Chem. Res. Toxicol. 1992,5, 548-552
548
Modeling Cyanide Release from Nitriles: Prediction of Cytochrome P450 Mediated Acute Nitrile Toxicity? James Grogan,$ Stephen C. DeVito,*g§Robert S. Pearlman,ll and Kenneth R. Korzekwat Laboratory of Molecular Carcinogenesis, National Cancer Institute, Bethesda, Maryland 20892, Office of Pollution Prevention and Toxics (TS-779),U. S. Environmental Protection Agency, Washington, D.C. 20460, and College of Pharmacy, University of Texas, Austin Texas 78712-1074 Received February 21, 1992
A mechanism-based model for prediction of acute nitrile toxicity was developed using octanol-
water partition coefficients (log P) and estimated rates of a-hydrogen atom abstraction as variables. Relative rates of hydrogen atom abstraction were derived from differences in heats of formation for ground-state and radical geometries and radical ionization potentials. Calculated energies of activation for all potential sites of oxidation for a given nitrile were used to estimate partitioning of metabolites among multiple oxidative pathways. log P and the resulting corrected rate constants for a-carbon oxidation were effective variables in an acute toxicity model of structurally diverse nitriles. T h e pharmacokinetics of substrate disposition is discussed in the context of multiple metabolic pathways. Structure-toxicity relationships are also discussed.
Introduction The nitrile (C=N) group is found in a variety of drug products and xenobiotics. Exposure of humans to certain nitriles produces toxic signs and symptoms remarkably similar to those of cyanide poisoning, suggesting that cyanide release is responsible for their acute toxicity (1, 2). The toxicity of cyanide is well known, and the potential for cyanide release from nitriles is of paramount concern to the medicinal chemist when designing compounds containing the nitrile group and to the toxicologist when dealing with routine or accidental releases of these widely used industrial compounds. Cyanide release from nitriles was reported as early as 1894, when Lang demonstrated increased thiocyanate excretion following administration of nitriles to dogs and rabbits (3). Cyanide release has subsequently been confirmed by other investigators for a wider range of nitriles and for other species (4-11). Ohkawa et al. hypothesized that release of cyanide from nitriles results from cytochrome P450 catalyzed hydroxylation of the a carbon to form a cyanohydrin intermediate, which undergoes rapid nonenzymatic conversion to the carbonyl derivative with the concomitant release of cyanide (5). Subsequent studies have demonstrated the role of the cytochrome P450 system in the release of cyanide from various nitriles (6-15). A minimal series of reaction steps required for cytochrome P450catalyzed hydroxylation includes substrate binding, oxygen activation, hydrogen atom abstraction, recombination of the resulting hydroxy radical equivalent with substrate, and dissociation of the hydroxylated product (16). In the case of nitriles, substrate molecules + The research described in this paper has been reviewed by the Office of Pollution Prevention and Toxics, U. S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency nor does mention of commercial products constitute endorsement or recommendation for use. * Author to whom correspondence should be addressed. National Cancer Institute. U. S.Environmental Protection Agency. 11 University of Texas.
hydroxylated at the carbon atom adjacent (a) to the cyano moiety undergo rapid dismutation to release cyanide (see Scheme I). Tanii and Hashimoto (9) observed a parabolic relationship in mice between acute toxicity, expressed as log (l/LDbO),and hydrophobicity, expressed as log P, for phenylalkyl nitriles and primary and secondary nitriles containing chloro- or unsubstituted alkyl groups of three carbons or less ( r = 0.96, n = 13). However, nitriles containing alkyl substituents of C-4 or larger (9)or dinitriles (11)do not follow a parabolic relationship with log P. Considering the fact that cytochrome P450 mediated oxidation of nitriles is a radical-based reaction, the acute toxicity of nitriles may be expected to relate to structural features which increase a-carbon radical stability. An inspection of the types of nitrile radicals possible, listed in order of increasing stability (i.e., primary < secondary tertiary.;: benzylic),provides a rough indication of parent nitrile toxicity. For example, acetonitrile (primary) is less toxic than propionitrile (secondary), which is less toxic than isobutyronitrile (tertiary). Since nitrile toxicity appears to be a function of log P (9) and the relative stability of a-carbon radicals, we were interested in developing a model utilizing both these parameters. Such a model may be generally useful in predicting the acute toxicity of structurally diverse nitriles. Two considerations suggested that quantitative prediction of acute toxicity for structurally diverse nitriles would be possible. First, a recent study has established a theoretical basis for estimation of the relative tendency of carbon-hydrogen bonds to undergo cytochrome P450 mediated hydrogen atom abstraction (17). Second, a large data set representing a broad range of nitrile structure and acute toxic response measured within the same laboratory is available (9,ll).We have evaluated 26 nitriles for their calculated relative rates of hydrogen atom abstraction and used these rates to formulate models of nitrile toxicity.
0893-228x/92/2705-0548$03.00/00 1992 American Chemical Society
Chem. Res. Toxicol., Vol. 5, No. 4,1992
Modeling Acute Toxicity of Nitriles
with those values estimated using CLOGP. No relation was found between estimated nitrile LDm values and residuals resulting from regression analyses.
Scheme I. Cytochrome P450 Mediated Cyanide Release from Nitriles
r
F-
P4EQ C-N
R2
[ol
P4EQ
R1-1-CmN
CY
['OH1 R t - F - e
RP
0 /
R2
II
C
\
Ri
+
HCN
R2
R,- RP. H. aky or aryl
Results Initially, we investigated the use of hydrophobicity (log P ) as the only variable to predict LDw values for the compounds listed in Table I using the following model:
Methods The semiempirical quantum chemical method AM1 (18), as provided in MOPAC 4.0 (QPCE No.455),was used for all structural calculations. Stable ground-state and radical geometries were optimized from approximate starting structures. Final configurations were optimized to A using the UHF Hamiltonian for open-shellcalculations,and visualized using QUANTA. We chose to begin the calculations on an unselective basis to include a wide diversity of nitriles. The compounds include straight chain alkyl nitriles, branched alkyl nitriles, dinitriles, chloro-substituted nitriles, amino-substituted nitriles, three nitriles with sites of unsaturation or phenyl groups, and a hydroxylated nitrile (Table I). Excluded from the data set were acrylonitrile and methacrylonitrile since a,&unsaturated nitriles are oxidized by a mechanism involving an epoxide intermediate (7). Benzonitrile, which cannot form a cyanohydrin intermediate, was also excluded. Acute toxicity (LDm) data were obtained from the literature (I,9,11, 19). The differences in calculated ground-state and radical heats of formation ( 6 A H f ) together with the ionization potential of the radical (IP) were used to calculate activation enthalpies (AH*) of each nitrile using eq 1.
