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#' Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability.
#'
#' @description
#' Given a CAS in the HTTK data set, a virtual population from HTTK-Pop, some
#' user specifications on the assumed distributions of Funbound.plasma and
#' Clint, draw "individual" values of Funbound.plasma and Clint from those
#' distributions. The methodology for this function was developed and described
#' by \insertCite{wambaugh2019assessing;textual}{httk}
#' (\doi{10.1093/toxsci/kfz205}).
#'
#' @details
#' The Monte Carlo methods used here were recently updated and described by
#' \insertCite{breen2022simulating;textual}{httk}.
#'
#' @param parameters.dt A data table of physiological and chemical-specific parameters
#'
#' @param parameters A list of chemical-specific model parameters containing at
#' least Funbound.plasma, Clint, and Fhep.assay.correction.
#'
#' @param samples The number of samples to draw.
#'
#' @param fup.meas.mc Logical -- should we perform measurment (uncertainty)
#' Monte Carlo for \code{Funbound.plasma} values (Default TRUE). If FALSE,
#' the user may choose to provide columns for "unadjusted.Funbound.plasma" or
#' "fup.mean" from their own methods.
#'
#' @param fup.pop.mc Logical -- should we perform population (variability)
#' Monte Carlo for \code{Funbound.plasma} values (Default TRUE)
#'
#' @param clint.meas.mc Logical -- should we perform measurment (uncertainty)
#' Monte Carlo for \code{Clint} values (Default TRUE)
#'
#' @param clint.pop.mc Logical -- should we perform population (variability)
#' Monte Carlo for \code{Clint} values (Default TRUE)
#'
#' @param fup.meas.cv Coefficient of variation of distribution of measured
#' \code{Funbound.plasma} values.
#'
#' @param clint.meas.cv Coefficient of variation of distribution of measured
#' \code{Clint} values.
#'
#' @param fup.pop.cv Coefficient of variation of distribution of population
#' \code{Funbound.plasma} values.
#'
#' @param clint.pop.cv Coefficient of variation of distribution of population
#' \code{Clint} values.
#'
#' @param poormetab Logical. Whether to include poor metabolizers in the Clint
#' distribution or not.
#'
#' @param fup.lod The average limit of detection for \code{Funbound.plasma}, below
#' which distribution will be censored if fup.censored.dist is TRUE. Default 0.01.
#'
#' @param fup.censored.dist Logical. Whether to draw \code{Funbound.plasma} from a
#' censored distribution or not.
#'
#' @param adjusted.Funbound.plasma Uses the
#' \insertCite{pearce2017evaluation;textual}{httk} lipid binding adjustment
#' for Funbound.plasma when set to TRUE (Default).
#'
#' @param adjusted.Clint Uses \insertCite{kilford2008hepatocellular;textual}{httk}
#' hepatocyte incubation
#' binding adjustment for Clint when set to TRUE (Default).
#'
#' @param clint.pvalue.threshold Hepatic clearance for chemicals where the in
#' vitro clearance assay result has a p-values greater than the threshold are
#' set to zero.
#'
#' @param minimum.Funbound.plasma Monte Carlo draws less than this value are set
#' equal to this value (default is 0.0001 -- half the lowest measured Fup in our
#' dataset).
#'
#' @param caco2.meas.sd Standard deviation of the measured oral absorption - numeric value (Default 0.3).
#'
#' @param caco2.pop.sd Standard deviation of the population level oral absorption - numeric value (Default 0.3).
#'
#' @param Caco2.Fabs = TRUE uses Caco2.Pab to calculate
#' fabs.oral, otherwise fabs.oral = \code{Fabs}.
#'
#' @param Caco2.Fgut = TRUE uses Caco2.Pab to calculate
#' fgut.oral, otherwise fgut.oral = \code{Fgut}.
#'
#' @param keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings.
#'
#' @return A data.table with three columns: \code{Funbound.plasma} and
#' \code{Clint}, containing the sampled values, and
#' \code{Fhep.assay.correction}, containing the value for fraction unbound in
#' hepatocyte assay.
