAC internal files via the BABEL shareware file conversion program. Molecular orbital quantum chemical calculations were performed using the MOPAC6 program (8). Each molecule was geometry optimized using the AM1 Hamiltonian in MOPAC6. The following keywords were employed: AM1 ENPART GEO-OK NOXYZ NOINTER LEVEL)-4.574 PRECISE. The energy of the lowest unoccupied molecular orbital (ELUMO) was obtained for each compound. The values of Amax were calculated as described by Schultz (2) following the double-summation technique (12). Briefly, the double summation includes an outer summation over all unoccupied orbitals and an inner summation, which sums the contributions of all atomic orbitals µ belonging to the relevant atom F. The Cµk is the linear combination of atomic orbital (LCAO-MO) wave function coefficient of µ in molecular orbital k at center F. The energy on MOk is ek. Statistical Analyses. Structure-toxicity relationships were generated using log(IGC50-1) in millimolar as the dependent variable and log P and either ELUMO or Amax as the independent variables. Response-surfaces were modeled using least-squares regression (regression procedure of MINITAB version 12.0). The fit of the model was quantified with the coefficient of determination (r2 value). Also noted were the root of the mean square for error (s value), the Fisher statistic (F value), and the probability greater than the F value (Pr > F). Outliers were identified by reference to their residual values, an outlier being defined as being outside the 95% confidence interval of the model. Partial least-squares (PLS) analysis was performed using TSAR 3D for Windows (ver. 3.3) using log(IGC50-1) in millimolar as the dependent variable and the log P, ELUMO, and Amax as the independent variables.

Results The toxicity and selected descriptor values are listed in Table 1. The compounds included in this evaluation were extremely heterogeneous. There is a strong likelihood that this data set contains compounds with a variety

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outliers, and possible reasons for these compounds being outliers, are listed in Table 4. Removal of the outliers and redevelopment of the QSARs results in the following improved equations:

log(IGC50-1) ) 0.44(0.04) log P 0.89(0.07)ELUMO - 1.30(0.11) (10) where n ) 199, r2 ) 0.65, s ) 0.44, F ) 187.

log(IGC50-1) ) 0.37(0.03) log P + 13.5(0.65)Amax - 4.49(0.22) (11) Figure 1. Plot showing the relationship between the two parameters for electrophilicity (ELUMO and Amax). Table 5. Partial Least-Squares Model for the Prediction of the Toxicity of All 203 Compounds dimension dimension dimension 1 2 3 coefficient for log P coefficient for ELUMO coefficient for Amax constant

0.240 -0.550 7.85 -3.03

0.389 -0.414 7.90 -3.17

0.365 -0.012 12.9 -4.26

predictive sum of squares cross validated (leave one out) r2 fraction of variance explained

73.5 0.636 0.643

65.1 0.670 0.678

63.6 0.685 0.694

Table 6. Partial Least-Squares Model for the Prediction of the Toxicity of 197 Compounds (Six Outliers Removed) dimension dimension dimension 1 2 3 coefficient for log P coefficient for ELUMO coefficient for Amax constant

0.243 -0.580 8.19 -3.20

0.392 -0.448 8.23 -3.35

0.369 -0.065 13.0 -4.38

predictive sum of squares cross validated (leave one out) r2 fraction of variance explained

54.4 0.702 0.707

51.6 0.737 0.744

48.8 0.751 0.759

of toxic mechanisms of action. A comparison of the two descriptors for electrophilicity (ELUMO and Amax) for the 203 compounds indicates that they are collinear (r ) 0.87: eq 1 in Table 2). This relationship is visualized in Figure 1. To investigate which of the two descriptors was better for the description of electrophilicity, the data set was artificially manipulated to obtain subsets of compounds with reduced collinearity between Amax and ELUMO. To achieve this, 50 and 25% of the compounds with the greatest absolute residual from eq 1 were selected. It is acknowledged that this is not a conventional use of regression analysis, but has been applied in this context only to select compounds with a low correlation between Amax and ELUMO and not for the purpose of building predictive models. By this method a reduction in collinearity between Amax and ELUMO for these smaller subsets (r ) 0.81 for 50% of the compounds; r ) 0.74 for 25% of the data) was achieved (eqs 2 and 3 in Table 2). Response-surfaces based on hydrophobicity and electrophilicity were developed for the complete set as well as the two reduced subsets of compounds (Table 3). Utilizing ELUMO as the descriptor for electrophilicity yielded eqs 4-6 for n ) 203, 102, and 51, respectively. QSARs with better fits (eqs 7-9) were obtained using Amax as the descriptor for electrophilicity. The QSARs for the complete data set (eqs 4 and 7) were examined further to identify statistical outliers. The

