<title>Methods</title> <p>The development of this new PBPK model framework for lifetime POP exposure in women can
be separated into three distinct phases: model representation, model parameterization,
and simulations. Model validation could not be achieved because of the evident lack of
data—namely, a lifetime follow-up study measuring blood concentration at different
moments and controlling all the input parameters. The model was coded using Advanced
Continuous Simulation Language (ACSLXtreme, Aegis Technologies Group, Inc., Huntsville,
AL, USA) and is available on request.</p> <title>Model representation</title> <p>For this model, the woman is best functionally described as a network of nine tissue
compartments perfused by blood circulation (##FIG##0##Figure 1##). Compartments are chosen for their known
relevance to POP kinetics or in light of objectives of this study. The liver is set
as the compartment where both intake via ingestion and metabolism (i.e., first-pass
organ) take place. Because POPs are highly hydrophobic compounds, adipose tissue is
defined as a compartment where significant chemical storage can occur. Because
mammary tissue is the cancer site studied and is also an excretory organ (i.e., via
lactation), it is also described as a separate compartment. Along with mammary and
adipose tissues, uterine tissue, placenta, and fetus are represented as compartments
because of their variations during pregnancy and postpartum periods (i.e., mainly
volume change). The brain is added as a potentially interesting organ for the study
of cognitive and motor effects of POPs. Finally, remaining tissues or organs, except
for teguments (i.e., bones, nails, hair, cartilage), are grouped into slowly perfused
tissues (mainly the skin, skeletal muscles, and heart) and richly perfused tissues
based on their volume:blood flow ratio.</p> <title>Absorption</title> <p>Absorption of POPs can occur through different routes, but primarily occurs
through food intake. For the purpose of this study, total intake is limited to a
direct input into the liver compartment, and each POP is assumed to be completely
bioavailable through the gastrointestinal tract, thus involving a hepatic first
pass.</p> <title>Distribution</title> <p>The distribution of POPs is managed by blood flow to different compartments and
partitioning from blood to tissues. This process is described by using mass
balance differential equations (MBDEs) that assume homogenous distributions in
tissues, as follows:</p> <p>where <italic>At</italic> represents the amount of chemical in the compartment,
<italic>Qt</italic> is the blood flow perfusing the compartment,
<italic>P</italic>
<italic><sub>t:b</sub></italic> is the tissue: blood coefficient for the compartment, and
<italic>Ca</italic> and <italic>Ct</italic> are concentrations in arterial
blood and the tissue, respectively.</p> <title>Metabolism</title> <p>Metabolism is assumed to be essentially limited to the liver compartment, and the
rate is described by the product of the hepatic extraction ratio
(<italic>Eh</italic>), the liver blood flow (<italic>Ql</italic> ), and the
arterial blood concentration (<italic>Ca</italic>) entering the compartment, as
follows:</p> <p>The value of <italic>Eh</italic> can be calculated from available data such as
half-life values, Michealis-Menten constants (Vmax and Km), or intrinsic
clearances. The MBDE in the liver therefore becomes</p> <p>where <italic>Al</italic>, <italic>Cl</italic>, and <italic>P</italic>
<italic><sub>l:blood</sub></italic> are the amount, concentration, and tissue:blood partition coefficient of
the POPs in the liver, respectively.</p> <title>Excretion</title> <p>Because most POPs are poorly metabolized, the main elimination route occurs
through excretion of unchanged chemicals. The model is adapted for two excretion
pathways: lactation and parturition. POP excretion via lactation is represented as
an output from the mammary tissue compartment through a partitioning process
between mammary tissue and milk, and milk withdrawal by the suckling such as
described by ##REF##16858609##Lee et al.
