Verification on the effectiveness of minimum relative entropy method for pharmacokinetic analysis

• Project/Area Number: 08672609
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: 応用薬理学・医療系薬学
• Research Institution: Shimane University
• Principal Investigator: AMISAKI Takashi Shimane University, Faculty of Science and Engineering, Associate Professor, 総合理工学部, 助教授 (20231996)
• Project Period (FY): 1996 – 1997
• Project Status: Completed (Fiscal Year 1997)
• Budget Amount: ¥1,000,000 (Direct Cost: ¥1,000,000); Fiscal Year 1997: ¥300,000 (Direct Cost: ¥300,000); Fiscal Year 1996: ¥700,000 (Direct Cost: ¥700,000)
• Keywords: pharmacokinetics / parameter estimation / relative entropy / least squares

Research Abstract

Primary aim of this research had been to examine the effectiveness of the minimum relative entropy method (MRE), which, in 1995, Amisaki and Eguchi proposed as the estimation method for phamacokinetic parameter analysis. However, as the research progressed, it revealed that MRE is mathematically identical to the iteratively reweighted least squares (IRLS) with a Poisson variance function. This result provides the theoretical basis for justifying the IRLS,that is, the method works by minimizing the relative entropy. A previous typical claim for the IRLS has been as follows : the method seems to perform well, but would be appropriate for data from a Poisson distribution, i.e., counted data. In addition to this result, this research theoretically and/or numerically compared the properties of many kinds of estimation methods, including weighted least squares, generalized least squares, extended least squares, and extended quasi-likelihood. As a result, it is shown that MRE is best suited for analyzing individual pharmacokinetic data where estimation of intra-individual variation of each parameter is of no concern.