| Project/Area Number |
12672096
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Physical pharmacy
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| Research Institution | Hoshi University |
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Principal Investigator |
TAKAYAMA Kozo Hoshi University, Pharmaceutics, Professor, 薬学部, 教授 (00130758)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2002: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2001: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2000: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Optimization / Artificial neural network / Response surface method / Formulation / Transdermal absorption / Chemical enhancer / Drug delivery / Ketoprofen / 薬物送達 / ケトプロフェン / メントール誘導体 / 構造活性相関 / 刺激性 |
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| Research Abstract |
A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, 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. A multi-objective simultaneous optimization method incorporating an artificial neural network (ANN) was developed. The method was applied to the optimization of ketoprofen hydrogel formulations including 1-O-ethyl-3-n-butylcyclohexanol as absorption enhancer. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. The observed results of several characteristics in the optimum formulations coincided well with the predictions, suggesting superior function of the ANN approach.
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