SECTION 4 QSARs for Environmental Toxicity and Fate © 2004 by CRC Press LLC C HAPTER 12 Development and Evaluation of QSARs for Ecotoxic Endpoints: The Benzene Response-Surface Model for Tetrahymena Toxicity T. Wayne Schultz and Tatiana I. Netzeva CONTENTS I. Introduction II. Background A. Toxicity Data B. Chemical Descriptor Data C. Statistical Methods III. Materials and Methods A. Test Chemicals B. Biological Data C. Molecular Descriptors D. Statistical Analyses E. Data Selection IV. Results A. Initial Benzene Response-Surface Model B. Evaluation of the Benzene Response-Surface Model C. Combined Benzene Response-Surface Model V. Discussion References I. INTRODUCTION As the uses of toxicological-based quantitative structure-activity relationships (QSARs) move into the arenas of priority setting, risk assessment, and chemical classification and labeling the demands for a better understanding of the foundations of these QSARs are increasing. Specifically, issues of quality, transparency, domain identification, and validation have been recognized as topics of particular interest (Schultz and Cronin, 2003). Quality QSAR can only be constructed and validated with quality data, but quality in a QSAR is more than a high coefficient of determination. Transparency has several different meanings as it © 2004 by CRC Press LLC applies to QSARs. First, transparency means that the data, both biological and chemical, that are used in QSAR development and validation are available for examination. Second, models, which are developed with descriptors that quantify the pivotal aspects of toxic expression, are considered to be mechanistic-based, fundamental, and more easily interpreted, and thus transparent. Transpar- ency can also mean the amount of process information obtainable from the statistical methodology; it goes from the black boxes of neural networks to interpretable multiple linear regression. Since the use of a particular QSAR is only valid within its domain (Schultz and Cronin, 2003), identifi- cation of that domain is critical to QSAR acceptability. In this present analysis concerns about quality, transparency, and domain identification are addressed in the validation of a previous developed QSAR. This QSAR examines the prediction of ectotoxic potency for population growth impairment to the aquatic ciliate Tetrahymena pyriformis by substituted benzenes. II. BACKGROUND The basic concept of QSAR as applied to toxicology has been reviewed several times; the most recent efforts include that of Walker and Schultz (2002). There are three elements to a QSAR: the toxicological data, the descriptor data, and the statistical method of linking the two data sets (Schultz et al., 2002). The function of a toxicological QSAR is to predict toxicity accurately. To meet this goal knowledge of the toxicological and chemical information on which the model is based is essential. A number of computer-assisted statistical methods are available for the development of QSAR models. Each method has advantages, disadvantages, and practical constraints. Issues of quality, transparency, and domain may apply to each of the three components of a QSAR and may be multifactoral because of interactions among components. The development of a toxicity-based QSAR is an integrated process requiring a working knowledge in chemistry, toxicology, and statistics. Determining the quality of a QSAR is frequently a difficult task. In part, this is because structure-toxicity relationships are simple approximations of complex processes that are not comprehended well (Nendza and Russom, 1991). Transparency is a critical issue for regulatory acceptance and wider use of QSARs (Blaauboer et al., 1999). It is worth noting that the use of mechanism-based descriptors, while transparent, differs from the QSAR being based on a mechanism of toxic action. The latter is biochemical based, while the former, at least as it related to aquatic toxicity, is physicochemical