where n ) 194, r2 ) 0.78, s ) 0.35, F ) 344. As a comparison to the development of the responsesurface, or two parameter QSAR technique, partial leastsquares (PLS) analysis was performed on the complete data set utilizing all three parameters. The results are shown in Table 5 and indicate that a model including three dimensions explains approximately 69% of the variance in the data (equivalent to an r2 of 0.69). There are a number of significant outliers to this PLS model with residuals greater than (1.0, these numbered six in all and are listed in Table 4. Recalculation of the model following the removal of the outliers results in approximately 76% of the variance (equivalent to an r2 of 0.76) being explained (see Table 6). The statistical fit of the PLS models is equivalent to that of the QSARs developed using regression analysis.

Discussion A large, chemically heterogeneous data set of compounds with toxicity data to T. pyriformis has been compiled for this study. The toxicity of such compounds has been reported previously to be in excess of nonpolar narcosis (1, 13, 14). Within the data set, a wide variety of toxic mechanisms of action is represented ranging from polar narcosis (e.g., alkylnitrobenzenes) to chemicals capable of acting by electrophilic interactions with biological macromolecules (e.g., multiply halogenated dinitrobenzenes). Examples of a number of mechanisms of toxic action that are present within the data set are listed in Table 7. It is not possible (nor is it the purpose of this study) to assign a definitive mechanism of action to each chemical in the data set. The reason for this is simply that this knowledge is not currently available. It is this lack of knowledge concerning mechanisms of toxic action, and the difficulty in assigning a mechanism for a novel chemical, that makes mechanism of action-based QSAR impractical for prediction of many compounds (1, 3). Thus, there has been considerable interest in the development of QSARs that do not require the a priori assignment of mechanism of action. Despite the lack of knowledge regarding specific mechanisms of toxic action for some compounds, it is recognized that, while it is not easy to quantify, electrophilicity is an important property governing the toxicity of these compounds. The purpose of this investigation was to assess the two descriptors commonly used in toxicological studies to describe molecular electrophilicity. The unique aspects of this study included the assembly of a large data set for compounds with reliable toxicity data, for which electrophilicity is known to be important. This allowed for a comparison of the methods for the description of electrophilicity. Amax and ELUMO have been observed to

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Table 7. Examples of Mechanisms of Toxic Action Present within the Data Set mechanism of toxic action polar narcosis weak acid respiratory uncouplers potential pro-electrophiles (via metabolism of nitro to the nitroso) direct acting electrophiles (aromatic nucleophilic substitution, SNAr) pro-redox cyclers (via oxidization to the quinone) unknown direct acting electrophile

types of compounds

representative examples

ref

alkyl substituted nitro- and cyanobenzenes 3-methylnitrobenzene and 3-tolunitrile 14 dinitro-phenols 2,4-dinitrophenol 23 halogenated mononitrobenzenes 3-chloronitrobenzene 18 dinitrobenzenes with a halogen leaving group nitrobenzenes substituted in the 2- or 4-position by a hydroxy or amino group multiply substituted pyrimidine

be collinear previously, e.g., the coefficient of determination between Amax and ELUMO for a structurally diverse series of 112 pyridines was found to be 0.90 (15). Amax has often been considered to be more successful than ELUMO at describing electrophilicity with regard to the toxicity of aromatic chemicals (2, 15). Equation 1 confirms that for the “complete” data set analyzed in this study, Amax and ELUMO are moderately related. To reduce this association, two reduced data sets were established containing the 50 and 25% “least” related chemicals, in terms of their descriptors for electrophilicity, respectively. It is acknowledged that this is a purely artificial selection procedure merely to reduce the relationship between the descriptors, rather than for the purposes of building QSAR models. Following the selection of the smaller subsets of compounds with reduced collinearity between Amax and ELUMO, an important difference in the statistical quality of the response-surfaces was observed (eqs 5, 6, 8, and 9). Assessment of the quality of these equations suggested Amax models biochemical electrophilicity better than ELUMO. To ascertain that the reduced data sets selected to develop eqs 5, 6, 8, and 9 were representative of the complete data (203 compounds) from which they were selected, the QSAR equations were compared with those for the complete data set (eqs 4 and 7). The QSARs for the reduced series of compounds are essentially identical to those for the complete data set. In other words, a comparison of the slopes and intercepts indicates that they are not significantly different. Reanalysis of the data using partial least-squares (Tables 5 and 6) reveals models utilizing three dimensions with similar levels of statistical fit to the regression based models. The physicochemical meaning of Tables 5 and 6 may also be interpreted in the same manner as for the regression based models. As the collinearity between Amax and ELUMO is decreased, i.e., consideration of the smallest data set as opposed to the complete data set, Amax clearly becomes superior to ELUMO for the modeling of toxicity (eq 9 and 6). In an attempt to determine more regarding what each of the parameters may be describing, the statistical outliers to eqs 4 and 7 were determined (see Table 3). There are fewer outliers to the QSAR for log P and ELUMO (eq 4) than that utilizing Amax (eq 7), due to the poorer statistical quality of the former equation. Potential reasons for these compounds being outliers are noted in Table 4. A general investigation of the outliers indicates a preponderance of 4-subsituted nitrophenols and nitroanilines. Such compounds have been observed to be outliers previously (14, 16-18). There are a number of reasons for this. Some compounds, such as 4-nitrophenol and 4-nitroaniline, show abiotic transformation (16, 17). It is also possible that 4-substituted nitroaromatics may

