Correlations of Nitrenium Ion Selectivities with Quantitative

Chem. Res. Toxicol. 2002, 15, 1495-1503

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Articles Correlations of Nitrenium Ion Selectivities with Quantitative Mutagenicity and Carcinogenicity of the Corresponding Amines Michael Novak* and Sridharan Rajagopal Department of Chemistry, Miami University, Oxford, Ohio 45056 Received July 24, 2002

There is a correlation (radj2 ) 0.5491-0.6338) of quantitative mutagenicity, log m, for a series of heterocyclic (HCAs) and carbocyclic (AAs) aromatic amines in Salmonella typhimurium TA 98 (18 amines) and TA 100 (15 amines) vs log S, the log of the azide/solvent selectivity of the corresponding nitrenium ion. Monocyclic aromatic amines, MAAs, are less mutagenic than other amines of similar log S. Multiple variable linear regression analysis led to a two parameter regression model, significant at the 95% confidence level for both variables, that includes log S and a ring index variable, Irings, that is 0 for MAAs and 1 for all other amines. These models have radj2 ) 0.8448 for TA 98, and 0.8927 for TA 100. Inclusion of a third variable, Clog P, increases radj2 to 0.8913 for TA 98 and 0.9011 for TA 100. This model is significant at the 95% confidence level for all variables for TA 98, but only for two of the three variables for TA 100. The confidence level is 80% for Clog P in TA 100. Quantitative carcinogenicity data in mice and rats are more weakly correlated with log S (radj2 ) 0.5357 for 12 amines in mice, radj2 ) 0.4216 for 10 amines in rats). Several two parameter regression models, all containing Clog P and one containing log S, adequately correlate the mouse data.

Introduction Over the past decade considerable effort has been expended on the development of QSARs1 that correlate quantitative bacterial mutagenicity data and quantitative carcinogenicity data for aromatic amines (AAs) and heterocyclic amines (HCAs) with calculated or observed properties of the amines, 1, or the nitrenium ions, 2, * To whom correspondence should be addressed. Phone: (513) 5292813. Fax: (513) 529-5715. E-mail: [email protected]. 1 Abbreviations: QSAR, quantitative structure-activity relationship; AA, aromatic amine; HCA, heterocyclic amine; MAA, monocyclic aromatic amine; PAA, polycyclic aromatic amine; IQ, heterocyclic amine containing an imidazoquinoline or imidazoquinoxaline ring system; non-IQ, heterocyclic amine that does not contain an imidazoquinoline or imidazoquinoxaline ring system; log m98 and log m100, log of the mutagenicity ([histidine revertants]/[nanomole of amine]) for Salmonella typhimurium, TA98 and TA100, respectively; -log TD50, negative log of the dose (mmol/kg/day) required to halve the probability of an animal remaining tumorless to the end of its standard life span; Eπ, the Huckel π-orbital energy; HOMO, highest occupied molecular orbital; LUMO, lowest unoccupied molecular orbital; EHOMO, energy of the HOMO; ELUMO energy of the LUMO; AM1, Austin Method 1 semiempirical molecular orbital procedure; RHF, restricted HarteeFock ab initio molecular orbital procedure; PCSpartan, software for molecular orbital calculations; PCB, polychlorinated biphenyl; log P, experimental log of the octanol/water partition coefficient; Clog P, calculated log P obtained from Clog P program; log S log of the azide/ solvent product ratio extrapolated to 1 M N3-; Nπ, number of π-electrons; Nrings, number of rings; Irings, 0 for MAAs, 1 for all other mutagenic amines; P, probability that a regression coefficient is 0; pcorr, partial correlation coefficient; std err, estimated standard error of the regression coefficient; radj2, coefficient of determination adjusted for the degrees of freedom; Fmodel, ratio of the variance estimate due to the model to the residual variance estimate; Pmodel, probability that all regression coefficients are 0; RMSE, square root of the mean square error.

derived from hydrolysis of the ester derivatives of their hydroxylamine metabolites (Scheme 1) (1-13). Since the metabolic and chemical pathways described in Scheme 1 are now well documented for both AAs and HCAs (1418), this research has focused in recent years on the interpretation of the QSAR results in terms of the processes depicted in Scheme 1. This work has recently been extensively reviewed (1-3). The mutagenicity correlations most often employ as the dependent variable log [(histidine revertants)/(nanomoles of amine)], log m, for two Salmonella typhimurium strains, TA 98 and TA 100, that are commonly used in Ames tests (4-9, 19). These amines are not mutagenic to Salmonella unless activated by mammalian liver homogenates, so mutagenicity data are taken under conditions in which the “S-9” fraction of rat liver homogenates, induced by Arachlor 1254 or other PCB preparations, are added to the Petri plates (14, 15, 19). A number of multiple variable linear regression models encompassing between three and six independent descriptor variables have been reported to correlate data sets (ca. 50-150 amines) containing both AAs and HCAs (4-9). These models generally achieve good correlation (r2 ≈ 0.8), but physical interpretations are not always straightforward. All of these models contain a descriptor related to molecular size such as the log of the octanol/water partition coefficient (log P), the number of rings, molecular volume, the Huckel π-orbital energy of the molecule, Eπ, or the number of π-electrons as the most significant

