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i

보건 사 논

Dose reconstruction

from human biomonitoring data

using PBPK modeling of

DEHP and its metabolites

인체 PBPK 모델 이용 DEHP 노출량 평가

2013 8월

울 보건 원

경보건 과 경보건 공

경 민

i

Abstract

Dose reconstruction

from human biomonitoring data

using PBPK modeling of

DEHP and its metabolites

Kyoungmin Kim

Department of Environmental Health

Graduate School of Public Health

Seoul National University

Di(2-ethylhexyl)phthalate (DEHP) is an economically important

phthalate used as a plasticizer for polyvinyl chloride (PVC); humans

are ubiquitously exposed to DEHP due to the extensive use. We

reconstructed Physiologically-based Pharmacokinetics (PBPK) model

for a comprehensive description of the kinetics of DEHP and its

metabolites in human. We use a published PBPK model of DEHP

after major modification to make better prediction of oxidative

metabolism of MEHP, more than 90% of metabolic flux for oxidative

ii

metabolites. Start by optimizing the adult human DEHP PBPK

model, dose-response relationship equations were deduced by

forward dosimetry. While the present PBPK model predicted

geometric mean of daily intake amount of DEHP 1.67 μg/kg/day, a

simple empirical calculation with urinary excretion coefficient did

1.48 μg/kg/day based on the geometric mean of DEHP metabolites

at US National Health and Nutrition Examination Survey (2009-

2010). As a result of reverse dosimetry using Monte Carlo analysis,

we obtained the distribution of estimated DEHP exposure using the

biomonitoring data of the Korean National Environmental Health

Survey (2009-2011). By a method using exposure conversion factors,

the 5th percentile value was shown to be 0.3 μg/kg/day and the 95th

percentile value was 41.4 μg/kg/day, and using a simplified Bayes’

formula, they were 0.5 μg/kg/day and 21.6 μg/kg/day, respectively.

The data-validated adult human DEHP PBPK model is expected to

provide more precise and scientific foundations for public health.

Keywords : PBPK, DEHP, pharmacokinetics, dose reconstruction,

risk assessment, phthalates

Student Number : 2010-22032

iii

Contents

I. Introduction -------------------------------------------------------------- 1

II. Methods ------------------------------------------------------------------ 5

1. PBPK model structure ----------------------------------------------- 5

2. Parameterization in the adult human DEHP PBPK model --- 10

3. PBPK model validation in the adult human ---------- ---------- 16

4. Sensitivity analysis ------------------------------------------------- 16

5. Forward dosimetry for dose-response relations ---------------- 17

6. Comparison estimated daily intakes with a simple

mathematical equation -------------------------------------------- 17

7. Reverse dosimetry --------------------------------------------------- 19

III. Results ----------------------------------------------------------------- 25

1. PBPK model validation in the adult human --------------------- 25

2. Sensitivity analysis ------------------------------------------------- 28




iv


IV. Discussion ------------------------------------------------------------- 41

1. PBPK model structure and validation ---------------------------- 41





V. Conclusion ------------------------------------------------------------- 48

References ----------------------------------------------------------------- 49

Appendix. Equations for the adult human DEHP PBPK model ---- 57

Abstract in Korean -------------------------------------------------------- 61

v

List of Tables

Table 1. Physiological parameters -------------------------------------- 11

Table 2. Kinetic parameters --------------------------------------------- 12

Table 3. Urinary excretion ratio (FUE) ---------------------------------- 18

Table 4. Model parameters and distributions used for the Monte

Carlo analysis --------------------------------------------------- 22

Table 5. Estimated DEHP exposure. Biomonitoring data is taken

from Korean National Environmental Health Survey

(2009-2011) (n=6274) ------------------------------------------ 36

Table 6. Comparison of estimated DEHP exposure. Biomonitoring

data is taken from NHANES (2009-2010) (n=1914

(m=1399, f=1350)) ----------------------------------------------- 38

Table 7. Distribution of exposure conversion factor (ECF)

by population percentile

((mg DEHP)/(μmol/L MEHHP+MEOHP)) -------------------- 40

Table 8. Distribution of estimated DEHP exposure (mg/day) ------ 40

vi

List of Figures

Figure 1. Metabolism of DEHP (Koch et al. 2005). -------------------- 4

Figure 2. DEHP PBPK model structure. -------------------------------- 9

Figure 3. Mean observed and predicted kinetic data. (A) Serum

concentration. (B) Urine concentration of MEHP. (C)

Urine concentration of MEHHP+MEOHP. --------------- 15

Figure 4. Schematic description of the reverse dosimetry approach

(Clewell et al. 2008). ------------------------------------------ 20

Figure 5. Mean observed and predicted urinary excretion kinetic

data. (A) MEHP following a single oral dose of 0.31 mg

DEHP (n=10). (B) MEHHP+MEOHP following a single oral

dose of 0.31 mg and 2.8 mg DEHP (n=20). Measured

data is taken from Anderson et al., 2011. --------------- 26

Figure 6. Mean observed and predicted serum concentration kinetic

data following a single oral dose of 0.645 mg/kg DEHP

(n=4). Measured data is taken from Kessler et al., 2012. -

------------------------------------------------------------------ 27

vii

Figure 7. Sensitivity coefficients for PBPK model parameters under

continuous exposure scenario at 20 μg/kg/day

(U.S. EPA RfD). -------------------------------------------- 29

Figure 8. Dose-response relations under a single exposure scenario.

(A) MEHP. (B) MEHHP+MEOHP. -------------------------- 31

Figure 9. Dose-response relations under a repeated exposure

scenario (8-hr interval: 0.5-hr exposure, then 7.5-hr

breaks). (A) MEHP. (B) MEHHP+MEOHP. --------------- 32

Figure 10. Dose-response relations under a continuous exposure

scenario. (A) MEHP. (B) MEHHP+MEOHP. ------------- 33

Figure 11. Predicted kinetic time courses under a repeated exposure

scenario (8-hr interval: 0.5-hr exposure, then 7.5-hr

breaks). (A) Serum concentration. (B) Urinary excreted

amount. Dotted lines indicate pseudo steady state. - 34

Figure 12. Predicted kinetic time courses under a continuous

exposure scenario. (A) Serum concentration. (B) Urinary

excreted amount. ------------------------------------------ 35

Figure 13. Cumulative distribution of estimated DEHP exposure

based on biomonitoring data from Korean National

Environmental Health Survey (2009-2011). ----------- 40

1

I. Introduction

Phthalates esters are a class of industrial chemicals extensively

used for plastics, lubricant, personal care products, food wrap, and

even in the coating of some medications (Clewell et al. 2008, Lorber

and Calafat 2012). Di(2-ethylhexyl)phthalate (DEHP) is one of the

classes of phthalate esters and a representative chemical used as a

plasticizer for polyvinyl chloride (PVC) in Korea. DEHP is ubiquitous

in the environment and is found at low levels in soil, water, and air;

therefore, it can be ingested, inhaled, or absorbed through the skin

(Keys et al. 1999). DEHP uptake, the most common route of

exposure, is assumed to take 15–95 % of the total external exposure

(Wormuth et al. 2006).

After oral exposure, DEHP is rapidly metabolized in the

gastrointestinal tract, where mono(2-ethylhexyl) phthalate (MEHP)

is formed and absorbed. De-esterification of DEHP absorbed from

the gastrointestinal (GI) tract occurs in the liver and blood by

lipases followed by side-chain hydroxylation and oxidation reactions

of MEHP in the liver (Schmid and Schlatter 1985). Also, DEHP can

easily contaminate biological samples during laboratory operations;

therefore, biomonitoring studies have been carried out to analyze

the concentration of DEHP metabolites in the laboratory (Gentry et

2

al. 2011). Recently, the secondary oxidative DEHP metabolites have

been shown to be up to 100 times more embryotoxic than MEHP

ensuing toxicity studies focusing on the metabolites (Koch et al.

2005).

Reproductive and developmental effects of DEHP have been

reported in animal studies. In human lymphocytes and mucosal

cells, it was shown to have genotoxicity (Kleinsasser et al. 2000).

Most of the effects were observed after in utero exposure to the male

offspring classifying the chemical as teratogenic (Abdul-Ghani et al.

2012). DEHP was previously classified as a group 3 carcinogen by

IARC. The classification was re-evaluated by IARC in 2011, and

DEHP was upgraded to a group 2B carcinogen indicating a possible

carcinogenic to humans (Grosse 2011).

