k.2 bouchard

Importance of toxicokinetics in understanding and

interpreting biological monitoring results

Michèle BouchardAssociate professor

Head of the Chair in Toxicological Risk Analysis and Management

Head of the Biomarker Unit of the Xenobiotics and Nanoparticles CFI plateform

University of Montreal, Canada

– Biomonitoring of worker exposure is routinely conducted in several industries

– National baseline concentration data (in blood and urine) of contaminants considered as a priority are being gathered in the general population:

– In Germany (GerES)

– In Canada (CHMS, MIREC)

– In the U.S. (NHANES)….

Biomonitoring, a recognized tool to assess exposure to environmental

contaminants

– Concentrations of biomarkers of exposure vary in time following an exposure period

– in exposed populations

– in workers in particular (during a workday and workweek)

– Concentration-time course also varies according to the exposure route and scenario and is subject to inter-individual variations

Knowledge of exposure biomarker time courses and modeling allows to help interpret kinetic behavior

Importance of kinetics to help interpret biomonitoring data

Kinetic examples with short-lived biomarkers of exposure

• Pyrethroid metabolites• PAH metabolites

0 24 48 72 96 120 144 168 300 4000.0

0.5

1.0

1.5

2.0

2.5

3.0

Volunteer 2Volunteer 1

Time since first application (h)

= Application

Urin

ary

1-O

HP

(µm

ol/m

ol c

reat

.)

5

• Increase in 1-OHP peak and troughlevels during the course of repeated application

• Plateau reached around the 3rd day followingonset of exposure

• Return to values close to background levels ~48-72 h following the end of exposure period

• Volunteer 1 > volunteer 2

Viau and Vyskocil (1995)

Time course of 1-OHP in volunteers following repeated dermal exposure to pyrene:

Variations in levels with time

t½elim ≈ 12 h

0 10 20 30 40 50

3050

300500

30005000

3000050000

10

100

1000

10000

Volunteer 2 - 500 µg p.c.

Volunteer 1 - 500 µg p.c.

Volunteer 2 - 500 µg p.o.

Volunteer 1 - 500 µg p.o.

1-O

HP

in u

rine

(pm

ol/h

)

Time since exposure (h)

t½elim ≈ 12 hViau et al. (1995)

Time course of 1-OHP in volunteers following single oral and dermal exposure:

Effect of the route of exposure and inter-individual differences

– Toxicokinetic models are increasingly used to reproduce the time course of a biomarker of exposure in the biological matrix of interest

– Links can be made with time-dependent variations in body burdens and dose per unit of time

– Simulations can help infer on main exposure routes

– provided some data are still available on the type of exposure (oral, respiratory, dermal)

– and in case of workers, ideally airborne concentrations and working hours.

Usefulness of kinetic models to interpret biomonitoring data

Two types of kinetic models to simulate the kinetics

• Toxicokinetic model• PBPK model

• Model development– Model conceptual and functional representation based on available

in vivo time course data– Each compartment represents a tissue or group of tissues or an

excreta– Mass balance is described– Transfers from one compartment to the other are represented by

rate constants – The rate of change in the amounts in a compartment is determined

by the difference between incoming and outgoing amounts per unit of time

– Saturable processes can also be described

Main modeling steps of the toxicokinetic model

Development of a toxicokinetic model based on human data

The case of pyrethroids and their metabolites

Toxicokinetic model permethrin and cypermethrin

• Determination of model parameter values– Established from in vivo time course data in humans

• In the current case: data of Ratelle et al. (unpublished) on the blood and urinary time course of metabolites common to permethrin and cypermethrin in volunteers orally exposed to these pyrethroids (0.1 mg/kg bw; trans:cis isomers: 60:40 or 58:42)

– Determined from best-fit adjustments to the time course data


Time (h)

0 12 24 36 48 60 72 84 96

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

0,0001

0,001

0,01

0,1

1

10 trans-DCCA simulationtrans-DCCA

0.01

0.001

0.0001

0.1

Modeling of the rate time courses of cypermethrin metabolites in urine

Experimental data of Ratelle et al. in orally exposed volunteers (unpublished)

Time (h)

0 12 24 36 48 60 72 84 96

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

0,0001

0,001

0,01

0,1

1

10 cis-DCCA simulationcis-DCCA

0.01

0.001

0.0001

0.1

Time (h)

0 12 24 36 48 60 72 84 96

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

0,0001

0,001

0,01

0,1

1

10 3-PBA simulation3-PBA

0.01

0.001

0.0001

0.1

Modeling of the cumulative urinary excretion profile of cypermethrin metabolites in urine

Experimental data of Ratelle et al. in orally exposed volunteers (unpublished)

Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

ary

excr

etio

n of

met

abol

ites

(%)

0

10

20

30


Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

ary

excr

etio

n of

met

abol

ites

(%)

0

2

4

6

8

10

12

14

16


Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

ary

excr

etio

n of

met

abol

ites

(%)

0

10

20

30

40

50


Time (h)

0 12 24 36 48 60 72 84 96 108 120

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

0,0001

0,001

0,01

0,1

1


0.01

0.001

0.0001

0.1

Modeling of the rate time courses of cypermethrin metabolites in urine

Experimental data of Woollen et al. in orally exposed volunteers (1992)

Time (h)

