| Project/Area Number |
16590035
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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) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2006: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2005: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2004: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Thin-plate spline interpolation / Response surface method / Optimization / Robustness / Bootstrap re-sampling / Self-organizing map / Clustering / Drug delivery system / 多目的同時最適化 / 経皮経粘膜吸収 / 人工ニューラルネットワーク |
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| Research Abstract |
The response surface method incorporating multivariate spline interpolation (RSM-S) is a powerful technique for the formulation optimization of pharmaceuticals. However, no satisfactory method has been developed to evaluate the reliability of the optimal solution. We integrated bootstrap (BS) resampling and Kohonen's self-organizing maps (SOM) into RSM-S using the formulation optimization of transderaml and transmucosal drug delivery systems as well as conventional tablets as the model experiments. Based on the data set obtained, the simultaneous optimal solution was estimated using RSM-S. Leave-one-out cross-validation showed the optimal solution to be reliable.
Concurrently, a large number of BS samples were generated from the original data set using BS resampling, and simultaneous optimal solutions for each BS sample (BS optimal solutions) were estimated. The distribution of the BS optimal solutions was far from a normal distribution, suggesting a mixture of global and local optimal solutions. SOM clustering was then used to identify the set of global optimal solutions. SOM clustering divided the BS optimal solutions into several clusters, and the reliability of the optimal solution was evaluated from the cluster that included the optimal solution. The method proposed in this study offers a promising way to evaluate the robustness of nonlinear optimal solutions.
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