There are different types of studies for DEHP metabolites:

controlled, biomonitoring, kinetics, and exposure model

development study. However, this process is often dependent upon

the use of animal and in vitro data to estimate human response in

the absence of quantitative human data. Application of animal data

to human using body weight correction factors, high to low dose

extrapolation and interspecies extrapolation are difficult to avoid.

The independent variable, dose, is simply replaced by administered

dose or inhaled concentration in the linearized multistage models

3

(Andersen and Krishnan 1994). Recently, a simple equation is

generally used to get daily intake of DEHP using urinary metabolite

excretion factors (Kohn et al. 2000, Koch et al. 2003).

To reduce the uncertainty inherent in such extrapolations and

simple mathematical models, there has been considerable interest

in the development of physiologically based pharmacokinetics

(PBPK) models around the USA and Europe. PBPK models simulate

the uptake, distribution, metabolism, and elimination of a chemical

that enter the body by integrating mechanistic pharmacokinetic

information through explicit description of physiological and

biochemical determinants of chemical disposition. Therefore, PBPK

models are well suited to perform interspecies, route-to-route,

human variability, and high-to-low dose extrapolations (Thomas et

al. 1996). For its use in risk assessment, PBPK modeling attempts

to describe the relationship between external measures of exposure

and internal measures of biologically effective dose (Clewell et al.

2000). PBPK models represent a class of dosimetry models that are

useful for predicting internal dose at target; also, predicting external

exposure as reverse dosimetry (U.S.EPA 2006).

The PBPK model structure was determined on the bases of

intended use, available data, and previous DEHP kinetic studies.

The purpose of this study was to develop a single model description

4

that could be used with confidence to predict DEHP, MEHP, and

oxidative metabolites of MEHP distribution and elimination in the

general adult human population.

Figure 1. Metabolism of DEHP (Koch et al. 2005).

5

II. Methods

1. PBPK model structure

The adult human DEHP PBPK model is established based on the

most recent PBPK model which is the human lactation model for Di-

n-butyl phthalate (DBP) and DEHP (Gentry et al. 2011). The original

model is the rat model for DEHP (Keys et al. 1999) and DBP (Keys et

al. 2000), which is extended to the rat lactation model for DBP

(Clewell et al. 2008) and further extrapolated for DEHP and DBP

together (Clewell 2009). The previous model describes urinary

excreted amount of MEHP only. However, we intended to describe

more aspects of DEHP metabolites kinetics: serum concentration of

MEHP, urinary excreted amount of MEHP and MEHHP+MEOHP,

and urine concentration of MEHP and MEHHP+MEOHP. The model

structure was set by iterative processes; wherein the controlled

study data was used to develop a structure, then other data sets

were simulated until describing comprehensively. In here,

simultaneous description of serum and urine kinetic was the goal.

For that purpose, exclusion of MEHP-glucuronide, here called by

MEHP-G, simplification of MEHP and oxidative metabolites of MEHP,

here called by MEHP-O, were tested. From those attempts, it was

clear that data of some major oxidative metabolites is not enough to

6

describe more than thirty kinds of oxidative metabolites en bloc.

Thus, the MEHP-O part was divided in two: one is described the

known-values and the other is covered all the other unknown-

values.

Final model comprises five submodels representing absorption

and disposition for DEHP, MEHO, MEHP-G and MEHP-O (Figure 2).

The submodels were linked at metabolism parts (hydrolysis,

glucuronide, and oxidation). The PBPK model was coded and

simulated with modeling and simulation software tool, acslX 3.0

(AEgis Technologies Group, Inc., Juntsville, AL). Details of model

equations were provided in the appendix.

DEHP

Enzymes responsible for the hydrolysis of DEHP are existed in

the intestinal mucosa, blood and liver (Rowland et al. 1977, Tanaka

et al. 1978). Hydrolysis in the stomach and small intestine is

described as a saturable process (VmaxGc, KmG). Furthermore,

hydrolysis of DEHP in the plasma (kbc) and liver (kc) is described as

a first order rate. Oral absorption into the liver from the small

intestine is described as a first order process (kad). Movement

between the upper intestine (GJs) and lower intestine (GJl) is a first

7

order rate (kgic). Fecal excretion (kfdc) and biliary transfer (kbdc) of

DEHP is described as a clearance rate (L/hr) (Keys et al. 1999,

Clewell et al. 2008). DEHP movement into the tissues is modeled

using flow-limited theory (Clewell et al. 2008).

Free MEHP

Oral absorption is described as a first order rate (kam).

Glucuronidation (VmaxLc, KmL) and oxidation (VmaxBc, KmB) in

the liver are described using saturable kinetics. The MEHP

transport into the lower intestine (GJl) is described as a first order

process (kgic). Urinary excretion is described as a first order

clearance rate (kelimmc) from the plasma (Keys et al. 1999). Fecal

excretion (kfmc) and biliary transfer (kbmc) of MEHP is described as

a clearance rate (L/hr). Transport of MEHP into the tissues from the

plasma is described in the manner of diffusion-limitation (Clewell et

al. 2008).

MEHP-glucuronide

MEHP-G is formed in the liver (VmaxLc, KmL) saturably and

distributed under flow-limited assumption. MEHP-G is moved

8

through the intestine (kgic) and hydrolyzed back to MEHP via β-

glucuronidase (khydrc) in the large intestine (GJl). Urinary excretion

is modeled as a first order clearance rate (kelimgc) (Clewell et al.

2008).

Oxidative Metabolites of MEHP

Oxidative metabolism to MEHP-O is described using a saturable

kinetics. In this study, there are two identical oxidative metabolites

submodels: one is for MEHHP and MEOHP, and the other is for the

rest of oxidized metabolites. All the other kinetics is same as the

previous model, but oxidative metabolisms are distinguished

(VmaxBc, KmB, VmaxBc2. KmB2). Urinary excretion is described

using the first order clearance rate (kelimoc). Distribution of MEHP-

O into the tissues is modeled using flow-limitation (Clewell 2009).

9

Figure 2. DEHP PBPK model structure.

10

2. Parameterization in the adult human DEHP

PBPK model

Physiological parameters

Physiological parameters are taken from the previous literatures

(Table 1). However, if specific parameters are given from a study,

those values were used for simulating the study data. Fractional

tissue volumes and blood flows were scaled using BW.

Adjustable kinetic parameters

The kinetic parameters are given in Table 2. The parameters are

mostly obtained from previous literatures. Kinetic parameters are

scaled allometrically as is typical for intra- and inter-species

extrapolation (Dedrick 1973).

Parameters related to the change of oxidative metabolites parts

were estimated by using a parameter estimation tool of acslX to

obtain the best fit of the model to time course data (Figure 3). Time

course data on serum concentration of MEHP and urinary excreted

amount of MEHP, MEHHP, and MEOHP after a single oral dose

(0.645mg/kg) of DEHP (Kessler et al. 2012) was used to derive

11

values for the parameters governing liver oxidations (VmaxBc, KmB,

VmaxBc2. KmB2) and urinary clearance rates (kelimmc, kelimoc).

Table 1. Physiological parameters.

Physiological parameter Adult human Source

Tissue volumes(%BW)

Body weight BW (kg) 82 Kessler et al., 2012

Slowly perfused VSc 0.481 Brown et al., 1997

Richly perfused VRc 0.0827 Brown et al., 1997

Fat VFc 0.273 Brown et al., 1997

Liver VLc 0.026 Brown et al., 1997

GI tract VGIc 0.0091 Brown et al., 1997

Upper GI contents VGJs 0.0088 Thompson et al., 1958

Lower GI contents VGJl 0.0052 Thompson et al., 1958

Blood VBc 0.04 Brown et al., 1997

Plasma Vplasc 0.04 Clewell et al., 2003

Urine VolUc 22 ICRP PUB

Blood flows(%QC)

Cardiac output QCc(L/h) 20 Brown et al., 1997

Slowly perfused QSc 0.26 Brown et al., 1997

Richly perfused QRc 0.431 Brown et al., 1997

Fat QFc 0.052 Brown et al., 1997

Liver QLc 0.046 Brown et al., 1997

GI QGIc 0.184 Brown et al., 1997

12

Table 2. Kinetic parameters

Parameter Value Source

Partition coefficient (unitless)

