ARTICLE pubs.acs.org/crt
Relating Skin Sensitizing Potency to Chemical Reactivity: Reactive Michael Acceptors Inhibit NF-jB Signaling and Are Less Sensitizing than SNAr- and SN2- Reactive Chemicals Andreas Natsch,* Tina Haupt, and Heike Laue Givaudan Schweiz AG, Ueberlandstrasse 138, CH-8600 Duebendorf, Switzerland
bS Supporting Information ABSTRACT: The skin sensitization potency of chemicals is partly related to their reactivity to proteins. This can be quantified as the rate constant of the reaction with a model peptide, and a kinetic profiling approach to determine rate constants was previously proposed. A linear relationship between the skin sensitization potency in the local lymph node assay (LLNA) and the rate constant for Michael acceptors was reported, characterized by a relatively flat regression line. Thus, a 10-fold increase of reactivity correlates to an increase of the sensitization potential of only 1.7-fold. Here, we first validate this model by repeating previous data and testing additional Michael acceptors and prove that the model is both reproducible and robust to the addition of new data. Chemicals of different mechanistic applicability domains, namely, SNAr- and SN2-reactive sensitizers, were then tested with the same kinetic profiling approach. A linear relationship between sensitization potency in the LLNA and rate constants was also found, yet with a much steeper slope, i.e., for SNAr- and SN2-reactive sensitizers, increasing reactivity correlates to a much stronger increase in sensitization potency. On the basis of the well-known inhibitory activity of some Michael acceptors on IKK kinase, it was hypothesized that the difference in the slopes is due to the specific anti-inflammatory potential of Michael acceptor chemicals. Therefore, all chemicals were tested for anti-inflammatory activity in a reporter gene assay for the inhibition of NF-kB activation. Increasingly reactive Michael acceptors have increasing anti-inflammatory potential in this assay, whereas no such biological activity was detected for the SNAr and SN2 reactive sensitizers. Thus, the increasing reactivity of Michael acceptors confers both antiinflammatory and skin sensitizing/pro-inflammatory potential, which may partially neutralize each other. This may be the reason for the relatively weak relationship between the potency in the LLNA and the rate constant of this particular group of chemicals.
’ INTRODUCTION Skin sensitizing chemicals have the ability to covalently modify skin proteins. These modified proteins are recognized by the immune system as foreign and trigger a specific T-cell mediated immune response.1 A key step in the skin sensitization process is the formation of a covalent adduct between the skin sensitizer and endogenous proteins and/or peptides in the skin. On the basis of this well-established toxicity mechanism, the most straightforward approach to predict skin sensitization involves the measurement of the reactivity of a test compound toward peptides and proteins.1 Gerberick et al.2 developed a peptide depletion assay using different heptapeptides (later coined the DPRA or “direct peptide reactivity assay”). This assay is currently under prevalidation by the European Center for the Validation of Alternative Methods to animal testing. We have further developed this approach by integrating LC/MS detection in order to simultaneously record peptide depletion and peptideadduct formation3,4 and by measuring the rate constant of the reaction. 5 Skin sensitization is currently estimated with the local lymph node assay in mice (LLNA) in which the cellular proliferation in the draining lymph nodes is measured after repeated topical application r 2011 American Chemical Society
of the test compound onto the ears. Results are expressed as EC3 values indicating the concentration which induces a 3-fold enhanced cellular proliferation.6 The EC3 value is considered to give the best available quantitative measure of the skin sensitizing potency of a chemical.7 Skin sensitizers are sometimes classified into applicability domains based on their putative reaction mechanisms,8 and it had been argued that distinct quantitative models should be made to describe the potency of chemicals in different applicability domains.9 This is based on the reasoning that the proportionality between reactivity with the skin protein and reactivity with a model nucleophile may differ according to the reaction mechanism.10 However, one may argue that discriminating applicability domains does not make sense a priori from a biological perspective: The immune system mounts a response to the modified protein, and there is no apparent biological mechanism as to how the immune system should discriminate between epitopes formed by different chemical reaction mechanisms, unless different reaction mechanisms Received: August 25, 2011 Published: October 24, 2011 2018
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Figure 1. Michael acceptors used in the previous study to derive the quantitative kinetic model. The sensitization potential is expressed within parentheses as the LLNA EC3 value in %.
Figure 2. Additional Michael acceptors of the current study. The sensitization potential is expressed within parentheses as the LLNA EC3 value in %. For all chemicals with the exception of 14, 20, and 21, the LLNA data were kindly provided by RIFM, the Research Institute for Fragrance Materials.
lead to selective modification of different target nuclophiles in proteins (such as Lys- vs Cys-residues).11 In a detailed kinetic profiling study on Michael acceptors (MA), we found a linear relationship between the EC3 values and the rate constant k of the reaction with a test peptide:5 pEC3 ¼ 0:24ð ( 0:04Þlog k þ 2:11ð ( 0:24Þ
ð1Þ
This indicates that the skin sensitization potency of MA in the LLNA can be estimated on the basis of the kinetics of the reaction with a test peptide, but interestingly, the regression line indicates that potency increases only slightly by a significant increase in reactivity. Thus, the slope of 0.24 signifies that a 10-fold increase in the rate constant of the reaction with the particular peptide in the DPRA correlates to only a 1.7-fold increase in the sensitization potential. Interestingly, literature data indicate that MA are somehow unique, if in vitro data of larger data sets of diverse skin sensitizing chemicals are analyzed: (i) Gerberick et al.12 proposed a classification tree approach to classify sensitizers on the basis of depletion values obtained in the original DPRA assay. Chemicals were divided in four potency classes and in four reactivity classes. The highest reactivity class contained 14 of the 17 strong/extreme sensitizers, but it also contained 11 moderate sensitizers. These 11 moderate sensitizers with the highest reactivity included seven clear MA and one potential MA (2-methyl-2H-isothiazol-3-one). (ii) Similarly, we noted in a data integration project, compiling data from an Nrf2-reporter gene assay and DPRA data, that MA are generally predicted too strong by a global model, thus making it difficult to accurately classify moderate sensitizers.13 At the time, we had no explanation for this unique behavior of MA. Here,
we pose the question whether these two observations (i) and (ii) on the overprediction of the potency of MA in global quantitative models are linked to the relatively weak relationship between reaction rate and potency for these chemicals. In a recent study, the difference between weak and strong sensitizers was proposed to be linked to their differential ability to induce pro-inflammatory cytokines and the balance of their pro- and anti-inflammatory effects.14 In this particular study, Arnica tinctures were tested as the weak sensitizer, and 2,4, 6-trinitrochlorobenzene (TNCB, attributed to the SNAr-reactive applicability domain) was tested as the strong sensitizer. Arnica tinctures contain as active principles sesquiterpenes lactones, namely, 11α,13-dihydrohelenalin esters and helenalin esters, which are reactive MA. Sesquiterpene lactones in general are known for their potent nuclear factor kappa B (NF-kB) inhibiting activity, which correlates to their reactivity and to the number of MA functionalities.15 The transcription factor NF-k B mediates a major signaling pathway associated with inflammation and oxidative stress. In unstimulated cells, the inhibitor of NF-kB (IkB) protein sequesters the inactive transcription factor in the cytoplasm. A variety of stimuli like cytokines, bacterial infection, and various forms of stress can activate NF-kB through phosphorylation of IkB by the IkBkinase complex (IKK). Upon phosphorylation, IkB is degraded, and the NF-kB dimer translocated into the nucleus where it activates the transcription of target genes.16 In this context, it is interesting to note that cyclopentenone prostaglandins, the sesquiterpene lactone parthenolide and the lipid metabolism product 4-hydroxy-2-nonenal have been found 2019
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Figure 3. SNAr- and SN2- reactive sensitizers tested. The sensitization potential is expressed within parentheses as the LLNA EC3 value in %.
