Assumptions based on absorption from single solvent systems may be inappropriate for risk assessment when chemical mixtures are involved. We used K-means and hierarchical cluster analyses to identify clusters in stratum corneum partitioning and porcine skin permeability datasets that are distinct from each other based on mathematical
indices of similarity and dissimilarity. Twenty four solvent systems consisting of combinations of water, ethanol,
propylene glycol, methyl nicotinate and sodium lauryl sulphate were used with 10 solutes, including phenol, pnitrophenol,
pentachlorophenol, methyl parathion, ethyl parathion, chlorpyrifos, fenthion, simazine, atrazine and propazine. Identifying the relationships between solvent systems that have similar effects on dermal absorption
formed the bases for hypotheses generation. The determining influence of solvent polarity on the partitioning data
structure supported the hypothesis that solvent polarity drives the partitioning of non-polar solutes. Solvent polarity
could not be used to predict permeability because solvent effects on diffusivity masked the effects of partitioning on
permeability. The consistent influence of the inclusion of propylene glycol in the solvent system supports the hypothesis that over saturation due to solvent evaporation has a marked effect on permeability. These results demonstrated the potential of using cluster analysis of large datasets to identify consistent solvent and chemical
mixture effects.
