computational toxicity: stochastic pbpk modeling

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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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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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


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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19

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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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 )

25

27

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


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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

27


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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