and quantum chemical based. One approach to developing QSARs has been the use of congeneric series of chemicals. While it is easy in the case of a congeneric series to identify the chemical domain, the congeneric series- derived QSAR is of little predictive value precisely because of the narrow structural domain on which they are based (Kaiser et al., 1999). Even within homologous series, efforts such as selecting derivatives with markedly different substitutents can be made to optimize molecular diversity and thus the domain. A. Toxicity Data Central to the issues of quality, transparency, and domain identification as they relate to toxicological QSAR is biological data. High quality toxicity data on a structurally diverse set of molecules are required to formulate and validate high quality QSARs. Quality toxicity data typically come from standardized assays measured in a consistent manner, with a clear and unambiguous endpoint, and low experimental error. In such cases, quality is associated with values, which are accurate, consistent with other data within the same set, and consistent with data for other similar endpoints. In the case of comparisons between endpoints, it is as important for data to be consistent between endpoints as for the inconsistencies to be consistent. © 2004 by CRC Press LLC The inhibition of growth of the ciliated protozoan T. pyriformis database (Schultz, 1997) is considered to be a high quality data set (Bradbury et al., 2003). It has been developed in a single laboratory over more than two decades. While numerous workers using slight variations in the static protocol and nominal concentrations have generated the data, the data set still remains an excellent primary source of information; it is also unique in terms of its size, molecular diversity, and quality. Moreover, these data have been compiled for the express purpose of QSAR development and validation. All toxicity measurements are subject to experimental error. The reality of toxicity testing is that however standardized the protocol, it is not possible to obtain precise potency data. Therefore, toxicity values are often reported as the mean from a series of replicates. However, different toxicological measurements have different amounts of error associated with them. Toxicity assess- ments made in a single laboratory by a single protocol tend to be the most precise. Even within such testing, there is varying reproducibility between toxicants. In a study of T. pyriformis toxicity data, it was observed that the variability in measured values was greater for chemicals considered to be reactive, than for those thought to act through a narcosis mode of action (Seward et al., 2001). B. Chemical Descriptor Data The primary supposition of any toxicological QSAR is that the potency of a compound is dependent upon its molecular structure, which is typically quantified by chemical properties (Schultz et al., 2002). Chemical descriptors include a variety of types, including atom, substituent, and molecular parameters. The most transparent of these are the molecular-based empirical and quantum chemical descriptors. Empirical descriptors are measured descriptors and include physicochemical properties such as hydrophobicity (Dearden, 1990). Quantum chemical properties are theoretical descriptors and include charge and energy values (Karelson et al., 1996). Physicochemical and quantum chemical descriptors are for the most part easily interpretable with regard to how that property may be related to toxicity. The classic example of this, the partitioning of a toxicant between aqueous and lipid phases, has been used as a measure of hydrophobicity for over a century (Livingstone, 2000). From the perspective of T. pyriformis population growth inhibition, there are limited controlling events (e.g., bio-uptake). One is able to develop probabilistic models where the analysis of single aspects of the system is replaced by the study of