computational toxicity: stochastic pbpk modeling
TRANSCRIPT
Stochastic PBPK modeling for estimating population-scale exposure risk attributable to
inorganic arsenic consumptionsPresenter: Wei-Chun Chou, Ph.D., Postdoctoral Fellow
National Health Research Institutes, National Institute of Environmental Health Sciences
Date: 2016/5/5
2016海峽兩岸環境、食品與健康之預測毒理學研討會
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IARC: Group 1 (Carcinogenic to humans) USEPA: Group A Source: Distributed throughout the earth's crustStandards for arsenic in drinking water: 10 μg
L-1
Arsenic
IARC: International Agency for Research on Cancer; USEPA: United States Environmental Protection Agency 2
As
As3+ As5+ MMA3+ MMA5+ DMA3+ DMA5+
Organic AsInorganic As
Arsenic exposure in Environment
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Many arsenic sources are exited in our living environment and food.
Drinking water from the groundwater, flour and rice grown or cooked in arsenic contaminated soil and water has contain large inorganic arsenic.
Seafood is a source of organic arsenic compounds (arsenobetaine, arenosugars, arsenolipids)
(Del Razo et al., 2002; Francesconi and Kuehnelt, 2004)
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Arsenic Effects on Human body
https://www.hrw.org/news/2016/04/06/bangladesh-20-million-drink-arsenic-laced-water
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As5+
As3+
reductionAs5+
oxidationAs3+
MMA5+
MMA3+
reductionMMA5
+
oxidationMMA3
+
reductionDMA5+
oxidationDMA3
+
UrinaryArsenic
Metabolites
SAM
As5+: ArsenateAs3+: ArseniteMMA5+: Monomethylarsonic acidMMA3+: Monomethylarsonous acidDMA5+: Dimethylarsinic acidDMA3+: Dimethylarsinous acidSAM: S-adenosyl-methionineSAH: S-adenosyl-homocysteine
(Kitchin, 2001; Gong et al., 2001; Aposhian and Aposhian, 2006)
Arsenic Metabolism
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DMA5+
DMA3+
SAH
Methyltransferase
Methyltransferase
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Environmental arsenic exposure
• In populations with low seafood intake, total urine arsenic and the sum of inorganic arsenic and methylated (MMA and DMA) urine arsenic species are established biomarkers that inorganic arsenic exposure for linking the biomonitoring data to health outcomes
Biomakers for inorganic arsenic exposure: the sum of iAs, MMA
and DMA
(Calderon et al., 1999; National Research Council, 1999; Hughes, 2006)
iAs: inorganic arsenic (As3+ and As5+)
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Alternative Approaches for Linking Biomonitoring Data to
Health OutcomesAnimal Dosimetry: Compare blood/urine concentration in population with blood/urine concentration at NOAEL in animal study to obtain MOE (Margin of Exposure )
Methods: Measurement of blood concentrations in toxicity studies or availability of PK model/data in animal to predict blood concentrations from external dose.
Results: To determine adequacy of MOE
dose
effec
t Slope=CSF Exposure risk
NOAEL: No observable adverse effect level
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Alternative Approaches for Linking Biomonitoring Data to
Health OutcomesForward dosimetry: To calculate internal does from external exposure
Methods: Human PBPK model (Ramsey and Andersen, 1984)
Results: Compare biomonitoring data with predicted biomarker at toxicity value (RfD, etc.)
Lung
Skin
Kidney
Liver
GI tract
External exposure
Target tissue does
Pollution (Arsenic, dioxin, etc,)
Human bodyTime
RfD: reference dose PBPK: Physiological based on pharmacokinetic
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• Various physiological and biological parameters (Weight, height, metabolize and exposure).
• How to characterize a population exposure risk9
Challenge for population risk
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Alternative Approaches for Linking Biomonitoring Data to
Health OutcomesReverse Dosimetry: Estimate external exposure in population from biomonitoring data and compare with toxicity value (RfD, MCL, etc.)
Methods: Human PBPK model can be applied to large and more poorly characterized human populations that have highly variable exposures, activities, physiology, and pharmacokinetics (Bois, 2001)
Results: Reconstructing a population exposures distribution corresponding to human biomonitoring data
Population exposure
Biomonitoring data
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Linking Biomonitoring Data to External Exposure Physiologically Based
Pharmacokinetic (PBPK) Modeling (Tan et al., 2006)
PBPK MODEL for chloroform In the Tan’s study, the PBPK model can be used in a
reverse dosimetry approach to assess a distribution of exposures related to specific blood levels of trihalomethanes (THMs).
