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Abstract
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 × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R2 = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN™ 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 × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.
Highlights
The skin, being the largest organ, is prone to exposure of organic chemicals found in environmental media [1, 2], and occupational settings [3], and in consumer products [4, 5]
We excluded ionized species from this data because our proposed models, parameter partitioning model (PPM) and GC × GC model, can theoretically account for the intermolecular interactions for neutral organic chemicals only. This resulted into a data size of 175 neutral organic chemicals, which are shown in Additional file 1: Table S1 along with the values of Abraham solute descriptors (ASDs)
Justification of 2P‐Linear Free Energy Relationship (LFER) As a starting point for developing a parsimonious LFER model, we propose that skin permeation of neutral organic chemicals may be adequately estimated by the use of only two parameters, Kow and Kaw
Summary
The skin, being the largest organ, is prone to exposure of organic chemicals found in environmental media [1, 2], and occupational settings [3], and in consumer products [4, 5]. Several environmental partitioning and diffusion-related properties ( logK ) of nonpolar complex organic mixtures were successfully estimated using LFER based on two solute parameters ( u1,i and u2,i ), which were derived from the first- and second-dimension retention times of analytes on GC × GC chromatogram. We hypothesized following: (1) a linear combination of Kow and Kaw (air–water partition coefficient) better explains the variability of skin permeation data as compared to DERMWINTM equation because Kaw brings in significant information about hydrogen-bonding interaction [29], which is not sufficiently provided by the combination of Kow and MW . (2) Given success of the GC × GC model with rate-related properties in previous studies, the GC × GC instrument provides suitable solute descriptors to model skin permeability of nonpolar complex mixtures We hypothesized following: (1) a linear combination of Kow and Kaw (air–water partition coefficient) better explains the variability of skin permeation data as compared to DERMWINTM equation because Kaw brings in significant information about hydrogen-bonding interaction [29], which is not sufficiently provided by the combination of Kow and MW . (2) Given success of the GC × GC model with rate-related properties in previous studies, the GC × GC instrument provides suitable solute descriptors to model skin permeability of nonpolar complex mixtures
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