The TOPS-MODE Approach - ACS Publications - American Chemical

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Chem. Res. Toxicol. 2003, 16, 1226-1235

Computer-Aided Knowledge Generation for Understanding Skin Sensitization Mechanisms: The TOPS-MODE Approach Ernesto Estrada,* Grace Patlewicz, Mark Chamberlain, David Basketter, and Sue Larbey Safety and Environmental Assurance Centre (SEAC), Unilever Colworth, Colworth House, Sharnbrook, Bedford, MK44 1LQ, United Kingdom Received May 20, 2003

The TOPS-MODE (topological substructural molecular descriptors) approach is used to derive models for understanding the molecular structural contribution to skin sensitization. A data set of 93 compounds was used in the development of the models; 29 new skin sensitization values (EC3) are reported here for the first time. The models developed possess high predictivity and have been validated through the use of cross-validation and external validation sets. The models have enabled the formulation of potential new structural alerts far faster and using less data than typically required by traditional approaches. Structural contributions to skin sensitization for various classes of chemicals are presented on the basis of bond contributions. The models have also been able to identify potential structural alerts for chemicals requiring metabolic activation.

Introduction The regulatory and regulated communities in Europe, North America, and Japan are faced with a need to increase the available knowledge on the ecological and health effects of industrial chemicals. This need arises as a result of recent revisions to the Canadian Environmental Protection Act, the publishing of the White Paper in the EU on Chemicals Policy, and other related legislative initiatives, relating to the priority setting, risk assessment, and classification of chemicals. Additionally, the publication of the 7th Amendment in the Cosmetics Directive poses a ban on all animal testing for evaluating sensitization hazard and potency of cosmetic ingredients within a 6 year time frame (1). Alternative hazard testing methods and hazard assessment strategies combined in a decision support system could potentially reduce the time and monetary cost to the chemical industry needed for compliance within REACH (registration, evaluation, authorization of chemicals) and other legislative initiatives such as the 7th Amendment. Such strategies should also reduce the time and resource investments required from competent authorities at national and EU levels and ensure that limited resources are allocated to the most relevant issues. There is a widely perceived need for alternative nonanimal methods for the hazard assessment of chemicals. (Q)SARs1 are now being increasingly viewed as one of * To whom correspondence should be addressed. Present address: X-ray Unit, BioComputing, RIAID, Edificio CACTUS, University of Santiago de Compostela, Santiago de Compostela 15706, Spain. E-mail: [email protected]. 1 Abbreviations: TOPS-MODE, topological substructural molecular design; LLNA, local lymph node assay; QSAR, quantitative structureactivity relationship; DEREK, deductive estimation of risk from existing knowledge.

the most cost effective alternatives to estimate ecological and health effects of chemicals. (Q)SAR predictions have the potential to save time and money and minimize the use of animal testing (2). Allergic contact dermatitis (ACD) is a complex endpoint involving a number of biochemical and physiological events (3). The outcome results in a stimulation of the immune system producing an inflammatory response in the skin. A chemical penetrates the skin and covalently binds with a carrier protein to form an antigenic complex. Correlations between the ability of chemicals to react with proteins to form covalently linked conjugates and their skin sensitization ability form the basis for QSARs models that have been described in the literature (4). A number of SARs have been developed in the area of contact allergy. These have typically been developed using two main approaches. Statistical QSARs, which use a wide range of different descriptors, provide a numeric estimate for skin sensitization, but this may present problems in interpretation as information is “encrypted” in descriptors. Knowledge-based approaches, which rely on the development of SARs, present a mechanistic picture of the chemical processes that could be involved in the development of the skin sensitization. Both approaches have been successful in incorporating new (Q)SARs into expert systems such as toxicity prediction by computer-assisted technology (5) and DEREK (6). A short review describing recent progress in the development of QSARs for skin sensitization has been published recently (7). The development of the LLNA has facilitated the use of sensitization QSARs because it provides an unambiguous, well-defined endpoint. The LLNA is described in detail in numerous publications (8-10). It involves repeated topical application of the test chemical to mouse ear skin followed by a quantitative assay of T-cell

10.1021/tx034093k CCC: $25.00 © 2003 American Chemical Society Published on Web 09/09/2003

Knowledge Generation for Skin Sensitization

Chem. Res. Toxicol., Vol. 16, No. 10, 2003 1227

response in the draining lymph node, measured as a function of the incorporation of tritiated thymidine. The sensitization response is quantified in terms of the stimulation index (SI) or the ratio of the activity in draining lymph nodes of treated mice as compared to that of the vehicle-treated mice. A given chemical is tested over a range of concentrations to generate a doseresponse relationship. The sensitization potential is defined in terms of the concentration required to give a specified SI value. Currently, the preference is to estimate the concentration of a chemical required to generate a SI of 3, known as the EC3 value (10).