They used the Monte Carlo sampling techniques to consider the probabilistic information about pharmacokinetics and exposure patterns.
Probabilistic information: physiological parameters and pharmacokinetics parameters
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Risk Assessment
PBPK model for arsenic
Human pharmacokine
tic parameters
Biomonitoring
dataSafe As guidelines
Reverse dosimetry
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Monte Carlo simulation
Concept
Objectives13
To develop a population scale PBPK model for arsenic risk assessment
PBPK: Physiologically-based pharmacokinetic modelling
To predict the arsenic exposure risk that are associated with specific biomarker levels in urine..
To provide a comprehensive assessment of safe ingested arsenic level.
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Materials and Methods
Cohort study Population based PBPK model Probabilistic reverse dosimetry Risk characterization
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Cohort Study
Subjects: 1,075 residents• Demography : Sex, Smoking, Age, Weight, High, Nutritional factor,
consumption etc,• Biomarker collection: Urine and blood.• Arsenic intake analysis: Rice, Water…..
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Cohort study
Subjects: An population living in industrial area of Taiwan. Study area: Changhua, central of Taiwan
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Parameter Symbol Unit Valuea Notes and references Body Height BH cm 163.31 (17.69) This study Body Weight BW kg 63.50 (14.46) This study Cardiac output QT L h-1 BW-0.75 16.50 (1.50) Clewell et al. (2000) Organs volume
Bloodb VB L 4.69 (0.96) (13.1×BH+18.05×BW-480)×0.001/0.5723
G.I.tractc VG L 1.20 (0.89) VG=BW×WG/DG Liverc VL L 1.81 (1.09) VL=BW×WL/DL Kidneyc VK L 0.28 (0.15) VK=BW×WK/DK Other organs VO L 52.21 (19) VO=BW-(VB+VG+VL
+VK) Tissue blood flow
To G.I tract QG L h-1 48.26 (24.23) QG=FG×QT×BW0.75 To liver QL L h-1 20.91 (10.61) QL=FL×QT×BW0.75 To kidney QK L h-1 61.13 (30.92) QK=FK×QT×BW0.75 To other organs QO L h-1 191.43 (96.49) QO=FO×QT×BW0.75
Tissue volume as percentage of body weight G.I.tract WG % 1.98 (0.59) Yu and Kim (2004). Liver WL % 2.99 (0.89) Yu and Kim (2004). Kidney WK % 0.52 (0.16) Yu and Kim (2004). Other organs WO % 94.51 (28.35) 100-other tissues
Blood flow to tissue as percentage of cardiac output G.I.tract FG % 15 (4.50) Yu and Kim (2004). Liver FL % 6.5 (1.95) Yu and Kim (2004). Kidney FK % 19 (5.70) Yu and Kim (2004). Other organs FO % 59.5 (17.85) 100-other tissues
Density G.I.tract DG kg L-1 1.04 (0.31) Yu and Kim (2004).