DEHP liver:plasma PL 21.80 Gentry et al., 2011

DEHP slowly perfused tissues:plasma PS 1.00 Gentry et al., 2011

DEHP richly perfused tissues:plasma PR 21.80 Gentry et al., 2011

DEHP fat:plasma PF 351.00 Gentry et al., 2011

MEHP liver:plasma PML 0.70 Gentry et al., 2011

MEHP slowly perfused tissues:plasma PMS 0.70 Gentry et al., 2011

MEHP richly perfused tissues:plasma PMR 0.70 Gentry et al., 2011

MEHP-G liver:plasma PGL 0.60 Gentry et al., 2011

MEHP-G liver:plasma PGS 0.30 Gentry et al., 2011

MEHP-O slowly perfused tissues:plasma POL 0.30 Gentry et al., 2011

MEHP-O slowly perfused tissues:plasma POS 0.30 Gentry et al., 2011

Max capacity, Vmaxc (mg/h/kg)

Hydrolysis of DEHP in GI VmaxGc 150.00 Gentry et al., 2011

Glucuronidation of MEHP in Liver VmaxLc 1.60 Gentry et al., 2011

Oxidation of MEHP in liver VmaxBc 39.80 Fitted-Kessler et al., 2012

Oxidation of MEHP in liver VmaxBc2 23.20 Fitted-Kessler et al., 2012

13


Affinity constants, Km (mg/L)

Hydrolysis of DEHP in GI KmG 300.00 Gentry et al., 2011

Glucuronidation of MEHP in Liver KmL 18.00 Gentry et al., 2011

Oxidation of MEHP in liver KmB 38.00 Fitted-Kessler et al., 2012

Oxidation of MEHP in liver KmB2 14.80 Fitted-Kessler et al., 2012

Permeability area cross products (L/h/kg)

MEHP liver PALc 1.00 Gentry et al., 2011

MEHP richly perfused tissue PARc 1.00 Gentry et al., 2011

MEHP slowly perfused tissue PASc 0.10 Gentry et al., 2011

MEHP-G in liver PAGLc 1.00 Gentry et al., 2011

MEHP-G slowly perfused tissue PAGSc 1.00 Gentry et al., 2011

MEHP-O lowly perfused tissues PAOSc 1.00 Gentry et al., 2011

Metabolism rates (/h)

Hydrolysis of DEHP in blood kbc 1.20 Gentry et al., 2011

Hydrolysis of DEHP in liver kc 0.10 Gentry et al., 2011

Hydrolysis of MEHP-G in Gut Lumen khydrc 100.00 Gentry et al., 2011

Movement of DEHP in GI contents KGIDc 0.10 Gentry et al., 2011

Movement of MEHP in GI contents KGIMc 0.05 Gentry et al., 2011

14


Clearance rates (L/h/kg)

MEHP urinary excretion KElimMc 0.011 Fitted-Kessler et al., 2012

MEHP-G urinary excretion KElimGc 0.35 Gentry et al., 2011

MEHP-O urinary excretion KElimOc 0.23 Fitted-Kessler et al., 2012

DEHP, MEHP, MEHP-O fecal excretion kfc 0.01 Gentry et al., 2011

Oral uptake (/h)

DEHP absorption in stomach and intestine kad 0.01 Gentry et al., 2011

MEHP absorption in stomach and intestine kam 0.40 Gentry et al., 2011

15

(A)

(B) (C)

Figure 3. Mean observed and predicted kinetic data. (A) Serum concentration. (B) Urine concentration of MEHP. (C) Urine concentration of MEHHP+MEOHP.

0.0001

0.001

0.01

0.1

1

0 5 10 15 20 25

Seru

m c

on

ce

ntr

ati

on

(u

mo

l/L

)

Time (hours)

Predicted

MEHP

0.01

0.1

1

10

0 10 20 30 40

Am

ou

nt

ex

cre

ted

(u

mo

l)

Time (hours)

Predicted

MEHP0.01

0.1

1

10

100

0 10 20 30 40

Am

ou

nt

ex

cre

ted

(u

mo

l)

Time (hours)

Predicted

MEHHP+MEOHP

16

3. PBPK model validation in the adult human

The predictability of the PBPK model and the final parameters

were tested with separate data sets against those used for

parameterization. Dataset used here is providing urinary kinetics

from 20 volunteers under controlled conditions (Anderson et al.

2011). All parameters were fixed except for body weight which was

matched reported in the study. For validation of serum

concentration of free MEHP, total MEHP (free MEHP+MEHP-

glucuronide) data was inevitably used (Kessler et al. 2012).

4. Sensitivity analysis

A normalized sensitivity analysis was performed in order to

identify the relative key parameters highly influencing on the model

outputs: urine concentration of MEHP and MEHHP+MEOHP. The

effect of a 1% change in parameters was evaluated using the

sensitivity analysis tool of acslX.

17

5. Forward dosimetry for dose-response relations

By simulating tissue concentrations at given exposures, the

dose-response relations were generated. In this study, the model

was set to predict single dosing profiles for 48-h period. However, in

order to predict the realistic exposure on general people, two types

of exposure scenarios were simulated. One is assumed to have

continuous exposure to DEHP during the lifetime; and the other

assumption is that DEHP exposure occurs at three mealtimes a day.

Monte Carlo analysis was performed by sampling pharmacokinetic

and exposure parameters randomly from defined distributions

(Table 4) and by running the model for 1000 iterations at each

exposure point.

6. Comparison estimated daily intakes with a

simple mathematical equation

To estimate Daily Intake, a simple mathematical equation

(Equation 1) is widely used (Fromme et al. 2007, Suzuki et al. 2009,

Hines et al. 2011). Equation 1 is calculated using fixed urinary

excreted ratios (Table 3) and creatinine excretion rate, which is set

to be 18 mg/kg/day for women and 23 mg/kg/day for men (Harper

18

et al. 1977, Kohn et al. 2000). With dose-response relationships,

point estimations are enabled both forward and reverse dosimetry.

Therefore, daily intakes using two approaches were compared based

on urinary concentration data of MEHP, mono(2-ethyl-5-hydroxy-

hexyl)phthalate (MEHHP) and mono(2-ethyl-5-oxyhexyl)phthalate

(MEOHP) (NHANES (2009-2010)).

DI( ⁄⁄ ) = ( ⁄ ) × ( ⁄⁄ )

× 1000×

Equation. 1. Daily intake calculating formula (Koch et al. 2003).

Table 3. Urinary excretion ratio (FUE).

Fue Koch(2003) Koch(2006)

MEHP 0.024 0.059

MEHHP 0.074 0.23

MEOHP 0.055 0.15

MEHP+MEHHP+MEOHP 0.442

19

7. Reverse dosimetry

The reverse dosimetry approach is to estimate the intake dose or

external environmental concentration based on a measured tissue

concentration (Mumtaz et al. 2012). In the present study, reverse

dosimetry was performed in three alternative reverse dosimetry

approaches: one is using the relationship equations from the

forward dosimetry, and others are using Monte Carlo analysis (Tan

et al. 2007). The parameter distributions used in the Monte Carlo

analysis for DEHP are expressed in Table 4. Those parameters were

based on the previous literatures (Gentry et al. 2011).

The first approach is to predict the distribution of urine

concentration of MEHHP+MEOHP at a given dose of DEHP (1

mg/day) by performing Monte Carlo analysis. The output

distribution is inverted to obtain a distribution of an exposure

conversion factor (ECF) in a unit of (mg/day DEHP)/(μmol/L

MEHHP+MEOHP in urine). The distribution of ECFs can then be

multiplied by MEHHP+MEOHP measured in urine to estimate a

distribution of DEHP exposure amount (Figure 4).

20

Figure 4. Schematic description of the reverse dosimetry approach (Clewell et al. 2008)

Unit Exposure Concentration

Population Variability

Physiological Parameters

Biochemical Parameters

Activity Patterns

PBPK ModelPredicted Biomarker Distribution

Biomarker Measurements

Exposure Conversion Factor Distribution

Predicted Distribution of Exposure

Monte Carlo Analysis

Invert

21

The second approach is based on a simplified Bayes’ formula

(Tan et al. 2007):

where E is a particular DEHP exposure amount, U is the urine

concentration observed, is the probability of a particular

DEHP exposure amount given the observed urine concentration of

MEHHP+MEOHP, is the probability of a specific urine

concentration predicted by the PBPK model at a given DEHP

exposure amount and the summation in the denominator is

over all the DEHP exposure amount used as input to the PBPK

model.

The DEHP exposure amount was between 0.001 mg and 10 mg

with geometric increment of 100.2 mg. For each exposure, 1000

urine concentrations of MEHHP+MEOHP were generated by Monte

Carlo analysis; and subsequent processes were followed the

previous study (Tan et al. 2007).