#'
#' @author Caroline Ring and John Wambaugh
#'
#' @references
#'
#' \insertAllCited{}
#'
#' @examples
#' \donttest{
#' #Simply generate a virtual population of 100 individuals,
#' #using the direct-resampling method
#' set.seed(42)
#'
#' # Pull mean vchemical=specific values:
#' chem.props <- parameterize_pbtk(chem.name="bisphenolaf")
#'
#' # Convert to data.table with one row per sample:
#' parameters.dt <- monte_carlo(chem.props,samples=100)
#'
#' # Use httk-pop to generate a population:
#' pop <- httkpop_generate(method='direct resampling', nsamp=100)
#'
#' # Overwrite parameters specified by httk-pop:
#' parameters.dt[,names(pop):=pop]
#' # Vary in vitro parameters:
#' parameters.dt <- invitro_mc(parameters.dt,samples=100)
#' }
#'
#' @keywords monte-carlo in-vitro
#'
#' @import stats
#'
#' @export invitro_mc
invitro_mc <- function(parameters.dt=NULL,
samples,
fup.meas.mc = TRUE,
fup.pop.mc = TRUE,
clint.meas.mc = TRUE,
clint.pop.mc = TRUE,
fup.meas.cv=0.4,
clint.meas.cv=0.3,
fup.pop.cv=0.3,
clint.pop.cv=0.3,
caco2.meas.sd = 0.3,
caco2.pop.sd = 0.3,
Caco2.Fgut = TRUE,
Caco2.Fabs = TRUE,
keepit100 = FALSE,
poormetab=TRUE,
fup.lod=0.01,
fup.censored.dist=FALSE,
adjusted.Funbound.plasma=TRUE,
adjusted.Clint=TRUE,
clint.pvalue.threshold=0.05,
minimum.Funbound.plasma=0.0001)
{
#R CMD CHECK throws notes about "no visible binding for global variable", for
#each time a data.table column name is used without quotes. To appease R CMD
#CHECK, a variable has to be created for each of these column names and set to
#NULL. Note that within the data.table, these variables will not be NULL! Yes,
#this is pointless and annoying.
Clint.mu<-Clint.sd<-unadjusted.Funbound.plasma<-Flipid<-physiology.data<-NULL
Parameter<-Funbound.plasma.adjustment<-fup.mean<-X<-Clint.dist<-Dow74<-NULL
Funbound.plasma.dist<-fup.sd<-Fhep.assay.correction <- NULL
PoorMetabolizer <- NULL
Caco2.Pab.dist <- Caco2.Pab.mu <- Fabs <- Fgut <- Fabsgut <- NULL
#End R CMD CHECK appeasement.
# Are we doing clint measurmement Monte Carlo?
if (is.null(clint.meas.cv))
{
clint.meas.mc <- FALSE
}
# Are we doing clint population Monte Carlo?
if (is.null(clint.pop.cv))
{
clint.pop.mc <- FALSE
}
# Are we doing fup measurmement Monte Carlo?
if (is.null(clint.meas.cv))
{
fup.meas.mc <- FALSE
}
# Are we doing fup population Monte Carlo?
if (is.null(fup.pop.cv))
{
fup.pop.mc <- FALSE
}
if (!("Funbound.plasma") %in% names(parameters.dt))
stop("Funbound.plasma needed in invitro_mc.")
if (any(!(c(
"Clint",
"pKa_Donor",
"pKa_Accept",
"Pow") %in% names(parameters.dt))) &
!("Dow74" %in% names(parameters.dt)))
stop("Either Dow74 or Clint, pKa_Donor, pKa_Accept and Pow needed in invitro_mc.")