Population-based PBPK
𝑑 𝐴𝑡
𝑑𝑡 =𝑄𝐿×(𝐶𝐴−𝐶𝐿
𝑃 𝐿)−𝑉𝑚𝑎𝑥×
𝐶𝐿
𝑃 𝐿(𝐾𝑀+𝐶𝐿 /𝑃𝐿)
Ca QK
VK CK
CK
Ca(K)
( )
KK
a K
CPC
Blood Tissue K
As
As
As As
As
AsAs
As
AsAs
As
AsAs
3 33
3( )K KK a
K
dA CQ Cdt P
QK
Ca
As3+
Tissue/Blood partition coefficients
(mol) (L/hr) (mol/L)
As3+
As3+
As5+MMADMA
Partition coefficients
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Parameters Symbol Unit Valuea
Metabolic constants for reduction and oxidationb Reduction (As3+As5+) k1 h-1 1.37 (0.41)c Oxidation (As5+As3+) k2 h-1 1.83 (0.55)c
Methylation constant of liverd Maximum rate ( As3+MMA)
3+As MAmax ,L
V μmol h-1 0.03 (0.01)c Maximum rate ( As3+DMA)
3+As DAmax,L
V μmol h-1 0.06 (0.02)c Maximum rate ( MMADMA) MA DA
max,LV μmol h-1 0.04 (0.01)c
Michaelis constant ( As3+MMA) 3+As MAm,L
k μmol L-1 0.1 (0.03)c Michaelis constant ( As3+DMA) 3+As DA
m,Lk μmol L-1 0.1 (0.03)c
Methylation constant of kidneyd Maximum rate ( As3+MMA)
3+As MAmax ,K
V μmol h-1 0.02 (0.006)c Maximum rate ( As3+DMA)
3+As DAmax,K
V μmol h-1 0.28 (0.08)c Maximum rate ( MMADMA) MA DA
max,KV μmol h-1 0.01 (0.004)c
Michaelis constant ( As3+MMA) 3+As MAm,K
k μmol L-1 0.1 (0.03)c Michaelis constant ( As3+DMA) 3+As DA
m,Kk μmol L-1 0.1 (0.03)c
Elimination constantsd As3+ for urine 3+As
urineK h-1 0.05 (0.01)e As5+ for fecal 5+As
fecalK h-1 0.001(0.0004)e As5+ for urine 5+As
urineK h-1 0.08 (0.02)e As5+ for biliary 5+As
biliaryK h-1 0.02 (0.005) e MMA for urine MA
urineK h-1 4.20 (1.26) e DMA for urine DA
urineK h-1 1.80 (0.54) e Species-specific tissue/blood partition coefficientd Tissues As3+ As5+ MMA DMA GI tract (PGI) 2.80 (0.56)e 2.80 (0.56) 1.20 (0.24) 1.40 (0.28) Liver (PL) 5.30 (1.06) 5.30 (1.06) 2.35 (0.47) 2.65 (0.53) Kidney (PK) 4.15 (0.83) 4.15 (0.83) 1.80 (0.36) 2.08 (0.42)
Metabolic parameters
Population-based PBPK
PBPK Model
Physiological parametersExposure patterns
Partition coefficient
Constant Individual Exposure
Monte CarloSimulation
Physiological
parameters
Arsenic biotransformati
on
Partition coefficient
Bloo
d le
vel
Days
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Physiological parameters
Prob
abili
ty
Physiological parameters
Metabolic parameters
Exposure patterns
Partition coefficients
Conc
entr
atio
nsTimes
Population
Ran
ge
(95
CI)
10,000 iterationsPopulation
based PBPK
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Population-based PBPK
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Probabilistic Reverse Dosimetry Approach (Tan et al., 2006, 2007)
μg g-1 of As in food or μg L-1 As in water)
PBPK modeling
Input Monte Carlo analysis
50%
97.5%
2.5%
95%
Exposure conversion factor distribution
(ECF)
Estimated distribution of arsenic in
urine
ECF (μg l-1 TAs ug iAs-
1)
Prob
abili
ty
×
Biomonitoring data (N=1,075)
UAs (μg l-1)
Prob
abili
ty
=Estimated population exposure
distribution
iAs (μg day-1)
Prob
abili
ty
UAs: Urinary arsenic; iAs: inorganic arsenic; InAs: Arsenic intake; ECF: Exposure converted factor
Invert distribution
Distribution of measured urine concentrations
(μg l-1 TAs per μg iAs)
(μg iAs per μg l TAs )
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Risk Characterization
Biomonitoring data
Arsenic intake
Modeling
Tolerable Daily Intake (WHO, 1999)
Population Risk
2.1 μg inorganic As/ day/kg body weight
Prob
abili
ty
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Results Demography (food consumption, others) Measured and predicted arsenic concentrations in
urine Prediction of urine arsenic concentrations Exposure conversion factor Risk characterization
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Demography
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Characteristics N Mean Median Range
Age (years) 1,075 50.73 51.00 35-70
Weight (kg) 1,075 64.32 64.41 46.55-82.05
Arsenic concentrations in rice and watera
Cooked Rice (μg g wet wt.-1)
