vLiverPBPK

Robert Pearce, Cory Strope, Woodrow Setzer, John Wambaugh

1 Introduction

Every day we are exposed to low levels of hundreds of environmental chemicals, most of which have not been tested for toxicity [14, 23, 3, 8]. However,

in vitro data for thousands of chemicals has been generated in the Tox21 [1]

and ToxCast [9] programs using a high-throughput robotic screening system

where chemicals are tested in at least 50 assays at varying concentrations.

Reverse dosimetry has been applied to this data in Wetmore et al. (2012)

[25] in estimating oral equivalent doses that yield steady state concentrations corresponding to various levels of biological activity. We introduce

vLiverPBPK, an R package for statistical analysis of pharmacokinetics for

chemicals undergoing high throughput screening (HTS) and other toxicity

studies. We provide tools for Monte Carlo simulation and reverse dosimetry

methods [20] with physiological and physicochemical pharmacokinetic data

[25] for 369 chemicals to simulate four pharmacokinetic models and solve

for their steady state concentrations and other pharmacokinetic summary

1
statistics.

Pharmacokinetics (PK) is a field of study for determining the absorption,

distribution, metabolism, and excretion of substances in the body [13]. The

necessary data for PK can be collected in rats and other animals, but if we

want to collect it for the thousands of untested chemicals, there is a high

cost in terms of time, money, and animals [17]. Creating a few harmonized

models for most chemicals that can be parameterized with in vitro PK data

that are more easily obtained than in vivo PK data can help address these

problems. Once we have an estimate of the amount of a chemical someone is

ordinarily exposed to, we can use this model to predict the concentrations in

various parts of the body and then compare these concentrations to known

concentrations that cause biological activity in the cells of a given tissue.

We can also use the model in a reverse manner to see what dose is needed

to produce a specific concentration such as the in vitro AC50 or other levels

of biological activity; as done in Wetmore et al. (2012) whose results are

contained in the package. If we find that many people are exposed to an

amount of a chemical that is likely to create a biological effect, we will place

a high priority on more testing of the chemical [21]. Although the package

has been developed primarily for the prioritization of testing for environmental chemicals, 75 of the chemicals contained are pharmaceuticals.

The reverse toxicokinetic model used in Wetmore et al. assumes a constant

infusion dose in reaching steady state where the dose rate is both continuous and constant and the concentration is the same for the entire body, but

2
this is not necessarily an accurate representation of reality. In our physiologically based pharmacokinetic (PBPK) models, we can use discrete doses

which create a fluctuating steady state that oscillates around the infusion

dose prediction as well as multiple compartments with different partition

coefficients and flows between them. These models are expressed as a set

of mass balance differential equations describing the rate of change of the

amount of a substance, and they use in vitro and physiological data in

calculating the partition coefficients, clearance, and flows between compartments or out of the kidneys. This in vitro data includes the intrinsic hepatic

clearance, the fraction of a chemical unbound to plasma (fup), and other

physicochemical properties. With this R package we provide data, models,

and examples to allow the inclusion of PK in statistical analysis of chemical

exposure and toxicity for 369 chemicals. The package is structured to be

modular and expandable to allow new modeling approaches chemical data

to be added as they become available.

3
Function Description

calc analytic css Calculates the analytic steady state plasma and blood concentrations

for the four models used in the package.

calc css Finds the day, max and average steady state concentrations along with

the fraction of the analytic steady state reached.

calc elimination rate Calculates the elimination rate for a one compartment model due to the

liver and kidneys, dividing the total clearance by volume of distribution.

calc hepatic clearance Calculates the hepatic clearance for a well-stirred model or other type

if specified.

calc mc css Monte Carlo simulation of steady state three compartment model.

calc mc oral equiv Converts a μM steady state plasma concentration to an oral equivalent

dose using a concentration obtained from calc mc css.

calc ratioblood2plasma Calculates the ratio of chemical concentration in blood to plasma.

calc stats Calculates the area under the curve, mean, and peak values for the blood

or plasma concentration of a specified chemical or all chemicals.

calc total clearance Calculates the total clearance rate for a one compartment model where

clearance is entirely due to metablism by the liver and glomerular filtration in the kidneys.

calc volume of distributionCalculates the volume of distribution for a one compartment model.

export pbpk jarnac Exports the PBPK model to jarnac.

export pbpk sbml Exports the PBPK model to sbml.

get cheminfo Provides the CAS numbers and other specified information for chemicals

that work with a given model.

get wetmore cheminfo Provides the names and CAS numbers of chemicals with information

from Wetmore et al. (2012) and (2013) [26].

get wetmore css Retrieves a steady state plasma concentration as a result of infusion

dosing from Wetmore et al. (2012) and (2013).

get wetmore

oral equiv

Converts a μM steady state plasma concentration to an oral equivalent

dose using the values from Wetmore et al. (2012) and (2013).

monte carlo Runs a monte carlo simulation of a given model.

parameterize 1comp Parameterizes the one compartment model.

parameterize 3comp Parameterizes the three compartment model.

parameterize pbpk Parameterizes the PBPK model.

parameterize steadystateParameterizes the three compartment steady state model.

solve 1comp Solves the one compartment model.

solve 3comp Solves the three compartment model.

solve pbpk Solves the PBPK model.

