Parameter estimation for physiologically based pharmacokinetics model using Bayesian inference
- Authors
- Kim, D.S.; Sung, J.H.; Lee, J.M.
- Issue Date
- 2013
- Publisher
- IFAC Secretariat
- Keywords
- Bayesian inference; Drug delivery system; Maximum a posteriori method; MCMC simulation; PBPK model; Tegafur
- Citation
- IFAC Proceedings Volumes (IFAC-PapersOnline), v.10, no.PART 1, pp.637 - 642
- Journal Title
- IFAC Proceedings Volumes (IFAC-PapersOnline)
- Volume
- 10
- Number
- PART 1
- Start Page
- 637
- End Page
- 642
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/17233
- DOI
- 10.3182/20131218-3-IN-2045.00064
- ISSN
- 1474-6670
- Abstract
- Physiologically based pharmacokinetics(PBPK) model can predict absorption, degradation, execration and other metabolism in drug delivery system. Thus it can be useful for regulating dose and estimating drug concentration at a particular time during the clinical demonstration. PBPK model is expressed as a set of differential equation with various parameters. Bio-chip experimental data are often noisy and sparse. This makes it difficult to estimate parameters with conventional least squares approaches. The resulting parameters often have a large confidence region. This work presents a Bayesian inference algorithm with an objective function suitable for PBPK model. A Markove Chain Monte Carlo(MCMC) method is employed to estimate the posterior distribution of the parameters. We illustrate the approach with a Tegafur delivery system. © IFAC.
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Collections - College of Engineering > Chemical Engineering Major > 1. Journal Articles
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