(2007)## for PCBs in rats. This partitioning process is further addressed
in the model parameterization section. The POP excretion via lactation is
described as follows:</p> <p>and</p> <p>where <italic>Amam</italic> and <italic>Cmam</italic> are the POP amount and
concentration in mammary tissue. <italic>Qmam</italic> is the blood flow to
mammary tissue compartment, <italic>Qmilk</italic> refers to the milk flow out of
the breast (i.e., the volume drunk per hour by infant in liters per hour), and
<italic>P</italic>
<italic><sub>mam:b</sub></italic> and <italic>P</italic>
<italic><sub>milk:b</sub></italic> are the mammary:blood and milk:blood partition coefficients.</p> <p>The placental transfer to the fetus is described using published equations in
##REF##12383718##Gentry et al. (2002)##. The
elimination of chemicals through parturition is described as a punctual extraction
of the baby body and placenta burdens at the time of birth.</p> <title>Model parameterization</title> <p>To simulate internal exposure in women throughout their entire life, compartments
size, blood flows, and biochemical properties are described as variables that change
as a function of age, body weight, body height, and pregnancy periods (see
Supplemental Material online at <ext-link ext-link-type="uri" xlink:href="http://www.ehponline.org/members/2008/10917/suppl.pdf">http://www.ehponline.org/members/2008/10917/suppl.pdf</ext-link>). Mathematical
equations describing these variable parameters are arranged so that information on
body weight and body height in relation to age, collected from questionnaires, can be
easily used as inputs throughout the entire simulation. Volume and blood flow
parameters for liver, richly perfused, slowly perfused, and adipose tissue
compartments are taken from ##REF##17060096##Haddad et
al. (2006)##. The equations describing early stages of development are used
to calculate organs growth for the 0–1 year interval, because no data are available
for that period. Because of the lack of data, uterine tissue and mammary tissue are
set as a function of body weight and age (##REF##12383718##Gentry et al. 2002##). Mammary tissue volume is
assumed to start from 0 L at 10 years of age and increase linearly to its final
volume at 14 years of age.</p> <p>Physiologic changes during pregnancy are considered for the uterine tissue, mammary
tissue, adipose tissue, placenta, and fetus compartments based on time elapsed since
the beginning of pregnancy, as described by ##REF##12383718##Gentry et al. (2002)##. Postpartum changes are set
as a 6-month linear return to normal in the volume of organs influenced by pregnancy
(##REF##12878049##Gentry et al. 2003##). Blood
flow to these compartments varies proportionally to their volume throughout pregnancy
and postpartum changes. Lactation parameters are also allowed to change over the
lactation period. Equations describing changes in daily excreted milk volume and milk
lipid content as a function of postnatal time are taken from ##REF##2058592##Neville et al. (1991)## (see Supplemental Material
online at <ext-link ext-link-type="uri" xlink:href="http://www.ehponline.org/members/2008/10917/suppl.pdf">http://www.ehponline.org/members/2008/10917/suppl.pdf</ext-link>). Placental
diffusion constant (PAF) describing the exchange between the mother and the baby is
given an arbitrary value of 1, because the model outputs in the woman (i.e., blood
concentrations) are virtually not influenced by this value.</p> <p>Tissue:blood and milk:blood partition coefficients are estimated using ##REF##8560465##Poulin and Krishnan’s (1996)##
approach based on tissue water and lipid composition (##REF##12712630##Price K et al. 2003##):</p> <p>where <italic>K</italic>
<italic><sub>ow</sub></italic> represents n-octanol:water partition coefficient, and <italic>Fl</italic>
and <italic>Fw</italic> stand for the lipid and water fraction, respectively, for
either tissue (subscript t) or blood (subscript b). Tissue composition is taken from
##REF##12712630##Price K et al. (2003)## (see
Supplemental Material online at <ext-link ext-link-type="uri" xlink:href="http://www.ehponline.org/members/2008/10917/suppl.pdf">http://www.ehponline.org/members/2008/10917/suppl.pdf</ext-link>). Slowly
perfused tissues compartment composition is calculated as scaled lipid and water
content of muscles, skin, and heart taken from the same paper as follows:</p> <p>where <italic>F</italic> is the fraction of either lipid or water for the whole
compartment, whereas <italic>Fi</italic> and <italic>Vi</italic> are the fraction of
either lipid or water and the volume for organs included in the compartment,
respectively. Richly perfused tissues compartment composition is calculated with the
same equation (Equation 7). The richly perfused tissue compartment includes the
lungs, kidneys, reproductive organs, spleen, glands, intestinal tract, and stomach
tissues. The organ volumes (<italic>Vi</italic>) used for the calculation of this
compartment composition are those at 18 years of age calculated with the equations in
##REF##11732696##Haddad et al. (2001)##. The
partition coefficients for richly and poorly perfused tissues compartments are
calculated with Equation 6 using these scaled composition parameters. Because of the
lack of data, the richly perfused tissues compartment partition coefficient is used
for the mammary tissue, placenta, and uterine tissue compartments. Because no
information is available on fetus compartment composition, we assessed the impact of
different partition coefficients on the model outputs. The lack of significant impact
of the tissue:blood partitioning for the fetus compartment on the toxicokinetic
profile of the mother led us to arbitrarily give it the partition coefficient of the
richly perfused tissues compartment.</p> <p>Metabolism is parameterized from half-life values for the compounds to be studied.
This assumes that POP elimination is essentially attributed to hepatic clearance.
