calc_ma

Membrane affinity (MA) is the membrane:water partition coefficient. MA
chacterizes chemical partitioning into membranes formed
from neutral phospholipids (KnPL). 
Pearce et al. (2017) compared five different methods for predicting 
membrane affinity using measured data for 59 compounds. The method of
Yun and Edgington (2013) was identified as the best: 
MA = 10^(1.294 + 0.304 * log10(Pow)

parameters

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

Kimberly Truong

Colin Thomson

Meredith Scherer

Annabel Meade

Celia Schacht

Todor Antonijevic

Miyuki Breen

Shannon Bell

Xiaoqing Chang

Jimena Davis

Elaina Kenyon

Gilberto Padilla Mercado

Katie Paul Friedman

Nathan Pollesch

Noelle Sinski

Nisha Sipes

James Sluka

Caroline Stevens

Barbara Wetmore

Lily Whipple

Woodrow Setzer

calc_ma function

<dl><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>suppress.messages</dt>
<dd>Whether or not the output message is suppressed.</dd>
<dt>pfas.calibration</dt>
<dd>Whether MA for chemicals in class PFAS should be
increased using the regression to the Droge (2019) dataset.</dd></dl>

Arguments

Author

Calculate the membrane affinity — calc_ma

<dl>

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


<dt>pfas.calibration</dt>
<dd>Whether MA for chemicals in class PFAS should be
increased using the regression to the Droge (2019) dataset.</dd>

</dl>

Calculate the membrane affinity

calc_ma: Calculate the membrane affinity

Description

Usage

Value

Arguments

Author

References