Mechanistic Applicability Domain Classification of a Local Lymph

Jun 8, 2007 - It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a...
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Chem. Res. Toxicol. 2007, 20, 1019-1030

1019

Mechanistic Applicability Domain Classification of a Local Lymph Node Assay Dataset for Skin Sensitization David W. Roberts,† Grace Patlewicz,‡ Petra S. Kern,§ Frank Gerberick,| Ian Kimber,⊥ Rebecca J. Dearman,⊥ Cindy A. Ryan,| David A. Basketter,# and Aynur O. Aptula*,# School of Pharmacy and Chemistry, LiVerpool John Moores UniVersity, LiVerpool, U.K., European Chemicals Bureau TP582, IHCP, Joint Research Centre, European Commission, 21020 Ispra (VA), Italy, The Procter & Gamble Eurocor, Brussels InnoVation Center, Strombeek-BeVer, Belgium, The Procter & Gamble Company, Miami Valley InnoVation Center, Cincinnati, Ohio, Syngenta Central Toxicology Laboratory, Alderley Park, Macclesfield, Cheshire, U.K., and Safety and EnVironmental Assurance Centre (SEAC), UnileVer, Colworth, Sharnbrook, Bedford, U.K. ReceiVed January 18, 2007

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable nonanimal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed. Introduction Under current regulatory frameworks, the identification of skin sensitization hazard is assessed through in ViVo testing. Although there has always been an appetite for the development of novel in Vitro methods, there has been with the advent of the EU Cosmetics Directive (1) and Registration Evaluation, Authorization of CHemicals (REACH) (2-4), an even greater drive to develop and apply alternatives such as (quantitative) structure-activity relationships ((Q)SARs) wherever possible. The development of alternative approaches involves the selection of a broad range of chemicals covering the major chemical mechanisms for skin sensitization, as well as an appropriate balance between confirmed skin sensitizers and non-sensitizers. Recently, a dataset was published (5) consisting of quantitative test results for over 200 compounds tested in the mouse local lymph node assay (LLNA) (6). A dataset of this size and quality is potentially very valuable for developing and evaluating (validating or invalidating) alternative approaches. Here, we analyze this new dataset by classifying it into the various chemical reaction mechanistic domains recognized for skin sensitization. Such a classification is necessary for the assessment of global (Q)SAR approaches that might be tested against * Corresponding author. Tel: +44 01234 264823. Fax: +44 01234 264722. E-mail: [email protected]. † Liverpool John Moores University. ‡ European Commission. § The Procter & Gamble Eurocor. | The Procter & Gamble Company. ⊥ Syngenta Central Toxicology Laboratory. # Unilever.

the dataset because it is important to know to what extent the known range of chemical mechanisms is covered. Furthermore, chemical reaction mechanistic domain classification is necessary if the dataset is to be used for mechanism-based approaches for the prediction of skin sensitization potential. In this article, we have hence classified the recently published LLNA dataset for more than 200 compounds (5) into reaction mechanistic domains. Our aims were as follows. (1) To characterize the population of each of the major domains by compounds in the dataset and in particular to identify any major domains that may be under-represented in the dataset. Six major domains (including one for non-reactive and non-pro-reactive compounds) have previously been described (7, 8): the reactive domains are Michael acceptors, SN2 electrophiles, SNAr electrophiles, Schiff base electrophiles and acyl transfer electrophiles (Scheme 1). (2) To determine the extent to which the 6 major domains cover the range of compounds in the dataset and to consider what can be learned from compounds outside these domains. (3) To consider the potential of the domain-classified dataset for predicting the sensitization potential of new compounds by mechanistic read-across or the use of quantitative mechanistic models (QMM). Before presenting the classification of the dataset, we need to discuss mechanistic domains and how mechanistic approaches might be developed.

Mechanism-Based Approaches Research dating back more than seven decades has established a very strong mechanistic understanding of skin sensitization

10.1021/tx700024w CCC: $37.00 © 2007 American Chemical Society Published on Web 06/08/2007

1020 Chem. Res. Toxicol., Vol. 20, No. 7, 2007 Scheme 1. Reaction Mechanistic Applicability Domains

(9, 10). Dermal exposure to contact allergens triggers highly regulated cellular and molecular interactions that together result in the stimulation of a cutaneous immune response and the acquisition of skin sensitization. In recent years, our understanding of the relevant biological mechanisms involved has become much more sophisticated (11, 12) but is nevertheless still incomplete. It is, however, by definition usually a low molecular weight chemical that initially triggers the acquisition of skin sensitization and the subsequent elicitation of contact allergic reactions. In order to sensitize an individual, a chemical has to cross the stratum corneum and react with protein or peptide, and then subsequently, the modified protein/peptide has to be processed by epidermal Langerhans cells (LC). Thereafter, antigen-laden LC are stimulated to migrate to draining lymph nodes, where antigen is presented to responsive T lymphocytes to stimulate an immune response. The fundamental chemical basis of skin sensitization is relatively well understood, despite some key gaps particularly with respect to the nature and location of carrier proteins. A proposed future approach to nonanimal-based estimation of relative skin sensitization potency has recently been outlined by Jowsey et al. (13). The theory is that if the ability of the chemical to meet each of the above requirements can be estimated quantitatively, and if the relative contributions of each step of the process to the overall skin sensitization potency can be determined, it should be possible to estimate the sensitization potency of a chemical relative to known contact allergens. Understanding the relative contributions of each of these steps to the overall skin sensitization potential is clearly essential and is an area of active research. However, the key importance of protein/peptide binding is

Roberts et al.