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Scheme 1

independent variable in the regression analysis (4-9). These variables correlate with each other (r2 ≈ 0.4-0.9), and log m typically correlates positively with each of these variables with r2 ≈ 0.4-0.6. The positive correlations have been suggested to be due to a size or lipophilicity dependence of transport phenomena, to a similar dependence of rates of P-450 and acyl- or sulfotransferase catalyzed metabolism of the mutagen into its ultimate reactive form, to a size or lipophilicity dependence of the specific genetic effects caused by the mutagen-DNA adduct, and to size or lipophilicity dependent repair rates of the DNA-mutagen lesions, as well as to a size dependent component of amine or nitrenium ion reactivity (1, 2, 4, 8). Several models show a positive correlation with HOMO energies of the amines and a negative correlation with LUMO energies of the amines (4, 6-8). The positive correlation with HOMO energies is thought to be due to the likely dependence of oxidative metabolism rates on HOMO energies, but the LUMO correlation does not appear to have a simple explanation (4, 8). Although these correlations are statistically significant, they account for far less of the variance in mutagenicities than does the variable related to molecular size (4, 6-8). Since the nitrenium ions 2 have been implicated as the active electrophile in the reactions with DNA bases, it is reasonable to expect that log m may correlate with properties of these ions. Ford and co-workers calculated ∆H of eq 1 by the semiempirical AM1 method for a series of AAs and HCAs, and observed a negative correlation of log m with ∆H (10, 11). A regression based on Ford’s data for 13 HCAs and 10 AAs for which TA 98 mutagenicity and ∆H are

ArNH2 + PhNH+ f ArNH+ + PhNH2

(1)

available provides r2 ) 0.242, so the correlation is weak. Better correlations were observed if the HCAs and AAs were treated separately, and data for which there was less confidence in log m were excluded (10, 11). The negative correlation shows that greater mutagenicity is associated with more stable nitrenium ions as defined

by eq 1. Sabbioni and Wild showed a positive correlation (r2 ) 0.748) of log m for a series of 19 aryl azides photolyzed in the presence of TA 98 with the AM1 LUMO energies of the corresponding nitrenium ions, the apparent reactive intermediates involved in the mutagenesis (12). Since LUMO energies of the nitrenium ions are strongly negatively correlated with ∆H of eq 1, this result is consistent with Ford’s observations (8, 12). These results suggest that, to the extent that ∆H of eq 1 can be related to the rates of ionization of the hydroxylamine esters, mutagenicity correlates positively with the rate of nitrenium ion formation from their ester precursors (10). An alternative explanation of the correlation provided by ∆H of eq 1 is that these enthalpy data indirectly measure the lifetimes and, therefore, the selectivities of the nitrenium ions (7, 8, 20). According to this explanation, the more stable and, therefore, longer lived the ion, the greater its mutagenic potential. It was not known if ∆H of eq 1 is a good predictor of nitrenium ion lifetimes, so this conclusion was tentative at best. Results from multiple variable models that include variables related to nitrenium ion stability (∆H of eq 1 or LUMO energies of the nitrenium ions) indicate that these variables are of only limited use in these regression models (6-8). The importance of nitrenium ion stability in determining the potency of amine mutagens in Salmonella has been questioned (8). Quantitative carcinogenicity data are generally expressed as TD50 (mmol/kg/day), the daily dose per kilogram of body weight required to halve the probability of an animal remaining tumorless to the end of its standard life span (1). Such data tend to correlate weakly with mutagenicity data. For example, -log TD50 for rats correlates with log m for Salmonella typhimurium TA 98 for a series of 8 HCAs and 24 AAs with r2 ) 0.441 and a slope of 0.26 (13). Since the carcinogenicity experiments are far more costly and time consuming to perform than mutagenicity experiments, TD50 data for laboratory animals such as rats and mice are not as readily available as mutagenicity data. For this reason, QSAR studies of carcinogenicity are considerably less abundant than similar studies for mutagenicity. We are aware of only two QSAR studies of the carcinogenicity of AAs and HCAs employing TD50 data (1, 13). No attempts to correlate amine carcinogenicity with nitrenium ion properties have been reported. Our interest in this area stems from our studies of the aqueous solution chemistry of nitrenium ions derived from mutagenic and carcinogenic AAs and HCAs. Our work allows us to indirectly measure the aqueous solution lifetime of a nitrenium ion from product studies that give the ratio of the second-order rate constant for trapping of the ion by N3- and the first-order rate constant for trapping of the ion by solvent water, kaz/ks (20, 21). We previously showed a positive linear correlation of log m for TA 98 and TA 100 with log(kaz/ks) or its functional equivalent, log S, the logarithm of the N3-/solvent product ratio extrapolated to 1 M N3-, for a limited series of five polycyclic AAs (20). Since kaz was diffusion-limited for all the nitrenium ions in the study, the correlation suggested that mutagenicity increased for those ions with longer aqueous solution lifetimes, 1/ks (20). Unfortunately, the small sample size made it difficult to make definitive conclusions. We now have selectivity data gathered in our lab, and by McClelland and co-workers,

Nitrenium Ion Selectivities

that allow us to extend this correlation and examine more closely the relationship of nitrenium ion stability to mutagenicity and carcinogenicity for a series of 18 AAs and HCAs.