First, the intrinsic clearance values per kilogram of liver (<italic>CLint</italic>
<italic><sub>C</sub></italic>) are calculated for the physiologic parameters at the age of half-life
[<italic>HL</italic>
<sub>(</sub>
<italic><sub>h</sub></italic>
<sub>)</sub>] measurements:</p> <p>where</p> <p>where <italic>Ql</italic> is the blood flow to the liver, <italic>Vl</italic> is the
volume of the liver, <italic>CL</italic> is the clearance for the studied compound,
<italic>Vd</italic> is the volume of distribution, <italic>P</italic> is the
tissue:blood partition coefficient, <italic>Vt</italic> is the volume of tissues, and
<italic>Vb</italic> is the volume of blood. The subscript <italic>age</italic>
means that the parameters are calculated with the physiologic features at the age of
individuals sampled for half-life value measurements, whereas the subscript
(<italic>h</italic>) means that the values of half-lives were in hours. Extraction
ratios are calculated from <italic>CLint</italic>
<italic><sub>C</sub></italic>, which is assumed to be age invariant, and from the liver volume and blood
flow, which change as a function of age, as follows:</p> <p>The liver weight–adjusted hepatic extraction ratio is used for the calculation of the
metabolism rate. Although recent modeling studies (e.g., ##REF##15056818##Clewell et al. 2004##) introduced gene ontology data
in their model to reflect age variations in intrinsic clearance, this was not
considered in the present simulations because of the relative little impact it has
shown on lifetime kinetics and on the data interpretation in this study (simulations
not shown). If needed in the future, such information can easily be incorporated into
the model.</p> <title>Model simulations</title> <p>Different scenarios were simulated to assess the impact of different physiologic
processes or changes that can occur during the lifetime of a woman on the
toxicokinetic profile of POPs as well as on the blood concentration at age of
diagnosis. The input parameters that were changed for the simulations were
breast-feeding in childhood, level of exposure through food intake, body weight and
body height in function of age, number of pregnancies, age at child birth(s),
lactation periods, and chemical properties for the chosen pollutants (log
<italic>K</italic>
<sub>ow</sub> and hepatic extraction ratio). All simulations used as input the normal
body weight profile and the height profile depicted in ##FIG##1##Figure 2##, unless stated otherwise.</p> <p>Three pollutants were chosen for the present studies: PCB-180, PCB-153, and HCB,
chosen for their relevant concentrations found in human blood and adipose tissue as
well as the fact that they differ in their <italic>K</italic>
<sub>ow</sub> and half-life. Log <italic>K</italic>
<sub>ow</sub> was 6.72 for PCB-153, 7.21 for PCB-180, and 5.73 for HCB [##UREF##0##Agency for Toxic Substances and Disease
Registry (ATSDR) 2000##, ##UREF##1##2002##]. Hepatic extraction ratios were calculated as described in the
parameterization section from the approximate half-lives of the chemicals, which were
27.5 years for PCB-153, 9.9 years for PCB-180, and 6 years for HCB in humans (##UREF##0##ATSDR 2000##; ##REF##10903610##To-Figueras et al. 2000##).</p> <p>Although the actual food consumption levels of these POPs declined from the 1970 to
the 1990s, it was kept constant for the purpose of the modeling effort. However, the
actual levels may be entered as a variable into the model. For all simulation
scenarios, the level of exposure through ingestion of contaminated food was set as a
background daily exposure of 10 ng/kg body weight/day (unless specified
otherwise).</p> <title>Impact of breast-feeding in childhood</title> <p>In this first set of simulations, the period of breast-feeding was set to 6
months, with a constant concentration of 2 μg/L to compare the kinetics of the
three chemicals. The volume of milk ingested was modeled with the same equation
used for the breast-feeding periods of the exposed woman. The milk concentrations
used in these simulations were arbitrarily chosen and do not necessarily reflect
specific actual levels of contaminants, although similar POP milk levels were
found in the literature (##REF##8806375##Dewailly
et al. 1996##; ##REF##12055065##Solomon and
Weiss 2002##). The purpose is simply to show how breast-feeding in
childhood will impact the LTP of a given individual.</p> <title>Impact of body weight change</title> <p>For some simulations, the body weight parameter was varied to investigate the
influence of adipose tissue volume and its variation throughout the lifetime of a
woman on the tissue pollutant concentration. Body weight and body height profiles
used in this study are depicted in ##FIG##1##Figure 2##. The normal weight and overweight scenarios represented linear
increases in weight from 50 kg at 14 years of age to 70 and 90 kg, respectively,
at 25 years of age. Weight loss scenario followed the overweight profile with a
drop from 90 to 70 kg on a 10-year interval between 25 and 35 years of age.</p> <title>Impact of pregnancy and lactation</title> <p>We performed simulations for several pregnancy history scenarios. The number of
pregnancies was either one or two. Lactation period length effect on tissue or
blood concentration was also assessed; lactation periods chosen for these
simulations were 6 and 12 months.</p> <title>Differences in toxicokinetic profiles for a given blood concentration at the
age of diagnosis</title> <p>For a given POP blood concentration, toxicokinetics profiles were obtained for
simulations in women having different physiologic histories (i.e., different
number of pregnancies, period of lactation, weight profile, and exposure). This
was done to assess how much the lifetime internal exposure can differ for a given
POP blood concentration at the age of diagnosis. The exposure values were
optimized to reach the same blood concentration at 55 years of age for different
physiologic histories.</p>
<title>Results</title> <title>Impact of breast-feeding in childhood</title> <p>We used the PBPK model to investigate the potential impact of additional early
exposure through breast-feeding in childhood. For this purpose, a scenario
considering breast-milk drinking for a period of 6 months was compared with a
scenario where the woman was not breast-fed (##FIG##2##Figure 3A##). The milk concentration was set at 2
μg/L, and the background oral exposure was set as a daily intake of 10 ng/kg body
weight. Scenarios were simulated for a woman with a normal body weight profile. For
the breast-fed female scenario, the blood concentration at the end of the
milk-drinking period reaches 1.87 μg/L for PCB-153, 1.81 μg/L for PCB-180, and 1.74
μg/L for HCB. These concentrations are even higher then those at the age of diagnosis