0 12 24 36 48 60 72 84 96 108 120

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e+1 cis-DCCA simulationcis-DCCA

0.01

0.001

0.0001

0.1

1

10

Time (h)

0 12 24 36 48 60 72 84 96 108 120

Urin

ary

excr

etio

n ra

te o

f met

abol

ites

(%)

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e+1 3-PBA simulation3-PBA

0.01

0.001

0.0001

0.1

1

10

Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

ary

excr

etio

n of

met

abol

ites

(%)

0

5

10

15

20

25


Modeling of the cumulative urinary excretion profile of cypermethrin metabolites in urine

Experimental data of Woollen et al. in orally exposed volunteers (1992)

Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

ary

excr

etio

n of

met

abol

ites

(%)

0

2

4

6

8

10

12


Time (h)

0 12 24 36 48 60 72 84 96

Tota

l urin

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excr

etio

n of

met

abol

ites

(%)

0

5

10

15

20


Development of a toxicokinetic model based on rat data

The case of BaP and 3-OHBaP

Toxicokinetic model of BaP and 3-OHBaP based on rat time-course data

Heredia-Ortiz et al. (2012)

• Determination of model parameter values– Established from in vivo time course data in animals

• In the current case: data of Marie et al. (2010) on the time course of BaP and 3-OHBaP in blood, tissues and excreta of rats intravenously injected with BaP (40 µmol/kg bw)

– Determined from best-fit adjustments to the time course data


Toxicokinetic model simulations compared with experimental time course data of Marie et al. (2010) in rats

Time courses of BaP in blood and tissues


Toxicokinetic model simulations compared with experimental time course data of Marie et al. (2010) in ratsTime courses of 3-OHBaP in blood and tissues


Modeling of the time course of 3-OHBaP in a worker based on the rat toxicokinetic model extrapolated to humans

Experimental data of Lafontaine et al. (2004)

Exposure on two consecutive days (shifts of 6.75 h and 4.75 h, respectively) Atmospheric concentration of 1514 ng/m3 and 3028 ng/m3 on days 1 and 2, respectively; Ventilation rate of 1.20 m3/h


• Advantages– Model is based on observed time course data– Only main biological determinants need to be represented such that

the model may be simplified

• Limits– Lack of physiological representation is often criticized – As with other models, uncertainty in model structure and parameter

values, when insufficient available data – As with other models, validity is dependent on available independent

sets of time course data to evaluate the model

Advantages and limits of this toxicokinetic modeling approach

Development of a PBPK model based on rat data

The case of BaP and 3-OHBaP

Main modeling steps of the PBPK model

• Model development– Model conceptual and functional representation based on animal and

human physiology– Each compartment represents a tissue, or group of tissues, or excreta– Mass balance is described– Transfers from one compartment to the other are represented by

tissue blood flow rates (% of cardiac output)– taken from the medical literature

– Transfers between tissues and blood are represented by tissue-blood partition coefficients

– usually determined from in vitro studies – may however be determined from in vivo time courses (as in the

following example)

PBPK model of BaP and 3-OHBaP based on rat time-course data

Heredia-Ortiz and Bouchard (submitted)

PBPK model simulations compared with experimental time course data of Marie et al. (2010) in ratsTime courses of BaP in blood and tissues


PBPK model simulations compared with experimental time course data of Marie et al. (2010) in rats

Time courses of 3-OHBaP in blood and tissues


Evaluation of the PBPK model with another set of iv experimental data: Bouchard and Viau (1996) and Lee et al. (2003)

Cumulative excretion-time course of 3-OHBaP in urine


Evaluation of the PBPK model with another set of inhalation/intratracheal experimental data: Weyand and Bevan

(1986) and Ramesh et al. (2001)Blood time course of BaP


Evaluation of the PBPK model with another set of dermalexperimental data: Payan et al. (2009) and Jongeneelen et al.

(1985)Cumulative excretion-time course of 3-OHBaP in urine


Evaluation of the PBPK model with another set of oralexperimental data: Cao et al. (2005)

Blood time course of BaP and 3-OHBaP


Modeling of the time course of 3-OHBaP in workers based on the rat PBPK model extrapolated to humans

Experimental data of Lafontaine et al. (unpublished)

• Limits– Model is often based on in vitro data

• As presented, this limit may be overcome by determining parameter values such as tissue:blood partition coefficients from in vivo time course data

– Many parameters to be determined– Lumping of tissues into highly and poorly perfused tissues increases

uncertainty in model structure and parameter values for these compartments

– As with other models, uncertainty in model structure and parameter values, when insufficient available data

– As with other models, validity is dependent on available independent sets of time course data to evaluate the model

Limits of this PBPK modeling approach

Kinetic models adapted to humans

• The models adapted to humans allows: – Reconstruction of daily absorbed doses in workers

– Good predictive value from cumulative amounts in urine over the longest feasible time periods

– Uncertainties based on creatinine-corrected urinary values

Kinetic models adapted to humans

• The models adapted to humans allows: – Predictions of main exposure route in workers

• Provided there is sufficient data on the urinary excretion profile during the course of a workday

– Proposing biological reference values• For example, a biological limit of 3-OHBaP in the urine of a

worker corresponding to an airborne BaP concentration limit

Acknowledgements

This work was funded by ANSES and Health Canada

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