time-ensemble averaging of a range of procedures. Such an approach allows one to development a QSAR without regular knowledge of the living system under investigation. Historically in the modeling of T. pyriformis toxicity, this ploy has worked well because it has been possible to identify global actions (e.g., bio-uptake) that appear to be autonomous from specific molecular events. Toxicity is a multivariate process based on events that are not well understood. For the purposes of modeling aquatic toxicity such as fish acute toxicity or Tetrahymena population growth impair- ment, the limited number of controlling aspects means that not every toxicological process must be evaluated, or even understood, in order to get useful QSARs. Experiences (Veith et al., 1983) have pointed toward the use of descriptors, which quantify information on a key process (i.e., bio- uptake). These experiences have shown that combinations of select descriptors can provide infor- mation on an integrated group of toxicological processes (Mekenyan and Veith, 1993). These might include macroscale measurements, such as measures of hydrophobicity and electrophilic reactivity, and microscale measurements of key processes such as steric hindrance (Karabunarliev et al., 1996a). The latter turn out to be especially useful for explaining observed variability in reactive-based ecotoxicity. Like toxicity assessments, descriptor values used in QSARs are also subject to variability. This fact is sometimes unnoticed, especially when values for descriptors are produced by software packages (Benfenati et al., 2001). In a study of the molecular orbital properties of pyridines, Seward © 2004 by CRC Press LLC et al. (2001) demonstrated that a mean of nine values was required to obtain consistent values for the energies of the highest occupied molecular orbital and lowest unoccupied molecular orbital. Moreover, Benfenati et al. (2001) demonstrated variability of up to 23% in conformationally dependent descriptors. C. Statistical Methods Some type of statistical technique is required to link the toxic potencies of the series of chemicals to their molecular descriptors. These techniques range from linear least squares regression analyses, to multivariate techniques including the use of principal component analysis and partial least squares, and to neural networks and genetic algorithms (see Chapter 7 and Livingstone [1995]). These statistical techniques vary in their transparency (i.e., the amount of process information obtainable from the statistical methodology). The automatic self-adapting methodologies of genetic algorithms and neural networks are largely black boxes, whereas multiple linear regression equa- tions are, at least from physicochemical and quantum chemical viewpoints, unambiguous. The best models to predict aquatic toxicity are ones that are simple and interpretable. A regression-based QSAR established with fundamental descriptors maximizes the interpretability of the model, while at the same time maintaining simplicity. Such QSARs are easily updated, capable of mechanistic-based interpretation, portable from one user to another, and allow the user to observe and comprehend how the prediction of toxic potency is made (Schultz and Cronin, 2003). III. MATERIALS AND METHODS A. Test Chemicals More than 400 substituted benzenes representing several mechanisms of toxic action were evaluated. The molecules were obtained commercially (Aldrich Chemical Co., Milwaukee, WI; MTM Research Chemicals or Lancaster Synthesis Inc., Windham, NH). In the large majority of cases purity was greater than 95%. B. Biological Data Population growth impairment testing with the common ciliate, T. pyriformis (strain GL-C), was conducted following the protocol described by Schultz (1997). This 40-h assay is static in design and uses population density quantified spectrophotometrically at 540 nm as its endpoint. The test protocol allows for 8 to 9 cell cycles in