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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
3. Forward dosimetry for dose-response relations ---------------- 30
4. Comparison estimated daily intakes with a simple
mathematical equation -------------------------------------------- 37
iv
5. Reverse dosimetry --------------------------------------------------- 39
IV. Discussion ------------------------------------------------------------- 41
1. PBPK model structure and validation ---------------------------- 41
2. Forward dosimetry for dose-response relations ---------------- 43
3. Comparison estimated daily intakes with a simple
mathematical equation -------------------------------------------- 44
4. Reverse dosimetry --------------------------------------------------- 46
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
Parameter Value Source
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
Parameter Value Source
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
Parameter Mean SD Lower Upper Distribution
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
Parameter Mean SD Lower Upper Distribution
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
3. Forward dosimetry for dose-response relations
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
simple mathematical equation
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
5. Reverse dosimetry
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
50th percentile:0.19 mg/day
5th percentile:0.03 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
2. Forward dosimetry for dose-response relations
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
simple mathematical equation
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
4. Reverse dosimetry
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
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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