Table 1. Testing the Published Quantitative Model for MA with a Test Set to Predict the LLNA EC3 Values no.
name
reference for
rate constant
LLNA result
103 k (s1M1)
EC3 predicted (%)
EC3 measured (%)
11
L-carvone
RIFM
0.4
7.7
13/10.7 a
12
damascenone
RIFM
52.2
3.0
1.2
2.5
13
β-Damascone
RIFM
4.5
5.5
6.7/2.4a
1.2
14
2-ethylhexyl acrylate
7.4
4.7
10.0
2.1
15
isojasmone
RIFM
4.4
4.7
2.4
2.0
16
δ-Damascone
RIFM
4.0
5.6
9.6/0.9/5.2a
1.1
17 18
3,7-dimethyl-2-methylene-6-octenal methyl 2-octinoate
RIFM RIFM
22.9 26.2
3.2 2.9
4.5 0.45
1.4 6.4
19
γ-Damascone
RIFM
3.2
5.9
4.5
1.3
20
1-(p-methoxyphenyl)-1-penten-3-one
0.8
8.2
9.3
1.1
21
4-vinylcyclohex-1-ene-carbaldehyde
16.9
2.8
3.4
30
31
internal
median a
margin of error (fold) a 1.5
1.2 1.4
Fold margin of error was calculated on the basis of the median in cases for which several LLNA results were available.
to exert anti-inflammatory activity by their ability to alkylate and thereby inhibit IKK which leads to the suppression of NF-kB signaling.1719 Interestingly, these chemicals are all MA. Moreover, in a screening on biological activities of essential oil ingredients, we noted the suppression of NF-kB signaling for a number of MA (unpublished observation). Reactive sesquiterpeneslactones were also reported to inhibit NF-kB signaling by directly alkylating the p65 subunit of NF-kB at Cys38.20,21 On the basis of these different, at first sight disparate, observations, we hypothesized that increasing reactivity of MA could confer to these chemicals a specific antiinflammatory potential partly counteracting their increasing sensitizing potential and thus leading to the relatively low dependence of potency on reactivity. Here, we report comparative data on MA and other skin sensitizers, mainly focusing on the well described groups of SNAr- and SN2-reactive sensitizers. These chemicals were tested both for their peptide reactivity with the quantitative kinetic profiling approach and suppression of NF-kB signaling in a luciferase reporter assay. The relatively weak relationship between potency and reactivity of MA was confirmed, and a much stronger linear relationship was found for the SNAr- and SN2-reactive sensitizers. This latter group was devoid of in vitro anti-inflammatory potential, whereas such a bioactivity was found for all skin sensitizing MA tested.
’ MATERIALS AND METHODS Caution: The test chemicals are mild to very potent skin sensitizers in the LLNA, and any skin contact should be avoided. Test Chemicals. All fragrance chemicals were obtained from Givaudan Schweiz AG, and all other chemicals were purchased from Sigma-Aldrich (Buchs Switzerland). TNF-α was from Invitrogen. Figures 13 list the test chemical structures along with their sensitization potential expressed as EC3 values in percent form as measured in the LLNA. The sources of the LLNA EC3 values are listed in Tables 1 and 2. Data on most Michael acceptors in the test set were kindly provided by the Research Institute for Fragrance materials (RIFM). For regression analysis, the pEC3 was used, which was calculated as follows: pEC3 ¼ logðMW=EC3 Þ
ð2Þ
Peptide Reactivity Experiments. The Cys-peptide (Ac-RFAACAA, MW 750.1) was obtained from Genscript Inc. (Piscataway, NJ, USA). It has a purity of 98.1%. The reaction conditions were taken from the assay described by Gerberick et al.2 (pH 7.5, 0.5 mM test peptide). To determine chemical modifications of peptides, reactions were carried out for 24 h in a final volume of 1 mL in HPLC vials and with a concentration of the test chemical of 5 mM. LC/MS analysis was then performed as previously described3 on a Finnigan LCQ classic mass spectrometer (Thermo Finnigan, San Jose, CA, U.S.A.) operated in the ESI(+) mode. 2020
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Table 2. Reaction Rates and the Sensitization Potential for the SNAr and SN2 Reactive Chemicals no.
a
name
reference for LLNA result
LLNA EC3
pEC3
rate constant 103 k (s1M1)
log k (s1M1)
22
DNCB
32
0.04
3.70
222.3
0.65
23
DNFB
33
0.032
3.76
9446.2
0.98
24
DCNB
34
20
0.98
0.3
3.47
25
DNBS
35
2
2.09
4.6
2.33
26
DNBB
33
0.085
3.46
341.0
0.47
27
DNBI
33
0.17
3.24
362.5
0.44
28
TNCB
36
0.26
3.69
100471a
2.00
29 30
TNBS benzyl bromide
37
0.3 0.2
2.99 2.93
3423 369
0.53 0.43
31
4-nitrobenzyl bromide
38
0.05
3.64
984
0.01
32
(2-bromoethyl)-benzene
39
6.2
1.47
0.9
3.03
33
DNTB
40
0.05b
3.68
160168a
2.20
34
chlorothalonile
41
0.004
4.82
4450a
0.65
38
b
Estimated minimal values from reactions performed under very dilute conditions. Extrapolated value.