if (!"Dow74"%in%names(parameters.dt))
{
pKa_Donor <- parameters.dt[["pKa_Donor"]]
pKa_Accept <- parameters.dt[["pKa_Accept"]]
Pow <- parameters.dt[["Pow"]] # Octanol:water partition coeffiecient
ion <- calc_ionization(pH=7.4,pKa_Donor=pKa_Donor,pKa_Accept=pKa_Accept)
dow <- Pow * (ion$fraction_neutral + 0.001 * ion$fraction_charged + ion$fraction_zwitter)
parameters.dt[,Dow74:=dow]
}
#
#
#
# MEASUREMENT UNCERTAINTY
#
#
#
# Hepatocyte clearance assay uncertainty:
#
#
#
# If the clint.meas.mc == FALSE then we just use the point estimate with no
# uncertainty:
if (!clint.meas.mc)
{
Clint <- parameters.dt[["Clint"]]
Clint.l95 <- NULL
Clint.u95 <- NULL
Clint.pvalue <- 0
parameters.dt[,Clint:=Clint]
} else {
# We need to determine what sort of information we have been provided about
# measurment uncertainty. We first check for a comma separated list with a
# median, lower, and upper 95th credible interval limits:
if (all(!is.na(parameters.dt$Clint.dist)))
{
if (nchar(parameters.dt$Clint.dist[1]) -
nchar(gsub(",","",parameters.dt$Clint.dist[1]))!=3)
{
stop("Clint distribution should be four values (median,low95th,high95th,pValue) separated by commas.")
}
temp <- strsplit(parameters.dt$Clint.dist,",")
Clint <- as.numeric(temp[[1]][1])
Clint.l95 <- as.numeric(temp[[1]][2])
Clint.u95 <- as.numeric(temp[[1]][3])
Clint.pvalue <- as.numeric(temp[[1]][4])
# If we don't have that, we use the default coefficient of variation to
# generate confidence limits:
} else {
Clint <- parameters.dt[["Clint"]]
Clint.l95 <- sapply(Clint*(1 - clint.meas.cv*1.96),function(x) max(x,10^-3))
Clint.u95 <- Clint*(1 + clint.meas.cv*1.96)
Clint.pvalue <- 0 # Currently we don't story the pvalue in parameters.dt
}
# Shrink it down if we don't have unique values:
if (all(c(length(unique(Clint))==1,
length(unique(Clint.l95))==1,
length(unique(Clint.u95))==1,
length(unique(Clint.pvalue))==1)))
{
Clint <- Clint[1]
Clint.l95 <- Clint.l95[1]
Clint.u95 <- Clint.u95[1]
Clint.pvalue <- Clint.pvalue[1]
}
# Now do the uncertainty Monte Carlo analysis -- draw a series of plausible
# "true" values for Clint that are consistent with the measurment .
# If a credible interval was specified for Clint, draw from that interval.
# First check if at least the upper limit on the 95 percent interval is non-zero:
if (Clint.u95>0)
{
# Check if the median is zero:
if (Clint == 0)
{
# Use our custom median 0 non-zero upper 95th function:
parameters.dt[, Clint := rmed0non0u95(
n=samples,
x.u95 = Clint.u95,
x.min = 0,
x.LOD = Clint.u95/1e2)]
} else {
# Optimize to find parameters.dt for a log-normal distribution that have
# the least squares difference using the three quantiles (median, l95, u95)
clint.fit <- suppressWarnings(optim(c(log(Clint),clint.meas.cv), function(x) (0.95-
plnorm(Clint.u95,x[1],x[2])+
plnorm(Clint.l95,x[1],x[2]))^2+
(Clint-qlnorm(0.5,x[1],x[2]))^2))
parameters.dt[,Clint:=rlnorm(n=samples,clint.fit$par[1],clint.fit$par[2])]
}
# Check to see if we should enforce some zero entries:
N.clint.zero <- dim(parameters.dt[Clint==0,])[1]
N.clint.zero.req <- round(Clint.pvalue*samples)
if (N.clint.zero < N.clint.zero.req)
{
# Pick the N.clint.zero.req lowest Clints:
thresh <- sort(parameters.dt[,Clint,with=TRUE])[N.clint.zero.req]
# Change some non-zero Clints to zero:
parameters.dt[Clint <= thresh, Clint:=0]
}
} else {
# All quantiles are zero:
parameters.dt[,Clint:=0]
}
}
# Determine the value for fraction unbound in hepatocyte assay (depends on
# phys-chem but does not vary biologically):
# First check that this model has phys-chem parameters:
if (all(c("Pow","pKa_Donor","pKa_Accept")%in%colnames(parameters.dt)))
parameters.dt[,Fhep.assay.correction:=calc_hep_fu(parameters=parameters.dt)]
# Correct for fraction of chemical unbound in in vitro hepatocyte assay:
if (adjusted.Clint)
{
parameters.dt[, Clint := apply_clint_adjustment(
Clint,
Fu_hep=Fhep.assay.correction,
suppress.messages=TRUE)]
}
#
#
#
# fup uncertainty Monte Carlo:
#
#
#
# If the fup.meas.mc == FALSE we just use the point estimate with no
# uncertainty simulation:
if (!fup.meas.mc)
{
Funbound.plasma <- parameters.dt$Funbound.plasma
Funbound.plasma.l95 <- NULL
Funbound.plasma.u95 <- NULL
if (!"unadjusted.Funbound.plasma" %in% colnames(parameters.dt))
{
parameters.dt[, unadjusted.Funbound.plasma:=Funbound.plasma]