Table 1: List of functions in package.

4
Model Hepatic Clearance

Partition CoefficientsFraction UnboundHematocritMolecular WeightRatio of Blood to PlasmaElimination RateVolume of Distribution*Dynamic PredictionSteady State PredictionPlasma without Blood

PBPK Yes Yes Yes Yes Yes Yes No No Yes Yes No

One CompartmentNo No No No Yes No Yes Yes Yes Yes Yes

Three CompartmentYes Yes Yes Yes Yes Yes No No Yes Yes No

Three Compartment

Steady State

Yes No Yes No Yes No No No No Yes Yes

Table 2: Model parameter and prediction comparison. *Partition coefficients needed in calculating volume of distirbution.

2 Methods

2.1 Models Used

Three general models are included in the package. The first, being the most

extensive, is referred to in the package as “pbpk” and contains separate

tissue compartments for the gut, liver, lungs (unused for dosing in our examples), arteries, veins, and kidneys. The rest of the tissues in the body

are aggregated into a single compartment termed “rest”. Tissue descriptors,

including volume, blood flow, cellular fraction of total volume, water, lipid

and protein fraction of cellular volume, and distribution of types of lipid

are taken from the corrected table in Schmitt (2008) [18]. Tissue flows and

volumes are scaled to cardiac output and body weights, respectively, from

Davies and Morris (1993) [2], which also provides values for the glomerular

filtration rate and hematocrit. Absorption from the gut lumen into gut tissue is modeled as a first order process with a rate of 1/h. Venous blood from

5
the gut and arterial blood feed separately into the liver, where metabolism

is modeled with the “well-stirred” approximation [27, 4] with two other options available from Ito and Houston (2004) [5].

Tissue to unbound fraction in plasma partition coefficients are predicted using Schmitt’s method [18] from the in vitro fraction of unbound chemical in

plasma, fup, the predicted hydrophobicity (EPI Suite), and any acid/base

dissociation constants available in the literature. We assume perfusionlimited tissue (i.e., tissue, red blood cells, and plasma come to equilibrium

rapidly with respect to the flow of blood). A constant ratio of blood to

plasma concentration is used throughout the body and is predicted using

the hematocrit and the predicted partitioning between red blood cells and

plasma from Schmitt (2008). With these assumptions, the chemical concentration in any tissue divided by that tissue’s partition coefficient is equal to

the unbound fraction of chemical in the plasma of that tissue. The rest of

body compartment is created by weighting the partition coefficients for all

tissues described in Schmitt (2008) by their volumes.

The second, an empirical, “three-compartment” model assumes no tissue

partitioning, and features elimination due to metabolism of the parent compound in the liver and renal elimination by passive glomerular filtration.

Hepatic metabolism was modeled using the “well-stirred” approximation

from the flow into the liver and the in vitro measured values for fraction unbound, intrinsic hepatic clearance, and the density of hepatocytes in the liver

(1.1 x 108 hepatocytes per gram liver and a liver density of 1.05 g/mL). This

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is the only model used exclusively in calculating the analytic steady state,

and is referred to in the package as “3compartmentss”, where “ss” stands

for steady state. The analytic solution to the 3compartmentss model is

the same equation used for determining steady state plasma concentrations,

Css, in previous work [16, 25, 26, 27]. The model referred to in the package

as “3compartment” and simulated with the function solve 3comp pbpk is a

variation of the 3compartmentss model that includes the partition coefficients from Schmitt’s method.

Finally, the one compartment model uses an elimination rate derived from

dividing the total clearance by the volume of distribution. The volume of

distribution is calculated by summing the products of the volumes of the

compartments in the pbpk model and their partition coefficients and is also

used as the volume the compartment. The total clearance is calculated by

summing the hepatic clearance from the well-stirred approximation and the

clearance from the kidneys, assumed to be the product of the glomerular

filtration rate and fup. The clearances for the one compartment model and

the 3compartmentss model are identical.

All models contain a compartment for absorption with an absorption rate

of 1/h.

