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

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• 作者：Hiromi Baba ; Jun-ichi Takahara ; Fumiyoshi Yamashita…
• 关键词：in silico prediction ; quantitative structure鈥損roperty relationship ; skin permeability ; solvent effect ; support vector regression
• 刊名：Pharmaceutical Research
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：32
• 期：11
• 页码：3604-3617
• 全文大小：2,410 KB
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• 作者单位：Hiromi Baba (1) (2)
  Jun-ichi Takahara (1)
  Fumiyoshi Yamashita (2)
  Mitsuru Hashida (2) (3)
  
  1. Kyoto R&D Center, Maruho Co., Ltd., 93 Awata-cho, Chudoji, Shimogyo-ku, 600-8815, Kyoto, Japan
  2. Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan
  3. Institute for Integrated Cell-Material Sciences, Kyoto University, 46-29, Yoshida-shimoadachicho, Sakyo-ku, Kyoto, 606-8501, Japan
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Pharmacology and Toxicology
  Pharmacy
  Biochemistry
  Medical Law
  Biomedical Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-904X

文摘

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.