controls. Following range finding, each chemical was tested in three replicate tests (or assays). Two controls were used to provide a measure of the acceptability of the test by indicating the suitability of the medium and test conditions as well as a basis for interpreting data from other treatments. The first control had no test substance, but was inoculated with T. pyriformis. The other, a blank, had neither test substance nor inoculum. Each test replicate consisted of six to ten different concentrations of each test material with duplicate flasks of each concentration. Only replicates with control-absorbency values greater than 0.60 but less than 0.90 were used in the analyses. C. Molecular Descriptors Hydrophobicity was quantified by the logarithm of the 1-octanol-water partition coefficient (log K ow ) values. The hydrophobicity values were measured or estimated by the ClogP (ver 3.55) software (BIOBYTE Corp., Claremont, CA, USA). The acceptor superdelocalizabilities were deter- mined as a sum of the ratios between the squared eigenvectors (coefficients) of the i-th atomic © 2004 by CRC Press LLC orbital in the j-th unoccupied molecular orbital and the eigenvalue (energy) of the j-th unoccupied molecular orbital, multiplied by two. The calculations were performed using the Austin Model 1 (AM1) method implemented in MOPAC 93 (Fujitsu Ltd., Windows 95/98/NT/2k adaptation and MO indices by J. Kaneti [1988–1994] MO-QC). The maximum acceptor superdelocalizabilities (A max ) were extracted by in-house macros in Microsoft Word and Excel. D.Statistical Analyses The 50% growth inhibitory concentration (IGC 50 ) was determined for each compound tested by Probit Analysis using the Statistical Analysis System (SAS) software (SAS Institute, 1989). The y-values were absorbencies normalized as percentage of control. The x-values were the toxicant concentrations in mg/L. QSARs were developed using the regression procedures of MINITAB version 13.0 (MINITAB Inc., State College, PA) and Statistical Package for Social Sciences (SPSS version 10.0.5) software (SPSS Inc., Chicago IL, USA). Log (IGC 50 ) –1 values reported as mM were used as the dependent variable. Log K ow and electrophilicity (A max )acted as the independent variables. Resulting models were measured for fit by the coefficient of determination adjusted to the degrees of freedom (R 2 adj). The uncertainty in the model was noted as the square root of the mean square for errors, while the predictivity of the model was noted as the R 2 pred. determined by the leave-one-out method (see Chapter 7). Outliers were identified as compounds with a standardized residual greater than three (Lipnick, 1991). E. Data Selection For structure-toxicity models data were confined to selected domains. Specifically, substructures not included in these evaluations were carboxylic acids, catechols, hydroquinones, and benzoquino- nes. The training set, the response-plane model, consisted of the 215 substituted benzenes for which measured toxic response data (i.e., IGC 50 ) prior to saturation were reported by Schultz (1999). The distribution of the training set chemicals based on their electrophilicity measured as A max is shown in Figure 12.1(a). The validation set was selected from an initial group of 450 candidates limited to commercially available substituted benzenes within the descriptor domain of the training set. Final selection of the validation set was based on attaining a data set that mimicked the A max distribution training set. The distribution of the 177 validation set chemicals based on their A max values is shown in Figure 12.1(b), which compares very favorably with Figure 12.1(a). IV. RESULTS A. Initial Benzene Response-Surface Model Earlier work by Schultz (1999) examined the toxicity (log (IGC 50 ) –1 ) of a heterogeneous series of 218 substituted benzenes (200 benzenes for training and 18 for external validation). Because of the use of a different algorithm