(i.e., 55 years of age). At 5 years of age, the difference between the two scenarios
was important for all three pollutants, because the blood concentration was about
2-fold higher for the breast-fed woman. However, by 20 years of age, these
differences almost completely disappeared. It can also be observed that blood
concentrations at 55 years of age cannot distinguish breast-fed from non-breast-fed
individuals, even though internal concentrations in the breast-fed group were very
high in infancy.</p> <title>Impact of body weight change</title> <p>We also assessed the impact of body weight on POP kinetics with the use of various
profiles shown in ##FIG##1##Figure 2##. All
these simulations used the same body height profile (##FIG##1##Figure 2##). Using these three physiologic profiles,
simulations considering a 6-month period of milk drinking in childhood (milk
concentration, 2 μg/L) and a daily exposure to 10 ng/kg of the chemicals were
performed for the three chemicals studied (##FIG##2##Figure 3B##). Simulations show that both the normal
and the overweight scenarios display similar kinetic profiles, despite the fact that
PCB blood concentration at 55 years of age is slightly higher in the overweight
profile than for the normal weight profile. A weight loss between 25 and 35 years of
age raises the immediate blood POP concentrations considerably. The effect is more
pronounced and lasts up to 55 years of age for PCB-153, where the blood
concentrations at 35 years of age are 1.13 μg/L and 1.38 μg/L for the normal weight
and weight loss profiles, respectively.</p> <title>Impact of pregnancy and lactation</title> <p>We also investigated the impact of pregnancy and subsequent breast-feeding on blood
POP concentrations. First, scenarios with pregnancy alone and pregnancy followed by a
12-month lactation starting at 30 years of age were simulated to compare the
respective effect of these two factors (##FIG##2##Figure 3C##). For PCB-153, the blood concentration at 31 years of age is
much lower in the woman who lactated for 12 months (0.53 μg/L blood) than in the
woman without lactation (1.01 μg/L blood). The difference is still present at 55
years of age, although smaller than at 31 years of age. For PCB-180, lactation has a
small effect on the blood concentration at 55 years of age, whereas for HCB it has no
impact. Simulations show that the pregnancy alone induces a small drop in the blood
concentration that rapidly returned to prepregnancy levels after postpartum
physiologic changes (##FIG##2##Figure 3C##,
normal lines).</p> <p>We assesssed the impact of the length of the lactation period. Simulations for two
lactation period lengths show that longer lactations have a greater impact on the
blood concentration (##FIG##2##Figure 3D##).
For PCB-153, the blood concentration at 55 years of age is 1.39 μg/L for a 6-month
lactation and 1.25 μg/L for a 12-month lactation starting at 35 years of age. The
difference between the two lactation periods is small for PCB-180 and negligible for
HCB when only blood concentration at 55 years of age is considered.</p> <p>POP toxicokinetic profiles were also compared for lactations of the same duration but
held at different ages. Two scenarios were compared: child birth at either 20 or 35
years of age, followed by a 12-month breast-feeding period (##FIG##2##Figure 3E##). As expected, POP kinetics differ
between the two scenarios. The effect of a lactation period later in life influences
significantly the blood concentration at 55 years of age for PCB-153, whereas this
effect is minimal for PCB-180 and practically absent for HCB in the simulated
scenarios.</p> <title>Differences in toxicokinetic profiles for a given blood concentration at
diagnosis</title> <p>To demonstrate that the same blood concentration at 55 years of age can be the result
of completely different kinetic profiles, we compared two scenarios with different
lifetime profiles (##FIG##2##Figure 3F##).
The first scenario represents a woman who was not breast-fed in childhood, was never
pregnant, and was exposed throughout life to the background level of 10 ng/kg/day for
each of the three pollutants. The second scenario is that of a woman who was
breast-fed for 6 months with breast milk at a POP concentration of 2 μg/L, had two
pregnancies, one at 35 and one at 40 years of age, followed by a 12-month lactation
period each time. To obtain a final concentration at 55 years of age identical to the
one in the first scenario, the daily exposure levels were optimized to the following
levels: 18.7 ng/kg/day for PCB-153, 13.8 ng/kg/day for PCB-180, and 11.6 ng/kg/day
for HCB. These simulations yielded completely different kinetic profiles despite
resulting in the identical blood concentration at 55 years of age (##FIG##2##Figure 3F##). The blood concentration
at 34 years of age showed the greatest difference, especially for PCB-153, where
approximately a 2-fold higher level was calculated for the
breast-feeding/pregnancy/higher exposure scenario (2.03 μg/L blood) than for the no
breast-feeding/no pregnancy/background exposure scenario (1.07 μg/L blood).</p>
<title>Discussion</title> <p>Over the last decades, many environmental epidemiology studies have focused on the
possible link between exposure to POPs and the development of breast cancer, but no
clear overall conclusion could be drawn from the different findings. Discrepancies among
conclusions from the various studies might be related to the false assumption that a
unique late-life sampling reflects lifetime POP exposure. Our study supports this
contention and proposes a new tool that could reduce this uncertainty in exposure
assessment by simulating lifetime toxicokinetics of POPs in women.</p> <p>The PBPK model built in this study can overcome exposure assessment problems by
simulating normal development (i.e., growth, blood flows) and various historical events
within the lifetime of a woman [i.e., breast feeding, changes in body mass index (BMI),
pregnancy] to obtain the lifetime toxicokinetics of POPs. Using this model, we simulated
several exposure and physiologic scenarios to assess the impact of different parameters
on POP blood concentrations from 0 to 55 years of age.</p> <p>Although <italic>in utero</italic> exposure is known to occur through placental
diffusion, body burden at birth was set to 0. This methodologic choice relies on two
facts: <italic>a</italic>) fetus tissue concentration estimation would require
information on the exposure of the mother to simulate the placental transfer, and
<italic>b</italic>) the baby’s body burden at birth is rapidly diluted by increasing
tissue volumes and therefore has a small impact on lifetime toxicokinetics when compared
with breast milk consumption (##REF##15056818##Clewell et
al. 2004##; ##REF##9195020##Kreuzer et al.
