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Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest

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Abstract

Purpose

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems.

Methods

We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated.

Results

The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds.

Conclusion

Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

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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 p :

Permeability coefficient

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

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Authors and Affiliations

  1. Kyoto R&D Center, Maruho Co., Ltd., 93 Awata-cho, Chudoji, Shimogyo-ku, 600-8815, Kyoto, Japan

    Hiromi Baba & Jun-ichi Takahara

  2. Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan

    Hiromi Baba, Fumiyoshi Yamashita & Mitsuru Hashida

  3. Institute for Integrated Cell-Material Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan

    Mitsuru Hashida

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  1. Hiromi Baba
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  2. Jun-ichi Takahara
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Correspondence to Hiromi Baba.

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Baba, H., Takahara, Ji., Yamashita, F. et al. Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest. Pharm Res 32, 3604–3617 (2015). https://doi.org/10.1007/s11095-015-1720-4

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