7
2.2 Model Equations

Inhaled Gas

Lung Blood

Lung Tissue Qcardiac

Gut Lumen

A. B.

Kidney Blood

QGFR

Kidney Tissue

Qkidney

Liver Plasma Portal Vein

Liver Tissue

kabsorption

Qgut

Gut Blood

Gut Lumen

Arterial Bl

o

enous Blood

CLmetab

Qhepatic artery/Rblood:plasma

Qliv = Qha + Qpv

Qliver

Qgut

Liver Blood

Q Liver Tissue

metab

ood

Ve

CL

Body Plasma

Rest of Body

Qportal vein/Rblood:plasma

Qrest

Body Blood

Rest of Body

CLGFR

Body Blood

Figure 1: (A) Three compartment model and (B) PBPK model.

2.2.1 PBPK Model Equations

d

dtAgutlumen = −kgutabsAgutlumen

d

dtCgut =



kgutabsAgutlumen + Qgut(Cart − Cgut)

.Vgut

d

dtCliver =



QliverCart+QgutCgut−

(Qliver + Qgut)Rb2p

fupKliver2plasma

Cliver−

CLmetabolism

Kliver2plasma

Cliver.

Vliver

d

dtCven =

Qliver + Qgut

Kliver2plasma

Cliver +

Qkidney

Kkidney2plasma

Ckidney

+

Qrest

Krest2plasma

Crest

Rb2p

fup

− QcardiacCven.

Vven

d

dtClung = Qcardiac(Cven − Clung)/Vlung

d

dtCart = Qcardiac(Clung − Cart)/Vart

8
d

dtCrest = Qrest

Cart −

Rb2p

Krest2plasmafup .

Vrest

d

dtCkidney =



QkidneyCart−

QKidneyRb2p

Kkidney2plasmafup

Ckidney−

Qgfr

Kkidney2plasma

Ckidney.Vkidney

2.2.2 Three Compartment Model Equations

d

dtAgutlumen = −kgutabsAgutlumen

d

dtCgut =



kgutabsAgutlumen +

QgutRb2p

Krest2plasmafup

Csyscomp − QgutCgutVgut

d

dtCliver =



QgutCportven +

QliverRb2p

Krest2plasmafup

Crest

−



(Qliver + Qgut)Rb2p

fupKliver2plasma

+

CLmetabolism

Kliver2plasma 

Cliver.

Vliver

d

dtCrest =



(Qgut + Qliver)Rb2p

fupKliver2plasma

Cliver −



Rb2p

fup



Qgut + Qliver

+ Qgfr

Crest

Krest2plasma .

Vrest

2.2.3 One Compartment Model Equations

d

dtAgutlumen = −kgutabsAgutlumen

d

dtCrest =

kgutabs

Vdist

Agutlumen − keliminationCrest

9
2.2.4 Steady State Three Compartment Model

Css = kdosefupQgfr +

(Qliver + Qgut)fupCLmetabolism

(Qliver + Qgut) + fupCLmetabolism 

2.3 In Vitro Chemical Data

Two types of in vitro data are used to parameterize the PK models: CLint,

the intrinsic metabolic clearance of the parent compound by primary hepatocytes (loss of parent over time) in a well on a multi-compound plate [19];

and fup, the fraction of chemical unbound in the presence of plasma protein as assessed using rapid equilibrium dialysis (RED) in which two wells

are separated by a membrane that is permeable by smaller molecules but

prevents the plasma protein added to one well from migrating to the other

well (the relative chemical concentration in the two linked wells gives the

free fraction of chemical) [24]. For environmental chemicals, data on these

two parameters are primarily from Wetmore et al. (2012) and Tonnelier

et al. (2012) [22]. For pharmaceutical compounds these values are compiled from Obach et al. (1999) [11], Jones et al. (2002) [7], Naritomi et al.

(2003) [10], Ito and Houston (2004), Riley et al. (2005) [15], Schmitt (2008),

and Obach et al. (2008) [12]. Where available, acid/base dissociation constants are curated from this literature; otherwise chemicals are treated as

neutral/unionized regardless of tissue pH.

2.4 Physicochemical data

Physico-chemical properties (e.g., logP) using EPAs Estimation Program Interface (EPI) Suite (4.11, November, 2012, http://www.epa.gov/opptintr/ex10
posure/pubs/episuite.htm). EPI Suite quantitative structure activity relationships (QSARs) were used to estimate physico-chemical properties if Simplified Molecular Input Line Entry System (SMILES) descriptions of chemical structure were available and the QSARs did not fail for that structure.