Chem. Res. Toxicol., Vol. 15, No. 12, 2002 1497 Scheme 2

Materials and Methods Mutagenicity data (log m) for the parent amines, 1, activated with the induced S-9 fraction of mammalian liver homogenates in Salmonella typhimurium TA 98 and TA 100 were collected from the literature (4-8, 10, 22-28). We used average values calculated by Ford and Herman from data gathered by multiple laboratories for several of the AAs (1b, f, k, o) (10). The data for the HCAs (1g, i, n, p, q, r) are due to Sugimura (22). Most of the rest of the data are from the compilations of Hatch and co-workers or Debnath and co-workers (4-8). Average values were used where more than one determination is available. The data for the 8 amines for which we previously published a preliminary correlation study (1a, b, f, h, j, k, m, o) are identical to those used in that study (20). Azide/solvent selectivities [log(kaz/ks) or log S] for 2 were obtained by direct measurements on ions generated by laser flash photolysis or by competition methods described in the literature (20, 21, 29-39). Average values were calculated for those ions for which multiple measurements by different methods are available. For some ions (2b, f, n, p, q, r) data are available only for the ArNAc+ analogues. Minor corrections (ca. 0.2 to 0.45 log units) were applied to these values to provide estimated values for the corresponding ArNH+. These corrections are based on observed differences for paired ArNAc+ and ArNH+ with similar overall azide/solvent selectivities to these six ions (40). Values of ∆H for eq 1 at the RHF/6-31G* level are available for most of the amines and their corresponding nitrenium ions (8, 41). Those that could not be obtained in this fashion (2e, h, j, m) were calculated at the RHF/6-31G* level using PCSpartan. Details of the calculation methodolgy have been described (41, 42). Values of Clog P are available in the literature (4), or were provided by Dr. Albert Leo of BioByte Corp. from the ClogP 4.0 software. Values of -log TD50 for 12 of the amines in mice and 10 of the amines in rats are available in the literature (43). Wherever possible, harmonic means of data gathered from several different experiments were employed (43). Regression analyses were performed with Small STATA version 7.0. In addition to the regression coefficients for each variable, the following statistical parameters are reported: P, the probability that an individual regression coefficient is zero; pcorr, the partial correlation coefficients for each variable; std err, the estimated standard error of the regression coefficient; radj2, the coefficient of determination adjusted for the degrees of freedom; Fmodel, the ratio of the variance estimate due to the model to the residual variance estimate; Pmodel, the probability derived from Fmodel that all regression coefficients are zero; and RMSE, the square root of the mean square error of the regression model.

Results and Discussion Mutagenicity data for 18 amines, 1, listed in Scheme 2, in Salmonella typhimurium TA 98, log m98, are provided in Table 1 with their literature sources. The logarithms of the azide/solvent product ratio at 1 M N3-, log S, are also provided for the corresponding nitrenium ions, 2. For log S g 1, this is equivalent to log(kaz/ks), but for less selective ions the azide/solvent product ratio contains contributions from ion pair trapping and preassociation processes (32, 40). The differences in the two ratios are not large until log S approaches 0 (32, 40). Calculated values for ∆H of eq 1 at the RHF/6-31G* level are provided for all cases, as is Nπ, the number of

π-electrons in 1 or 2, and Nrings, the number of rings in each structure, a variable that ranges from 1 to 3 for this data set. These latter two variables have been shown to correlate strongly with log m98 for a larger series of 80 AAs and HCAs (8). Values of Clog P are provided for each amine. These correlate well with observed log P for amines (r2 ) 0.95 for 67 amines that include 10 used in this study) (4). They are used here because log P is not available in the literature for all 18 amines. Additionally, log m100, the mutagenicity of the amines to Salmonella typimurium TA 100, is provided for 15 of the amines for which literature data were found. Each amine is also classified into one of four structural groups: monocyclic aromatic amines (MAAs), polycyclic aromatic amines (PAAs), IQ type heterocyclic amines (IQs), and non-IQ type heterocyclic amines (non-IQs). The heterocyclic amines are classified IQ or non-IQ based on the presence or absence of an imidazoquinoline or imidazoquinoxaline ring system (22). The data collected for the 18 amines in Table 1 show that the chemical selectivity of the nitrenium ions vary over 7 orders of magnitude, while log m98 varies over almost 8 orders of magnitude and log m100 varies over more than 6 orders of magnitude. There appears to be sufficient range in the magnitude of the data to detect correlations among the variables. The correlation matrices for log m98 or log m100, the five variables described above, and the variable Irings that is defined as 0 for MAAs and 1 for all other structural types, are provided in Table 2. As expected, log m for both strains correlates strongly with Nπ and Nrings, but weakly with ∆H of eq 1 (8). In fact, log m98 correlates rather better with those variables for these 18 amines than for the larger set of 80 amines used by Hatch and co-workers (r ) 0.7492 for Nπ, 0.7870 for Nrings, and -0.3825 for ∆H) (8). There is essentially no observable correlation of log m for either strain with Clog P. In studies confined

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Table 1. Mutagenicity Data, log S, ∆H, and Other Variables Used in the Correlations amine or ion a b c d e f g h i j k l m n o p q r

log Sa 0.0229 0.0220 0.3729 0.4729 0.4930 0.6320 1.0031 1.3832 3.0833 3.1034 3.4821,35,36 3.4837,38 3.6837,38 5.0139 5.0736 5.3839 6.6639 7.0139

log m98b

log m100c

∆Hd

Nπ

Nrings

Clog Pf

structural type

-3.397,25

-2.7024

-0.6510 -1.807 -2.024,7,26 -2.237,27 0.1410 0.8522 -2.504,28 1.7422 0.4623 0.7210 -2.317,27 -2.304,26 4.1722 1.7510 4.4922 2.5422 3.9922

-0.3410

0 -17.841 -7.98 -10.68 -8.0e -15.28 1.68 2.7e -11.88 -26.4e -20.08 -25.78 -27.2e -22.98 -22.441 -25.88 -25.28 -26.58

8 12 8 8 8 12 14 8 16 16 14 10 10 16 14 16 16 16

1 2 1 1 1 2 2 1 3 2 2 1 1 3 3 3 3 3

0.92 2.09 1.41 1.91 1.41 2.09 2.21 1.93 2.23 3.61 2.80 1.02 1.55 0.57 2.70 1.07 1.78 2.28

MAA PAA MAA MAA MAA PAA non-IQ MAA non-IQ PAA PAA MAA MAA IQ PAA IQ non-IQ non-IQ

-1.434,26 0.8310 0.6022 -1.504,28 0.6022 1.1023 1.2310 -0.604,26 2.4722 1.4410 3.4722 2.3422 2.8022

a log of the azide/solvent product ratio at 1 M N -. Listed with literature source. b log of histidine revertants/nanomole of amine for 3 Salmonella typhimurium TA 98. Listed with literature source. c log of histidine revertants/nanomole of amine for Salmonella typhimurium d TA 100. Listed with literature source. ∆H of eq 1 in kcal/mol. Listed with literature source. e This work. f Source: ref 4, or Dr. Albert Leo.

