Prediction of eye irritation from organic chemicals using membrane-interaction QSAR analysis.

Eye irritation potency of a compound or mixture has traditionally been evaluated using the Draize rabbit-eye test (Draize et al., 1944). In order to aid predictions of eye irritation and to explore possible corresponding mechanisms of eye irritation, a methodology termed "membrane-interaction QSAR analysis" (MI-QSAR) has been developed (Kulkarni and Hopfinger 1999). A set of Draize eye-irritation data established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) (Bagley et al., 1992) was used as a structurally diverse training set in an MI-QSAR analysis. Significant QSAR models were constructed based primarily upon aqueous solvation-free energy of the solute and the strength of solute binding to a model phospholipid (DMPC) monolayer. The results demonstrate that inclusion of parameters to model membrane interactions of potentially irritating chemicals provides significantly better predictions of eye irritation for structurally diverse compounds than does modeling based solely on physiochemical properties of chemicals. The specific MI-QSAR models reported here are, in fact, close to the upper limit in both significance and robustness that can be expected for the variability inherent to the eye-irritation scores of the ECETOC training set. The MI-QSAR models can be used with high reliability to classify compounds of low- and high-predicted eye irritation scores. Thus, the models offer the opportunity to reduce animal testing for compounds predicted to fall into these two extreme eye-irritation score sets. The MI-QSAR paradigm may also be applicable to other toxicological endpoints, such as skin irritation, where interactions with cellular membranes are likely.     

A robust structure-activity relationship (SAR) model for esters that cause skin irritation in humans.

A structure-activity relationship (SAR) model has been developed to discriminate skin irritant from nonirritant esters. The model is based on the physicochemical properties of 42 esters that were tested in humans for skin irritation. Nineteen physicochemical parameters that represent transport, electronic, and steric properties were calculated for each chemical. Best subsets regression analysis indicated candidate models for further analysis. Regression analyses identified significant models (p < 0.05) that had variables that were also significant (p < 0.05). These candidate models were evaluated using linear discriminant analysis to determine if the irritant esters could be discriminated from nonirritant esters. The stability of the model was evident from the consistency of parameters among ten submodels generated using multiple random sampling of the database. The sensitivity of the ten models, evaluated by "leave-one-out" cross-validation, ranged from 0. 846 to 0.923, with a mean of 0.885 +/- 0.025 (95% CI). The specificity ranged from 0.615 to 0.923, with a mean of 0.738 +/- 0.06 (CI). Compared with nonirritant esters, irritant esters had lower density, lower water solubility, lower sum of partial positive charges, higher Hansen hydrogen bonding parameter, and higher Hansen dispersion parameter. The results indicate that physicochemical features of esters contribute to their ability to cause skin irritation in humans, and that chemical partitioning into the epidermis and intermolecular reactions are likely important components of the response. This model is applicable for prediction of human irritation of esters yet untested.     

A quantitative structure-activity relationship for the eye irritation potential of neutral organic chemicals

Quantitative structure-activity relationships (QSARs) have been derived relating eye irritation data of a set of neutral organic chemicals to log(octanol-water partition coefficient), the minor principal inertial axes (R_y and R_z) and dipole moment. Datasets were analysed using principal components analysis; plots of the first 2 principal components of the above parameters showed that the analysis was able to discriminate well between the irritant and non-irritant chemicals in the dataset. The derived QSAR could be useful for the prediction of the eye irritation potential of new or untested chemicals within this category.