for the determination of A max values, previously reported data on benzene toxicity were re-evaluated. The data for toxicity along with hydrophobicity and newly calculated electrophilicity are reported in Table 12.1. Toxicity values varied uniformly over four orders of magnitude (from –1.13 to 2.82 on a log scale). Hydrophobicity varied over about six orders of magnitude (from –0.55 to 5.76 on a log scale). Reactivity measured by A max varied on a linear scale from 0.280 to 0.385. To investigate the influence of the change of the algorithm for A max calculation on the coefficients in the model, only the compounds, considered in Schultz (1999) for training (n = 200) were used in the analysis (see Table 12.1). The compounds, being not toxic at saturation as well as those detected as outliers in Schultz (1999), were excluded prior to the modeling. The resulting equation: © 2004 by CRC Press LLC Figure 12.1 Histogram charts of (a) compounds used for the initial response-surface (Equation 12.3) and (b) for external validation (Equation 12.4). Figure 12.1 (continued). Amax Frequency 40 30 20 10 0 .285 .295 .305 .315 .325 .335 .345 .355 .365 .375 .385 22 32 28 34 23 23 17 13 11 8 3 Amax Frequency 40 30 20 10 0 .285 .295 .305 .315 .325 .335 .345 .355 .365 .375 .385 22 32 28 35 21 23 13 2 © 2004 by CRC Press LLC Table 12.1 Toxicity to T. pyriformis (Log [IGC 50 ] –1 ), Octanol-Water Partition Coefficient (Log K ow ), and Maximum Acceptor Superdelocalizability (A max ) Values for the Compounds Published by Schultz (1999) No. CAS Name Log (IGC 50 ) –1 Log K ow A max 1 71-43-2 Benzene –0.12 2.13 0.280 2 106-42-3 4-xylene 0.25 3.15 0.283 3 120055-09-6 1-Phenyl-2-butanol –0.16 2.02 0.284 4 108-88-3 Toluene 0.25 2.73 0.284 5 104-51-8 n-Butylbenzene 1.25 4.26 0.284 6 538-68-1 n-Amylbenzene 1.79 4.90 0.284 7 100-46-9 Benzylamine –0.24 1.09 0.284 8 98-82-8 Isopropylbenzene 0.69 3.66 0.285 9 2430-16-2 6-Phenyl-1-hexanol 0.87 3.30 0.285 10 10521-91-2 5-Phenyl-1-pentanol 0.42 2.77 0.285 11 103-05-9 E,E-Dimethylbenzenepropanol –0.07 2.42 0.285 12 3360-41-6 4-Phenyl-1-butanol 0.12 2.35 0.285 13 122-97-4 3-Phenyl-1-propanol –0.21 1.88 0.285 14 100-51-6 Benzyl alcohol –0.83 1.05 0.285 15 98-85-1 (Sec)phenethyl alcohol –0.66 1.42 0.285 16 768-59-2 4-Ethylbenzyl alcohol 0.07 2.13 0.285 17 2722-36-3 3-Phenyl-1-butanol 0.01 2.11 0.286 18 22144-60-1 (R+-)-1-Phenyl-1-butanol –0.01 2.47 0.286 19 3597-91-9 4-Biphenylmethanol 0.92 2.99 0.287 20 5707-44-8 4-Ethylbiphenyl 1.97 5.06 0.288 21 92-52-4 Biphenyl 1.05 3.98 0.288 22 1565-75-9 (s)-2-Phenyl-2-butanol 0.06 2.34 0.288 23 5342-87-0 (s)-1,2-Diphenyl-2-propanol 0.80 3.23 0.290 24 29338-49-6 1,1-Diphenyl-2-propanol 0.75 2.93 0.290 25 95-64-7 3,4-Dimethylaniline b –0.16 1.86 0.293 26 1877-77-6 3-Aminobenzyl alcohol b –1.13 –0.55 0.293 27 4344-55-2 4-Butoxyaniline 0.61 2.59 0.293 28 39905-50-5 4-Pentyloxyaniline 0.97 3.12 0.293 29 39905-57-2 4-Hexyloxyaniline 1.38 3.65 0.293 30 106-49-0 4-Methylaniline –0.05 1.39 0.293 31 99-88-7 4-Isopropylaniline b 0.22 2.47 0.293 32 587-02-0 3-Ethylaniline –0.03 1.94 0.294 33 589-16-2 4-Ethylaniline 0.03 1.96 0.294 34 108-44-1 3-Methylaniline –0.28 1.40 0.294 35 104-13-2 4-Butylaniline 1.07 3.18 0.294 36 103-63-9 (2-Bromoethyl)benzene 0.42 3.09 0.294 37 95-53-4 2-Methylaniline –0.16 1.43 0.294 38 24544-04-5 2,6-Diisopropylaniline 0.76 3.18 0.294 39 62-53-3 Aniline –0.23 0.90 0.295 40 578-54-1 2-Ethylaniline –0.22 1.74 0.295 41 579-66-8 2,6-Diethylaniline 0.31 2.87 0.295 42 100-68-5 Thioanisole 0.18 2.74 0.296 43 150-76-5 4-Methoxyphenol –0.14 1.34 0.298 44 527-54-8 3,4,5-Trimethylphenol 0.93 2.87 0.298 45 100-44-7 Benzyl chloride 0.06 2.30 0.298 46 104-93-8 4-Methylanisole 0.25 2.81 0.299 47 697-82-5 2,3,5-Trimethylphenol 0.36 2.92 0.299 48 527-60-6 2,4,6-Trimethylphenol 0.42 2.73 0.299 49 98-54-4 4-(Te r t )butylphenol 0.91 3.31 0.300 50 80-46-6 4-(Te r t )-pentylphenol 1.23 3.83 0.300 51 2416-94-6 2,3,6-Trimethylphenol 0.28 2.67 0.300 52 103-73-1 