This is the HTTK master function for creating a data table for use with
Monte Carlo methods to simulate parameter uncertainty and variabilit.
Each column of the output table corresponds to an HTTK model parameter and
each row corresponds
to a different random draw (for example, different individuals when
considering biological variability). This function call three different key
functions to simulate parameter parameter uncertainty and/or variability in
one of three ways. First parameters can be varied in an uncorrelated manner
using truncated normal distributions by the function
monte_carlo. Then,
physiological parameters can be varied in a correlated manner according to
the ring2017identifying;textualhttk
(tools:::Rd_expr_doi("10.1016/j.envint.2017.06.004")) httk-pop
approach by the function httkpop_mc. Next, both uncertainty
and variability of in vitro HTTK parameters can be simulated by the function
invitro_mc as described by
wambaugh2019assessing;textualhttk
(tools:::Rd_expr_doi("10.1093/toxsci/kfz205")). Finally, tissue-specific partition
coefficients are predicted for each draw using the
schmitt2008general;textualhttk
(tools:::Rd_expr_doi("10.1016/j.tiv.2007.09.010")) method as calibrated to in vivo
data by pearce2017evaluation;textualhttk
(tools:::Rd_expr_doi("10.1007/s10928-017-9548-7")) and
implemented in predict_partitioning_schmitt.
create_mc_samples(
chem.cas = NULL,
chem.name = NULL,
dtxsid = NULL,
parameters = NULL,
samples = 1000,
species = "Human",
suppress.messages = FALSE,
model = "3compartmentss",
httkpop = TRUE,
invitrouv = TRUE,
calcrb2p = TRUE,
censored.params = list(),
vary.params = list(),
return.samples = FALSE,
tissue = NULL,
httkpop.dt = NULL,
invitro.mc.arg.list = NULL,
adjusted.Funbound.plasma = TRUE,
adjusted.Clint = TRUE,
httkpop.generate.arg.list = list(method = "direct resampling"),
convert.httkpop.arg.list = NULL,
propagate.invitrouv.arg.list = NULL,
parameterize.arg.list = NULL,
Caco2.options = NULL
)A data table where each column corresponds to parameters needed for the specified model and each row represents a different Monte Carlo sample of parameter values.
Chemical Abstract Services Registry Number (CAS-RN) -- if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
Chemical name (spaces and capitalization ignored) -- if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) -- if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
Parameters from the appropriate parameterization function for the model indicated by argument model
Number of samples generated in calculating quantiles.
Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Species must be set to "Human" to run httkpop model.
Whether or not to suppress output message.
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.
Whether or not to use the ring2017identifying;textualhttk "httkpop" population generator. Species must be 'Human'.
Logical to indicate whether to include in vitro parameters such as intrinsic hepatic clearance rate and fraction unbound in plasma in uncertainty and variability analysis
Logical determining whether or not to recalculate the chemical ratio of blood to plasma
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.
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.
Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.
Desired steady state tissue conentration.
A data table generated by httkpop_generate.
This defaults to NULL, in which case httkpop_generate is
called to generate this table.
Additional parameters passed to
invitro_mc.
Uses pearce2017evaluation;textualhttk lipid binding adjustment for Funbound.plasma when set to TRUE (Default).
Uses kilford2008hepatocellular;textualhttk hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
Additional parameters passed to
httkpop_generate.
Additional parameters passed to the convert_httkpop_* function for the model.
Additional parameters passed to model's associated in vitro uncertainty and variability propagation function
Additional parameters passed to the parameterize_* function for the model.
Arguments describing how to handle Caco2 absorption data
that are passed to invitro_mc and the parameterize_[MODEL]
functions.
See get_fabsgut for further details.
Caroline Ring, Robert Pearce, and John Wambaugh
The Monte Carlo methods used here were recently updated and described by breen2022simulating;textualhttk.
We aim to make any function that uses chemical identifiers (name, CAS, DTXSID) also work if passed a complete vector of parameters (that is, a row from the table generated by this function). This allows the use of Monte Carlo to vary the parameters and therefore vary the function output. Depending on the type of parameters (for example, physiological vs. in vitro measurements) we vary the parameters in different ways with different functions.
# \donttest{
# We can use the Monte Carlo functions by passing a table
# where each row represents a different Monte Carlo draw of parameters:
p <- create_mc_samples(chem.cas="80-05-7")
# Use data.table for steady-state plasma concentration (Css) Monte Carlo:
calc_mc_css(parameters=p)
# Using the same table gives the same answer:
calc_mc_css(parameters=p)
# Use Css for 1 mg/kg/day for simple reverse toxicokinetics
# in Vitro-In Vivo Extrapolation to convert 15 uM to mg/kg/day:
15/calc_mc_css(parameters=p, output.units="uM")
# Can do the same with calc_mc_oral_equiv:
calc_mc_oral_equiv(15, parameters=p)
# }
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