Read-across to rank skin sensitization potential: subcategories for the Michael acceptor domain.

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.

Screening of potentially hormonally active chemicals using bioluminescent yeast bioreporters.

Saccharomyces cerevisiae bioluminescent bioreporter assays were developed previously to assess a chemical's estrogenic or androgenic disrupting potential. S. cerevisiae BLYES, S. cerevisiae BLYAS, S. cerevisiae BLYR, were used to assess their reproducibility and utility in screening 68, 69, and 71 chemicals for estrogenic, androgenic, and toxic effects, respectively. EC(50) values were 6.3 +/- 2.4 x 10(-10)M (n = 18) and 1.1 +/- 0.5 x 10(-8)M (n = 13) for BLYES and BLYAS, using 17beta-estradiol and 5alpha-dihydrotestosterone over concentration ranges of 2.5 x 10(-12) through 1.0 x 10(-6)M, respectively. Based on analysis of replicate standard curves and comparison to background controls, a set of quantitative rules have been formulated to interpret data and determine if a chemical is potentially hormonally active, toxic, both, or neither. The results demonstrated that these assays are applicable for Tier I chemical screening in Environmental Protection Agency's Endocrine Disruptor Screening and Testing Program as well as for monitoring endocrine-disrupting activity of unknown chemicals in water.

Quantitative and mechanistic read across for predicting the skin sensitization potential of alkenes acting via Michael addition.

Read across is a powerful tool to predict toxicity from structure: It relies on "obvious" chemical similarities to allow for interpolation of activity. This study has extended the read across concept within a known mechanism of action to be quantitative. The chemicals that have been chosen are skin sensitizers and are considered to elicit this response by direct interaction through a direct-acting Michael type addition electrophilic mechanism of action. The Michael addition domain is well-defined for skin sensitizers; however, developing quantitative models for predicting potency within the domain has proven to be difficult. This study highlights the ability of an electrophilicity index (omega) to be used as a measure of similarity for sensitizing chemicals acting through the Michael addition mechanism. The index is shown to offer a chemically interpretable qualitative ranking of the chemicals within the Michael acceptor domain, enabling potentially nonsensitizing and extremely sensitizing chemicals to be easily identified. This study also demonstrates the utility of omega to make predictions of skin sensitization using a mechanism-based read across model. Predictions were made for 19 chemicals within the Michael acceptor domain, with the majority being in good agreement with the experimentally determined values. The mechanism-based read across predictions are in keeping with the OECD principles of transparency and simplicity for quantitative structure-activity relationships and are likely to be of significant benefit to regulators and risk assessors.

Prediction of aquatic toxicity: use of optimization of correlation weights of local graph invariants.

Quantitative structure-activity relationships (QSARs) were developed for three sets of toxicity data. Chemicals in each set represented a number of narcoses and electrophilic mechanisms of toxic action. A series of quantitative structure-toxicity models correlating toxic potency with a number of optimization of correlation weights of local graph invariants were developed. In the case of the toxicity of a heterogeneous set of benzene derivatives to Tetrahymena pyriformis, the QSARs were based on the Descriptor of Correlation Weights (DCW) using atoms and extended connectivity (EC) graph invariants. The model [log (IGC(50)(-1)) = 0.0813 DCW(a(k),(3)EC(k)) + 2.636; n = 157, r(2) = 0.883, s = 0.27, F = 1170, Pr > F = 0.0001] based on third-order EC of 89 descriptors was observed to be best for the benzene data. However, fits for these data of > 0.800 were achieved ECs with as few as 23 variables. The relationship between the toxicity predicted by this model and experimental toxicity values for the test set [obs. log(IGC(50)(-1))) = 0.991 (pred. (log(IGC(50)(-1))) - 0.012; n = 60, r(2) = 0.863, s = 0.28, F = 372, Pr > F = 0.0001] is excellent. The utility of the approach was demonstrated by the model [log (IGC(50)(-1)) = 0.1744(DCW (a(k), (2)EC) - 3.505; n = 39, r(2) = 0.900, s = 0.35, F = 333, Pr > F = 0.0001] for the toxicity data for T. pyriformis exposed to halo-substituted aliphatic compounds and the model [log (IC(50)(-1)) = 0.1699(DCW (a(k), (2)EC)) - 2.610; n = 66, r(2) = 0.901, s = 0.31, F = 583, Pr > F = 0.0001] for the Vibrio fischeri toxicity data.