already well recognized and is a process that can be modeled by combining, for example, reactivity and hydrophobicity parameters using the relative alkylation index approach (RAI) (14). Modeling approaches to prediction of toxicity fall into four broad categories: read-across, SAR, QSAR, and QMM. We define these concepts as follows. Read-Across. This process may be defined as estimating a range within which toxicity for a given compound is expected to lie, by comparing it with related compounds whose toxicity or effects are known. This simple definition implies two issues: how to know which compounds are related and which are not and what parameters to use for the comparison. These are difficult issues when the chemical mechanism of toxicity is not well understood and when a (Q)SAR has been established based on statistically selected parameters. In such cases, relatedness can only be defined in terms of statistically based similarity indices, which in our experience can often fail to pick up important mechanistic subtleties that are obvious to organic chemists. In contrast, where the chemical basis of the toxic effect is well understood, a mechanism-based approach can avoid these difficulties. For skin sensitization, if a compound can be assigned to a reaction mechanistic domain, its sensitization potential can be assessed by comparison with known sensitizers in the same domain. We will illustrate how mechanism-based read-across can be applied in the Discussion section. SAR. The term SAR (structure-activity relationship) is used with two rather different meanings. In the QSAR literature, SAR is often taken to refer to a statistical function that classifies compounds in terms of their biological activity (e.g., active/ nonactive; strong/moderate/weak/nonactive) using quantitative parameters. Often, the statistical methods for such SARs are more complex and less mechanistically transparent than those for QSARs. In other contexts, the term SAR is used more broadly to refer to a method of relating structural parameters, which are not quantified, to biological effects, which are also not quantified, for example, expert systems using structural alerts, or schemes for the classification of compounds in terms of their biological activity (e.g., active/nonactive; strong/ moderate/weak/nonactive) on the basis of structural features. QSAR. A QSAR (quantitative structure-activity relationship) is an equation relating quantitative parameters, either experimentally determined or derived from chemical structure, to a quantitative measure of biological activity. It is derived by statistical analysis (e.g., linear regression) rather than by mathematical simulation of a mechanistic model. The term (Q)SAR is often generally used to refer to both QSARs and SARs. Often (Q)SARs are not directly mechanism-based, and in particular, there is a growing trend to use software that can search from a large repertoire of parameters calculated from structure to look for combinations that give statistically significant correlations to biological activity. Critical analyses of several (Q)SARs of this type, applied to skin sensitization, have recently been reported (15, 16). At the other end of the range are mechanism-based QSARs, where the parameters are selected on the basis of mechanistic understanding. Some QSARs of this sort fit our definition of a QMM (see below); others may fall short of QMM status because there is uncertainty as to whether the chosen parameters are adequate. For example, if the mechanistic understanding is that the toxic effect is dependent on reactivity, then a QSAR developer may use a calculated molecular orbital index that purports to model reactivity but may only partly do so.

Mechanistic Applicability Domain Classification

QMM. QMM (quantitative mechanistic modeling) is a recently introduced term (15) for a long-established approach. A QMM is an equation relating toxicity (or more generally an effect) and physicochemical parameters, which can be derived mathematically from the known mechanism of action, applying established principles of chemistry and physics. An example is the QMM for skin sensitization for Schiff base domain sensitizers (17).

pEC3 ) 1.12 Σσ* + 0.42 log P - 0.62 The relationship pEC3 ) a [log k] + b log P + c (k being a rate constant and a, b, and c being constants) is derived from the RAI model (14). The use of Σσ* (the sum of the Taft σ* values of the two groups bonded to the reactive carbonyl group) to represent log k is based on the application of linear free energy relationships to the known reaction mechanism. The coefficients 1.12 and 0.42 and the constant -0.62 are obtained from regression analysis. The key difference between QSAR and QMM is as follows. In QSAR, statistical techniques are applied not only to determine coefficients for the parameters but also to confirm that the parameters are really meaningful and that the correlation did not arise simply by chance. Thus, there are rules defining the minimum number of compounds needed for each descriptor and statistical parameters such as F-values have to be quoted. In QMM, the relevance of the parameters can be taken for granted (because they have been derived from the appropriate mechanism-based differential equations), and the regression statistics are only needed in order to define the confidence limits on the coefficients (and hence the error limits of predictions). Attempts to develop global QSAR models for skin sensitization, by analyzing datasets diverse in chemical structure and types of reaction chemistry, have been met at best with only limited success. They have been evaluated in refs 15 and 16 when assessed against the OECD principles for the validation of (Q)SARs (18). In contrast, QMM, that is, using a mechanistic understanding of the physicochemical basis of skin sensitization to develop mathematical models relating sensitization potency to chemical properties, has proved successful in interpreting many small sets of sensitization data derived from both guinea pig and murine assays. Examples include refs 19-22. However, until recently the QMM approach was considered to be limited in scope by being applicable only to datasets on compounds that are structurally closely related (thus with the implication not only that their reaction mechanisms are common but also that they produce the same or very similar antigenic determinants). Recently, the concept of the reaction mechanistic applicability domain and rules for defining these domains for electrophilic toxicity were introduced (7). Applicability domain is a term that comes from the field of QSAR. Particularly with a statistical QSAR without a transparent mechanistic basis, QSAR practitioners face the problem of how to define the applicability domain, that is, what compounds the QSAR can (and should) and what compounds it cannot (and should not) use to predict. For predictions of toxicological properties where the effect results from chemical reaction (reactive toxicity) with a target, for example, a protein/peptide, it is argued that if chemicals are grouped together according to the chemical mechanisms by which they react (i.e., grouping into reaction mechanistic domains), then in principle it should be possible to develop QSARs or QMMs that would apply across single reaction mechanistic domains. A reaction mechanistic domain for which such a QSAR or QMM has been developed can be referred to

Chem. Res. Toxicol., Vol. 20, No. 7, 2007 1021

as the reaction mechanistic applicability domain for that QSAR or QMM. At this point, it is worth noting that the mechanistic and physical organic chemistry principles underlying reactive toxicity are not unique to skin sensitization and have been discussed in depth in relation to other toxic endpoints (8, 2325). For skin sensitization, which is a reactive toxicity endpoint, correlations were found to exist between potency and a combination of reactivity and hydrophobicity parameters within a reaction mechanistic domain. These correlations have made it feasible to develop QMMs based on the RAI model (14) covering much wider ranges of structures (17) than was previously thought possible.

Classification into Reaction Mechanistic Domains The rules shown in Scheme 1, (7) were applied in order to try to classify the compounds into the six major reaction mechanistic domains: Michael acceptors, SN2 electrophiles, SNAr electrophiles, Schiff base formers, acylating agents, nonreactive and non-pro-reactive, and special cases.

Results Domains Populated by the Gerberick et al. Data Set (5). The compounds are shown, classified into their appropriate domains, in Tables 1-6. The reaction chemistry corresponding to each domain has already been discussed (8, 9). Here, we limit the chemistry discussion to that not already covered in these two references as well as to special cases. Michael Acceptors. These are listed in Table 1. Some of the compounds can be confidently assigned as definite Michael acceptors, that is, they can clearly be seen to have an electrondeficient double bond that is susceptible to nucleophilic attack, or as definite pro-Michael acceptors, that is, although not Michael acceptors themselves, they are able to be converted to Michael acceptors by well-established transformations (metabolic or abiotic), for example, hydroquinone oxidized to benzoquinone. Other compounds that we have included in Table 1 are less confidently assigned because plausible alternative mechanisms such as free radical binding can be proposed. Several eugenol and isoeugenol derivatives fall into this category. Many of these ambiguous cases are aromatic compounds with two or more hydroxyl or amino groups. A subgroup of such compounds consists of aromatic compounds in which two hydroxy or amino groups are meta to each other. For these compounds, two mechanisms can be suggested: reaction with molecular oxygen to introduce a hydroxy group either ortho or para to the original hydroxy or amino groups and direct binding to protein via attack of a protein-centered radical. In the first case, the oxidation product would be a proMichael acceptor analogous to hydroquinone, 2-aminophenol, and similar compounds. The compound 4-(N-ethyl-N-2-methan-sulfonylamino-ethyl)2-methyl-1,4-phenylenediamine (CD3) (CAS 25646-71-3) requires special comment. It is structurally similar to 1,4phenylenediamine (PPD) but cannot be oxidized to a neutral quinoneimine because one of the aromatic amino groups is tertiary. However, it can in principle be oxidized to a charged analogue as shown in Scheme 2. This derivative would be more reactive as a Michael acceptor than the quinoneimine derived from PPD. Similar to PPD, a free radical binding via a Wu¨rstertype radical cannot be excluded. Not all of the compounds in this domain are sensitizers. Six of them are non-sensitizers and can be rationalized in terms of