Experimental Procedures Theoretical: TOPS-MODE Approach. The TOPS-MODE (11-20) approach involves developing linear QSARs using the spectral moments of a molecular bond matrix. QSARs developed using these moments contain encrypted structural information, which can hence be transformed into quantitative contributions for each bond in the molecule, so-called bond contributions. These bond contributions are used to formulate structural rules describing those features of the molecule that drive a specific mechanism of action as well as identifying potential pharmacophores and/or toxicophores. There are several stages in the development of a TOPSMODE model. Chemical structure(s) of interest are drawn and input into the Modeslab program (21) as simplified molecular input line entry specification (SMILES). Bond weights used to account for different molecular or physicochemical effects are computed. TOPS-MODE uses bond weights for hydrophobicity (H), polar surface area (PS), molar refraction (MR), molecular polarizability (Pol), van der Waals radii (vdW), and atomic charges (Ch). The sums of each bond weight equate to the corresponding physicochemical or molecular property; for example, the sum of hydrophobicity weights generates the calculated value of the n-octanol/water partition coefficient, logP. Quantitative structure-property relationships (QSPR) or QSARs are determined, and their predictive capacity is verified using cross-validation techniques. Each bond contribution is then computed in order to derive new mechanistic insights and structural rules. Bond Weights. Many approaches used in computing physicochemical properties from fragments are based on atom additive methods. Here, the atomic contributions are transformed into bond contributions for the corresponding physicochemical property. The current selection of bond weights for calculating TOPSMODE descriptors is carried out through accounting for hydrophobic/polarity, electronic, and steric features of molecules. Thus, atomic contributions for partition coefficient (H) (22), polar surface (PS) (23), and polarizability (Pol) (24) are transformed into bond contributions. A similar approach is used to transform Gasteiger-Marsilli atomic charges (Ch) (25), van der Waals atomic radii (vdW) (26), and molar refraction (MR) (27) into bond weights. The approach is very simple. The weight of a bond (i, j), where i and j are the corresponding atoms linked together, is calculated as follows:

w (i, j) )

wi wj + δi δj

(1)

where wi and δi are the atomic weight and vertex degree of the atom i. Definition of TOPS-MODE Descriptors. The total spectral moments of the bond matrix are defined as (11-20): s

µTk ) Tr(Bk) )

∑ (e )

k

ii

i)1

(2)

where Tr is the trace of the matrix, i.e., the sum of the diagonal entries of the matrix where the elements (eii)k are the diagonal entries of the k-th power of the bond matrix (28). Local spectral moments are defined as the sum of diagonal entries of the different powers of the bond matrix corresponding to a given molecular fragment (29, 30). In mathematical terms, local spectral moments of the bond matrix are defined as follows: f

µk (f) )

∑ (e )

k

(3)

ii

i)1

where f is the corresponding fragment for which the moments are defined and the sum is carried out over all bonds forming the fragment f. The simplest case is when f corresponds to a single bond, and in this case, the k-th local moment is defined as the diagonal entry corresponding to this bond in the matrix raised to the k-th power. Consequently, total spectral moments of order k can be expressed as the sum of the bond spectral moments of the same order:

µTk )

∑µ

k (B)

(4)

B

where B means the corresponding bond. Bond Contributions. This means that we can substitute eq 4 into the QSAR/QSPR (eq 5) in such a way that the total contribution of the different bonds in a specific molecule is obtained as follows:

P ) b0 +

∑a k

k µk

) b0 +

∑∑a k

k µk (B)

(5)

B

These contributions represent the additive features of the property modeled, and they can be visualized as spheres centered on the corresponding bonds. Meaning of TOPS-MODE Descriptors. TOPS-MODE descriptors are spectral moments of the bond matrix weighted in the main diagonal with different physicochemical parameters. The spectral moment of order k in a graph (i.e., the spectral moment of the vertex adjacency matrix) counts the number of self-returning walks visiting k bonds in the graph. The spectral moments of the bond matrix count the number of self-returning walks in the line graph of the molecular graph, i.e., the graph in which vertexes represent the bonds of the original graph. More importantly is the case where physicochemical parameters are used as bond weights in the main diagonal of the bond matrix, which is the particular case of the TOPS-MODE approach. Here, TOPS-MODE descriptors measure the degree of concentration of these physicochemical properties in a region of the molecule. For instance, consider the polar surface area of 1,2-, 1,3-, and 1,4-diazines, where two nitrogen atoms are in an aromatic six-membered ring and separated by either one, two, or three bonds. The topological polar surface area of these three molecules is identical as they are defined as the sum of contributions arising from the two nitrogen atoms. However, the polar area is clearly more concentrated in the small region of the 1,2-diazine than in either the 1,3- or the 1,4-diazine molecules. Such differences will affect physicochemical characteristics such as the partition coefficient octanol/water to a given pH for the mixture of the neutral and ionic forms of the compounds, logD. This parameter is lowest for 1,2-diazine (-0.77), increases for 1,3-diazine (-0.33), and reaches a maximum for 1,4-diazine (-0.28). The TOPS-MODE descriptors shown below clearly reflect this behavior as they give higher values for the first compound and the lowest for the last, therefore measuring the concentration of the polar surface area in determined regions of the molecule. This is simply a consequence of counting the self-returning walks in the weighted graphs. There are fewer self-returning walks visiting both nitrogens in 1,4-diazine than in 1,3- or 1,2-diazine. Note that spectral moments of low order such as µ2 or µ3 are not able to

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Estrada et al.

differentiate simple structures such as 1,3- and 1,4-diazine.

diazine 1,21,31,4-

µ1

µ2

µ3

µ4

µ5

µ6

25.78 261.23 2831.80 33 751.77 421 695.96 5 411 103.51 25.78 178.15 1225.53 8765.30 63 994.28 475 763.51 25.78 178.15 1225.53 8599.15 61 317.16 443 210.81

