本文主要探討二筆實際的資料，並運用基礎之四個空腔的PBTK 模型(Leung, 1992; Tomas et al 1996)描述化學物質在身體內的運行機制，接著應用貝氏架構與 MCMC 方法(Gelman 1996; Bois 1996)求出PBTK 模型的參數並同時求得外在的 曝露濃度，首先探討的化學物質為三氯乙烯(TCE; Trichloroethylene)，資料來自 Fisher et al. (1998)的文章，包含一群健康的自願實驗對象，實驗過程曝露在50ppm 與100ppm TCE 下四小時，詳細紀錄血液與尿液中化學物質的濃度，我們先利用 血液中的TCE 濃度推估當時的暴露濃度與PBTK 體內參數，接著再推廣探討只 利用單筆血液資料與多筆尿液資料的推估；第二筆討論的資料來自Wang et al (1996)的文章，化學物質為苯乙烯(Styrene)，由資料中觀察得知，資料中的誤差 項同時包含量測誤差與過程誤差，我們將PBTK 模型先簡化成為單個空腔的模 型，並將化學物質濃度隨時間變化的常微分方程式(Ordinary differential equation;ODE)轉化成隨機微分方程式(Stochastic differential equation; SDE)討論。 Physiologically based toxicokinetic (PBTK) modeling has been well established to study the distributions of chemicals in target tissues. In addition, to address the uncertainties in model parameters and inter-individual variability in PBTK models, the hierarchical Bayesian statistical approach using Markov Chain Monte Carlo (MCMC) simulations has been successfully applied for parameter estimation. Thus, employing PBTK models would be a highly plausible way to estimate the constant inhalation exposure concentration using hierarchical Bayesian approaches. In this dissertation, we first discuss the estimations of parameters for PBTK model and exterior exposure. By treating the exterior exposure as an unknown parameter of a four-compartment PBTK model, we apply MCMC simulations to obtain the posterior distributions of the exposure and other model parameters with prior information from the literature. Next, considering stochastic variations to the toxicokinetic model, the solution to the resultant stochastic differential equation (SDE), together with measurement error, is transformed into a dynamic linear state-space model. The proposed method is used in the analysis of the styrene data (Wang et al. in Occup Environ Med 53:601–605, 1996) to backward estimate the exterior exposure.