22

Table 4. Model parameters and distributions used for the Monte Carlo analysis.

Parameter Mean SD Lower Upper Distribution

Blood flows (fraction of cardiac output)

QCC Cardiac output (L/hr scaled by BW3/4) 20.00 4.40 6.80 33.20 Normal

QLC Liver 0.046 0.023 0.0009 0.091

QFC Fat 0.052 0.026 0.0010 0.103

QGIC GI Tract 0.184 0.092 0.0037 0.364

QMC Mammary 0.027 0.014 0.0005 0.053

QSC Slowly Perfused 0.260 0.130 0.0052 0.515

QRC Richly Perfused 0.431 0.216 0.0086 0.853

Tissue volume (fraction of body weight)

VOLUC Volume of Urine 2.979 0.472 7.796 49.667 Lognormal

BWINIT Initial body weight (kg) 4.137 0.472 24.805 158.03

VBC Blood 0.070 0.035 0.0014 0.139 Normal

VPLASC Plasma 0.040 0.020 0.0008 0.079

VLC Liver 0.026 0.013 0.0005 0.051

VFC Fat 0.273 0.137 0.0055 0.541

VGIC GI Tract 0.009 0.005 0.0002 0.018

VGJSC Small Intestine 0.009 0.004 0.0002 0.017

VGJLC Large Intestine 0.005 0.003 0.0001 0.010

VMC Mammary 0.006 0.003 0.0001 0.012

VSC Slowly Perfused 0.481 0.241 0.0096 0.952

VRC Richly Perfused 0.083 0.041 0.0017 0.164

VREMC Remainder (non perfused tissue) 0.122 0.061 0.0024 0.242

23


Partition Coefficients

PL DEHP in Liver 2.970 0.472 7.725 49.215 Lognormal

PF DEHP in Fat 5.749 0.472 124.38 792.41

PS DEHP in Slowly Perfused -0.112 0.472 0.354 2.258

PR DEHP in Richly Perfused 2.970 0.472 7.725 49.215

PML MEHP in Liver -0.468 0.472 0.248 1.580

PMS MEHP in Slowly Perfused -0.468 0.472 0.248 1.580

PMR MEHP in Richly Perfused -0.468 0.472 0.248 1.580

PGL MEHP-G in Liver -0.622 0.472 0.213 1.355

PGS MEHP-G in Slowly Perfused -1.316 0.472 0.106 0.677

POL MEHP-O in Liver -1.316 0.472 0.106 0.677

POS MEHP-O in Slowly Perfused -1.316 0.472 0.106 0.677

Permeation Coefficients (scaled to BW3/4)

PALC MEHP in Liver -0.112 0.472 0.354 2.258 Lognormal

PASC MEHP in Slowly Perfused -2.414 0.472 0.035 0.226

PARC MEHP in Richly Perfused -0.112 0.472 0.354 2.258

PAGLC MEHP-G in Liver -0.112 0.472 0.354 2.258

PAGSC MEHP-G in Slowly Perfused -0.112 0.472 0.354 2.258

PAOSC MEHP-O in Slowly Perfused -0.112 0.472 0.354 2.258

24


Kinetic Parameters (scaled to BW3/4)

VMXGC Hydrolysis of DEHP in Gut Lumen 0.294 0.472 0.532 3.386 Lognormal

VMXLC Glucuronidation of MEHP in Liver 0.294 0.472 0.532 3.386

VMXBC Oxidation of MEHP in Liver 3.499 0.472 13.111 83.531

Affinity Constants (mg/L)

KMG Hydrolysis of DEHP in Gut Lumen 5.699 0.096 247.35 360.52 Lognormal

KML Glucuronidation of MEHP in Liver 2.779 0.472 6.379 40.636

KMB Oxidation of MEHP in Liver 3.221 0.472 9.922 63.212

Rate Constants (L/hr/kg)

KC Hydrolysis of DEHP in Liver -2.414 0.472 0.035 0.226 Lognormal

KBC Hydrolysis of DEHP in Blood -0.468 0.472 0.248 1.580

KAD DEHP Absorption in Small Intestine (/hr) -1.721 0.472 0.071 0.452

KAM MEHP Absorption in Small Intestine (/hr) -1.028 0.472 0.142 0.903

KGIC Movement of MEHP in GI Contents (/hr) -2.414 0.472 0.035 0.226

KGIC2 Movement of MEHP-G in GI Contents (/hr) -3.107 0.472 0.018 0.113

KELIMMC MEHP Urinary Excretion -2.771 0.472 0.025 0.158

KELIMGC MEHP-G Urinary Excretion -1.161 0.472 0.124 0.790

KELIMOC MEHP-O Urinary Excretion -2.414 0.472 0.035 0.226

KFDC DEHP Fecal Excretion -4.534 0.472 0.004 0.027

KFMC MEHP Fecal Excretion -4.534 0.472 0.004 0.027

KFGC MEHP-G Fecal Excretion -4.534 0.472 0.004 0.027

KFOC MEHP-O Fecal Excretion -4.534 0.472 0.004 0.027

25

III. Results

1. PBPK model validation in the adult human

The final model (Figure 2) is differ from the previous model

(Gentry et al. 2011) due to changes in the intensive description of

oxidative metabolites and consequential structure. Parameters were

not altered to improve fits to the validation data (Anderson et al.

2011). The urinary excretion kinetics for MEHP after a single oral

dose of 0.31mg DEHP is suitably reproduced (Figure 5A); predicted

kinetic for MEHHP+MEOHP were almost agreed with the measured

data of DEHP dose, 0.31mg and 2.8mg (Figure 5B). Serum data was

not included in the validation dataset. For that reason, serum

concentration for total MEHP (free MEHP+MEHP-glucuronide)

(Kessler et al. 2012) was inevitably used; and, it correctly described

serum concentration of total MEHP (Figure 6).

26

(A) (B)

Figure 5. Mean observed and predicted urinary excretion kinetic data. (A) MEHP following a single oral dose

of 0.31 mg DEHP (n=10). (B) MEHHP+MEOHP following a single oral dose of 0.31 mg and 2.8 mg DEHP

(n=20). Measured data is taken from Anderson et al., 2011.

0.001

0.01

0.1

0 10 20 30 40

Am

ou

nt

exc

rete

d (

um

ol)

Time (hours)

0.31 mg DEHP0.01

0.1

1

10

0 10 20 30 40Am

ou

nt

exc

rete

d (

um

ol)

Time (hours)

2.8 mg DEHP

0.31 mg DEHP

27

Figure 6. Mean observed and predicted serum concentration kinetic

data following a single oral dose of 0.645 mg/kg DEHP (n=4).

Measured data is taken from Kessler et al., 2012.

0.0001

0.001

0.01

0.1

1

10

0 10 20 30 40

Se

rum

co

nc

en

trati

on

(u

mo

l/L

)

Time (hours)

28

2. Sensitivity analysis

The normalized sensitivity coefficients for the model parameters

with respect to urine concentrations of MEHP and MEHHP+MEOHP

were shown in Figure 7. Sensitivity analysis was performed under a

continuous exposure conditions with 20 μg/kg/day DEHP (U.S.

EPA reference dose, RfD (U.S.EPA 2007)); as a result, parameters

with sensitivity coefficient greater than 0.1 were determined only.

The urine concentration of MEHP is more sensitive to parameters

governing glucuronidation (VmaxLc, KmL); meanwhile, the urine

concentration of MEHHP+MEOHP is more sensitive to parameters

describing oxidation to MEHHP+MEOHP (VmaxBc, KmB). The

volume of urine is the most sensitive parameter on both of them.

29

Figure 7. Sensitivity coefficients for PBPK model parameters under

continuous exposure scenario at 20 ug/kg/day (U.S. EPA RfD).

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

VmaxLc VmaxBc VmaxBc2 KmL KmB KmB2 KelimMc VolUc

Sen

sit

ivit

y C

oe

ffic

ien

t

MEHP

MEHHP+MEOHP

30


The predicted dose-response curves and relationship equations

were shown in Figure 8 for single-exposure, Figure 9 for repeated

exposure, and Figure 10 for continuous exposure conditions. For

repeated exposures, three meals a day were assumed as definite

exposure sources (8-hour interval as 0.5-hour exposure, then 7.5-

hr breaks). Continuous exposure represented daily persistent

exposure condition. As the final outcome, the steady-state values of

repeated and the pseudo-steady-state values of continuous

exposure were converged to almost the same value (Figure 11, 12).

