Electrophilic reactivity and skin sensitization potency of SNAr electrophiles.

We published in 2011 a quantitative mechanistic model (QMM) for skin sensitization potency of SNAr electrophiles in the mouse local lymph node assay (LLNA). In this model, potency was correlated with a combination of σ* for the leaving group and the total σ(-) values of the other substituents in the aromatic ring. Shortly afterward Natsch et al. published a kinetic study in which rate constants were determined for reactions of SNAr electrophiles with the cysteine-based peptide Ac-RFAACAA (Cys-peptide) that is used in the direct peptide reactivity assay (DPRA), and correlations were sought between these rate constants and sensitization potency in the LLNA. These two publications together have enabled the present study, aiming to develop a linear free energy relationship (LFER) correlating Cys-peptide reactivity with a reactivity parameter (RP) based on a combination of σ* and σ(-) substituent constants and, by analyzing differences between the QMM based on RP and the QMM based on Cys-peptide rate constants, to gain further insights into the underlying chemistry of skin sensitization. For the 2,4-dinitro-X-subsituted benzenes (DNXB), the rate constants of Natsch et al. are well correlated with the reactivity parameter used in our earlier work, with two outliers. These are the compounds with X = F and X = SCN, which are both substantially more reactive toward Cys-peptide than predicted from comparison of their RP values with those of the other DNXB compounds. These two compounds are both negative outliers from a correlation of sensitization potency with experimental rate constants, but fit well to the correlation of sensitization potency with RP values. With these two compounds excluded, sensitization potency is well correlated with the experimental rate constants for the DNXB compounds (X = SO3(-), I, Br, Cl) together with 2,4-dichloro-1-nitrobenzene and 1,3,4,5-tetrachloro-2,6-dicyanobenzene. The regression equation is pEC3 = 0.88 log k + 4.03, R(2) = 0.966. The implication of DNFB being an outlier is that the model Cys-peptide nucleophile is substantially more sterically hindered than the cutaneous nucleophile(s) involved in the sensitization process. The pattern seen with 2,4-dinitrothiocyanatobenzene suggests that this compound reacts as an SNAr electrophile in the sensitization process, but by a different pathway, acting as a CN transfer agent, with the model Cys-peptide. For two further compounds, 2,4,6-trinitrochlorobenzene and 2,4,6-trinitrobenzenesulfonate, the Cys-peptide rate constants are well predicted by the reactivity parameter based on displacement of the Cl or SO3(-) substituent, but their sensitization potency is underestimated by both the Cys-peptide rate constant and this reactivity parameter. However, potency of these two compounds is well predicted by a reactivity parameter calculated on the basis of displacement of the 2-nitro group. This is interpreted as a case of sensitization being driven by the thermodynamically favored rather than the kinetically favored reaction product.

Chemistry-based risk assessment for skin sensitization: quantitative mechanistic modeling for the S(N)Ar domain.

There is a strong impetus to develop nonanimal based methods to predict skin sensitization potency. An approach based on physical organic chemistry, whereby chemicals are classified into reaction mechanistic domains and quantitative models or read-across methods are derived for each domain, has been the basis of several recent publications. This article is concerned with the S(N)Ar reaction mechanistic domain. Electrophiles able to react by the S(N)Ar mechanism have long been recognized as skin sensitizers and have been used extensively in research studies on the biology of skin sensitization. Although qualitative discriminant analysis approaches have been developed for estimating the sensitization potential for S(N)Ar electrophiles on a yes/no qualitative basis, no quantitative mechanistic model (QMM) has so far been developed for this domain. Here, we derive a QMM that correlates skin sensitization potency, quantified by murine local lymph node assay (LLNA) EC3 data on a range of S(N)Ar electrophiles. It is based on the Hammett σ(-) values for the activating groups and the Taft σ* value for the leaving group. The model takes the form pEC3=2.48 Σσ(-) + 0.60 σ* - 4.51. This QMM, generated from mouse LLNA data, provides a reactivity parameter 2.48 Σσ(-) + 0.60 σ*, which was applied to a set of 20 compounds for which guinea pig test results were available in the literature and was found to successfully discriminate the sensitizers from the nonsensitizers. The reactivity parameter correctly predicted a known human sensitizer 2,4-dichloropyrimidine. New LLNA data on two further S(N)Ar electrophiles are consistent with the QMM.

Refinement of the Dermal Sensitisation Threshold (DST) approach using a larger dataset and incorporating mechanistic chemistry domains.

An essential step in ensuring the toxicological safety of ingredients in consumer products is the evaluation of their skin sensitising potential. Where skin exposure is low, it is possible to conduct a risk assessment using the Dermal Sensitisation Threshold (DST), a process similar to that of the Threshold of Toxicological Concern. This paper describes work building on that previously published, whose aim was to produce a more robust tool for assessing the safety of consumer products. This consisted of expanding the Local Lymph Node Assay dataset used to define the original DST and classifying chemicals in the dataset according to their mechanistic chemistry domains. A DST of 900µg/cm(2) was derived for chemicals classified as non-reactive and non-proreactive. This value was benchmarked against human potency data for 58 fragrance allergens and was lower than the measured No Expected Sensitisation Levels for those classified as non-reactive. Use of this DST will help to eliminate the need for animal testing of non-reactive and non-proreactive chemicals where skin exposure is sufficiently low. For chemicals where a Quantitative Risk Assessment based on the DST does not give an adequate margin of safety, and those classified as reactive, a case-by-case risk assessment will be required.

