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# Add the 3compartment model (Pearce et al., 2017) to the list of models:
#
# Pearce, Robert G., et al. "Httk: R package for high-throughput
# toxicokinetics." Journal of statistical software 79.4 (2017): 1.
# Model identifier for the model.list:
THIS.MODEL <- "3compartment"
# Analytic expression for steady-state plasma concentration to be used by
# calc_analytic_css:
model.list[[THIS.MODEL]]$analytic.css.func <- "calc_analytic_css_3comp"
# What units does the analytic function return in calc_analytic_css:
model.list[[THIS.MODEL]]$steady.state.units <- "mg/L"
# When calculating steady-state with calc_css, which compartment do we test?
# ("C" is preprended):
model.list[[THIS.MODEL]]$steady.state.compartment <- "plasma"
# Function used for generating model parameters:
model.list[[THIS.MODEL]]$parameterize.func <- "parameterize_3comp"
# Function called for running the model:
model.list[[THIS.MODEL]]$solve.func <- "solve_3comp"
# Here are the tissues from tissue.data that are considered:
# They should correspond
# in name to the names present in the tissue.data object, if the parameters
# necessary for describing the tissue/compartment aren't going to be provided
# otherwise.
model.list[[THIS.MODEL]]$alltissues=c(
"adipose",
"bone",
"brain",
"gut",
"heart",
"kidney",
"liver",
"lung",
"muscle",
"skin",
"spleen",
"red blood cells",
"rest")
# Which tissues from tissue.data are not lumped together when forming
# the model: 3 compartment model has only liver and gut compartments;
# everything else is lumped.
model.list[[THIS.MODEL]]$tissuelist = list(
liver=c("liver"),
gut=c("gut"))
# These are all the parameters returned by the R model parameterization function.
# Some of these parameters are not directly used to solve the model, but describe
# how other parameters were calculated:
model.list[[THIS.MODEL]]$param.names <- c(
"BW",
"Caco2.Pab",
"Caco2.Pab.dist",
"Clint",
"Clint.dist",
"Clmetabolismc",
"Funbound.plasma",
"Funbound.plasma.dist",
"Funbound.plasma.adjustment",
"Fabsgut",
"Fhep.assay.correction",
"hematocrit",
"Kgut2pu",
"Krbc2pu",
"kgutabs",
"Kliver2pu",
"Krest2pu",
"liver.density",
"million.cells.per.gliver",
"MW",
"Pow",
"pKa_Donor",
"pKa_Accept",
"MA",
"Qcardiacc",
"Qgfrc",
"Qgutf",
"Qliverf",
"Rblood2plasma",
"Vgutc",
"Vliverc",
"Vrestc"
)
#
# String representations of the R version of names of
# the parameters are assigned to the C variable name in this scheme.
model.list[[THIS.MODEL]]$Rtosolvermap <- list(
BW = "BW",
CLmetabolismc="Clmetabolismc",
kgutabs="kgutabs",
Qcardiacc="Qcardiacc",
Qgfrc="Qgfrc",
Qgutf="Qgutf",
Qliverf="Qliverf",
Vportvenc="Vgutc",
Vliverc="Vliverc",
Vsyscompc="Vrestc",
Fraction_unbound_plasma="Funbound.plasma",
Kliver2plasma="Kliver2pu",
Krest2plasma="Krest2pu",
Ratioblood2plasma="Rblood2plasma"
)
# This function translates the R model parameters into the compiled model
# parameters:
model.list[[THIS.MODEL]]$compiled.parameters.init <- "getParms3comp"
# This is the ORDERED full list of parameters used by the compiled code to
# calculate the derivative of the system of equations describing the model
model.list[[THIS.MODEL]]$compiled.param.names <- c(
"BW",
"Clmetabolismc",
"kgutabs",
"Qcardiacc",
"Qgfrc",
"Qgutf",
"Qliverf",
"Vportvenc",
"Vliverc",
"Vsyscompc",
"Vportven",
"Vliver",
"Vsyscomp",
"Fraction_unbound_plasma",
"Clmetabolism",
"Qcardiac",
"Qgfr",
"Qgut",
"Qliver",
"Kliver2plasma",
"Krest2plasma",
"Ratioblood2plasma"
)
# This function initializes the state vector for the compiled model:
model.list[[THIS.MODEL]]$compiled.init.func <- "initmod3comp"
# This is the function that calculates the derviative of the model as a function
# of time, state, and parameters:
model.list[[THIS.MODEL]]$derivative.func <- "derivs3comp"
# This is the ORDERED list of variables returned by the derivative function
# (from Model variables: Outputs):
model.list[[THIS.MODEL]]$derivative.output.names <- c(
