Abstract

Allergic contact dermatitis resulting from skin sensitization is a common occupational and environmental health problem. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin sensitization potential of chemicals. In addition to accurate identification of skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency; information that is pivotal in successful management of human health risks. However, even with the significant animal welfare benefits provided by the LLNA, there is still interest in the development of nonanimal test methods for skin sensitization testing. One characteristic of a chemical allergen is its ability to react with proteins prior to the induction of skin sensitization. The majority of chemical allergens is electrophilic and as such reacts with nucleophilic amino acids like cysteine or lysine. In order to determine if reactivity correlates with sensitization potential, 38 chemicals representing allergens of different potencies (weak to extreme) and nonsensitizers were evaluated for their ability to react with glutathione or three synthetic peptides containing either cysteine, lysine, or histidine. Following a 15-min reaction time for glutathione or a 24 h reaction period for the three synthetic peptides, the samples were analyzed by HPLC. UV detection was used to monitor the depletion of glutathione or the peptide following reaction. The results demonstrate that a significant correlation (Spearman correlation) exists between allergen potency and the depletion of glutathione (p = 0.001), lysine (p = 0.025), and cysteine (p = 0.020), but not histidine. The peptide with the highest sensitivity was cysteine (80.8%) whereas histidine was the least sensitive (11.5%). The data presented show that measuring peptide reactivity has utility for screening chemicals for their skin sensitization potency and thus potential for reducing our reliance on animal test methods.

Allergic contact dermatitis (ACD) resulting from skin sensitization is a common occupational and environmental health problem, and the most common manifestation of immunotoxicity in humans. The acquisition of skin sensitization, and the subsequent elicitation of an allergic hypersensitivity reaction in the skin, are processes dependent upon recognition of chemical allergens in the skin by Langerhans cells (LC) and the induction of specific T lymphocyte responses (Kimber et al., 2000, 2002). For many years guinea pigs were the species of choice for the hazard identification of skin sensitizing chemicals. More recently, however, the local lymph node assay (LLNA) has been developed as an alternative approach based upon characterization of induced proliferative responses in draining lymph nodes following topical exposure of mice to chemicals (Basketter et al., 2002; Dearman et al., 1999; Gerberick et al., 2000; Kimber et al., 1994, 2002). The LLNA has been adopted recently, as Testing Guideline 429, by the Organization for Economic Cooperation and Development (OECD, 2002) as a stand-alone test method for skin sensitization testing. However, one challenge facing investigators is the need to develop nonanimal based methods for the evaluation of new chemicals that will reduce significantly or eliminate the need for animals in skin sensitization testing in the future (Ryan et al., 2001).

There are a variety of characteristics that determine whether a chemical can function as a contact sensitizer (or allergen) including the ability to penetrate into the skin, react with protein, and be recognized as antigenic by immune cells. The correlation of protein reactivity with skin sensitization potential is well established (Dupuis and Benezra, 1982; Lepoittevin et al., 1998). In fact, Landsteiner and Jabcobs (1936) presented the origin of the reactivity hypothesis in their landmark paper looking at the underlying mechanisms of contact allergy. Thus, if a chemical is capable of reacting with protein either directly or after appropriate biotransformation, then it has the potential to act as a contact allergen.

The majority of chemical allergens (or their metabolites) have electrophilic properties and are able to react with various nucleophiles to form covalent bonds. In proteins, the side chains of many amino acids contain electron-rich groups, nucleophiles, capable of reacting with electrophilic allergens. Lysine and cysteine are those most often cited, but other amino acids containing nucleophilic heteroatoms, such as histidine, methionine, and tyrosine can also react with electrophiles (Ahlfors et al., 2003; Dupuis and Benezra, 1982; Lepoittevin et al., 1998). Thus, electrophilic allergens have the capability to react with nucleophilic amino acids in proteins, forming extremely stable covalent bonds, and therefore are involved in the triggering of skin sensitization resposnes.

Since protein reactivity is a key step in the induction of skin sensitization it was hypothesized that reactivity could be used to screen for the sensitization potential of chemicals. Therefore, a peptide-based assay was developed and chemicals of different allergenic potencies (weak to extreme) along with nonsensitizers were evaluated to determine if reactivity could be used as a potential skin sensitization screening tool. All chemicals tested have been evaluated in the LLNA and for each assigned a skin sensitization potency category: extreme, strong, moderate, weak, and nonsensitizers. These data demonstrate that a significant correlation exists between a chemical's skin sensitization potency and its ability to react with peptides containing nucleophilic amino acids such as cysteine and lysine.

MATERIALS AND METHODS

Test chemicals. The following chemicals with accompanying CAS numbers were purchased from the Sigma Chemical Company (St. Louis, MO): 2,4-dinitrochlorobenzene [97-00-7], p-benzoquinone [106–51-4], octanoic acid [124-07-2], 1,4-hydroquinone [123-31-9], glutaraldehyde [111-30-8], 4-(N-ethyl-N-2-methansulphonamido-ethyl)-2-methyl-1,4-phenylenediamine [CD3; 25646-71-3], 1-(4-methoxyphenyl)-1-penten-3-one [104-27-8], and lactic acid [50-21-5].

The following chemicals with accompanying CAS numbers were purchased from the Aldrich chemical company (Milwaukee, WI): diphenylcyclopropenone [97-00-7], phthalic anhydride [85-44-9], 2-hydroxyethylacrylate [818-61-1], 3-dimethylaminopropylamine [109-55-7], cinnamic aldehyde [104-55-2], 3-aminophenol [591-27-5], 3,4-dihydrocoumarin [119-84-6], α-hexylcinnamaldehyde [101-86-0], ethleneglycol dimethacrylate [97-90-5], diethyl phthalate [84-66-2], 2-hydroxypropylmethacrylate [923-26-2], oxazolone [5646-46-5], 1,2-benzisothiazolin-3-one [2634-33-5], phenylacetaldehyde [122-78-1], squaric acid [2892-51-5], citral [5392-40-5], diethyl maleate [141-05-9], α-amyl cinnamaldehyde [122-40-7], benzyl benzoate [120-40-7], hydroxycitronellal [107-75-5], lilial [80-54-6], 1-butanol [71-36-3], 4-hydroxybenzoic acid [99-96-7], 6-methylcoumarin [92-48-8], methyl salicylate [119-36-8], chlorobenzene [108-90-7].

