r_left_censored_norm

Returns draws from a normal distribution with a lower censoring limit of lod
(limit of detection)

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

r_left_censored_norm function

<dl><dt>n</dt>
<dd>Number of samples to take</dd>
<dt>mean</dt>
<dd>Mean of censored distribution. Default 0.</dd>
<dt>sd</dt>
<dd>Standard deviation of censored distribution. Default 1.</dd>
<dt>lod</dt>
<dd>Bound below which to censor. Default 0.005.</dd>
<dt>lower</dt>
<dd>Lower bound on censored distribution. Default 0.</dd>
<dt>upper</dt>
<dd>Upper bound on censored distribution. Default 1.</dd></dl>

Arguments

Returns draws from a normal distribution with a lower censoring limit of lod
(limit of detection) — r_left_censored_norm

<dl>

<dt>n</dt>
<dd>Number of samples to take</dd>


<dt>mean</dt>
<dd>Mean of censored distribution. Default 0.</dd>


<dt>sd</dt>
<dd>Standard deviation of censored distribution. Default 1.</dd>


<dt>lod</dt>
<dd>Bound below which to censor. Default 0.005.</dd>


<dt>lower</dt>
<dd>Lower bound on censored distribution. Default 0.</dd>


<dt>upper</dt>
<dd>Upper bound on censored distribution. Default 1.</dd>

</dl>

Returns draws from a normal distribution with a lower censoring limit of lod
(limit of detection)

r_left_censored_norm: Returns draws from a normal distribution with a lower censoring limit of lod (limit of detection)

Description

Usage

Value

Arguments