Skin Sensitization Measure. Currently, the criterion for a positive LLNA response is based on the increment in the lymph node cell proliferative activity as compared with a control. A test substance is regarded as a sensitizer in the LLNA if exposure to at least one concentration of the test substance results in an incorporation of tritiated thymidine 3-fold or greater than that recorded in control mice, as indicated by the SI. The criterion of 3-fold has been established on the basis of the experience in the use of this method and on comparison with experimental results of skin sensitization in humans (10, 3134). This 3-foldness is measured through the use of the EC3 value, which is the concentration estimated to give a SI of exactly 3. The EC3 value is calculated by linear interpolation of the data points lying immediately above and below 3, and it has been well-described in numerous references in the literature (10, 31-34). This criterion was statistically evaluated using data on 134 chemicals tested in the LLNA and in the guinea pig and/or where there existed clear evidence related to human skin sensitization potential. The results indicated that linear interpolation of values on either side of the 3-fold SI on a LLNA dose-response curve was an acceptable and practical method for hazard identification (10, 31). For regulatory purposes, the EC3 value might provide a simple route through which to grade allergens and has been described as such in a number of publications (35-38). The predictive identification and discrimination of classes of compounds were proposed by Basketter et al. (38) for the following groups: strong, moderate, weak, and extremely weak allergens. The thresholds for this classification were selected on the basis of comparison with experimental results in humans, and they are as follows: EC3 lower than 0.1% for strong, EC3 between 0.1 and 10% for moderate, EC between 10 and 30% for weak, EC3 between 30 and 50% for extremely weak, and EC3 greater than 50% for nonsensitizer. In this way, risk management can be effectively targeted toward chemicals that pose the greatest risk to human health. Data Set and Classification Strategy. LLNA sensitization study data conducted to the same conditions (e.g., same vehicle) where an EC3 value had been successfully determined were selected from Unilever’s database. The EC3 values of 48 compounds in these database were previously reported by Unilever in the literature (references are provided in the Supporting Information). The EC3 values of 29 compounds are presented here for the first time. The details of the experiments will be published in further papers. EC3 values were averaged for repeat studies. Chemicals tested were identified, and their structures were drawn or downloaded from www.chemfinder.com. These were then collected into the TSAR spreadsheet (Version 3.3, Accelrys, U.S.A.) for derivation of canonical SMILES. The EC3 values were then ranked qualitatively according to their potency into the five classes before mentioned (38): strongly sensitizing, moderately sensitizing, weakly sensitizing, extremely weakly sensitizing, and nonsensitizing. Because of limitations in the breadth of test data available, only three classifications were ultimately used. Class 1 signified strong and moderate sensitizers, class 2 signified weak sensitizers, and class 3 signified extremely weak/nonsensitizing chemicals. The criteria for such classifications are as follows: strong/ moderate (EC3 < 10%), weak (EC3 ) 10-30%), and extremely weak/nonsensitizing (EC3 > 30%). These new classification groups were necessary due to the lack of sufficient data for some of the original classes, such as strong or nonsensitizers, which made it impossible to do good assessments for them.

The resulting data set contained 93 compounds. A strategy of developing two classification models was conducted due to utilitarian reasons. In practical situations of risk assessment of chemicals, a fast response indicating whether a compound is a strong sensitizer or not is required; hence, classification in the other groups is not necessary. In other situations, a complete classification is relevant. Consequently, we have chosen to develop two distinct models that could be used in differing classification strategies. The data set was randomly divided into two groups; 78 compounds were used as the training set for development of the first of two models (model 1); 15 compounds formed the cross-validation set. Model 1 would differentiate strong/moderate sensitizers from all remaining chemicals. The second model (model 2) was developed to differentiate weak sensitizers from extremely weak or nonsensitizing chemicals. A subset of the original data set where strong and moderate sensitizers were removed left a data set of 42 compounds, which was divided into two groups. Here, a training set of 36 compounds was selected with six remaining compounds forming the cross-validation set. A decision tree illustrating the classification scheme based on these two models is shown in Figure 1. The list of compounds is provided as Supporting Information accompanying this paper. Linear discriminant analysis was used in order to develop the classification models as implemented in STATISTICA (39). In this process, three compounds were found to be statistical outliers for model 1 according to at least one of the three following criteria: residual, deleted residuals, or Mahalanobis distance. The compounds in question were compounds 45 (c4azlactone), 46 (c6-azlactone), and 47 (c9-azlactone).

Results and Discussion The first classification model (model 1) was able to correctly classify 80% of the 75 compounds in class 1. Thirty-one of the 39 strong/moderate sensitizers were correctly predicted. Eight compounds were predicted to be “false negatives”, i.e., compounds that were strong/ moderate sensitizers but predicted to be weak/nonsensitizers. Three compounds of the 39 used in the training set to develop model 1 are reported as nonclassified because the difference in their probabilities of belonging or not belonging to class 1 was found to be less than 5%. Only one compound from classes 2 or 3 could not be classified by model 1, resulting in 80% (28/35) good classification. The equation of the regression model “model 1” is given below: H PS class (1) ) 1.331µH 1 - 0.00598µ4 + 0.00781µ2 MR (2.1366 × 10-4µPS + 0.0319µMR 3 ) + 0.0755µ1 2 Ch Ch Ch 1.1133µPol 5 - 2.3797µ1 + 0.1547µ3 + 0.00425µ6 +

2.0932µvdW - 0.8683µvdW + 0.7954; Wilks - λ ) 1 2 0.61; F (12,63) ) 3.39; D2 ) 2.52; p < 0.0007 (6) where λ is the Wilks’ statistics, D2 is the squared Mahalanobis distance, and F is the Fisher ratio. The Wilks’ λ for the overall discrimination can take values in the range of 0 (perfect discrimination) to 1 (no discrimination). The D2 statistics indicate the separation of the respective groups, showing if the model possesses an appropriate discriminatory power for differentiating between the respective two groups. The tabulated Fisher ratio for 12 variables and 76 compounds, F (12,60), is 2.50. As can be seen, the value of the Fisher ratio of the model is greater than the tabulated value showing the statistical significance of the model despite the high


Figure 1. Decision tree illustrating the classification scheme for models 1 and 2.