AH* = 2.60 + 0.22(6AHf)+ 2.38(IP)
549
(eq 1)
Such a calculation of AH*is based on thep-nitrosophenoxy radical based model of the cytochrome P450 active oxygen (17). The Arrhenius equation was used to obtain estimated relative rate constants a t each C-H bond where hydrogen atom abstraction could occur. Aromatic C-H bonds were not considered in these calculations because aromatic hydrogen atom abstraction is unlikely. The temperature used for these calculations was 37 O C , and it was assumed that the variation in the entropy of activation was negligible. For each nitrile, rate constants for hydrogen atom abstraction from the a-carbon were corrected (k,,,,,) by estimating the influence of oxidation a t other positions using eq 2. The rates constants k,, kp, k,, and k , represent rate constants for hydrogen atom abstraction from the a,8, y, and n carbon atoms of a given nitrile. The coefficients A, B , C, and N represent the number
of hydrogen atoms covalently bonded to each carbon atom. The origin of this correction is based on two assumptions. First, it is assumed that metabolism a t positions other than the a-carbon results in elimination of the metabolite without toxic effects. Second, total metabolism partitions between multiple metabolic pathways. If all metabolites arise from the same enzymesubstrate complex,one can expect that the observed rate constant for one pathway will be modified by the fractional importance of that pathway, k,,, (20, 21). Similar behavior will also be observed in systems involving multiple enzymes under a given set of pharmacokinetic conditions (see Discussion). Both corrected and uncorrected rate constants were used in the regression analyses and the results compared. Unrestricted multivarate regression analyses using the SAS software package (SAS Institute, Inc., Cary, NC) were made on an IBM PS/2. The general regression procedure was used to obtain best coefficients for variables predicting the acute LDbO data. log P values were estimated using CLOGP (22). Experimentally determined log P values, available for 22 of the 26 compounds investigated (9,I I), were generally in close agreement
log (1/LD,) = C,(log R 2+ C,(log P) + C, (eq 3) The graphical output of the best fit of individual compounds to the model is depicted in Figure 1. The bisecting line of the graph represents an ideal fit to the model. This model, using only log P as a variable (eq 3), predicts experimental toxicity with an r value of 0.59. Since cyanide release from nitriles may be influenced by the ease of a-hydrogen atom abstraction, we estimated relative rates of a-hydrogen atom abstraction (k,) from the nitriles in Table I and used these rates as a variable in toxicity modeling. The estimated rate constants (lz,) are shown in Table I. Addition of this variable to the model did not improve the prediction of acute toxicity ( r = 0.60; see Table 11, model 2). In fact, the rate constant variable is given a low weight after regression with this model. Rates of hydrogen atom abstraction were also estimated at every C-H bond where cytochrome P450 catalyzed hydroxylation could occur. This allowed for the calculation of corrected rate constants at the a-carbon (k,,,; see Table I) using eq 2, which takes into account oxidation at positions other than the cy-carbon. Use of the corrected rate constant and log P as variables in the model results in a markedly improved prediction of acute toxicity ( r = 0.85; see Table 11,model 3). The graphical output using this model is given in Figure 2, and the calculated values of log (1/LD50) are shown in Table I. Attempts were made to refine model 3 by the use of different variable combinations or compound exclusions. The resulting coefficients and regression parameters for these models are included in Table I1 (models 4-8), together with the three models discussed above (models 1-3). Exclusion of any variable from model 3 reduced the predictive quality of this model in all instances. However, it is noteworthy that exclusive use of the variable In (k,,,,) (Table 11, model 6) results in an r value of 0.76, which is substantially better than that for hydrophobicity alone. Model 7 in Table I1 shows the effect of substitution of the corrected rate constant term by the individual statistical correction terms themselves (Le., k,,,,/k, =f"). Removal of compounds 17 and 26, whose acute toxicity is not believed to be due to cyanide release (9, l o ) ,and 23-25, where olefinic oxidation is possible, resulted in a minor improvement of model 3 (Table 11, model 8; r = 0.90).
Discussion Tanii and Hashimoto (9) observed that as a group the acute toxicity of nitriles 1-3,8,11,19,and 23-26 in mice follows a well-correlated parabolic relationship with log P. They concluded that the acute toxicity of these nitriles is largely a function of hydrophobicity with respect to susceptibility to hepatic biotransformation to release cyanide. They also observed that the acute toxicity of nitriles 4-7,9,10,and 17 follows a linear relationship with log P and that as a group they are considerably less toxic.
SSO Chem. Res. Toxicol., Vol. 5, No. 4, 1992
Grogan et al.
Table I. log P,Theoretical Reaction Rate Constants, and Acute Toxicity Data of Some Nitriles name acetonitrile propionitrile butyronitrile valeronitrile capronitrile caprylonitrile pelargononitrile isobutyronitrile 3-methylbutyronitrile 4-methylvaleronitrile 2-methylbutyronitrile malononitrile succinonitrile glutaronitrile adiponitrile pimelonitrile chloroacetonitrile 3-chloropropionitrile 4-chlorobutyronitrile propionitrile, 3-(dimethylamino)3-(isopropylamino)3,3'-iminodi3-butenenitrile phenylacetonitrile 3-phenylpropionitrile 3-hydroxypropionitrile
compound no. 1 2 3 4 5 8 9 10 11 12 13 14 15 16 17 18 19
log F -0.39 0.14 0.66 1.19 1.72 2.78 3.31 0.44 1.06 1.59 0.97 -1.20 -0.82 -0.96 -0.43 0.11 0.22 0.20 0.38
kOb 4.0 287.9 310.0 324.9 324.9 348.8 348.8 8422.2 97.4 349.9 5811.7 5.4 28.9 70.6 226.9 495.0 107.0 45.8 131.1
4.0 251.0 58.4 24.7 16.9 8.1 5.5 8288.5 0.98 6.6 4288.2 5.4 28.9 41.7 113.5 202.1 107.0 4.9 16.2
20 21 22 23 24 25 26
-0.24 0.12 -0.92 0.12 1.56 1.55 -1.10
788.9 515.2 300.0 12703.4 34808.9 703.9 70.2
500.2 0.005 0.041 12703.4 34808.9 3.3 0.82
structure
6 7
kamrr'
LDwd (log (1/LDw)) e=P calcde 6.551 (-0.816) 3.42 (-0.534) 0.651 (0.187) 0.89 (0.051) 0.57f (0.244) 1.15 (-0.059) 2.3W (-0.362) 1.54 (-0.188) 4.77f (-0.679) 2.25 (-0.352) 14.09' (-1.149) 8.71 (-0.940) 14.79' (-1.170) 23.28 (-1.367) 0.37f (0.432) 0.34 (0.470) 2.8W (-0.447) 3.31 (-0.520) 5.09 (-0.701) 2.61 (-0.416) 0.29f (0.538) 0.39 (0.398) 1.8W (-0.255) 7.59 (-0.880) 1.6D (-0.210) 3.13 (-0.495) 2.838 (-0.452) 3.34 (-0.524) 1.59 (-0,201) 1.53 (-0.185) 1.038 (-0,013) 0.95 (0.023) 1.u(-0.265) 1.07 (-0.029) 0.57f (0.244) 2.32 (-0.366) 0.59 (0.284) 1.64 (-0.213) 15.30" (-1.185) 19.40" (-1.288) 27.50' (-1.439)
l.w (0.Ooo)
0.39' (0.409) 0.89' (0.051) 48.79 (-1.688)
9.19 (-0.963) 13.37 (-1.126) 17.99 (-1.255) 0.34 (0.474) 0.25 (0.598) 3.03 (-0.481) 10.62 (-1.026)
a Estimated from CLOGP; see ref 22. Theoretical reaction rate constants for cytochrome P450 mediated a-hydrogen atom abstraction; see text for details. c Theoretical reaction rate constants statistically corrected for metabolism at other positions; calculated according to eq 2. d Acute median lethal dose in mice. e Calculated by model 3. f Obtained from ref 9. 8 Obtained from ref 11. Obtained from ref 1. i Obtained from ref 19.