1022 Chem. Res. Toxicol., Vol. 20, No. 7, 2007

Roberts et al. Table 1. Michael Acceptor Domain

#

chemical name

CAS #

LLNA EC3%

potency category

1 2 3 4 5 6 7 8 9 10

p-benzoquinone hydroquinone 1,4-phenylenediamine 2,5-diamino-toluene lauryl gallate (dodecyl gallate) 2-aminophenol 2-nitro-p-phenylenediamine 2-methyl-5-hydroxyethylaminophenol 3-phenylenediamine 4-(N-ethyl-N-2-methan-sulfonamido-ethyl)2-methyl-1,4-phenylenediamine (CD3) isopropyl isoeugenol metol 1,2-dibromo-2,4-dicyanobutane 3-methyl-4-phenyl-1,2,5thiadiazole-1,1-dioxide (MPT) 2-hydroxyethyl acrylate 6-methylisoeugenol vinyl pyridine isoeugenol 5,5-dimethyl-3-methylenedihydro-2(3H)-furanone 2-amino-6-chloro-4-nitrophenol HC Red No3 trans-anethol trans-2-decenal methyl 2-nonynoate cinnamic aldehyde 3-aminophenol 3-bromomethyl-5,5-dimethyldihydro-2(3H)-furanone 3-methylisoeugenol benzylidene acetone (4-phenyl3-buten-2-one) 2,4-heptadienal R-methyl cinnamic aldehyde trans-2-hexenal 2-methoxy-4-methyl-phenol diethyl maleate dihydroeugenol safranal (1,1,3-trimethyl-2formylcyclohexa-2,4-diene) perillaldehyde 1-(p-methoxyphenyl)-1-penten-3-one hexyl cinnamic aldehyde amyl cinnamic aldehyde R-butyl cinnamic aldehyde eugenol 5-methyleugenol 6-methyleugenol 4-allylanisole cinnamic alcohol ethyl acrylate ethylene glycol dimethacrylate 3-methyleugenol isopropyl eugenol coumarin 2-hydroxypropyl methacrylate 6-methylcoumarin vinylidene dichloride resorcinol

106-51-4 123-31-9 106-50-3 95-70-5 1166-52-5 95-55-6 5307-14-2 55302-96-0 108-45-2 25646-71-3

0.0099 0.11 0.16 0.2 0.3 0.4 0.4 0.4 0.49 0.6

extreme strong strong strong strong strong strong strong strong strong

1 3 4 4 3 4 4 4 4 4

55-55-0 35691-65-7 3775-21-1

0.6 0.8 0.9 1.4

strong strong strong moderate

4 4 3 1

818-61-1 13041-12-8 1337-81-1 97-54-1 29043-97-8

1.4 1.6 1.6 1.7 1.8

moderate moderate moderate moderate moderate

1 4 1 4 1

6358-09-4 2871-01-4 104-46-1 3913-71-1 111-80-8 104-55-2 591-27-5 154750-20-6

2.2 2.2 2.3 2.5 2.5 3.0 3.2 3.6

moderate moderate moderate moderate moderate moderate moderate moderate

4 4 4 1 1 1 4 4

186743-29-3 122-57-6

3.6 3.7

moderate moderate

4 1

5910-85-0 101-39-3 6728-26-3 93-51-6 141-05-9 2785-87-7 116-26-7

4.0 4.5 5.5 5.8 5.8 6.8 7.5

moderate moderate moderate moderate moderate moderate moderate

1 1 1 3 1 4 1

2111-75-3 104-27-8 101-86-0 122-40-7 7492-44-6 97-53-0 186743-25-9 186743-24-8 140-67-0 104-54-1 140-88-5 97-90-5 186743-26-0

8.1 9.3 11 11 11 13 13 17 18 21 28 28 32 NC NC NC NC NC NC

moderate moderate weak weak weak weak weak weak weak weak weak weak weak non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer

1 1 2 2 2 4 4 4 4 2 1 1 4 4 1 1 1 1 4, 5

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 48 49 50 51 52 53 54 55

91-64-5 923-26-2 92-48-8 75-35-4 108-46-3

further groupinga

a 1. Definite assignment as a Michael acceptor. 2. Probably/possibly a Michael acceptor, but other possibilities cannot be ruled out. 3. Definite assignment as a pro-Michael acceptor. 4. Probably/possibly a pro-Michael acceptor, but other possibilities cannot be ruled out. 5. The re-testing of resorcinol, recently reported (54), gives an EC3 value of 6%, corresponding to classification as a moderate sensitizer.

low reactivity, relative to the other compounds, of the compounds themselves or of the Michael acceptors into which they can be converted. SN2 Electrophiles. These are shown in Table 2. Within the group of direct-acting electrophiles, it is useful for QMM purposes to distinguish between nonpolar compounds (25 of these) and H-polar compounds (17 of these). The distinction does not imply a difference in the mechanism of protein binding;

instead it proves necessary on account of the different relationships between log P (octanol/water partition coefficient) and in cutaneo partitioning properties (9). The six non-sensitizers in this domain can be rationalized in terms of their nonoptimal hydrophobicity (9). SNAr Electrophiles. There are only three compounds of this domain in the Gerberick et al. dataset (5), and we cannot exclude the possibility that one of these (pentachlorophenol, weak

