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.

Ranking of hair dye substances according to predicted sensitization potency: quantitative structure-activity relationships.

Allergic contact dermatitis following the use of hair dyes is well known. Many chemicals are used in hair dyes and it is unlikely that all cases of hair dye allergy can be diagnosed by means of patch testing with p-phenylenediamine (PPD). The objectives of this study are to identify all hair dye substances registered in Europe and to provide their tonnage data. The sensitization potential of each substance was then estimated by using a quantitative structure-activity relationship (QSAR) model and the substances were ranked according to their predicted potency. A cluster analysis was performed in order to help select a number of chemically diverse hair dye substances that could be used in subsequent clinical work. Various information sources, including the Inventory of Cosmetics Ingredients, new regulations on cosmetics, data on total use and ChemId (the Chemical Search Input website provided by the National Library of Medicine), were used in order to identify the names and structures of the hair dyes. A QSAR model, developed with the help of experimental local lymph node assay data and topological sub-structural molecular descriptors (TOPS-MODE), was used in order to predict the likely sensitization potential. Predictions for sensitization potential were made for the 229 substances that could be identified by means of a chemical structure, the majority of these hair dyes (75%) being predicted to be strong/moderate sensitizers. Only 22% were predicted to be weak sensitizers and 3% were predicted to be extremely weak or non-sensitizing. Eight of the most widely used hair dye substances were predicted to be strong/moderate sensitizers, including PPD - which is the most commonly used hair dye allergy marker in patch testing. A cluster analysis by using TOPS-MODE descriptors as inputs helped us group the hair dye substances according to their chemical similarity. This would facilitate the selection of potential substances for clinical patch testing. A patch-test series with potent, frequently used, substances representing various chemical clusters is suggested. This may prove useful in diagnosing PPD-negative patients with symptoms of hair dye allergy and would provide some clinical validation of the QSAR predictions.

Contact allergy: the role of skin chemistry and metabolism.

Chemical reactivity plays the driving role in the biological processes that result in the induction of allergic contact dermatitis. This paper presents an overview of the chemical basis of allergic contact dermatitis, including the physicochemical parameters governing skin penetration, chemical reaction mechanisms associated with haptenation of skin proteins, (quantitative) structure-activity relationships (Q)SARs for contact allergens and prohaptens/skin metabolism of contact allergens. Despite the complexities and poor understanding of some of the metabolic processes leading to skin sensitization, it is possible to describe some of the relationships between chemical structures and the ability to form covalent conjugates with proteins. This knowledge, which relates chemical structure to a specific endpoint, can be programmed into an expert system. The Deductive Estimation of Risk from Existing Knowledge (DEREK) is one such expert system which is described in further detail.

Contact allergy: the local lymph node assay for the prediction of hazard and risk.

The prospective identification of potential contact allergens and their subsequent safety assessment are pivotal in successful management of this risk to human health. Although much can be learned from the chemical and physical properties of a substance, the definitive information in respect of sensitizing hazard/risk derives from an assessment of the integrated response of the immune system. In recent years, the focus for such assessments has begun to switch from the guinea pig to the mouse, notably to the local lymph node assay (LLNA). In this paper, the current value of the LLNA for hazard identification is reviewed and its regulatory status defined. Once a potential contact allergen has been identified, however, the vital clue to accurate safety evaluation is the assessment of the potency of the allergen. How this can be achieved using the LLNA and employed in safety evaluation is discussed in detail, together with practical suggestions for the deployment of such processes in regulatory toxicology.

Structure-activity relationships for selected fragrance allergens.

Fragrance substances represent a very diverse group of chemicals, a proportion of them providing not only desirable aroma characteristics, but also being associated with adverse effects, notably the ability to cause allergic reactions in the skin. However, efforts to find substitute materials are hampered by the need to undertake animal testing to evaluate both the presence and the degree of skin sensitization hazard. One potential route to avoid such testing is to understand the relationships between chemical structure and skin sensitization. In the present work we have evaluated two groups of fragrance chemicals, saturated aldehydes (aryl substituted and aliphatic aldehydes) and alpha,beta-unsaturated aldehydes. Data on their skin sensitization potency defined using the local lymph node assay has been evaluated in relation to their physicochemical properties. The initial outcome has been consistent with the concept that alpha,beta-unsaturated aldehydes react largely via Michael addition, whilst the group of saturated aldehydes form Schiff bases with proteins. Simple models of chemical reactivity based on these mechanisms suggest that it may be possible to predict allergenic potency. Accordingly, the evaluation of an additional group of similar aldehydes is now underway to assess the robustness of these models, with some emphasis being based on ensuring a wider spread of chemical reactivity.

A QSAR model for the eye irritation of cationic surfactants.

A QSAR model for the eye irritation of cationic surfactants has been constructed using a dataset consisting of the maximum average scores (MAS-accordance to Draize) for 29 in vivo rabbit eye irritation tests on 19 different cationic surfactants. The parameters used were logP (log [octanol/water partition coefficient]) and molecular volume (to model the partition of the surfactants into the membranes of the eye), logCMC (log critical micelle concentration-a measure of the reactivity of the surfactants with the eye) together with surfactant concentration. The model was constructed using neural network analysis. MAS showed strongly positive, non-linear correlations with surfactant concentration and logCMC and a strongly negative, non-linear correlation with logP. The Pearson correlation between the actual and predicted values of MAS was 0.838 showing that around 70% (r(2)=0.702) of the variance in the dataset is explained by the model. This value is consistent with levels of biological variability reported historically for the Draize rabbit eye test. The relationship provides a potentially useful prediction model for the eye irritation potential of new or untested cationic surfactants with physicochemical properties lying within the parameter space of the model.