Prediction Of Percutaneous Absorption Research Articles

Modeling transdermal permeation. Part 2. Predicting the dermatopharmacokinetics of percutaneous solute

AbstractThe prediction of percutaneous absorption and bioavailability in vivo, using the recently reported “bricks‐and‐mortar” model is discussed. Two sets of in vivo data have been simulated: the tape‐striping data of 4‐cyanophenol and Raman spectrometry data of transretinol. The predicted transdermal permeation using theoretically derived properties agreed well with the experimental data. The prediction shows that about 2/3 of the 4‐cynophenol in the SC partitioned into the corneocytes, indicating diffusion of moderately hydrophobic solutes across the corneocytes is also important. Only for highly hydrophobic solute like transretinol, diffusion across the corneocytes is negligibly small. The study demonstrates that with the mechanism‐based computer model, many dermatopharmacokinetic parameters can be derived, providing much insight into how vehicle formulation and topical administration affects the absorption and distribution of solute in the SC, as well as its bioavailability in epidermis/dermis. © 2010 American Institute of Chemical Engineers AIChE J, 2010

The application of Gaussian processes in the prediction of percutaneous absorption

Abstract Objectives The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models. Methods Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures. Key findings Principal component analysis showed that the dataset is inherently nonlinear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight. Conclusions A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.

The application of Gaussian processes in the prediction of percutaneous absorption

The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models. Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures. Principal component analysis showed that the dataset is inherently non-linear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight. A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.

Evaluation of in-vivo animal and in-vitro models for prediction of dermal absorption in man

Risk assessment of dermal exposure to chemicals requires percutaneous absorption data to link the external exposure to the systemic uptake. The most reliable data on percutaneous absorption are obtained from in-vivo human volunteer studies. In addition to ethical constrains, the conduct of these studies is not feasible for the large number of industrial chemicals in use today. Therefore, there is an increasing need for alternative methods to determine percutaneous absorption such as in-vitro assays and methods performed in vivo in experimental animals. In this article, recent comparative in-vitro and in-vivo studies on percutaneous absorption have been addressed with emphasis on the factors that may affect the predictive value of the in-vitro models. Furthermore, the use of animal models, in particular the rat skin, in prediction of percutaneous absorption in the human skin has been reviewed. In-vitro assays showed to be largely influenced by the experimental circumstances, such as type and thickness of the skin, receptor fluid, and the way in which percutaneous absorption is calculated. Rat skin showed consistently to be more permeable than human skin. However, the difference between human and rat skin does not show a consistent pattern between chemicals hampering prediction of human percutaneous absorption. To increase predictive value of in-vitro and animal models, the influence of experimental factors on the percutaneous absorption should be systematically investigated by comparison with human in-vivo data, resulting in more prescriptive guidelines.

Prediction of percutaneous absorption from physicochemical data: A model based on data of in vitro experiments

Correlations between in vitro percutaneous absorption data and physicochemical properties of industrial chemicals are evaluated in order to develop predictive mathematical models based on said properties. Percutaneous diffusion of 16 compounds of occupational interest, eight of which were polycyclic aromatic hydrocarbons (acenaphthene, anthracene, benzo(a)anthracene, chrysene, phenanthrene, fluorene, naphthalene, pyrene), six organophosphorus insecticides (acephate, chlorpyrifos, dimethoate, fenitrothion, methamidophos, omethoate) and two phenoxycarboxylic herbicides (2,4-D, MCPA), were tested in vitro using monkey (Cercopithecus aetiops) skin. The test apparatus consisted of nine static diffusion cells with normal saline, gentamycin sulphate and 4% bovine serum albumin as receiving solution. Test compounds were applied at various concentrations in 30 μl of acetone solution and determined, in the receiving phase, by chemical analysis. Values for In Kow (octanol/water partition coefficient) were correlated with experimentally determined values of the permeability constant Kp (r = 0.90, P < 0.001) and lag time (r = 0.81, P < 0.01). Analysis of variance in a model of multiple linear regression between Kp, Inow and water solubility [water] of the compounds, showed that the data had a highly significant fit (P < 0.0001). A more general model which also included molecular weight (MW) and vapour pressure was evaluated as well, but the two variables made no substantial difference. Multiple regression analysis between lag time, In Kow and [water] was significant (P < 0.0001), whereas introduction of vapour pressure and MW as independent variables did not significantly improve the predictive effect on lag time. Our experimental system, therefore, enables the values of Kp and lag time to be predicted with reasonable precision on the basis of In Kow and [water] values, using the algorithm derived from the multiple linear regression equation.

The prediction of percutaneous absorption: I. Influence of the dermis on in vitro permeation models

In percutaneous penetration in vitro techniques, excised full-thickness skin with its stratum corneum, viable epidermis and dermis is often used. Since penetrants can be absorbed in vivo immediately below the viable epidermis, the dermal layer could act as an additional barrier in the in vitro experiments relative to the actual in vivo process. In the present paper, in vitro penetration studies through excised hairless rat skin devoid of its dermal layer are reported and compared with those previously carried out with the excised full-thickness skin of the same animal. A homologous series of 4-alkylanilines was used in all the studies, and the correlations found between permeability coefficients and n-octanol partition coefficients were analyzed. Correlations are bilinear in nature in both cases, but in the absence of the dermal layer the correlation line seems to tend to hyperbolicity, as assessed by a significant increment in permeability coefficients for the highly lipophilic compounds of the series and by a displacement of the optimal lipophilicity value (vertex of the correlation line) to a higher partition coefficient. It can be concluded that the heterogeneous nature of the skin, as far as absorption is concerned, may be due to the presence of the two anatomical hydrophilic layers, dermis and viable epidermis, rather than to the stratum corneum itself. A critical review of the results reported in the literature showed good agreement with these conclusions. The biophysical penetration model was identical and the optimal lipophilicity values very similar, so it may be that these features are independent of the type of epidermis used (rat, mouse or man) and also of the chemical composition of the penetrants.