B] 2 .,
0
.21/ -2
Experimental .1 I= (ULD60)
Figure 1. Experimental versus predicted log (1/LDm) for nitriles 1-26 (eq 3), obtained from multiple regression of (log P12 and log P with LDw data. See Table 11, model 1,for regression coefficients.
Release of cyanide from these nitriles is much slower, and hence their acute toxicity is not believed to be due to cyanide release, but rather due to the parent compounds (9, IO). They later reported that the acute toxicity of dinitriles 12-16 does not follow any relationship with log P, and they attributed the acute toxicity of 12-16 to a combination of cyanide release and the parent compounds (11). From our investigation, we found that as a group the acute toxicity of nitriles 1-26 correlates poorly with log P (Table 11, model 1). It is readily apparent from the data provided in Table I that many of the nitriles have the same hydrophobicity but differ greatly in acute toxicity. Phenylacetonitrile (241, for example, has essentially the same log P as 10 but is more toxic by greater than 1order of magnitude. Similar analogiescan be made in comparing 12 with 26 or 11 with 9 or 4. Thus, use of log P alone as a parameter in predicting acute toxicity of nitriles may not always be sufficient, especially in instances where it is unclear if the acute toxicity is cyanide-dependent or not.
Our original intent in this research was to develop a single model that would be useful for predicting the acute toxicity of structurally diverse nitriles, by testing the usefulness of variables relating to rates of cytochrome P450 mediated oxidation. Estimation of the rates of toxic metabolite formation by cytochrome P450 requires, as a minimum, an understanding of the electronic and steric features of these reactions. The electronics of the oxidation reaction are thought to involve a triplet-like oxygenating species in the form of an iron(IV)-oxene porphyrin radical cation (23). The high reactivity of this species results in the metabolism of a wide variety of substrates by multiple mechanisms. A semiempirical quantum chemical model of the cytochrome P450 active oxygenating species has been previously developed (13,and we have used this model for the prediction of relative rate constants. The analysis of steric interactions associated with substrate binding and orientation is a more difficult task. On the basis of relatively broad regio- and substrate selectivity, the binding interactions for many substrates appear to be hydrophobic and generally nonspecific (24). The overlapping but distinct selectivities for the cytochrome P450's along with a multiplicity of isozymes suggest that a relatively !age database may be necessary to observe significant predictive correlations. The relative rates of oxidation estimated in this study originate from electronic considerations only and do not include steric effects involvingenzyme-substrate association. However, nitriles 1-26 are relatively small molecules, suggestingthat differences in rates arising from steric considerations may be less important. Modification of the hydrophobicity model (Table 11, model 1) to include a variable for rates of a-carbon oxidation resulted in little improvement in the correlation (Table 11, model 2). However, implicit in the use of the
Chem. Res. Toricol., Vol. 5, No. 4, 1992 551
Modeling Acute Toxicity of Nitriles
Table 11. log (1/LDw) Models, Resulting Variable Coefficients, and Best Fit Parameters regression outputb CA cz c3 c1 -0.23 (0.07) 0.40 (0.14) -0.22 (0.12) nae 0.59 -0.21 (0.07) -0.16 (0.05) -0.05 (0.08)
nae nae -0.20 (0.05) -0.12 (0.04)
0.34 (0.17) 0.22 (0.10)
nae -0.08 (0.03) nae 0.40 (0.11) 0.08 (0.09)
0.04 (0.06) 0.11 (0.20) 0.13 (0.02) 0.12 (0.02) 0.13 (0.02) 0.12 (0.03) 0.13 (0.02)
1.78 (3.22) 5.67 (1.07) 6.65 (1.25) 6.43(1.09) 6.49 (1.21) 0.01 (0.11) 7.08 (1.06)
0.60 0.85 0.76 0.82 0.76 0.80 0.90
RMSEd 0.54 0.54 0.35 0.43 0.38 0.42 0.41 0.29
a See text for model description and Methods for derivation of variables k, and . ,k Sample size (n)= 26. C1, C2, CB,and C4 are best fit coefficients. Numbers in parentheses are the standard errors of the coefficients (95% confidence level). Correlation coefficient. d Root mean standard error of the fit. e Nonapplicable. f Sample size (n)= 21;compounds 17 and 23-26 were omitted from this analysis (see text).
'1
24 0
2Y .2
,
1
.l
0
1
Exwrimental log WLDW)
Figure 2. Experimental versus predicted log (l/LDw)for nitriles 1-26, obtained from multiple regression of (log log P, and In k,,, with LDw data. See Table 11, model 3, for regression coefficients and Methods for calculation of k,,,.
