Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air- water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC x GC). The PPM successfully explained variability in the skin permeability data (n=175) with R-2 =0.82 and root mean square error (RMSE)= 0.47 log unit. In comparison, the US-EPA's model DERMWIN (TM) exhibited an RMSE of 0.78 log unit. The Zhang model-a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)-performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC x GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n =79) with R-2 =0.90 and RMSE= 0.23 log unit. The PPM can easily be implemented in US-EPA's Estimation Program Interface Suite (EPI Suite (TM)). The GC x GC model can be applied to the complex mixtures of nonpolar chemicals.

Automated summary

This study presents two models to estimate the skin permeability coefficients of neutral organic chemicals, with the 2-parameter partitioning model showing better performance in explaining the variability in skin permeability data compared to existing models.