1997##).</p> <p>Simulations showed that although pregnancy alone did not have a strong impact on blood
POP concentrations, lactation exerted major changes in the toxicokinetic profiles. The
longer and later in life a lactation period occurs, the greater its impact on blood POP
concentration of the woman at 55 years of age. Thus, quantitative information on
lactation is critical when evaluating past exposure to POPs. Moreover, simulations
showed that body weight variations throughout life seemed to have a greater impact on
blood POP concentrations than body weight level itself. A loss of weight can be regarded
as a decrease in the adipose tissue volume in which POPs are preferentially stored, a
phenomenon that leads to the unloading of POPs into blood. It has been previously shown
that PCB-153, HCB, β-hexa-chlorocyclohexane, <italic>p,p</italic>′-DDE, and Aroclor 1260
levels in blood increase with weight loss (Imbeault et al. 2002). Therefore, BMI changes
should be regarded as an important factor in POP kinetics. New approaches in exposure
assessment that consider physiologic parameters such as BMI and lactation were developed
by ##REF##16172236##Wolff et al. (2005)## with the
use of first-order pharmacokinetic and predictor-based multivariate models. They
concluded that possible exposure misclassifications in epidemiologic studies can occur
if the impacts of BMI and lactation on POP concentrations are ignored. In accordance
with such results, the current study clearly showed that sampling at the age of
diagnosis is a questionable end point for lifetime exposure estimation. A more recent
study from ##REF##17932339##Wolff et al. (2007)##
stresses the importance of considering pharmacokinetic variability in epidemiologic
studies. PBPK modeling as proposed here is particularly well suited for such
considerations The use of PBPK models such as the one reported in our manuscript go much
further than Wolff’s strategy in considering pharmacokinetics. This new approach is
being proposed to epidemiologists. Instead of simply proposing pharmacokinetic factors
such as BMI as other covariables in the epidemiologic studies, we suggest directly using
PBPK model estimates of blood or tissue concentrations during different periods of life
for each subject of the study to analyze if there is a relationship between disease and
internal exposure.</p> <p>We investigated the impact of breast milk consumption in childhood on internal exposure
by comparing POP venous concentration in breast-fed and a bottle-fed women. Results
showed that early-life blood POP concentrations are strongly influenced by breast-milk
drinking for the first years of life, but that these effects are almost fully attenuated
by 20 years of age. These findings are supported by a study on a Faroese birth cohort in
which the primary contributors to the serum total PCB concentrations at 7 and 14 years
of age were breast-feeding and blubber consumption, respectively (##REF##16169054##Barr et al. 2006##). The work reported herein showed
that blood or tissue POP concentration at the age of diagnosis does not reflect the
important body burden resulting from early life breast-feeding, a possibly important
time window of exposure.</p> <p>By simulating the lifetime blood concentrations for two distinct exposure and life
history scenarios with the same level at 55 years of age, this study showed the poor
predictive value of late-life sampling for past exposure assessment. Late lactations can
dramatically decrease POP concentrations and lead to lower blood concentrations at the
age of diagnosis, even for a high-exposure profile. To eliminate this artifact, it is
crucial that the estimation takes into account such physiologic events.</p> <p>This PBPK model enables the consideration of chemical-specific parameters affecting
distribution (log <italic>K</italic>
<sub>ow</sub>) and elimination (half-life). The simulations performed with the three
contaminants showed that blood PCB-153 levels were more sensitive to the main
factors—that is, lactation and body weight change—than those for PCB-180 or HCB. This
can be explained by their different half-lives, a parameter that strongly correlates
with the time required for the pollutant to reach steady state in the body. Chemicals
with a shorter half-life reach steady state more rapidly, leading to the faster
attenuation of the impact that these physiologic events may have on blood concentration.