ka variable of model 2 is the assumption that cytochrome P450 oxidizes nitriles at only the a-carbon. That this assumption may not always be valid is supported by several independent studies involving amino nitriles. Froines et al. found that a series of N-methylated 3-aminopropionitriles are oxidized at positions not resulting in cyanide release (15). In addition, Mumtaz et al. recently reported that in rata approximately 44% of orally administered 34dimethy1amino)propionitrile (20) is excreted unchanged while only 3-aminopropionitrile and cyanoacetic acid are identified as urinary metabolites (25). Keiser et al. reported that 3-aminopropionitrile is almost completely excreted in the urine as cyanoacetic acid when administered to mice or rats (26). In our investigation, a preliminary survey of radical stabilities a t all possible sites of oxidation showed that hydrogen atom abstraction at the a-carbon was often less favorable than a t other positions of the same nitrile. This was especially true in the case of the amino nitriles and nitriles containing long alkyl substituents. On the basis of the low toxicity of 3-hydroxypropionitrile relative to propionitrile (Table I) and the apparent elimination of amino nitrile metabolites (15,25, 26),we considered that oxidation of nitriles a t other positions might curtail toxicities based solely on k,. Cytochrome P450 enzymes are also generally known to favor hydrophobic substrates, suggesting that hydroxy metabolites may be eliminated rather than metabolized further at the a-carbon. To include consideration of multiple oxidation sites and elimination of hydroxylated nitriles into our model, correction factors (fc0r.I.) were calculated for the rate of a-carbon oxidation for each nitrile in Table I. Predicted LDm values were markedly improved in the toxicity model when the corrected rate
constants of a a-hydrogen atom abstraction (k,,,,) were used as variables (cf. Figures 1 and 2). Relative rates of a-hydrogen atom abstraction (k,,,,) appear to be a more critical parameter than hydrophobicity in determining acute toxicity, as is evident in comparing model 6 with model 1 (Table 11). Both parameters are important, however, as exemplified by model 3; removal of either term results in lower correlation coefficients (Table 11, model 1 and model 6). Of the possible "hydrophobicity" terms (models 3-5), the combination of (log PI2and log P was most effective. Also evaluated were the relative contributions of the k, and fco, factors to the kacorr variable. Separate regressions (Table 11, models 2 and 8) made it clear that f o r , had a relatively greater influence than k, on shaping the outcome of model 3. In k, values, relating to substituent group effects on the acarbon, represent a relatively narrow numerical range compared to Infcorr values. The use of these latter values, representing the influence of multiple pathways of oxidation, results in a model which can estimate the toxicity of a wide array of nitriles whose acute toxicity may or may not be cyanide-dependent. Although there are no statistical outliers to model 3, exclusion of specific compounds from the analysis may be justified for other reasons. Administration of chloroacetonitrile (17) to mice pretreated with CCl4 results in decreased cyanide liberation, but the acute toxicity of this compound increases (9,lO).These observations suggest that the normal hepatic metabolism of 17 is a detoxifying process and that the acute toxicity of this compound is not cyanide-dependent. Nitriles with sites of unsaturation, such as 23-25, can also be excluded because olefinic or aromatic oxidation may occur with these compounds (7). 3-Hydroxypropionitrile (26) can be excluded on the basis of the premise that hydroxylated nitriles are not metabolized by cytochrome P450 enzymes to release cyanide but are eliminated. Removal of these five compounds from the analysis (Table 11, model 8) resulted in a modest improvement in the correlation coefficient (r = 0.90). It appears that the variance of model 3 is not limited to specific classes of nitriles. For example, model 3 appears to accurately predict the acute toxicity of dinitriles 14-16 but not 12 and 13. The same is true for chloroacetonitrile (17), when compared with chloro nitriles 18 and 19, as well as for phenylacetonitrile (24) when compared with its homolog, 25. Willhite and Smith (6)observed considerable variations in cyanide concentrations within groups of animals given equivalent doses of the same nitrile, and they observed that the time course for onset of death within these groups varies greatly. Thus, individual tolerances
662 Chem. Res. Toxicol., Vol. 5, No. 4, 1992
Scheme 11. Possible Metabolic Pathways for in vivo Nitrile Disposition
to cyanide may be another important variable and may account for some of the variance in model 3 (Figure 2). The physiologicalsignificance of fcom and k, is suggested by the minimal scheme for nitrile disposition shown in Scheme 11. The elimination of the substrate, hydroxylated nitriles, and cyanide occurs with rate constants ke, k,,, and k,,, respectively. The rate of cyanide formation is determined by k,, and the rate of formation of all other metabolites is determined by k,. Therefore, the fraction of metabolism resulting in cyanide release (fcom) is kal(ka + kn). The assumptions are made that metabolites resulting from hydroxylation at positions other than the a-carbon atom are eliminated without further metabolism and that cyanide toxicity is a function of cyanide concentration. For substrates which are not eliminated (12, = 01,the acute toxicity may be a function of either fcom or both k , and fcorr, depending on the rate of cyanide elimination (kae). If cyanide elimination is slow relative to the rates of metabolite formation, acute toxicity will depend primarily on fcom, since any cyanide formed is committed to toxic effects. Therefore, substrates with lower fcom values will require proportionally higher doses for cyanidemediated acute toxicity. If cyanide elimination (ha,) is significant,the maximum cyanide concentration for a given dose will depend on both k , and fcom. When the rate of cyanide elimination is similar to the rate of cyanide formation, the P450 system may be saturated at doses required for toxicity. At high substrate doses, toxicity may result from nonspecific toxicity (27) or other toxicities. Finally, when the rate of substrate elimination (ke) is significant, cyanide concentration will be a function of both k, and fcorr, since k, is in competition with substrate elimination. The above analysis suggests that both k, and fcom may be correlated with cyanide-mediated toxicity.
Conclusion The present theoretical study has determined molecular parameters useful for predicting the acute toxicity of structurally diverse nitriles. The resulting toxicity model includes hydrophobicity, predicted relative rates of cytochrome P450 mediated metabolism, and detoxification pathways. We are presently attempting to expand this model to include olefinicand aromatic oxidation pathways. Acknowledgment. We thank Drs. Harry Gelboin and Roger L. Garrett for their support and encouragement. We also thank Drs. Bill Trager and Deanna Kroetz for their careful review of the manuscript.
Grogan et al.
References (1) Zeller, Von H., Hofmann, H. Th., Theiss, A. M., and Hey, W. (1969)