20 0.020 0.019 0.015-0.03
Water (μg L-1) 20 4.88 4.89 4.78-5.20Daily rice and water intakesb
Cooked Rice (g wet wt. d-1) 776 801.97 486-1045
Water (L d-1) 3.10 3.28 0.91-6.00
Urinary arsenic (μg L-1) 109.36 84.71 3.88-1139.46 aMeasured the total arsenic concentration from cooked rice and drinking waterbrice and water intake is calculated from the questionnaire
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Selected percentile (95% confidence interval)
5th 10th 25th 50th 75th 90th 95th Measured arsenic concentrations [NHANES data]a Total arsenic - 2.10 4.10 7.70 16.00 37.40 65.40 DMA - - 2.00 3.90 6.00 11.00 16.00
Predicted arsenic concentrations [PBPK model]b As3+ 0.08 0.09 0.13 0.50 0.75 1.05 1.12 As5+ 0.07 0.06 0.15 0.18 1.13 1.72 1.83 MMA 0.30 0.49 0.18 0.45 3.42 3.06 4.75 DMA 1.66 2.12 3.02 4.43 11.42 13.40 17.23 Total arsenic 2.11 2.76 3.48 5.56 16.72 19.23 24.93
aData adopted from Caldwell et al., 2009bValue estimated from PBPK model
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Measured and predicted arsenic concentrations in urine (μg L-1)
National Health and Nutrition Examination Survey (NHANES)
Prediction of urine arsenic concentrations
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0 24 48 720.00
0.02
0.04
0.0
0.1
0.2
0 24 48 720.00
0.02
0.04
0.0
0.1
0.2
0 24 48 720.00
0.02
0.04
0.0
0.1
0.2
0 24 48 720.00
0.02
0.04
0.0
0.1
0.2
0 0.03 0.06 0 0.04 0.08
0 0.1 0.2 0 0.4 0.8
As3+ As5+
DMAMMA
Uri
ne a
rsen
ic c
once
ntra
tion
s (μ
g L-
1 )
Time (hour)
Prob
abil
ity Pr
obab
ilit
yPr
obab
ilit
y
Prob
abil
ity
Urinary arsenic conc. in unit arsenic intake (μg L-1)
A
B
C
D
LN (0.03 μg L-1, 0.02 )
LN (0.13 μg L-1, 0.11 )
LN (0.6 μg L-1, 0.2 )
LN (0.04 μg L-1, 0.03 )
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0 1 2 3 4 50.00
0.03
0.06
0.09
0.12
0.15
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.40.0
0.1
0.2
0.3
0.40.5 0.4 0.3 0.2 0.1 0.0
0
100
200
300
400
Inorganic arsenic intake (μg kg-1 d-1)
Fit curve InAs intake
Area of risk
Prob
abili
ty
TDI:2.1 Risk=0.27
ECF Urinary TAs
B A
Prob
abili
ty
ECF (μg L-1 ug InAs-
1)
Probability
Urinary total
arsenic (μg l -1)
Exposure conversion factor
Risk Characterization
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0 1 2 3 4 5 6 7 80.00
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 40.00
0.01
0.02
0.03
0.04
0.05
Daily InAs intake (μg kg-1 d-
1)
Cum
ulati
ve p
roba
bilit
y
Daily InAs intake (μg kg-1 d-1)
Risk from drinking waterRisk from rice consumptionRisk from others
Prob
abili
tyOthers (49%) Rice
(41%)
Water (10%)
TDI
Risk=0.27
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Risk Characterization
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0 5 10 15 200.0
0.2
0.4
0.6
0.8
1.0
0
20
40
60
80
100
EP
of T
DI (%
) Bangladesh64.17%
Korean34.69%This study
27.21%
Mexico4.82%
Standard
0.04%
Korea 127.4 μg L-1
TDI
2.1
Mexico, 65.4 μg L-1
This study, 106 μg L-1
Standard, 50 μg L-1
Bangladesh, 263.7 μg L-1
Cum
ulati
ve p
roba
bilit
y
Daily inorganic arsenic intake (μg kg-1 d-1)
Conclusion Population based PBPK mode indicate that study subject
have arsenic exposure risk of 27% (daily inorganic arsenic intake for 20% study subjects exceedance the WHO recommended MTDI value, 2.1 μg InAs day-1 kg-1 body wt).
Daily quantities of arsenic ingestion by study population from water, rice and others are 10%, 41% and 49%, respectively.
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MTDI: Maximum Tolerable Daily Intake
Future Perspectives
Quantitative structure activity relationships (QSAR)-PBPK/PD models (e.g. QSAR to predict metabolic rate constants)
PBPK modeling provides an effective framework for conducting quantitative in vitro to in vivo extrapolation (QIVIVE)
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Thanks foryour attention