Table 2. Correlation Matrices for log m98, log m100, and Regression Variables n ) 18

log m98

log S

Nπ

Nrings

∆H

Irings

Clog P

log m98 log S Nπ Nrings ∆H Irings Clog P

1.0000 0.7587 0.9124 0.9484 -0.5177 0.8367 0.1144

1.0000 0.7288 0.7228 -0.7431 0.4847 0.0556

1.0000 0.9246 -0.5701 0.9141 0.3462

1.0000 -0.4820 0.8884 0.2106

1.0000 -0.4050 -0.1209

1.0000 0.4543

1.0000

n ) 15

log m100

log S

Nπ

Nrings

∆H

Irings

Clog P

log m100 log S Nπ Nrings ∆H Irings Clog P

1.0000 0.8124 0.9016 0.8824 -0.6921 0.8029 0.0629

1.0000 0.7100 0.7333 -0.7245 0.4369 -0.0151

1.0000 0.9080 -0.5997 0.8921 0.2247

1.0000 -0.5343 0.8484 0.0396

1.0000 -0.4522 -0.1392

1.0000 0.3295

1.0000

primarily to AAs, a strong correlation of log m98 and log m100 with log P has been observed (4). In data sets consisting of more diverse structures, including IQs and non-IQs, weak or negligible correlations with log P are observed (8). The correlation of log m with log S is weaker than for Nπ and Nrings, but stronger than for ∆H for both TA 98 and TA 100. Variables similar to Irings have been utilized in other regression studies of amine mutagenicity (4-8). The use of Irings in this study is described below. A closer examination of the correlation of log m with log S provided in Figure 1 shows that for both TA 98 and TA 100 the mutagenicity of MAAs is consistently about 2 orders of magnitude less than that for other amines with equivalent log S. The IQs may also be somewhat more mutagenic than PAAs or non-IQs with equivalent log S, but the number of observations is small. Linear regression of log m vs log S for both strains shows that r2 adjusted for the degrees of freedom, radj2, improves if the MAAs are excluded (for TA 98 radj2 increases from 0.5491 to 0.6510, and for TA 100 from 0.6338 to 0.7135). The slopes of the correlation lines decrease from 0.82 ( 0.18 to 0.61 ( 0.14 for TA 98 and from 0.59 ( 0.12 to 0.41 ( 0.08 for TA 100, if the MAAs are excluded. These trends were noted in our previous study (20). Similar observations have been made by others who have incorporated the number of rings as an independent variable

into amine mutagenicity regression analyses (4-8). It has been suggested that the number of rings reflects the delocalization of π-electrons in the nitrenium ion and, therefore, is related to nitrenium ion stability (5-8). log S is a direct experimental measure of nitrenium ion stability, so the residual effect of ring size evident in Figure 1 must be primarily due to some other factors. These factors may include size dependence of intercalation into the DNA helix, size-dependent repair rates, and size-dependent mutation rates for transcription of the DNA adducts (4-8, 20). The correlation of log m98 and log m100 with log S is superior to that with ∆H of eq 1 (radj2 ) 0.2223 for log m98, and 0.4390 for log m100). This variable measures the thermodynamic stability of the nitrenium ion with respect to the parent amine (eq 1), but that is not what determines the kinetic lability of the ion in aqueous solution. In fact, the aqueous solution stability of the ion is related to the thermodynamics of cation hydration as shown in eq 2. We have previously shown that log S has a strong linear correlation (radj2 ) 0.8797) with ∆H of eq 2 calculated at the RHF/6-31G*//3-21G level of theory for 18 structurally diverse nitrenium ions derived from AAs (42). log S and ∆H of eq 1 are also correlated (Table 2), but radj2 is much smaller (0.5423 for all 18 amines). A plot of ∆H of eq 1 vs log S (not shown) exhibits a decidedly


Figure 1. log m for ArNH2 (1) expressed as log(revertants/ nmol) vs log S for ArNH+ (2) for S. typhimurium TA 98 (A) and TA 100 (B): IQ type HCAs ([, magenta), non-IQ type HCAs (1, blue), PAAs (2, green), MAAs (b, red). The solid lines are the least-squares regression lines excluding the MAAs. Dashed lines are the least-squares regression lines for the entire data set.

nonrandom distribution of points around the correlation line. For log S e 2.0, the residuals for the data points are much greater, so that ∆H of eq 1 is not strongly related to kinetic stability for the less stable ions. Since log S is tedious to determine experimentally, it may be that ∆H of eq 2 calculated by ab initio or semiempirical methods can be profitably used in regression analyses of larger data sets. One drawback to this approach is the necessity of knowing the identity of the major initial product of attack of water on the nitrenium ion. This is usually, but not always, predictable on the basis of nitrenium ion structure (40, 42).

