monte_carlo

This function performs basic, uncorrelated Monte Carlo to simulate
uncertainty and/or variability for parameters of 
toxicokinetic models. Parameters can be varied according to either a
normal distribution that is truncated at zero 
(using argument <code>cv.params</code>) or from a normal distribution that is 
censored for values less than the limit of detection 
(<code>censored.params</code>). Coefficient of variation (cv) and limit of
of detectin can be specified separately for each parameter.

Monte-Carlo

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

monte_carlo function

<dl><dt>parameters</dt>
<dd>These parameters that are also listed in either
cv.params or censored.params are sampled using Monte Carlo.</dd>
<dt>cv.params</dt>
<dd>The parameters listed in cv.params are sampled from a
normal distribution that is truncated at zero. This argument should be a
list of coefficients of variation (cv) for the normal distribution. Each
entry in the list is named for a parameter in "parameters". New values are
sampled with mean equal to the value in "parameters" and standard deviation
equal to the mean times the cv.</dd>
<dt>censored.params</dt>
<dd>The parameters listed in censored.params are sampled
from a normal distribution that is censored for values less than the limit
of detection (specified separately for each parameter). This argument should
be a list of sub-lists. Each sublist is named for a parameter in "params"
and contains two elements: "cv" (coefficient of variation) and "LOD" (limit
of detection), below which parameter values are censored. New values are
sampled with mean equal to the value in "params" and standard deviation
equal to the mean times the cv. Censored values are sampled on a uniform
distribution between 0 and the limit of detection.</dd>
<dt>samples</dt>
<dd>This argument is the number of samples to be generated for
calculating quantiles.</dd>
<dt>suppress.messages</dt>
<dd>Whether or not the output message is suppressed.</dd></dl>

Arguments

Author

Monte Carlo for toxicokinetic model parameters — monte_carlo

<dl>

<dt>parameters</dt>
<dd>These parameters that are also listed in either
cv.params or censored.params are sampled using Monte Carlo.</dd>


<dt>cv.params</dt>
<dd>The parameters listed in cv.params are sampled from a
normal distribution that is truncated at zero. This argument should be a
list of coefficients of variation (cv) for the normal distribution. Each
entry in the list is named for a parameter in "parameters". New values are
sampled with mean equal to the value in "parameters" and standard deviation
equal to the mean times the cv.</dd>


<dt>censored.params</dt>
<dd>The parameters listed in censored.params are sampled
from a normal distribution that is censored for values less than the limit
of detection (specified separately for each parameter). This argument should
be a list of sub-lists. Each sublist is named for a parameter in "params"
and contains two elements: "cv" (coefficient of variation) and "LOD" (limit
of detection), below which parameter values are censored. New values are
sampled with mean equal to the value in "params" and standard deviation
equal to the mean times the cv. Censored values are sampled on a uniform
distribution between 0 and the limit of detection.</dd>


<dt>samples</dt>
<dd>This argument is the number of samples to be generated for
calculating quantiles.</dd>


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

</dl>

Monte Carlo for toxicokinetic model parameters

monte_carlo: Monte Carlo for toxicokinetic model parameters

Description

Usage

Value

Arguments

Author

References

Examples