Subscriber access provided by UNIV LAVAL
Article
A Permeability-limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic Acid (PFOA) in Male Rats Weixiao Cheng, and Carla Ng Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02602 • Publication Date (Web): 31 Jul 2017 Downloaded from http://pubs.acs.org on August 3, 2017
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 34
Environmental Science & Technology
1
A Permeability-limited Physiologically Based Pharmacokinetic (PBPK)
2
Model for Perfluorooctanoic acid (PFOA) in Male Rats
3
Weixiao Cheng and Carla A. Ng*
4
Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh,
5
Pennsylvania 15261, USA
6
* Address correspondence to: Carla A. Ng, Department of Civil & Environmental Engineering,
7
University of Pittsburgh, 3700’Hara St, Pittsburgh, PA 15261. Tel.: 412-383-4075. Fax: 412-
8
624-0135. E-mail:
[email protected]
1 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
9
ABSTRACT: Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico
10
tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic
11
substances, such as perfluorooctanoic acid (PFOA) in organisms. However, most existing PBPK
12
models have been based on the flow-limited assumption and largely rely on in vivo data for
13
parameterization. In this study, we propose a permeability-limited PBPK model to estimate the
14
toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular
15
uptake and efflux of PFOA via both passive diffusion and transport facilitated by various
16
membrane transporters, association with serum albumin in circulatory and extracellular spaces,
17
and association with intracellular proteins in liver and kidney. Model performance is assessed
18
using seven experimental datasets extracted from three different studies. Comparing model
19
prediction with these experimental data, our model successfully predicts the toxicokinetics and
20
tissue distribution of PFOA in rats following exposure via both IV and oral routes. More
21
importantly, rather than requiring in vivo data fitting, all PFOA-related parameters were obtained
22
from in vitro assays. Our model thus provides an effective framework to test in vitro-in vivo
23
extrapolation and holds great promise for predicting toxicokinetics of per- and polyfluorinated
24
alkyl substances in humans.
25
2 / 30
ACS Paragon Plus Environment
Page 2 of 34
Page 3 of 34
Environmental Science & Technology
26
1. INTRODUCTION
27
Perfluorooctanoic acid (PFOA) is one of the most important perfluoroalkyl substances that has
28
been widely used in industrial and consumer products since 19501. The strong carbon-fluorine
29
bonds in PFOA make it very resistant to metabolic and environmental degradation, which,
30
coupled with its widespread use, results in its worldwide presence2-4. Although production of
31
PFOA has been eliminated by many manufacturers5, worldwide human exposure to PFOA
32
continues6-9.
33
PFOA toxicokinetics have been studied extensively in mammals, and results show that the
34
substance is well absorbed orally and not metabolized10-12. It is primarily accumulated in plasma,
35
liver, and kidney, with lowest levels in adipose and muscle10, 13-17. In addition, PFOA can be
36
eliminated through urine and feces, with urine being the major route. It has been reported that
37
renal elimination rates are both species- and sex-dependent18. In humans, the half-life of PFOA
38
in blood is estimated to be about 3.5 years with no significant gender difference19. However, the
39
clearance of PFOA in rats is considerably sex-dependent, with reported half-lives ranging from
40
hours to several days in female and male rats, respectively20, 21.
41
Two principal underlying molecular mechanisms have been identified to explain observed PFOA
42
toxicokinetics: protein binding and cell membrane transport. Studies revealed that PFOA is
43
strongly bound to serum albumin as well as cytosolic fatty acid binding proteins (FABPs)22-25,
44
which are pervasive in different tissues such as liver and kidney. Therefore, binding to different
45
proteins is an important determinant for high accumulation in blood, liver, and kidney. For
46
membrane transport, both passive diffusion and protein-facilitated transport play important roles
47
in cellular uptake of PFOA26-29. A number of transporters (named using all capital letters to
48
denote human transporters, or by one capital followed by lowercase letters for animal 3 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
49
transporters30), such as organic anion transporters (OATs/Oats) and organic anion transporting
50
polypeptides (OATPs/Oatps) have been identified that are responsible for renal tubular excretion
51
and reabsorption of PFOA in humans and rats27-29.
52
Due to its persistence and bioaccumulation, the potential human health risks of PFOA have
53
received intense attention from environmental scientists and regulatory agencies5. As
54
toxicokinetics data are particularly scarce for humans, in silico tools, such as physiologically
55
based pharmacokinetic (PBPK) models, are becoming a promising alternative to inform risk
56
assessment of chemicals like PFOA31. By appropriate specification of species- and chemical-
57
specific parameters, PBPK models can be employed to simulate absorption, distribution,
58
metabolism, and excretion (ADME) of compounds in animals and humans, providing a useful
59
tool to understand and extrapolate pharmacokinetics across different species and dosing
60
scenarios32.
61
A number of PBPK models have been developed to simulate the toxicokinetics and distribution
62
of PFOA in humans, rats, and monkeys33-39. However, all these models were based on the flow-
63
limited assumption, which means that the substance uptake rate to the tissue compartment is
64
limited by the blood flow rate, not by cell membrane permeability32. Flow limited kinetics
65
commonly occur for small and lipophilic molecules (molecular weight < 300 Da)32. But for large
66
and charged molecules like PFOA (molecular weight = 414.09 Da40 and pKa < 1, meaning >99%
67
ionized at environmentally relevant pH30), permeability across the cell membrane becomes the
68
limiting process. Therefore, a model that takes this into account may provide more insight into
69
realistic behavior32. In addition, existing models use in vivo test data to fit parameters, and thus
70
the predictive power of these models largely depends on the in vivo data used (in these studies, a
71
range from 14 of 30 to 88 of 105 total parameters were obtained by fitting33-39). Although the 4 / 30
ACS Paragon Plus Environment
Page 4 of 34
Page 5 of 34
Environmental Science & Technology
72
flow-limited assumption performs well for animals with sufficient in vivo data for fitting
73
parameters, when applied to humans, the flow-limited model does not work well. Take Fabrega
74
et al.39 for example: in that study, most perfluoroalkyl substances (PFASs), including PFOA,
75
were under-predicted substantially in human liver. The authors ascribed this to the high
76
uncertainty caused by the limited in vivo data they relied on for model parameterization. Beyond
77
the need for more reliable models in the absence of in vivo data for humans, there is increasing
78
motivation to reduce in vivo experiments in animal studies. The National Research Council’s
79
2007 report, Toxicity Testing in the 21st Century, proposes a shift from in vivo animal studies to
80
in vitro assays and sophisticated modeling approaches41. Such a shift means in vitro and in silico
81
approaches must become better developed and independent of in vivo data41, 42. A PBPK model
82
that could reasonably predict tissue distribution without need of in vivo data would therefore be
83
of great benefit for human toxicology and risk assessment. To develop such a mechanistic model,
84
a better and more reliable understanding of the organism of interest and the specific molecular
85
mechanisms that drive PFOA toxicokinetics is required.
86
To the best of our knowledge, the PBPK model developed by Ng and Hungerbühler43 is the only
87
one that explicitly considers membrane permeability, active transport, and protein binding. It was
88
successfully used to evaluate bioconcentration of perfluorinated alkyl acids in fish. However, due
89
to limited fish-specific protein data, the protein binding and active transport processes were
90
parameterized utilizing existing data from studies for humans and rats43. Despite the overall good
91
prediction results, given the physiological difference between mammals and fish, further
92
development of this model warrants implementation in a mammalian system with better
93
availability of data for parameterization.
94
In this study, we have substantially modified the original fish model and applied it to estimate 5 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
95
the plasma toxicokinetics and tissue distribution of PFOA in male rats, for which most protein-
96
related parameters were available. We then evaluated whether the predicted results are consistent
97
with measured experimental data from 3 separate studies10, 14, 15, where different PFOA dose
98
levels and administration routes were used (providing a total of seven data sets) for male rats.
