ABSTRACT
A methodology called quantitative component analysis of mixtures (QCAM) was used to analyze an existing set of product formulation data to determine if the irritating ingredients in the mixtures could be identified. Eye irritation scores, based on a rat model, for 18 mixtures having a composite total of 37 components, were analyzed by QCAM. QCAM relates a net toxicity measure of a mixture to the toxicities of the individual components of the mixture through linear, quadratic, and pairwise cross-component concentration-dependent interactions. A correlation model is established using a particular genetic algorithm employing either multidimensional linear regression or partial least-squares regression fitting. Cornea eye irritation and average eye irritation are well-explained in terms of a linear model of, at most, three components over the set of mixtures. Moreover, extensive cornea and average eye irritations are due to only one of these three components of the mixtures. Also, one of the three significant components was predicted to decrease the extent of eye irritation, and subsequently identified as an "anti-irritant" in contact lens solutions. A reasonable linear correlation model could also be developed for conjunctiva irritation, but no significant iris irritation model could be constructed. The addition of quadratic and/or cross-component concentration terms to a linear correlation model did not statistically improve the overall resultant model. The QCAM models permit estimation of the intrinsic (self) toxicity of each of the components of a mixture, and may aid in the reduction, and ultimate elimination, of the need for animal eye irritation studies.
