calc_mc_tk | R Documentation |
This function finds the analytical steady state plasma concentration(from calc_analytic_css) using a monte carlo simulation (monte_carlo).
calc_mc_tk(
chem.cas = NULL,
chem.name = NULL,
dtxsid = NULL,
parameters = NULL,
samples = 1000,
species = "Human",
suppress.messages = FALSE,
model = "pbtk",
httkpop = TRUE,
httkpop.dt = NULL,
invitrouv = TRUE,
calcrb2p = TRUE,
censored.params = list(),
vary.params = list(),
return.samples = FALSE,
tissue = NULL,
output.units = "mg/L",
solvemodel.arg.list = list(times = c(0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5)),
Caco2.options = list(),
invitro.mc.arg.list = NULL,
httkpop.generate.arg.list = list(method = "direct resampling"),
convert.httkpop.arg.list = NULL,
parameterize.arg.list = NULL,
return.all.sims = FALSE
)
chem.cas |
Either the CAS number, parameters, or the chemical name must be specified. |
chem.name |
Either the chemical parameters, name, or the CAS number must be specified. |
dtxsid |
EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs |
parameters |
Parameters from parameterize_steadystate. Not used with httkpop model. |
samples |
Number of samples generated in calculating quantiles. |
species |
Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Species must be set to "Human" to run httkpop model. |
suppress.messages |
Whether or not to suppress output message. |
model |
Model used in calculation: 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model. This only applies when httkpop=TRUE and species="Human", otherwise '3compartmentss' is used. |
httkpop |
Whether or not to use population generator and sampler from httkpop. This is overwrites censored.params and vary.params and is only for human physiology. Species must also be set to 'Human'. |
httkpop.dt |
A data table generated by |
invitrouv |
Logical to indicate whether to include in vitro parameters in uncertainty and variability analysis |
calcrb2p |
Logical determining whether or not to recalculate the chemical ratio of blood to plasma |
censored.params |
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 "parameters" 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 "parameters" 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. Not used with httkpop model. |
vary.params |
The parameters listed in vary.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. Not used with httkpop model. |
return.samples |
Whether or not to return the vector containing the samples from the simulation instead of the selected quantile. |
tissue |
Desired steady state tissue conentration. |
output.units |
Plasma concentration units, either uM or default mg/L. |
solvemodel.arg.list |
Additional arguments ultimately passed to
|
Caco2.options |
A list of options to use when working with Caco2 apical to
basolateral data |
invitro.mc.arg.list |
List of additional parameters passed to
|
httkpop.generate.arg.list |
Additional parameters passed to
|
convert.httkpop.arg.list |
Additional parameters passed to the convert_httkpop_* function for the model. |
parameterize.arg.list |
Additional parameters passed to the parameterize_* function for the model. |
return.all.sims |
Logical indicating whether to return the results of all simulations, in addition to the default toxicokinetic statistics |
The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).
All arguments after httkpop only apply if httkpop is set to TRUE and species to "Human".
When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.
Tissue concentrations are calculated for the pbtk model with oral infusion dosing. All tissues other than gut, liver, and lung are the product of the steady state plasma concentration and the tissue to plasma partition coefficient.
The six sets of plausible in vitro-in vivo extrpolation (IVIVE) assumptions identified by Honda et al. (2019) (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0217564")}) are:
in vivo Conc. | Metabolic Clearance | Bioactive Chemical Conc. | TK Statistic Used* | |
Honda1 | Veinous (Plasma) | Restrictive | Free | Mean Conc. |
Honda2 | Veinous | Restrictive | Free | Max Conc. |
Honda3 | Veinous | Non-restrictive | Total | Mean Conc. |
Honda4 | Veinous | Non-restrictive | Total | Max Conc. |
Honda5 | Target Tissue | Non-restrictive | Total | Mean Conc. |
Honda6 | Target Tissue | Non-restrictive | Total | Max Conc. |
*Assumption is currently ignored because analytical steady-state solutions are currently used by this function.
If return.all.sims == FALSE (default) a list with:
means |
The mean concentration for each model compartment as a function of time across the Monte Carlo simulation |
sds |
The standard deviation for each model compartment as a function of time across the Monte Carlo simulation |
If return.all.sums == TRUE then a list is returned with:
stats |
The list of means and sds from return.all.sums=FALSE |
sims |
The concentration vs. time results for each compartment for every (samples) set of parameters in the Monte Carlo simulation |
John Wambaugh
create_mc_samples
NSAMP <- 50
chemname="Abamectin"
times<- c(0,0.25,0.5,0.75,1,1.5,2,2.5,3,4,5)
age.ranges <- seq(6,80,by=10)
forward <- NULL
for (age.lower in age.ranges)
{
label <- paste("Ages ",age.lower,"-",age.lower+4,sep="")
set.seed(1234)
forward[[label]] <- calc_mc_tk(
chem.name=chemname,
samples=NSAMP,
httkpop.generate.arg.list=list(
method="d",
agelim_years = c(age.lower, age.lower+9)),
solvemodel.arg.list = list(
times=times))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.