To determine kinetic doseresponse curves with the Cys-peptide, the peptide reactivity assay was run in microtiter plates in a final volume of 160 μL. Test agents were dissolved in acetonitrile at concentrations of 20, 16, 12, 8, and 4 mM. These solutions (40 μL) were added to individual wells of the microtiter plates, and then 120 μL of a peptide solution (0.666 mM of Ac-RFAACAA) in 100 mM phosphate buffer (pH 7.5) was added to each well. Control wells contained solvent and buffer only to determine background fluorescence. The plates were covered with impermeable foil to avoid evaporation, and they were incubated protected from light at 30 °C for different time intervals (5, 15, 30, 90, 150, 270, and 1440 min). The reactions were then stopped by the addition of a monobromobimane solution (80 μL per well; 1.5 mM in 100 mM NaCO3, pH 8.8). After 5 min of incubation at room temperature, the fluorescence at 385/480 nm was determined to measure the free cysteine in the parent peptide derivatized with monobromobimane. The data were plotted and rate constants calculated as described before (see a detailed example for diethylmaleate in ref 5). For each chemical, only those time and concentration points were evaluated for which the peptide was not depleted by >80%. For chemicals with slow reaction rates (less than 30% peptide depletion at the highest concentration after 24 h), the rate constant was calculated from the depletion value (dp) obtained with the LC-MS method after 24 h according to the following formula:5 k ¼ ½lnð100=ð100 dpÞÞ=½E0 t
ð3Þ
Anti-Inflammatory Effect on NF-jB Signaling. The 293/ NF-kB-luc cell line was obtained from Panomics/Affymetrics (Santa Clara, CA, USA). The cell line is based on human embryonic kidney 293 cells, which contain a stable chromosomal integration of a luciferase reporter construct regulated by six copies of the NF-kB response element. Induction of luciferase activity is very specific with cells only responding to TNF-α. 293/NF-kB-luc were maintained in Dulbecco’s modified Eagle’s medium containing glutamax (Invitrogen) supplemented with 10% heat inactivated fetal calf serum (FCS, Amimed) and 100 μg/mL Hygromycin B (Invitrogen). Cells were grown at 37 °C in the presence of 5% CO2. Test chemicals (0.5 M) were dissolved in dimethyl sulfoxide (DMSO) and serial half dilutions prepared in DMSO (0.5500 mM). These DMSO solutions were diluted 125-fold in DMEM with glutamax (44000 μM). All compounds were tested in at least two independent repetitions with triplicate analysis at 12 concentrations in each repetition. 293/NF-kB-luc cells were dissociated with 0.05% trypsin/EDTA (Invitrogen), resuspended in assay medium (DMEM with glutamax and 10% FCS and without Hygromycin B), and seeded in 96-well plates at a
density of 25,000 cells in 100 μL. Ninety-six-well plates had been coated with 10 ppm polyethylene imine (MW 600,0001,000,000). After 24 h of growth, 50 μL of assay medium without FCS with 40 ng/mL TNF-α (final concentration 10 ng/mL) was added. At the same time, the test chemicals (44000 μM) dissolved in 50 μL of assay medium without FCS were added resulting in final concentrations of test chemicals ranging from 1 to 1000 μM except for the controls β-Damascone (18.8300 μM) and Apigenin (4.7150 μM). Additional controls included medium with TNF-α without test chemicals (induced NF-kB) and without TNF-α (no induction of NF-kB). Inhibition of NF-kB induction was based on the full induced luciferase activity with TNF-α in the absence of test chemicals. Final solvent concentration was 0.2% in all experiments including the controls without test chemicals. Plates were sealed with sterile sealing tape. After 24 h of incubation, cytotoxicity of the compounds was tested before the determination of luciferase activity using the PrestoBlue Cell Viability assay (Invitrogen). Growth medium was removed and 90 μL of fresh medium plus 10 μL of PrestoBlue Cell Viability Reagent added. After 30 min of incubation at 37 °C and 5% CO2, bottom-read fluorescence (excitation at 560 nm, emission at 590 nm; Flexstation, Molecular Devices) was measured. Then, the PrestoBlue reagent was removed and cells washed once with PBS. Cells were lysed using Passive Lysis buffer (Promega), and luciferase activity was determined as described previously.22
’ RESULTS AND DISCUSSION Reproducibility of the Rate Constant Determinations for MA. The chemicals in Figure 1 had been tested in the previous
study, and all except 10 were now retested to evaluate intralaboratory reproducibility of the rate constant determinations. Table S1 in the Supporting Information presents the rate constants in the previous study and the new data. Regression analysis with the new data (including the published value for 10) against the pEC3 values resulted in the regression eq 4: pEC3 ¼ 0:21ð ( 0:04Þlog k þ 2:06ð ( 0:07Þ
ð4Þ
where n = 10, R2 = 0.80, R2 (adjusted) = 0.77, s = 0.125, F = 31.7, and p < 0.0005. This equation is very close to eq 1 obtained in the previous study, and we can conclude that deriving rate constants for the reaction with the test peptide in the DPRA assay to build a quantitative model is a reproducible method. 2021
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Prediction of the New MA Based on the Published Quantitative Model. The additional chemicals in Figure 2 have a
MA structural alert. For these chemicals, new LLNA data have become available since the previous study, and they were now used as a test set. Reactions were first performed for 24 h under the standard conditions of the DPRA and analyzed by LC-MS. All were directly peptide-reactive, and all with one exception formed as major product the direct adducts of the expected molecular weight (Table S2 in the Supporting Information). Next, the rate constants were determined for these chemicals by the kinetic profiling approach (Table 1) and used to predict the EC3 values based on published eq 1. The predicted and the measured EC3 values and the margin of error (fold-difference between predicted and measured) are listed in Table 1. For most chemicals, the EC3 could be predicted with a margin of error of less than two (median 1.4). The key outlier is compound 18: this chemical is the only one containing an α,β-unsaturated triple bond, and its sensitization potential as determined with the LLNA is 6-fold underpredicted by the model. When comparing these quantitative predictions, one should be aware of the variability of the LLNA result itself. Thus, for example, for 16, the actual LLNA result is the median of three tests with individual EC3 values of 9.6/0.9/5.2%. For Isoeugenol, detailed results on the intralaboratory variability of the LLNA have been published,23 indicating that the geometric standard deviation of the EC3 value for within-lab reproducibility is at 1.6 (i.e., variation of a factor of 1.6-fold on either side). Thus, if an in vitro prediction gives a margin of error of less than 2-fold as compared to the LLNA EC3, this is a good approximation. Updating the Model for MA with the Complete Data Set. The additional data were then used to redefine the quantitative model and validate how robust it is to the addition of new data. Thus, we did regression analysis with all MA, using the average of the two rate constant determinations in Table S1 (Supporting Information) for compounds 19. The resulting regression eq 5 is very close to the published eq 1; hence, the published model is robust to the addition of new data, considering that eq 5 is based on n = 20 instead of n = 10. No improvement of the regression was obtained by adding c log P as second independent variable, confirming our earlier observation that the rate constant but not c log P is critical for predicting the potency of MA.9 pEC3 ¼ 0:253ð ( 0:04Þlog k þ 2:142ð ( 0:10Þ