Lauryl gallate [1166-52-5] was purchased from Alfa Aesar (Ward Hill, MA). Glycerol [56-81-5] was purchased from J.T. Baker (Phillipsburg, NJ). Hexane [110-54-3] was purchased from EM Science (Gibbstown, NJ). 5-Methy-2,3-hexanedione [13706-86-0] was purchased from Penta MFG (Livingston, NJ).

The purity of these chemicals was equal to or greater than 95% except for the following: α-hexylcinnamaldehyde (85%), oxazolone (90%), glutaraldehyde (70%), phenylacetaldehyde (90%), and lactic acid (85%). Stock solutions that were prepared from chemicals with less than 95% purity were adjusted for purity.

LLNA protocol and chemicals tested. The LLNA was conducted as described elsewhere (Basketter et al., 1996, 2002; Dearman et al., 1999; Gerberick et al., 2000; Kimber et al., 1994, 2002). Briefly, groups of CBA female mice (7–12 weeks of age) were exposed topically on the dorsum of both ears to 25 μl of test material, or to an equal volume of the relevant vehicle alone. Treatment was performed daily for three consecutive days. Five days following the initiation of exposure, all mice were injected via the tail vein with 250 μl of phosphate buffered saline (PBS) containing 20 μCi of tritiated thymidine. Mice were sacrificed 5 h later and the draining lymph nodes excised for each experimental group. The incorporation of tritiated thymidine measured by β-scintillation counting was reported in disintegrations per minute (dpm). A stimulation index (SI) was calculated for each allergen-treated group as the ratio of the dpm of the treated group over the dpm of the concurrent vehicle control. A substance was classified as a skin sensitizer if at one or more test concentrations it induced a three-fold or greater increase in local lymph node proliferative activity compared with concurrent vehicle-treated controls. The data reported in this article are derived from previously conducted studies. References for the sources of LLNA data for each of the chemicals are provided in Table 2.

Potency estimation in the LLNA. The approach to the estimation of relative skin sensitization potency of chemicals in the LLNA has been described previously in detail (Basketter et al., 1999). It is based upon the mathematical estimation of the concentration of chemical necessary to obtain a three-fold increase in proliferative activity in draining lymph nodes compared with concurrent vehicle-treated controls. It is termed the estimated concentration that yields a three-fold stimulation value (EC3). In these present investigations, existing dose response data for 38 chemicals evaluated in the LLNA have been used to derive EC3 values. In most cases calculation of the EC3 values was conducted by linear interpolation according to the equation:  

\[\mathrm{EC}3\ =\ \mathrm{c}\ +\ \left[\left(3\ {-}\ \mathrm{d}\right)/\left(\mathrm{b}\ {-}\ \mathrm{d}\right)\right]\ {\times}\ \left(\mathrm{a}\ {-}\ \mathrm{c}\right)\]
where the data points lying immediately above and below the SI value of 3 on the LLNA dose response plot have the coordinates (a,b) and (c,d), respectively.

For the remainder of the chemicals for which the lowest concentration tested resulted in a stimulation index of greater than 3, an EC3 value was extrapolated from the two lowest doses utilized (Gerberick et al., in press). The extrapolated EC3 value is calculated by log-linear interpolation between these two points on a plane where the x-axis represents the dose level and the y-axis represents the SI. The point with the higher SI is denoted (a,b) and the point with the lower SI is denoted (c,d). The formula for the extrapolated EC3 value is as follows:  

\[\mathrm{EC}3\ =\ 2\left(\mathrm{log}2\left(\mathrm{c}\right)\ +\ \left(3\ {-}\ \mathrm{d}\right)/\left(\mathrm{b}\ {-}\ \mathrm{d}\right)\ ^{{\ast}}\ \left(\mathrm{log}2\left(\mathrm{a}\right)\ {-}\ \mathrm{log}2\left(\mathrm{c}\right)\right)\right)\]

The relative sensitizing potencies of the chemical allergens were categorized using an arbitrary classification scheme that has recently been proposed (Kimber et al., 2003). The system, shown in Table 1, is comprised of four sensitization potency categories based on EC3 values. Compounds that did not induce a three-fold increase at any concentration tested are categorized as nonsensitizing.

TABLE 1

Classification of Relative Skin Sensitization Potency Using LLNA EC3 Values

EC3 value (%)
 
Potency classification
 
≥10–≤100 Weak 
≥1–<10 Moderate 
<1 Strong 
<0.1 Extreme 
EC3 value (%)
 
Potency classification
 
≥10–≤100 Weak 
≥1–<10 Moderate 
<1 Strong 
<0.1 Extreme 

GSH reactivity assay. Glutathione (GSH), glutathione disulfide (GSSG), iodoacetic acid, potassium bicarbonate, dimethyl sulfoxide (DMSO), bathophenanthrolinedisulfonic acid (BPDS), ethanol, and 2,4-dinitrofluorobenzene (DNFB) were purchased from Sigma. Perchloric acid (70%) and m-cresol purple were purchased from Aldrich. Glacial acetic acid, sodium acetate, methanol, and potassium hydroxide were purchased from J.T. Baker (Phillispsburg, NJ). A 2 mM stock solution of GSH and GSSG were prepared fresh in nitrogen purged water. A 200 mM stock solution of each test chemical was prepared in DMSO. Iodoacetic acid and m-cresol purple were prepared separately in water and combined by transferring 5 ml of 200 mM iodoacetic acid to 5 ml of 0.4 mM m-cresol purple. DNFB was prepared at 1% in ethanol. A working solution of 2 M potassium hydroxide/2.4 M potassium bicarbonate was prepared by adding 20 ml of 10 M potassium hydroxide to 80 ml of 3 M potassium bicarbonate. A 2.6 M sodium acetate solution was prepared by transferring 695 ml of glacial acetic acid to 500 g of sodium acetate in 224 ml of water. All other reagents were prepared in water.

