The Prediction of Skin Permeability by Using Physicochemical Data

///The Prediction of Skin Permeability by Using Physicochemical Data

The Prediction of Skin Permeability by Using Physicochemical Data

Lisa A. Kirchner, Richard P. Moody, Edward Doyle, Ranjan Bose, Jamie Jeffery and Ih Chu

A database on physicochemical properties and skin permeation compiled by Health Canada was analysed by using linear regression analysis. The correlation between permeability coefficient (Kp) and the octanol–water partition coefficient (Kow) has been improved by grouping the compounds according to their respective molar volumes. Linear regression analysis of the individual groups has demonstrated a positive correlation for the majority of the groups, with the compounds in the lowest molar volume range (≤ 75Å3) having the best correlation (r2 = 0.86), and the compounds in the highest molar volume range (≥ 301Å3) being the least well-correlated (r2 = 0.55). Due to the diversity of the chemicals used in this analysis, and the statistically significant correlations obtained, this model could permit the prediction of skin permeation of a wide variety of chemical compounds. Although of a simplistic nature, and not yet experimentally validated, this quantitative structure-activity relationship may be useful for predicting human skin permeability coefficients for compounds that fall within the constraints
of this data set.
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2017-01-09T06:26:21+00:00