ð5Þ
where n = 20, R2 = 0.65, R2 (adjusted) = 0.63, s = 0.18, F =32.7, and p < 0.0005. The outlier 18 was omitted from final regression analysis. Figure 4 displays the scatter plot of all the data. Certainly, the regression is not perfect. This may partly be due to the fact that the kinetic profiling approach is a simplification, as we do not take into account bioavailability and cytotoxicity/danger signal formation by the test chemicals, which, besides the reactivity of the test chemicals, are thought to be modifying factors in the skin sensitization reaction.24 However, as discussed above, the intrinsic variability of the animal data also may contribute to less than optimal regression statistics. Deriving a Quantitative Model for Another Mechanistic Applicability Domain: SNAr- and SN2-Reactive Chemicals. Besides the MA, the domains of SNAr and SN2-reactive chemicals are probably the best studied skin sensitizers. The chemicals selected for this study (Figure 3) were first tested in the LC-MS-based DPRA
Figure 4. pEC3 of all tested MA plotted vs the rate constant. The outlier 18 is indicated as an open triangle.
Figure 5. Kinetic plots for 22 (DNCB) as a representative example. In this case, the rate constants were derived from the data at the 5, 15, and 30 min time points.
to verify their reaction mechanism (Table S3, Supporting Information). For most chemicals, the simple and predicted substitution at the halogen atom could experimentally be verified. Compound 34 apparently has the ability to react at two sites under the elimination of two HCl molecules, thereby cross-linking two peptide molecules. In addition, 28 appears to undergo reactions with the elimination of NO2 groups, and it reacts at an additional site in the peptide, most likely at the arginine residue, leading to double adducts. Furthermore, if 28 and 29 were incubated with an excess of the test peptide, they formed an adduct with an m/z of 1665.4, which is consistent with a reaction with two peptide molecules under elimination of both HCl and HNO2. The three chemicals 28, 29, and 34 thus have two reactive sites and the intrinsic ability to cross-link proteins, which is a property known for some strongly sensitizing dialdehydes and di-isocyanates, but so far not described for these extreme sensitizers. Therefore, we cannot describe these three compounds as simple monofunctional SNAr or SN2 chemicals. Interestingly, NO2 as the leaving group in SNAr sensitizers had already been described in the pioneering work by Landsteiner and Jacobs.25 Next, the rate constants were determined (Table 2). In most cases, the method developed for the MA was appropriate. In Figure 5, the kinetic plot is shown for 22 (DNCB) as a 2022
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representative example. For 23 and 29, the reaction proceeded so fast that it had to be stopped after shorter time intervals (1, 2, and 5 min) and at concentrations ranging from 0.5 to 2.5 mM test chemical. Compounds 28 and 33 reacted extremely fast, and we could only estimate their rate constants from reactions performed under dilute concentrations: At 0.05 mM test chemical and 0.05 mM test peptide concentration, 28 depleted the peptide by 88% and 33 depleted the peptide by 91% within 5 min. Rate constants were derived from these values according eq 3, but they must be considered with caution; they are probably an underestimation as the reaction already was near completion. Compound 34 is poorly soluble in the ACN/buffer system. The rate constant was estimated from a reaction of 0.05 mM of test chemical with 0.05 mM of the peptide for 30 min according to eq 3, but the resulting constant could also be an underestimation due to the limited solubility. Regression analysis was performed first with the classical SNAr-reactive chemicals 2227, which gave direct addition elimination reactions according to the LC-MS analysis summarized in Table S3 (Supporting Information). A linear relationship with the following regression equation and statistics was obtained: pEC3 ¼ 0:664ð ( 0:11Þlog k þ 3:58ð ( 0:20Þ 2
ð6Þ
2
where n = 6, R = 0.89, R (adjusted) = 0.87, s = 0.40, F = 33.7, and p = 0.004 . No significant contribution was obtained by adding c log P as second independent variable (data not shown), indicating that sensitization potency in the LLNA for these SNAr electrophiles is dependent on reactivity alone and that no hydrophobicity parameter is needed for prediction, which is in line with a recent modeling study.10 Interestingly, the regression line was not changed (data not shown) if the data for the three tested SN2-chemicals (30, 31, and 32) were added. This indicates that these particular chemicals do not need to be treated with a separate model, although the picture may change if more SN2-chemicals are added. A general difference in theoretical models for SN2-chemicals and SNArreactive chemicals had been described10,26 with the EC3 of SN2but not of SNAr-chemicals being dependent on c log P. The regression was also not significantly changed (eq 7), although the resulting correlation coefficient is slightly lower, if in addition to 30, 31, and 32 also the two compounds 29 and 34, for which the reaction rates could be measured with adapted conditions, were added. It should, however, be kept in mind that these two chemicals have two reactive sites, which, besides the speed of the reaction, may additionally impact the sensitization potential. pEC3 ¼ 0:660ð ( 0:11Þlog k þ 3:53ð ( 0:18Þ
ð7Þ
where n = 11, R2 = 0.80, R2 (adjusted) = 0.78, s = 0.33, F = 36.8, and p < 0.0005. This regression line is plotted in Figure 6, which also displays the estimated data for the two extremely reactive chemicals 28 and 33, for which the given reaction rate is an approximation. These chemicals do not fit the regression line: A very marked increase in the reaction rate does not further increase their sensitization potential. Thus, above a certain reactivity, the sensitization potential in the LLNA appears not to increase any further. For 33, it had indeed been reported that the increased reactivity even leads to a loss of sensitization potential in humans. In a very elegant study, this could be linked to the fact that 33
Figure 6. pEC3 of the SNAr and SN2 chemicals plotted vs the rate constant. The estimated values for the extremely reactive chemicals 28 and 33 are indicated as open triangles and do not form part of the regression line.