Mechanistic Applicability Domain Classification Scheme 2. Proposed Transformation for 4-(N-ethyl-N-2-methan-sulfonylamino-ethyl)-2-methyl-1,4phenylenediamine (CD3)

sensitizer) may act by a different mechanism. The compounds are 1-chloro-2,4-dinitrobenzene, EC3 ) 0.06%; 2,4,6-trichloro1,3,5-triazine (cyanuric chloride), EC3 ) 0.09%; and pentachlorophenol, EC3 ) 20%. Schiff Base Electrophiles. The LLNA dataset (5) contains 40 compounds in this domain (Table 3). Three of them are aliphatic amines that are proposed to act as pro-Schiff base electrophiles by conversion of the CH-N entity into CdO. This is a working hypothesis based on a proposal by Benezra, quoted by Foussereau et al. (26). We are not in a position to formulate a definitive prediction rule for compounds with aliphatic amino groups: provisionally, the rule would be to consider the Schiff base potency of the compound with CH-N replaced by CdO. However, more data are needed to assess the generality and scope of this rule. A further proposed pro-Schiff base electrophile is the weak sensitizer geraniol, which may be activated by oxidation of the allylic CH2OH group to CHdO. (This carbonyl group is R,β-unsaturated, but the compound is unlikely to sensitize by Michael addition because of the deactivating effect of the methyl group on the double bond.) Another possibility for geraniol is sulfation of the allylic CH2OH group to give an SN2-reactive allylic sulfate, -CH2OSO3-. A QMM for the Schiff base domain, based on reactivity and hydrophobicity parameters, has been recently published (17). This QMM can rationalize the seven non-sensitizers shown in Table 3. Acyl Transfer Agents. Acyl transfer agents, listed in Table 4, have the general structure RCOX, and their reactions with nucleophiles proceed via a tetrahedral intermediate as shown in Scheme 3. Ability to act as an acyl transfer agent depends very much on the ability of group X to be expelled from the tetrahedral intermediate, which in turn depends on the pKa of the conjugate acid XH: the more acidic XH is, the more reactive RCOX is. Thus, acyl halides (X ) halogen) and anhydrides (X ) OCOR) are highly reactive, aryl esters (XH is a phenol) are somewhat less reactive but usually reactive enough to sensitize, and simple alkyl esters (X ) alkyl) are not reactive enough to sensitize. The azlactones, of which there are eight in the dataset, (Scheme 4) merit some further discussion. They can be regarded as aza-analogues of cyclic anhydrides. One of these compounds, oxazolone, is different from the others in having an exocyclic double bond: it is classed as an extreme sensitizer, and it is likely that it has more complex reaction chemistry than that of the other compounds. The remaining

Chem. Res. Toxicol., Vol. 20, No. 7, 2007 1023 Table 2. SN2 Domain #

chemical name

CAS #

LLNA potency EC3% category

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

1-chloromethylpyrene 4-nitrobenzyl bromidea propiolactonea dimethyl sulfatea benzyl bromide methyl dodecane sulfonatea methyl hexadecene sulfonatea bisphenol A-diglycidyl ethera 1-bromohexadecanea diethyl sulfatea 2-bromotetradecanoic acida 1-bromoheptadecane 1-bromopentadecane 1-bromoeicosane 12-bromo-1-dodecanola methyl methanesulfonatea 1-bromodocosane dodecyl methane sulfonatea 1-chlorohexadecane 1-bromotetradecane 1-bromohexane 1-bromotridecane 1-iodododecane 1-iodotetradecane 1-bromooctadecane 1-chlorooctadecane benzyl benzoatea 1-bromododecane 12-bromododecanoic acida 1-iodohexadecane 1-bromoundecane 1-chlorotetradecane 7-bromotetradecane 1-iodononane oleyl methane sulfonatea butyl glycidyl ethera 1-bromobutane 1-bromononane 1-chlorononane 1-iodohexane 1-iodooctadecane methyl hexadecane sulfonatea

1086-00-6 100-11-8 57-57-8 77-78-1 100-39-0 2374-65-4 26452-48-2 1675-54-3 112-82-3 64-67-5 10520-81-7 3508-00-7 629-72-1 4276-49-7 3344-77-2 66-27-3 6938-66-5 51323-71-8 4860-03-1 112-71-0 111-25-1 765-09-3 4292-19-7 19218-94-1 112-89-0 3386-33-2 120-51-4 143-15-7 73367-80-3 544-77-4 693-67-4 2425-54-9 74036-97-8 4282-42-2 35709-09-2 2426-08-6 109-65-9 693-58-3 2473-01-0 638-45-9 629-93-6 4230-15-3

0.005 0.05 0.15 0.19 0.2 0.39 0.8 1.5 2.3 3.3 3.4 4.8 5.1 6.1 6.9 8.1 8.3 8.8 9.1 9.2 10 10 13 14 15 16 17 18 18 19 20 20 21 24 25 31 NC NC NC NC NC NC

a

extreme extreme strong strong strong strong strong moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer

H-polar.

seven azlactones are members of a homologous series. A plot of pEC3 versus log P is shown in Figure 1. Sensitization potential increases with log P between the C4 and C6 homologues, and then beyond some point between the C6 and C9 homologues (in the log P range 2.9-4.4), sensitization potential becomes a decreasing function of log P. This is indicative of stratum corneum (SC) penetration becoming sensitizationdetermining for hydrophobic compounds in the LLNA, a phenomenon that has previously been recognized in the SN2 and SB domains for sulfonate esters, alkyl halides, and reactive carbonyl compounds (8, 9, 17, 19). The argument is that with the high log P compounds, not all of the material penetrates the stratum corneum; therefore, the effective dose (dose reaching the site of action) is reduced, and as log P increases, the effective dose gets smaller. For the material that does get through, protein binding is positively dependent on both reactivity and log P. The reduction in effective dose (bioavailability) is the larger effect; therefore, the net log P dependence is negative (19). Overall, the acyl transfer agent domain is one of the more difficult to quantitatively predict; as mentioned in our earlier paper (9), the LLNA data do not always correlate well with earlier guinea pig data. We have plans for a detailed mechanistic analysis of the guinea pig data for this domain.

1024 Chem. Res. Toxicol., Vol. 20, No. 7, 2007

Roberts et al. Table 3. Schiff Base Domain LLNA EC3%

potency category

50-00-0 111-30-8 579-07-7 107-22-2 109-55-7 107-15-3 122-78-1 111-40-0 93-53-8 112-45-8 167998-73-4 167998-76-7

0.61 0.1 1.3 1.4 2.2 2.2 3 5.8 6.3 6.8 8.3 9.6

strong strong moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate

465-29-2 110-41-8 431-03-8 55846-68-9 502-67-0 5392-40-5 56290-55-2 5406-12-2 31906-04-4 80-54-6 326-06-7 103-95-7 2277-19-2 106-24-1 13706-86-0 1118-71-4 6668-24-2 170928-69-5