Phenetole –0.14 2.51 0.300 53 100-66-3 Anisole –0.10 2.11 0.300 54 105-67-9 2,4-Dimethylphenol 0.14 2.35 0.300 55 127-66-2 2-Phenyl-3-butyn-2-ol –0.18 1.88 0.300 56 106-44-5 p-Cresol (4-methylphenol) –0.16 1.97 0.300 © 2004 by CRC Press LLC Table 12.1 (continued) Toxicity to T. pyriformis (Log [IGC 50 ] –1 ), Octanol-Water Partition Coefficient (Log K ow ), and Maximum Acceptor Superdelocalizability (A max ) Values for the Compounds Published by Schultz (1999) No. CAS Name Log (IGC 50 ) –1 Log K ow A max 57 123-07-9 4-Ethylphenol 0.21 2.50 0.300 58 645-56-7 4-Propylphenol 0.64 3.20 0.300 59 620-17-7 3-Ethylphenol 0.29 2.50 0.300 60 104-40-5 Nonylphenol 2.47 5.76 0.300 61 108-39-4 m-Cresol (3-methylphenol) –0.08 1.98 0.300 62 95-48-7 o-Cresol (2-methylphenol) –0.29 1.98 0.301 63 90-00-6 2-Ethylphenol 0.16 2.47 0.301 64 108-95-2 Phenol –0.35 1.50 0.301 65 1745-81-9 2-Allylphenol 0.33 2.55 0.301 66 591-50-4 Iodobenzene 0.36 3.25 0.301 67 106-47-8 4-Chloroaniline 0.05 1.83 0.302 68 529-19-1 2-Tolunitrile –0.24 2.21 0.302 69 501-94-0 4-Hydroxyphenethyl alcohol b –0.83 0.52 0.303 70 615-65-6 2-Chloro-4-methylaniline 0.18 2.41 0.303 71 95-51-2 2-Chloroaniline –0.17 1.88 0.304 72 500-66-3 5-Pentylresorcinol 1.31 3.42 0.305 73 150-19-6 3-Methoxyphenol –0.33 1.58 0.305 74 136-77-6 4-Hexylresorcinol a 1.80 3.45 0.306 75 88-04-0 4-Chloro-3,5-dimethylphenol 1.20 3.48 0.306 76 106-38-7 4-Bromotoluene 0.47 3.50 0.306 77 1585-07-5 1-Bromo-4-ethylbenzene 0.67 4.03 0.306 78 623-12-1 4-Chloroanisole 0.60 2.79 0.307 79 59-50-7 4-Chloro-3-methylphenol 0.80 3.10 0.307 80 108-46-3 1,3-Dihydroxybenzene –0.65 0.80 0.307 81 108-86-1 Bromobenzene 0.08 2.99 0.308 82 106-48-9 4-Chlorophenol 0.54 2.39 0.308 83 540-38-5 4-Iodophenol b 0.85 2.90 0.311 84 156-41-2 2(4-Chlorophenyl)ethylamine b 0.14 2.00 0.311 85 104-86-9 4-Chlorobenzylamine 0.16 1.81 0.311 86 554-00-7 2,4-Dichloroaniline 0.56 2.78 0.311 87 108-90-7 Chlorobenzene a –0.13 2.84 0.311 88 108-42-9 3-Chloroaniline 0.22 1.88 0.312 89 99-51-4 1,2-Dimethyl-4-nitrobenzene 0.59 2.91 0.314 90 5736-91-4 4-(Pentyloxy)benzaldehyde 1.18 3.89 0.315 91 99-99-0 4-Nitrotoluene 0.65 2.37 0.315 92 122-03-2 4-Isopropylbenzaldehyde 0.67 2.92 0.316 93 83-41-0 1,2-Dimethyl-3-nitrobenzene 0.56 2.83 0.316 94 108-43-0 3-Chlorophenol 0.87 2.50 0.317 95 99-08-1 3-Nitrotoluene 0.42 2.45 0.317 96 88-72-2 2-Nitrotoluene 0.26 2.30 0.317 97 106-37-6 1,4-Dibromobenzene a 0.68 3.79 0.317 98 100-52-7 Benzaldehyde –0.20 1.48 0.317 99 121-32-4 3-Ethoxy-4-hydroxybenzaldehyde 0.02 1.58 0.317 100 121-33-5 3-Methoxy-4-hydroxybenzaldehyde b –0.03 1.21 0.318 101 70-70-2 4d-Hydroxypropiophenone b 0.12 2.03 0.318 102 120-83-2 2,4-Dichlorophenol 1.04 3.17 0.318 103 1009-14-9 Valerophenone 0.56 3.17 0.318 104 93-55-0 Propiophenone –0.07 2.19 0.318 105 495-40-9 Butyrophenone 0.21 2.77 0.318 106 90-02-8 2-Hydroxybenzaldehyde 0.42 1.81 0.318 107 1671-75-6 Heptanophenone 1.56 4.23 0.318 108 98-86-2 Acetophenone –0.46 1.63 0.318 109 98-95-3 Nitrobenzene 0.14 1.85 0.318 110 1674-37-9 Octanophenone 1.89 4.75 0.318 111 95-82-9 2,5-Dichloroaniline 0.58 2.75 0.318 © 2004 by CRC Press LLC Table 12.1 (continued) Toxicity to T. pyriformis (Log [IGC 50 ] –1 ), Octanol-Water Partition Coefficient (Log K ow ), and Maximum Acceptor Superdelocalizability (A max ) Values for the Compounds Published by Schultz (1999) No. CAS Name Log (IGC 50 ) –1 Log K ow A max 112 95-75-0 3,4-Dichlorotoluene 1.07 3.95 0.318 113 99-09-2 3-Nitroaniline 0.03 1.43 0.319 114 626-43-7 3,5-Dichloroaniline 0.71 2.90 0.319 115 7530-27-0 4-Bromo-6-chloro-2-cresol b 1.28 3.61 0.319 116 95-50-1 1,2-Dichlorobenzene 0.53 3.38 0.319 117 555-03-3 3-Nitroanisole 0.72 2.17 0.321 118 119-61-9 Benzophenone 0.87 3.18 0.321 119 65262-96-6 3-Chloro-5-methoxyphenol 0.76 2.50 0.322 120 100-14-1 4-Nitrobenzyl chloride b 1.18 2.45 0.323 121 615-58-7 2,4-Dibromophenol b 1.40 3.25 0.323 122 5922-60-1 2-Amino-5-chlorobenzonitrile 0.44 1.79 0.323 123 552-41-0 2-Hydroxy-4-methoxyacetophenone 