reacts so quickly in the thiol-rich surface layers of the human skin that insufficient amounts reach the viable epidermis to trigger the priming of the immune response.27 A similar effect might explain the fact that 28 (commonly referred to as TNCB) has no increased potency as compared to 22 (DNCB) despite the dramatic increase in reaction rate with the test peptide (estimated to be above 450-fold). As TNCB stochiometrically reacts with peptide-thiols at low concentration and within minutes, it may also largely get quenched in the surface layers of the skin. Difference in the Quantitative Model for MA and SNArand SN2-Reactive Chemicals. The most interesting observation from the kinetic analysis is the fact that eqs 6 and 7 have a much steeper slope as compared to eq 1 or eq 5. Thus for SNAr- and SN2-reactive chemicals, the relationship between increasing reactivity and increasing sensitization potential appears to be much stronger as compared to the MA. Indeed a 10-fold increase in reactivity does only confer a 1.7-fold increase in potency to MA based on eq 1. Equation 6 has a slope of 0.66, which is much closer to 1 (the latter would conform to the naïve assumption that a 10-fold increase in reactivity increases sensitization potential 10-fold). The slope of 0.66 indicates that a 10-fold increase in reactivity with the test peptide still parallels a 4.6-fold increase in sensitization potency in the LLNA, which is a dramatic difference as compared to the 1.7-fold increase for MA. To illustrate the difference with a specific example, the rate constant of 22 (DNCB) is only half of the MA 14, but its sensitization potential is 35-fold higher as compared to that of 14. Anti-Inflammatory Potential of the Test Chemicals in a NF-jB Reporter Assay. As outlined in the Introduction, the relatively weaker potency of MA might eventually be linked to their anti-inflammatory potential,14 as the anti-inflammatory potential itself is mechanistically linked to reactivity for some MA.1719 We therefore systematically evaluated the anti-inflammatory potential of all the chemicals listed in Figures 13. In this assay, the inhibition of TNF-α-induced NF-kB-dependent luciferase activity is measured and expressed as EC50 values (concentration to reduce TNF-α-dependent gene induction by 50%). Cytotoxicity is measured in parallel and expressed as IC50 values (concentration to reduce cellular viability by 50%). Anti-inflammatory effects are only 2023
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Table 3. Inhibition of NF-jB Activation by MA, SNAr-, and SN2-Chemicals no.
EC50a (μM)
name parthenolide (reference sesquiterpene lactone)
IC50b (μM)
ratio
2.8
6.7
2.4
7.6
Michael Acceptors 1
diethyl maleate
62.7
479.2
2
2-hydroxy-ethyl acrylate
45.6
152.8
3.4
3
α-methylcinnamaldehyde
568.9
>1000
>1.8
4
α-Damascone
32.9
104.7
3.2
5
benzylidene acetone
47.2
153.9
3.3
6
4-vinyl-pyridine
44.6
69.5
1.6
7 8
(2E)-5,6,7-trimethyl-2,5-octadien-4-one benzyl cinnamate
30.3 307.5
141.1 >1000
4.7 >3.2
9
cinnamic aldehyde
61.4
102.8
1.7
10
2-transdecenal
45.8
57.3
1.3
11
L-carvone
603.2
>1000
>1.6
12
Damascenone
31.9
158.7
5.0
13
β-Damascone
70.0
300.0
4.3
14
2-ethylhexyl acrylate
>1000
>1000
n.a.
15 16
isojasmone δ-Damascone
74.9 87.8
359.0 >1000
4.8 >11.4
17
3,7-dimethyl-2-methylene-6-octenal
80.3
169.0
2.1
18
methyl 2-octinoate
25.3
202.3
8.0
19
γ-Damascone
115.6
>1000
>8.65
20
1-(p-methoxyphenyl)-1-penten-3-one
66.0
228.0
3.5
21
4-vinylcyclohex-1-enecarbaldehyde
264.7
415.6
median Michael acceptors
1.6 3.4
SNAr and SN2 Chemicals 22
DNCB
9.5
10.2
1.1
23
DNFB
17.4
16.7
1.0
24
DCNB
>1000
>1000
n.a.
25
DNBS
459.8
489.7
1.1
26
DNBB
5.5
5.0
0.9
27
DNBI
8.2
7.8
1.0
28
TNCB
266.1
272.3
1.0
29 30
TNBS (2-bromoethyl)-benzene
>1000 >1000
>1000 >1000
n.a. n.a.
31
4-nitrobenzyl bromide
1.5
3.1
2.0
32
benzyl bromide
67.2
85.0
1.3
33
DNTB
17.4
16.3
median SNAr- and SN2-chemicals
0.9 1.0
a
EC50 indicates the concentration for 50% inhibition of TNFα-induced Luciferase. b IC50 indicates the concentration for 50% reduction in viability as measured with the Presto Blue assay.
evident if the luciferase response is reduced at lower concentrations as compared to the reduction of cellular viability. Otherwise, reduction of luciferase activity is just due to cell toxicity. Table 3 lists the EC50 and the IC50 values of the tested chemicals and the ratio between the two values. Figure 7 shows the doseresponse curves for compounds 1 and 22 as examples of a MA and an SNArreactive chemical. Interestingly, all MA have an anti-inflammatory potential with EC50 values below the IC50 values. The median of the ratio between IC50 and EC50 for the MA is 3.4; thus, the inhibition occurs clearly below the cytotoxic level. Especially, for some of the more cytotoxic aldehydes the difference between EC50 and IC50 is relatively small, but also for parthenolide, a well-known NF-kB-inhibiting sesquiterpene lactone, the ratio is only 2.4. For the
SNAr- and SN2-chemicals, however, we found no anti-inflammatory activity. The ratio between IC50 and EC50 is generally very close to one (median = 1.0); thus, the observed reduction of luciferase activity is just due to cytotoxicity. Interestingly, the same holds true when looking at a series of other skin sensitizers mainly taken from the list published by Casati et al.28 Table S4 in the Supporting Information lists the EC50 and IC50 values for this more diverse set of chemicals, and with the exception of two additionally tested epoxides, we found no general anti-inflammatory effects for sensitizing chemicals with the ratio IC50/EC50 mostly close to 1. This would indicate that NF-kB inhibition is rather specific to MA. Finally, we did regression analysis between the logarithmic rate constants and the logarithmic EC50 values for all MA (eq 8). 2024
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Chemical Research in Toxicology
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Figure 7. Inhibition of TNFα-induced luciferase activity (4) and cellular viability (9) in the 293/NF-kB-luc cell line of (A) the MA diethylmaleate (1) and (B) the SNAr-reactive chemical DNCB (22) as representative examples.
