ニューラルネットワークによる製剤処方の最適化

• 髙山 幸三

  星薬科大学薬剤学教室

• 藤川 未来人

  星薬科大学薬剤学教室

• 小幡 誉子

  星薬科大学薬剤学教室

• 森下 真莉子

  星薬科大学薬剤学教室

• 永井 恒司

  星薬科大学薬剤学教室

書誌事項

タイトル別名

• Optimization of Pharmaceutical Formulations Based on Artificial Neural Networks

抄録

A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness and stability, as well as safety, must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM is often limited to low levels, resulting in poor estimations of optimal formulations. In this review, a multi-objective simultaneous optimization method incorporating an artificial neural network (ANN) is introduced. Further, usefulness of the method is demonstrated by its application to the optimization of ketoprofen hydrogel formulations including 1-O-ethyl-3-n-butylcyclohexanol as a newly developed transdermal absorption enhancer.

収録刊行物

• 薬剤学

  薬剤学 64 (1), 2-12, 2004

  公益社団法人 日本薬剤学会

キーワード

• optimization
• artificial neural network
• response surface method
• formulation design
• transdermal absorption

詳細情報

• CRID: 1390001288136328192
• NII論文ID: 10012925550
• NII書誌ID: AN00266708
• DOI: 10.14843/jpstj.64.2
• ISSN: 21883149
• ISSN: 03727629
• Web Site: https://search.jamas.or.jp/link/ui/2004122172
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可