Toxicity of nitriles. Results of animal experiments and industrial experiencesduring 15 years. Zentralbl.Arbeitsmed. Arbeitsschutz 19,225-238. (2) Hartung, R. (1982) Cyanide and nitriles. In Patty’s Industrial Hygiene and Toxicology,3rd revised ed., pp 4845-4900,Wiley-Interscience, New York. (3) Lang, S.(1894)Ueber die umwandlung des acetonitrile und seiner homologenim thierkorper. Arch. Exp. Pathol. Pharmukol. 34,247258. (4) Stern, J., Weil-Malherbe, H., and Green, R. H. (1952)The effects and the fate of malononitrile and related compounds in animal tissues. Biochem. J. 52, 114-125. (5) Ohkawa, H., Ohkawa, R., Yamamoto, I., and Casida, J. E. (1972) Enzymic mechanisms and toxicological significance of hydrogen cyanide liberation from various organothiocyanates and organonitriles in mice and houseflies. Pestic. Biochem. Physiol. 2,95-112. (6)Willhite,C. C., and Smith, R. P. (1981)The role of cyanide liberation in the acute toxicity of aliphatic nitriles. Toxicol. Appl. Pharmacol. 59,589-602. (7)Silver, E. H., Kuttab, S. H., Hasan, T., and Hasean, M. (1982) Structural considerations in the metabolism of nitriles to cyanide in vivo. Drug Metab. Dispos. 10,496-498. (8) Ahmed, A. A., andFarooqui, M. Y. H. (1982)Comparative toxicities of aliphatic nitriles. Toxicol. Lett. 12,157-163. (9) Tanii, H., and Hashimoto, K. (1984)Studies on the mechanism of acute toxicity of nitriles in mice. Arch. Toxicol. 55,47-54. (10) Tanii, H.,and Hashimoto,K. (1984)Structure-toxicity relationships of aliphatic nitriles. Toxicol. Lett. 22,267-272. (11)Tanii, H., and Hashimoto, K. (1985) Structure-acute toxicity relationship of dinitriles in mice. Arch. Toxicol. 57,-93. (12)Johannsen, F. R.,and Levinskas, G .J. (1986)Relationshipsbetween toxicity and structure of aliphatic nitriles. Fundam. Appl. Toxicol. 7,690-697. (13) Yoshikawa, H. (1968)Toxicity of nitrile compounds. 1. Aliphatic nitriles. Med. Biol. 77,1-4. (14)Davis, R. H. (1981)Cyanide detoxification in the domestic fowl. In Cyanide in Biology (Vennesland, B., Conn, E. E., Knowlee, C. J., Westley,J.,and Wissing, F., E&.) pp 51-60,AcademicPres, London. (15) Froines, J. R., Postlethwait, E. M., LaFuente, E. J., and Liu, W. C. V. (1985)In vivo and in vitro release of cyanide from neurotoxic aminonitriles. J. Toxicol. Enoiron. Health 16 (3-4),449-460. (16)Ortiz, de Montellano, P. R. (1986)Cytochrome P450 Structure, Mechanism and Biochemistry, Plenum Press, New York. (17) Korzekwa, K. R.,Jones, J. P., and Gillette, J. R. (1990)Theoretical studies on cytochrome P-450mediated hydroxylation: a predictive 112,7042model for hydrogen atom abstraction. J.Am. Chem. SOC. 7047. (18) Dewar, M. J. S., Zoebisch, E. G., Healy, E. F., and Stuart, J. J. P. (1985)Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical 107,3902-3909. molecular model. J. Am. Chem. SOC. (19) EPA document 8(e) HQ-0988-0754. (20) Cleland, W.W. (1975)Partition analysis and the concept of net rate constants aa tools in enzyme kinetics. Biochemistry 14,3220-3224. (21) Korzekwa, K. R.,Trager, W. F., and Gillette, J. R. (1989)Theory for the observed isotope effects from enzymatic systems that form multiple products via branched reaction pathways: cytochromeP450. Biochemistry 28,9012-9018. (22) CLOCP version 3.42,Pomona College Medicinal Chemistry Project, Claremont, CA 91711. (23)McMurry, T. J.,and Groves,J. T. (1986)Metalloporphyrin Models for Cytochrome P450. In Cytochrome P450 Structure, Mechanism and Biochemistry (Ortiz, de Montellano, P. R., Ed.) pp 1-28, Plenum Press, New York. (24)Wilson,L.Y., and Famini, G . R. (1991)Using theoretical descriptors in quantitative structure-activity relationships: some toxicological indices. J.Med. Chem. 34, 1668-1674. (25) Mumtaz, M. M., Farooqui, M. Y. H., Ghanayem, B. I., and Ahmed, A. E. (1991)The urotoxic effects of N,N’-dimethylaminopropionitrile. 2. In vivo and in vitro metabolism. Toxicol. Appl. Pharmacol. 110,61-69. (26) Keiser, H. R.,Harris, E. D., and Sjoerdama, A. (1967)Studies on beta-aminopropionitrilein animale. Clin. Pharmacol. Ther. 8,587592. (27) (a) Meyer, H.(1899)Zur theorie der alkoholnarkose. Arch. Exp. Pathol. Pharmakol. 42, 109-118. (b) Overton, E. (1901)Studien uber die Narkose, zugleich ein Beitrag zur allgemeiner Pharmakologie (Fischer,G., Ed.) Jena, Germany. For an English translation, see Lipnick, R. L. (1991)Studies of Narcosis, Charles Ernest Ouerton, Chapman and Hall, London.
548
Modeling Cyanide Release from Nitriles: Prediction of Cytochrome P450 Mediated Acute Nitrile Toxicity? James Grogan,$ Stephen C. DeVito,*g§Robert S. Pearlman,ll and Kenneth R. Korzekwat Laboratory of Molecular Carcinogenesis, National Cancer Institute, Bethesda, Maryland 20892, Office of Pollution Prevention and Toxics (TS-779),U. S. Environmental Protection Agency, Washington, D.C. 20460, and College of Pharmacy, University of Texas, Austin Texas 78712-1074 Received February 21, 1992
A mechanism-based model for prediction of acute nitrile toxicity was developed using octanol-
water partition coefficients (log P) and estimated rates of a-hydrogen atom abstraction as variables. Relative rates of hydrogen atom abstraction were derived from differences in heats of formation for ground-state and radical geometries and radical ionization potentials. Calculated energies of activation for all potential sites of oxidation for a given nitrile were used to estimate partitioning of metabolites among multiple oxidative pathways. log P and the resulting corrected rate constants for a-carbon oxidation were effective variables in an acute toxicity model of structurally diverse nitriles. T h e pharmacokinetics of substrate disposition is discussed in the context of multiple metabolic pathways. Structure-toxicity relationships are also discussed.
Introduction The nitrile (C=N) group is found in a variety of drug products and xenobiotics. Exposure of humans to certain nitriles produces toxic signs and symptoms remarkably similar to those of cyanide poisoning, suggesting that cyanide release is responsible for their acute toxicity (1, 2). The toxicity of cyanide is well known, and the potential for cyanide release from nitriles is of paramount concern to the medicinal chemist when designing compounds containing the nitrile group and to the toxicologist when dealing with routine or accidental releases of these widely used industrial compounds. Cyanide release from nitriles was reported as early as 1894, when Lang demonstrated increased thiocyanate excretion following administration of nitriles to dogs and rabbits (3). Cyanide release has subsequently been confirmed by other investigators for a wider range of nitriles and for other species (4-11). Ohkawa et al. hypothesized that release of cyanide from nitriles results from cytochrome P450 catalyzed hydroxylation of the a carbon to form a cyanohydrin intermediate, which undergoes rapid nonenzymatic conversion to the carbonyl derivative with the concomitant release of cyanide (5). Subsequent studies have demonstrated the role of the cytochrome P450 system in the release of cyanide from various nitriles (6-15). A minimal series of reaction steps required for cytochrome P450catalyzed hydroxylation includes substrate binding, oxygen activation, hydrogen atom abstraction, recombination of the resulting hydroxy radical equivalent with substrate, and dissociation of the hydroxylated product (16). In the case of nitriles, substrate molecules + The research described in this paper has been reviewed by the Office of Pollution Prevention and Toxics, U. S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency nor does mention of commercial products constitute endorsement or recommendation for use. * Author to whom correspondence should be addressed. National Cancer Institute. U. S.Environmental Protection Agency. 11 University of Texas.
hydroxylated at the carbon atom adjacent (a) to the cyano moiety undergo rapid dismutation to release cyanide (see Scheme I). Tanii and Hashimoto (9) observed a parabolic relationship in mice between acute toxicity, expressed as log (l/LDbO),and hydrophobicity, expressed as log P, for phenylalkyl nitriles and primary and secondary nitriles containing chloro- or unsubstituted alkyl groups of three carbons or less ( r = 0.96, n = 13). However, nitriles containing alkyl substituents of C-4 or larger (9)or dinitriles (11)do not follow a parabolic relationship with log P. Considering the fact that cytochrome P450 mediated oxidation of nitriles is a radical-based reaction, the acute toxicity of nitriles may be expected to relate to structural features which increase a-carbon radical stability. An inspection of the types of nitrile radicals possible, listed in order of increasing stability (i.e., primary < secondary tertiary.;: benzylic),provides a rough indication of parent nitrile toxicity. For example, acetonitrile (primary) is less toxic than propionitrile (secondary), which is less toxic than isobutyronitrile (tertiary). Since nitrile toxicity appears to be a function of log P (9) and the relative stability of a-carbon radicals, we were interested in developing a model utilizing both these parameters. Such a model may be generally useful in predicting the acute toxicity of structurally diverse nitriles. Two considerations suggested that quantitative prediction of acute toxicity for structurally diverse nitriles would be possible. First, a recent study has established a theoretical basis for estimation of the relative tendency of carbon-hydrogen bonds to undergo cytochrome P450 mediated hydrogen atom abstraction (17). Second, a large data set representing a broad range of nitrile structure and acute toxic response measured within the same laboratory is available (9,ll).We have evaluated 26 nitriles for their calculated relative rates of hydrogen atom abstraction and used these rates to formulate models of nitrile toxicity.