Although the correlation is relatively weak with an R2 of 0.57 (Figure 8), it is highly significant with a negative slope (p < 0.0005). Thus, overall, the increasing reactivity does confer to the skin sensitizing MA an increasing anti-inflammatory potential, which is in line with the mode of action (alkylation of IKK by some MA).1719 This regression is further improved (R2 of 0.67) by adding c log P as second independent variable, which makes a significant contribution (data not shown). However, this does not quantitatively affect the dependence of log EC50 on the rate constant. log EC50 ¼ 0:335ð ( 0:69Þlog k þ 1:24ð ( 0:15Þ ð8Þ where n = 20, R2 = 0.57, R2 (adjusted) = 0.54, s = 0.28, F = 23.45, and p < 0.0005 . These data give a more systematic underpinning of the earlier work by Lass et al.14 An anti-inflammatory effect thus appears to be common to most MA, whereas SNAr- and SN2-chemicals are devoid of this biological activity. As the anti-inflammatory effect increases with increasing reactivity for MA, this effect may partly counterbalance the increasing sensitization potential. We thus propose this anti-inflammatory effect as the currently most plausible explanation for the weaker relationship between potency in the LLNA and reactivity observed for the MA. Currently, we do not know why there appears to be a specific effect for MA on NF-kB inhibition and how the target (presumably IKK) discriminates between MA and other reactive chemicals. We can just note that the inhibitory effects observed for many sensitizing MA are in line with literature data indicating that many sesquiterpenes lactones and other MA have a similar biological activity. Alternative Explanations for the Difference in the Reactivity Potency Correlations between MA and SNAr-and SN2-Reactive Chemicals. Certainly, the anti-inflammatory effect of the MA is not the only possible explanation for the relatively lower sensitization potential. The Michael addition is a reversible reaction, whereas the substitution reactions are irreversible in dilute aqueous solution. In the peptide reactivity assays, the electrophile is not removed from the reaction site if the reverse reaction happens. In the skin, the electrophile is likely to become depleted with time by diffusion and metabolism, and we cannot
Figure 8. Correlation between the rate constants and the NF-kB inhibitory activity expressed as EC50 values for the MA chemicals.
exclude the possibility that the epitopes formed as Michael adducts are then prone to the reverse reaction, diluting them in time, which may also dampen the sensitization potential. There is also an alternative biological explanation: Skin sensitizers in general have the ability to induce the Nrf2-response,11 and thus, they can induce enhanced levels of glutathione in keratinocytes. Although this is true for most sensitizers, we have noted that some Michael-acceptors have a particular high capacity to induce GSH formation (our unpublished observation). Increased detoxification potential of the cells induced by MA thus could be another alternative explanation for the relatively lower sensitization potential of highly reactive MA. Finally, we had earlier speculated that the slope of eq 1 might indicate that the relevant nucleophilic sites in the skin (which are unknown) might be more reactive and less selective as compared to the test peptide in the DPRA conditions.5 Since we now get a much steeper slope with the SNAr- and SN2-chemicals, this can be only a partial explanation, but it is indeed possible that both slopes would get steeper with a more reactive target peptide, and we may envisage that the slope for the SNAr and SN2 then approximates 1, which we would expect based on a simplistic quantitative reasoning. 2025
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Chemical Research in Toxicology An additional discussion on possible reasons for a lower than expected dependence of potency on reactivity can be found in the very recent work by Roberts et al.10 In the absence of experimental data or a clear mechanistic biological hypothesis, they raised the possibility that an additional cell process is part of the cascade of events leading to sensitization and that this cell process itself must be dependent on the degree of protein modifications. Indeed, the NF-kB inhibition effect might represent such an additional cell process dependent on reactivity and which is dampening the sensitization potency.
’ CONCLUSIONS As in vitro testing is moving from its first goal (hazard identification) to attempts to predict potency, we have to increase our understanding of the concept of applicability domains. So far, there has been no clear biological rationale to divide skin sensitizers into applicability domains based on chemical reaction mechanisms (with the notable exception of specifically lysine- or cysteine- reactive chemicals assumed to preferentially trigger TH1 and TH2 reactions).11,29 However, the concept made sense since successful models have been developed within specific applicability domains. Our data, for the first time, prove that adduct formation by different reaction mechanisms may have a dramatic impact on the relationship between skin sensitizing potency and chemical reactivity. At the same time, we propose a mechanistic and experimentally proven biological mechanism for this difference. However, we cannot conclude from this observation that in general all chemicals can only be predicted within such defined applicability domains. The MA, based on their unique action on the IKK, might be an exception from the rule, the rule being the simple biological rationale that the amount of epitope formed is the key determinant of potency and not the chemical mechanism behind epitope formation. Thus, we cannot conclude from these data that formation of global models to predict skin sensitization potency based on in vitro data is generally impossible, but the data clearly give a possible reason why MA are overpredicted by some global models proposed so far (see review of refs 12 and 13 in the Introduction). As a simple first guidance, the data indicate that MA might need to be treated separately in such global models or that a correction factor may have to be introduced. Animal models are holistic, and a single readout such as the EC3 value in the LLNA represents the sum of a number of underlying biological processes and signaling cascades. By their very nature, most in vitro tests lack this holistic aspect, and the current data indicate how knowledge on different relevant biological processes may be needed to obtain a global understanding of the factors affecting a holistic potency estimate such as the EC3. Applicability domains therefore will make practical sense as long as we cannot model all the biological events enhancing or also weakening the actual sensitization potential of a certain chemical. ’ ASSOCIATED CONTENT
bS
Supporting Information. Intralaboratory reproducibility of the rate constant determinations; adduct formation by the Michael acceptors of the test set in the LC-MS-based DPRA assay; adduct formation by SNAr and SN2 reactive chemical in the LC-MS-based DPRA assay; and inhibition of NF-kB activation by diverse skin sensitizers. This material is available free of charge via the Internet at http://pubs.acs.org.