Chem. Res. Toxicol., Vol. 15, No. 12, 2002 1499

The data shown in Table 1 were used to develop multiple variable linear regression models for mutagenicity. Table 2 shows that there is a strong correlation among the variables Nπ, Nrings, and Irings (|r| g 0.8). They are not independent variables and would give unreliable coefficients if used together in multiple regression models (8, 44). Hatch and co-workers have recently shown that Nπ and Nrings correlate strongly (|r| g 0.8) with molecular volume, EB, and ELUMO, the LUMO energy of the amines, for a larger data set of 80 amines containing 12 of the amines in Table 1 (8). For this reason it was decided not to examine these commonly used regression variables in this study. EHOMO, the HOMO energy of the amines, was not used because it correlates poorly with log m in studies that contain a wide variety of structural types (7, 8). Finally, another commonly used variable, the LUMO energy of the nitrenium ion, was excluded because of its strong correlation with ∆H of eq 1 (8, 12). Multiple regression models of mutagenicity were examined for pairs of the six variables with |r| e 0.8. Only one model, log S and Irings, met the criteria of (a) radj2 g 0.8, (b) radj2 greater than that for the individual variables, and (c) nonzero regression coefficients for each variable at the 95% confidence level for both TA 98 and TA 100. The regression equations for both log m98 and log m100 are provided in Table 3 along with relevant statistical data. For comparison, the best one variable regression for TA 98 has radj2 ) 0.8932 (Nrings), and for TA 100 has radj2 ) 0.7985 (Nπ). Nrings in TA 98 will admit no other variable into a statistically significant multiple regression at the 95% confidence level, but Nπ in TA 100 is involved in a statistically significant regression in combination with log S (see Table 3). Irings was included in the multiple regressions because of our observations above concerning the noticeably smaller log m values for MAAs. Although the reasons for this observation are not well understood, taking this into account in a regression model including log S accounts for 84.5% (log m98) to 89.3% (log m100) of the variation in log m for these mutagenicity data sets. Figure 2, the square roots of the mean square errors (RMSE) in Table 3, and residual plots shown in the Supporting Information indicate that for both strains the model predicts log m typically within (1 log unit, and the residuals are about 55-60% of those of a single variable regression with log S. The partial correlation coefficients, pcorr, that attempt to measure the correlation of log m with each variable after taking into account the effect of the others, suggest that log S and Irings are roughly of equal importance in determining the overall model. There were three other two variable regression models that met the fitting criteria for one of the two Salmonella strains. These are also listed in Table 3. These models have radj2 comparable to the log S and Irings model. Two of these models, Nπ and Clog P for TA 98, and Nrings and ∆H for TA 100, share features with models recently developed by Hatch and co-workers, including the negative coefficient for Clog P in the TA 98 model and the dominance of the regression variable that is more closely related to molecular size (8). Models containing three regression variables were also examined. Only one of these models met the fitting criteria in TA 98. This model is also listed in Table 3. It includes the variables log S, Irings, and Clog P. No threevariable model met all the fitting criteria for TA 100. The one that came closest is the same one that did meet these criteria for TA 98. One regression coefficient, for Clog P,

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Table 3. Regression Models for Mutagenicity TA 98 +3.0773 × Irings 0.000 0.8230 0.5485

log m ) P pcorr std err radj2 Fmodel Pmodel RMSE

0.4963 × log S 0.001 0.7372 0.1175 0.8448 47.27 0.0000 0.9922


0.4384 × log S 0.001 0.7588 0.1006 0.8913 47.47 0.0000 0.8303

+3.7788 × Irings 0.000 0.8868 0.5263

-0.8390 × Clog P 0.016 -0.5886 0.3080


0.7393 × Nπ 0.000 0.9365 0.0715 0.8624 54.26 0.0000 0.9343

-0.7674 × Clog P 0.031 -0.5245 0.3217

-7.4832


0.4151 × log S 0.000 0.8608 0.0708 0.8927 59.21 0.0000 0.5718


0.3967 × log S 0.000 0.8655 0.0692 0.9011 43.52 0.0000 0.5488

+2.3151 × Irings 0.000 0.8762 0.3840

-0.2932 × Clog P 0.182 -0.3943 0.2060


0.2528 × log S 0.035 0.5654 0.1064 0.8515 41.13 0.0000 0.6726

+0.3577 × Nπ 0.001 0.7910 0.0799

-4.7619


-0.0506 × ∆H 0.039 -0.5548 0.0219 0.8212 33.15 0.0000 0.7379

+1.5014 × Nrings 0.000 0.8401 0.2798

-3.3498

TA 100 +2.1109 × Irings 0.000 0.8541 0.3711

is significant at the 80% confidence level. The regression equation for this model is included in Table 3. Figure 2, the RMSE values listed in Table 3, and the residual plots shown in the Supporting Information show that the three variable model predicts log m better than the two variable model for both strains, but the effect is more pronounced for TA 98. An examination of Figures 1 and 2 demonstrates the effects of Irings and Clog P on the fit of the data. The major effect of inclusion of Irings is to bring the data for MAAs into line with the rest of the amines. Inclusion of Clog P into the models for both strains has little effect on the regression coefficients for log S or Irings (Table 3). The coefficients remain well within the 95%

-3.0655

-1.7671

-2.1335

-1.6454

confidence limit for both variables calculated in the absence of the third. The major effect on the fit of inclusion of Clog P is to decrease the residuals for the IQs. The points for the PAAs also come somewhat closer to the observed values, while the residuals for the MAAs tend to increase somewhat. It is not clear if there is a mechanistic reason for this or whether it is a fortuitous result of the characteristics of Clog P. The partial correlation coefficients, pcorr, suggest, in agreement with Figure 2, that the dominant terms in these models are log S and Irings. This analysis demonstrates that log S can be employed to correlate mutagenicity data in a minimal regression


Chem. Res. Toxicol., Vol. 15, No. 12, 2002 1501 Table 4. Carcinogenicity Data amine

-log TD50 (mice)a

-log TD50 (rats)a

1a 1c 1d 1e 1f 1h 1i 1k 1o 1p 1q 1r

-1.83b -0.77 -0.60 0.24 0.59 0.26 0.57 1.91 1.47 0.94 1.06 1.56

-0.32 0.52 0.02 0.37 1.33 2.32 2.26 2.11 0.64 1.63

a Harmonic means calculated by the Carcinogenic Potential Data Base, ref 43. b Calculated from the raw data in ref 43.