number of variables included. The number of compounds per variable in this model is 6.3, higher than the traditionally accepted value of about 5. However, as we will remark later in this paper, this is not a rule of thumb for the selection of QSAR models, which should be made on the basis of the statistical quality of the model as measured by different statistical parameters (see comments below eq 7). All of these statistics indicate that model 1 is appropriate for the discrimination of active/ inactive compounds studied here. The subscripts in the spectral moments correspond to the order of the corresponding moment. The superscripts correspond to the bond weights used as described in the previous section. Regarding the predictability of this model, model 1 was able to correctly classify seven of the nine class 1 compounds used as the cross-validation set. One of these compounds was unclassified, giving (7/8) good classification in class 1. An 83.3% amount of the compounds in class 2 or 3 was well-classified as nonclass 1 compounds. From the training set of 36 compounds used to develop model 2, 80.5% of the compounds were predicted correctly. Twenty-nine compounds were correctly classified, and seven were false positives. The overall correct classification for this model was 80.26%. Model 2 was able to classify 96% of class 2 compounds (27/28) correctly in the training set. Additionally, six of the eight compounds in class 3 were correctly classified. The overall classification was 91.7%. The equation for model 2 is given below: H PS class (2) ) 0.946µH 1 - 0.00468µ7 - 0.894µ1 + PS Pol Ch 0.1004µPS 2 - 0.0024µ3 + 0.0057µ3 - 1.429µ1 + Ch 0.0053µCh 8 - 0.00111µ9 - 5.309; Wilks - λ )

0.38; F (9,26) ) 4.63; D2 ) 8.76; p < 0.001 (7) The statistical parameters for this model show that it is significant from the statistical point of view. For instance, the tabulated Fisher ration for this number of variables and compounds is 3.18, well below the value obtained for the model. The separation between the groups is also higher in this model as compared to model 1, and there is a lower value of the λ of Wilk indicating better discrimination. The number of compounds per variable in this model is four. However, as Franke has


stated, “it was considered almost as a dogma by many investigators that a meaningful QSAR should be based on at least five observations per variable included. This is, of course, not correct. The ratio ‘number of observations per variable included’ will simply be reflected in Fand t-statistics and in the confidence intervals of regression coefficients and the predicted values which increase as this ratio decreases” (40). Model 2 correctly classified 5/6 class 2 compounds in the cross-validation set. Model 2 was considered sufficiently robust for the classification of class 2 compounds as more than 95% were correctly classified in both training and validation sets. The results of the classification of compounds by models 1 and 2 are given in Tables 1 and 2. A more challenging and realistic external validation set was used to test the predictive power of the models developed as well as the classification scheme. The external data set comprised 15 structurally different compounds taken from three recently published papers in the literature (41-43). None of the 15 compounds were included in the original training or cross-validation sets. In general, all strong/moderate sensitizers were classified correctly as class 1 compounds, with the exception of 1-(pmethoxyphenyl)-1-penten-3-one, which was classified as class 2. Benzocaine was classified as a class 1 compound using the model although this has shown contradictory results in LLNA experiments. Basketter et al. (44) have classified benzocaine as a moderate sensitizer, but Och et al. (40) have classified it as a weak sensitizer. Our prediction is in agreement with that published by Basketter et al. (44). Our model classified melatonin as a nonsensitizer (class 3) and nimesulide as a strong/ moderate sensitizer although these compounds were classified, respectively, as nonsensitizers by Kanikkannan et al. (43). The experiments by Kanikkannan et al. (43) were conducted only up to a 10% concentration; hence, these were insufficiently conclusive to classify them as nonsensitizers. These results are shown in Table 3. Structural Interpretation. Because of the greater importance of detecting strong/moderate sensitizers among those compounds used in consumer and health care products, we have focused our interest on the interpretation of the mechanisms that lead to such sensitization. Similar analysis has been carried out for the weak sensitizers, but it will not be presented here for the sake of simplicity and space although it will be published elsewhere. The results are presented in a graphical form. The red-colored spheres represent the positive contributions to skin sensitization, that is, the parts of the molecules contributing to the development of such toxicity. The blue spheres are those with negative contributions. The radius of each sphere is proportional to the magnitude of the contribution. ACD results from an immune response to a chemical allergen that comes into contact with the skin. The primary elements associated with the induction of ACD therefore involve skin penetration, possible metabolic activation of unreactive chemicals, and covalent binding to skin protein. The induction phase is dependent on the molecular structure of the allergen and could be termed the chemical phase of sensitization. Our approach provides some insights about the structural factors influencing the mechanism occurring in the induction (or chemical) phase of sensitization (3).


Estrada et al.

Table 1. Predictions Made by Using Model 1 for Compounds in Classes 1-3 no.

compda

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

trimellitic anhydride 1-bromoeicosane 1-bromododecane 1-bromohexadecane 1-bromooctadecane 1-bromopentadecane 1-bromotetradecane 1-bromotridecane 1-chloro-2,4-dinitrobenzene 1-chlorohexadecane 1-chlorooctadecane 1-chlorotetradecane 1-iododoecane 1-iodohexadecane 1-iodononane 1-naphthol 2,3-butanedione 2,4-heptadienal 2-amino-6-chloro-4-nitrophenol 2-ethyl butraldehyde 2-methoxy-4-methylphenol 2-methyl-5-hydroxyethylaminophenol 2-methyl-2H-isothiazol-3-one 2-methylundecanal 2-nitro-p-phenylenediamine 2-phenylpropionaldehyde (() 3-bromomethyl-5,5-dimethyldihydro-2(3H)-furanone 3-methyl eugenol 3-methyl isoeugenol 4-allylanisole 4-chloroaniline 4-nitrobenzyl bromide 5-methyl eugenol 6-methyl eugenol 7-bromotetradecane 12-bromo-1-dodecanol 12-bromododecanoic acid abietic acid R-amyl cinnamaldehyde R-butyl cinnamaldehyde R-methyl cinnamaldehyde bromohexane bromoundecane butyl glycidyl ether c4-azlactone c6-azlactone c9-azlactone

class EC3 class (pred) pred

prob

no.