99
Like the original model, our model is also a permeability-limited model including both protein
100
binding and active transport. Moreover, rather than requiring fitting to experimental data, all
101
PFOA-related parameters were obtained from in vitro studies, and then used to predict in vivo
102
ADME (i.e., in vitro-in vivo extrapolation, an alternative approach to traditional animal testing42).
103
Key improvements were also made to the original model. Enterohepatic circulation of PFOA is
104
now included, given the observation that it may contribute to PFOA accumulation in liver and
105
blood44. A gut compartment, bile and feces flows, and a “rest of body” compartment for those
106
tissues not explicitly modeled, were also added. Moreover, additional protein interactions, such
107
as active PFOA uptake into hepatocytes mediated by Na+/taurocholate cotransporting
108
polypeptide (Ntcp) and renal efflux by organic solute transporter (Ost) were considered, in order
109
to reflect recent observations for male rats45. Finally, the method used for deriving active
110
transport rates and the sensitivity analysis were improved, as described in sections 2.3.2 and 2.5.
111
Our model predicts the concentration of PFOA in different rat tissues as a function of time,
112
following either intravenous (IV) or oral dosing, and provides a flexible framework to test in
113
vitro-in vivo extrapolation.
114
2. MATERIALS AND METHODS
115
2.1 Rat Model Structure.
116
The model includes 7 tissues: blood, kidney, liver, gut, muscle, adipose and the rest of the body 6 / 30
ACS Paragon Plus Environment
Page 6 of 34
Page 7 of 34
Environmental Science & Technology
117
(Figure 1). Since this is a permeability-limited model, the consideration of tissue
118
subcompartments is required. Except for blood, each tissue contains both a vascular space and
119
tissue space, the latter of which can be further divided into two subcompartments: interstitial
120
fluid and tissue. To characterize absorption and elimination processes of PFOA, gut lumen, renal
121
filtrate and bile compartments were newly incorporated.
122
The blood compartment functions as systemic circulation, connecting each tissue compartment.
123
In blood, PFOA binds to serum albumin based on the equilibrium association constant, KAlb a .
124
Interstitial fluids of other compartments also contain albumin to which PFOA could bind46, 47.
125
Enterohepatic circulation may play a role in the distribution of PFOA in liver44 and thus was
126
considered in our model. Due to scarcity of data, we only included two transporters that could be
127
associated with the cycling of PFOA in liver: Oatp1a1 and Ntcp, both of which are located at the
128
basolateral membrane of hepatocytes48. Oatp1a1 has been demonstrated to transport PFOA27,
129
while for Ntcp only interactions with perfluorooctane sulfonate (PFOS) were reported45. Given
130
the structural similarity between PFOA and PFOS, we assume that Ntcp could also transport
131
PFOA. Once in the hepatocyte, PFOA can bind to liver-type fatty acid binding protein (L-FABP),
132
while the free fraction is available for excretion into the bile duct via passive diffusion. Biliary
133
PFOA is then circulated to gut lumen, where reabsorption of PFOA from the intestine back to
134
systemic circulation can occur, as well as elimination of PFOA through defecation.
135
The kidney is another major elimination tissue, involving glomerular filtration, tubular secretion,
136
and reabsorption processes. The free fraction of PFOA can transport from blood into filtrate
137
through both glomerular filtration and renal tubular secretion. The latter process is mainly
138
mediated via organic anion transporters (Oat1 and Oat3) located at the basolateral membrane of
139
proximal tubular cells18. PFOA is actively reabsorbed by Oatp1a1 from filtrate back to the tissue 7 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
140
compartment18, where PFOA can bind to two different proteins, L-FABP and α2µ-globulin
141
(traditionally but erroneously called kidney fatty acid binding protein49), both of which are
142
present in rat kidney tissue50, 51. The free fraction of PFOA in kidney tissue might be excreted
143
into blood through organic solute transporters (Ostα/β). Based on the observation of lower
144
kidney:blood PFOA concentration in male rats compared to female rats, it is hypothesized that
145
male rats have more effective efflux transporters on the renal basolateral membrane excreting
146
intracellular PFOA back to blood29; Ostα/β and Mrp6 are proposed to be promising candidates
147
for PFOA efflux18. Given available kinetic data for Ostα/β, it was included in our model.
148
Finally, muscle and adipose were selected for comparison to other tissues, since they typically
149
have the lowest levels of PFOA10,
150
compartment, “rest of body”.
151
2.2 Experimental Data.
152
Several studies have reported toxicokinetics and tissue distribution of PFOA in male rats for both
153
oral and IV dosing10, 14, 15. In order to compare different administration routes, data on single oral
154
and IV doses of 1 mg PFOA/kg body weight (BW) were chosen for our model evaluation10, 14.
155
Moreover, data from a single oral dose of 0.1 mg PFOA/kg BW and IV dose of 0.041 mg
156
PFOA/kg BW were used to verify model performance at low doses10, 15. This is of particular
157
toxicological relevance since the general population is exposed to low levels of PFOA, with
158
estimated daily intakes in the range of 1 to 130 ng PFOA/kg BW52. A total of seven experimental
159
datasets collected from three different studies were used and are briefly described below.
160
We extracted four datasets from the Kemper study10. The first two datasets are toxicokinetic data
161
for male rats administered by two routes: oral and IV dose. Specifically, four male Sprague-
15
. All remaining tissues were lumped into a single
8 / 30
ACS Paragon Plus Environment
Page 8 of 34
Page 9 of 34
Environmental Science & Technology
162
Dawley (SD) rats were dosed 1 mg PFOA/kg BW through oral or IV administration, respectively,
163
then blood samples were collected at different times and analyzed. A third experiment measuring
164
terminal tissue distribution was chosen for comparison to predicted PFOA levels in each organ.
165
In this experiment, 1 mg 14C-FPOA/kg BW was administered orally to four male SD rats. After
166
28 days, tissue samples were collected for analysis. Finally, the fourth dataset is a toxicokinetic
167
experiment with an oral dose of 0.1 mg PFOA/kg BW, where the PFOA concentration in blood
168
of four male SD rats, collected at different timepoints, was analyzed.
169
From a second study, conducted by Kim et al.14, two datasets were extracted wherein 1 mg
170
PFOA/kg BW oral and IV dose, respectively, were administrated to 5 male and 5 female SD rats.
171
Blood samples were collected and analyzed at different time points. At the end, tissue samples
172
including liver, kidney, heart, lung, and spleen were collected for analysis.
173
To further assess our model, we selected another dataset from a third study15 where four male
174
Wistar rats were intravenously dosed with the low dose of 0.041 mg [1-14C]PFOA/kg BW.
175
PFOA concentrations in blood collected at different time points and tissues, including liver,
176
kidney, intestine, testis, spleen, fat, heart, lung, brain, stomach, and carcass, analyzed after 2
177
hours, were available.
178
For all three studies (seven datasets in all), data were taken directly from tables if available or
179
extracted from plots using WebPlotDigitizer53.
180
2.3 Model Parameterization.
181
All parameters used in our model were rat-specific, except capillary surface area, albumin
182
concentrations in interstitial fluid compartments, and the transport kinetics of Ostα/β. The first
183
two parameters were estimated from other mammalian studies46, 47, 54, and Ostα/β kinetics were 9 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 10 of 34
184
based on an uptake study in human cells45. All these parameters are explained in detail below.
185
2.3.1 Rat Physiology.
186
Physiological parameters were obtained from the literature. The average body weight of rats in
187
each experimental dataset was used for model simulation. Fractional tissue volume, blood flow
188
rate, interstitial fluid and blood volumes, and capillary surface area for each compartment are
189
summarized in Supporting Information (SI) Table S1. Other parameters, including volume of
190
bile, renal filtrate, and gut content, as well as urinary, biliary, and fecal flow rates are also
191
indicated. Detailed information on derivation of physiological parameters is provided in Table
192
S1.