Conjugation of GSH to several different chemical classes has been examined under various conditions and these data were used to design the reaction conditions employed in this work (Boyland and Chasseaud, 1967; Esterbauer et al., 1975; Fry et al., 1992; Garle and Fry, 1989; Portoghese et al., 1989). GSH reactivity of a test chemical was evaluated in a 15 ml plastic Corning conical tube (Corning, NY) by transferring 50 μl of the GSH stock solution to 400 μl of sodium phosphate buffer, 100 mM (pH 7.4), followed by the addition of 50 μl of the test chemical stock solution. The final reaction contained 0.2 mM GSH and 20 mM of the test chemical. The reaction was incubated in a 25°C shaking water bath for 15 min. Additional samples containing GSH (0.05 to 200 mM) or GSSG (0.025 to 100 mM) were prepared without test chemical and these samples were used to define the calibration curve for each analysis. Immediately following incubation, GSH and GSSG were derivatized with DNFB according to the method of Farriss and Reed (1987). To each sample, 50 μl of BPDS, 100 μl of concentrated perchloric acid, 50 μl of 100 mM iodoacetic acid/0.2 mM m-cresol purple, and 480 μl of the working solution of 2 M potassium hydroxide/2.4 M potassium bicarbonate were added. The sample was mixed on a Vortex (Bohemia, NY) and placed in the dark at room temperature for 10 min. Unreacted GSH was derivatized by adding 1 ml of 1% DNFB. The sample was mixed on a Vortex and placed in the dark at room temperature for 60 min. The sample was spun in a centrifuge at 2000 rpm for 10 min and the supernatant was analyzed by HPLC.

Derivatized GSH and GSSG were separated and quantitated by reversed-phase (RP)-HPLC according to the method of Farriss and Reed (1987) on a Waters Alliance 2695 system (Waters Corporation, Milford, MA) using a Waters 2487 dual channel UV detector (365 nm) and a Waters Spherisorb analytical column (5 μm, NH2, 4.6 mm × 250 mm). A gradient mobile phase was employed with a flow-rate of 1.5 ml/min, an injection volume of 25 μl, and a column temperature of 35°C. An initial mobile phase composition of 80% A (80% methanol) and 20% B (500 mM sodium acetate/64% methanol) was held for 5 min, followed by a 25 min linear gradient to a final composition of 20% A and 80% B. The column was allowed to equilibrate for 10 min between injections. GSH and GSSG had retention times of approximately 8 and 11 min, respectively.

All test chemicals were prepared and analyzed in duplicate on two different days. The mass of GSH and GSSG on column was calculated with the respective calibration curves. GSH depletion was determined by dividing the GSH in a sample containing a test chemical by the GSH in a sample with no test chemical, and multiplying by 100. GSSG was measured to determine if GSH depletion was a result of GSH conjugation to the test chemical or due to an oxido-reduction reaction with the test chemical.

Lysine, cysteine, and histidine reactivity assay. Peptides, Ac-RFAAKAA-COOH (lysine peptide, Fig. 1), Ac-RFAACAA-COOH (cysteine peptide), and Ac-RFAAHAA-COOH (histidine peptide), were made by the SynPep Corp. (Dublin, CA), and purified >90% by HPLC (Keough et al., 1997). Molecular weight confirmation was done by flow injection mass spectrometry with electrospray ionization in the positive mode. Ammonium acetate, ammonium hydroxide, sodium phosphate monobasic and sodium phosphate dibasic for the preparation of buffers were purchased from J.T. Baker. DMSO was obtained from Sigma. Acetonitrile (HPLC grade) and trifluoroacetic acid (TFA, 99%) for the preparation of the HPLC mobile phase were purchased from EMD (Gibbstown, NJ) and Aldrich, respectively.

FIG. 1.

Structure of synthetic peptide, Ac-RFAAKAA-COOH, showing the lysine side chain. The pKa of the amines is shown. The other synthetic peptides were similarly structured, except a cysteine or histidine residue was substituted for the lysine.

FIG. 1.

Structure of synthetic peptide, Ac-RFAAKAA-COOH, showing the lysine side chain. The pKa of the amines is shown. The other synthetic peptides were similarly structured, except a cysteine or histidine residue was substituted for the lysine.

Peptide stock solutions were prepared to a final concentration of 1.25 mM in either 100 mM ammonium acetate buffer, pH 10.2 (lysine peptide), or 100 mM phosphate buffer, pH 7.5 (histidine and cysteine peptides). Test chemical solutions at a concentration of 100 mM were prepared in acetonitrile, or solubilized in DMSO and then diluted with an equal part acetonitrile. Triplicate reactivity samples were prepared containing 0.5 mM peptide, and either 5 mM or 25 mM test chemical for a peptide:test chemical ratio of 1:10 or 1:50. A Biomek 2000 automated workstation (Beckman Coulter, Fullerton, CA) was used to make additions of the peptide stock solution (400 μl), the appropriate buffer (350 μl), and the test chemical solutions (50 or 250 μl) into autosampler vials. In the case of the 1:10 ratio reactivity samples, 200 μl of acetonitrile was added to each vial. Samples without the test chemicals were also prepared in triplicate to function as controls. The autosampler vials were capped, gently vortexed, and incubated for 24 h at room temperature in the autosampler (dark) prior to HPLC analysis.

Calibration standards were prepared manually from the peptide stock solution, diluted into the appropriate buffer for the peptide, and contained either 5 or 25% acetonitrile. The peptide concentrations were 0.0156, 0.0313, 0.0625, 0.125, 0.25, 0.50, 1.0 mM.

A Waters Alliance 2695 and 996 PDA detector comprised the chromatographic system. A 10 μl injection of the reactivity samples was made onto the column. The peptides were separated from the test chemicals and products on a Zorbax SB-C18 (2.1 × 100 mm) stationary phase (Agilent Technologies, Wilmington, DE) which was preceded by a SecurityGuard cartridge guard system (Phenomenex, Torrance, CA) containing a C18 cartridge (2.0 × 4.0 mm). The column temperature was 30°C. The mobile phase consisted of 0.1% TFA in water (A) and 0.085% TFA in acetonitrile (B). A gradient of 90% (A) to 60% (A) over 25 min at a flow rate of 0.3 ml/min was used for the separation. The diode array detector scanned the wavelengths 210–400 nm. Chromatograms were extracted at 220 nm. Quantitation was performed using either Millenium32 or Empower software packages. Peptide reactivity with the test chemicals was reported as percent peptide depletion, which was determined as the reduction of the peptide concentration in the samples relative to the average concentration of the controls.

Data analysis. Sensitivity, specificity, positive predictivity, negative predictivity, and accuracy were calculated for each peptide based on an arbitrary depletion cut-off value used to determine whether the chemical had reacted with the peptide.