compda

9.2 6.1 15.47 2.3 16.6 5.15 9.2 10.2 0.1 9.1 16.3 20.2 13.1 19.1 24.2 1.3 11.3 4 2.2 68.15 5.8 0.4 1.9 10 0.4 6.3 3.6 31.6 3.5 20.15 6.5 0 13.2 16.9 21.3 6.9 7.6 11.27 12.05 13.7 4.5 10.3 19.6 30.9 2.1 1.3 2.8

91.08 56.68 75.92 67.01 61.98 69.39 71.68 73.85 97.33 34.88 30.07 40.03 4.12 2.69 5.63 80.03 26.33 87.86 95.57 18.10 24.83 10.14 90.37 14.83 95.71 50.19 77.21 21.09 17.68 32.52 79.08 97.35 16.75 17.31 24.48 94.27 95.10 11.30 34.30 36.82 58.75 85.91 77.87 35.85 19.64 16.40 12.37

48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

c11-azlactone c15 azlactone c17-azlactone c19-azlactone camphorquinone cinnamic alcohol cinnamic aldehyde cis-6-nonenal clotrimazole cyclamen aldehyde dibromodicyanobutane diethyl maleate dihydroeugenol dimethyl sulfoxide dodecyl methane sulfonate ethylene glycol dimethacrylate eugenol farnesal formaldehyde glutaraldehyde glyoxal hc red no. 3 hexyl cinnamic aldehyde hydroquinone hydroxycitronellal 1-bromodocosane isoeugenol 1-(-)-perillaldehyde lactic acid linalool lyral MPT o-aminophenol oleyl methane sulfonate palmitoyl chloride paraphenylenediamine phenyl benzoate phenylacetaldehyde pyridine qrm 2113 safranal tetradecyl iodide tetramethylthiuram disulfide trans-2-decenal trans-2-hexenal xylene

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

1 1 1 1 1 1 1 1a 1 2, 3 2, 3 2, 3 2, 3 2, 3 2, 3a 1 2, 3 1 1 2, 3 2, 3 2, 3a 1 2, 3a 1 U 1 2, 3 2, 3 2, 3 1a 1 2, 3 2, 3 2, 3 1 1a 2, 3 2, 3 2, 3 1 1a 1 2, 3a outlier outlier outlier

2.32 0.27 1.15 0.71 0.49 0.82 0.93 1.04 3.59 -0.62 -0.84 -0.40 -3.15 -3.59 -2.82 1.39 -1.03 1.98 3.07 -1.51 -1.11 -2.18 2.24 -1.75 3.11 0.01 1.22 -1.32 -1.54 -0.73 1.33 3.61 -1.60 -1.56 -1.13 2.80 2.97 -2.06 -0.65 -0.54 0.35 1.81 1.26 -0.58 -1.41 -1.63 -1.96

class EC3 class (pred) pred 16.1 17.8 19 26.4 10 20.6 2.2 23.1 4.8 20.5 2.3 4.7 12.45 71.9 8.8 36.5 13.95 11.7 1.2 0.1 0.6 2.2 12.54 0.1 25.25 8.3 3.5 7.95 14.3 30.4 17.1 1.4 0.5 25 8.8 0.29 17.05 4.7 71.9 36.8 7.5 13.8 5.2 2.5 5.5 95.8

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

2, 3 2, 3 2, 3 2, 3 1 1a 1 1 1 2, 3 1 Ua 2, 3 2, 3 U 2, 3 2, 3 2, 3a 1 1 1 1 2, 3 1a 2, 3 U 2, 3 2, 3 1 2, 3a 2, 3 1 1 U 2, 3 1a 1 1 1 2, 3 2, 3 2, 3a 1 1 1 2, 3

-2.18 -2.62 -2.84 -3.06 3.05 2.18 2.45 0.69 3.51 -3.78 5.71 0.01 -1.88 -1.49 -0.10 -1.21 -0.14 -4.88 1.19 2.09 2.50 2.91 -0.76 2.29 -2.41 0.05 -0.37 -2.61 1.25 -1.44 -2.44 0.87 2.28 -0.01 -1.52 1.78 1.05 1.44 1.22 -2.27 -0.95 -3.37 2.80 0.72 1.16 -1.62

prob 10.17 6.80 5.53 4.49 95.47 89.87 92.05 66.59 97.11 2.24 99.67 50.16 13.20 18.37 47.50 23.02 46.50 0.75 76.74 89.01 92.38 94.81 31.87 90.84 8.25 51.22 40.91 6.84 77.73 19.22 8.00 70.48 90.72 49.87 17.92 85.58 74.13 80.92 77.19 9.35 27.86 3.33 94.26 67.22 76.11 16.55

a Cross-validation set for model 1. Positive values of class 1 are for compounds in class 1, and negative values are for compounds in classes 2 and 3. A complete list of references for LLNA results not reported here is given as Supporting Information.