193
2.3.2 Protein-related Parameters.
194
Protein Concentrations. Table S2 indicates the concentrations of albumin, two specific
195
intracellular proteins (L-FABP and α2µ-globulin), and total protein in different compartments.
196
Total protein content is used to calculate tissue-specific uptake kinetics into cells, as explained in
197
SI section S2-1. Plasma albumin concentration was obtained from the study of Davies and
198
Morris55. For interstitial fluid compartments, the albumin concentrations related to serum levels
199
were available from other mammalian studies46, 47, and the same fluid:plasma albumin ratios for
200
liver, muscle and adipose were used in our rat model. Kidney and gut were assumed to have
201
similar interstitial albumin levels as liver, while the “rest of body” was assumed to be the same
202
as muscle. We assumed no albumin is present in tissue intracellular compartments.
203
Finally, for the two intracellular proteins considered, L-FABP is dominant in liver cytosol56 but
204
also present in kidney cells50; while α2µ-globulin is a male-specific protein predominant in rat
205
kidney cytosol51. The concentrations of these two proteins are indicated in Table S2. 10 / 30
ACS Paragon Plus Environment
Page 11 of 34
Environmental Science & Technology
206
Protein Interaction. Two forms of protein interaction were considered in our model: protein
207
binding and active transport.
208
For protein binding, the equilibrium association constants (Ka) for PFOA binding to albumin, L-
209
FABP, and α2µ-globulin were available from the literature22,
210
estimate the Ka for PFOA binding to rat serum albumin to be 3.1 × 103 M-1. It is worth noting
211
that this value is much smaller than Ka for human serum albumin24, 57. Since this is, to the best of
212
our knowledge, the only available value for rats, its uncertainty was considered in our model
213
sensitivity analysis. The interaction of L-FABP with PFCAs of different chain length has been
214
measured using fluorescence displacement and isothermal titration calorimetry techniques, and it
215
was shown that three binding sites exist for L-FABP interacting with PFOA: a single high-
216
affinity binding site and two additional low-affinity binding sites23. We assume these three
217
binding events are independent of one another. The interaction between α2µ-globulin and PFOA
218
was also determined and indicates a relatively weak binding affinity of about 2 mM49. However,
219
given the high concentration of α2µ-globulin in kidney51, we took this protein into consideration
220
when building our model.
221
Utilizing Ka, the individual rate constants for PFOA association and dissociation from each
222
protein (bon and boff) were derived in the same manner as in the original fish model43, as
223
described in SI Section S2-2.
224
For active transport, uptake kinetics of five organic anion transport proteins including Oat1, Oat3,
225
Oatp1a1, Ostα/β, and Ntcp (Table S4) were used for parameterization of active transport of
226
PFOA. Similar to the original model, the uptake kinetics of Oat1, Oat3, and Oatp1a1 transporters
227
were obtained from the Weaver et al. study27. Based on another study where uptake rates of
228
PFOS for rat Ntcp and human Ostα/β were available45, we assumed that rat Ntcp has the same 11 / 30
ACS Paragon Plus Environment
23, 49
(Table S3). Han et al.22
Environmental Science & Technology
Page 12 of 34
229
kinetics for transporting PFOA as PFOS and that kinetics of rat Ostα/β transporting PFOA is the
230
same as human Ostα/β transporting PFOS. Finally, all uptake or efflux proteins were assumed to
231
be unidirectional. These in vitro to in vivo extrapolations are further described in SI Section S3-3
232
and Table S6.
233
2.4 Mass Balance Equations.
234
Our model is based on permeability-limited equations that consider three or four
235
subcompartments for each tissue. Based on the original model43, the effective membrane
236
permeabilities for each tissue were obtained from a PFOA cellular uptake study27, as summarized
237
in Table S5. Each compartment is described by mass balance equations that are established
238
according to PFOA-protein interactions and exchange between connected compartments.
239
For an individual tissue i (i = A, G, K, L, M, and R, for adipose, gut, kidney, liver, muscle, and
240
rest of body, respectively), the mass of free and bound PFOA in each subcompartment j (j = F
241
and T, for fluid and tissue subcompartment, respectively) is determined. In blood compartment
242
(B), which is in contact with and can be considered a subcompartment of each tissue:
243
244
= ∑ b M − ∑ b M + b M − b M
+ b M − b M
(1)
245
246
In interstitial fluid subcompartment iF:
247
!"
248
= b M − b M
(2)
# # # = b M − b M + b # M − b # M + b # $ % M − b$ % M
+ b M − b M
(3)
12 / 30
ACS Paragon Plus Environment
Page 13 of 34
Environmental Science & Technology
249
!"
250
In tissue subcompartment iT (i = A, M, and R):
251
!&
252
!&
253
For tissues (i = K, L, and G) containing additional compartments, namely filtrate, bile, or gut
254
lumen (GL), the mass balance equations in tissue subcompartment iT and corresponding
255
additional compartments are:
256
'&
# # # = b # M − b M
(6)
(# (# (# (# −b(# M + b (# M + b(# $ % M − b M
258
'&
259
"!)*+*
261
(# (# (# = b(# M − b M
263
.&
264
!)
265
0&
266
0.
(8)
, -
M
(9)
/ /# / /# /# = b/ /# M − b/# / M + b/ /# M − b/# M $ % M + b
262
(7)
(# = b M − b M + b(# M − b (# M
− b (# M − $ %
.&
(5)
( (# ( (# ( (# (# = b( (# M − b(# ( M + b( (# M $ % M − b$ % M + b
257
260
(4)
# # # # # # # = b # M − b # M + b # $ % M − b$ % M + b M − b M
= b M − b M
/# /# /# +b/# M − b M
(10)
/# /# /# = b/# M − b M
(11)
/# = b/# M − b /# M −
, -
M
(12)
1 1# 1/ 1# = b1 1# M − b1# 1 M + b1/ 1# M − b1# 1/ M
1# 1/ = b1# 1/ M − b1/ 1# M +
, -
M −
,$2 -1/
1# M
13 / 30
ACS Paragon Plus Environment
(13)
(14)
Environmental Science & Technology
Page 14 of 34
267
M ijfree represents unbound PFOA in ij subcompartment, which can freely transport between
268
ij compartments, while M bound refers to PFOA bound to albumin, L-FABP or α2µ-globulin.
269
Parameters bij-ik and bij-jk active are first-order rate constants for passive diffusion and active transport
270
between subcompartments ij and ik (j ≠ k), respectively. Q and V are the flow rate and volume of
271
a compartment, respectively. The protein-binding rate constant in subcompartment ij is bijon and
272
the dissociation rate constant is bijoff. All rate constants for transport among compartments are
273
explained in detail in SI Section S3.
274
2.5 Sensitivity and Uncertainty Analysis.
275
There were 72 independent parameters used in our model to predict the toxicokinetics and tissue
276
distribution of PFOA in rats, and no parameters were fit to match the data. We conducted a
277
Monte Carlo uncertainty analysis to characterize the propagation of uncertainty to predicted
278
PFOA concentration profiles in three key tissues: blood, liver, and kidney. For each of the 10000
279
iterations, parameters were sampled from uniform, normal or lognormal distributions (see SI
280
Section S4 and Table S7). Correlation analysis was then conducted between sampled parameters
281
and predicted PFOA concentrations to quantify the sensitivity of the model to each input
282
parameter.
283
2.6 Software and Model Code.
284
The PBPK model considered in this study was programmed in MATLAB (MATLAB R2016a),
285
and the code is available in SI section S6.