Therefore, the dose-response relationships under a continuous

exposure scenario were used to back-calculate the external

exposure to DEHP. The biomonitoring data of KNEHS (2009-2011)

was applied; consequentially, Korean adult populations were

expected to be daily exposed to 3.8 μg/kg DEHP (Table 5).

31

(A) (B)

Figure 8. Dose-response relations under a single exposure scenario. (A) MEHP. (B) MEHHP+MEOHP.

y = 64.086x

0.0001

0.001

0.01

0.1

1

10

100

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne

co

nc

en

tra

tio

n

(nm

ol/

L/k

g)

Dose (mg/kg/day)

y = 700.6x

0.001

0.01

0.1

1

10

100

1000

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne c

on

cen

tra

tio

n

(nm

ol/L

/kg

)

Dose (mg/kg/day)

32

(A) (B)

Figure 9. Dose-response relations under a repeated exposure scenario (8-hr interval: 0.5-hr exposure, then

7.5-hr break). (A) MEHP. (B) MEHHP+MEOHP.

y = 70.31x

0.0001

0.001

0.01

0.1

1

10

100

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne c

on

cen

trati

on

(n

mo

l/L

/kg

)

Dose (mg/kg/day)

y = 532.05x

0.001

0.01

0.1

1

10

100

1000

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne

co

nc

en

tra

tio

n

(nm

ol/

L/k

g)

Dose (mg/kg/day)

33

(A) (B)

Figure 10. Dose-response relations under a continuous exposure scenario. (A) MEHP. (B) MEHHP+MEOHP.

y = 64.991x

0.0001

0.001

0.01

0.1

1

10

100

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne c

on

cen

trati

on

(n

mo

l/L

/kg

)

Dose (mg/kg/day)

y = 511.91x

0.001

0.01

0.1

1

10

100

1000

0.00001 0.0001 0.001 0.01 0.1 1

Uri

ne

co

nc

en

trati

on

(n

mo

l/L

/kg

)

Dose (mg/kg/day)

34

(A) (B)

Figure 11. Predicted kinetic time courses under a repeated exposure scenario (8-hr interval: half-hr

exposure, then 7.5-hr break). (A) Serum concentration. (B) Urinary excreted amount. Dotted lines indicate

pseudo steady state.

0.01

0.1

0 20 40 60

Seru

m c

on

cen

tra

tio

n

(um

ol/L

)

Time (hours)

MEHP0.1

1

10

100

0 20 40 60

Am

ou

nt

Ex

cre

ted

(u

mo

l)

Time (hours)

MEHP

MEHHP+MEOHP

35

(A) (B)

Figure 12. Predicted kinetic time courses under a continuous exposure scenario. (A) Serum concentration.

(B) Urinary excreted amount.

0.01

0.1

0 20 40 60

Seru

m c

on

cen

trati

on

(u

mo

l/L

)

Time (hours)

MEHP0.1

1

10

100

0 20 40 60

Am

ou

nt

Ex

cre

ted

(u

mo

l)

Time (hours)

MEHP

MEHHP+MEOHP

36

Table 5. Estimated DEHP exposure. Biomonitoring data is taken from Korean National Environmental

Health Survey (2009-2011) (n=6274).

Observed urine concentration of MEHHP+MEOHP (nmol/L/kg)

Estimated DEHP exposure (μg/kg/d)

GM 25th 50th 75th 95th GM 25th 50th 75th 95th

Total 1.9 1.1 2.0 3.4 7.2 3.8 2.2 4.0 6.6 14.0

Sex

Male 2.0 1.2 2.1 3.5 7.4 3.9 2.3 4.1 6.8 14.4

Female 1.9 1.1 2.0 3.2 7.0 3.6 2.2 3.8 6.3 13.7

Age

19∼29 1.9 1.1 2.0 3.5 7.1 3.7 2.2 3.8 6.7 13.8

30∼39 1.8 1.1 2.0 3.3 6.6 3.5 2.1 3.8 6.4 12.9

40∼49 2.0 1.2 2.1 3.3 7.1 3.8 2.3 4.1 6.5 14.0

50∼59 1.9 1.1 1.9 3.2 6.8 3.6 2.2 3.8 6.2 13.3

60∼69 2.0 1.1 2.1 3.6 7.9 4.0 2.2 4.1 7.0 15.4

70+ 2.2 1.3 2.2 3.5 7.9 4.3 2.5 4.4 6.9 15.5

37

4. Comparison daily intake estimations with the


Daily intakes of DEHP were calculated with the dose-response

relationship equation (Figure 10) under continuous exposures using

the present PBPK model and the simple mathematical equation

(Equation 1) with the urinary excretion factors (FUE) of each

metabolites (Table 3) (Koch et al. 2006). Using NHANES (2009-2010)

data, Americans’ daily exposure level for DEHP was estimated: 1.0-

1.9 μg/kg/day according to the PBPK model and 0.7-1.8 μg/kg/day

according to the equation 1 (Table 6). The estimates by the PBPK

model were higher than those by the Equation 1.

38

Table 6. Comparison of estimated DEHP exposure. Biomonitoring data is taken from NHANES (2009-

2010)* (n=1914 (m=1399, f=1350)).

Observed urine concentration* Estimated Exposure (μg/kg/day)

(μg/g cr) (nmol/L/kg) By equation1 By the present

model

GM 75th 95th GM 75th 95th GM 75th 95th GM 75th 95th

Older than 20

MEHP 1.7 1.5 11.1 0.1 0.2 0.7 0.8 0.7 5.4 1.1 2.4 10.1

MEHHP 13.1 20.9 86.3 0.9 1.7 6.8 1.5 2.5 10.2 1.7 3.3 13.3

MEOHP 8.0 12.7 47.4 1.5 2.3 8.7

MEHP+MEHHP+MEOHP 22.8 35.1 144.8 1.5 2.3 9.4

Males#

MEHP 1.6 1.5 13.0 0.1 0.2 0.8 0.9 0.8 7.1 1.2 2.5 11.6

MEHHP 13.6 22.9 103.0 1.0 1.9 7.8 1.8 3.0 13.7 1.9 3.7 15.3

MEOHP 8.2 13.4 52.1 1.7 2.7 10.7

MEHP+MEHHP+MEOHP 23.4 37.8 168.1 1.7 2.8 12.3

Females#

MEHP 1.7 1.5 10.5 0.1 0.1 0.5 0.7 0.7 4.5 1.0 2.3 7.8

MEHHP 13.3 21.9 74.0 0.8 1.8 6.1 1.4 2.3 7.7 1.6 3.4 11.8

MEOHP 8.6 13.6 43.2 1.4 2.2 6.9

MEHP+MEHHP+MEOHP 23.5 37.0 127.7 1.3 2.1 7.3

* http://www.cdc.gov/exposurereport, Updated Tables, March, 2013.

39


Dose reconstruction was performed with two alternative reverse

dosimetry approaches using Monte Carlo analysis. From first

approach, distribution of ECF was predicted (Table 7) and then

approximate distribution of DEHP exposure was deduced with a

measured urine concentrations of MEHHP+MEOHP (KNEHS (2009-

2011)) (Table 8). From second approach, the output of the Monte

Carlo DEHP exposure was estimated and presented as cumulative

probability distribution (Figure 13).

Taking the observed values from the KNEHS (2009-2011), the

median urine concentration of MEHHP+MEOHP (0.13 μmol/L) is

likely to be associated with 0.23 mg/day DEHP exposure using the

first approach; 0.19 mg/day DEHP exposure using the second

approach. The estimated DEHP exposure range of the first approach

is 0.1-107.4 ug/kg/day; 0.2-26.9 ug/kg/day for the second

approach. The first approach resulted in higher estimates of DEHP

exposures distribution.

40

Table 7. Distribution of exposure conversion factor (ECF) by

population percentile. ((mg DEHP)/(μmol/L MEHHP+MEOHP)).

