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Contact Dermatitis ; 60(1): 21-31, 2009 Jan.
Article in English | MEDLINE | ID: mdl-19125718

ABSTRACT

BACKGROUND: Eliminating animal testing for skin sensitization is a significant challenge in consumer safety risk assessment. To be able to perform resilient risk assessments in the future, one will need alternative approaches to fill the data gaps. OBJECTIVES: To this end, we propose a subcategory-based read-across approach to estimate and rank skin sensitization potential of chemicals. The example described here is for the mechanism of Michael-type nucleophilic addition with subcategories being limited to carbonyl-containing compounds. PATIENTS/METHODS: In this approach, in silico tools based on structural alerts were used to determine both the mechanism of protein binding and the relative subcategories within that mechanism. RESULTS: Fifty compounds previously evaluated in the in vivo mouse local lymph node assay (LLNA) were placed in 10 subcategories defined by their polarized alpha,beta-unsaturated substructure. To offset the limitations and skewness of the published in vivo data, in chemico glutathione (GSH) depletion data also were included. CONCLUSIONS: It was shown that the read-across approach can be successfully used to rank qualitatively skin sensitization potential of an untested carbonyl-containing Michael acceptor chemical by using subcategories. Moreover, the use of the more resilient in chemico GSH depletion data added further support to the read-across result.


Subject(s)
Animal Testing Alternatives/methods , Dermatitis, Irritant/immunology , Irritants/toxicity , Skin Irritancy Tests/methods , Toxicity Tests/methods , Animals , Catalytic Domain , Dermatitis, Allergic Contact/diagnosis , Dermatitis, Allergic Contact/immunology , Dermatitis, Irritant/diagnosis , Disease Models, Animal , Glutathione/immunology , Glutathione/toxicity , Guinea Pigs , Humans , Immunization/methods , Local Lymph Node Assay , Mice , Models, Chemical , Protein Binding/immunology , Risk Factors , Sensitivity and Specificity , Severity of Illness Index
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