The role of structural factors driving skin sensitization and their mechanistic interpretation is discussed for several examples of classes of chemicals that were wellclassified by model 1. Other classes not included in this paper but studied in a similar way encompass haloalkanes, ketones, and esters, among others. Aldehydes and their skin sensitization potential have been the subject of several papers (34, 45, 46). Here, the aldehydes studied belonged to one of two classes, saturated aldehydes or R,β-unsaturated aldehydes. Saturated aldehydes are those where the carbonyl is not directly bonded to unsaturated carbons in contrast with aromatic or R,β-unsaturated aldehydes. The main reaction pathway for saturated aldehydes is the Schiff base formation. Our predictions compare well to previous works (34, 46). It is a fact that our model identifies the carbonyl group as well as the R,β-double bond conjugated to it as structural alerts for skin sensitization. These elements are well-recognized in the literature and are implemented into expert systems such as DEREK (6). An additional value of the current methodology as compared to those implemented in actual expert systems such as DEREK

is that it enables a ranking (on the basis of potency) of compounds having the same functional group in different classes. For instance, compounds 85 (phenylacetaldehyde), 57 (cyclamen aldehyde), and 20 (2-ethyl butyraldehyde) have saturated carbonyl groups. They are classified by the DEREK expert system as sensitizers, but our current models discriminate them as strong/moderate, weak, and extremely weak/nonsensitizers, respectively. An explanation of these differences should be found in the differences in reactivity or distribution/ metabolism of these compounds. The bond contributions of these compounds are given in Figure 2. R,β-Unsaturated aldehydes were also studied. These compounds are able to react by the formation of Schiff bases, but additionally, they are able to react via nucleophilic addition at the double bond; i.e., Michael addition. Schiff base formation occurs from the reaction between the aldehydes and the amine group of basic amino acids, such as lysine. Although amino groups participate in Michael addition reactions, other nucleophilic groups such as thiol and hydroxy groups are also able to participate in Michael addition reactions with aldehydes.



Table 2. Predictions Made by Using Model 2 for Compounds in Classes 2 and 3a class class no. EC3 class (pred) (2) prob

class class no. EC3 class (pred) (2) prob

3 5 11 12 13 14 15 17 20 28 30 33 34 35 38 39 40 43 44 48 49

50 51 53 55 57 60 61 63 64 65 70 72 76 77 78 81 84 86 87 89 94

15.47 16.6 16.3 20.2 13.1 19.1 24.2 11.3 68.15 31.6 20.15 13.2 16.9 21.3 11.27 12.05 13.7 19.6 30.9 16.1 17.8

2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 3 2 2

2 2 2 2 2 2 2b 2 2 2 3 2 2b 2 2 2 2 2b 3 2 2

4.28 9.04 8.84 5.66 5.22 8.39 2.84 1.66 0.35 3.31 -1.30 2.76 3.39 3.37 7.52 8.69 7.90 3.49 -6.57 2.49 5.66

98.6 99.9 99.9 99.6 99.5 99.9 94.5 84.1 58.7 96.5 78.6 94.1 96.8 96.7 99.9 99.9 99.9 97.0 99.9 92.3 99.6

19 26.4 20.6 23.1 20.5 12.45 71.9 36.5 13.95 11.7 12.54 25.25 14.3 30.4 17.1 25 17.05 71.9 36.8 13.8 95.8

2 2 2 2 2 2 3 3 2 2 2 2 2 3 2 2 2 3 3 2 3

2 2 2b 2 2 2 3 3 2 2 2 2 2 2b 2 2b 2 3 3 2 3

7.25 8.83 4.99 2.88 5.06 3.02 -6.23 -2.09 3.33 5.75 9.49 6.15 5.86 4.44 7.23 12.94 5.85 -4.48 -8.30 6.80 -1.00

99.9 99.9 99.3 94.7 99.4 95.3 99.8 89.0 96.5 99.7 99.9 99.8 99.7 98.8 99.9 99.9 99.7 98.9 99.9 99.9 73.0

a Positive values of class 2 are for compounds in class 2, and negative values are for those in class 3. b Cross-validation set for model 2. A complete list of references for LLNA results not reported here is given as Supporting Information.

The order of nucleophilicity is SH > NH2 > OH. Bond contributions for skin sensitization of several unsaturated aldehydes are presented in Figure 2. As we can see, model 1 correctly identifies the carbonyl group and the conjugated double bond of strong/moderate sensitizers as structural alerts. However, only the carbonyl groups of those R,β-unsaturated aldehydes having some steric hindrance to the double bond are identified as alerts. These compounds (26, 2-phenyl propionaldehyde; 65, farnesal; and 39, R-amyl cinnamaldehyde) are only weak sensitizers. This highlights how our model points in the right direction in terms of differentiating compounds with the same functional group but having differing sensitization profiles. This represents an important step change as compared with expert systems currently available. Experimental evidence (47) supports the fact that aldehydes such as compound 65 (farnesal) do not react by Michael addition but only by Schiff base formation. This evidence strongly supports our finding here with the use of the TOPS-MODE approach. There are several phenols reported in the literature as skin sensitizers. Phenol itself is not a sensitizer (31) although other phenols such as compound 16 (1-naphthol) have been shown to be strong or moderate sensitiz-

Figure 2. Bond contributions to skin sensitization of some saturated and R,β-unsaturated aldehydes according to model 1.

ers. 1-Naphthol is a moderate sensitizer in the LLNA with EC3 1.3% (see Figure 3). Model 1 correctly identified the OH group as a structural alert for skin sensitization, which is a well-recognized fact in the literature and has been incorporated into expert systems such as DEREK. This functional group, i.e., the phenolic OH, is recognized by TOPS-MODE in other compounds used in the current study where the group is in different structural environments such as compound 80 (o-aminophenol) in Figure 3. Six aromatic amines were included in the current study: compound 19 (2-amino-6-chloro-4-nitrophenol), 25 (2-nitro-p-phenylenediamine), 31 (4-chloroaniline), 69 (HC Red No. 3), 80 (o-aminophenol), and 83 (paraphenylenediamine). All of them are strong sensitizers. In Figure 3, we illustrate the high positive bond contribution of the amine group in some of these compounds. Compounds 25 (2-nitro-p-phenylene diamine), 69 (HC Red No. 3), and 83 (para-phenylenediamine) all possess