Percentile 5% 10% 25% 50% 75% 90% 95%

ECF 5.67 4.29 3.10 1.74 1.16 0.78 0.66

Table 8. Distribution of estimated DEHP exposure (mg/day)

Percentile 5% 10% 25% 50% 75% 90% 95%

DEHP Exposure

0.02 0.03 0.09 0.23 0.59 1.51 2.65

Figure 13. Cumulative distribution of estimated DEHP exposure

based on biomonitoring data from Korean National Environmental

Health Survey (2009-2011).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cu

mu

lati

ve d

en

sit

y f

un

cti

on

DEHP Exposure (mg/day)

95th percentile:1.38 mg/day



41

IV. Discussion

1. PBPK model structure and validation

We aimed to develop the adult human PBPK model for mainly

DEHP from the human gestation model for mainly DBP and

subsidiary DEHP (Gentry et al. 2011). Fundamentally, PBPK model

supports extrapolations across exposure routes, species (Clewell

and Andersen 1987), individuals (Mezzetti et al. 2003), age (Clewell

et al. 2004), and exposure conditions (Jonsson et al. 2001). Hence,

a previous model was modified from two aspects: DBP to DEHP, and

pregnant women to general adult human. Although DEHP is larger

and more lipophilic and has higher affinity for P450s (Keys et al.

2000), DEHP and DBP share the similar metabolic pathways (Albro

1986, Frederiksen et al. 2007). This characteristic enables

application of the chemical-specific parameters for DEHP. In the

same manner, extrapolation to general adult human could be

achieved by replacing the physiological parameters.

While the earlier DEHP PBPK model (Gentry et al. 2011)

described urinary excreted amount of MEHP only, the present

model is able to describe urinary kinetics of MEHHP and MEOHP as

well as MEHP, and serum kinetics of MEHP. The model validation

42

for urinary kinetics was performed with data from twenty volunteers

(Figure 4) (Anderson et al. 2011). There are three human controlled

studies for DEHP (Koch et al. 2005, Anderson et al. 2011, Kessler et

al. 2012); among them, we used one for model development and the

other for model validation. A previous human gestation model

(Gentry et al. 2011) was validated with urinary excreted amount

data of MEHP from the human controlled study (n=1) (Koch et al.

2004). There is no controlled study data for pregnant women;

therefore, even the data of a man was used for the validation by

excluding the gestation part from the model. In cases of other

materials such as BPA and vinyl chloride, validation of the

extrapolation from animal to human was performed with few

human data available (Clewell et al. 2001, Fisher et al. 2011). As

the predicted urine concentration of MEHP passed close by the

median of the observed value, the model predictions of MEHHP and

MEOHP agreed well with the observations both at low and high

doses. Due to data shortage, the present model was validated

against total MEHP (Kessler et al. 2012); whereas the model for the

serum kinetics was developed using free MEHP. The predicted time-

course of serum concentration of total MEHP was partially fitted;

however, its time-course trend had a similar pattern with the

observed data (Figure 5).

43


We developed the present model under a single dosing scenario,

as the human controlled studies available were conducted following

the single dose (Koch et al. 2005, Anderson et al. 2011, Kessler et al.

2012). However, DEHP is existed ubiquitously and exposed in many

ways in real life (ATSDR 2002, Kato et al. 2004). For that reason,

the internal dose levels in human were considered to reach at

steady state (GFEA 2005); so, we expansively applied the model to

reflect the repeated and continuous exposure conditions. As the

final result, repeated and continuous exposure scenarios showed

the steady-state values almost to the same degree (Figure 9, Figure

10). Therefore, we adopted the continuous exposure scenario as it

could embrace the fluctuations of exposure levels as well.

Under the continuous exposure scenario, both forward and

reverse dosimetry approaches were applicable through the dose-

response relationship (Figure 8). According to the result of reverse

dosimetry using the Korean National Environmental Health Survey

data (2009-2011), average daily intake of DEHP for Korean general

adult was 3.8 μg/kg/day (Table 4); and, the 95th percentile value

(14.0 μg/kg/day) was lower than the U.S. EPA RfD value (20

μg/kg/day).

44

3. Comparison estimated daily intakes with a


In order to estimate the daily intakes of DEHP, a simple

mathematical equation (Equation 1) is used widely; even though it

has limitations on urinary excretion factors (FUE) (Table 3) and

creatinine excretion rates. Given that the value of FUE are based on

a controlled study (n=1) (Koch et al. 2005); it is dependent on the

data. For other data from the controlled study (n=4) (Kessler et al.

2012), the estimated daily intakes could not reproduce the known

dose. Inversely, the estimated daily intakes using the present model

could not describe the dose of the data used for Equation 1. To

overcome the discrepancy, we decided to utilize another dataset

(Anderson et al. 2011).

MEHP, MEHHP, and MEOHP induced different daily intakes by

Equation 1 (Table 5); moreover, none of these could represent the

daily intakes. Therefore, a urinary excretion factor for three

metabolites as one was added at revision (Koch et al. 2006). In

contrast, MEHP and MEHHP+MEOHP can estimate the same daily

intakes theoretically by the PBPK model; the estimates were

practically the same (Table 5). This supports the strength of the

present model because MEHHP and MEOHP are better biomarkers

45

than MEHP that has lesser background exposure and higher

toxicity (Koch et al. 2005, Gentry et al. 2011).

Also, the creatinine excretion rate was set to be 18 mg/kg/day

for women and 23 mg/kg/day for men (Kohn et al. 2000) according

to Equation 1; however, the creatinine excretion rates have several

uncertainties (Garde et al. 2004): higher values for men, less

amount with age (Simpson et al. 1978), more with exercise (Calles-

Escandon et al. 1984) and muscle mass (Edwards and Whyte 1959).

Equation 1 considered kinetics but not physiological characteristics.

On the other hand, the PBPK model took physiological and

biochemical characteristics into account. For example, the intakes

could be different due to the body weight difference, even though

two persons were exposed to same amount of DEHP. The present

PBPK model was able to account the individuals unique features;

thus, the estimations (Table 4, 5) were able to recognize daily

intakes of population.

46


In this study, two reverse dosimetry approaches using Monte

Carlo analysis were demonstrated to interpret biomonitoring data

(Tan et al. 2007). The first approach using ECF estimated percentile

distributions of exposure; while the second approach could cover

wider range of exposures and deduce probability or cumulative

density of DEHP exposure. In the present study, the first approach

estimated DEHP exposure higher than the second approach (Table 8)

(Figure 13).

To apply the reverse dosimetry in risk assessment, the estimated

distributions of exposure were compared with the regulatory

guidelines such as U.S. EPA RfD or TDI. We assessed the

percentage of a population having daily DEHP intakes above RfD

(20 μg/kg/day). The 88th percentile value was 20.0 ug/kg/day

using the first approach; and, the 94th percentile value was 20.3

ug/kg/day using the second approach. In case of the second

approach, 99th percentile value (26.9 ug/kg/day) was slightly over

RfD, but lower than OECD TDI (40 ug/kg/day); in contrast, for the

first approach, 99th percentile value was 107.4 ug/kg/day, which

was around Japan TDI (40-140 ug/kg/day) (GFEA 2005, KFDA

2010). The differences in the inducement methods may cause the

47

gap between two approaches even though both were adopted to the

Monte Carlo analysis.

The feature of the present PBPK model is capability to represent

the concurrent kinetics of key biomarkers of DEHP exposure in

humans and application of the reverse dosimetry with less

uncertainty. By integrating PBPK model and biomonitoring data into

exposure and risk assessments, more scientific foundations can be

obtained to protect the public health.

48

V. Conclusion

The adult human DEHP PBPK model described in this article

was drawn by the extension of a human gestation model for DBP

and DEHP. This model provided reasonably accurate estimates of

kinetics of MEHP, MEHHP, and MEOHP and daily intakes of DEHP.

The use of data-validated kinetics and physiological parameters

reduced uncertainties in use of the model to predict target tissue

exposures. Tissue dose metrics and external exposures calculated

with the PBPK model should be advantageous in risk assessments

for DEHP exposure. This validated PBPK model can be applied for

extrapolations and exposure scenarios to provide quantitative

measures of target tissue dosimetry and reverse dosimetry in the

population of interest.

49

References

Abdul-Ghani, S., J. Yanai, R. Abdul-Ghani, A. Pinkas and Z.

Abdeen. (2012). The teratogenicity and behavioral teratogenicity of

di(2-ethylhexyl) phthalate (DEHP) and di-butyl phthalate (DBP) in

a chick model. Neurotoxicol Teratol. 34(1), 56-62.

Albro, P. W. (1986). Absorption, metabolism, and excretion of di(2-

ethylhexyl) phthalate by rats and mice. Environ Health Perspect.

65, 293-298.

Andersen, M. E. and K. Krishnan. (1994). Physiologically based

pharmacokinetics and cancer risk assessment. Environ Health

Perspect. 102 Suppl 1, 103-108.