On the other hand, the log <italic>K</italic>
<sub>ow</sub> is unlikely to account for differences in POP toxicokinetics, because it
has been reported that compounds with a log <italic>K</italic>
<sub>ow</sub> value &gt; 4 will partition similarly between blood and organs (##REF##10718576##Haddad et al. 2000##). Like physiologic
parameters, interindividual variability also exists in half-life values as well as blood
and adipose tissue lipid content, which are sensitive parameters of the PBPK model (see
Supplemental Material online at <ext-link ext-link-type="uri" xlink:href="http://www.ehponline.org/members/2008/10917/suppl.pdf">http://www.ehponline.org/members/2008/10917/suppl.pdf</ext-link>). Apart from doing
a toxicokinetic study in each individual, there currently exists no method to estimate
half-lives in individuals. Similarly, determining lipid fractions in blood and adipose
tissue of individuals represents another difficulty, and such a practice can be very
costly. Using average values of these parameters is an acceptable surrogate, because
they lead to prediction errors under a 1.5-fold difference in blood concentrations in
the case of intrinsic clearance and 1.8-fold in the case of lipid composition in adipose
tissues and blood (see Supplemental Material online at <ext-link ext-link-type="uri" xlink:href="http://www.ehponline.org/members/2008/10917/suppl.pdf">http://www.ehponline.org/members/2008/10917/suppl.pdf</ext-link>).</p> <p>The PBPK model developed herein refines the assessment of past tissue exposure by
incorporating physiologic processes that greatly affect POP kinetics. The use of such a
tool should permit epidemiologists to better assess past exposure and to investigate the
potential critical windows of exposure to POPs in cancer development, a commonly
reported concern in epidemiology studies. The importance of exposure assessment for
different critical time windows is supported by studies on breast cancer incidence among
Japanese women who were exposed to radiation; these studies show that exposure at a
lower age has a higher impact on cancer development than exposure at later-life stages
(##REF##2269215##Hoel and Dinse 1990##; ##REF##8183991##Tokunaga et al. 1994##). Moreover, a
recent study reported that exposure to <italic>p,p</italic>′-DDT early in life may
increase the risk of breast cancer (##REF##17938728##Cohn
et al. 2007##). By using estimated LTPs and internal exposure levels for
different time frames, this hypothesis could be further addressed.</p> <p>Furthermore, the toxicity of certain POPs may not be attributable entirely to parent
compound. Some evidence indicates that metabolites may also elicit a biochemical or
toxic response (##REF##15025504##Machala et al.
2004##; ##REF##15310862##Meerts et al.
2004##; ##REF##8037844##Safe 1994##; ##UREF##6##You et al. 2006##). Because the PBPK
model describes the metabolism of the chemicals, the amount of metabolites formed in
different periods of life can be assessed and used for epidemiologic analysis in the
same way that we use internal concentrations of the parent compound. Thus, PBPK modeling
can also add another dimension to POP epidemiologic studies.</p> <p>To perform simulations, information on the proposed important variables to be used as
inputs in the model must be gathered within the epidemiologic questionnaire. The model
was constructed so that it requires information on body weight and height as a function
of age. Information on pregnancy and lactation periods is also a prerequisite to model
simulation. This information must be associated with the age of the woman at birth of
her children and the duration of the lactation periods. The model can easily include any
information on exposure levels that could vary as a function of changes in dietary
lifestyle (e.g., increase in fish consumption or in fatty foods for certain periods of
life) as well as geographic/temporal monitoring data on environmental levels of selected
pollutants. Once these parameters are included in the model, the exposure scenario can
be optimized with the use of information on both estimated exposure and blood or tissue
samples.</p> <p>Although the proposed PBPK model framework for POP lifetime exposure assessment in women
is constructed on validated descriptions of absorption, distribution, metabolism, and
excretion of various POPs in rodents or humans, further studies are needed to validate
the model. The model proposed herein can be used as a generic framework in which
adjustments/modifications can be made to incorporate additional toxicokinetic processes
that may be specific to particular POPs (e.g., diffusion limitation, plasma or tissue
protein binding) if relevant for a lifetime scale in humans.</p>
<title>Conclusion</title> <p>This study is the first to propose a PBPK modeling approach for the assessment of
lifetime internal exposure to POPs in the context of epidemiologic studies. The proposed
model has the potential to be used in environmental epidemiology research to reduce the
uncertainty in past tissue exposure estimation. This approach can not only strengthen
the validity and reproducibility of studies on the impact of POPs on breast cancer
incidence in humans, but also help to assess the effect of exposure during critical time
windows in breast cancer development and other late-life diagnosis pathologic end
points.</p>
<p>Both authors contributed equally to all parts of the work. The authors declare
they have no competing financial interests.</p> <title>Background</title> <p>Despite experimental evidence, most epidemiologic studies to date have not
supported an association between exposure to persistent organic pollutants (POP)
and breast cancer incidence in humans. This may be attributable to difficulties in
estimating blood/tissue POP concentration at critical time periods of
carcinogenesis.</p> <title>Objectives</title> <p>In this work we aimed to develop a tool to estimate lifetime POP blood/tissue
exposure and levels during any hypothesized time window of susceptibility in
breast cancer development.</p> <title>Methods</title> <p>We developed a physiologically based pharmacokinetic (PBPK) model that can account
for any given physiologic lifetime history. Using data on pregnancies, height,
weight, and age, the model estimates the values of physiologic parameters (e.g.,
organ volume, composition, and blood flow) throughout a woman’s entire life. We
assessed the lifetime toxicokinetic profile (LTP) for various exposure scenarios
and physiologic factors (i.e., breast-feeding, growth, pregnancy, lactation, and
weight changes).</p> <title>Results</title> <p>Simulations for three POPs [hexachlorobenzene, polychlorinated biphenyl (PCB)-153,
PCB-180] using different lifetime physiologic profiles showed that the same blood
concentration at 55 years of age can be reached despite totally different LTP.