In addition to QSAR model estimates, EPI Suite contains a database of experimentally obtained physico-chemical properties that were used in place of

estimated values when available. EPI Suite does not predict acid/base dissociation, so chemicals were assumed to be neutral unless data was available

from the literature. Molecular weight and structure are determined from

the DSStox database (http://www.epa.gov/dsstox) and hydrophobicity is

predicted for all compounds using EPI Suite.

2.5 Determination of Steady State

In our model, we can simulate discrete doses to approximate real world

exposure scenarios. However, we do assume an equivalent constant infusion

dosing in the calculation of the analytic steady state concentrations from the

differential equations. The day a chemical reaches steady state is found by

determining when the average concentration for a given day comes within

1% of the analytic solution. The concentration resulting from discrete dosing

oscillates around the analytic steady state, and the lower the concentration

is, the greater the oscillation is relative to the analytic concentration, as can

be seen in the examples.

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2.6 Monte Carlo Sampler

The package contains the results in Wetmore et al. (2012) and (2013) obtained with the SimCYP population simulator [6], which uses Monte Carlo

to simulate human variability in steady state plasma concentrations using

the 3compartmentss model. Normal distributions, truncated to ensure positive values, were used with mean values drawn from the references above

and a coefficient of variation of 0.2. Body weight, liver volume, blood flow to

the liver, and CLint were all varied in this manner. The fup was drawn from

a censored distribution with identical properties to the other distributions

if the mean was above the limit of detection (assumed to be 1% unbound)

and a uniform distribution between 0% and 1% unbound otherwise. For

each chemical, 1000 different combinations of parameters were used to determine Css, allowing estimation of the 5%, median, and 95% quantiles for

humans but only the median for rats. These concentrations were determined with doses of 1 mg/kg bw/day but, assuming a linear concentration

response, can be extrapolated to other doses with get wetmore css. Using

get wetmore oral equiv, we can, in a reverse manner, back calculate the dose

for a given concentration. The package also includes a function, calc mc css,

that runs a Monte Carlo simulation of the 3compartmentss model, emulating

the results in Wetmore et al. (2012) and (2013). This function implements

the simulation with another function, monte carlo, that allows the user to

use his own PK model or a model contained in the package besides the 3compartmentss model. Analogously to get wetmore oral equiv, get mc oral equiv

calculates the oral equivalent dose through division of the given concentra12
tion by the steady state concentration generated by calc mc css with a dose

of 1 mg/kg bw/day.

3 Examples

3.1 Accessing and Changing Model Parameters

VLiverPBPK allows the user to access and change the parameters used in

each of the models. The models each contain their own parameterize function

that generates a list of the parameters required by the model. For example,

to get a list of parameters for the pbpk model of triclosan in a rat:

R> parameters <- parameterize_pbpk(chem.name = "triclosan",

+ species = "rat")

To see the effect a change in parameters has on the model, we can modify

the desired entries in the list and use the new parameter list as an input for

the parameters argument of a function that uses that model. For example,

to change the fup in the previous parameters list to 0.1 from the default of

0.005 and use it in a simulation of the pbpk model for a single dose of 1

mg/kg bw of triclosan in a rat:

R> parameters["Fraction_unbound_plasma"] <- 0.1

R> out <- solve_pbpk(parameters=parameters)

Individual parameters, such as the ratio of blood to plasma, total clearance,

volume of distribution, hepatic clearance, and elimination rate, can also be

calculated using the functions with the prefix calc followed by the parameter

name and the same arguments as the above parameterize function.

13
3.2 Making Data Frames and Tables

VLiverPBPK is capable of generating large amounts of data, and in order

to compare chemicals or models, we need to construct tables or data frames.

Suppose we want to look at how the steady state concentrations compare at

1 mg/kg bw/day for the pbpk model, the Monte Carlo simulation, and the

Wetmore data, formatted as a data frame (used with ggplot2 in following

examples). We can do this using a for loop and the as.data.frame and cheminfo functions. The intersection of get wetmore cheminfo and get cheminfo

for the pbpk and 3compartmentss (used in calc mc css) models contains all

the CAS numbers that will work for all three functions. The for loop then

binds the CAS number and function outputs into a row with cbind, and each

row is added to the bottom of the data frame with rbind

R> table <- NULL

R> for(this.cas in intersect(get_cheminfo(model="3compartmentss"),

+ intersect(get_cheminfo(model = "pbpk"),get_wetmore_cheminfo()))){

+ this.row <- as.data.frame(this.cas)

+ this.row <- cbind(this.row,as.data.frame(calc_analytic_css(

+ chem.cas = this.cas,model = model)))