For the sensitizers, LLNA EC3 values and the peptide depletion percentages were compared using a Spearman correlation analysis (Hollander and Wolfe, 1973). Spearman correlations were calculated since both the raw and log-transformed EC3 values are not normally distributed and since Spearman correlations are based on the ranks of the data points rather than the actual values (i.e., no distributional assumptions are made). Correlation analyses and graphs were generated using S-Plus 6.2 (Insightful Corporation, Seattle, WA). A level of significance (alpha) of 0.05 was used for the correlation analyses.

RESULTS

Test Chemical Dataset: Chemical Information and Biological Data

Table 2 lists 38 chemicals along with their respective molecular weights and chemical structures. It is clear from reviewing the structures themselves that the dataset embraces the wide chemical diversity known to exist among skin allergens. For example, aldehydes, ketones, aromatic amines, quinones, and acrylates are represented in the dataset. In addition, the LLNA EC3 values and each chemical's potency category are displayed in Table 2. The dataset includes weak, moderate, strong, and extreme skin sensitizers, as well as nonsensitizers. For each chemical, the data shown in Table 2 derive from one representative experiment that we feel reflects accurately the results obtained with the chemical as the chemical might have been tested multiple times in LLNA studies. The specific reference for the source of the LLNA data for each chemical is indicated in Table 2. The dataset comprises 11 nonsensitizers; 7 weak sensitizers; 11 moderate sensitizers; 5 strong sensitizers; and 4 extreme sensitizers; a total of 38 compounds. For the skin sensitizers, the range of EC3 values spans from 0.003% for the extreme sensitizers, oxazolone and diphenylcyclopropenone, to 36.5% for the weak sensitizer, ethyleneglycol dimethacrylate. EC3 values estimated by the log-linear extrapolation method are marked with an asterisk (*) and the potency class for the chemical is shown in italics.

TABLE 2

Chemical Structures and Their Potency Category Based on LLNA Data

Note. NC, not calculated; NS, non-sensitizing in LLNA.

a

Chemical name—Each chemical listed in the table is associated with representative LLNA data and its specific literature citation.

b

EC3 values calculated using the log-linear extrapolation are indicated by an asterisk (*).

c

Potency category was determined by the following EC3 cutoff values: Extreme, <0.1%; Strong, ≥0.1% − <1%; Moderate, ≥1% − <10%; and Weak, ≥10% − ≤100%. Potency categories derived from extrapolated EC3 values are given in italics.

Optimization of Peptide Reactivity Assays

Conjugation of GSH to chemical allergens was evaluated using reaction conditions previously described for other chemicals (Boyland and Chasseaud, 1967; Esterbauer et al., 1975; Fry et al., 1992; Garle and Fry, 1989; Portoghese et al., 1989). The GSH and test chemicals were mixed at a ratio of 1:100 for 15 min at pH 7.4. The reactions were stopped at 15 min and the percent depletion of free, unreacted GSH was measured. Thus, the reactions conditions previously published for evaluating GSH reactivity with chemicals were used for evaluating our test chemical dataset.

For the synthetic peptides (containing lysine, cysteine, or histidine), a number of optimization experiments were conducted to determine the reaction conditions that would be most optimal for the majority of chemicals tested. Initially, the kinetics of the reaction using the lysine peptide and a peptide to test chemical ratio of 1:10 was evaluated. Figure 2 shows that reaction times of 3, 12, or 24 h gave similar results with the chemical allergens tested. For example, maximum reactivity (percent depletion of lysine peptide) was still evident at 24 h for both 1,4-benzoquinone and hydroxyethyl acrylate. For logistic reasons, the 24 h time point was chosen for analysis of the other chemicals in the dataset. The pH of the lysine peptide reaction was set at 10.5 based on its pKa and the need to deprotonate the primary amine of the lysine side chain to make it available for reactivity. Prior to analysis of the chemicals, the optimal test chemical to peptide ratio was determined for each peptide. Figure 3 shows clearly that the use of a peptide (lysine) to test chemical ratio of 1:50 was optimal for three of the four allergens tested. Thus, for the lysine peptide we used a 1:50 peptide to test chemical ratio with a 24 h reaction period. In preliminary experiments, the optimal pH for evaluating the reactivity of the lysine peptide was found to be 10.2 (data not shown). For the cysteine and histidine peptides, similar reaction conditions were used except that the reaction was conducted at pH 7.5 and a 1:10 peptide to test chemical ratio was used for the cysteine peptide.

FIG. 2.

Kinetics of lysine reactivity with allergens. The lysine peptide (0.5 mM) was examined with various test chemicals (5 mM) at 3, 12, and 24 h.

FIG. 2.

Kinetics of lysine reactivity with allergens. The lysine peptide (0.5 mM) was examined with various test chemicals (5 mM) at 3, 12, and 24 h.

FIG. 3.

Optimization of peptide to test chemical ratio for reactivity. The lysine peptide (0.5 mM) was tested with chemicals 5 mM (gray), 12.5 mM (black), and 25 mM (white) with a 24 h reaction time.

FIG. 3.

Optimization of peptide to test chemical ratio for reactivity. The lysine peptide (0.5 mM) was tested with chemicals 5 mM (gray), 12.5 mM (black), and 25 mM (white) with a 24 h reaction time.

Relationship of Peptide Reactivity and Potency Data

There are multiple hurdles a chemical must pass in order to initiate a sensitization response, including skin absorption, peptide reactivity, and immune recognition by T cells. Thus, it would not be expected that measuring peptide reactivity would predict the total biological response that occurs in animals or humans. However, this peptide reactivity information might be useful in helping to screen chemicals for their potential to cause a skin sensitization response. Table 3 summarizes our reactivity data for GSH and the three synthetic peptides that contained lysine, cysteine, or histidine. The chemicals in Table 3 are listed according to the four potency categories described previously (Table 1). For discussion purposes, we chose a peptide depletion of greater than 10% to indicate that the chemical had reacted with the starting peptide.