Table 3. Results of the Predictions Using Models 1 and 2 for the External Prediction Set compd

CAS

EC3

class

class (1)/class (2)

class

diethylamine 2-mercaptobenzothiazole oxazolone toluene diisocyanate phthalic anhydride benzocaine benzylidene acetone diethylphthalate isopropyl myristate 4-methoxyacetophenone 1-(p-methoxyphenyl)-1-penten-3-one 5-methyl-2,3-hexanedione 3-propylidenephthalide melatonin nimesulide

109-89-7 149-30-4 15646-46-5 584-84-9 85-44-9 94-09-7 122-57-6 84-66-2 110-27-0 100-06-1 104-27-8 13706-86-0 17369-59-4 8041-44-9 51803-78-2

39.78 9.669 0.013 0.109 0.357 22.026 50 10


Figure 3. Bond contributions to strong/moderate skin sensitization of some of the aromatic phenols and amines included in the current study.

a structural motif of p-phenylene diamine. Compound 83, p-phenylendiamine (48), shows high positive contributions to strong/moderate sensitization for both amino groups as well as the two aromatic bonds alternated between these groups (see Figure 3). This type of structure suggests the formation of a conjugated system highly susceptible to oxidization to a 1,4-diimine. The implications of this structure in the skin sensitization of these compounds need experimental verification, but the current findings provide a focus for searching to explain the strong sensitization of p-phenylene diamine-like compounds. Compound 22 (2-methyl-5-hydroxyethylaminophenol) (see Figure 3) was shown to be a potent sensitizer (EC3 0.4%). Although this compound is not an amine, we have chosen to discuss it in this section on account of the presence of the hydroxylamine group present in it. This group is recognized by TOPS-MODE as one of the possible structural alerts for this compound together with the phenolic OH group. The hydroxylamine group could be oxidized to a nitroso group in a similar way like that of an amine. In fact, amines are first oxidized to Nhydroxylamines and then onto nitroso compounds. The latter reacts with proteins forming haptens, as has been widely recognized as the possible sensitizing mechanism for amines. Compound 22 has some of the structural features of these intermediates, i.e., the hydroxylamine. Model 1 recognizes this motif as a possible structural alert for sensitization, which ought to be explored for possible inclusion into expert systems such as DEREK, which itself contains no alerts for hydroxylamines. Other compounds unrelated to chemical classes already analyzed were also included in the current work. In some cases, interesting structural contributions have been

Estrada et al.

Figure 4. Bond contributions to strong/moderate skin sensitization for TMP (compound 79) and its tautomer.

Scheme 1

identified by TOPS-MODE. Compound 79 (3-methyl-4phenyl-1,2,5-thiadiazole-1,1-dioxide; TMP) is a strong/ moderate sensitizer with EC3 values of 1.4%. TMP is predicted by model 1 to be a strong/moderate sensitizer with a probability of 70%. TOPS-MODE identified several regions of this compound as possible structural alerts. These included the SO2 group, one of the CdN bonds in the ring, and a wide region of the phenyl ring (see Figure 4A). There is sufficient experimental evidence that supports the formation of a tautomeric isomer of TMP (Scheme 1). Aimone et al. (49) found that this tautomer could produce a resonance-stabilized anion that could be dimerized. We explored this tautomer further using model 1. The tautomeric isomer of TMP is predicted to be a strong/moderate sensitizer with a probability of 90%, which is greater than that obtained for TMP (70%). The SO2 group continues to have the largest positive bond contribution toward skin sensitization although the next highest contribution is that for the CdCH2 group (see Figure 4B). According to several experimental results, nucleophilic addition of alcohols and thiols has been observed to take place at the CdN bond of the thiadiazole ring of TMPlike compounds (49-53). For TMP, Caram et al. have found that the ethanol molecule does indeed add to the CdN bond positioned on the Me-substituted side of the



