| Project/Area Number |
25670073
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Medical pharmacy
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Renkei-kenkyūsha) |
YANO Ikuko 京都大学, 薬学研究科, 准教授 (50273446)
AMANO Akira 立命館大学, 生命科学部, 教授 (60252491)
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| Project Period (FY) |
2013-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2013: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 母集団薬物動態解析 / 共変量モデル / 遺伝的アルゴリズム / 遺伝的数式プログラミング / 個体間変動 / 拡張最小二乗法 / 薬物動態 / 母集団解析 / パラメータ推定 / 機械学習 |
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| Outline of Final Research Achievements |
A population pharmacokinetic model is composed of average pharmacokinetic parameters, their inter- and intra-individual variabilities. In order to predict blood concentration profile of a drug in each individual, we have to develop an accurate covariate model that represents quantitative relationship between pharmacokinetic parameters and patient information. However, it is a labor-intensive process because there are too many possibilities comprising covariate candidates. In the present study a full-automatic system for covariate modeling was developed, that utilizes an algorithm of evolving mathematical formulae on computers.
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