286
3. RESULTS
287
3.1 Plasma Toxicokinetics.
14 / 30
ACS Paragon Plus Environment
Page 15 of 34
Environmental Science & Technology
288
We first compare the toxicokinetics of PFOA predicted by our model to six datasets extracted
289
from three different studies where different administration routes and dose levels were
290
available10, 14, 15 (Figure 2). In order to compare between predicted concentrations and these data,
291
we ran the model using the BWs and doses given in each experimental study. Since the
292
information on BW was not given by Kim et al14., we assumed these rats were the same average
293
BW as in Kemper10, given that both studies used SD rats as test species.
294
As indicated in Figure 2, all experimental datasets fall within the 95% confidence interval of the
295
predicted concentrations of PFOA in plasma, and the geometric means of simulated results (solid
296
lines) indicate similar time course behaviors observed in each experiment. In addition, based on
297
the geometric means of predicted results, the toxicokinetic parameters, including the maximum
298
plasma concentration of PFOA (Cmax), the time required to reach the peak concentration (Tmax),
299
the half-life (T1/2), and the clearance of PFOA (CL), were calculated and compared with the
300
Kemper study10, as shown in Table S8. Compared to the mean of Cmax in the Kemper study, the
301
model-predicted results after 0.1 mg PFOA/kg BW and 1 mg PFOA/kg BW oral dose are
302
underestimated by a factor of 1.9 and 3, respectively, but the predicted Tmax fall well within the
303
range of the experimental values for both dose scenarios. Furthermore, the T1/2 (6.80, 6.93, and
304
6.93 days for 0.1 mg PFOA/kg BW oral dose, 1 mg PFOA/kg BW oral dose, and 1 mg PFOA/kg
305
BW IV dose, respectively) predicted by our model are in very good agreement with the reported
306
mean values (8.41, 5.76, and 7.73 days for 0.1 mg PFOA/kg BW oral dose, 1 mg PFOA/kg BW
307
oral dose, and 1 mg PFOA/kg BW IV dose, respectively) under three different dose scenarios.
308
This suggests that the T1/2 is independent of dose scenarios. Finally, all predictions of the
309
clearance of PFOA are overestimated by about a factor of 3.
15 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
310
Page 16 of 34
Table 1. Correlation coefficients between parameters and predicted PFOA levels in blood, liver, and kidney. Parametersa
Bloodb
Liverb
Kidneyb
-
0.13
0.11
Blood volume (VB)
0.18
-
-
Plasma volume (Vplasma)
-0.16
-
-
Glomerular filtration rate (QGFR)
-0.26
-0.25
-
Urine flow rate (Qurine)
-0.28
-0.25
-0.36
Capillary surface area of kidney (AK)
0.27
0.26
-
-
-
0.16
Effective permeability for blood (PeffB)
0.24
0.23
-0.17
Effective permeability for liver (PeffL)
0.10
-0.34
0.11
-
-
-0.17
Steady-state cell-water concentration ratio for kidney (CRssK)
0.25
0.24
0.38
Albumin concentration in blood (CAlbB)
0.29
-
-
Albumin concentration in kidney fluid (CAlbKF)
-
-
0.13
L-FABP concentration in liver tissue (CLFABPLT)
-
0.33
-0.10
L-FABP concentration in kidney tissue (CLFABPKT)
-
-
0.12
Association constant of L-FABP binding site 1 (KLFABP1)
-
0.22
-
Association constant of albumin (Ka)
0.35
-
0.15
Renal clearance rate constant (bclear)
-0.16
-0.16
-
Renal reabsorption rate constant (breab)
0.26
0.26
0.39
Renal efflux rate constant (befflux)
0.17
0.16
-
Absorption rate constant of hepatocyte (babs)
-0.11
0.33
-0.12
Dose
Enlargement factor of apical membrane of proximal tubule (n)
Effective permeability for kidney (PeffK)
a
Only parameters with correlation coefficients ≥ 0.1 and P values < 0.05 are indicated here. The results for other
parameters can be found in Figures S1-S3. b
Coefficient values are the average of three simulation results for different doses and administration routes (1 mg/kg oral
dose, 1 mg/kg IV dose and 0.1 mg/kg oral dose). Sensitivities were similar for the three scenarios.
16 / 30
ACS Paragon Plus Environment
Page 17 of 34
Environmental Science & Technology
311
In comparison with the experimental results of the Kim et al. study14, our model performs best
312
for the 1 mg PFOA/kg BW IV dose scenario, with all experimental data close to the predicted
313
geometric mean. However, for 1 mg PFOA/kg BW oral dose, the elimination rate is
314
underestimated by our model by about a factor of 3. It is worth noting that even under the same
315
dose scenarios, the PFOA toxicokinetics vary substantially among different studies (Figure 2, 1
316
mg PFOA/kg BW IV and oral dose), reflecting both natural biological variation and differences
317
in experimental procedures (e.g., sampling and analysis methods).
318
The last experimental dataset is from Kudo et al.15, where the PFOA concentration profile only
319
up to 5 hours after dosing was observed. Our model was also able to capture the behavior of
320
PFOA in plasma for this short-term experiment, including during the distribution phase, which is
321
considered to be the first 2 hours in the Kudo et al. study.
322
3.2 Tissue Distribution
323
We next evaluate our model performance in predicting the tissue distribution of PFOA through
324
comparison with four datasets extracted from the three studies10, 14, 15 (Figure 3 and Figures S4-
325
S6). As indicated in Figure 3, all measured PFOA concentrations in each tissue fall well within
326
or overlap with the model-predicted ranges, except the hepatic PFOA concentration from the
327
Kudo et al. study15, which is 4.5 times higher than the predicted result. It is worth noting that the
328
four experimental datasets were sampled at different time points, namely: dataset A at 28 days
329
following dosing, dataset B and C at 12 days following dosing, and dataset D at 2 hours after
330
dosing. Due to propagating uncertainty in the model, variability in predicted results increases
331
with time.
332
With respect to distribution patterns, our model prediction (liver > kidney > blood > gut > 17 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 18 of 34
333
muscle ≈ adipose) is similar to that measured by Kemper (liver > kidney ≈ blood > gut > muscle >
334
adipose). In the Kim et al. study14, PFOA concentrations were available for blood, kidney, and
335
liver, and the comparison between these data and model predictions indicates the same
336
distribution pattern: liver > kidney > blood. Finally, the model under-predicts the PFOA
337
concentration in the short-term dosing simulation compared with measured data in Kudo et al.15.
338
In this case alone, predicted PFOA concentrations in liver are lower than in blood.
339
A further comparison was conducted between the geometric mean of predicted results and the
340
mean of measured data for each tissue in each study. In the blood compartment, predicted values
341
are well within a factor of 1.5 of measured concentrations for both high and low IV dose (Figure
342
3, C and D). For the oral study, even though both were at the same dose level of 1 mg PFOA/kg
343
BW, our predicted results are either underestimated or overestimated by a factor of 4.7 or 3.5,
344
respectively, which may reflect the biological or experimental variability. In the liver, our model
345
generally under-predicts the results by a factor ranging from 1.6 to 4.5 for the four different dose
346
scenarios. Similarly, in the kidney compartment, PFOA concentrations are generally under-
347
predicted within a factor ranging from 1.2 to 2.7 for all dose scenarios except Kudo et al15., for
348
which the prediction is overestimated by a factor of 2.5. For the gut compartment, data were
349
available from the Kemper10 and Kudo et al.15 studies, and our model underestimates or
350
overestimates by a factor of 3.1 or 1.4, respectively. In adipose, the predicted geometric mean
351
falls within the range of measured values in the Kemper study10, and for Kudo et al.15, the
352
prediction is overestimated by only a factor of 1.7. Finally, only one dataset from Kemper10 was
353
available for comparison with the muscle compartment, and the PFOA concentration is under-
354
predicted by our model, but within a factor of 4.