Anderson, W. A., L. Castle, S. Hird, J. Jeffery and M. J. Scotter.

(2011). A twenty-volunteer study using deuterium labelling to

determine the kinetics and fractional excretion of primary and

secondary urinary metabolites of di-2-ethylhexylphthalate and di-

iso-nonylphthalate. Food Chem Toxicol. 49(9), 2022-2029.

ATSDR. (2002). Toxicological profile for Di(2-ethyhexyl)phthalate.

Calles-Escandon, J., J. J. Cunningham, P. Snyder, R. Jacob, G.

Huszar, J. Loke and P. Felig. (1984). Influence of exercise on urea,

creatinine, and 3-methylhistidine excretion in normal human

subjects. Am J Physiol. 246(4 Pt 1), E334-338.

50

Clewell, H. J., 3rd, P. R. Gentry, T. R. Covington and J. M. Gearhart.

(2000). Development of a physiologically based pharmacokinetic

model of trichloroethylene and its metabolites for use in risk

assessment. Environ Health Perspect. 108 Suppl 2, 283-305.

Clewell, H. J. and M. E. Andersen. (1987). Dose, species, and route

extrapolation using physiologically based pharmacokinetic models.

Pharmacokinetics in Risk Assessment. 8, 159–184.

Clewell, H. J., P. R. Gentry, T. R. Covington, R. Sarangapani and J.

G. Teeguarden. (2004). Evaluation of the potential impact of age-

and gender-specific pharmacokinetic differences on tissue

dosimetry. Toxicol Sci. 79(2), 381-393.

Clewell, H. J., P. R. Gentry, J. M. Gearhart, B. C. Allen and M. E.

Andersen. (2001). Comparison of cancer risk estimates for vinyl

chloride using animal and human data with a PBPK model. Sci

Total Environ. 274(1-3), 37-66.

Clewell, H. J., Y. M. Tan, J. L. Campbell and M. E. Andersen. (2008).

Quantitative interpretation of human biomonitoring data. Toxicol

Appl Pharmacol. 231(1), 122-133.

Clewell, R. A. (2009). Mode of Action Studies with Phthalate Acid

Monoesters: Pharmacokinetic and Pharmacodynamic Factors

Affecting Steroidogenesis.

Clewell, R. A., J. J. Kremer, C. C. Williams, J. L. Campbell, Jr., M. E.

Andersen and S. J. Borghoff. (2008). Tissue exposures to free and

glucuronidated monobutylyphthalate in the pregnant and fetal rat

51

following exposure to di-n-butylphthalate: evaluation with a PBPK

model. Toxicol Sci. 103(2), 241-259.

Dedrick, R. L. (1973). Animal scale-up. J Pharmacokinet Biopharm.

1(5), 435-461.

Edwards, K. D. and H. M. Whyte. (1959). Creatinine excretion and

body composition. Clin Sci. 18, 361-366.

Fisher, J. W., N. C. Twaddle, M. Vanlandingham and D. R. Doerge.

(2011). Pharmacokinetic modeling: prediction and evaluation of

route dependent dosimetry of bisphenol A in monkeys with

extrapolation to humans. Toxicol Appl Pharmacol. 257(1), 122-

136.

Frederiksen, H., N. E. Skakkebaek and A. M. Andersson. (2007).

Metabolism of phthalates in humans. Mol Nutr Food Res. 51(7),

899-911.

Fromme, H., L. Gruber, M. Schlummer, G. Wolz, S. Bohmer, J.

Angerer, R. Mayer, B. Liebl and G. Bolte. (2007). Intake of

phthalates and di(2-ethylhexyl)adipate: results of the Integrated

Exposure Assessment Survey based on duplicate diet samples

and biomonitoring data. Environ Int. 33(8), 1012-1020.

Garde, A. H., A. M. Hansen, J. Kristiansen and L. E. Knudsen.

(2004). Comparison of uncertainties related to standardization of

urine samples with volume and creatinine concentration. Ann

Occup Hyg. 48(2), 171-179.

52

Gentry, P. R., H. J. Clewell, 3rd, R. Clewell, J. Campbell, C. Van

Landingham and A. M. Shipp. (2011). Challenges in the

application of quantitative approaches in risk assessment: a case

study with di-(2-ethylhexyl)phthalate. Crit Rev Toxicol. 41 Suppl

2, 1-72.

GFEA. (2005). Substance Monograph: Di(2-ethylhexyl)phthalate

(DEHP) – Reference values for 5oxo-MEHP and 5OH-MEHP in

urine. The German Federal Environmental Agency, 706-722.

Grosse, Y. B., R. (2011). IARC(2011)_Carcinogenicity of chemicals in

industrial and consumer products, food contaminants and

flavourings, and water chlorination byproducts.

Harper, H. A., V. W. Rodwell and P. A. Mayes. (1977). Review of

physiological chemistry. Lange Medical Publications.

Hines, C. J., N. B. Hopf, J. A. Deddens, M. J. Silva and A. M.

Calafat. (2011). Estimated daily intake of phthalates in

occupationally exposed groups. J Expo Sci Environ Epidemiol.

21(2), 133-141.

Jonsson, F., F. Bois and G. Johanson. (2001). Physiologically based

pharmacokinetic modeling of inhalation exposure of humans to

dichloromethane during moderate to heavy exercise. Toxicol Sci.

59(2), 209-218.

Kato, K., M. J. Silva, J. A. Reidy, D. Hurtz, 3rd, N. A. Malek, L. L.

Needham, H. Nakazawa, D. B. Barr and A. M. Calafat. (2004).

Mono(2-ethyl-5-hydroxyhexyl) phthalate and mono-(2-ethyl-5-

53

oxohexyl) phthalate as biomarkers for human exposure

assessment to di-(2-ethylhexyl) phthalate. Environ Health

Perspect. 112(3), 327-330.

Kessler, W., W. Numtip, W. Volkel, E. Seckin, G. A. Csanady, C.

Putz, D. Klein, H. Fromme and J. G. Filser. (2012). Kinetics of

di(2-ethylhexyl) phthalate (DEHP) and mono(2-ethylhexyl)

phthalate in blood and of DEHP metabolites in urine of male

volunteers after single ingestion of ring-deuterated DEHP. Toxicol

Appl Pharmacol. 264(2), 284-291.

Keys, D. A., D. G. Wallace, T. B. Kepler and R. B. Conolly. (1999).

Quantitative evaluation of alternative mechanisms of blood and

testes disposition of di(2-ethylhexyl) phthalate and mono(2-

ethylhexyl) phthalate in rats. Toxicol Sci. 49(2), 172-185.

Keys, D. A., D. G. Wallace, T. B. Kepler and R. B. Conolly. (2000).

Quantitative evaluation of alternative mechanisms of blood

disposition of di(n-butyl) phthalate and mono(n-butyl) phthalate

in rats. Toxicol Sci. 53(2), 173-184.

KFDA. (2010). Phthalate Risk Profile.

Kleinsasser, N. H., E. R. Kastenbauer, H. Weissacher, R. K.

Muenzenrieder and U. A. Harreus. (2000). Phthalates demonstrate

genotoxicity on human mucosa of the upper aerodigestive tract.

Environ Mol Mutagen. 35(1), 9-12.

Koch, H. M., H. M. Bolt and J. Angerer. (2004). Di(2-

ethylhexyl)phthalate (DEHP) metabolites in human urine and

54

serum after a single oral dose of deuterium-labelled DEHP. Arch

Toxicol. 78(3), 123-130.

Koch, H. M., H. M. Bolt, R. Preuss and J. Angerer. (2005). New

metabolites of di(2-ethylhexyl)phthalate (DEHP) in human urine

and serum after single oral doses of deuterium-labelled DEHP.

Arch Toxicol. 79(7), 367-376.

Koch, H. M., H. Drexler and J. Angerer. (2003). An estimation of the

daily intake of di(2-ethylhexyl)phthalate (DEHP) and other

phthalates in the general population. Int J Hyg Environ Health.

206(2), 77-83.

Koch, H. M., R. Preuss and J. Angerer. (2006). Di(2-

ethylhexyl)phthalate (DEHP): human metabolism and internal

exposure-- an update and latest results. Int J Androl. 29(1), 155-

165; discussion 181-155

.

Kohn, M. C., F. Parham, S. A. Masten, C. J. Portier, M. D. Shelby, J.