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’ AUTHOR INFORMATION Corresponding Author
*Tel: ++41 44 824 21 05. Fax: ++41 44 824 29 26. E-mail:
[email protected].
’ ACKNOWLEDGMENT We thank RIFM (the Research Institute for Fragrance Materials) for providing additional LLNA data on MA chemicals. ’ ABBREVIATIONS DCNB, 2,4-dichloro-nitrobenzene; DMSO, dimethyl sulfoxide; DNBB, 2,4-dinitrobromobenzene; DNBI, 2,4-dinitroiodobenzene; DNBS, 2,4-dinitrobenzene sulfonic acid; DNCB, 2,4-dinitrochlorobenzene; DNFB, 2,4-dinitrofluorobenzene; DNTB, 2,4dinitro-1-thiocyanatobenzene; DPRA, direct peptide reactivity assay; GSH, reduced glutathione; IkB, inhibitor of NF-kB; IKK, IkB-kinase; LLNA, local lymph node assay; MA, Michael acceptor; Nrf2, nuclear factor-erythroid 2-related factor; TNCB, 2,4,6trinitrochlorobenzene; TNBS, 2,4,6-trinitrobenzene sulfonic acid; TNF, tumor necrosis factor ’ REFERENCES (1) Gerberick, F., Aleksic, M., Basketter, D., Casati, S., Karlberg, A. T., Kern, P., Kimber, I., Lepoittevin, J. P., Natsch, A., Ovigne, J. M., Rovida, C., Sakaguchi, H., and Schultz, T. (2008) Chemical reactivity measurement and the predictive identification of skin sensitisers. Altern. Lab. Anim. 36, 215–242. (2) Gerberick, G. F., Vassallo, J. D., Bailey, R. E., Chaney, J. G., Morrall, S. W., and Lepoittevin, J. P. (2004) Development of a peptide reactivity assay for screening contact allergens. Toxicol. Sci. 81, 332–343. (3) Natsch, A., and Gfeller, H. (2008) LC-MS-based characterization of the peptide reactivity of chemicals to improve the in vitro prediction of the skin sensitization potential. Toxicol. Sci. 106, 464–478. (4) Natsch, A., Gfeller, H., Rothaupt, M., and Ellis, G. (2007) Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro. Toxicol. in Vitro 21, 1220–1226. (5) Roberts, D. W., and Natsch, A. (2009) High throughput kinetic profiling approach for covalent binding to peptides: Application to skin sensitization potency of michael acceptor electrophiles. Chem. Res. Toxicol. 22, 592–603. (6) Basketter, D. A., Evans, P., Fielder, R. J., Gerberick, G. F., Dearman, R. J., and Kimber, I. (2002) Local lymph node assay - Validation, conduct and use in practice. Food Chem. Toxicol. 40, 593–598. (7) Basketter, D. A., Gerberick, F., and Kimber, I. (2007) The local lymph node assay and the assessment of relative potency: Status of validation. Contact Dermatitis 57, 70–75. (8) Aptula, A. O., and Roberts, D. W. (2006) Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: General principles and application to reactive toxicity. Chem. Res. Toxicol. 19, 1097–1105. (9) Roberts, D. W., Aptula, A. O., Patlewicz, G., and Pease, C. (2008) Chemical reactivity indices and mechanism-based read-across for nonanimal based assessment of skin sensitisation potential. J. Appl. Toxicol. 28, 443–454. (10) Roberts, D. W., Aptula, N. O., and Patlewicz, G. Y. (2011) Chemistry-based risk assessment for skin sensitization: Quantitative mechanistic modelling for the SNAr domain. Chem. Res. Toxicol. 24, 1003–1011. (11) Natsch, A. (2010) The Nrf2-Keap1-ARE toxicity pathway as a cellular sensor for skin sensitizers: Functional relevance and a hypothesis on innate reactions to skin sensitizers. Toxicol. Sci. 113, 284–292. (12) Gerberick, G. F., Vassallo, J. D., Foertsch, L. M., Price, B. B., Chaney, J. G., and Lepoittevin, J. P. (2007) Quantification of chemical 2026
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Chemical Research in Toxicology peptide reactivity for screening contact allergens: A classification tree model approach. Toxicol. Sci. 97, 417–427. (13) Natsch, A., Emter, R., and Ellis, G. (2009) Filling the concept with data: Integrating data from different in vitro and in silico assays on skin sensitizers to explore the battery approach for animal-free skin sensitization testing. Toxicol. Sci. 107, 106–121. (14) Lass, C., Merfort, I., and Martin, S. F. (2010) In vitro and in vivo analysis of pro- and anti-inflammatory effects of weak and strong contact allergens. Exp. Dermatol. 19, 1007–1013. (15) Siedle, B., Garcia-Pineres, A. J., Murillo, R., Schulte-Monting, J., Castro, V., Rungeler, P., Klaas, C. A., Da Costa, F. B., Kisiel, W., and Merfort, I. (2004) Quantitative structure-activity relationship of sesquiterpene lactones as inhibitors of the transcription factor NF-kappaB. J. Med. Chem. 47, 6042–6054. (16) Ghosh, S., and Hayden, M. S. (2008) New regulators of NF-kB in inflammation. Nature Rev. Immunol. 