Parameter estimation for physiologically based pharmacokinetics model using Bayesian inference
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, D.S. | - |
dc.contributor.author | Sung, J.H. | - |
dc.contributor.author | Lee, J.M. | - |
dc.date.accessioned | 2021-11-11T04:43:00Z | - |
dc.date.available | 2021-11-11T04:43:00Z | - |
dc.date.created | 2021-11-10 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 1474-6670 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/17233 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IFAC Secretariat | - |
dc.subject | Bayesian networks | - |
dc.subject | Controlled drug delivery | - |
dc.subject | Differential equations | - |
dc.subject | Drug dosage | - |
dc.subject | Inference engines | - |
dc.subject | Monte Carlo methods | - |
dc.subject | Pharmacokinetics | - |
dc.subject | Physiology | - |
dc.subject | Process control | - |
dc.subject | Targeted drug delivery | - |
dc.subject | Bayesian inference | - |
dc.subject | Drug delivery system | - |
dc.subject | Maximum a posteriori methods | - |
dc.subject | MCMC simulation | - |
dc.subject | Tegafur | - |
dc.subject | Physiological models | - |
dc.title | Parameter estimation for physiologically based pharmacokinetics model using Bayesian inference | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Sung, J.H. | - |
dc.identifier.doi | 10.3182/20131218-3-IN-2045.00064 | - |
dc.identifier.scopusid | 2-s2.0-84896348127 | - |
dc.identifier.bibliographicCitation | IFAC Proceedings Volumes (IFAC-PapersOnline), v.10, no.PART 1, pp.637 - 642 | - |
dc.relation.isPartOf | IFAC Proceedings Volumes (IFAC-PapersOnline) | - |
dc.citation.title | IFAC Proceedings Volumes (IFAC-PapersOnline) | - |
dc.citation.volume | 10 | - |
dc.citation.number | PART 1 | - |
dc.citation.startPage | 637 | - |
dc.citation.endPage | 642 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Bayesian networks | - |
dc.subject.keywordPlus | Controlled drug delivery | - |
dc.subject.keywordPlus | Differential equations | - |
dc.subject.keywordPlus | Drug dosage | - |
dc.subject.keywordPlus | Inference engines | - |
dc.subject.keywordPlus | Monte Carlo methods | - |
dc.subject.keywordPlus | Pharmacokinetics | - |
dc.subject.keywordPlus | Physiology | - |
dc.subject.keywordPlus | Process control | - |
dc.subject.keywordPlus | Targeted drug delivery | - |
dc.subject.keywordPlus | Bayesian inference | - |
dc.subject.keywordPlus | Drug delivery system | - |
dc.subject.keywordPlus | Maximum a posteriori methods | - |
dc.subject.keywordPlus | MCMC simulation | - |
dc.subject.keywordPlus | Tegafur | - |
dc.subject.keywordPlus | Physiological models | - |
dc.subject.keywordAuthor | Bayesian inference | - |
dc.subject.keywordAuthor | Drug delivery system | - |
dc.subject.keywordAuthor | Maximum a posteriori method | - |
dc.subject.keywordAuthor | MCMC simulation | - |
dc.subject.keywordAuthor | PBPK model | - |
dc.subject.keywordAuthor | Tegafur | - |
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