TABLE 3

Reactivity of Chemical Substances to Glutathione or Synthetic Peptides with Results Expressed as Percent Depletion

LLNA category
 
(Conc. peptide:Conc. test substance)
 
Glutathione (0.2 mM:20 mM)
 
 Lysine (0.5 mM:25 mM)
 
 Cysteine (0.5 mM: 5 mM)
 
 Histidine (0.5 mM:25 mM)
 
 

 

 
Average
 
SD
 
Average
 
SD
 
Average
 
SD
 
Average
 
SD
 
Extreme 2,4 Dinitrochlorobenzene 43.6 2.6 14.7 4.2 100.0 0.0 0.3 4.9 
 Diphenylcyclopropenone 22.0 7.5 0.7 3.8 98.8 2.0 −1.0 3.1 
 Oxazolone 22.6 9.5 49.6 1.8 75.5 1.4 −4.9 11.6 
 p-Benzoquinone 100.0 0.0 91.0 0.2 99.0 1.8 94.0 3.8 
Strong Phthalic anhydride 100.0 0.0 75.0 3.9 −1.9 1.0 −4.6 5.8 
 1,4-Hydroquinone 76.5 6.1 51.1 6.5 83.3 0.9 30.0 0.9 
 Glutaraldehyde 20.8 4.0 85.4 3.5 30.2 0.5 2.7 1.0 
 Lauryl gallate 42.2 13.6 8.7 4.2 90.9 13.1 −0.1 1.0 
 CD3 63.6 13.6 13.6 0.5 90.1 1.1 −8.9 4.0 
Moderate 2-Hydroxyethyl acrylate 98.1 1.8 88.9 0.3 92.6 0.5 8.2 4.3 
 3-Dimethylaminopropylamine 2.8 5.2 −1.8 1.9 10.2 3.4 1.9 2.5 
 Cinnamic aldehyde 46.7 5.2 43.2 4.1 70.6 1.0 −3.8 7.8 
 3-Aminophenol 2.0 2.4 1.2 1.7 6.6 1.5 2.0 2.4 
 3,4-Dihydrocoumarin 2.3 2.6 7.5 1.0 ND — −1.8 2.8 
 1,2-Benzisothiazolin-3-one 14.5 1.3 ND — 97.7 0.1 ND — 
 Phenylacetaldehyde −4.7 0.7 22.6 1.9 60.7 13.3 −3.1 0.8 
 Squaric acid 16.5 4.0 4.8 4.9 46.9 8.7 3.1 0.4 
 Citral 37.5 14.4 16.9 0.3 85.7 3.2 −7.9 1.0 
 1-(4-Methoxyphenyl)-1-penten-3-one −0.2 1.5 14.3 3.2 29.9 5.6 1.7 2.1 
 Diethyl maleate 83.3 4.5 85.5 1.6 100.0 0.0 0.5 0.8 
Weak α-Hexylcinnamaldehyde −2.6 3.2 −1.6 2.9 −0.3 1.2 −0.4 1.5 
 5-Methyl-2,3-Hexandione −2.6 9.9 7.5 1.1 25.8 4.0 23.1 3.9 
 Hydroxycitronellal −1.8 3.9 6.5 2.0 17.5 1.7 5.6 6.2 
 Ethyleneglycol dimethacrylate 3.6 5.6 12.4 3.0 87.3 5.0 −1.2 1.8 
 α-Amyl cinnamaldehyde 0.2 10.1 3.9 1.5 0.6 0.2 −1.1 1.7 
 Benzyl benzoate 0.7 5.5 3.0 5.3 0.2 1.1 −2.5 0.8 
 Lilial 7.7 0.8 0.7 0.2 14.0 6.4 −0.4 0.3 
Non Glycerol 1.2 4.2 2.1 0.9 −3.8 5.2 0.2 0.6 
Sensitizers Hexane −0.8 4.1 −5.1 0.6 −0.4 0.8 −1.8 3.5 
 Diethyl phthalate 10.9 13.3 −0.7 0.9 0.8 1.7 0.7 2.8 
 Octanoic acid −1.6 3.1 0.9 0.1 −1.0 0.7 0.7 0.3 
 2-Hydroxypropyl methacrylate 5.5 4.8 ND — 58.4 5.9 ND — 
 1-Butanol 6.1 7.5 1.2 0.8 −0.4 1.4 0.5 0.4 
 4-Hydroxybenzoic acid −1.0 5.8 2.2 2.1 −0.3 0.8 −0.4 0.2 
 6-Methylcoumarin −1.6 8.6 4.0 5.6 1.4 0.3 −1.6 2.5 
 Methyl salicylate 4.2 3.5 1.6 0.3 0.3 0.8 0.5 1.1 
 Chlorobenzene 3.2 2.3 1.3 0.2 0.4 0.2 −1.8 2.0 
 Lactic acid −1.1 11.1 0.8 0.5 −0.9 0.3 −0.8 1.3 
LLNA category
 
(Conc. peptide:Conc. test substance)
 
Glutathione (0.2 mM:20 mM)
 
 Lysine (0.5 mM:25 mM)
 
 Cysteine (0.5 mM: 5 mM)
 
 Histidine (0.5 mM:25 mM)
 
 

 