4-chloro-1,3-dinitrobenzene

24

4-nitrophenol and 2-nitroaniline

19

4,6-dichloro-5-nitropyrimidine

25

be metabolized to quinone-type compounds (19). Furthermore, Schu¨u¨rmann et al. (18) postulated that other metabolic and toxicokinetic factors could result in poor predictions for 4-substituted nitrobenzenes. Interestingly, all six significant outliers to the PLS model (Table 5) are the same as those found for the regression-based models. While both Amax and ELUMO describe electrophilic reactivity, there are differences in their meaning that could account for Amax being a better descriptor in a given toxicological QSAR. The ELUMO is related directly to the electron affinity of a molecule and as such characterizes the susceptibility of the molecule to attack by nucleophiles. Both EHOMO and ELUMO have been reported to be important in radical reactions (20). Superdelocalizabilities provide an index of reactivity of occupied and unoccupied orbitals. Individual superdelocalizabilties are considered to provide a description of the extent of the contribution of an atom to the stabilization energy in the formation of a charge-transfer complex with a second molecule. The acceptor superdelocalizability describes reactions with the electrophilic center (20). The maximum value for this is considered to provide an indication of the relative ability of the center to react as an electrophile. The superiority of Amax as a descriptor in toxicological QSAR may be due to the fact that it represents more accurately the electrophilic nature of particular reactive groups within the molecule. It may achieve this by being a dynamic index better able to describe transition states of reactions (20). With the compounds considered within this study, it is possible that Amax better discriminates highly electrophilic compounds from those of less reactivity than does ELUMO. There is a rich history of the use of superdelocalizabilities in QSAR. Purdy (21) demonstrated their use to describe the toxicity of electrophilic compounds to fish. Inclusion of at least one electronic descriptor in tandem with hydrophobicity was successfully applied to a heterogeneous data set of 114 π-conjugated chemicals (22). Acute toxic effects were explained as a plane in threedimensional space. The plane was described using log P to model penetration and ELUMO or Aave to model interaction. Chemicals that elicit baseline noncovalent toxicity were distributed along the toxicity-log P face. Bioreactive chemicals that reacted covalently were located in the upper part of the plane indicative of the lowest ELUMO or highest Aave values. Chemicals of moderate electrophilicity (noncovalent bioreactivity) were distributed in the middle of the plane. The use of Amax in conjunction with log P was also employed by Karabunarliev et al. (12) in the modeling of benzenes found in the fathead minnow toxicity database whose reactivity was independent of the electrophilicity of a single substituent. This investigation resulted in the model log(LC50-1) ) 0.62(log P) + 9.17(Amax) - 0.22, where n ) 122; r2 ) 0.83, s ) 0.16, F ) 292. In a subsequent effort to model toxic potency

Electrophilicity in Ecotoxicity

with a limited number of variables, toxicity data for 220 substituted benzenes tested in Tetrahymena were evaluated by Schultz (2). The assessed compounds represented a wide range of hydrophobicity and electrophilic reactivity as well as several mechanisms of toxic action. A QSAR relating toxicity with hydrophobicity and electrophilic reactivity quantified by Amax was developed. The model log(IGC50-1) ) 0.50(log P) + 9.85(Amax) - 3.47, where n ) 197; r2 ) 0.82, s ) 0.34, F ) 429, was not unlike that reported in eq 7. In conclusion, a chemically diverse data set has been assembled for which toxicity data to T. pyriformis were available. A selection of 51 and 102 compounds was made in order to obtain a data set with reduced collinearity between Amax and ELUMO. Using the subset of compounds, Amax appears to be a superior descriptor of electrophilicity for the purposes of toxicological QSARs for aromatic molecules. Validation of the smaller subset of compounds by comparison with the larger subset revealed no statistical difference of the QSARs with those developed from the complete data set.