W. Brock and L. L. Needham. (2000). Human exposure estimates

for phthalates. Environ Health Perspect. 108(10), A440-442.

Lorber, M. and A. M. Calafat. (2012). Dose Reconstruction of Di(2-

ethylhexyl) Phthalate Using a Simple Pharmacokinetic Model.

Environ Health Perspect

.

Mezzetti, M., J. G. Ibrahim and F. Y. Bois. (2003). A Bayesian

compartmental model for the evaluation of 1,3-butadiene

metabolism. J. R. Stat. Soc. Ser. 52, 291–305.

55

Mumtaz, M., J. Fisher, B. Blount and P. Ruiz. (2012). Application of

physiologically based pharmacokinetic models in chemical risk

assessment. J Toxicol. 2012, 904603.

Rowland, I. R., R. C. Cottrell and J. C. Phillips. (1977). Hydrolysis of

phthalate esters by the gastro-intestinal contents of the rat. Food

Cosmet Toxicol. 15(1), 17-21.

Schmid, P. and C. Schlatter. (1985). Excretion and metabolism of

di(2-ethylhexyl)phthalate in man. Xenobiotica. 15(3), 251-256.

Simpson, F. O., E. R. Nye, P. Bolli, H. J. Waal-Manning, A. W.

Goulding, E. L. Phelan, F. A. de Hamel, R. D. Stewart, G. F.

Spears, G. M. Leek and A. C. Stewart. (1978). The Milton survey:

Part 1, General methods, height, weight and 24-hour excretion of

sodium, potassium, calcium, magnesium and creatinine. N Z Med

J. 87(613), 379-382.

Suzuki, Y., M. Niwa, J. Yoshinaga, C. Watanabe, Y. Mizumoto, S.

Serizawa and H. Shiraishi. (2009). Exposure assessment of

phthalate esters in Japanese pregnant women by using urinary

metabolite analysis. Environ Health Prev Med. 14(3), 180-187.

Tan, Y. M., K. H. Liao and H. J. Clewell, 3rd. (2007). Reverse

dosimetry: interpreting trihalomethanes biomonitoring data using

physiologically based pharmacokinetic modeling. J Expo Sci

Environ Epidemiol. 17(7), 591-603.

56

Tanaka, A., A. Matsumoto and T. Yamaha. (1978). Biochemical

studies on phthalic esters. III. Metabolism of dibutyl phthalate

(DBP) in animals. Toxicology. 9(1-2), 109-123.

Thomas, R. S., W. E. Lytle, T. J. Keefe, A. A. Constan and R. S. Yang.

(1996). Incorporating Monte Carlo simulation into physiologically

based pharmacokinetic models using advanced continuous

simulation language (ACSL): a computational method. Fundam

Appl Toxicol. 31(1), 19-28.

U.S.EPA. (2006). Approaches for the Application of Physiologically

Based Pharmacokinetic (PBPK) Models and Supporting Data in

Risk Assessment. EPA.

U.S.EPA. (2007). Phthatlates TEACH Chemical Summary.

Wormuth, M., M. Scheringer, M. Vollenweider and K. Hungerbuhler.

(2006). What are the sources of exposure to eight frequently used

phthalic acid esters in Europeans? Risk Anal. 26(3), 803-824.

57

Appendix. Equations for adult human DEHP PBPK

model

Flow-limited tissues:

Distribution of DEHP and MEHP-glucuronide into the tissues and

the liver were modeled using flow-limitation. RAS is the rate of

change in the amount in the tissues, QT is the fractional blood flow

to the tissues, CT is the concentration of DEHP in the tissues and

PT is the tissue:blood partition coefficient.

( )

= = ×

( − )

Diffusion-limited tissues:

Diffusion-limited distribution of MEHP is demonstrated for the

slowly perfused tissues. RAMSE is the rate of MEHP change in

extracellular fluid, and RAMSI is rate of MEHP change in

intracellular space. QS is the fractional blood flow to the slowly

perfused tissues, PMS is the slowly perfused tissue:plasma partition

coefficient, PAS is the permeability area cross product for MEHP in

the slowly perfused tissue.

= = { × ( − )} −

58

= = ×

( − )

GI contents and tissue:

The kinetics of DEHP in the GI, including oral absorption,

hydrolysis to MEHP, reuptake via bile, movement of through the GI,

and fecal excretion is defined below. RAGJs is rate of change in the

amount of DEHP in the upper intestine (GJs), OD is the oral dose

(mg/hr), RAD is the rate of oral absorption, RAM is the rate of

hydrolysis to MEHP, RAGJl is the rate of transport to the lower

intestine (GJl). RAGJl is the rate of change in the amount of DEHP

in the lower intestine (GJl), radFec is the fecal excretion rate for

DEHP. RAGI rate of change in the gut wall, QG is the fractional

blood flow to the GI, CA is the concentration of DEHP in the plasma,

and CGI is the concentration of DEHP in the gut tissue.

dA(GI)

= = × ( − ) + + 2

dA(GJs)

=

= − − − ( × × ) + ( ×

× )

dA(GJl)

= = ( × × ) − − 2 −

59

dAD

= = × ×

dAM

= =

×

( + )

Liver:

Distribution and metabolism of DEHP in liver are shown below.

Like the other tissues, DEHP is assumed to be flow-limited.

Hydrolysis to MEHP is modeled as a first order rate. RAL is the rate

of change in the amount of DEHP in the liver, QL and QGI are the

fractional blood flows to the liver (hepatic artery) and GI (portal vein),

CGI is the concentration of DEHP in the gut tissue, CL is the

concentration of DEHP in the liver.

( )

=

= ( × ) + ( × ) − {( + ) × } −

× − ×

Distribution and metabolism of MEHP in liver are shown below.

RAMLE is the rate of change in the amount of extracellular MEHP,

CMGI is the concentration of MEHP in the gut tissue, RLMet is the

mass adjusted rate of hydrolysis of DEHP to MEHP in the liver, PAL

is the permeability area cross product for DEHP in the liver, PML is

the liver:plasma partition coefficient for MEHP. RALM rate of

60

glucuronide conjugation in the liver, described with VmaxL and KmL,

which are the maximum capacity and affinity constants for MEHP-G

formation. RAOM is the rate of oxidative metabolism, VmaxB is the

maximum capacity and KmB is the affinity constant for oxidation of

MEHP in the liver.

=

= × + × − {( + ) × }

− − + − × ( − )

−

×

= =

×

( + )

= =

×

( + )

61

국

인체 PBPK 모델 이용

DEHP 노출량 평가

경 민

울 보건 원

경보건 과

지도 균

Di(2-ethylhexyl)phthalate (DEHP)는 PVC 등 라스틱 품

인 가소 범 게 사용 는 탈 이트 종류이다. 본

연구에 는 용체 장 (organs) 생리 구조 능 탕

질 흡 (absorption), 분포 (distribution), 사 (metabolism)

(excretion) 는 Physiologically-based

pharmacokinetics (PBPK) 모델 사용 여 DEHP 체내 거동

이해 고자 다. 본 연구 목 1) DEHP에 인체 PBPK 모델

, 2) PBPK 모델 이용 DEHP 인체 노출량 역추 존

노출모델과 상 평가, 3) Monte Carlo 법 사용 인구

62

집단 노출량 분포 이다. 존에 DEHP 인체 PBPK 모

모델에 DEHP가 2차 사산 인 MEHHP, MEOHP 등 90% 이상

사 는 특 여 oxidative metabolites 부분 강 PBPK

모델 재구 다. 미국 National Health and Nutrition Examination

Survey (2009-2010) 데이 를 본 모델에 얻 용량- 계식

통해 역추 결과, 평균 DEHP 노출량 1.67 μg/kg/day

었고, 존 노출모델 이용 면 1.48 μg/kg/day 었다.

국민 경보건 조사 (2009-2011) 데이 를 Monte Carlo 법

통해 역추 결과, 우리나라 일 인 5-95th percentile 노출

범 가 Exposure Conversion Factor를 사용 법 는 0.3 – 41.4

μg/kg/day, Bayes’ formula를 이용 법 는 0.5 – 21.6 μg/kg/day

임 있었다. 이 게 실 데이 를 통해 검증 고

인 DEHP PBPK 모델 추 내 용량 노출량 이용 면

불 실도가 낮고 좀 과 인 보건 근거를 시 있

것이다.

주요어 : PBPK, DEHP, 용량 재구 , 약 동 , 해 평가,

탈 이트

번 : 2010-22032

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