Aside from exposure levels, lactation periods and weight profile history were
shown to be the factors that had the greatest impact on the LTP.</p> <title>Conclusions</title> <p>This new lifetime PBPK model, which showed the limitations of using a single
sample value obtained around the time of diagnosis for lifetime exposure
assessment, will enable researchers conducting environmental epidemiology studies
to reduce uncertainty linked to past POP exposure estimation and to consider
exposure during time windows that are hypothesized to be mechanistically critical
in carcinogenesis.</p>
<p>Exposure to ubiquitous persistent organic pollutants (POPs) such as polychlorinated
biphenyls (PCBs), dichlorodiphenyldichloroethylene (DDE), and hexachlorobenzene (HCB) has
attracted attention in breast cancer etiology. These compounds have high solubility in
lipids and long half-lives in organisms, and are present in measurable amounts in human
tissues, blood, and milk. Although the mechanistic actions of these chemicals in
carcinogenesis remain unclear, studies showed that some POPs have the potential to promote
cancer development in various experimental models such as rodents and human cell lines.
<italic>In vitro</italic> assays using MCF-7 human breast cancer cells showed that POPs
can promote cell proliferation (##REF##10398763##Andersson et
al. 1999##; ##REF##11036508##Du et al. 2000##;
##REF##8593856##Soto et al. 1995##). POPs have also
been shown to inhibit epidermal growth factor withdrawal-induced apoptosis (##REF##11309286##Davis et al. 2001##).</p> <p>Despite the experimental evidence, controversy still exists regarding breast carcinogenic
properties of POPs in humans. An epidemiologic study first suggested that DDE blood
concentration may be an important etiologic factor in breast cancer (##REF##8468722##Wolff et al. 1993##). This finding led epidemiologists to
address further the issue of environmental exposure to POPs and their potential implication
in breast cancer. In subsequent years, several environmental epidemiology studies on the
subject were published and their findings were greatly variable. Reviews and meta-analyses
concluded, on the lack of evidence to support the hypothesis, that POP exposure could be
linked to an increase in female breast cancer risk (##REF##12363327##Calle et al. 2002##; ##REF##11353787##Laden et al. 2001##; ##REF##14754575##Lopez-Cervantes et al. 2004##). On the other hand, some
studies showed a positive correlation between POP levels and breast cancer incidence (##REF##10667464##Aronson et al. 2000##; ##REF##12709520##Charlier et al. 2003##; ##REF##11914190##Demers et al. 2002##; ##REF##10760644##Hoyer et al. 2000##; ##REF##10968381##Romieu et al. 2000##).</p> <p>The variability in conclusions among epidemiologic studies might arise from methodologic
challenges. One conclusion relates to the lack of tools for past exposure to pollutants
assessment (##REF##12826474##Brody and Rudel 2003##). In
most cases, biologic assessment of exposure has been limited to measurements of blood or
tissue levels in samples collected around the date of diagnosis. It is uncertain that blood
or tissue concentrations sampled a few years before diagnosis reflect the body burden
during potentially critical time windows such as fetal, neonatal, and pubertal periods.
Epidemiologic conclusions on the link between POP exposure and breast cancer development
are still based on the premise that single sampling is indicative of past POP exposures,
highlighting the need for tools to assess lifetime toxicokinetic profiles (LTPs).</p> <p>Physiologically based pharmacokinetic (PBPK) modeling represents a possible approach to
estimating POP exposure during specific time windows. PBPK models are mathematic
representations of xenobiotic pharmacokinetics (i.e., processes of absorption,
distribution, metabolism, and excretion) based on the physiologic and biochemical
parameters of a given organism (e.g., humans) and the physicochemical properties of the
selected xenobiotic (##UREF##3##Krishnan and Andersen
2001##). Such models allow the prediction of blood or tissue concentrations at a
given time after a given dose of the xenobiotic. Various types of physiologic changes,
which can be mathematically described within a PBPK model, may affect the kinetics of a
compound in an individual throughout his or her life. Examples of relevant physiologic
changes are body weight variations, excretion of POPs through lactation (##REF##2058592##Neville et al. 1991##), and physiologic
changes due to aging (##REF##17060096##Haddad et al.
2006##; ##REF##12712630##Price K et al. 2003##;
##REF##14594104##Price PS et al. 2003##) or pregnancy
(##REF##12383718##Gentry et al. 2002##, ##REF##12878049##2003##). PBPK modeling can also
accommodate a variety of exposure scenarios such as changes in the lifestyle of the subject
and geographic/temporal monitoring data on environmental levels of POPs. Thus, development
of a PBPK model able to simulate exposure throughout life while considering such
physiologic changes would be very useful to assess past exposure during critical time
windows.</p> <p>In the past, several PBPK modeling efforts have described the toxicokinetics of different
POPs. Many of these models are based on the assumption that these chemicals, which are
highly lipophilic, are distributed uniformly between blood and tissues according to their
contents in lipids (for PCBs: ##REF##410580##Anderson et al.