Figure 2. Plots of calculated vs observed log m for TA 98 (A) and TA 100 (B) for the 2 variable (log S and Irings) model (closed symbols) and the three variable (log S, Irings, and Clog P) model (open symbols). Symbols have the same meaning as in Figure 1.

model. The demonstrated correlation of log m for both Salmonella strains with log S in single variable and multiple variable regression analyses is necessary, but insufficient, to show that nitrenium ion stability plays an important role in determining the mutagenicity potential of these amines, although the lack of a correlation could have been used to prove the negative. Particularly in a situation such as this in which many of the descriptor variables correlate strongly with each other, it is wise to remember that correlations cannot prove cause and effect. Nonetheless, these correlations, and other data that show that the more selective nitrenium ions are more effectively trapped by 2′-deoxyguanosine in aqueous solution (16-18, 40), do strongly suggest a role for nitrenium ion stability in bacterial mutagenicity. Carcinogenicity data, expressed as -log TD50, in mice and rats were found for some of the amines used in this study. These data are provided in Table 4. Wherever possible, harmonic means computed by the Carcinogenic

Figure 3. Plots of -log TD50 of ArNH2 (1) for mice (A) and rats (B) vs log S of ArNH+ (2). Least-squares regression lines are shown. Symbols have the same meaning as in Figure 1.

Potential Data Base were used (43). In one case (1a in mice) raw data from the same database were utilized to estimate -log TD50. Plots of -log TD50 for mice and rats vs log S (Figure 3) show considerable scatter and less

1502


Novak and Rajagopal

Table 5. Regression Models for Carcinogenicity in Mice -log TD50 ) P pcorr std err radj2 Fmodel Pmodel RMSE -log TD50 ) P pcorr std err radj2 Fmodel Pmodel RMSE -log TD50 ) P pcorr std err radj2 Fmodel Pmodel RMSE -log TD50 ) P pcorr std err radj2 Fmodel Pmodel RMSE

0.9285 × Clog P 0.010 0.7321 0.2880 0.7606 18.48 0.0007 0.5302 0.9068 × Clog P 0.013 0.7173 0.2937 0.7553 17.97 0.0007 0.5361 0.8700 × Clog P 0.022 0.6779 0.3145 0.7301 15.88 0.0011 0.5630 0.9189 × Clog P 0.022 0.6759 0.3340 0.6902 13.25 0.0021 0.6033

+0.2389 × log S 0.005 0.7746 0.0650

-1.9710

-0.06309 × ∆H 0.006 -0.7688 0.01749

-2.1501

+0.1663 × Nπ 0.009 0.7409 0.0502

-3.1789

+0.6021 × Nrings 0.018 0.6944 0.2080

-2.4795

03-CNE). We thank Dr. Albert Leo for providing some values of Clog P. Supporting Information Available: Residual plots for log m regressions vs log S, log S, and Irings, and log S, Irings, and Clog P. This material is available free of charge via the Internet at http://pubs.acs.org.

dependence on log S than do log m98 or log m100 (radj2 ) 0.5357, slope ) 0.31 ( 0.08 for mice, radj2 ) 0.4216, slope ) 0.25 ( 0.09 for rats). This is not unprecedented. Other correlations of -log TD50 for amines have shown significant scatter and less sensitivity to many of the same variables used in mutagenesis correlations (1, 13). MAAs, unlike the case for mutagenicity, do not appear to be particularly less carcinogenic than other amines of similar log S. Two parameter regression models using the same variables utilized in the mutagenicity correlations were attempted for the mouse data. Four models exhibited radj2 greater than that for the individual variables, and nonzero regression coefficients for both variables at the 95% confidence level. These are listed in Table 5. The common feature of all models is a strong dependence on Clog P with similar coefficients for that variable in all four cases. Similar strong dependence on log P was recently observed in a multiple variable regression study of carcinogenicity in a larger data set of 37 amines (1). Since the number of data points is relatively small, our conclusions should be viewed as tentative, but it appears that log S may be as useful as several regression variables other than Clog P in predicting carcinogenicity in mice. It is impractical to utilize the experimentally determined log S in large scale (50-150 amines) correlation analyses of mutagenicity and carcinogenicity. Because of the strong correlation observed between log S and ∆H of eq 2 for AAs it may be possible to use that calculated thermodynamic property of the nitrenium ions as a stand-in variable for log S in correlations of larger data sets. We are currently examining the relationship of log S to ∆H of eq 2 to determine if the linear correlation extends to HCAs.

