calc_fup_correction

Poulin and Haddad (2012) observed "...that for
a highly lipophilic compound, the calculated 
fup is by
far [less than] the experimental values observed under
in vitro conditions." Pearce et al. (2017) hypothesized that there was additional lipid
binding in vivo that acted as a sink for lipophilic compounds, reducing the
effective fup in vivo. It is 
possible that this is due to the binding of lipophilic compounds on the non
plasma-side of the rapid equilibrium dialysis plates (Waters et al., 2008).
 Pearce et al. (2017) compared predicted and observed 
 tissue partition coefficients
for a range of compounds. They showed that predictions were improved by 
adding additional binding proportional to the distribution coefficient 
Dow 
(<code>calc_dow</code>)
and the fractional volume of lipid in
plasma (Flipid). 
We calculate
Flipid as the 
sum of the physiological plasma neutral lipid fractional volume and 30 percent of 
the plasma neutral phospholipid fractional volume. We use values
from Peyret et al. (2010) for rats and Poulin and Haddad (2012)
for humans. The estimate of 30 percent of the
neutral phospholipid volume as neutral lipid was used for simplictity's sake in
place of our membrane affinity predictor. To account for additional binding to lipid, 
plasma to water partitioning
(Kplasma:water = 1/fup)
is increased as such:
Kcorrectedplasma:water = 1/fcorrectedup = 1/fin vitroup + Dow*Flipid

in-vitro

Pre-made models that can be rapidly tailored to various chemicals
and species using chemical-specific in vitro data and physiological
information. These tools allow incorporation of chemical
toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE")
into bioinformatics, as described by Pearce et al. (2017)
(<doi:10.18637/jss.v079.i04>). Chemical-specific
in vitro data characterizing toxicokinetics have been obtained
from relatively high-throughput experiments. The
chemical-independent ("generic") physiologically-based ("PBTK") and empirical
(for example, one compartment) "TK" models included here can be
parameterized with in vitro data or in silico predictions which are
provided for thousands of chemicals, multiple exposure routes,
and various species. High throughput toxicokinetics ("HTTK") is the
combination of in vitro data and generic models. We establish the
expected accuracy of HTTK for chemicals without in vivo data
through statistical evaluation of HTTK predictions for chemicals
where in vivo data do exist. The models are systems of ordinary
differential equations that are developed in MCSim and solved
using compiled (C-based) code for speed. A Monte Carlo sampler is
included for simulating human biological variability
(Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
and propagating parameter uncertainty
(Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>).
Empirically calibrated methods are included for predicting
tissue:plasma partition coefficients and volume of distribution
(Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>).
These functions and data provide a set of tools for using IVIVE to
convert concentrations from high-throughput screening experiments
(for example, Tox21, ToxCast) to real-world exposures via reverse
dosimetry (also known as "RTK")
(Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

John Wambaugh

httk

High-Throughput Toxicokinetics

Sarah Davidson-Fritz

Robert Pearce

Caroline Ring

Greg Honda

Mark Sfeir

Matt Linakis

Dustin Kapraun

Nathan Pollesch

Miyuki Breen

Shannon Bell

Xiaoqing Chang

Todor Antonijevic

Jimena Davis

Elaina Kenyon

Katie Paul Friedman

Celia Schacht

Meredith Scherer

James Sluka

Noelle Sinski

Nisha Sipes

Barbara Wetmore

Lily Whipple

Woodrow Setzer

calc_fup_correction function

<dl><dt>fup</dt>
<dd>Fraction unbound in plasma, if provided this argument overides
values from argument parameters and <code>chem.physical_and_invitro.data</code></dd>
<dt>chem.cas</dt>
<dd>Chemical Abstract Services Registry Number (CAS-RN) -- if
parameters is not specified then the chemical must be identified by either
CAS, name, or DTXISD</dd>
<dt>chem.name</dt>
<dd>Chemical name (spaces and capitalization ignored) -- if
parameters is not specified then the chemical must be identified by either
CAS, name, or DTXISD</dd>
<dt>dtxsid</dt>
<dd>EPA's 'DSSTox Structure ID (<a href="https://comptox.epa.gov/dashboard">https://comptox.epa.gov/dashboard</a>)
 -- if parameters is not specified then the chemical must be identified by 
either CAS, name, or DTXSIDs</dd>
<dt>parameters</dt>
<dd>Parameters from the appropriate parameterization function
for the model indicated by argument model</dd>
<dt>Flipid</dt>
<dd>The fractional volume of lipid in plasma (from <code>physiology.data</code>)</dd>
<dt>plasma.pH</dt>
<dd>pH of plasma (default 7.4)</dd>
<dt>dow74</dt>
<dd>The octanol-water distribution ratio (DOW).</dd>
<dt>species</dt>
<dd>Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or
default "Human").</dd>
<dt>default.to.human</dt>
<dd>Substitutes missing fraction of unbound plasma with
human values if true.</dd>
<dt>force.human.fup</dt>
<dd>Returns human fraction of unbound plasma in
calculation for rats if true.
When species is specified as rabbit, dog, or mouse, the human unbound
fraction is substituted.</dd>
<dt>suppress.messages</dt>
<dd>Whether or not the output message is suppressed.</dd></dl>

Arguments

Author

Calculate the correction for lipid binding in plasma binding assay — calc_fup_correction

<dl>

<dt>fup</dt>
<dd>Fraction unbound in plasma, if provided this argument overides
values from argument parameters and <code>chem.physical_and_invitro.data</code></dd>


<dt>chem.cas</dt>
<dd>Chemical Abstract Services Registry Number (CAS-RN) -- if
parameters is not specified then the chemical must be identified by either
CAS, name, or DTXISD</dd>


<dt>chem.name</dt>
<dd>Chemical name (spaces and capitalization ignored) -- if
parameters is not specified then the chemical must be identified by either
CAS, name, or DTXISD</dd>


<dt>dtxsid</dt>
<dd>EPA's 'DSSTox Structure ID (<a href='https://comptox.epa.gov/dashboard'>https://comptox.epa.gov/dashboard</a>)
 -- if parameters is not specified then the chemical must be identified by 
either CAS, name, or DTXSIDs</dd>


<dt>parameters</dt>
<dd>Parameters from the appropriate parameterization function
for the model indicated by argument model</dd>


<dt>Flipid</dt>
<dd>The fractional volume of lipid in plasma (from <code>physiology.data</code>)</dd>


<dt>plasma.pH</dt>
<dd>pH of plasma (default 7.4)</dd>


<dt>dow74</dt>
<dd>The octanol-water distribution ratio (DOW).</dd>


<dt>species</dt>
<dd>Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or
default "Human").</dd>


<dt>default.to.human</dt>
<dd>Substitutes missing fraction of unbound plasma with
human values if true.</dd>


<dt>force.human.fup</dt>
<dd>Returns human fraction of unbound plasma in
calculation for rats if true.
When species is specified as rabbit, dog, or mouse, the human unbound
fraction is substituted.</dd>


<dt>suppress.messages</dt>
<dd>Whether or not the output message is suppressed.</dd>

</dl>

Calculate the correction for lipid binding in plasma binding assay

calc_fup_correction: Calculate the correction for lipid binding in plasma binding assay

Description

Usage

Value

Arguments

Author

Details

References

See Also