+ this.row <- cbind(this.row,as.data.frame(get_wetmore_css(

+ chem.cas = this.cas,which.quantile = .50)))

+ this.row <- cbind(this.row,as.data.frame(calc_mc_css(

+ chem.cas = this.cas,which.quantile = .50)))

+ table <- rbind(table,this.row)

+ }

14
R> colnames(table) <- c("CAS","PBPK","Wetmore","MC")

3.3 Plotting

3.3.1 Days to Steady State Histogram

Creating histograms can allow us to visualize how a given value varies across

all of the chemicals contained within the package. To create a histogram

using ggplot2 for the number of days to steady state for all chemicals with

sufficient data, we must first set up a for loop with get cheminfo and calc css

to generate a vector containing the data. Then the vector must be converted

to a data frame before entering it into the ggplot command and adding all

the additional features. The ggplot command is used to initially format the

data for any type of figure in ggplot2. We first enter the data frame we want

to use in the plot and then the specific column or columns in aes. Then

additional commands are added to the basic ggplot object. We can just as

easily create a histogram containing the average or maximum steady state

concentrations by typing avg or max in place of the.day.

R> days <- NULL

R> for(this.cas in get_cheminfo(model="pbpk")){

+ css.info <- calc_css(chem.cas=this.cas,model="pbpk")

+ days[["this.cas"]] <- css.info[["the.day"]]

+ }

R> days.data <- as.data.frame(days)

R> ggplot <- ggplot(days.data,aes(days))

R> hist <- ggplot + geom_histogram(fill="blue",binwidth=1/6) +

15
+ scale_x_log10() + ylab("Number of Chemicals") + xlab("Days") +

+ theme(axis.text=element_text(size=13,type="bold"),

+ axis.title=element_text(size=15))

Figure 2: Days to steady state histogram.

3.3.2 Concentration vs Time

The function solve pbpk plots and returns values for each of the compartment

concentrations vs. time, but we can make a plot with ggplot2 that includes

a horizontal line through the y axis at the predicted steady state plasma or

venous blood concentration for constant infusion dosing. We first turn the

output into a data frame and enter it into the ggplot command along with

the desired x and y axes in the aes argument. Then we use calc analytic css

to get the steady state plasma concentration and enter it into geom hline as

the y intercept and add all the other options to our ggplot object.

16
R> out<-solve_pbpk(chem.name="Bisphenol A",days=25,doses.per.day=3)

R> plot.data<-as.data.frame(out)

R> ggplot <- ggplot(plot.data,aes(time,Cplasma))

R> css <- calc_analytic_css(chem.name="Bisphenol A")

R> plot <- ggplot + geom_line() + geom_hline(yintercept=css) +

+ ylab("Plasma Concentration (uM)") + xlab("Day") +

+ theme(axis.text=element_text(size=16),axis.title=element_text(size=16),

+ plot.title=element_text(size=17)) + ggtitle("Bisphenol A")

This example plots the concentration vs. time of 1 mg/kg bw/day of

Bisphenol A broken into three doses per day. The same plots can be made

for the other models by substituting one of the other two solve functions,

solve 3comp pbpk or solve 1comp pk, for solve pbpk and setting the model

argument of calc analytic css to the corresponding model. These three functions also have the option of simulating a single oral or i.v. dose and setting

the initial values of each compartment with units matching the specified

output units (defaults are μM).

17
0.00

0.25

0.50

0.75

0 5 10 15 20 25

Day

Plasma Concentration (uM)

Bisphenol A

Figure 3: Steady state plasma concentration at 3 doses per day, 1 mg/kg

bw/day.

3.3.3 Average vs Maximum Concentration

Using calc css and the same technique used to generate the histogram data,

we can get a vector for the average concentration and the maximum concentration at steady state. Binding the columns together and converting it

into a data frame will allow us to create another plot using ggplot2.

R> ggplot <- ggplot(data.frame,aes(avg,max))

R> Plot <- ggplot + geom_point() + geom_abline() + scale_x_log10() +

+ scale_y_log10() + xlab("Average Concentration at Steady State (uM)") +

+ ylab("Max Concentration at Steady State (uM)") +

+ theme(axis.text=element_text(size=16),axis.title=element_text(size=16))

18
Figure 4: Average vs. maximum concentration at steady state for 3 doses

per day, 1 mg/kg bw/day.

