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A Use of Monte Carlo Integration for Population Pharmacokinetics with Multivariate Population Distribution

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Abstract

This paper describes a use of Monte Carlo integration for population pharmacokinetics with multivariate population distribution. In the proposed approach, a multivariate lognormal distribution is assumed for a population distribution of pharmacokinetic (PK) parameters. The maximum likelihood method is employed to estimate the population means, variances, and correlation coefficients of the multivariate lognormal distribution. Instead of a first-order Taylor series approximation to a nonlinear PK model, the proposed approach employs a Monte Carlo integration for the multiple integral in maximizing the log likelihood function. Observations below the lower limit of detection, which are usually included in Phase 1 PK data, are also incorporated into the analysis. Applications are given to a simulated data set and an actual Phase 1 trial to show how the proposed approach works in practice.

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Authors and Affiliations

  1. Department of Clinical Pharmacology, Bio-Iatric Center, the Kitasato Institute, 5-9-1 Shirokane, Minato-ku, Tokyo, 108-8642, Japan

    Akifumi Yafune & Masato Takebe

  2. Department of Pharmacoepidemiology, Faculty of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Akifumi Yafune

  3. Department of Pharmaceutics, Meiji College of Pharmacy, 1-22-1 Yato-cho, Tanashi-shi, Tokyo, 188-0001, Japan

    Hiroyasu Ogata

Authors
  1. Akifumi Yafune
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  2. Masato Takebe
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  3. Hiroyasu Ogata
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Yafune, A., Takebe, M. & Ogata, H. A Use of Monte Carlo Integration for Population Pharmacokinetics with Multivariate Population Distribution. J Pharmacokinet Pharmacodyn 26, 103–123 (1998). https://doi.org/10.1023/A:1023280909207

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  • DOI: https://doi.org/10.1023/A:1023280909207

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