10 10 11 11 12 13 13 14 17 19 20 22 23 26 26 27 29 33

weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak weak

107-75-5 620159-84-4

33 37

weak weak

97-96-1 874-23-7

76 NC NC

weak non-sensitizer non-sensitizer

94-02-0 492-94-4

NC NC NC

non-sensitizer non-sensitizer non-sensitizer

135099-98-8

NC

non-sensitizer

100-52-7

NC

non-sensitizer

#

chemical name

CAS #

1 2 3 4 5 6 7 8 9 10 11 12

formaldehyde glutaraldehyde 1-phenyl-1,2-propanedione glyoxal 3-dimethylaminopropylaminea ethylenediamine free basea phenylacetaldehyde diethylenetriaminea R-methylphenylacetaldehyde undec-10-enal 1-(2′,3′,4′,5′-tetramethylphenyl)butane-1,3-dione 1-(2′,5′-diethylphenyl) butane-1,3-dione camphoroquinone 2-methylundecanal 2,3-butanedione 1-phenyloctane-1,3-dione farnesal citral 1-(2′,5′-dimethylphenyl)butane-1,3-dione p-methylhydrocinnamic aldehyde lyral lilial (p-tert-butyl-a-ethyl hydrocinnamal) 4,4,4-trifluro-1-phenylbutane-1,3-dione cyclamen aldehyde cis-6-nonenal geraniolb 5-methyl-2,3-hexanedione 2,2,6,6-tetramethyl-heptane-3,5-dione 1-phenyl-2-methylbutane-1,3-dione 3-ethoxy-1-(2′,3′,4′,5′-tetramethylphenyl) propane-1,3-dione hydroxycitronellal 2-(4-tert-amylcyclohexyl) acetaldehyde (QRM 2113) diethyl acetaldehyde 2-acetylcyclohexanenonec bis-1,3-(2′,5′-dimethylphenyl)propane-1,3-dioned ethyl benzoylacetated furild 1-(2′,3′,4′,5′-tetramethylphenyl)-3(4′-tertbutylphenyl) propane-1,3-dioned 1-(3′,4′,5′-trimethoxyphenyl)-4dimethylpentane-1,3-dioned benzaldehyded

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 a

Proposed pro-SB via oxidative deamination. b Possibly pro-SB via oxidation of CH2OH to CHO but possibly pro-SN2 via sulfation of allylic CH2OH to CH2OSO3-. c Correctly predicted NS by QMM (17). d Relatively unreactive.

Nonreactive. All of the compounds in this domain are either weak sensitizers (8 compounds) or non-sensitizers (23 compounds); where sensitization is observed, it can be attributed to impurities such as oxidative degradation products. The compounds are presented in Table 5. Special Cases. Under this heading we have included the following: (a) compounds that we can assign to one of the above domains, although in some cases only provisionally, but that require further comment. These are pro-SN2 PAHs, via oxidation (9); N-nitroso derivatives, which act as hard SN2 or pro-SN2 electrophiles, discussed in some detail in ref 9; compounds for which SN2-reactions at sulfur can be proposed (9); and 1-naphthol. We suggest that this acts as a Michael acceptor via its keto-tautomer (Scheme 5). This keto-tautomer, in a protonated form, is commonly accepted as being an intermediate in the Bucherer-Lepetit reaction (27, 28) in which 1-napthol is treated with ammonia and sodium bisulfite to give 1-naphthylamine. (In our view, the protonated keto-tautomer seems unlikely except at pH values lower than those used in the Bucherer-Lepetit reaction or those encountered in the skin; we consider the unprotonated keto-tautomer more plausible.) (b)

Compounds which do not fit any of the above domains. There are only 2 such compounds: clotrimazole and potasssium dichromate. Clotrimazole is known to undergo rapid hydrolysis upon heating in aqueous acids (29), which is indicative of an SN1 electrophile. It can dissociate to a delocalised electrophilic triarylmethyl cation (Scheme 6). As far as we are aware, clotrimazole is the first compound that has been proposed to sensitize via the SN1 mechanism. It is possible that other SN1 sensitizers exist but have not been recognized as such, for example, tertiary allylic hydroperoxides, which have been proposed to sensitize via a free radical mechanism (30). Potasssium dichromate is not only an oxidizing agent but also a source of electrophilic trivalent chromium ions (31). Transition state metal ions can be regarded as analogous to carbonium ions (SN1 electrophiles), able to covalently bind to proteins in covalent coordination complexes.

Discussion Domain Coverage. Table 7 summarizes the number of compounds in the data set that have been assigned to the

Mechanistic Applicability Domain Classification

Chem. Res. Toxicol., Vol. 20, No. 7, 2007 1025

Scheme 3. Acyl Transfer

Scheme 4. Azlactone Structures

currently defined reaction mechanistic applicability domain. With the exception of the SNAr domain, all of the domains are well represented in the data set (5). Although the SNAr domain is comparatively poorly represented in this LLNA dataset, it is a domain that has been well investigated in guinea pig studies, beginning with the pioneering work of Landsteiner and Jacobs more than 70 years ago (32). By analyzing these guinea pig data, models that can be used for read-across have been developed (33, 34). Mechanism-Based Approach. The following approach for the non-animal prediction of skin sensitization potential, combining in silico (we include expert chemistry insight under this term) and experimental chemistry, was recently proposed (9) and is strengthened by the present findings. Presented with a new compound: 1. The first step would be to classify it into its reaction mechanistic domain. This may often be possible by inspection of structure, but inevitably in some cases, a confident prediction may not be possible. In such situations, experimental work will be needed to determine the reaction chemistry, in particular to determine if the compound is electrophilic or pro-electrophilic, and the nature of the reactions. 2. Having assigned the compound to its reaction mechanistic domain, the next step is to quantify its reactivity/hydrophobicity

Figure 1. Azlactones. pEC3 vs log P.

relative to known sensitizers in the same mechanistic applicability domain. These properties may sometimes be confidently predictable from structure, using physical organic chemistry approaches such as linear free energy relationships based on substituent constants or on molecular orbital parameters. In other cases, experimental physical organic chemistry will be required, for example, to measure reactivity to appropriate model nucleophiles such as glutathione or other model peptides (35-37). 3. Having assigned the compound to its reaction mechanistic applicability domain and having quantified its reactivity/ hydrophobicity relative to known sensitizers in the same domain, mechanistic read-across can be used to predict, within a range, the likely sensitization potential. The logic behind mechanistic read-across is that sensitization potential is always related to a combination of reactivity and hydrophobicity. We may not always know how to adequately model reactivity (other than by experimental rate constants) or what the relative weightings of reactivity and hydrophobicity are, but if we can assign two compounds A and B to the same reaction mechanistic domain and if we know that A is both more reactive and more

Table 4. Acyl Transfer Agents Domain #

chemical name

CAS #

LLNA EC3%

potency category

1 2 3 4

oxazolone tetrachlorosalicylanilide fluorescein-5-isothiocyanate 2-methyl-4H,3,1-benzoxazin-4-one (product 2040) C6-azlactone 2-mercaptobenzothiazole nonanoyl chloride C4-azlactone methyl 2-sulfophenyl octadecanoate isononanoyl chloride 3,5,5-trimethylhexanoyl chloride C9-azlactone 3-propylidenephthalide 3,4-dihydrocoumarin sodium 3,5,5-trimethylhexanoyloxy benzenesulfonate palmitoyl chloride 1,2,4-benzenetricarboxylic anhydride (trimellitic anhydride) pationic 138C (sodium lauroyl lactylate) C11-azlactone C15-azlactone C17-azlactone phenyl benzoate imidazolidinyl urea C19-azlactone penicillin G saccharin