 
Average
 
SD
 
Average
 
SD
 
Average
 
SD
 
Average
 
SD
 
Extreme 2,4 Dinitrochlorobenzene 43.6 2.6 14.7 4.2 100.0 0.0 0.3 4.9 
 Diphenylcyclopropenone 22.0 7.5 0.7 3.8 98.8 2.0 −1.0 3.1 
 Oxazolone 22.6 9.5 49.6 1.8 75.5 1.4 −4.9 11.6 
 p-Benzoquinone 100.0 0.0 91.0 0.2 99.0 1.8 94.0 3.8 
Strong Phthalic anhydride 100.0 0.0 75.0 3.9 −1.9 1.0 −4.6 5.8 
 1,4-Hydroquinone 76.5 6.1 51.1 6.5 83.3 0.9 30.0 0.9 
 Glutaraldehyde 20.8 4.0 85.4 3.5 30.2 0.5 2.7 1.0 
 Lauryl gallate 42.2 13.6 8.7 4.2 90.9 13.1 −0.1 1.0 
 CD3 63.6 13.6 13.6 0.5 90.1 1.1 −8.9 4.0 
Moderate 2-Hydroxyethyl acrylate 98.1 1.8 88.9 0.3 92.6 0.5 8.2 4.3 
 3-Dimethylaminopropylamine 2.8 5.2 −1.8 1.9 10.2 3.4 1.9 2.5 
 Cinnamic aldehyde 46.7 5.2 43.2 4.1 70.6 1.0 −3.8 7.8 
 3-Aminophenol 2.0 2.4 1.2 1.7 6.6 1.5 2.0 2.4 
 3,4-Dihydrocoumarin 2.3 2.6 7.5 1.0 ND — −1.8 2.8 
 1,2-Benzisothiazolin-3-one 14.5 1.3 ND — 97.7 0.1 ND — 
 Phenylacetaldehyde −4.7 0.7 22.6 1.9 60.7 13.3 −3.1 0.8 
 Squaric acid 16.5 4.0 4.8 4.9 46.9 8.7 3.1 0.4 
 Citral 37.5 14.4 16.9 0.3 85.7 3.2 −7.9 1.0 
 1-(4-Methoxyphenyl)-1-penten-3-one −0.2 1.5 14.3 3.2 29.9 5.6 1.7 2.1 
 Diethyl maleate 83.3 4.5 85.5 1.6 100.0 0.0 0.5 0.8 
Weak α-Hexylcinnamaldehyde −2.6 3.2 −1.6 2.9 −0.3 1.2 −0.4 1.5 
 5-Methyl-2,3-Hexandione −2.6 9.9 7.5 1.1 25.8 4.0 23.1 3.9 
 Hydroxycitronellal −1.8 3.9 6.5 2.0 17.5 1.7 5.6 6.2 
 Ethyleneglycol dimethacrylate 3.6 5.6 12.4 3.0 87.3 5.0 −1.2 1.8 
 α-Amyl cinnamaldehyde 0.2 10.1 3.9 1.5 0.6 0.2 −1.1 1.7 
 Benzyl benzoate 0.7 5.5 3.0 5.3 0.2 1.1 −2.5 0.8 
 Lilial 7.7 0.8 0.7 0.2 14.0 6.4 −0.4 0.3 
Non Glycerol 1.2 4.2 2.1 0.9 −3.8 5.2 0.2 0.6 
Sensitizers Hexane −0.8 4.1 −5.1 0.6 −0.4 0.8 −1.8 3.5 
 Diethyl phthalate 10.9 13.3 −0.7 0.9 0.8 1.7 0.7 2.8 
 Octanoic acid −1.6 3.1 0.9 0.1 −1.0 0.7 0.7 0.3 
 2-Hydroxypropyl methacrylate 5.5 4.8 ND — 58.4 5.9 ND — 
 1-Butanol 6.1 7.5 1.2 0.8 −0.4 1.4 0.5 0.4 
 4-Hydroxybenzoic acid −1.0 5.8 2.2 2.1 −0.3 0.8 −0.4 0.2 
 6-Methylcoumarin −1.6 8.6 4.0 5.6 1.4 0.3 −1.6 2.5 
 Methyl salicylate 4.2 3.5 1.6 0.3 0.3 0.8 0.5 1.1 
 Chlorobenzene 3.2 2.3 1.3 0.2 0.4 0.2 −1.8 2.0 
 Lactic acid −1.1 11.1 0.8 0.5 −0.9 0.3 −0.8 1.3 

For GSH, all of the strong and extreme sensitizers caused depletion of greater than 10% with a range of 20.8 to 100% (Table 3). For the moderate sensitizers, 6 of 11 tested gave depletions of greater than 10% with a range of 14.5 to 98.1%. The only weak or nonsensitizer that caused GSH depletion of greater than 10% was the nonsensitizer diethyl phthalate which yielded 10.9% depletion.

The peptide depletion results for the three synthetic peptides were quite varied. The majority of chemical allergens tested with the cysteine peptide at a 1:10 ratio (21/26) caused greater than 10% depletion. All of the extreme sensitizers and four of five strong sensitizers caused depletion of greater than 20% with most of them with values greater than 75%. Interestingly, no depletion of the cysteine peptide was observed with the strong allergen phthalic anhydride. For the moderate sensitizers tested, all but one gave depletion values greater than 10% with a range of 10.2 to 100%. No data was obtained with 3,4-dihydrocoumarin because it co-eluted with the cysteine peptide. Four of seven weak allergens gave depletion of cysteine peptide at levels greater than 10% with a range of 14.0 to 87.3%. The only nonsensitizer to give a response of greater than 10% was 2-hydroxypropyl methacrylate with a value of 58.4%. Using a peptide to test chemical ratio of 1:50 for the cysteine peptide increased the sensitivity of the assay as indicated by an increase in percent depletion values for weak and moderate allergens, but also increased the number of nonsensitizers detected (data not shown). Thus, the 1:10 peptide to test chemical ratio gave a better discrimination of sensitizers and nonsensitizers when using the cysteine peptide.

Depletion of the lysine peptide was generally seen more often with moderate to extreme sensitizers than with weak sensitizers. Three of four extreme and four of five of the strong sensitizers demonstrated depletion values of greater than 10%. For the moderate sensitizers, 6 of the 10 tested had values greater than 10%, whereas, only one of weak sensitizers, ethyleneglycol dimethacrylate, had a value of greater than 10%. Thus, 14 of 26 allergens tested gave lysine depletion results of 10% or greater. None of the nonsensitizers tested gave lysine depletion levels of greater than 10%.

For the histidine peptide that was run at a 1:50 ratio at pH of 7.4, only 3 of 26 sensitizers tested gave depletion results of greater than 10%. None of the nonsensitizers gave values of greater than 10%.

To summarize the potential usefulness of a 10% depletion cut-off for categorizing chemicals as sensitizers or nonsensitizers, the sensitivity, specificity, positive predictivity, and negative predictivity were calculated for each peptide. These statistics are shown in Table 4. Based on the specificity, most nonsensitizers (90.9% to 100.0%) are called negative, regardless of peptide. In terms of sensitivity, cysteine is the most promising peptide (80.8%) and histidine is not sensitive at all (11.5%). Most chemical substances called sensitizers are true sensitizers (93.8 to 100.0%) regardless of peptide, but many chemicals called nonsensitizers based on a 10% depletion cut-off are actually sensitizers (i.e., the negative predictivity range is 30.3 to 66.7%). The accuracy values for the glutathione, lysine, cysteine, and histidine were 65.8, 66.7, 83.8, and 36.1%, respectively.