3.4 Calculating AUC, Peak, and Mean Values

The function calc stats gives the option to calculate the area under the curve

(AUC), peak, or mean concentrations of any of the solve functions. If a

chemical name or CAS number is specified, it will calculate the specified

statistics for that chemical, and if not, it will calculate the values for all

chemicals with sufficient data for the given model. To calculate the peak

statistics for all chemicals that work with the pbpk model simulated for 10

days at 1 mg/kg bw/day with 3 doses per day and a list containing the AUC,

peak, and mean for a single 1 mg dose of ibuprofen over 10 days, we have:

R> all.peak.stats <- calc_stats(days=10,doses.per.day=3,stats="peak")

19
R> ibuprofen.stats <- calc_stats(days=10,chem.name="Ibuprofen")

3.5 Monte Carlo Sampler

The Monte Carlo data and simulations in the package use the 3compartmentss model. The published results from Wetmore et al. (2012) and (2013)

are accessible with get wetmore css, and calc mc css runs an identical simulation. However, with this simulation, we can specify all of the parameters

and any quantile we desire. Below are examples of these two functions,

comparing the median of the Wetmore steady state μM concentration in

humans for 1 mg/kg bw/day of Bisphenol A with the calc mc css simulation

but with probability distributions containing half the standard deviation,

half the limit of detection for the fraction unbound to plasma, and double

the number of samples of the default Wetmore parameters.

R> get_wetmore_css(chem.cas="80-05-7",daily.dose=1,

+ which.quantile=0.5,output.units="uM")

R> calc_mc_css(chem.cas="80-05-7",daily.dose=1,which.quantile=0.5,

+ censored.params=list(fub=list(cv=0.1,lod=0.005)),vary.params=

+ list(BW=0.1,liver.volume.per.kgBW=0.1,QGFRc=0.1,Qhc=0.1,

+ million.cells.per.gliver=0.1,CLint=0.1),output.units="uM",samples=2000)

The oral equivalent functions convert a plasma concentrations into a dose.

Below is an example of a 50 μM plasma concentration of Bisphenol A converted to an oral equivalent dose using the Wetmore data for the 95th quantile of humans. We can call calc mc oral equiv in the same manner, passing

20
additional arguments to calc mc css within the function and specifying any

quantile we want.

R> get_wetmore_oral_equiv(50,chem.cas="80-05-7")

3.6 Export Functions

The package has the ability to export the multiple compartment pbpk model

to sbml and jarnac files. Both functions, export pbpk sbml and export pbpk jarnac,

have the same arguments and only differ in the file extension names (.xml

and .jan) entered into the filename argument. Both use liters as the units for

volume, but the amounts are unitless and to be determined by the user. If

we suppose that we enter an initial amount of 1 mg in the gut lumen, then

all the other compartments will contain amounts in mg. Below is a typical

call of an export function for a dose of 1 given to a rat.

R> export_pbpk_sbml(chem.name="Bisphenol A",species="Rat",

+ initial.amounts=list(Agutlumen=1),filename="PBPKmodel.xml")

References

[1] John R. Bucher. Guest editorial: Ntp: New initiatives, new alignment.

Environ Health Perspect, 116(1), 2008.

[2] Brian Davies and Tim Morris. Physiological parameters in laboratory

animals and humans. Pharmaceutical Research, 10(7):1093–1095, 1993.

21
[3] Peter P. Egeghy, Daniel A. Vallero, and Elaine A. Cohen Hubal.

Exposure-based prioritization of chemicals for risk assessment. Environmental Science & Policy, 14(8):950–964, 2011.

[4] J. B. Houston and D. J. Carlile. Prediction of hepatic clearance from

microsomes, hepatocytes, and liver slices. Drug Metab Rev, 29(4):891–

922, 1997. Houston, J B Carlile, D J Comparative Study Journal Article

Review United states Drug Metab Rev. 1997 Nov;29(4):891-922.

[5] K. Ito and J. B. Houston. Comparison of the use of liver models for

predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res, 21(5):785–92, 2004. Ito,

Kiyomi Houston, J Brian Comparative Study Journal Article United

States Pharm Res. 2004 May;21(5):785-92.

[6] Masoud Jamei, S. Marciniak, K. Feng, A. Barnett, G. Tucker, and

A. Rostami-Hodjegan. The simcyp population-based adme simulator. Expert Opin Drug Metab Toxicol, 5(2):211–23, 2009. 1744-

7607 Jamei, Masoud Marciniak, Steve Feng, Kairui Barnett, Adrian

Tucker, Geoffrey Rostami-Hodjegan, Amin Journal Article Review

England Expert Opin Drug Metab Toxicol. 2009 Feb;5(2):211-23. doi:

10.1517/17425250802691074 .

[7] O. A. Jones, N. Voulvoulis, and J. N. Lester. Aquatic environmental

assessment of the top 25 english prescription pharmaceuticals. Water

Res, 36(20):5013–22, 2002. Jones, O A H Voulvoulis, N Lester, J N

Journal Article England Water Res. 2002 Dec;36(20):5013-22.