15646-46-5 1154-59-2 3326-32-7 525-76-8

0.003 0.04 0.14 0.7

extreme extreme strong strong

176665-02-4 149-30-4 764-85-2 176664-99-6 57077-36-8 36727-29-4 176665-04-6 17369-59-4 119-84-6 94612-91-6

1.3 1.7 1.8 1.8 2 2.7 2.7 2.8 3.7 5.6 6.4

moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate moderate

112-67-4 552-30-7

8.8 9.2

moderate moderate

13557-75-0 176665-06-8 176665-09-1 176665-11-5 93-99-2 39236-46-9

15 16 18 19 20 24 26 30 NC

weak weak weak weak weak weak weak weak non-sensitizer

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

61-33-6 81-07-2

1026 Chem. Res. Toxicol., Vol. 20, No. 7, 2007

Roberts et al.

Table 5. Neither Reactive nor Pro-Reactive Domain LLNA EC3%

#

chemical name

CAS #

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

sodium lauryl sulfate abietic acid oxalic acid linalool isopropyl myristate R(+)-limonene dimethylsulfoxide pyridine aniline benzocaine chlorobenzene ethyl vanillin glycerol hexane 4-hydrobenzoic acid isopropanol kanamycin lactic acid 4′-methoxyacetophenone methyl 4-hydroxybenzoate (methylparaben) methyl salicylate octanoic acid propylene glycol propyl paraben salicylic acid sulfanilamide sulfanilic acid vanillin 1-butanol diethylphthalate streptomycin sulfate tartaric acid

151-21-3 514-10-3 144-62-7 78-70-6 110-27-0 5989-27-5 67-68-5 110-86-1 62-53-3 94-09-7 108-90-7 121-32-4 56-81-5 110-54-3 99-96-7 67-63-0 8063-07-8 50-21-5 100-06-1 99-76-3

1.3 15 15 30 44 69 72 72 89 NC NC NC NC NC NC NC NC NC NC NC

moderate (false positive)a weakb weak (false positive)a weakb weak (false positive)a (56) weakb weak (false positive)a weak (false positive)a weak (false positive)a non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer

119-36-8 124-07-2 57-55-6 94-13-3 69-72-7 63-74-1 121-57-3 121-33-5 71-36-3 84-66-2 3810-74-0 87-69-4

NC NC NC NC NC NC NC NC NC NC NC NC

non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer non-sensitizer

21 22 23 24 25 26 27 28 29 30 31 32

potency category

a These compounds, irritant at the concentrations tested, meet the criteria for giving false positives in the LLNA (55). b These compounds when pure are non-sensitizers but, depending on storage history, can cause sensitiation via autoxidation derived hydroperoxides (57-60).

hydrophobic than B, then we could predict that A will be the stronger sensitizer1. If a QMM has previously been developed for the mechanistic domain, the sensitization potency of the compound under consideration can be predicted, more precisely than by read-across, from the QMM. We can illustrate how the classified data set could be used for mechanism-based read-across by considering the compound 1-nitrododec-1-ene, C10H21CHdCHNO2. We are not aware of any test data on this compound. It is a shorter chain homologue of a naturally occurring compound, 1-nitropentadec-1-ene, which is used as a defense chemical by termites (38). C10H21CHdCHNO2 can immediately be assigned to the Michael acceptor domain, the double bond being activated by the electronegative nitro group. There is currently no QMM for the Michael acceptor domain in general, although a QSAR has been developed for a subdomain, the R,β-unsaturated aldehydes (39). One of the compounds from the 210 dataset, which is in the Michael acceptor domain, is C7H15CHdCHCHO. It has the same basic structure as that of C10H21CHdCHNO2, the alkyl group being shorter and the activating group being CHO rather than NO2. The Clog P values (Clog P indicates a log P value calculated by the method of Leo and Hansch (40)) for the two compounds are 4.01 for C10H21CHdCHNO2 and 3.17 for C7H151 As noted earlier, at high log P values sensitization potency in the LLNA becomes a decreasing function of log P. This needs to be taken into account if the intention is to predict the LLNA potency per se. However, the inverse correlation with log P at high log P values seems to be a feature that is unique to the LLNA but does not occur (unless perhaps with compounds above a higher log P limit rarely encountered) in guinea pig assays or in humans. If the aim is to estimate potency for purposes of human risk assessment, it is better to predict what the EC3 would be if the inverse log P dependence effect did not apply.

CHdCHCHO. We can be confident that the NO2 group is a much stronger activator than the CHO group (41), as can be seen by comparison of substituent constants (42).

Hammett σ Hammett Taft σTaft σ*

CHO

NO2

0.44 1.03 2.15

0.78 1.24 4.25

When the two compounds are compared, C10H21CHdCHNO2 is more hydrophobic and more reactive than C7H15CHd CHCHO. Therefore, C10H21CHdCHNO2 is predicted to be the stronger sensitizer. The EC3 of C7H15CHdCHCHO (Table 1) is 2.5% (1.6 × 10-2 mol %). Therefore, the EC3 of C10H21CHdCHNO2 is predicted to be less than 1.6 × 10-2 mol %, that is, less than 3.5%. Bearing in mind its higher log P value and its larger reactivity parameters, there is a high probability that C10H21CHdCHNO2 would have an EC3 value below 1% and would be classed as a strong sensitizer. Scheme 5. Reaction of 1-Naphthol via a Keto-Tautomer

Mechanistic Applicability Domain Classification

Chem. Res. Toxicol., Vol. 20, No. 7, 2007 1027 Table 6. Special Cases

#

chemical name

CAS #

LLNA EC3%

potency category

1 2 3 4 5

clotrimazole potassium dichromate benzo[a]pyrene 7,12-dimethylbenz[a]anthracene 5-chloro-2-methyl-4isothiazolin-3-one