355
3.3 Uncertainty and Sensitivity Analysis 18 / 30
ACS Paragon Plus Environment
Page 19 of 34
Environmental Science & Technology
356
In order to explore the effect of parameter uncertainties on model performance, Monte Carlo
357
uncertainty and sensitivity analysis was conducted for all 72 independent model parameters (SI
358
Section S4). As shown in Figures S1-S3 and Table 1, the most important parameters are related
359
to protein binding (i.e., the concentration of albumin and L-FABP in corresponding
360
compartments and the equilibrium association constants for binding to PFOA) and active
361
transport (i.e., renal clearance rate constant, renal reabsorption rate constant, renal efflux rate
362
constant, and absorption rate constants for hepatocytes). Second most important are parameters
363
relevant to passive diffusion, including the effective membrane permeability (Peff) for blood,
364
liver, and kidney, the steady-state cell-water concentration ratio for kidney (CRssK) describing
365
the back-diffusion from kidney tissue to filtrate43 and the surface area for exchange between
366
blood and kidney (AK, Table 1). Finally, some physiological parameters such as blood and
367
plasma volume, glomerular filtration rate and urine flow rate also have a significant effect on
368
predictions of tissue-specific PFOA concentrations.
369
4. DISCUSSION
370
In this study, we developed a permeability-limited PBPK model that explicitly considers PFOA-
371
protein interactions for toxicokinetics and distribution of PFOA in male rats. A total of seven
372
datasets from three different studies were used to evaluate model performance, and all the
373
experimental data fall within or overlap the prediction ranges, except PFOA concentration in the
374
liver from the Kudo et al.15 study. It is possible, given that the Kudo et al. tissue distributions
375
were sampled after only two hours, that the distribution from blood to liver in our model is
376
slower than in vivo. This prediction was the only one in which PFOA concentration was higher
377
in blood than in liver, in contrast with both the other experimental results and the other model
378
predictions. Such a lag time could be caused by our parameterization of liver blood flow, surface 19 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 20 of 34
379
area for exchange blood-liver exchange, or passive diffusion. It is also worth noting that in the
380
Kudo et al. study the subcellular distribution of PFOA in the liver 2 hours after IV administration
381
showed that only 3% of the PFOA was detected in the cytosolic fraction, with most found in the
382
membrane fraction. It is hypothesized that PFOA might bind to some membrane components
383
such as proteins or lipids58. However, given the limited information available on membrane
384
binding, our model did not consider these interactions, which could cause the failure to capture
385
PFOA localization on hepatic membranes. Our model did successfully predict the hepatic
386
distribution of PFOA at time points of 12 and 28 days.
387
In comparisons with all three experimental studies, our model was able to predict plasma
388
toxicokinetics and tissue distribution of PFOA well within a factor of 5. Considering natural
389
biological variation and experimental differences among studies, the agreement is remarkable. In
390
the blood compartment, PFOA concentrations were generally under-predicted in comparison
391
with the Kemper study10 (Figure 2). In addition to natural biological variation, the reason for this
392
under-prediction could also be attributed to the equilibrium association constant (Ka) for binding
393
to albumin, which is the most influential parameter for the prediction in the blood. As described
394
in the Methods section 2.3.2, only one study22 was available for the Ka of rats, and the value (3.1
395
× 103 M-1) is much smaller compared with the Ka of human serum albumin in other studies (e.g.,
396
1 × 104 M-1 and 3.12 × 104 M-1). If we assume the Ka used in our model is a lower bound, then
397
the predictions would fit better with the experimental data. However, more studies are needed to
398
measure Ka for PFOA in rats before making this assumption.
399
Similarly, in each tissue compartment, our model generally under-predicts PFOA levels to a
400
small extent in comparison with all three studies except Kudo et al. (Figure 3 D). Currently, there
401
are two cytosolic proteins included in our model: L-FABP (in the liver and kidney) and α2µ20 / 30
ACS Paragon Plus Environment
Page 21 of 34
Environmental Science & Technology
402
globulin (only in the kidney). However, other FABPs, which could also bind to PFOA, were not
403
considered due to the lack of protein-binding data. For example, the heart-type FABP (H-FABP),
404
which is ubiquitous in many tissues including liver, kidney, gut, and heart in the rat59, is reported
405
to have a higher fatty acid binding affinity than L-FABP60. Given the similarity of perfluorinated
406
alkyl acids to fatty acids within organisms, H-FABP may also have a higher PFOA binding
407
affinity, and the lack of H-FABP binding may explain the underestimation of PFOA levels in
408
kidney, liver, and gut compartments. In addition, it is possible that another sensitive parameter,
409
the albumin concentration in the interstitial fluid subcompartment of each tissue, was under-
410
predicted since this parameter was derived based on other mammalian studies.
411
This work is the first permeability-limited PBPK model that explicitly considers PFOA-protein
412
interactions for rats. Compared to the original fish model43, prediction accuracy is further
413
improved by incorporation of rat-specific parameters and inclusion of new tissues and proteins.
414
The good agreement between the simulation results and experimental data illustrate our model’s
415
ability to predict the toxicokinetics and tissue distribution of PFOA in rats. Our model also
416
provided several new insights on processes that drive the tissue distribution of PFOA compared
417
with previous flow-limited models. We demonstrate that L-FABP, Oatp1al and Ntcp transporters
418
play key roles for PFOA accumulation in rat liver; in kidney tissue, the efflux of PFOA back to
419
the blood by Ostα/β is also important.
420
This model includes a Monte Carlo-based uncertainty and sensitivity analyses that could be used
421
in future to address population polymorphism. Each parameter is sampled from a pre-defined
422
distribution to account for biological variation and uncertainty. A generalized model that can be
423
applied to a population (not just an individual) could therefore be easily derived by defining the
424
distributions for protein- or physiology-related parameters in a way that captures observed 21 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 22 of 34
425
polymorphisms. Because our model is mechanistic, the influence of such polymorphisms on
426
tissue distribution, whole-body half-lives, and accumulation potentials could thus be directly
427
probed in a way that is not accessible to a model whose parameters are fit to in vivo data. Our
428
model makes these relationships explicit, thus providing an effective framework to conduct in
429
vitro-in vivo extrapolation. The in vitro-in vivo extrapolations used in our model also represent a
430
promising avenue to reduce the use of whole animals in toxicology and risk assessment, and a
431
potential solution to the challenges caused by limited human in vivo data.
432
Extrapolation to Humans. To extrapolate our model approach to humans, human physiology and
433
PFOA-protein interaction parameters are needed. All physiological parameters including
434
compartmental volume, blood and fluid flow rates, and capillary surface area for each tissue can
435
be obtained from the literature (the surface areas for some tissues in humans can be estimated
436
from other mammals)54, 55, 61, 62. The concentrations and the equilibrium association constants of
437
serum albumin and liver-type fatty acid binding protein (L-FABP) are also available46, 47, 50, 55, 63.
438
However, some protein binding parameters, such as binding characteristics of H-FABP, are still
439
missing. Importantly, more data on transporter kinetics are required to extrapolate the transport
440
rate constants of PFOA to different tissues. In vitro kinetic studies have indicated that OAT1,
441
OAT3, OAT4, and urate transporter 1 (URAT1) play key roles in human renal secretion and
442
reabsorption of PFOA29. However, kinetic data for other transporters such as OSTα/β, breast
443
cancer resistance protein (BCRP), and multidrug resistance-associated proteins (MRP2, MRP4,
444
and MRP6) are currently not available18. It is important for future studies to investigate whether
445
these transporters are involved in PFAS transport in humans.