Reactivity-based toxicity modelling of five-membered heterocyclic compounds: application to Tetrahymena pyriformis.

A diverse set of 57 heterocyclic organic chemicals, consisting of a five-membered unsaturated ring of four carbon atoms and one oxygen (furans), or sulfur (thiophenes), or nitrogen (pyrroles) were evaluated for reactivity with thiol and acute aquatic toxicity assays using glutathione (GSH) as a model nucleophile and the ciliate Tetrahymena pyriformis, respectively. Reactivity was quantified by the RC50 value, the concentration of test compound that produced 50% reaction of the GSH thiol groups in 2 hours. Under standard conditions, RC50 values are mathematically proportional to reciprocal rate constants. Toxicity was quantified by the IGC50, the concentration of the test compound that produces 50% inhibition of population growth in 40 hours. Pyrroles with polarized α,ß-unsaturated substructures were found to be non-reactive with GSH and did not exhibit excess toxicity in the Tetrahymena assay. In contrast, those furans and thiophenes with polarized α,ß-unsaturated substructures were reactive with GSH via the Michael addition mechanism and did exhibit excess acute aquatic toxicity in Tetrahymena. For furans and thiophenes, reactivity and toxicity varied with the number, type, and location on the ring of the π-bond-containing polarized moieties. Comparisons of reactivity and toxicity potency between furan and thiophene derivatives revealed furans to be twice as potent as thiophenes. QSAR analysis revealed that aquatic toxicity IGC50 to Tetrahymena is correlated with RC50 values: log (IGC50(-1)) = 1.13 log (RC50(-1)) + 1.43; n = 23, r²= 0.815, r²(adj) = 0.806, s = 0.41, F = 92.

An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential.

Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification.

Global (Q)SARs for skin sensitisation: assessment against OECD principles.

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.

Structure-activity relationships for abiotic thiol reactivity and aquatic toxicity of halo-substituted carbonyl compounds.

Using abiotic thiol reactivity (EC50) and Tetrahymena pyriformis toxicity (IGC50) data for a group of halo-substituted ketones, esters and amides (i.e. SN2 electrophiles) and related compounds a series of structure-activity relationships are illustrated. Only the alpha-halo-carbonyl-containing compounds are observed to be thiol reactive with the order I > Br > Cl > F. Further comparisons disclose alpha-halo-carbonyl compounds to be more reactive than non-alpha-halo-carbonyl compounds; in addition, the reactivity is reduced when the number of C atoms between the carbonyl and halogen is greater than one. Comparing reactivity among alpha-halo-carbonyl-containing compounds with different beta-alkyl groups shows the greater the size of the beta-alkyl group the lesser the reactivity. A comparison of reactivity data for 2-bromoacetyl-containing compounds of differing dimensions reveals little difference in reactivity. Regression analysis demonstrates a linear relationship between toxicity and thiol reactivity: log (IGC(50)(-1)) = 0.848 log (EC(50)(-1)) + 1.40; n=19, s=0.250, r2=0.926, r2(pred)=0.905, F=199, Pr > F=0.0001.

Prediction of hERG K+ blocking potency: application of structural knowledge.

Modelling of QT-prolongation has been performed using data for 19 structurally diverse hERG K+ channel blocking drugs taken from literature. The modelling used hydrophobicity corrected for ionisation (log D) and various 2D and 3D physico-chemical molecular descriptors. Stepwise regression produced a two parameter, interpretable and transparent QSAR with good statistical fit, including log D and the maximum diameter of molecules (Dmax). Two strategies were applied for model validation: (i) a scrambling procedure, i.e., training the total set of 19 chemicals after randomising the hERG K+ channel blocking activity data and (ii) use of external validation sets. Validation of the models showed them to be stable and statistically significant. The effect of molecular size on QT-prolongation side effect is discussed.

Partial least squares modelling of the acute toxicity of aliphatic compounds to Tetrahymena pyriformis.

The aim of this study was to evaluate a multivariate statistical model, utilising Partial Least Squares (PLS) analysis, for the prediction of the acute toxicity of aliphatic chemicals to the ciliate Tetrahymena pyriformis. A model was developed that was capable of making a prediction regardless the mechanism of toxic action. The toxicity of 476 compounds, possessing different mechanisms of toxic action was considered. A set of 74 descriptors, including the octanol-water partition coefficient, molecular-orbital descriptors, geometrical, topological and connectivity indices, was generated. A three-component, eight-descriptor PLS model was developed. It was validated by a Y-permutation test and by simulation of external prediction for complementary subsets. A comparison with existing class or mechanism-based models, derived on the same data set, was made.