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.

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When C_max was plotted against log K_oct, there is a two-segment linear correlation of C_max and log K_oct (r2 = 0.984, p < 0.05) (see Figure 2). The outliers were clonidine, fentanyl and nitroglycerin, with high C_max values, and estradiol with a low C_max value. This significant pattern of correlation suggests that log K_oct = 3 is an inflection point below which log Koct is negatively correlated with Cmax, and above that, there is a positive correlation between C_max and log K_oct.

If the skin permeability coefficient (log Kp) was considered as the only parameter determining C_max, then according to Eq. 1, which is applicable in the range of −3 < log K_oct < 6 , there should be a direct linear correlation between C_max and log K_oct. This contrast supports that the effects of physicochemical parameters on C_max are expressed through different in vivo processes such as absorption, clearance metabolist, etc., which are responsible for observed plasma concentration profiles.

Conclusion
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 K_oct values, in the range of 200 < MW < 400 and 1.6 < log K_oct < 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 C_max 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 C_max 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.