應用人體毒物動力學模式與生物標記量測資料推估環境曝露濃度

Environmental exposure estimation with biomarker measurements using Physisologically-based toxicokinetic model (PBTK model)

施銘權(Meng-Chiuan Shih)

指導教授 ： 陳主智 ； 錢傳仁

淡江大學/理學院/數學學系博士班/博士(2010年)

https://doi.org/10.6846/TKU.2010.00624

摘要

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。

關鍵字

生物標記 ； 曝露評估 ； 馬可夫鏈蒙地卡羅法 ； 量測誤差 ； 過程誤差 ； 人體毒物動力學模式 ； 隨機微分方程式

並列摘要

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.

並列關鍵字

Biomarker ； exposure assessment ； hierarchical Bayesian statistics ； Important resampling ； Markov chain Monte Carlo ； measurement error ； physiologically based toxicokinetic model ； Process error ； Stochastic differential equation

參考文獻

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