Computational Modelling of the Impact of Evaporation on In-Vitro Dermal Absorption.

PURPOSE: Volatiles are common in personal care products and dermatological drugs. Determining the impact of evaporation of volatiles on skin permeation is crucial to evaluate and understand their delivery, bioavailability, efficacy and safety. We aim to develop an in-silico model to simulate the impact of evaporation on the dermal absorption of volatiles. METHOD: The evaporation of volatile permeants was modelled using vapour pressure as the main factor. This model considers evaporation as a passive diffusion process driven by the concentration gradient between the air-vehicle interface and the ambient environment. The evaporation model was then integrated with a previously published physiologically based pharmacokinetic (PBPK) model of skin permeation and compared with published in vitro permeation test data from the Cosmetics Europe ADME Task Force. RESULTS: The evaporation-PBPK model shows improved predictions when evaporation is considered. In particular, good agreement has been obtained for the distributions in the evaporative loss, and the overall percutaneous absorption. The model is further compared with published in-silico models from the Cosmetics Europe ADME Task Force where favourable results are achieved. CONCLUSION: The evaporation of volatile permeants under finite dose in vitro permeation test conditions has been successfully predicted using a mechanistic model with the intrinsic volatility parameter vapour pressure. Integrating evaporation in PBPK modelling significantly improved the prediction of dermal delivery.

An examination of published datasets of skin permeability and partition coefficients.

Permeability and partition coefficients of the skin barrier are important for assessing dermal absorption, bioavailability, and safety of cosmetics and medicine. We use the Potts and Guy equation to analyse the dependence of skin permeability on the hydrophobicity of permeants and highlight the significant differences in published datasets. Correlations of solute partition to skin are examined to understand the likely causes of the differences in the skin permeability datasets. Recently published permeability datasets show weak correlation and low dependence on hydrophobicity. As expected, early datasets show good correlation with hydrophobicity due to the related derivation. The weaker correlation of later datasets cannot be explained by the partition to skin lipids. All the datasets of solute partition to skin lipid showed a similar correlation to hydrophobicity where the log-linear correlation coefficient of partition is almost the same of the log-linear coefficient of Potts and Guy equation. Weak correlation and dependence of the late permeability datasets with SC lipid/water partition and that they are significantly under predicted by the Potts and Guy equation suggests either additional non-lipid pathway at play or a weaker skin barrier property.

A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin.

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2 = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.