create_mc_samples: Create a table of parameter values for Monte Carlo

In httk: High-Throughput Toxicokinetics

Create a table of parameter values for Monte Carlo

Description

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 Ring et al. (2017) (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 Wambaugh et al. (2019) (doi: 10.1093/toxsci/kfz205). Finally, tissue-specific partition coefficients are predicted for each draw using the Schmitt (2008) (doi: 10.1016/j.tiv.2007.09.010) method as calibrated to in vivo data by Pearce et al. (2017) (doi: 10.1007/s10928-017-9548-7) and implemented in predict_partitioning_schmitt.

Usage

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,
  httkpop.generate.arg.list = list(method = "direct resampling"),
  convert.httkpop.arg.list = NULL,
  propagate.invitrouv.arg.list = NULL,
  parameterize.arg.list = NULL
)

Arguments

chem.cas	Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name	Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid	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	Parameters from the appropriate parameterization function for the model indicated by argument 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 the Ring et al. (2017) "httkpop" population generator. Species must be 'Human'.
invitrouv	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
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.
httkpop.dt	A data table generated by httkpop_generate. This defaults to NULL, in which case httkpop_generate is called to generate this table.
invitro.mc.arg.list	Additional parameters passed to invitro_mc.
httkpop.generate.arg.list	Additional parameters passed to httkpop_generate.
convert.httkpop.arg.list	Additional parameters passed to the convert_httkpop_* function for the model.
propagate.invitrouv.arg.list	Additional parameters passed to model's associated in vitro uncertainty and variability propagation function
parameterize.arg.list	Additional parameters passed to the parameterize_* function for the model.

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

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.

Value

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.

Author(s)

Caroline Ring, Robert Pearce, and John Wambaugh

References

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences 147.1 (2015): 55-67.

Ring, Caroline L., et al. "Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability." Environment international 106 (2017): 105-118.

Examples

sample_set = create_mc_samples(chem.name = 'bisphenol a')

httk documentation built on March 7, 2023, 7:26 p.m.