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Predicting the Percutaneous Penetration of Cosmetic Ingredients
By: Sara Farahmand, PhD, University of Cincinnati College of Pharmacy; and Howard I. Maibach, MD, PhD, University of California School of Medicine
Posted: March 30, 2010, from the April 2010 issue of Cosmetics & Toiletries.
page 10 of 11
When Cmax was plotted against log Koct, there is a two-segment linear correlation of Cmax and log Koct (r2 = 0.984, p < 0.05) (see Figure 2). The outliers were clonidine, fentanyl and nitroglycerin, with high Cmax values, and estradiol with a low Cmax value. This significant pattern of correlation suggests that log Koct = 3 is an inflection point below which log Koct is negatively correlated with Cmax, and above that, there is a positive correlation between Cmax and log Koct.
If the skin permeability coefficient (log Kp) was considered as the only parameter determining Cmax, then according to Eq. 1, which is applicable in the range of −3 < log Koct < 6 , there should be a direct linear correlation between Cmax and log Koct. This contrast supports that the effects of physicochemical parameters on Cmax are expressed through different in vivo processes such as absorption, clearance metabolist, etc., which are responsible for observed plasma concentration profiles.
Interindividual variation in pharmacokinetics remains an important challenge in transdermal drug delivery. The present results suggest an increased interindividual variability by decreased MW and log Koct values, in the range of 200 < MW < 400 and 1.6 < log Koct < 4.3. This could be explained by augmented vulnerability of smaller and more hydrophilic molecules in this range to permeation and elimination, the main sources of intersubject variability.
In an attempt to develop a predictive model for Cmax based on physicochemical characteristics of drugs, two statistically significant empirical equations were established for 10 molecules. The results demonstrated that the number of HA has the largest contribution in predicting Cmax in both of the equations, in the context of the other predictor variables in the model.
This data analysis provides entry into predicting the penetration of cosmetic ingredients based on an in vivo human data set. Such an approach might be helpful when the systemic absorption of a cosmetic active or formulation ingredient is of specific concern.17-19 A validated QSAR model for predicting the fate of a cosmetic ingredient in the human body would eliminate the need for in vivo experiments and aid in predicting the permeation of novel cosmetic compounds before their synthesis.