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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
Main modeling steps of the toxicokinetic model
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
40 trans-DCCA simulationtrans-DCCA
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
18 cis-DCCA simulationcis-DCCA
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
60 3-PBA simulation3-PBA
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
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 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
30 trans-DCCA simulationtrans-DCCA
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
14 cis-DCCA simulationcis-DCCA
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 3-PBA simulation3-PBA
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
Main modeling steps of the toxicokinetic model
Toxicokinetic model simulations compared with experimental time course data of Marie et al. (2010) in rats
Time courses of BaP in blood and tissues
Heredia-Ortiz et al. (2012)
Toxicokinetic model simulations compared with experimental time course data of Marie et al. (2010) in ratsTime courses of 3-OHBaP in blood and tissues
Heredia-Ortiz et al. (2012)
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
Heredia-Ortiz et al. (2012)
• 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
Heredia-Ortiz and Bouchard (submitted)
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
Heredia-Ortiz and Bouchard (submitted)
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
Heredia-Ortiz and Bouchard (submitted)
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
Heredia-Ortiz and Bouchard (submitted)
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
Heredia-Ortiz and Bouchard (submitted)
Evaluation of the PBPK model with another set of oralexperimental data: Cao et al. (2005)
Blood time course of BaP and 3-OHBaP
Heredia-Ortiz and Bouchard (submitted)
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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