"Cportven",
"Cliver",
"Csyscomp",
"Cplasma"
)
model.list[[THIS.MODEL]]$default.monitor.vars <- c(
"Cliver",
"Csyscomp",
"Cplasma",
"Atubules",
"Ametabolized",
"AUC"
)
# Allowable units assigned to dosing input:
model.list[[THIS.MODEL]]$allowed.units.input <- list(
"oral" = c('umol','mg','mg/kg'),
"iv" = c('umol','mg','mg/kg'))
# Allowable units assigned to entries in the output columns of the ode system
model.list[[THIS.MODEL]]$allowed.units.output <- list(
"oral" = c('uM','mg/L','umol','mg','uM*days','mg/L*days'),
"iv" = c('uM','mg/L','umol','mg','uM*days','mg/L*days'))
model.list[[THIS.MODEL]]$routes <- list(
"oral" = list(
# We need to know which compartment gets the dose
"entry.compartment" = "Aintestine",
# desolve events can take the values "add" to add dose C1 <- C1 + dose,
# "replace" to change the value C1 <- dose
# or "multiply" to change the value to C1 <- C1*dose
"dose.type" = "add",
"dosing.params" = c("daily.dose",
"initial.dose",
"doses.per.day",
"dosing.matrix")),
"iv" = list(
"entry.compartment" = "Asyscomp",
"dose.type" = "add",
"dosing.params" = c("initial.dose",
"dosing.matrix"))
)
# ORDERED LIST of state variables (must match Model variables:
# States in C code, each of which is associated with a differential equation),
# mostly calculated in amounts, though AUC (area under plasma concentration
# curve) also appears here:
model.list[[THIS.MODEL]]$state.vars <- c(
"Aintestine",
"Aportven",
"Aliver",
"Asyscomp",
"Ametabolized",
"Atubules",
"AUC"
)
# Default set of units assigned to correspond to each of the time dependent
# variables of the model system including state variables and any transformed
# outputs (for example, concentrations calculated from amounts.)
# AUC values should also be included.
model.list[[THIS.MODEL]]$compartment.units <-c(
"Aintestine"="umol",
"Aportven"="umol",
"Aliver"="umol",
"Asyscomp"="umol",
"Ametabolized"="umol",
"Atubules"="umol",
"Cportven"="uM",
"Cliver"="uM",
"Csyscomp"="uM",
"Cplasma"="uM",
"AUC"="uM*days"
)
# Compartment state of matter, needed for proper unit conversion, if all
# comaprtments of the same only include one state and set it to "all":
model.list[[THIS.MODEL]]$compartment.state <- list(liquid="all")
# Parameters needed to make a prediction (this is used by get_cheminfo):
model.list[[THIS.MODEL]]$required.params <- c(
"Clint",
"Funbound.plasma",
"Pow",
"pKa_Donor",
"pKa_Accept",
"MW"
)
# Function for calculating Clmetabolismc after Clint is varied:
model.list[[THIS.MODEL]]$propagateuv.func <- "propagate_invitrouv_3comp"
# If httk-pop is enabled:
# Function for converting httk-pop physiology to model parameters:
model.list[[THIS.MODEL]]$convert.httkpop.func <- NULL# We want all the standard physiological calculations performed:
# We want all the standard physiological calculations performed:
model.list[[THIS.MODEL]]$calc.standard.httkpop2httk <- TRUE
# These are the model parameters that are impacted by httk-pop:
model.list[[THIS.MODEL]]$httkpop.params <- c(
"BW",
"Qcardiacc",
"Qgfrc",
"Qgutf",
"Qliverf",
"Vportvenc",
"Vliverc",
"Vsyscompc",
"Vportven",
"Vliver",
"Vsyscomp",
"Qcardiac",
"Qgfr",
"Qgut",
"Qliver",
"Ratioblood2plasma")
# Do we need to recalculate partition coefficients when doing Monte Carlo?
model.list[[THIS.MODEL]]$calcpc <- TRUE
# Do we need to recalculate first pass metabolism when doing Monte Carlo?
model.list[[THIS.MODEL]]$firstpass <- FALSE
# Do we ignore the Fups where the value was below the limit of detection?
model.list[[THIS.MODEL]]$exclude.fup.zero <- TRUE
# These are the parameter names needed to describe steady-state dosing:
model.list[[THIS.MODEL]]$css.dosing.params <- list(
oral=c("hourly.dose"))
# Filter out volatile compounds with Henry's Law Constant Threshold
model.list[[THIS.MODEL]]$log.henry.threshold <- c(-4.5)
# Filter out compounds belonging to select chemical classes
model.list[[THIS.MODEL]]$chem.class.filt <- c("PFAS")
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