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Chem. Res. Toxicol., Vol. 14, No. 11, 2001 1505 (11) Morley, S. D., Abraham, R. J., Haworth, I. S., Jackson, D. E., Saunders: M. R., and Vinter, J. G. (1991) COSMIC(90): An improved molecular mechanics treatment of hydrocarbons and conjugated systems. J. Comput.-Aided Mol. Des. 5, 475-504. (12) Karabunarliev, S., Mekenyan, O. G., Karcher, W., Russom, C. L., and Bradbury, S. P. (1996) Quantum-chemical descriptors for estimating the acute toxicity of substituted benzenes to the guppy (Poecilia reticulata) and the fathead minnow (Pimephales promelas). Quant. Struct.-Act. Relat. 15, 311-320. (13) Cronin, M. T. D., Bryant, S. E., Dearden, J. C., and Schultz, T. W. (1995) Quantitative structure-activity study of the toxicity of benzonitriles to the ciliate Tetrahymena pyriformis. SAR QSAR Environ. Res. 3, 1-13. (14) Dearden, J. C., Cronin, M. T. D., Lin, D. T., and Schultz, T. W. (1995) QSAR study of the toxicity of nitrobenzenes to Tetrahymena pyriformis. Quant. Struct.-Act. Relat. 14, 427-432. (15) Seward, J. R., Cronin, M. T. D., and Schultz, T. W. (2001) Structure-toxicity analyses of Tetrahymena pyriformis exposed to pyridinessAn examination into extension of surface-response domains. SAR QSAR Environ. Res. 11, 489-512. (16) Schultz, T. W., Lin, D. T., and Arnold, L. M. (1991) QSARs for monosubstituted anilines eliting the polar narcosis mechanism of action. Sci. Total Environ. 109/110, 569-580. (17) Schultz, T. W., Wesley, S. W., and Lin, D. T. (1992) QSARs for monosubstituted phenols and the polar narcosis mechanism of toxicity. Qual. Assur.: Good Pract., Reg., Law 1, 132-143. (18) Schu¨u¨rmann, G., Flemming, B., Dearden, J. C., Cronin, M. T. D., and Schultz, T. W. (1997) CoMFA study of acute toxicity of nitrobenzenes to Tetrahymena pyriformis. In Quantitative Structure-Activity Relationships in Environmental Sciences-VII (Fredenslund, F. C., and Schu¨u¨rmann, G., Eds.) pp 315-327, SETAC Press, Pensacola, FL. (19) Dupuis, G., and Benezra, C. (1982) Allergic Contact Dermatitis to Simple Chemicals, p 183, Marcel Dekker, New York. (20) Karelson, M., Lobanov, V. S., and Katritzky, A. R. (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev. 96, 1027-1043. (21) Purdy, R. (1990) The utility of computed superdelocalizability for predicting the LC50 values of epoxides to the guppy. Sci. Total Environ. 109/110, 553-556. (22) Veith, G. D., and Mekenyan, O. G. (1993) A QSAR approach for estimating the aquatic toxicity of soft electrophiles (QSAR for soft electrophiles). Quant. Struct.-Act. Relat. 12, 349-356. (23) Schultz, T. W., and Cronin, M. T. D. (1997) Quantitative structureactivity relationships for weak acid respiratory uncouplers to Vibrio fisheri. Environ. Toxicol. Chem. 16, 357-360. (24) Roberts, D. W. (1995) Linear free energy relationships for reactions of electrophilic halo- and pseudohalobenzenes, and their application in prediction of skin sensitization potential of SNAr electrophiles. Chem. Res. Toxicol. 8, 545-551. (25) Cronin, M. T. D., Roberts, D. W., Sinks, G. D., and Schultz, T. W. (2001) QSAR analyses of the toxicity of selected nitrobenzenes and halogenated nitrogen heterocyclics to Tetrahymena pyriformis. In Quantitative Structure-Activity Relationships in Environmental Sciences-VIII (Walker, J. D., Ed.) SETAC Press, Pensacola, FL (in press).

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