0893-228x/92/2705-0548$03.00/00 1992 American Chemical Society
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Modeling Acute Toxicity of Nitriles
with those values estimated using CLOGP. No relation was found between estimated nitrile LDm values and residuals resulting from regression analyses.
Scheme I. Cytochrome P450 Mediated Cyanide Release from Nitriles
r
F-
P4EQ C-N
R2
[ol
P4EQ
R1-1-CmN
CY
['OH1 R t - F - e
RP
0 /
R2
II
C
\
Ri
+
HCN
R2
R,- RP. H. aky or aryl
Results Initially, we investigated the use of hydrophobicity (log P ) as the only variable to predict LDw values for the compounds listed in Table I using the following model:
Methods The semiempirical quantum chemical method AM1 (18), as provided in MOPAC 4.0 (QPCE No.455),was used for all structural calculations. Stable ground-state and radical geometries were optimized from approximate starting structures. Final configurations were optimized to A using the UHF Hamiltonian for open-shellcalculations,and visualized using QUANTA. We chose to begin the calculations on an unselective basis to include a wide diversity of nitriles. The compounds include straight chain alkyl nitriles, branched alkyl nitriles, dinitriles, chloro-substituted nitriles, amino-substituted nitriles, three nitriles with sites of unsaturation or phenyl groups, and a hydroxylated nitrile (Table I). Excluded from the data set were acrylonitrile and methacrylonitrile since a,&unsaturated nitriles are oxidized by a mechanism involving an epoxide intermediate (7). Benzonitrile, which cannot form a cyanohydrin intermediate, was also excluded. Acute toxicity (LDm) data were obtained from the literature (I,9,11, 19). The differences in calculated ground-state and radical heats of formation ( 6 A H f ) together with the ionization potential of the radical (IP) were used to calculate activation enthalpies (AH*) of each nitrile using eq 1.
AH* = 2.60 + 0.22(6AHf)+ 2.38(IP)
549
(eq 1)
Such a calculation of AH*is based on thep-nitrosophenoxy radical based model of the cytochrome P450 active oxygen (17). The Arrhenius equation was used to obtain estimated relative rate constants a t each C-H bond where hydrogen atom abstraction could occur. Aromatic C-H bonds were not considered in these calculations because aromatic hydrogen atom abstraction is unlikely. The temperature used for these calculations was 37 O C , and it was assumed that the variation in the entropy of activation was negligible. For each nitrile, rate constants for hydrogen atom abstraction from the a-carbon were corrected (k,,,,,) by estimating the influence of oxidation a t other positions using eq 2. The rates constants k,, kp, k,, and k , represent rate constants for hydrogen atom abstraction from the a,8, y, and n carbon atoms of a given nitrile. The coefficients A, B , C, and N represent the number
of hydrogen atoms covalently bonded to each carbon atom. The origin of this correction is based on two assumptions. First, it is assumed that metabolism a t positions other than the a-carbon results in elimination of the metabolite without toxic effects. Second, total metabolism partitions between multiple metabolic pathways. If all metabolites arise from the same enzymesubstrate complex,one can expect that the observed rate constant for one pathway will be modified by the fractional importance of that pathway, k,,, (20, 21). Similar behavior will also be observed in systems involving multiple enzymes under a given set of pharmacokinetic conditions (see Discussion). Both corrected and uncorrected rate constants were used in the regression analyses and the results compared. Unrestricted multivarate regression analyses using the SAS software package (SAS Institute, Inc., Cary, NC) were made on an IBM PS/2. The general regression procedure was used to obtain best coefficients for variables predicting the acute LDbO data. log P values were estimated using CLOGP (22). Experimentally determined log P values, available for 22 of the 26 compounds investigated (9,I I), were generally in close agreement
log (1/LD,) = C,(log R 2+ C,(log P) + C, (eq 3) The graphical output of the best fit of individual compounds to the model is depicted in Figure 1. The bisecting line of the graph represents an ideal fit to the model. This model, using only log P as a variable (eq 3), predicts experimental toxicity with an r value of 0.59. Since cyanide release from nitriles may be influenced by the ease of a-hydrogen atom abstraction, we estimated relative rates of a-hydrogen atom abstraction (k,) from the nitriles in Table I and used these rates as a variable in toxicity modeling. The estimated rate constants (lz,) are shown in Table I. Addition of this variable to the model did not improve the prediction of acute toxicity ( r = 0.60; see Table 11, model 2). In fact, the rate constant variable is given a low weight after regression with this model. Rates of hydrogen atom abstraction were also estimated at every C-H bond where cytochrome P450 catalyzed hydroxylation could occur. This allowed for the calculation of corrected rate constants at the a-carbon (k,,,; see Table I) using eq 2, which takes into account oxidation at positions other than the cy-carbon. Use of the corrected rate constant and log P as variables in the model results in a markedly improved prediction of acute toxicity ( r = 0.85; see Table 11,model 3). The graphical output using this model is given in Figure 2, and the calculated values of log (1/LD50) are shown in Table I. Attempts were made to refine model 3 by the use of different variable combinations or compound exclusions. The resulting coefficients and regression parameters for these models are included in Table I1 (models 4-8), together with the three models discussed above (models 1-3). Exclusion of any variable from model 3 reduced the predictive quality of this model in all instances. However, it is noteworthy that exclusive use of the variable In (k,,,,) (Table 11, model 6) results in an r value of 0.76, which is substantially better than that for hydrophobicity alone. Model 7 in Table I1 shows the effect of substitution of the corrected rate constant term by the individual statistical correction terms themselves (Le., k,,,,/k, =f"). Removal of compounds 17 and 26, whose acute toxicity is not believed to be due to cyanide release (9, l o ) ,and 23-25, where olefinic oxidation is possible, resulted in a minor improvement of model 3 (Table 11, model 8; r = 0.90).
Discussion Tanii and Hashimoto (9) observed that as a group the acute toxicity of nitriles 1-3,8,11,19,and 23-26 in mice follows a well-correlated parabolic relationship with log P. They concluded that the acute toxicity of these nitriles is largely a function of hydrophobicity with respect to susceptibility to hepatic biotransformation to release cyanide. They also observed that the acute toxicity of nitriles 4-7,9,10,and 17 follows a linear relationship with log P and that as a group they are considerably less toxic.
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Grogan et al.
