Cosmetics Europe evaluation of 6 in silico skin penetration models

Grégoire, Sébastien; Sorrell, Ian; Lange, Daniela; Najjar, Abdulkarim; Schepky, Andreas; Ellison, Corie A.; Troutman, John; Fabian, Eric; Duplan, Hélène; Géniès, Camille; Jacques, Carine; Klarić, Martina; Hewitt, Nicola J.

doi:10.1016/j.comtox.2021.100177

Cited by 20 publications

(14 citation statements)

References 20 publications

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“…Many models already exist for predicting skin permeation, including the DERMWIN™ model ( https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface ). This model is based on an equation similar to that proposed by Potts and Guy 17 in that it uses the same physicochemical properties to predict log K p values and is often used by researchers for comparison with newly proposed models 21 , 35 , 36 . For comparative analysis in this work, log K p values for the 214 compounds were analysed using DERMWIN™ and the values obtained compared with those from HuskinDB (as selected for Eq.…”

Section: Resultsmentioning

confidence: 99%

“…However, the variability in experimental conditions undertaken to create these datasets has been noted, such as if the solute is analysed under finite or infinite dosing conditions 19 and the chosen experimental volume 20 . These effects can result in variability in dataset K p values which will ultimately affect the suitability and success of the model created 21 – 23 . It has therefore been stated that ideally values should be obtained under the same experimental conditions 24 yet this is difficult to achieve in reality.…”

Section: Introductionmentioning

confidence: 99%

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Predicting skin permeability using HuskinDB

Waters

Quah

2022

Sci Data

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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 (Kp) data from within this resource, several models were established for Kp 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 Kp 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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting skin permeability using HuskinDB

Waters

Quah

2022

Sci Data

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“…For the corneocytes layer: Partition Coefficient (Kcor/w) is given by [38] 𝐾 / = 𝑃𝐶 / 𝜔 + 𝜐 𝜔 𝜌 𝜌 + 𝜐 [16] and diffusion coefficient (Dcor) is given by [20,22] (𝐷 ) = 𝐷 1 − 𝜑 (0.9999 − 1.2762𝜆 + 0.0718𝜆 + 0.1195𝜆 ) [17] 𝐾 / = 1 − 𝜑 [18] 𝐾 / 𝐷 / = 𝐾 / (𝐷 ) [19] 𝜑 = (0.1928)(1 + 𝜆 ) [20] 𝜆 = 𝑟 (35 Å) [21] Where (Kcor/w )free and (Dcor)free are corneocytes partition and diffusion coefficient of free molecules.…”

Section: Governing Equations: Scmentioning

confidence: 99%

“…Various insilico models have been investigated to address this gap. Grѐgoire et al [18], investigated 3 open source (DermWin™, CDC and the University of Surrey models) and 3 commercial (DSkin, SimCyp and TCAT) in silico models for permeation of 25 cosmetic relevant compounds and compared their dermal delivery (DD: amount present in dermis, VE and receptor fluid (RF)) with those from in vitro study done by Hewitt et al [17]. They reported correlation coefficient (r 2 ) for the various model under different physiochemical properties of molecules; TCAT(r 2 ~0.8), DSkin (r 2 ~0.6), SimCyp(r 2 ~0.57), CDC(r 2 ~0.58), Surrey(r 2 ~0.28).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the 7th Amendment to the Cosmetic Directive (Directive 76/768/EEC) and REACH demanded the development of novel in vitro methods for future risk assessment of chemicals for their potential to cause toxicity [15,16]. In response to this, the Cosmetics Europe ADME Task Force (TF) launched several projects towards alternative testing methods and came up standardization protocol of in-vitro and in-silico studies [16,17,18]. Ellison et al.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Modeling of Molecule Transport through Skin’s Deeper Layers

Verma

Gajula

Gupta

et al. 2022

Preprint

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Accurate in-silico models of human skin are required to obtain the uptake/release of molecules across the skin layers to supplement the in-vivo/in-vitro experiments for faster development/testing of cosmetics and drugs. We aim to develop an in-silico skin permeation model by extending the multiscale modeling framework developed earlier for skin’s top layer to deeper layer and compared the outcomes with in-vitro experimental permeation data of 43 cosmetic-relevant molecules across human skin. In this study, we have extended a multiscale modeling framework, with realistic heterogeneous stratum corneum (SC) comprising of network of permeable lipids and corneocytes, followed by homogeneous viable epidermis and dermis. The diffusion coefficients of molecules in lipid layer were determined using molecular dynamics simulations, whereas the diffusion coefficients in other layers and all the partition coefficients were calculated from correlations reported in literature. These parameters were then used in the macroscopic models to predict the release profiles of drugs through the deeper skin layers. The obtained release profiles were in good agreement with available experimental data for most of the molecules. The reported model could provide insight into cosmetics/drugs skin permeation and act as a time-saving and efficient guiding tool for performing targeted experiments

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

Evans

Moxon

Lian

et al. 2023

J of Applied Toxicology

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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.

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Cosmetics Europe evaluation of 6 in silico skin penetration models

Cited by 20 publications

References 20 publications

Predicting skin permeability using HuskinDB

Predicting skin permeability using HuskinDB

Multiscale Modeling of Molecule Transport through Skin’s Deeper Layers

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

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