Evaluation on the reliability of the permeability coefficient (Kp) to assess the percutaneous penetration property of chemicals on the basis of Flynn's dataset

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.

Transdermal drug delivery in horses: An in vitro comparison of skin structure and permeation of two model drugs at various anatomical sites

BACKGROUND

Oral and parenteral drug delivery in horses can be difficult. Equine-specific transdermal drug formulations offer improved ease of treatment; development of such formulations requires a deeper understanding of the structural and chemical tissue barrier of horse skin.

HYPOTHESIS/OBJECTIVES

To compare the structural composition and barrier properties of equine skin.

ANIMALS

Six warmblood horses (two males, four females) with no skin diseases.

MATERIALS AND METHODS

Routine histological and microscopic analyses were carried out with image analysis for skin from six different anatomical locations. In vitro drug permeation was analysed using a standard Franz diffusion cell protocol coupled with reversed phase-high-performance liquid chromatography detailing flux, lag times and tissue partitioning ratios of two model drug compounds.

RESULTS

Epidermal and dermal thicknesses varied between sites. The dermal and epidermal thicknesses of the croup were 1764 ± 115 μm and 36 ± 3.6 μm, respectively, and were significantly different (p < 0.05) from the inner thigh thicknesses which were 824 ± 35 μm and 49 ± 3.6 μm. Follicular density and size also varied. The highest flux for the model hydrophilic molecule (caffeine) was for the flank (3.22 ± 0.36 μg/cm² /h), while that for the lipophilic molecule (ibuprofen) was for the inner thigh (0.12 ± 0.02 μg/cm² /h).

CONCLUSIONS AND CLINICAL RELEVANCE

Anatomical location differences in equine skin structure and small molecule permeability were demonstrated. These results can aid in the development of transdermal therapies for horses.

Predicting skin permeability using HuskinDB

A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (K_p) data from within this resource, several models were established for K_p values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict K_p through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.

A Data-Mining Approach for the Quantitative Assessment of Physicochemical Properties of Molecular Compounds in the Skin Flux

This paper aimed to provide an insight into the mechanism of transdermal penetration of drug molecules with respect to their physicochemical properties, such as solubility (S), the presence of enantiomer (ET) and logarithm of octanol-water partition coefficient (log P), molecular weight (MW), and melting point (MP). Propionic acid derivatives were evaluated for their flux through full-thickness skin excised from hairless mice upon being delivered from silicone-based pressure-sensitive adhesive (PSA) matrices in the presence or absence of various enhancers. The skin fluxes of model compounds were calculated based on the data obtained using the method engaged with the diffusion cell system. The statistical design of experiments (DoE) based on the factorial approach was used to find variables that have a significant impact on the outcomes. For the prediction of skin flux, a quantitative equation was derived using the data-mining approach on the relationship between skin permeation of model compounds (~125 mg/ml) and involved physicochemical variables. The most influential variables for the skin flux of propionic acid derivatives were the melting point (0.97) followed by the presence of enantiomer (0.95), molecular mass (0.93), log P values (0.86), and aqueous solubility (0.80). It was concluded that the skin flux of molecular compounds can be predicted based on the relationship between their physicochemical properties and the interaction with cofactors including additives and enhancers in the vehicles.

Percutaneous penetration of drugs applied in transdermal delivery systems: an in vivo based approach for evaluating computer generated penetration models

Human skin is a viable pathway for administration of therapeutics. Transdermal delivery systems (TDS) have been approved by the US-FDA since 1981. To enable the risk assessment of dermal exposure, predictive mathematical models are used. In this work the accuracy of predicted flux of the models is compared to experimental human in vivo data of drugs applied in US-FDA approved TDS. A database of pharmacokinetic data of drugs applied in TDS was used and updated. Three mathematical models (QSAR) were used to calculate predicted fluxes, and compared to the human in vivo data. For more than half of the drugs applied in TDS, the predicted flux by the mathematical models was an underestimation compared to the flux calculated with the experimental in vivo data. The flux was over- or underestimated by a factor 10-100. All mathematical models were significantly correlated with the experimental in vivo data. The process of percutaneous penetration has several influencing factors, TDS minimize some of these factors. Limitations are discussed. Further research is needed, with a focus on validation and standardization of the permeability coefficient. This work offers observations that should give a stimulus for refinement on the appropriate usage and limitations of mathematical models.