頁籤選單縮合
題名 | Prediction of Human Drug Clearance Using a Single-Species, Fixed-Exponent Allometric Approach=以單一種動物固定指數法預測人體之藥物清除率 |
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作者 | 胡德民; 邱士娟; Hu, Teh-min; Chiu, Shih-jiuan; |
期刊 | Journal of Medical Sciences |
出版日期 | 20091200 |
卷期 | 29:6 2009.12[民98.12] |
頁次 | 頁331-339 |
分類號 | 418.4 |
語文 | eng |
關鍵詞 | 固定指數法; 藥物清除率; Allometry; Clearance; Interspecies scaling; Allometric scaling; Pharmacokinetics; |
英文摘要 | Background: Human pharmacokinetics can be predicted from animal data using the principle of allometry, which assumes a mathematical power-law relationship between pharmacokinetic parameters and body weights of animal species. The objective of the present study was to investigate the feasibility of extrapolating human drug clearance (CL) from a single animal species using simple allometry with a fixed body-weight exponent. Methods: CL values from rat, monkey, dog and human for 109 compounds were obtained from the literature. A normalization procedure based on the concept of a characteristic CL value was first introduced to homogenize and pool the CL data for a regression analysis. The allometric exponent from the regression analysis was then used as the exponent for CL extrapolation. The prediction performance of the proposed method was compared with methods that incorporate liver blood flow (LBF) or maximum lifespan potential (MLP). Results: An allometric exponent of 0.67 (95% CI, 0.64 to 0.71) adequately described the pooled CL data. A fixed value of 0.67 as the body-weight scaling exponent and monkey CL provided the best estimate of human CL, followed by rat and dog. CL prediction by the LBF approach was comparable to that of the fixed-exponent method. The MLP approach systematically underestimated the human CL. Conclusions: It is feasible to predict human drug CL from CL measured in a single animal species using simple allometry with a fixed body-weight exponent of 0.67. While monkey provides the best estimate of human CL, rat, but not dog, offers an acceptable prediction when monkey data are unavailable. |
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