estimate_gfr

Predict GFR using CKD-EPI equation (for adults) or BSA-based equation (for children).

httk-pop

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

estimate_gfr function

<dl><dt>gfrtmp.dt</dt>
<dd>A data.table with columns <code>gender</code>, <code>reth</code>, 
<code>age_years</code>, <code>age_months</code>, <code>BSA_adj</code>, <code>serum_creat</code>.</dd>
<dt>gfr_resid_var</dt>
<dd>Logical value indicating whether or not to include
residual variability when generating GFR values. (Default is TRUE.)</dd>
<dt>ckd_epi_race_coeff</dt>
<dd>Logical value indicating whether or not to use the
"race coefficient" from the CKD-EPI equation when estimating GFR values.
(Default is FALSE.)</dd></dl>

Arguments

Author

Predict GFR. — estimate_gfr

<dl>

<dt>gfrtmp.dt</dt>
<dd>A data.table with columns <code>gender</code>, <code>reth</code>, 
<code>age_years</code>, <code>age_months</code>, <code>BSA_adj</code>, <code>serum_creat</code>.</dd>


<dt>gfr_resid_var</dt>
<dd>Logical value indicating whether or not to include
residual variability when generating GFR values. (Default is TRUE.)</dd>


<dt>ckd_epi_race_coeff</dt>
<dd>Logical value indicating whether or not to use the
"race coefficient" from the CKD-EPI equation when estimating GFR values.
(Default is FALSE.)</dd>

</dl>

Predict GFR.

estimate_gfr: Predict GFR.

Description

Usage

Value

Arguments

Author

Details

References