Acknowledgment. This work was supported by a grant from the American Cancer Society (RPG-96-078-

References (1) Benigni, R., Giuliani, A., Franke, R., and Gruska, A. (2000) Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines. Chem. Rev. 100, 3697-3714. (2) Colvin, M. E., Hatch, F. T., and Felton, J. S. (1998) Chemical and biological factors affecting mutagen potency. Mutat. Res. 400, 479-492. (3) Chung, K.-T., Kirkovsky, L., Kirkovsky, A., and Purcell, W. P. (1997) Review of the mutagenicity of monocyclic aromatic amines: quantitative structure-activity relationships. Mutat. Res. 387, 1-16. (4) Debnath, A. K., Debnath, G., Shusterman, A. J., and Hansch, C. (1992) A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: I. Mutagenicity of aromatic and heterocyclic amines in Salmonella typhimurium TA 98 and TA 100. Environ. Mol. Mutagen. 19, 37-52. (5) Hatch, F. T., Knize, M. G., and Felton, J. S. (1991) Quantitative structure-activity relationships of heterocyclic amine mutagens formed during the cooking of foods. Environ. Mol. Mutagen. 17, 4-19. (6) Hatch, F. T., Colvin, M. E., and Seidl, E. T. (1996) Structural and quantum chemical factors affecting mutagenic potency of aminoimidazoazaarenes. Environ. Mol. Mutagen. 27, 314-330. (7) Hatch, F. T., and Colvin, M. E. (1997) Quantitative structureactivity (QSAR) relationships of mutagenic aromatic and heterocyclic amines. Mutat. Res. 376, 87-96. (8) Hatch, F. T., Knize, M. G., and Colvin, M. E. (2001) Extended quantitative structure-activity relationships for 80 aromatic and heterocyclic amines: structural, electronic, and hydropathic factors affecting mutagen potency. Environ. Mol. Mutagen. 38, 268291. (9) Maran, U., Karelson, M., and Katritzky, A. R. (1999) A comprehensive QSAR treatment of the genotoxicity of heteroaromatic and aromatic amines. Quant. Struct.-Act. Relat. 18, 3-10. (10) Ford, G. P., and Herman, P. S. (1992) Relative stabilities of nitrenium ions derived from polycyclic aromatic amines: relationship to mutagenicity. Chem.-Biol. Interact. 81, 1-18. (11) Ford, G. P., and Griffin, G. R. (1992) Relative stabilities of nitrenium ions derived from heterocyclic amine food carcinogens: relationship to mutagenicity. Chem.-Biol. Interact. 81, 1933. (12) Sabbioni, G., and Wild, P. (1992) Quantitative structure-activity relationships of mutagenic aromatic and heteroaromatic azides and amines. Carcinogenesis 13, 709-713. (13) Hatch, F. T., Knize, M. G., Moore, D. H., II, and Felton, J. S. (1992) Quantitative correlation of mutagenic and carcinogenic potencies for heterocyclic amines from cooked foods and additional aromatic amines. Mutat. Res. 271, 269-287. (14) Beland, F. A., and Kadlubar, F. F. (1990) Metabolic activation and DNA-adducts of aromatic amines and nitroaromatic hydrocarbons. In Handbook of Experimental Pharmacology (Cooper, C. S., and Grover, P. L., Eds.) Vol. 94/1, pp 267-325, SpringerVerlag, Berlin. (15) King, R. S., Kadlubar, F. F., and Turesky, R. J. (2000) In Vivo Metabolism. In Food Borne Carcinogens Heterocyclic Amines (Nagao, M., and Sugimura, T., Eds.) pp 90-111, Wiley, New York. (16) Novak, M., and Kennedy, S. A. (1995) Selective trapping of N-acetyl-N-(4-biphenylyl)-nitrenium and N-acetyl-N-(2-fluorenyl)nitrenium ions by 2′-deoxyguanosine in aqueous solution. J. Am. Chem. Soc. 117, 574-575. (17) Kennedy, S. A., Novak, M., and Kolb, B. A. (1997) Reactions of ester derivatives of carcinogenic N-(4-biphenylyl)hydroxylamine and the corresponding hydroxamic acid with purine nucleosides. J. Am. Chem. Soc. 119, 7654-7664. (18) McClelland, R. A., Ahmad, A., Dicks, A. P., and Licence, V. P. (1999) Spectroscopic characterization of the initial C8 intermediate in the reaction of the 2-fluorenylnitrenium ion with 2′deoxyguanosine. J. Am. Chem. Soc. 121, 3303-3310. (19) Maron, D. M., and Ames, B. N. (1983) Revised methods for the salmonella mutagenicity test. Mutat. Res. 113, 173-215. (20) Novak, M., VandeWater, A. J., Brown, A. J., Sanzenbacher, S. A., Hunt, L. A., Kolb, B. A., and Brooks, M. E. (1999) Hydrolysis


(21)

(22) (23) (24)

(25) (26)

(27)

(28)

(29)

(30) (31) (32)