TABLE 4

Contingency Probability Statistics

 Peptide
 
   

 
Glutathione
 
Lysine
 
Cysteine
 
Histidine
 
Sensitivity 55.6% 53.8% 80.8% 11.5% 
Specificity 90.9% 100.0% 90.9% 100.0% 
Positive predictivity 93.8% 100.0% 95.5% 100.0% 
Negative predictivity 45.5% 45.5% 66.7% 30.3% 
Accuracy 65.8% 66.7% 83.8% 36.1% 
 Peptide
 
   

 
Glutathione
 
Lysine
 
Cysteine
 
Histidine
 
Sensitivity 55.6% 53.8% 80.8% 11.5% 
Specificity 90.9% 100.0% 90.9% 100.0% 
Positive predictivity 93.8% 100.0% 95.5% 100.0% 
Negative predictivity 45.5% 45.5% 66.7% 30.3% 
Accuracy 65.8% 66.7% 83.8% 36.1% 

The LLNA EC3 values for each sensitizer and the peptide depletion percentages were compared using a Spearman correlation analysis. Spearman correlations are based on the ranks of the data points rather than the actual values (Hollander and Wolfe, 1973). By computing Spearman correlations rather than Pearson correlations, weak sensitizers have the same amount of influence as stronger sensitizers on the results. In addition, potential outliers do not have a strong influence on the results. Scatter plots of EC3 values and the depletion percentage of each peptide, along with correlation analysis results, are shown in Figure 4. The results indicate that a moderately strong correlation exists between LLNA EC3 values and glutathione depletion (r = −0.645, p = 0.0010), lysine depletion (r = −0.445, p = 0.0259), and cysteine depletion (r = −0.463, p = 0.0205). LLNA EC3 values and histidine depletion are not significantly correlated (r = 0.005, p = 0.9795).

FIG. 4.

Spearman correlation of LLNA EC3 data and peptide reactivity. LLNA potency category is denoted as Extreme (□), Strong (○), Moderate (▵), or Weak (⋄). Peptides include glutathione (A), lysine (B), cysteine (C), and histidine (D).

FIG. 4.

Spearman correlation of LLNA EC3 data and peptide reactivity. LLNA potency category is denoted as Extreme (□), Strong (○), Moderate (▵), or Weak (⋄). Peptides include glutathione (A), lysine (B), cysteine (C), and histidine (D).

DISCUSSION

Over the past 25 years there have been significant advances in our understanding of the chemical and biological processes associated with skin sensitization and allergic contact dermatitis. For example, there is an increased understanding of how chemical reactions play an important role in the initiation of skin sensitization (reviewed in Lepoittevin et al., 1998). Specifically, they are involved in the formation of hapten-protein complexes which are processed by antigen-presenting Langerhans cells in the skin for presentation to antigen-specific T cells.

Chemical allergens (haptens) or their metabolites are small molecular weight compounds (generally less than 500 Da) with electrophilic properties. In this regard, they are able to react with various nucleophiles to form covalent bonds. In proteins, the side chains of many amino acids contain electron-rich groups capable of reacting with allergens. Lysine and cysteine are those most often cited, but other amino acids containing nucleophilic heteroatoms, such as histidine, methionine, and tyrosine can react with electrophiles (Ahlfors et al., 2003; Dupuis and Benezra, 1982; Lepoittevin et al., 1998). Thus, electrophilic allergens react with nucleophilic amino acids to form extremely stable covalent bonds, which is critical to the initiation of a skin sensitization response. It is the understanding of these reaction types that led to the development of DEREK, an expert system designed to identify skin sensitization alerts (Barratt et al., 1994). Protein reactivity was also an important consideration in the development of several QSARs (e.g., Patlewicz et al., 2001; Roberts and Patlewicz, 2002). Since reactivity is one key step in the induction of skin sensitization it was our intention to investigate whether reactivity could be used to develop a quantitative peptide-based reactivity assay that would have utility for screening a chemical's skin sensitization potency as defined in the LLNA. The conditions of the peptide reactivity assays were set up to optimize our ability to screen for allergens, not to necessarily reproduce all of the varied physiological conditions on and in the skin where these reactions might take place. What we try to compare is the intrinsic reactivity of different haptens in water towards specific nucleophiles such as lysine. Even if the physiological pH is around 7.4 (this is an average value that does not reflect specific compartments and/or situations) the protonation status of amino groups in proteins is very diverse. Thus it has been shown for some lysine residues that they have very low pKa values compared to ‘normal’ primary amino groups. In human serum albumin for example, Lysine 166 has a low pKa value and is therefore able to react with electrophiles such as acetylsalicylic acid while other residues are not. As we are considering small peptides for which a matrix effect does not exist, the only way to mimic such reactive amino groups is to increase the pH to get a significant reactivity within 24 h time period used for the lysine peptide.

The LLNA EC3 values listed in Table 2 show a range of potency from 0.003% for the extreme allergen, oxazolone, to 36.5% for the weak allergen, ethyleneglycol dimethacrylate. The chemicals selected for the dataset are known skin allergens which have been reported to induce sensitization in animals and/or man. For each chemical listed in Table 2, a specific reference is given for the representative LLNA data shown, since most of these compounds have been tested several times in different laboratories (e.g., Dearman et al., 1998, 2001; Kimber et al., 1998; Loveless et al., 1996) and are well known allergens for the purpose of using them for evaluation of new methodologies (Gerberick et al., in press). The chemicals represented in the database comprise weak, moderate, strong, and extreme sensitizers, as well as nonsensitizing materials, as based on potency categorization criteria that have been developed recently by a European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Task Force (Kimber et al., 2003).

The results, as summarized in Table 3, demonstrate clearly that an association between the degree of peptide reactivity (as measured by peptide depletion) and sensitization potency is evident. For ease of discussion, we chose 10% depletion as a cutoff for classifying whether a test chemical had been detected or not with the different peptides. Additional studies are underway to increase the number of chemicals tested so that we can more formally analyze the data to determine more appropriate cutoffs to employ for the peptide reactivity assays. With the current dataset, a greater number of the extreme, strong, and moderate allergens reacted with GSH, cysteine, and lysine than did the weak and nonsensitizers. Based on the number of compounds with greater than 10% depletion, the cysteine peptide seems to be best at discriminating between the different potency categories followed by the GSH and lysine peptides (Table 3). The histidine peptide was limited in its ability to react with the known allergens. It is important to note that conditions used for each of the peptides were optimized in a general way and are not specific for any chemical on an individual basis. Thus, some chemicals were not detected because they either co-eluted with the peptide (e.g., 3,4-dihydrocoumarin with cysteine peptide) or were incompatible with the solvent system (e.g., diphenylcyclopropenone in buffer with 25% acetonitrile). Even with these limitations, the results show that analysis of peptide reactivity demonstrates a significant correlation with sensitization potency (Fig. 4). Of course, one would not expect an extremely high correlation between reactivity and potency since other factors, such as skin penetration and immune recognition by T cells, are critical for the acquisition of skin sensitization.