Scheme 2

substrate and this was verified by 13C NMR and cyclic voltametry (51). This nucleophilic reaction can proceed by an addition of the nucleophile to the double bond of the tautomer of TMP as shown in Scheme 2. Possible Metabolic Activation Pathways. The skin’s metabolic capability is known to be involved in the metabolism of drugs as well as xenobiotics (54). Two important metabolic reactions that occur in the skin are the epoxidation reaction and the reduction of nitro groups. The latter is of interest as there were five structures containing aromatic nitro groups: compounds 9 (1-chloro-2,4-dinitrobenzene), 19 (2-amino-6-chloro-4nitrophenol), 25 (2-nitro-p-phenylenediamine), 32 (4nitrobenzyl bromide), and 69 (HC Red No. 3). All were strong sensitizers with values of EC3 not greater than 2.2. As can be seen (see compound 25 in Figure 3), the NO bonds of the nitro group are predicted to have high contributions to skin sensitization. The nitro groups are unable to react directly with proteins but are metabolically activated to reactive species. The nitro group reduction is a well-known mechanism of biological activation for nitro aromatic compounds and is indeed related to both mutagenic and carcinogenic activity. Here, nitro reduction is considered as a possible pathway for bioactivation in the skin taking as an example the compound 2-nitro-p-phenylendiamine (2NPPD), compound 25 (see Figure 3), for which experimental evidence exists. 2-Nitro-p-phenylendiamine is a “coal-tar dye” used in semipermanent (nonoxidative) and permanent (oxidative) hair dye formulations. At current concentrations (up to 1%), the Cosmetic Ingredient Review Panel has concluded that it is safe (55). However, it was found to be mutagenic in the Ames test as well as other mammalian tests (55, 56). It is also a strong skin sensitizer (EC3 0.4%). In a recent in vitro study using fuzzy rat skin, 2NPPD was found to be metabolized mainly to N4-acetyl-2NPPD (65%) and triaminobenzene (17%) (57). The latter is the product of reduction of a NO2 group in this molecule. In human skin, it was observed that most of this compound was metabolized to N4-acetyl-2NPPD (90%) and 7% of triaminobenzene was formed. However, when 2NPPD was applied to human skin as a semipermanent formulation, equal amounts of N4-acetyl-2NPPD and triaminobenzene were formed. This work concluded that, “in human and rat skin, it appears that 2NPPD is being metabolized via two pathways” (57). They are the N4acetylation of the corresponding amine and the nitro reduction of the 2-nitro group to an amine. Whether or not the reduction of nitro group plays a role in the skin sensitization of these compounds and other ones related to it is a matter of discussion. However, there is experimental evidence about the reduction of this group in the skin when it is present in some formulations. It has been detected that it is reduced to amines, and there is a wide range of evidence that amines are skin sensitizers. TOPSMODE identifies this group as a possible structural alert

Figure 5. Bond contributions to strong/moderate skin sensitization for styrene and patch test results for its metabolites (see ref 63).

for skin sensitization. Thus, it is recommendable to carry out further experimental studies on this type of compounds to confirm this possibility. Furthermore, some data supporting this alert already do exist in the literature. Barratt and Langowski (58) proposed rules for DEREK addressing the sensitization potential of musk ambrette that encompassed both 1,3,5-trinitro- and 1,3dinitrobenzenes substituted by alkyl and methoxy at different positions. Another possible metabolic activation pathway identified by TOPS-MODE was that for exocyclic double bonds (excluding those double bonds conjugated to carbonyl, ester, amido, or carboxylic acid, which are able to participate in Michael addition reaction). One possible activation pathway for such double bonds is the formation of epoxides, which can react directly with proteins to form adducts (59). The epoxidation of aldrin, a pesticide having an isolated double bond in a cyclic system, has been reported to take place during skin absorption (60). According to this paper, more than 99% of the epoxidation product, dieldrin, was probably formed locally by dermal metabolism of percutaneously absorbed aldrin. Other papers detecting the metabolism of aldrin to dieldrin by rat skin have also been published in the literature (61, 62). An interesting case that could be directly related with the epoxidation of an exocyclic double bond is that of styrene for which experimental confirmation exists. Styrene has no groups other than the exocyclic double bond, which could interfere in the metabolic activation of this chemical. Our prediction using model 1 classifies this compound as a strong/moderate sensitizer. The bond contributions for styrene according to model 1 are given in Figure 5.


The main contribution as predicted by TOPS-MODE is that of the exocyclic double bond. Styrene has been found to produce skin sensitization (63). In this study, it was concluded that styrene epoxide was the hapten due to the stronger reaction as compared to styrene when tested in equimolar concentrations. This proposes that styrene is the prohapten, which has to undergo oxidation in the skin before becoming a true hapten, i.e., the epoxide. It was pinpointed that styrene epoxide could be formed by enzymatic transformation by aryl hydrocarbon hydroxylase. The other possible metabolites of styrene are 4-hydroxystyrene and 1-phenylethanol. The first of them shows positive contact allergic reactions when tested in the same study, but there was no conclusion about whether the proper compound or its epoxide was responsible for the reaction (63). TOPS-MODE also identified the para position of the phenyl ring as a possible site responsible for sensitization. In closing, we believe that the presence of double bonds in certain conditions can constitute a toxicophore for skin sensitization due to metabolic activation to the epoxide hapten. The structural conditions for which these double bonds can be metabolized to epoxides remains unexplored in the current study, as there is insufficient data to arrive at definitive rules.

Conclusions Two general models have been developed that could be used in the screening and prioritization/classification of new ingredients for sensitization. Currently, there are no validated and regulatory accepted in vitro methodologies to replace animal testing. As animal testing of ingredients will become increasingly difficult over the coming years and the fact that a ban is being imposed in the European Union on the animal testing of cosmetic ingredients, QSARs as alternative methods will become and are playing an increasingly important role as tools for facilitating screening and classification of new ingredients as well as maximizing knowledge. As a component of a battery of alternative tools, this knowledge generator could be useful in deriving new structural alerts more efficiently and systemically than by conventional QSAR methods alone. Rules developed could be incorporated into such expert systems as DEREK, which are effectively knowledge archives requiring substantial inputs of data for the evolvement of new rules. The models presented have been shown to work well for a good breadth of chemicals and are able to discriminate well between compounds containing the same functional groups. The models compare well with rules that are currently contained within DEREK for sensitization although this will be the subject of a further paper (64). In summation, the models presented could work as a tool to generate new structural alerts and insights. New mechanistic insights could be described for specific chemicals on the basis of our findings, which will need further evaluation with biological measurements.

Acknowledgment. We thank both reviewers for useful comments that improved the presentation of this work. E.E. thanks the Ministerio de Ciencia y Tecnolgia, Spain, for a Ramon y Cajal contract as a researcher at the University of Santiago de Compostela.

Estrada et al. Supporting Information Available: Names and numbers of chemicals used and additional references. This material is available free of charge via the Internet at http://pubs.acs.org.

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