reactions of N-sulfonatooxy-N-acetyl-1-aminonaphthalene and N-sulfonatooxy-N-acetyl-2-aminonaphthalene: limited correlation of nitrenium ion azide/solvent selectivities and mutagenicities of the corresponding amines. J. Org. Chem. 64, 6023-6031. Novak, M., Kahley, M. J., Eiger, E., Helmick, J. S., and Peters, H. E. (1993) Reactivity and selectivity of nitrenium ions derived from ester derivatives of carcinogenic N-(4-biphenylyl)hydroxylamine and the corresponding hydroxamic acid. J. Am. Chem. Soc. 115, 9453-9460. Wakabayashi, K., Nagao, M., Esumi, H., and Sugimura, T. (1992) Food-derived mutagens and carcinogens. Cancer Res. 52, 2092s2098s. Scribner, J. D., Fisk, S. R., and Scribner, N. K. (1979) Mechanisms of action of carcinogenic aromatic amines: an investigation using mutagenesis in bacteria. Chem.-Biol. Interact. 26, 11-25. Rashid, K. A., Arjmand, M., Sandermann, H., Mumma, R. O. (1987) Mutagenicity of choroaniline/lignin metabolities in the Salmonella/microsome assay. J. Environ. Sci. Health B22, 721729. Haworth, S., Lawlor, T., Mortelmans, K., Speck, W., Zeiger, E. (1983) Salmonella mutagenicity test results for 250 chemicals. Environ. Mutagen. 5 (Suppl. 1), 3-142. Zeiger, E., Anderson, B., Haworth, S., Lawlor, K., and Mortelmans, K. (1988) Salmonella mutagenicity tests: IV. Results from the testing of 300 chemicals. Environ. Mol. Mutagen. 11 (Suppl. 12), 1-158. Zeiger, E., Anderson, B., Haworth, S., Lawlor, T., and Mortelmans, K. (1992) Salmonella mutagenicity tests: V. Results from the testing of 311 chemicals. Environ. Mol. Mutagen. 19 (Suppl. 2), 2-141. Mortelmans, K., Haworth, S., Lawlor, T., Speck, W., Tainer, B., and Zeiger, E. (1986) Salmonella mutagenicity tests: 11. Results from the testing of 270 chemicals. Environ. Mutagen. 8 (Suppl. 7), 1-119. Fishbein, J. C., and McClelland, R. A. (1996) Halide ion trapping of nitrenium ions formed in the Bamberger rearrangement of N-arylhydroxylamines. Lifetime of the parent phenylnitrenium ion in water. Can. J. Chem. 74, 1321-1328. Novak, M., Kahley, M. J., Lin, J., Kennedy, S. A., and Swanegan, L. A. (1994) Reactivity patterns of N-arylnitrenium ionsslack of correlation with σ+. J. Am. Chem. Soc. 116, 11626-11627. Novak, M., Xu, L., and Wolf, R. A. (1998) Nitrenium ions from food-derived heterocyclic arylamine mutagens. J. Am. Chem. Soc. 120, 1643-1644. Novak, M., Kahley, M. J., Lin, J., Kennedy, S. A., and James, T. G. (1995) Involvement of free nitrenium ions, ion pairs, and preassociation trapping in the reactions of ester derivatives of N-arylhydroxylamines and N-arylhydroxamic acids in aqueous solution. J. Org. Chem. 60, 8294-8304.

Chem. Res. Toxicol., Vol. 15, No. 12, 2002 1503 (33) Novak, M., and Kazerani, S. (2000) Characterization of the 2-(Rcarbolinyl) nitrenium ion and its conjugate base produced during the decomposition of the model carcinogen 2-N-(pivaloyloxy)-2amino-R-carboline in aqueous solution. J. Am. Chem. Soc. 122, 3606-3616. (34) Bose, R., Ahmad, A. R., Dicks, A. P., Novak, M., Kayser, K. J., and McClelland, R. A. (1999) Spectroscopic characterization by laser flash photolysis of electrophilic intermediates derived from 4-aminostilbenes. Stilbene “nitrenium” ions and quinone methide imines. J. Chem. Soc., Perkin Trans. 2, 1591-1599. (35) Ren, D., and McClelland, R. A. (1998) Carbocation-like reactivity patterns in X′-substituted-4-biphenylylnitrenium ions. Can. J. Chem. 76, 78-84. (36) McClelland, R. A., Davidse, P. A., and Hadzialic, G. (1995) Electron-deficient strong bases-generation of the 4-biphenylylnitrenium and 2-fluorenylnitrenium ions by nitrene protonation in water. J. Am. Chem. Soc. 117, 4173-4174. (37) Sukhai, P., and McClelland, R. A. (1996) Direct observation of 4-alkoxyphenylnitrenium ions upon irradiation of 4-alkoxyphenyl azides in aqueous solution. J. Chem. Soc., Perkin Trans. 2, 15291530. (38) Ramlall, P., and McClelland, R. A. (1999) Photochemical generation and lifetimes in water of p-aryloxy- and p-alkoxyphenylnitrenium ions. J. Chem. Soc., Perkin Trans. 2, 225-232. (39) Novak, M., Toth, K., Rajagopal, S., Brooks, M., Hott, L. L., and Moslener, M. (2002) Reactivity and Selectivity of the N-acetylGlu-P-1, N-acetyl-Glu-P-2, N-acetyl-MelQx, and N-acetyl-IQx nitrenium ions: comparison to carbocyclic N-arylnitrenium ions. J. Am. Chem. Soc. 124, 7972-7981. (40) Novak, M., and Rajagopal, S. (2001) N-arylnitrenium ions. Adv. Phys. Org. Chem. 36, 167-254. (41) Ford, G. P., and Herman, P. S. (1991) Comparison of the relative stabilities of polycyclic arylnitrenium ions and arylmethyl cations: ab initio and semiempirical molecular orbital calculations. THEOCHEM 236, 269-282. (42) Novak, M., and Lin, J. (1999) Correlation of azide/solvent selectivities for nitrenium ions with ab initio hydration energies: understanding the kinetic lability of nitrenium ions in aqueous solution. J. Org. Chem. 64, 6032-6040. (43) http://potency.berkeley.edu/cpdb.html. See Gold, L. S., Sawyer, C. B., Magaw, R., Backman, G. M., deVeciana, M., Levinson, R., Hooper, N. K., Havender, W. R., Bernstein, L., Petro, R., Pike, M. C., and Ames, B. N. (1984) A Carcinogenic potency database of the standardized results of animal bioassays. Environ. Health Perspect. 58, 9-319. (44) Wittink, D. L. (1988) The Application of Regression Analysis, pp 61-140, Allyn and Bacon, Boston.

TX025584S

Correlations of Nitrenium Ion Selectivities with Quantitative

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