1977##; ##REF##16009653##Emond et al. 2005##;
##UREF##4##Lutz et al. 1977##, ##REF##6149901##1984##; ##REF##20292##Tuey and Matthews 1977##, ##REF##6247786##1980a##, ##REF##6109607##1980b##; for dioxins: ##UREF##2##Carrier
1991##, ##REF##12946389##Maruyama et al. 2003##;
##REF##8998957##van der Molen et al. 1996##; for
HCB: ##UREF##5##Yesair et al. 1986##). Other
models have added diffusion limitation to the fat compartment [##REF##17382449##Belfiore et al. 2007## (mirex); ##REF##11752682##Lee et al. 2002##, ##REF##16858609##2007## (PCB-153); ##REF##9705893##Parham and Portier 1998## (PCBs); ##REF##10366546##You et al. 1999## (<italic>p,p</italic>′-DDE)] or
diffusion limitation between erythrocytes and plasma [##REF##16481338##Lu et al. 2006## (HCB)] to their model structure to
improve model predictions of animal kinetic data. Although the addition of diffusion
limitation has definitely had an impact on the initial uptake phase on a short time scale,
it is not likely to be an important determinant for the toxicokinetics on a scale spanning
many years. Most agree that partitioning for these compounds is driven by lipid solubility,
and this has been corroborated with human and rodent <italic>in vivo</italic> data (##REF##10718576##Haddad et al. 2000##).</p> <p>Our overall goal in this work was to develop an exposure assessment tool that could be used
in breast cancer epidemiologic studies to estimate lifetime POP blood/tissue exposure and
levels during any hypothesized time window of susceptibility in breast cancer development.
The specific objectives were to build a generic PBPK model framework to simulate POP
toxicokinetics for any given physiologic profile and exposure data, and to evaluate through
model simulation the impact of exposure scenarios and different physiologic factors such as
pregnancy, lactation, and body weight on the lifetime internal exposure profile and the
blood POP concentration at 55 years of age, a surrogate time representing the age of
diagnosis. For the purposes of this study, three POPs with half-lives varying from 6 to
27.5 years were chosen to run the simulations: HCB; 2,2′,3,4,4′,5,5′-heptachlorobiphenyl
(PCB-180); and 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB-153).</p>
<fig id="f1-ehp0116-000886" position="float"><label>Figure 1</label><caption><p>Conceptual representation of the PBPK model.</p></caption></fig> <fig id="f2-ehp0116-000886" position="float"><label>Figure 2</label><caption><p>Body weight and body height profiles used for the simulations:
(<italic>A</italic>) body height, (<italic>B</italic>) normal weight,
(<italic>C</italic>) overweight, and (<italic>D</italic>) weight loss profiles.
The normal weight and overweight scenarios represent linear increases in weight
from 50 kg at 14 years of age to 70 and 90 kg, respectively, at 25 years of age.
Weight loss scenario followed the overweight profile with a drop from 90 to 70 kg
on a 10-year interval between the ages of 25 and 35 years.</p></caption></fig> <fig id="f3-ehp0116-000886" position="float"><label>Figure 3</label><caption><p>Toxicokinetic profiles for PCB-153, PCB-180, and HCB blood concentration for
(<italic>A</italic>) normal body weight history and 10 ng/kg/day exposure to
each of these chemicals for a woman who was not breast-fed in childhood (blue
line) or breast-fed for 6 months (black line); (<italic>B</italic>) normal weight
(gray line), weight loss (black line), or overweight (blue line) profiles and 10
ng/kg/day exposure to each of these chemicals for women who were breast-fed for 6
months in childhood; (<italic>C</italic>) normal body weight history and 10
ng/kg/day exposure to each of these chemicals for a woman who was breast-fed for 6
months in childhood and had a pregnancy at 30 years of age followed by no
lactation (blue line) or a 12-month lactation period (black line);
(<italic>D</italic>) normal body weight history and 10 ng/kg/day exposure to
each of these chemicals for a woman who had a pregnancy at 35 years of age
followed by a 6-month lactation period (blue line) or a 12-month lactation period
(black line); (<italic>E</italic>) normal body weight history and 10 ng/kg/day
exposure to each of these chemicals for a woman who was breast-fed for 6 months in
childhood and had a pregnancy followed by a 12-month lactation period at 20 years
of age (blue line) or 35 years of age (black line); (<italic>F</italic>) normal
body weight history for a woman who was exposed to 10 ng/kg/day of each of the
three chemicals and had no pregnancy (black line) or was breast-fed for 6 months
in childhood, was exposed to 18.7 ng/kg/day PCB-153, 13.8 ng/kg/day PCB-180, 11.6
ng/kg/day HCB, and who had two pregnancies at 35 and 40 years of age followed by
12-month lactation periods (blue line).</p></caption></fig>
<disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula> <disp-formula></disp-formula>
<fn-group><fn><p>Supplemental Material is available online at
http://www.ehponline.org/members/2008/10917/suppl.pdf</p><p>We thank R. McDougall from Aegis Technologies for his technical help during this
project. This work was performed at Université du Québec à Montréal, C.P. 8888 Succ.
Centreville, Montréal (Québec), Canada H3C 3P8.</p><p>S.H. is recipient of a research scholarship from Fonds de Recherche en Santé du
Québec.</p></fn></fn-group>
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Environ Health Perspect. 2008 Jul 19; 116(7):886-892