8, 837–848. (17) Rossi, A., Kapahi, P., Natoli, G., Takahashi, T., Chen, Y., Karin, M., and Santoro, M. G. (2000) Anti-inflammatory cyclopentenone prostaglandins are direct inhibitors of IkappaB kinase. Nature 403, 103–108. (18) Ji, C., Kozak, K. R., and Marnett, L. J. (2001) IkappaB kinase, a molecular target for inhibition by 4-hydroxy-2-nonenal. J. Biol. Chem. 276, 18223–18228. (19) Kwok, B. H., Koh, B., Ndubuisi, M. I., Elofsson, M., and Crews, C. M. (2001) The anti-inflammatory natural product parthenolide from the medicinal herb Feverfew directly binds to and inhibits IkappaB kinase. Chem. Biol. 8, 759–766. (20) Garcia-Pineres, A. J., Castro, V., Mora, G., Schmidt, T. J., Strunck, E., Pahl, H. L., and Merfort, I. (2001) Cysteine 38 in p65/ NF-kB plays a crucial role in DNA binding inhibition by sesquiterpene lactones. J. Biol. Chem. 276, 39713–39720. (21) Lyss, G., Knorre, A., Schmidt, T. J., Pahl, H. L., and Merfort, I. (1998) The anti-inflammatory sesquiterpene lactone helenalin inhibits the transcription factor NF-kB by directly targeting p65. J. Biol. Chem. 273, 33508–33516. (22) Natsch, A., and Emter, R. (2008) Skin sensitizers induce antioxidant response element dependent genes: Application to the in vitro testing of the sensitization potential of chemicals. Toxicol. Sci. 102, 110–119. (23) Basketter, D. A., and Cadby, P. (2004) Reproducible prediction of contact allergenic potency using the local lymph node assay. Contact Dermatitis 50, 15–17. (24) Basketter, D. A., and Kimber, I. (2009) Updating the skin sensitization in vitro data assessment paradigm in 2009. J. Appl. Toxicol. 29, 545–550. (25) Landsteiner, K., and Jacobs, J. (1936) Studies on the sensitization of animals with simple chemical compounds. J. Exp. Med 64, 625–639. (26) Roberts, D. W., Aptula, A. O., and Patlewicz, G. (2007) Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay. Chem. Res. Toxicol. 20, 44–60. (27) Pickard, C., Louafi, F., McGuire, C., Lowings, K., Kumar, P., Cooper, H., Dearman, R. J., Cumberbatch, M., Kimber, I., Healy, E., and Friedmann, P. S. (2009) The cutaneous biochemical redox barrier: a component of the innate immune defenses against sensitization by highly reactive environmental xenobiotics. J. Immunol. 183, 7576–7584. (28) Casati, S., Aeby, P., Kimber, I., Maxwell, G., Ovigne, J. M., Roggen, E., Rovida, C., Tosti, L., and Basketter, D. (2009) Selection of chemicals for the development and evaluation of in vitro methods for skin sensitisation testing. Altern. Lab. Anim. 37, 305–312. (29) Vandebriel, R., Callant Cransveld, C., Crommelin, D., Diamant, Z., Glazenburg, B., Joos, G., Kuper, F., Natsch, A., Nijkamp, F., Noteborn, H., Pieters, R., Roberts, D., Roggen, E., Rorije, E., Seed, M., Sewald, K., van den Heuvel, R., van Engelen, J., Verstraelen, S., and van Loveren, H. (2011) Respiratory sensitization: Advances in assessing the risk of respiratory inflammation and irritation. Toxicol. in Vitro 25, 1251–1258. (30) Dearman, R. J., Betts, C. J., Farr, C., McLaughlin, J., Berdasco, N., Wiench, K., and Kimber, I. (2007) Comparative analysis of skin
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sensitization potency of acrylates (methyl acrylate, ethyl acrylate, butyl acrylate, and ethylhexyl acrylate) using the local lymph node assay. Contact Dermatitis 57, 242–247. (31) Ryan, C. A., Gerberick, G. F., Cruse, L. W., Basketter, D. A., Lea, L., Blaikie, L., Dearman, R. J., Warbrick, E. V., and Kimber, I. (2000) Activity of human contact allergens in the murine local lymph node assay. Contact Dermatitis 43, 95–102. (32) Gerberick, G. F., Ryan, C. A., Kern, P. S., Dearman, R. J., Kimber, I., Patlewicz, G. Y., and Basketter, D. A. (2004) A chemical dataset for evaluation of alternative approaches to skin-sensitization testing. Contact Dermatitis 50, 274–288. (33) Basketter, D. A., Dearman, R. J., Hilton, J., and Kimber, I. (1997) Dinitrohalobenzenes: evaluation of relative skin sensitization potential using the local lymph node assay. Contact Dermatitis 36, 97–100. (34) Basketter, D. A., Scholes, E. W., Fielding, I., Dearman, R. J., Hilton, J., and Kimber, I. (1996) Dichloronitrobenzene: a reappraisal of its skin sensitization potential. Contact Dermatitis 34, 55–58. (35) Ryan, C. A., Cruse, L. W., Skinner, R. A., Dearman, R. J., Kimber, I., and Gerberick, G. F. (2002) Examination of a vehicle for use with water soluble materials in the murine local lymph node assay. Food Chem. Toxicol. 40, 1719–1725. (36) Smith, C. K. and Hotchkiss, S. A. M. (2010) Allergic contact dermatitis chemical and metabolic mechanisms. Taylor & Francis, UK. (37) Peiser, M. (2010) Background on skin sensitization hazard identification: Aspects in the development of in-vitro based alternatives for sensitization testing, LRI workshop, Brussels, 02.02.2010, http:// www.cefic-lri.org/uploads/Events%202010/LRI_LLNA-Workshop_100202-Peiser.pdf. (38) Gerberick, G. F., Ryan, C. A., Kern, P. S., Schlatter, H., Dearman, R. J., Kimber, I., Patlewicz, G. Y., and Basketter, D. A. (2005) Compilation of historical local lymph node data for evaluation of skin sensitization alternative methods. Dermatitis 16, 157–202. (39) Patlewicz, G., Dimitrov, S. D., Low, L. K., Kern, P. S., Dimitrova, G. D., Comber, M. I., Aptula, A. O., Phillips, R. D., Niemela, J., Madsen, C., Wedebye, E. B., Roberts, D. W., Bailey, P. T., and Mekenyan, O. G. (2007) TIMES-SS: A promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity. Regul. Toxicol. Pharmacol. 48, 225–239. (40) Dearman, R. J., Cumberbatch, M., Hilton, J., Fielding, I., Basketter, D. A., and Kimber, I. (1997) A re-appraisal of the skinsensitizing activity of 2,4-dinitrothiocyanobenzene. Food Chem. Toxicol. 35, 261–269. (41) Boman, A., Montelius, J., Rissanen, R. L., and Liden, C. (2000) Sensitizing potential of chlorothalonil in the guinea pig and the mouse. Contact Dermatitis 43, 273–279.
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