In Silico Estimation of Skin Concentration Following the Dermal Exposure to Chemicals

Hatanaka, Tomomi; Yoshida, Shun; Kadhum, Wesam R.; Todo, Hiroaki; Sugibayashi, Kenji

doi:10.1007/s11095-015-1756-5

Cited by 20 publications

(13 citation statements)

References 37 publications

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“…Sugibayashi et al and Hatanaka et al reported that steady-state chemical concentration in the skin after topical application could be calculated using P and K values obtained from the skin permeation profile. 14,29) Thus, understanding of changes in DL…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a Silicone Membrane as an Alternative to Human Skin for Determining Skin Permeation Parameters of Chemical Compounds

Uchida

Yakumaru

Nishioka

et al. 2016

CHEMICAL & PHARMACEUTICAL BULLETIN

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We evaluated the effectiveness of a silicone membrane as an alternative to human skin using the skin permeation parameters of chemical compounds. An in vitro permeation study using 15 model compounds was conducted, and permeation parameters comprising permeability coefficient (P), diffusion parameter (DL 2 ), and partition parameter (KL) were calculated from each permeation profile. Significant correlations were obtained in log P, log DL 2 , and log KL values between the silicone membrane and human skin. DL 2 values of model compounds, except flurbiprofen, in the silicone membrane were independent of the lipophilicity of the model compounds and were 100-fold higher than those in human skin. For antipyrine and caffeine, which are hydrophilic, KL values in the silicone membrane were 100-fold lower than those in human skin, and P values, calculated as the product of a DL 2 and KL, were similar. For lipophilic compounds, such as nbutyl paraben and flurbiprofen, KL values for silicone were similar to or 10-fold higher than those in human skin, and P values for silicone were 100-fold higher than those in human skin. Furthermore, for amphiphilic compounds with log K o/w values from 0.5 to 3.5, KL values in the silicone membrane were 10-fold lower than those in human skin, and P values for silicone were 10-fold higher than those in human skin. The silicone membrane was useful as a human skin alternative in an in vitro skin permeation study. However, depending on the lipophilicity of the model compounds, some parameters may be over-or underestimated.Key words silicone membrane; skin; membrane permeation; alternative membrane Skin forms an interface between the body and environment and functions as a firm barrier against undesirable elements. Many skin permeation studies of therapeutic drugs have been conducted to develop transdermal drug delivery systems (TDDSs) for the purpose of preventing the hepatic first-pass effect, which is observed for some oral dosage forms, and for controlling release over long periods of time.1-3) For the development and evaluation of TDDSs, in vitro skin permeation tests using excised human skin have been performed in the European Union (EU) and United States; however, in Japan, it is difficult to obtain human skin, so permeation studies using human skin are not performed as frequently. Therefore, animal skins, particularly those of hairless rat, mouse, and guinea pig, have been used for the evaluation of percutaneous absorption. Recently, however, sales of cosmetics in the EU, 4) for which animal experiments were performed during their development have been prohibited out of concern for animal welfare, and alternative membranes are now required to determine percutaneous absorption. In addition, permeation studies using animal or human skin are associated with several difficulties and limitations for assessing the effects of formulations components, because of the complex nature of this biological tissue and inter- and intra-individuality of skin.Artificial model membranes offer a simple and ...

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Section: Discussionmentioning

confidence: 99%

Evaluation of a Silicone Membrane as an Alternative to Human Skin for Determining Skin Permeation Parameters of Chemical Compounds

Uchida

Yakumaru

Nishioka

et al. 2016

CHEMICAL & PHARMACEUTICAL BULLETIN

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“…We previously proposed a two-layered diffusion model to estimate the mean skin concentration and concentration at each position in the skin layers [11,12]. However, the use of this model may result in an inaccurate estimation of skin concentration for chemicals extensively metabolized in the skin.…”

Section: Discussionmentioning

confidence: 99%

“…Because the lipid matrix in the stratum corneum greatly contributes to the skin's barrier function [10] and only a small area of skin surface is occupied by appendages such as hair follicles, sebaceous glands and sweat glands [9], the two-layered membrane consisting of the stratum corneum and its lower layers has been often assumed in in silico models. We previously demonstrated that the mean skin concentration and concentration at each depth in the skin layers can be estimated based on the two-layered diffusion model for drugs and chemicals with a wide range of lipophilicity [11,12].…”

Section: Introductionmentioning

confidence: 99%

In Silico Estimation of Skin Concentration of Dermally Metabolized Chemicals

Sugibayashi¹

2018

Int J Pharm Sci Dev Res

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Background: A great deal of in silico estimation methods were proposed for skin concentration and permeation of drugs by many researchers including us. The aim of the present study was to expand our in silico estimation method of skin concentration to dermally metabolized chemicals. Materials and Methods: A three-layered diffusion model consisting of stratum corneum, viable epidermis and dermis was constructed based on Fick's second law of diffusion incorporated with Michaelis-Menten equation and plasma clearance in the viable epidermis and dermis, respectively. Ethyl nicotinate was used as a model chemical, and the in vivo skin concentration of the ester and its metabolite, nicotinic acid were measured after topical application to hairless rats. Permeation parameters were determined from the in vitro permeation data through full-thickness skin and stripped skin after application of the ester or acid with and without esterase inhibitor treatment. Metabolic parameters were obtained from the metabolic profi le of the ester using skin homogenate. Results and Conclusion: The skin concentrations calculated from our improved model using the permeation and metabolic parameters obtained beforehand were similar to the observed values. Influence of cutaneous enzyme distribution and plasma clearance on the skin concentrations were also estimated using appropriately modifi ed models, resulting in higher influence on the acid than the ester. This estimation method will become an effective tool to assess the effi cacy and safety of dermally metabolized chemicals.

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“…Based on a non-exhaustive assessment of several different end points relevant to pharmaceutical research, while it appears that most have seen utilization of Bayesian or SVM approaches to develop predictive models, few have so far utilized DL (Table 2). Recent examples of computational models appearing in this journal alone over the past 18 months include: modeling thermodynamic proxies (35), predicting mouse liver microsomal stability (36), predicting autooxidation (37), drug solubility in human intestinal fluid (38), site of metabolism prediction in CYP2C9 (39), human skin permeability prediction (40, 41), blood brain barrier penetration modeling (42), predicting clearance mechanism (41) and skin concentration due to dermal exposure (43). Many of these datasets could likely utilize and benefit from DL and it would be of interest to see for how many an improvement in predictions could be obtained.…”

Section: Pharmaceutical Applications Of Deep Learningmentioning

confidence: 99%

The Next Era: Deep Learning in Pharmaceutical Research

2016

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Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

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In Silico Estimation of Skin Concentration Following the Dermal Exposure to Chemicals

Cited by 20 publications

References 37 publications

Evaluation of a Silicone Membrane as an Alternative to Human Skin for Determining Skin Permeation Parameters of Chemical Compounds

Evaluation of a Silicone Membrane as an Alternative to Human Skin for Determining Skin Permeation Parameters of Chemical Compounds

In Silico Estimation of Skin Concentration of Dermally Metabolized Chemicals

The Next Era: Deep Learning in Pharmaceutical Research

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