0.55 1.98 0.324 124 591-35-5 3,5-Dichlorophenol 1.56 3.61 0.325 125 104-88-1 4-Chlorobenzaldehyde 0.40 2.13 0.325 126 134-85-0 4-Chlorobenzophenone 1.50 3.97 0.325 127 108-70-3 1,3,5-Trichlorobenzene a 0.87 4.19 0.325 128 636-30-6 2,4,5-Trichloroaniline 1.30 3.69 0.325 129 90-90-4 4-Bromobenzophenone b 1.26 4.12 0.326 130 120-82-1 1,2,4-Trichlorobenzene 1.08 4.02 0.326 131 88-06-2 2,4,6-Trichlorophenol 1.41 3.69 0.326 132 616-86-4 4-Ethoxy-2-nitroaniline 0.76 2.39 0.326 133 2973-76-4 5-Bromovanillin 0.62 1.92 0.326 134 100-29-8 4-Nitrophenetole 0.83 2.53 0.328 135 89-59-8 4-Chloro-2-nitrotoluene 0.82 3.05 0.328 136 585-79-5 1-Bromo-3-nitrobenzene 1.03 2.64 0.328 137 3217-15-0 4-Bromo-2,6-dichlorophenol b 1.78 3.52 0.329 138 83-42-1 2-Chloro-6-nitrotoluene 0.68 3.09 0.329 139 3481-20-7 2,3,5,6-Tetrachloroaniline 1.76 4.10 0.330 140 619-24-9 3-Nitrobenzonitrile 0.45 1.17 0.330 141 95-95-4 2,4,5-Trichlorophenol 2.10 3.72 0.330 142 95-94-3 1,2,4,5-Tetrachlorobenzene 2.00 4.63 0.331 143 89-62-3 4-Methyl-2-nitroaniline b 0.37 1.82 0.331 144 121-73-3 1-Chloro-3-nitrobenzene 0.73 2.47 0.332 145 88-74-4 2-Nitroaniline 0.08 1.85 0.332 146 634-83-3 2,3,4,5-Tetrachloroaniline 1.96 4.27 0.333 147 118-79-6 2,4,6-Bromophenol 1.91 4.08 0.334 148 7149-70-4 2-Bromo-5-nitrotoluene 1.16 3.25 0.334 149 3819-88-3 1-Fluoro-3-iodo-5-nitrobenzene 1.09 3.15 0.335 150 88-75-5 2-Nitrophenol 0.67 1.77 0.335 151 121-87-9 2-Chloro-4-nitroaniline 0.75 2.05 0.336 152 42454-06-8 5-Hydroxy-2-nitrobenzaldehyde 0.33 1.75 0.336 153 576-55-6 3,4,5,6-Tetrabromo-2-cresol 2.57 4.97 0.336 154 58-90-2 2,3,4,6-Tetrachlorophenol 2.18 3.88 0.337 155 350-46-9 1-Fluoro-4-nitrobenzene 0.10 1.89 0.338 156 771-60-8 Pentafluoroaniline 0.26 1.87 0.338 157 577-19-5 1-Bromo-2-nitrobenzene 0.75 2.51 0.338 158 90-59-5 3,5-Dibromosalicylaldehyde 1.65 3.42 0.338 159 618-62-2 3,5-Dichloronitrobenzene 1.13 3.09 0.339 160 610-78-6 4-Chloro-3-nitrophenol 1.27 2.46 0.339 161 4901-51-3 2,3,4,5-Tetrachlorophenol a 2.72 4.21 0.339 162 2227-79-4 Thiobenzamide 0.09 1.50 0.339 163 100-00-5 1-Chloro-4-nitrobenzene 0.43 2.39 0.340 164 2357-47-3 E,E,E-4-Tetrafluoro-3-toluidine 0.77 2.51 0.341 165 88-73-3 1-Chloro-2-nitrobenzene 0.68 2.52 0.343 166 7147-89-9 4-Chloro-6-nitro-3-cresol b 1.63 2.93 0.343 © 2004 by CRC Press LLC [...]... 52 6-7 5-0 9 5-8 7 -4 49 8-0 0-0 8 8-6 9-7 5322 2-9 2-7 9 5-6 9-2 9 7-5 4- 1 9 0-7 2-2 34 8-5 4- 9 3 54 4- 2 5-0 62 6-0 1-7 43 6 0 -4 7-8 45 6 -4 7-3 223 7-3 0-1 37 1 -4 1-5 61 5 -4 3-0 37 2-1 9-0 157 0-6 4- 5 141 4 3-3 2-9 112 4- 0 4- 5 50 0-9 9-2 141 9 1-9 5-8 237 4- 0 5-2 1898 2-5 4- 2 61 5-7 4- 7 36 7-1 2 -4 10 0-1 0-7 10 6 -4 1-2 9 5-7 9 -4 9 5-7 4- 9 8 7-6 0-5 187 5-8 8-3 87 3-7 6-7 662 7-5 5-0 60 3-7 1 -4 87 3-6 3-2 9 5-5 6-7 44 2 1-0 8-3 61 9-2 5-0 1653 2-7 9-9 87 4- 9 0-8 3 643 6-6 5 -4 13 5-0 2 -4 9 5-8 8-5 ... 8 1-9 0-3 157 0-6 4- 5 651 5-3 6-2 62 0-1 7-7 15 3-1 8 -4 12 3-0 7-9 10 6 -4 4- 5 5 047 1 -4 4- 8 12 1-3 3-5 11 5-8 6-6 655 4- 9 8-9 88 6-6 5-7 5 0-3 5-1 5 8-2 2-0 48 0-1 8-2 50 5 -4 8-6 8 3 -4 6-5 12 2-3 4- 9 13 5-9 8-8 11 7-3 9-5 161 0-1 8-0 5 7-8 3-0 10 8-9 5-2 213 2-7 0-9 7 2 -4 3-5 5 0-2 9-3 7 2-5 5-9 7 2-5 4- 8 342 4- 8 2-6 5 3-1 9-0 721 2 -4 4- 4 10 4- 5 1-8 1023 6 -4 7-2 238 5-8 5-5 5121 8 -4 5-2 7 3-3 1 -4 5 8-8 9-9 9 7-5 4- 1 11 1-2 7-3 11 8-7 4- 1 52 0-3 3-2 11 1-7 1-7 7 6 -4 4- 8 52 9-5 9-9 5 9-3 0-3 ... 8 7-8 6-5 9 9-6 5-0 12 1-1 4- 2 6 64 1-6 4- 1 77 1-6 1-9 60 8-7 1-9 35 0-3 0-1 10 0-2 5 -4 9 9-5 4- 7 8 9-6 1-2 268 3 -4 3 -4 79 54 4- 3 1-3 61 1-0 6-3 320 9-2 2-1 52 8-2 9-0 10 3-7 2-0 8 8-3 0-2 30 5-8 5-1 60 9-8 9-2 1870 8-7 0-8 8 9-6 9-0 1770 0-0 9-3 636 1-2 1-3 65 3-3 7-2 70 9 -4 9-9 11 7-1 8-0 32 9-7 1-5 9 7-0 2-9 87 9-3 9-0 77 1-6 9-7 630 6-3 9 -4 60 6-2 2 -4 53 4- 5 2-1 40 9 7 -4 9-8 58 4- 4 8-5 5 1-2 8-5 2868 9-0 8-9 353 1-1 9-9 181 7-7 3-8 31 4- 4 1-0 57 3-5 6-8 9 7-0 0-7 7 0-3 4- 8 88 0-7 8 -4 ... 