Table I. log P,Theoretical Reaction Rate Constants, and Acute Toxicity Data of Some Nitriles name acetonitrile propionitrile butyronitrile valeronitrile capronitrile caprylonitrile pelargononitrile isobutyronitrile 3-methylbutyronitrile 4-methylvaleronitrile 2-methylbutyronitrile malononitrile succinonitrile glutaronitrile adiponitrile pimelonitrile chloroacetonitrile 3-chloropropionitrile 4-chlorobutyronitrile propionitrile, 3-(dimethylamino)3-(isopropylamino)3,3'-iminodi3-butenenitrile phenylacetonitrile 3-phenylpropionitrile 3-hydroxypropionitrile
compound no. 1 2 3 4 5 8 9 10 11 12 13 14 15 16 17 18 19
log F -0.39 0.14 0.66 1.19 1.72 2.78 3.31 0.44 1.06 1.59 0.97 -1.20 -0.82 -0.96 -0.43 0.11 0.22 0.20 0.38
kOb 4.0 287.9 310.0 324.9 324.9 348.8 348.8 8422.2 97.4 349.9 5811.7 5.4 28.9 70.6 226.9 495.0 107.0 45.8 131.1
4.0 251.0 58.4 24.7 16.9 8.1 5.5 8288.5 0.98 6.6 4288.2 5.4 28.9 41.7 113.5 202.1 107.0 4.9 16.2
20 21 22 23 24 25 26
-0.24 0.12 -0.92 0.12 1.56 1.55 -1.10
788.9 515.2 300.0 12703.4 34808.9 703.9 70.2
500.2 0.005 0.041 12703.4 34808.9 3.3 0.82
structure
6 7
kamrr'
LDwd (log (1/LDw)) e=P calcde 6.551 (-0.816) 3.42 (-0.534) 0.651 (0.187) 0.89 (0.051) 0.57f (0.244) 1.15 (-0.059) 2.3W (-0.362) 1.54 (-0.188) 4.77f (-0.679) 2.25 (-0.352) 14.09' (-1.149) 8.71 (-0.940) 14.79' (-1.170) 23.28 (-1.367) 0.37f (0.432) 0.34 (0.470) 2.8W (-0.447) 3.31 (-0.520) 5.09 (-0.701) 2.61 (-0.416) 0.29f (0.538) 0.39 (0.398) 1.8W (-0.255) 7.59 (-0.880) 1.6D (-0.210) 3.13 (-0.495) 2.838 (-0.452) 3.34 (-0.524) 1.59 (-0,201) 1.53 (-0.185) 1.038 (-0,013) 0.95 (0.023) 1.u(-0.265) 1.07 (-0.029) 0.57f (0.244) 2.32 (-0.366) 0.59 (0.284) 1.64 (-0.213) 15.30" (-1.185) 19.40" (-1.288) 27.50' (-1.439)
l.w (0.Ooo)
0.39' (0.409) 0.89' (0.051) 48.79 (-1.688)
9.19 (-0.963) 13.37 (-1.126) 17.99 (-1.255) 0.34 (0.474) 0.25 (0.598) 3.03 (-0.481) 10.62 (-1.026)
a Estimated from CLOGP; see ref 22. Theoretical reaction rate constants for cytochrome P450 mediated a-hydrogen atom abstraction; see text for details. c Theoretical reaction rate constants statistically corrected for metabolism at other positions; calculated according to eq 2. d Acute median lethal dose in mice. e Calculated by model 3. f Obtained from ref 9. 8 Obtained from ref 11. Obtained from ref 1. i Obtained from ref 19.
B] 2 .,
0
.21/ -2
Experimental .1 I= (ULD60)
Figure 1. Experimental versus predicted log (1/LDm) for nitriles 1-26 (eq 3), obtained from multiple regression of (log P12 and log P with LDw data. See Table 11, model 1,for regression coefficients.
Release of cyanide from these nitriles is much slower, and hence their acute toxicity is not believed to be due to cyanide release, but rather due to the parent compounds (9, IO). They later reported that the acute toxicity of dinitriles 12-16 does not follow any relationship with log P, and they attributed the acute toxicity of 12-16 to a combination of cyanide release and the parent compounds (11). From our investigation, we found that as a group the acute toxicity of nitriles 1-26 correlates poorly with log P (Table 11, model 1). It is readily apparent from the data provided in Table I that many of the nitriles have the same hydrophobicity but differ greatly in acute toxicity. Phenylacetonitrile (241, for example, has essentially the same log P as 10 but is more toxic by greater than 1order of magnitude. Similar analogiescan be made in comparing 12 with 26 or 11 with 9 or 4. Thus, use of log P alone as a parameter in predicting acute toxicity of nitriles may not always be sufficient, especially in instances where it is unclear if the acute toxicity is cyanide-dependent or not.
Our original intent in this research was to develop a single model that would be useful for predicting the acute toxicity of structurally diverse nitriles, by testing the usefulness of variables relating to rates of cytochrome P450 mediated oxidation. Estimation of the rates of toxic metabolite formation by cytochrome P450 requires, as a minimum, an understanding of the electronic and steric features of these reactions. The electronics of the oxidation reaction are thought to involve a triplet-like oxygenating species in the form of an iron(IV)-oxene porphyrin radical cation (23). The high reactivity of this species results in the metabolism of a wide variety of substrates by multiple mechanisms. A semiempirical quantum chemical model of the cytochrome P450 active oxygenating species has been previously developed (13,and we have used this model for the prediction of relative rate constants. The analysis of steric interactions associated with substrate binding and orientation is a more difficult task. On the basis of relatively broad regio- and substrate selectivity, the binding interactions for many substrates appear to be hydrophobic and generally nonspecific (24). The overlapping but distinct selectivities for the cytochrome P450's along with a multiplicity of isozymes suggest that a relatively !age database may be necessary to observe significant predictive correlations. The relative rates of oxidation estimated in this study originate from electronic considerations only and do not include steric effects involvingenzyme-substrate association. However, nitriles 1-26 are relatively small molecules, suggestingthat differences in rates arising from steric considerations may be less important. Modification of the hydrophobicity model (Table 11, model 1) to include a variable for rates of a-carbon oxidation resulted in little improvement in the correlation (Table 11, model 2). However, implicit in the use of the
Chem. Res. Toricol., Vol. 5, No. 4, 1992 551
Modeling Acute Toxicity of Nitriles
Table 11. log (1/LDw) Models, Resulting Variable Coefficients, and Best Fit Parameters regression outputb CA cz c3 c1 -0.23 (0.07) 0.40 (0.14) -0.22 (0.12) nae 0.59 -0.21 (0.07) -0.16 (0.05) -0.05 (0.08)
nae nae -0.20 (0.05) -0.12 (0.04)
0.34 (0.17) 0.22 (0.10)
nae -0.08 (0.03) nae 0.40 (0.11) 0.08 (0.09)
0.04 (0.06) 0.11 (0.20) 0.13 (0.02) 0.12 (0.02) 0.13 (0.02) 0.12 (0.03) 0.13 (0.02)
1.78 (3.22) 5.67 (1.07) 6.65 (1.25) 6.43(1.09) 6.49 (1.21) 0.01 (0.11) 7.08 (1.06)
0.60 0.85 0.76 0.82 0.76 0.80 0.90
RMSEd 0.54 0.54 0.35 0.43 0.38 0.42 0.41 0.29
a See text for model description and Methods for derivation of variables k, and . ,k Sample size (n)= 26. C1, C2, CB,and C4 are best fit coefficients. Numbers in parentheses are the standard errors of the coefficients (95% confidence level). Correlation coefficient. d Root mean standard error of the fit. e Nonapplicable. f Sample size (n)= 21;compounds 17 and 23-26 were omitted from this analysis (see text).