Glutathione (GSH), a cysteine containing peptide, is the most prevalent cellular thiol and most abundant low molecular weight peptide present in cells (Deneke and Fanburg, 1989). Cellular GSH protects cells by detoxifying electrophilic compounds through conjugation of its nucleophilic sulfur, and by acting as an antioxidant (Deneke and Fanburg, 1989). It is unknown whether GSH is directly involved in ACD, however a clear correlation exists between sensitizing activity and ability to perturb GSH status in mouse skin (Schmidt and Chung, 1992, 1993). The perturbation of the GSH status is likely driven by direct GSH conjugation or by oxidative stress via free radical formation (Schmidt et al., 1990; Schmidt and Chung, 1992, 1993). Regardless of the mechanism, in vivo application of electrophilic chemicals to the skin depletes GSH and this is driven by the ubiquitous nature of GSH and its reactivity to soft electrophiles, particularly α,β-unsaturated carbonyl compounds. Our results show that all of the extreme and strong sensitizers demonstrated GSH depletion, whereas only 6 of 11 moderate sensitizers showed values greater than 10% (Table 3). None of weak sensitizers caused GSH depletion greater than 10%. In contrast, the cysteine peptide demonstrated greater sensitivity with most of the moderate sensitizers giving depletion values greater than 10%. In fact, the only two compounds that didn't react with cysteine were 3-aminophenol and 3,4-dihydrocoumarin which are prohaptens and are thought to require biotransformation for their ability to react with nucleophilic amino acids such as cysteine.

Only one compound, phthalic anhydride, reacted with GSH but not the cysteine peptide. The differences between the GSH and the cysteine peptide results might be due to the different test conditions used for the two peptide assays or to a difference in the peptide sequence. Thus, while in the cysteine peptide the terminal α-NH2 group is blocked by an acetyl function, this terminal α-NH2 group is free in the GSH peptide. It has been shown in previous studies on the covalent binding of 5-chloro-2-methylisothiazol-3-one (MCI) to model peptides that terminal α-NH2 function can play a role in protein modifications (Alvarez-Sanchez et al., 2004). Not all sensitizers are expected to show the same reactivity towards SH/NH2. This is well described by the Hard and Soft Acid and Base (HSAB) theory (Pearson, 1963) which reflects qualitatively interaction mechanisms between a nucleophile and an electrophile. Thus, GSH, which is a good model for a soft nucleophile, is expected to react more selectively with soft electrophiles such as α,β-unsaturated aldehydes, ketones, or esters while lysine which is a good example of a hard nucleophile should react more selectively with hard electrophiles such as phthalic anhydride.

In recent studies, lysine has been demonstrated to be an important nucleophile for allergens such as sultone and methylisothiazolone derivaties (Alvarez-Sanchez et al., 2003; Meschkat et al., 2001). Thus, we thought lysine would be an important nucleophile to use in our peptide reactivity studies. Overall, the lysine peptide worked well with extreme, strong, and some moderate sensitizers. However, only one weak allergen, ethyleneglycol dimethacrylate, was detected with the lysine peptide. The only compound detected with the lysine peptide but not the cysteine peptide was phthalic anhydride due to a chemical selectivity of phthalic anhydride for amino groups.

From a specificity standpoint, the GSH, lysine and cysteine peptides performed extremely well. Only two nonsensitizers gave depletion results greater than 10%. Diethyl phthalate gave 10.9% depletion of GSH and 2-hydroxypropyl methacrylate gave 58.4% depletion with the cysteine peptide. Although 2-hydroxypropyl methacrylate is reactive with cysteine it might be that its limited ability to penetrate the skin or be recognized by the immune system accounts for it being a nonsensitizer in the LLNA. As mentioned previously, the use of a 1:50 ratio for the cysteine peptide yielded additional nonsensitizers demonstrating reactivity (data not shown). Although these compounds have the ability to react with peptides in our artificial systems they might lack that ability to do so in vivo. Moreover, there could be other reasons such as a lack of immune recognition or materials are appropriately detoxified before they have the ability to initiate a sensitization response.

One potential challenge for developing in vitro methods for skin sensitization testing is that it is well known that some chemical allergens are prohaptens, and as such require biotransformation prior to initiating a skin sensitization response in vivo (Smith and Hotchkiss, 2001; Smith-Pease et al., 2003), The need for biotransformation has been demonstrated with many chemicals, such as the formation of benzoquinonediimine from azo hair dyes (Basketter and Goodwin, 1988), or orthoquinone from isoeugenol (Bertrand et al., 1997; Hotchkiss, 1998). Since prohaptens are an important class of sensitizers a few of these chemicals (aminophenol and 3,4-dihydrocoumarin) were also included and evaluated in this assay (Bertrand et al., 1997; Smith and Hotchkiss, 2001). The observation that neither of these chemicals reacted with the three peptides is consistent with the fact that they are prohaptens. However, 1,4-hydroquinone, which requires oxidation for reactivity, reacted with all of the peptides used, including histidine. It is believed that the 1,4-hydroquinone was air oxidized under the conditions of testing to benzoquinone. Based on the knowledge that some chemical allergens need to be biotransformed prior to reacting with proteins/peptides, it will be critical to incorporate a metabolism component to address these types of molecules. We are evaluating currently a number of metabolic and oxidizing systems for use in our peptide reactivity work.

The goal of this work was to evaluate the use of peptide reactivity as a means for screening the skin sensitization potential of chemicals. The results presented show clearly that each of the peptides evaluated, except histidine, was useful in quantifying reactivity as measured by the depletion of the peptide. Measurement of peptide reactivity will be helpful in reducing our need for animal testing by allowing us to deselect chemicals with high reactivity and thus, the potential to be moderate to strong allergens. Thus, by performing a peptide reactivity assay one can reduce the number of compounds that would require testing in an animal model. It is hoped that with additional modification of the peptide reactivity assays (e.g., addition of metabolism component) and use of other nonanimal methods (e.g., dendritic cell based assay, skin penetration models), we will be even more successful in reducing our reliance on animals for skin sensitization testing in the future.

The authors wish to acknowledge Cindy Ryan and Petra Kern for their excellent review of the manuscript. None of the P&G authors will benefit financially from this work other than their salaries. Dr. Lepoittevin works in collaboration with the P&G scientists and is not receiving any financial support from P&G for this work.

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Author notes

*The Procter & Gamble Company, Miami Valley Laboratories, Cincinnati, Ohio 45253-8707, and †Université Louis Pasteur, Laboratorie de Dermatochimie, UMR 7123, Strasbourg, France