10 4- 8 4- 7 58 7-0 3-1 10 4- 5 4- 1 87 7-6 5-6 69 9-0 2-5 61 8-3 6-0 8 9-9 5-2 10 0-8 6-7 58 9-0 8-2 58 2-2 2-9 2213 5 -4 9-5 9 3-5 4- 9 6 0-1 2-8 112 3-8 5-9 61 7-9 4- 7 8910 4- 4 6-1 9 1-0 1-0 62 2-3 2-2 10 8-6 9-0 76 9-9 2-6 9 5-6 8-1 2 04 6-1 8-6 8 8-0 5-1 64 5-5 9-0 3027 3-1 1-1 8 7-5 9-2 14 0-2 9 -4 9 5-7 8-3 182 3-9 1-2 64 3-2 8-7 8 7-6 2-7 10 3-6 9-5 182 1-3 9-2 10 0-6 1-8 119 9 -4 6-8 9 0-0 4- 0 100 8-8 8 -4 5 34 4- 9 0-1 10 1-8 2-6 113 8-5 2-9 565 1-8 8-7 62 2-6 2-8 12 2-9 4- 1 211 6-6 5-6 100 8-8 9-5 ... 180 6-2 6 -4 52 8 -4 8-3 48 5-6 3-2 49 1-8 0-5 265 7-2 5-2 9 7-2 3 -4 61 1-9 9 -4 9 4- 1 8-8 2 042 6-1 2 -4 13 1-5 6-6 651 5-3 7-3 185 2-5 3-5 633 5-8 3-7 55 2-5 9-0 1537 2-3 4- 6 52 9 -4 4- 2 9 2-0 4- 6 5 8-7 2-0 9 4- 4 1-7 78 9-0 2-6 1303 7-8 6-0 52 1-1 8-6 48 5-7 2-3 62 0-9 2-8 9 2-6 9-3 49 1-6 7-8 42 5 0-7 7-5 9 4- 2 6-8 8 0-0 9-1 48 0-1 6-0 12 6-0 0-1 5 6-3 3-7 9 4- 1 3-3 12 0 -4 7-8 14 3-7 4- 8 13 04 9-1 3-3 8 0 -4 6-6 Log RBA –1 .48 –1.50 –1.51 –1.53 –1.55 –1.61 –1.65 –1.65 –1.73 –1. 74. .. 10 3-2 3-1 5 0-0 2-2 5 0-2 2-6 14 0-1 0-3 47 0-8 2-6 48 0 -4 0-0 21 8-0 1-9 5 7-8 8-5 5 7-7 4- 9 15 4- 2 3 -4 156 3-6 6-2 6 3-2 5-2 5 8-0 8-2 8 5-6 8-7 11 7-8 4- 0 246 7-0 2-9 11 7-8 1-7 10 0-5 1-6 191 2-2 4- 9 91 5-6 7-3 30 9-0 0-2 5 2-3 9-1 1597 2-6 0-8 666 5-8 6-7 10 1-8 0 -4 9 4- 2 5-7 1781 7-3 1-1 10 1-7 7-9 10 1-6 1-1 11 8-8 2-1 205 0-6 8-2 547 6 0-7 5-7 3259 8-1 3-3 13 1-5 7-7 9 0 -4 3-7 9 8-0 1-1 9 0-0 0-6 9 5-5 7-8 9 4- 7 5-7 9 3-7 6-5 24 3-1 7 -4 83 5-1 1-0 13 1-5 3-3 Log RBA Inactive... 13 1-5 7-7 270 0-2 2-3 62 0-8 8-2 13 6-3 6-7 145 4 8 -4 5-9 12 1-8 9-1 9 9-6 1-6 45 5 3-0 7-5 9 1-2 3-6 49 2 0-7 7-8 84 4- 5 1-9 61 0-1 5-1 290 5-6 9-3 55 2-8 9-6 11 9-3 3-5 13 1-5 5-5 55 5-1 6-8 70 0-3 8-9 9 0-6 0-8 6921 2-3 1-3 9 9-7 7 -4 87 4- 4 2-0 1360 8-8 7-2 83 5-1 1-0 61 9-5 0-1 297 3-1 9-5 40 2 -4 5-9 529 2 -4 5-5 63 7-5 3-6 60 1-8 9-8 168 9-8 4- 5 44 0-6 0-8 42 08 7-8 0-9 149 3-2 7-2 © 20 04 by CRC Press LLC Name (Trans)methyl cinnamate Ethyl -4 - methoxybenzoate Phenylacetic... 1 24 125 126 127 128 129 130 131 132 133 1 34 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 1 54 155 156 157 158 159 160 161 162 163 1 64 165 166 167 168 10 3-2 6 -4 9 4- 3 0 -4 93 7-3 9-3 10 0-8 3 -4 8 7-6 5-0 249 5-3 7-6 652 1-3 0-8 249 1-3 2-9 12 0-5 1 -4 113 7 -4 2 -4 542 8-5 4- 6 62 1 -4 2-1 303 4- 3 4- 2 8 6-0 0-0 712 0 -4 3-6 55 4- 8 4- 7 62 6-1 9-7 579 8-7 5 -4 8 9-8 4- 9 101 6-7 8-0 1769 6-6 2-7 9 3-9 9-2 13 1-5 7-7 270 0-2 2-3 ... 597 5-7 8-0 10 54 0-2 9-1 8977 8-2 6-7 6511 8-8 1-2 47 9-1 3-0 521 9-1 7-0 1 84 5-1 1-0 91 1 -4 5-5 122 9-2 4- 9 42 42 2-6 8 -4 360 1-9 7-6 252 9-6 4- 8 44 6-7 2-0 65 9-2 2-3 297 1-3 6-0 7593 8-3 4- 0 6 8-2 3-5 539 4- 9 8-9 53 1-9 5-3 63 04 6-0 9-3 2 846 3-0 3-8 57 1-2 0-0 7 7 -4 0-7 6 0-8 2-2 777 3-3 4- 4 96 1-2 9-5 605 2-8 4- 2 5390 5-2 8-5 Table 13.1 (continued) The NCTR Data Set, Containing ER Binding Data (RBA) for 232 Diverse Chemicals Name 16 -OH-16-Methyl-3-methyl-estradiol... 4, 4 -Sulfonyldiphenol Morin Diphenolic acid 1,3-Diphenyltetramethyldisiloxane n-Propyl 4- hydroxybenzoate Ethyl 4- hydroxybenzoate Phenol red 3,3 ,5,5 -Tetrachloro -4 , 4 -biphenyldiol 4- Tert-Amylphenol © 20 04 by CRC Press LLC CAS 510 8-9 4- 1 60 3 -4 5-2 50 0-3 8-9 2515 4- 5 2-3 52 0-3 6-5 52 0-1 8-3 48 6-6 6-8 343 4- 7 9-5 10 4- 4 3-8 515 3-2 5-3 14 0-6 6-9 7 7-0 9-8 14 3-5 0-0 108 5-1 2-7 8 0-0 5-7 48 0 -4 1-1 2803 4- 9 9-3 5 3 -4 5-2 59 9-6 4- 4 . 10 0-0 0-5 1-Chloro -4 - nitrobenzene 0 .43 2.39 0. 340 1 64 235 7 -4 7-3 E,E,E -4 - Tetrafluoro-3-toluidine 0.77 2.51 0. 341 165 8 8-7 3-3 1-Chloro-2-nitrobenzene 0.68 2.52 0. 343 166 7 14 7-8 9-9 4- Chloro-6-nitro-3-cresol . 0.2 84 5 326 1-6 2-9 2-( 4- tolyl)ethylamine –0. 04 1.78 0.2 84 6 10 4- 8 4- 7 4- Methyl benzylamine –0.01 2.81 0.2 84 7 58 7-0 3-1 3-Methylbenzyl alcohol –0. 24 1.60 0.285 8 10 4- 5 4- 1 3-Phenyl-2-propen-1-ol. 4. 08 0.3 34 148 7 14 9-7 0 -4 2-Bromo-5-nitrotoluene 1.16 3.25 0.3 34 149 381 9-8 8-3 1-Fluoro-3-iodo-5-nitrobenzene 1.09 3.15 0.335 150 8 8-7 5-5 2-Nitrophenol 0.67 1.77 0.335 151 12 1-8 7-9 2-Chloro -4 - nitroaniline