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A membrane-coated fiber (MCF) array approach was developed for quantitative assessment of skin absorption from chemical mixtures, which was based on the similarity in the absorption mechanisms of the MCF membrane and the stratum corneum of the skin. A set of probe compounds were used to detect the relative molecular interaction strengths of chemicals with the vehicle and the membranes, which provided a linkage between the skin permeability (log k) and MCF partition coefficients (log KF). A predictive model was established via multiple linear regression analysis of the data matrix of experimentally measured log k value and log KFm values; log k=a0+a1 log KF1+a2 log KF2+...+an log KFm, where m is the number of diverse MCFs. Twenty-five probe compounds and three MCFs (polydimethylsiloxane for lipophilic, polyacrylate for polarizable, and CarboWax for polar interactions) were used to demonstrate the model development processes in the MCF array approach. The skin permeability of the probe compounds was measured with conventional diffusion cell experiments using dermatomed porcine skin. Three predictive models were established for skin permeability prediction from chemical mixtures in water, 50% ethanol, and 1% sodium lauryl sulfate (SLS) with R2 values of 93, 91, and 83, respectively. The log k and log KF values were considerably altered by the addition of ethanol or SLS into the dose vehicle; however, their correlations to skin permeability remained strong under various conditions. These results suggested that the experimentally based MCF array approach can be used to predict skin absorption from chemical mixtures in different vehicles or formulations.

Membrane-Coated Fiber Array Approach for Predicting Skin Permeability of Chemical Mixtures from Different Vehicles