446
Model Limitations. Some limitations that might affect the model performance need to be
447
addressed. First, many new parameters are incorporated into our model, and some are based on a 22 / 30
ACS Paragon Plus Environment
Page 23 of 34
Environmental Science & Technology
448
single study (e.g., Ka for binding to proteins) or extrapolated from in vitro studies (e.g., effective
449
permeabilities and active transport rate constants), indicating that their uncertainties are very
450
high. Furthermore, some protein binding parameters such as H-FABP were not included in our
451
model due to limited data. Finally, female rats were not considered in our model due to lack of
452
data to account for the substantial gender differences in PFOA elimination in male and female
453
rats. Especially little information is available about the kinetics of some Mrps (e.g., Mrp364),
454
which are female-dominant and might play an important role in faster PFOA efflux in female
455
rats. Most of these parameters are protein-related and play important roles in predicting PFOA
456
toxicokinetics, based on our sensitivity analysis.
457
To overcome these limitations, we first call for more in vitro data on protein interactions for
458
PFOA and other PFAS. The measurement of equilibrium association constants for binding to H-
459
FABP and the transport kinetics of Mrps are urgently required, as these parameters have the
460
potential to substantially affect toxicokinetics. Further measurements of Ka for PFOA binding to
461
albumin and L-FABP are also needed, since these parameters were each obtained from a single
462
in vitro study. If more data were available, the associated uncertainties would decrease. From the
463
model perspective, statistical tools like Bayesian analysis and Markov chain Monte Carlo
464
simulations can be applied to reduce the uncertainty of parameters65 and molecular modeling
465
tools can provide suggestions for promising protein targets for further research.
466
However, it should be noted that these are in a way one-time costs for a given organism and class
467
of chemicals. It is our vision that, once sufficiently parameterized, this type of model can become
468
a flexible and generalizable predictive tool that can be applied to a variety of simulations in a
469
way that is not possible with a model trained on experimental data. The sensitivity analysis we
470
include here can serve, as we further develop such general PBPK models, to give us an idea of 23 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
471
when “enough is enough” as relates to the importance of specifying each physiological and
472
protein interaction parameter. The ability of our current model, uncertainties notwithstanding, to
473
successfully simulate a variety of experiments and dosing scenarios provides a first
474
demonstration of the power of this approach.
475
476
ASSOCIATED CONTENT
477
Supporting Information
478
Details on model parameterization and sensitivity analysis are available in the Supporting
479
Information (SI). This material is available free of charge via the Internet at http://pubs.acs.org/.
480
481
AUTHOR INFORMATION
482
Corresponding Author
483
*Address: 3700 O’Hara St, Pittsburgh, PA 15261 USA. Tel: 412-383-4075; Fax: 412-624-0135;
484
E-mail:
[email protected].
485
Notes
486
The authors declare no competing financial interest.
24 / 30
ACS Paragon Plus Environment
Page 24 of 34
Page 25 of 34
Environmental Science & Technology
487
REFERENCES
488
(1) Wang, Z.; Cousins, I. T.; Scheringer, M.; Buck, R. C.; Hungerbühler, K. Global emission inventories for C 4–C
489
14 perfluoroalkyl carboxylic acid (PFCA) homologues from 1951 to 2030, part II: The remaining pieces of the
490
puzzle. Environ. Int. 2014, 69, 166-176.
491
(2) Lau, C.; Anitole, K.; Hodes, C.; Lai, D.; Pfahles-Hutchens, A.; Seed, J. Perfluoroalkyl acids: a review of
492
monitoring and toxicological findings. Toxicol. Sci. 2007, 99 (2), 366-394.
493
(3) Krafft, M. P.; Riess, J. G. Per-and polyfluorinated substances (PFASs): Environmental challenges. Curr. Opin.
494
Colloid Interface Sci. 2015, 20 (3), 192-212.
495
(4) Kannan, K. Perfluoroalkyl and polyfluoroalkyl substances: current and future perspectives. Environ. Chem. 2011,
496
8 (4), 333-338.
497
(5) Wang, Z.; Cousins, I. T.; Scheringer, M.; Hungerbuehler, K. Hazard assessment of fluorinated alternatives to
498
long-chain perfluoroalkyl acids (PFAAs) and their precursors: status quo, ongoing challenges and possible solutions.
499
Environ. Int. 2015, 75, 172-179.
500
(6) Olsen, G. W.; Lange, C. C.; Ellefson, M. E.; Mair, D. C.; Church, T. R.; Goldberg, C. L.; Herron, R. M.;
501
Medhdizadehkashi, Z.; Nobiletti, J. B.; Rios, J. A. Temporal trends of perfluoroalkyl concentrations in American
502
Red Cross adult blood donors, 2000–2010. Environ. Sci. Technol. 2012, 46 (11), 6330-6338.
503
(7) Schröter-Kermani, C.; Müller, J.; Jürling, H.; Conrad, A.; Schulte, C. Retrospective monitoring of
504
perfluorocarboxylates and perfluorosulfonates in human plasma archived by the German Environmental Specimen
505
Bank. Int. J. Hyg. Environ. Health 2013, 216 (6), 633-640.
506
(8) Wu, M.; Sun, R.; Wang, M.; Liang, H.; Ma, S.; Han, T.; Xia, X.; Ma, J.; Tang, L.; Sun, Y. Analysis of
507
perfluorinated compounds in human serum from the general population in Shanghai by liquid chromatography-
508
tandem mass spectrometry (LC-MS/MS). Chemosphere 2017, 168, 100-105.
509
(9) Toms, L.-M.; Thompson, J.; Rotander, A.; Hobson, P.; Calafat, A. M.; Kato, K.; Ye, X.; Broomhall, S.; Harden,
510
F.; Mueller, J. F. Decline in perfluorooctane sulfonate and perfluorooctanoate serum concentrations in an Australian
511
population from 2002 to 2011. Environ. Int. 2014, 71, 74-80.
512
(10) Kemper, R. A. Perfluorooctanoic acid: toxicokinetics in the rat. Project ID: DuPont 2003, 7473.
513
(11) Kuslikis, B. I.; Vanden Heuvel, J. P.; Peterson, R. E. Lack of evidence for perfluorodecanoyl ‐ or 25 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 26 of 34
514
perfluorooctanoyl‐coenzyme a formation in male and female rats. J. Biochem. Mol. Toxicol. 1992, 7 (1), 25-29.
515
(12) Heuvel, J. P. V.; Kuslikis, B. I.; Van Rafelghem, M. J.; Peterson, R. E. Tissue distribution, metabolism, and
516
elimination of perfluorooctanoic acid in male and female rats. J. Biochem. Toxicol. 1991, 6 (2), 83-92.
517
(13) Vestergren, R.; Cousins, I. T. Tracking the pathways of human exposure to perfluorocarboxylates. Environ. Sci.
518
Technol. 2009, 43 (15), 5565-5575.
519
(14) Kim, S.-J.; Heo, S.-H.; Lee, D.-S.; Hwang, I. G.; Lee, Y.-B.; Cho, H.-Y. Gender differences in
520
pharmacokinetics and tissue distribution of 3 perfluoroalkyl and polyfluoroalkyl substances in rats. Food Chem.
521
Toxicol. 2016, 97, 243-255.
522
(15) Kudo, N.; Sakai, A.; Mitsumoto, A.; Hibino, Y.; Tsuda, T.; Kawashima, Y. Tissue distribution and hepatic
523
subcellular distribution of perfluorooctanoic acid at low dose are different from those at high dose in rats. Biol.
524
Pharm. Bull. 2007, 30 (8), 1535-1540.
525
(16) Martin, J. W.; Mabury, S. A.; Solomon, K. R.; Muir, D. C. Bioconcentration and tissue distribution of
526
perfluorinated acids in rainbow trout (Oncorhynchus mykiss). Environ. Toxicol. Chem. 2003, 22 (1), 196-204.
527
(17) Conder, J. M.; Hoke, R. A.; Wolf, W. d.; Russell, M. H.; Buck, R. C. Are PFCAs bioaccumulative? A critical
528
review and comparison with regulatory criteria and persistent lipophilic compounds. Environ. Sci. Technol. 2008, 42
529
(4), 995-1003.
