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In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models

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Abstract

Purpose

Predicting human skin permeability of chemical compounds accurately and efficiently is useful for developing dermatological medicines and cosmetics. However, previous work have two problems; 1) quality of databases used, and 2) methods for prediction models. In this paper, we attempt to solve these two problems.

Methods

We first compile, by carefully screening from the literature, a novel dataset of chemical compounds with permeability coefficients, measured under consistent experimental conditions. We then apply machine learning techniques such as support vector regression (SVR) and random forest (RF) to our database to develop prediction models. Molecular descriptors are fully computationally obtained, and greedy stepwise selection is employed for descriptor selection. Prediction models are internally and externally validated.

Results

We generated an original, new database on human skin permeability of 211 different compounds from aqueous donors. Nonlinear SVR achieved the best performance among linear SVR, nonlinear SVR, and RF. The determination coefficient, root mean square error, and mean absolute error of nonlinear SVR in external validation were 0.910, 0.342, and 0.282, respectively.

Conclusions

We provided one of the largest datasets with purely experimental log k p and developed reliable and accurate prediction models for screening active ingredients and seeking unsynthesized compounds of dermatological medicines and cosmetics.

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Abbreviations

ALOGP:

Ghose–Crippen octanol–water partition coefficient

ANN:

Artificial neural network

C d :

Chemical concentration in dose formulation

J ss :

Steady state flux of the solute

K :

Skin–vehicle partition coefficient

k p :

Permeability coefficient

L :

Thickness of the skin

log P:

Octanol–water partition coefficient

MAE:

Mean absolute error

MW:

Molecular weight

PCA:

Principal component analysis

QSPR:

Quantitative structure–property relationship

R 2 :

Determination coefficient

RF:

Random forest

RMSE:

Root mean square error

SVR:

Support vector regression

SVR-G:

Support vector regression with Gaussian (radial basis function) kernel

SVR-L:

Support vector regression with linear kernel

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Authors and Affiliations

  1. Kyoto R&D Center, Maruho Co., Ltd., Shimogyo-ku, Kyoto, Japan

    Hiromi Baba & Jun-ichi Takahara

  2. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan

    Hiroshi Mamitsuka

Authors
  1. Hiromi Baba
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  2. Jun-ichi Takahara
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  3. Hiroshi Mamitsuka
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Correspondence to Hiromi Baba.

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Baba, H., Takahara, Ji. & Mamitsuka, H. In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models. Pharm Res 32, 2360–2371 (2015). https://doi.org/10.1007/s11095-015-1629-y

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