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A Bayesian Approach to Robust Parameter Estimation of Physiologically Based Pharmacokinetics Model with Drug Dissolution Model
약물 용해 모델이 포함된 생리학적 약동학 모델의 베이즈 접근을 통한 강건한 변수 추정

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Authors
김대식
Advisor
이종민
Major
공과대학 화학생물공학부
Issue Date
2014-02
Publisher
서울대학교 대학원
Keywords
Bayesian approachParameter estimationPBPK modelPharmacokineticsDrug delivery systemMaximum a posteriori
Description
학위논문 (석사)-- 서울대학교 대학원 : 화학생물공학부, 2014. 2. 이종민.
Abstract
Physiologically based pharmacokinetics (PBPK) model can predict absorption, degradation, execration and 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. While PBPK model is generally expressed as a set of ordinary differential equations with a large number of parameters, in-vivo experimental data are often noisy and sparse. This makes it difficult to estimate parameters with conventional least squares approaches. Therefore, maximum a posteriori method from Bayesian approach that is the robust parameter estimation technique can be used to estimate parameters of PBPK model. However, the scheme of maximum a posteriori method by using Markov Chain Monte Carlo sampling is hard to use for parameter estimation of PBPK model because of the large number of parameters. This work introduces the Bayesian approach estimation method for parameter estimation of PBPK model. In addition, a scheme of maximum a posteriori method is proposed to find maximum of the posterior distribution without using Markov Chain Monte Carlo sampling.\\

To regulate the concentration of drug and prevent side effect, the studies of drug dosage form are developed. However, since PBPK models and drug dissolution models are studied independently, there is no consideration of the drug dissolution dynamics in PBPK model. Therefore, accurate description of oral administrated drug delivery system requires an improved PBPK model. This work proposes a PBPK model that can describe orally administrated drug dissolution model by combining the drug dissolution model and PBPK model.\\

This thesis simulates parameter estimation of PBPK model to compare the performance of least squares method and maximum a posteriori method. In addition, the case study for Tegafur delivery system is conducted with in-vivo data and drug dissolution model included PBPK model to predict concentration profile of Tegafur, and to evaluate the proposed PBPK model.
Language
English
URI
http://hdl.handle.net/10371/127094
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Chemical and Biological Engineering (화학생물공학부)Theses (Master's Degree_화학생물공학부)
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