530
(18) Han, X.; Nabb, D. L.; Russell, M. H.; Kennedy, G. L.; Rickard, R. W. Renal elimination of
531
perfluorocarboxylates (PFCAs). Chem. Res. Toxicol. 2011, 25 (1), 35-46.
532
(19) Olsen, G. W.; Burris, J. M.; Ehresman, D. J.; Froehlich, J. W.; Seacat, A. M.; Butenhoff, J. L.; Zobel, L. R.
533
Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexanesulfonate, and perfluorooctanoate in
534
retired fluorochemical production workers. Environ. Health Perspect. 2007, 1298-1305.
535
(20) Kennedy, G. L.; Butenhoff, J. L.; Olsen, G. W.; O'Connor, J. C.; Seacat, A. M.; Perkins, R. G.; Biegel, L. B.;
536
Murphy, S. R.; Farrar, D. G. The toxicology of perfluorooctanoate. Crit. Rev. Toxicol. 2004, 34 (4), 351-384.
537
(21) Naomi, K.; Kawashima, Y. Toxicity and toxicokinetics of perfluorooctanoic acid in humans and animals. J.
538
Toxicol. Sci. 2003, 28 (2), 49-57.
539
(22) Han, X.; Snow, T. A.; Kemper, R. A.; Jepson, G. W. Binding of perfluorooctanoic acid to rat and human
540
plasma proteins. Chem. Res. Toxicol. 2003, 16 (6), 775-781.
541
(23) Woodcroft, M. W.; Ellis, D. A.; Rafferty, S. P.; Burns, D. C.; March, R. E.; Stock, N. L.; Trumpour, K. S.; Yee, 26 / 30
ACS Paragon Plus Environment
Page 27 of 34
Environmental Science & Technology
542
J.; Munro, K. Experimental characterization of the mechanism of perfluorocarboxylic acids' liver protein
543
bioaccumulation: The key role of the neutral species. Environ. Toxicol. Chem. 2010, 29 (8), 1669-1677.
544
(24) Hebert, P. C.; MacManus-Spencer, L. A. Development of a fluorescence model for the binding of medium-to
545
long-chain perfluoroalkyl acids to human serum albumin through a mechanistic evaluation of spectroscopic
546
evidence. Anal. Chem. 2010, 82 (15), 6463-6471.
547
(25) Luebker, D. J.; Hansen, K. J.; Bass, N. M.; Butenhoff, J. L.; Seacat, A. M. Interactions of flurochemicals with
548
rat liver fatty acid-binding protein. Toxicology 2002, 176 (3), 175-185.
549
(26) Han, X.; Yang, C.-H.; Snajdr, S. I.; Nabb, D. L.; Mingoia, R. T. Uptake of perfluorooctanoate in freshly
550
isolated hepatocytes from male and female rats. Toxicol. Lett. 2008, 181 (2), 81-86.
551
(27) Weaver, Y. M.; Ehresman, D. J.; Butenhoff, J. L.; Hagenbuch, B. Roles of rat renal organic anion transporters
552
in transporting perfluorinated carboxylates with different chain lengths. Toxicol. Sci. 2009, kfp275.
553
(28) Yang, C.-H.; Glover, K. P.; Han, X. Organic anion transporting polypeptide (Oatp) 1a1-mediated
554
perfluorooctanoate transport and evidence for a renal reabsorption mechanism of Oatp1a1 in renal elimination of
555
perfluorocarboxylates in rats. Toxicol. Lett. 2009, 190 (2), 163-171.
556
(29) Yang, C.-H.; Glover, K. P.; Han, X. Characterization of cellular uptake of perfluorooctanoate via organic
557
anion-transporting polypeptide 1A2, organic anion transporter 4, and urate transporter 1 for their potential roles in
558
mediating human renal reabsorption of perfluorocarboxylates. Toxicol. Sci. 2010, 117 (2), 294-302.
559
(30) Armitage, J. M.; Erickson, R. J.; Luckenbach, T.; Ng, C. A.; Prosser, R. S.; Arnot, J. A.; Schirmer, K.; Nichols,
560
J. W. Assessing the bioaccumulation potential of ionizable organic compounds: Current knowledge and research
561
priorities. Environ. Toxicol. Chem. 2017, 36 (4), 882-897.
562
(31) Benfenati, E.; Gini, G.; Hoffmann, S.; Luttik, R. Comparing in vivo, in vitro and in silico methods and
563
integrated strategies for chemical assessment: problems and prospects. Altern. Lab. Anim. 2010, 38 (2), 153.
564
(32) Lin, Z.; Gehring, R.; Mochel, J.; Lavé, T.; Riviere, J. Mathematical modeling and simulation in animal health–
565
Part II: principles, methods, applications, and value of physiologically based pharmacokinetic modeling in
566
veterinary medicine and food safety assessment. J. Vet. Pharmacol. Ther. 2016, 39 (5), 421-438.
567
(33) Loccisano, A. E.; Campbell, J. L.; Butenhoff, J. L.; Andersen, M. E.; Clewell, H. J. Comparison and evaluation
568
of pharmacokinetics of PFOA and PFOS in the adult rat using a physiologically based pharmacokinetic model.
569
Reprod. Toxicol. 2012, 33 (4), 452-467. 27 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 28 of 34
570
(34) Tan, Y.-M.; Clewell, H. J.; Andersen, M. E. Time dependencies in perfluorooctylacids disposition in rat and
571
monkeys: a kinetic analysis. Toxicol. Lett. 2008, 177 (1), 38-47.
572
(35) Worley, R. R.; Fisher, J. Application of physiologically-based pharmacokinetic modeling to explore the role of
573
kidney transporters in renal reabsorption of perfluorooctanoic acid in the rat. Toxicol. Appl. Pharmacol. 2015, 289
574
(3), 428-441.
575
(36) Fàbrega, F.; Kumar, V.; Schuhmacher, M.; Domingo, J. L.; Nadal, M. PBPK modeling for PFOS and PFOA:
576
Validation with human experimental data. Toxicol. Lett. 2014, 230 (2), 244-251.
577
(37) Loccisano, A. E.; Campbell, J. L.; Andersen, M. E.; Clewell, H. J. Evaluation and prediction of
578
pharmacokinetics of PFOA and PFOS in the monkey and human using a PBPK model. Regul. Toxicol. Pharmacol.
579
2011, 59 (1), 157-175.
580
(38) Andersen, M. E.; Clewell, H. J.; Tan, Y.-M.; Butenhoff, J. L.; Olsen, G. W. Pharmacokinetic modeling of
581
saturable, renal resorption of perfluoroalkylacids in monkeys—probing the determinants of long plasma half-lives.
582
Toxicology 2006, 227 (1), 156-164.
583
(39) Fàbrega, F.; Kumar, V.; Benfenati, E.; Schuhmacher, M.; Domingo, J. L.; Nadal, M. Physiologically based
584
pharmacokinetic modeling of perfluoroalkyl substances in the human body. Toxicol. Environ. Chem. 2015, 97 (6),
585
814-827.
586
(40) Drinking Water Health Advisory for Perfluorooctanoic Acid (PFOA). U.S. Environmental Protection Agency,
587
Washington, DC, EPA-822-R-16-003, 2016.
588
(41) Krewski, D.; Acosta Jr, D.; Andersen, M.; Anderson, H.; Bailar III, J. C.; Boekelheide, K.; Brent, R.; Charnley,
589
G.; Cheung, V. G.; Green Jr, S. Toxicity testing in the 21st century: a vision and a strategy. J. Toxicol. Environ.
590
Health 2010, 13 (2-4), 51-138.
591
(42) Yoon, M.; Campbell, J. L.; Andersen, M. E.; Clewell, H. J. Quantitative in vitro to in vivo extrapolation of cell-
592
based toxicity assay results. Crit. Rev. Toxicol. 2012, 42 (8), 633-652.