22
[8] Richard Judson, Ann Richard, David J. Dix, Keith Houck, Matthew

Martin, Robert Kavlock, Vicki Dellarco, Tala Henry, Todd Holderman,

Philip Sayre, Shirlee Tan, Thomas Carpenter, and Edwin Smith. The

toxicity data landscape for environmental chemicals. Environ Health

Perspect, 117(5):685–695, 2008.

[9] Richard S. Judson, Keith A. Houck, Robert J. Kavlock, Thomas B.

Knudsen, Matthew T. Martin, Holly M. Mortensen, David M. Reif,

Daniel M. Rotroff, Imran Shah, Ann M. Richard, and David J. Dix.

In vitro screening of environmental chemicals for targeted testing prioritization: The toxcast project. Environmental Health Perspectives,

118(4):485–492, 2010.

[10] Yoichi Naritomi, Shigeyuki Terashita, Akira Kagayama, and Yuichi

Sugiyama. Utility of hepatocytes in predicting drug metabolism: Comparison of hepatic intrinsic clearance in rats and humans in vivo and in

vitro. Drug Metabolism and Disposition, 31(5):580–588, 2003.

[11] R. S. Obach. Prediction of human clearance of twenty-nine drugs from

hepatic microsomal intrinsic clearance data: An examination of in vitro

half-life approach and nonspecific binding to microsomes. Drug Metab

Dispos, 27(11):1350–9, 1999. Obach, R S Journal Article United states

Drug Metab Dispos. 1999 Nov;27(11):1350-9.

[12] R. S. Obach, F. Lombardo, and N. J. Waters. Trend analysis of a

database of intravenous pharmacokinetic parameters in humans for 670

drug compounds. Drug Metab Dispos, 36(7):1385–405, 2008. 1521-

23
009x Obach, R Scott Lombardo, Franco Waters, Nigel J Journal Article United States Drug Metab Dispos. 2008 Jul;36(7):1385-405. doi:

10.1124/dmd.108.020479. Epub 2008 Apr 21.

[13] Ellen J. O’Flaherty. Toxicants and Drugs: Kinetics and Dynamics.

John Wiley & Sons Inc, New York, NY, 1981.

[14] Youngja H. Park, Kichun Lee, Quinlyn A. Soltow, Frederick H. Strobel, Kenneth L. Brigham, Richard E. Parker, Mark E. Wilson, Roy L.

Sutliff, Keith G. Mansfield, Lynn M. Wachtman, Thomas R. Ziegler,

and Dean P. Jones. High-performance metabolic profiling of plasma

from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring. Toxicology, 295(13):47–55,

2012.

[15] R. J. Riley, D. F. McGinnity, and R. P. Austin. A unified model for

predicting human hepatic, metabolic clearance from in vitro intrinsic

clearance data in hepatocytes and microsomes. Drug Metab Dispos,

33(9):1304–11, 2005. Riley, Robert J McGinnity, D F Austin, R P

Journal Article United States Drug Metab Dispos. 2005 Sep;33(9):1304-

11. Epub 2005 Jun 2.

[16] D. M. Rotroff, B. A. Wetmore, D. J. Dix, S. S. Ferguson, H. J. Clewell,

K. A. Houck, E. L. Lecluyse, M. E. Andersen, R. S. Judson, C. M.

Smith, M. A. Sochaski, R. J. Kavlock, F. Boellmann, M. T. Martin,

D. M. Reif, J. F. Wambaugh, and R. S. Thomas. Incorporating human

dosimetry and exposure into high-throughput in vitro toxicity screen24
ing. Toxicol Sci, 117(2):348–58, 2010. Rotroff, Daniel M Wetmore,

Barbara A Dix, David J Ferguson, Stephen S Clewell, Harvey J Houck,

Keith A Lecluyse, Edward L Andersen, Melvin E Judson, Richard S

Smith, Cornelia M Sochaski, Mark A Kavlock, Robert J Boellmann,

Frank Martin, Matthew T Reif, David M Wambaugh, John F Thomas,

Russell S Toxicol Sci. 2010 Oct;117(2):348-58. Epub 2010 Jul 16.

[17] Costanza Rovida and Thomas Hartung. Re-evaluation of animal numbers and costs for in vivo tests to accomplish reach legislation requirements for chemicalsa report by the transatlantic think tank for toxicology (t (4)). Altex, 26(3):187–208, 2009.

