A QSAR model for the eye irritation of cationic surfactants

A QSAR model for the eye irritation of cationic surfactants has been constr ucted using a dataset consisting of the maximum average scores (MAS-accorda nce to Draize) for 29 in vivo rabbit eye irritation tests on 19 different c ationic surfactants. The parameters used were logP (log \octanol/water part ition coefficient\) and molecular volume (to model the partition of the sur factants into the membranes of the eye), logCMC (log critical micelle conce ntration-a measure of the reactivity of the surfactants with the eye) toget her with surfactant concentration. The model was constructed using neural n etwork analysis, MAS showed strongly positive, non-linear correlations with surfactant concentration and logCMC and a strongly negative, non-linear co rrelation with logP. The Pearson correlation between the actual and predict ed values of MAS was 0.838 showing that around 70% (r(2) = 0.702) of the va riance in the dataset is explained by the model. This value is consistent w ith levels of biological variability reported historically for the Draize r abbit eye test. The relationship pro, ides a potentially useful prediction model for the eye irritation potential of new or untested cationic surfacta nts with physicochemical properties lying within the parameter space of the model. (C) 2000 Elsevier Science Ltd. AII rights reserved.