593
(43) Ng, C. A.; Hungerbühler, K. Bioconcentration of perfluorinated alkyl acids: how important is specific binding?
594
Environ. Sci. Technol. 2013, 47 (13), 7214-7223.
595
(44) Johnson, J. D.; Gibson, S. J.; Ober, R. E. Cholestyramine-enhanced fecal elimination of carbon-14 in rats after
596
administration of ammonium [14C] perfluorooctanoate or potassium [14C] perfluorooctanesulfonate. Fundam. Appl.
597
Toxicol. 1984, 4 (6), 972-976. 28 / 30
ACS Paragon Plus Environment
Page 29 of 34
Environmental Science & Technology
598
(45) Zhao, W.; Zitzow, J. D.; Ehresman, D. J.; Chang, S.-C.; Butenhoff, J. L.; Forster, J.; Hagenbuch, B.
599
Na+/taurocholate cotransporting polypeptide and apical sodium-dependent bile acid transporter are involved in the
600
disposition of perfluoroalkyl sulfonates in humans and rats. Toxicol. Sci. 2015, 146 (2), 363-373.
601
(46) Levitt, D. G. The pharmacokinetics of the interstitial space in humans. BMC Clin. Pharmacol. 2003, 3 (1), 3.
602
(47) Ellmerer, M.; Schaupp, L.; Brunner, G. A.; Sendlhofer, G.; Wutte, A.; Wach, P.; Pieber, T. R. Measurement of
603
interstitial albumin in human skeletal muscle and adipose tissue by open-flow microperfusion. Am. J. Physiol.
604
Endocrinol. Metab. 2000, 278 (2), E352-E356.
605
(48) Faber, K. N.; Müller, M.; Jansen, P. L. Drug transport proteins in the liver. Adv. Drug Del. Rev. 2003, 55 (1),
606
107-124.
607
(49) Han, X.; Hinderliter, P. M.; Snow, T. A.; Jepson, G. W. Binding of Perfluorooctanoic Acid to Rat Liver‐form
608
and Kidney‐form α2u‐Globulins. Drug Chem. Toxicol. 2004, 27 (4), 341-360.
609
(50) Maatman, R. G.; van de Westerlo, E. M.; Van Kuppevelt, T.; Veerkamp, J. H. Molecular identification of the
610
liver-and the heart-type fatty acid-binding proteins in human and rat kidney. Use of the reverse transcriptase
611
polymerase chain reaction. Biochem. J. 1992, 288 (1), 285-290.
612
(51) Kimura, H.; Odani, S.; Nishi, S.; Sato, H.; Arakawa, M.; Ono, T. Primary structure and cellular distribution of
613
two fatty acid-binding proteins in adult rat kidneys. J. Biol. Chem. 1991, 266 (9), 5963-5972.
614
(52) Trudel, D.; Horowitz, L.; Wormuth, M.; Scheringer, M.; Cousins, I. T.; Hungerbühler, K. Estimating consumer
615
exposure to PFOS and PFOA. Risk Anal. 2008, 28 (2), 251-269.
616
(53) Rohatgi, A. WebPlotDigitizer. URL http://arohatgi. info/WebPlotDigitizer/app 2011.
617
(54) Crone, C. The permeability of capillaries in various organs as determined by use of the ‘indicator
618
diffusion’method. Acta Physiol. Scand. 1963, 58 (4), 292-305.
619
(55) Davies, B.; Morris, T. Physiological parameters in laboratory animals and humans. Pharm. Res. 1993, 10 (7),
620
1093-1095.
621
(56) OcKNER, R. K.; Manning, J.; Kane, J. Fatty acid binding protein. Isolation from rat liver, characterization, and
622
immunochemical quantification. J. Biol. Chem. 1982, 257 (13), 7872-7878.
623
(57) Wu, L.-L.; Gao, H.-W.; Gao, N.-Y.; Chen, F.-F.; Chen, L. Interaction of perfluorooctanoic acid with human
624
serum albumin. BMC Struct. Biol. 2009, 9 (1), 31.
625
(58) Armitage, J. M.; Arnot, J. A.; Wania, F.; Mackay, D. Development and evaluation of a mechanistic 29 / 30
ACS Paragon Plus Environment
Environmental Science & Technology
Page 30 of 34
626
bioconcentration model for ionogenic organic chemicals in fish. Environ. Toxicol. Chem. 2013, 32 (1), 115-128.
627
(59) Crisman, T. S.; Claffey, K. P.; Saouaf, R.; Hanspal, J.; Brecher, P. Measurement of rat heart fatty acid binding
628
protein by ELISA. Tissue distribution, developmental changes and subcellular distribution. J. Mol. Cell. Cardiol.
629
1987, 19 (5), 423-431.
630
(60) Richieri, G. V.; Ogata, R. T.; Zimmerman, A. W.; Veerkamp, J. H.; Kleinfeld, A. M. Fatty acid binding
631
proteins from different tissues show distinct patterns of fatty acid interactions. Biochemistry 2000, 39 (24), 7197-
632
7204.
633
(61) Brown, R. P.; Delp, M. D.; Lindstedt, S. L.; Rhomberg, L. R.; Beliles, R. P. Physiological parameter values for
634
physiologically based pharmacokinetic models. Toxicol. Ind. Health 1997, 13 (4), 407-484.
635
(62) Aukland, K.; Nicolaysen, G. Interstitial fluid volume: local regulatory mechanisms. Physiol. Rev. 1981, 61 (3),
636
556-643.
637
(63) Wang, G.; Bonkovsky, H. L.; de Lemos, A.; Burczynski, F. J. Recent insights into the biological functions of
638
liver fatty acid binding protein 1. J. Lipid Res. 2015, 56 (12), 2238-2247.
639
(64) Sabolić, I.; Asif, A. R.; Budach, W. E.; Wanke, C.; Bahn, A.; Burckhardt, G. Gender differences in kidney
640
function. Pflug. Arch. Eur. J. Physiol. 2007, 455 (3), 397.
641
(65) Weijs, L.; Yang, R. S.; Das, K.; Covaci, A.; Blust, R. Application of Bayesian population physiologically based
642
pharmacokinetic (PBPK) modeling and Markov chain Monte Carlo simulations to pesticide kinetics studies in
643
protected marine mammals: DDT, DDE, and DDD in harbor porpoises. Environ. Sci. Technol. 2013, 47 (9), 4365-
644
4374.
645
30 / 30
ACS Paragon Plus Environment
MODEL Environmental ScienceTISSUE & Technology Vascular Space Q in Albumin
Page 31 of 34
Interstitial Fluid
Transporters
Intracellular Space
In
sil
ic
o
Albumin
α2μ-globulin
L-FABP
MODEL ASSESSMENT In vi vo
ACS Paragon Plus Environment
Q out
LIVER(L) Bile
Environmental ScienceGUT(G) & Technology Q Bile Gut Lumen Q Feces
GT
LT
GF
LF
Q BL
LB
Page 32 of 34
Q BG
GB
Q Hepatic artery
LEGEND Passive diffusion Active transport
BLOOD(B)
Q Cardiac input
Q Cardiac output
Blood flow Bile, feces or urine outflow
MUSCLE(M)
Q BM
Q BG
Q BM
MB
L-FABP
MF
α2μ-globulin
MT
Albumin Oat1
ADIPOSE(A)
Q BA
Q BA
AB
Oat3
AF
Oatp1a1
AT
Ntcp Ostα/β
REST OF BODY(R)
Q BR
Q BR
RB RF RT
KIDNEY(K)
Q BK
KB
Q BK - Q GFR
KF KT
Q Urine
ACS Paragon Plus Environment Q Filtrate
GFR
Page 33 of 34
a
c
Environmental Science & Technology
b
d
ACS Paragon Plus Environment
Environmental Science & Technology
a
b
c
ACS Paragon Plus Environment
Page 34 of 34
d