23593-75-1 7778-50-9 50-32-8 57-97-6 26172-55-4

4.8 0.08 0.0009 0.006 0.009

moderate extreme extreme extreme extreme

6

1-methyl-3-nitro-1nitrosoguanidine

70-25-7

0.03

extreme

7

N-methyl-N-nitrosourea, toxic

684-93-5

0.05

extreme

8

N-ethyl-N-nitrosourea

759-73-9

1.1

moderate

9

2-methyl-2H-isothiazol-3-one

2682-20-4

1.9

moderate

10

2634-33-5

2.3

moderate

11

1,2-benzisothiazolin-3-one (proxel active) tetramethylthiuram disulfide

137-26-8

5.2

moderate

12

1-naphthol

90-15-3

1.3

moderate

Table 7. Summary of Mechanistic Applicability Domain Classification domain (pro)-Michael acceptors SNAr SN2 Schiff base acyl transfer non-(pro)electrophilic special cases

subdomain

non H-polar H-polar

total # of cases 55 3 25 17 40 26 32 12

The above is a hypothetical illustration. Below we give some examples based on real data. The first example illustrates how read-across can be applied, in this case using a combination of physicochemical data and data from a completely different toxicological end point. The basic principle of mechanistic readacross is that if two compounds in the same mechanistic domain are similar in their toxicity-determining parameters, then they should be similar in their toxicity, irrespective of whether or not they are similar in structure. The best situation is to have a large database such that for a compound whose toxicity is to be predicted, there are several compounds with known toxicity and similar toxicity-determining parameters such as reactivity and hydrophobicity. At present, the reactivity database of known skin sensitizers is still quite small. However, Schultz (43) has Scheme 6. Proposed SN1 Reaction Mechanism for Clotrimazole

group

pro-SN2 PAH, via oxidation (9) pro-SN2 PAH, via oxidation (9) compounds for which SN2-reaction at the S-atom can be proposed (9) N-nitroso derivatives, which act as hard SN2 or pro-SN2 electrophiles, discussed in some detail in (9) N-nitroso derivatives, which act as hard SN2 or pro-SN2 electrophiles, discussed in some detail in (9) N-nitroso derivatives, which act as hard SN2 or pro-SN2 electrophiles, discussed in some detail in (9) compounds for which SN2-reaction at the S-atom can be proposed (9) compounds for which SN2-reaction at the S-atom can be proposed (9) compounds for which SN2-reaction at the S-atom can be proposed (9) we suggest that this acts as a Michael acceptor via its keto-tautomer

generated, and continues to extend, a large database of aquatic toxicity to the unicellular organism Tetrahymena pyriformis, which includes numerous electrophiles. Aquatic toxicity of electrophiles is correlated with their reactivity but only weakly, if at all, with hydrophobicity (44-46). Thus, aquatic toxicity can be regarded as a quantitative index of the reactivity of an electrophile to the unknown biological nucleophiles undergoing reaction in Tetrahymena. Here, we use this reactivity, expressed as pIGC50, to model reactivity to the unidentified carrier protein nucleophiles involved in skin sensitization. IGC50 is the concentration giving 50% inhibition of population growth, and p denotes the operator -log. Suppose we wish to estimate the sensitization potency of methyl 2-nonynoate, C6H13C#CCO2Me. This compound is clearly assigned to the Michael acceptor mechanistic domain. Although we do not have direct reactivity data for this compound, its aquatic toxicity to Tetrahymena pyriformis is available: its IGC50 value is 0.05 mM. Although hydrophobicity does not have much influence on aquatic toxicity of electrophiles, it is an important determinant of skin sensitization potency. We therefore need the log P value for methyl 2-nonynoate. By the Leo and Hansch method (40), it is calculated to be 3.25. In the Tetrahymena database, we searched for two compounds in the Michael acceptor domain, whose IGC50 and log P values are at either side of the methyl 2-nonynoate figures, and whose LLNA EC3 values are known. We find the following: 2-hexenal, C3H7CHdCHCHO, IGC50 ) 0.17; log P ) 1.58; and EC3 ) 5.5% and β-phenyl cinammaldehyde, PhCHdCPh.CHO, IGC50 ) 0.02; log P ) 3.4, and EC3 ) 2.5%. The smaller the IGC50, the more reactive the chemical. Because the reactivity (as modeled by IGC50) and hydrophobicity of methyl 2-nonynoate lies between those of 2-hexenal and β-phenyl cinnammaldehyde, we can predict that its sensitization potency will also be between their EC3 values and closer to β-phenyl cinnamaldehyde than to 2-hexenal. We can do a rough calculation as follows. IGC50 is inversely proportional

1028 Chem. Res. Toxicol., Vol. 20, No. 7, 2007 Scheme 7. trans-2-Methyl-2-butenal and Tetrachloroisophthalonitrile (TCPN)

to the rate constant; therefore, we can use pIGC50 to represent log k. By analogy with many structure-sensitization correlations published for various types of compounds (e.g., sultones, lactones, alkyl alkane sulfonates, Schiff base electrophiles, etc.), we can assume that for the present compounds, sensitization potency (expressed as a mol % pEC3 value) is influenced by reactivity twice as much as that by log P. (The accuracy of this assumption is not critical because the log P values of methyl 2-nonynoate and β-phenyl cinnamaldehyde differ only slightly.) Therefore, we can define a relative sensitization parameter, RSP, as RSP ) pIGC50 + 0.5 log P, and, on the basis of the RAI model (14), we can write the following equation.

pEC3mol % ) aRSP + b From their pIGC50 and log P values, we can calculate the RSP for all three compounds, and from the EC3 (wt%) values for 2-hexenal and β-phenyl cinnamaldehyde and their molecular weights, we can calculate their pEC3mol % values as follows: 2-hexenal, C3H7CHdCHCHO, RSP ) 1.56; pEC3mol % ) 1.25; β-phenyl cinammaldehyde, RSP ) 3.40; pEC3mol % ) 1.92; and methyl 2-nonynoate, RSP ) 2.93; pEC3mol % ) ?. By linear interpolation, we get 1.75 as the pEC3mol % for methyl 2-nonynoate. Its molecular weight is 168. Hence, we can calculate its EC3 as 2.98%. Considering the approximate nature of the calculations, we would quote the estimate to be about 2-3.5%. The observed EC3 for methyl 2-nonynoate is actually 2.5%. Read-across estimates made in this manner are very dependent on the EC3 values of the two known compounds being reliable and on the reactivity and log P parameters for all three compounds being accurate. We next illustrate the application of QMM and read-across for two compounds that are not in the LLNA database discussed here but that are intended to be included in a future extended version of it. LLNA data for these compounds came to our attention while this article was undergoing review. trans-2-Methyl-2-butenal. Prediction by QMM. This compound has the structure shown in Scheme 7. It is an R,βunsaturated aldehyde, but the methyl groups on the olefinic double bond have a deactivating effect toward Michael addition, and therefore, we assign it to the Schiff Base domain, for which a QMM has been reported (17). Other R,β-unsaturated aldehydes with methyl substitution in the R-position have been found to fit the Schiff Base QMM. The Schiff base QMM equation is as follows.

pEC3 ) 1.12 Σσ* + 0.42 log P - 0.62 For trans-2-methyl-2-butenal, Σσ* is calculated by the method of Perrin et al. (42) to be 0.65. Log P is calculated, using the Leo and Hansch method (40) with the PDBF modification (47), to be 0.43. Using these Σσ* and log P values, pEC3 is calculated from the QMM to be 0.29, corresponding to an EC3 value of 43%, that is, it is predicted to be a weak sensitizer. The experimental LLNA stimulation index values are as follows: at 10%, 1.5; at 25%, 1.0; and at 50%, 2.8. These figures correspond to an EC3 value >50% (probably, given the SI value