[18] W. Schmitt. General approach for the calculation of tissue to plasma

partition coefficients. Toxicol In Vitro, 22(2):457–67, 2008. Schmitt,

Walter Journal Article England Toxicol In Vitro. 2008 Mar;22(2):457-

67. Epub 2007 Nov 5.

[19] Yoshihiro Shibata, Hiroyuki Takahashi, Masato Chiba, and Yasuyuki

Ishii. Prediction of hepatic clearance and availability by cryopreserved

human hepatocytes: An application of serum incubation method. Drug

Metabolism and Disposition, 30(8):892–896, 2002.

[20] Yu-Mei Tan, Kai H. Liao, and III Clewell, Harvey J. Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling. J Expos Sci Environ Epidemiol,

17(7):591–603, 2006.

25
[21] Russell S. Thomas, Martin A. Philbert, Scott S. Auerbach, Barbara A.

Wetmore, Michael J. Devito, Ila Cote, J. Craig Rowlands, Maurice P.

Whelan, Sean M. Hays, Melvin E. Andersen, M. E. Meek, Lawrence W.

Reiter, Jason C. Lambert, Harvey J. Clewell, Martin L. Stephens,

Q. Jay Zhao, Scott C. Wesselkamper, Lynn Flowers, Edward W. Carney, Timothy P. Pastoor, Dan D. Petersen, Carole L. Yauk, and Andy

Nong. Incorporating new technologies into toxicity testing and risk assessment: Moving from 21st century vision to a data-driven framework.

Toxicological Sciences, 136(1):4–18, 2013. (Bette).

[22] Arnaud Tonnelier, Sandra Coecke, and Jos-Manuel Zaldvar. Screening

of chemicals for human bioaccumulative potential with a physiologically

based toxicokinetic model. Archives of Toxicology, 86(3):393–403, 2012.

[23] John F. Wambaugh, R. Woodrow Setzer, David M. Reif, Sumit Gangwal, Jade Mitchell-Blackwood, Jon A. Arnot, Olivier Jolliet, Alicia

Frame, James Rabinowitz, Thomas B. Knudsen, Richard S. Judson, Peter Egeghy, Daniel Vallero, and Elaine A. Cohen Hubal. High throughput models for exposure-based chemical prioritization in the expocast

project. Environmental Science & Technology, 47(15):8479–8488, 2013.

[24] Nigel J. Waters, Rachel Jones, Gareth Williams, and Bindi Sohal.

Validation of a rapid equilibrium dialysis approach for the measurement of plasma protein binding. Journal of Pharmaceutical Sciences,

97(10):4586–4595, 2008.

26
[25] B. A. Wetmore, J. F. Wambaugh, S. S. Ferguson, M. A. Sochaski, D. M.

Rotroff, K. Freeman, 3rd Clewell, H. J., D. J. Dix, M. E. Andersen,

K. A. Houck, B. Allen, R. S. Judson, R. Singh, R. J. Kavlock, A. M.

Richard, and R. S. Thomas. Integration of dosimetry, exposure, and

high-throughput screening data in chemical toxicity assessment. Toxicol

Sci, 125(1):157–74, 2012. 1096-0929 Wetmore, Barbara A Wambaugh,

John F Ferguson, Stephen S Sochaski, Mark A Rotroff, Daniel M Freeman, Kimberly Clewell, Harvey J 3rd Dix, David J Andersen, Melvin

E Houck, Keith A Allen, Brittany Judson, Richard S Singh, Reetu

Kavlock, Robert J Richard, Ann M Thomas, Russell S Journal Article

Research Support, U.S. Gov’t, Non-P.H.S. United States Toxicol Sci.

2012 Jan;125(1):157-74. doi: 10.1093/toxsci/kfr254. Epub 2011 Sep 26.

[26] Barbara A. Wetmore, John F. Wambaugh, Stephen S. Ferguson, Lili

Li, Harvey J. Clewell, Richard S. Judson, Kimberly Freeman, Wenjun

Bao, Mark A. Sochaski, Tzu-Ming Chu, Michael B. Black, Eric Healy,

Brittany Allen, Melvin E. Andersen, Russell D. Wolfinger, and Russell S. Thomas. Relative impact of incorporating pharmacokinetics on

predicting in vivo hazard and mode of action from high-throughput in

vitro toxicity assays. Toxicological Sciences, 132(2):327–346, 2013.

[27] G. R. Wilkinson and D. G. Shand. Commentary: a physiological approach to hepatic drug clearance. Clin Pharmacol Ther, 18(4):377–90,

1975. Wilkinson, G R Shand, D G Journal Article Research Support, U.S. Gov’t, P.H.S. United states Clin Pharmacol Ther. 1975

Oct;18(4):377-90.

27