'1
24 0
2Y .2
,
1
.l
0
1
Exwrimental log WLDW)
Figure 2. Experimental versus predicted log (l/LDw)for nitriles 1-26, obtained from multiple regression of (log log P, and In k,,, with LDw data. See Table 11, model 3, for regression coefficients and Methods for calculation of k,,,.
ka variable of model 2 is the assumption that cytochrome P450 oxidizes nitriles at only the a-carbon. That this assumption may not always be valid is supported by several independent studies involving amino nitriles. Froines et al. found that a series of N-methylated 3-aminopropionitriles are oxidized at positions not resulting in cyanide release (15). In addition, Mumtaz et al. recently reported that in rata approximately 44% of orally administered 34dimethy1amino)propionitrile (20) is excreted unchanged while only 3-aminopropionitrile and cyanoacetic acid are identified as urinary metabolites (25). Keiser et al. reported that 3-aminopropionitrile is almost completely excreted in the urine as cyanoacetic acid when administered to mice or rats (26). In our investigation, a preliminary survey of radical stabilities a t all possible sites of oxidation showed that hydrogen atom abstraction at the a-carbon was often less favorable than a t other positions of the same nitrile. This was especially true in the case of the amino nitriles and nitriles containing long alkyl substituents. On the basis of the low toxicity of 3-hydroxypropionitrile relative to propionitrile (Table I) and the apparent elimination of amino nitrile metabolites (15,25, 26),we considered that oxidation of nitriles a t other positions might curtail toxicities based solely on k,. Cytochrome P450 enzymes are also generally known to favor hydrophobic substrates, suggesting that hydroxy metabolites may be eliminated rather than metabolized further at the a-carbon. To include consideration of multiple oxidation sites and elimination of hydroxylated nitriles into our model, correction factors (fc0r.I.) were calculated for the rate of a-carbon oxidation for each nitrile in Table I. Predicted LDm values were markedly improved in the toxicity model when the corrected rate
constants of a a-hydrogen atom abstraction (k,,,,) were used as variables (cf. Figures 1 and 2). Relative rates of a-hydrogen atom abstraction (k,,,,) appear to be a more critical parameter than hydrophobicity in determining acute toxicity, as is evident in comparing model 6 with model 1 (Table 11). Both parameters are important, however, as exemplified by model 3; removal of either term results in lower correlation coefficients (Table 11, model 1 and model 6). Of the possible "hydrophobicity" terms (models 3-5), the combination of (log PI2and log P was most effective. Also evaluated were the relative contributions of the k, and fco, factors to the kacorr variable. Separate regressions (Table 11, models 2 and 8) made it clear that f o r , had a relatively greater influence than k, on shaping the outcome of model 3. In k, values, relating to substituent group effects on the acarbon, represent a relatively narrow numerical range compared to Infcorr values. The use of these latter values, representing the influence of multiple pathways of oxidation, results in a model which can estimate the toxicity of a wide array of nitriles whose acute toxicity may or may not be cyanide-dependent. Although there are no statistical outliers to model 3, exclusion of specific compounds from the analysis may be justified for other reasons. Administration of chloroacetonitrile (17) to mice pretreated with CCl4 results in decreased cyanide liberation, but the acute toxicity of this compound increases (9,lO).These observations suggest that the normal hepatic metabolism of 17 is a detoxifying process and that the acute toxicity of this compound is not cyanide-dependent. Nitriles with sites of unsaturation, such as 23-25, can also be excluded because olefinic or aromatic oxidation may occur with these compounds (7). 3-Hydroxypropionitrile (26) can be excluded on the basis of the premise that hydroxylated nitriles are not metabolized by cytochrome P450 enzymes to release cyanide but are eliminated. Removal of these five compounds from the analysis (Table 11, model 8) resulted in a modest improvement in the correlation coefficient (r = 0.90). It appears that the variance of model 3 is not limited to specific classes of nitriles. For example, model 3 appears to accurately predict the acute toxicity of dinitriles 14-16 but not 12 and 13. The same is true for chloroacetonitrile (17), when compared with chloro nitriles 18 and 19, as well as for phenylacetonitrile (24) when compared with its homolog, 25. Willhite and Smith (6)observed considerable variations in cyanide concentrations within groups of animals given equivalent doses of the same nitrile, and they observed that the time course for onset of death within these groups varies greatly. Thus, individual tolerances
662 Chem. Res. Toxicol., Vol. 5, No. 4, 1992
Scheme 11. Possible Metabolic Pathways for in vivo Nitrile Disposition
to cyanide may be another important variable and may account for some of the variance in model 3 (Figure 2). The physiologicalsignificance of fcom and k, is suggested by the minimal scheme for nitrile disposition shown in Scheme 11. The elimination of the substrate, hydroxylated nitriles, and cyanide occurs with rate constants ke, k,,, and k,,, respectively. The rate of cyanide formation is determined by k,, and the rate of formation of all other metabolites is determined by k,. Therefore, the fraction of metabolism resulting in cyanide release (fcom) is kal(ka + kn). The assumptions are made that metabolites resulting from hydroxylation at positions other than the a-carbon atom are eliminated without further metabolism and that cyanide toxicity is a function of cyanide concentration. For substrates which are not eliminated (12, = 01,the acute toxicity may be a function of either fcom or both k , and fcorr, depending on the rate of cyanide elimination (kae). If cyanide elimination is slow relative to the rates of metabolite formation, acute toxicity will depend primarily on fcom, since any cyanide formed is committed to toxic effects. Therefore, substrates with lower fcom values will require proportionally higher doses for cyanidemediated acute toxicity. If cyanide elimination (ha,) is significant,the maximum cyanide concentration for a given dose will depend on both k , and fcom. When the rate of cyanide elimination is similar to the rate of cyanide formation, the P450 system may be saturated at doses required for toxicity. At high substrate doses, toxicity may result from nonspecific toxicity (27) or other toxicities. Finally, when the rate of substrate elimination (ke) is significant, cyanide concentration will be a function of both k, and fcorr, since k, is in competition with substrate elimination. The above analysis suggests that both k, and fcom may be correlated with cyanide-mediated toxicity.
Conclusion The present theoretical study has determined molecular parameters useful for predicting the acute toxicity of structurally diverse nitriles. The resulting toxicity model includes hydrophobicity, predicted relative rates of cytochrome P450 mediated metabolism, and detoxification pathways. We are presently attempting to expand this model to include olefinicand aromatic oxidation pathways. Acknowledgment. We thank Drs. Harry Gelboin and Roger L. Garrett for their support and encouragement. We also thank Drs. Bill Trager and Deanna Kroetz for their careful review of the manuscript.
Grogan et al.
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