Roberts et al.

of 2.8 at 50%, in the range 55-60%), that is, it is a weak sensitizer. Bearing in mind the error limits for the LLNA (typically a factor of 2 (48)), the agreement between the QMM prediction and the experimental data is good. Tetrachlorosiophthalonitrile (TCPN). Prediction by ReadAcross. This compound, used as a wood preservative and a known occupational allergen (49), has the structure shown in Scheme 7. It can be assigned confidently to the SNAr domain. Although there are very few SNAr electrophiles in the 210 LLNA dataset, a prediction can be made, by comparison with 2,4-dinitrochlorobenzene (DNCB), as follows. For guinea pig sensitization data on halo- and nitro-substituted benzenes, the reactivity parameter (RP) Σσ-(o,m,p) + 0.45 σ*(ipso) has been found to discriminate between sensitizers and non-sensitizers (10 compounds in each group), and between chemically reactive (toward the model nucleophile aniline) and unreactive compounds (33). The larger the RP, the more reactive the compound. The RP values of TCPN and DNCB have been reported (33). They are as follows: TCPN, RP ) 4.95; and DNCB, RP ) 4.02. The log P values are TCPN, log P ) 2.90; and DNCB, log P ) 2.14. Thus, TCPN is more reactive and more hydrophobic than DNCB and should therefore be a significantly stronger sensitizer than DNCB. The reported (50) LLNA data for TCPN are as follows. [TCPN] (wt %) stimulation index (SI)

0.003 2.1

0.01 9.4

0.03 13.8

0.1 18.4

0.3 27.2

A plot of SI against log[TCPN] (not shown) gives SI ) 11.8 log[TCPN] + 32.1, and R2 ) 0.9827, from which the EC3 value is determined to be 0.0035%. Comparing this with the EC3 value of 0.06% for DNCB, it can be seen that as predicted by readacross, TCPN is a significantly stronger sensitizer than DNCB.

Conclusions Historically, the predictive identification of chemicals that have the capacity to cause skin sensitization required the use of one of a number of guinea pig assays (51). More recently, the mouse LLNA has become widely used because of both technical and animal welfare advantages (52). Results from both types of animal assay have been used extensively as part of investigations aimed at determining whether it is possible to identify skin sensitization potential as a function of their chemical characteristics (10, 12, 51, 52). To a large extent, where (Q)SAR models have focused on a narrow window of chemistry, the outcomes have been encouraging, whereas attempts to build global QSARs have been much less successful, as illustrated in refs 15 and 16. This is not unexpected; the narrow range of chemistry effectively dictates a single chemical mechanism, and it was on this mechanism that a (Q)SAR was usually built. Widening the chemical scope, however, demands a knowledge of chemical mechanisms, and how to integrate these into a single model is a challenge that only infrequently has met with any success (13). Thus, the deployment of global (Q)SARs as nonanimal alternatives to help meet the demands of new legislation designed to eliminate in ViVo methods such as the LLNA might well be expected to meet with only limited success. In this article, we have sought to lay the foundations of a new strategy based on read-across and QMM and, in doing so, to address the extent to which the LLNA dataset (5) provides a good representation of the repertoire of skin sensitization chemistry. We have shown that with the exception of the SNAr domain, which has been well investigated in guinea pig studies, coverage of major domains by the dataset is good; all the

Mechanistic Applicability Domain Classification

domains are well represented in the 210 dataset (5). Furthermore, we have shown that the domains previously defined (7-9) accommodate almost all of the 210 compounds in the dataset. (There are indications that a further domain, for SN1 electrophiles, may need to be added.) Because more than half of the 210 compounds in the dataset analyzed here have not been discussed in our previous publications on mechanistic applicability domains (7-9), this finding supports the view that most if not all sensitizers act by a chemical mechanism corresponding to one of these domains. It is important to note that not all of the domain assignments are unequivocal. Some assignments can be made more confidently than others, depending on how well established the organic chemistry is. Where our assignment of a compound to a mechanistic domain is provisional, based on our assessment of the probable reaction chemistry, we have indicated this in the Tables. Where assignment of a sensitizer to a domain is uncertain, that compound should not be used for prediction of new compounds by read-across. In such situations of uncertainty, particularly in the case with new compounds to be predicted, chemistry experiments will be needed to assign the mechanistic domain. A good illustration of the type of investigation needed is provided by the work of Alvarez et al. (53) on 5-chloro-2methyl-4-isothiazolin-3-one (MCI) and 2-methyl-2H-isothiazol3-one (MI). The chemistry of these compounds was not obvious, multiple possibilities seeming plausible on paper, but the experimental findings revealed the importance of a ring cleavage reaction via nucleophilic attack on the sulfur atom as the initial step in a multistep cascade of reactions (53). One group of compounds for which such an exercise would enhance predictive capability comprises aromatic compounds containing two or more hydroxyl or amino groups. For these compounds, there are potentially several mechanisms for protein binding. Another such group is that of compounds proposed to act as SN1 electrophiles such as clotrimazole discussed above. Currently, work is underway to extend the dataset discussed here by inclusion of results obtained with other compounds previously tested in the LLNA but not yet released into the public domain. It is hoped that these new data will enable us to assess the value of the present domain-classified dataset for readacross purposes and to develop QMMs for reaction-mechanistic domains currently lacking them. Acknowledgment. We are grateful to Professor T. W. Schultz for access to his Tetrahymena toxicity database and for permission to quote new data contained in it, which have not yet been published.

Chem. Res. Toxicol., Vol. 20, No. 7, 2007 1029

(5)

(6) (7)

(8) (9)

(10) (11)

(12)

(13) (14)

(15)

(16)

(17)

(18)

References (1) EU. (2003) Directive 2003/15/EC of the European Parliament and the Council of 27 February 2003 Amending Council Directive 76/768/ EEC on the Approximations of Laws of the Member States Relating to Cosmetic Products. Official Journal of the European Union, L: Legislation 66, 26-35. (2) Commission of the European Communities. (2001) White Paper on the Strategy for a Future Chemicals Policy. COM(2001)88, Brussels, Belgium. http://europa.eu.int/comm/environment/chemicals/whitepaper.htm. (3) Commission of the European Communities. (2003) Proposal for a Regulation of the European Parliament and of the Council Concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency and Amending Directive 1999/45/EC and Regulation (EC) {on Persistent Organic Pollutants}. http://europa.eu.int/comm/enterprise/chemicals/ chempol/whitepaper/reach.htm. (4) Commission of the European Communities. (2006) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation

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