}
} else {
# We need to determine what sort of information we have been provided about
# measurment uncertainty.
#
# We first check for a comma separated list with a
# median, lower, and upper 95th credible interval limits:
if(!is.na(parameters.dt$Funbound.plasma.dist[1]))
{
# Check formatting:
if (nchar(parameters.dt$Funbound.plasma.dist[1]) -
nchar(gsub(",","",parameters.dt$Funbound.plasma.dist[1]))!=2)
{
stop("Funbound.plasma distribution should be three values (median,low95th,high95th) separated by commas.")
}
# Split into quantiles:
temp <- strsplit(parameters.dt$Funbound.plasma.dist,",")
Funbound.plasma <- as.numeric(temp[[1]][1])
Funbound.plasma.l95 <- as.numeric(temp[[1]][2])
Funbound.plasma.u95 <- as.numeric(temp[[1]][3])
# If we don't have actual quantiles, use the coefficient of
# variation (fup.meas.cv), to approximate confidence limits:
} else {
Funbound.plasma <- parameters.dt$Funbound.plasma
Funbound.plasma.l95 <- sapply(Funbound.plasma*(1-fup.meas.cv*1.96),
function(x) max(x,0))
Funbound.plasma.u95 <- sapply(Funbound.plasma*(1+fup.meas.cv*1.96),
function(x) min(x,1))
}
# For computational efficiency, shrink the median and quantiles down to a
# single value if there is only one value for each quantile
if (all(c(length(unique(Funbound.plasma))==1,
length(unique(Funbound.plasma.l95))==1,
length(unique(Funbound.plasma.u95))==1)))
{
Funbound.plasma <- Funbound.plasma[1]
Funbound.plasma.l95 <- Funbound.plasma.l95[1]
Funbound.plasma.u95 <- Funbound.plasma.u95[1]
}
# Check to see if median fup was below lod:
if (Funbound.plasma==0)
{
# Is the upper bound non-zero?
if (Funbound.plasma.u95 > minimum.Funbound.plasma)
{
# If so, make the median LOD and the upper 95th quantile match the measured value:
parameters.dt[, unadjusted.Funbound.plasma := rmed0non0u95(
n=samples,
x.u95 = Funbound.plasma.u95,
x.min = minimum.Funbound.plasma,
x.LOD = fup.lod)]
} else {
# Otherwise, assign values between the minimum and the lod:
parameters.dt[, unadjusted.Funbound.plasma := runif(
n=samples,
minimum.Funbound.plasma,
fup.lod)]
}
} else {
# Median Fup is non-zero.
# Check to see if lower bound of confidence/credible interval is 1:
if (Funbound.plasma.l95 == 1)
{
# If so all values set to 1:
parameters.dt[, unadjusted.Funbound.plasma:=1]
} else {
# Lower bound is not 1.
# Check to see if median fup was below minimum allowed value:
if (Funbound.plasma<minimum.Funbound.plasma)
{
# Is the upper bound non-zero?
if (Funbound.plasma.u95 > minimum.Funbound.plasma)
{
# If so, make the median LOD and the upper 95th quantile match the measured value:
parameters.dt[, unadjusted.Funbound.plasma := rmed0non0u95(
n=samples,
x.u95 = Funbound.plasma.u95,
x.min = minimum.Funbound.plasma,
x.LOD = fup.lod)]
} else {
# Otherwise, assign the minimum to all samples:
parameters.dt[, unadjusted.Funbound.plasma := minimum.Funbound.plasma]
}
} else {
# Median is above minimum allowed value
# Initial conditions for optimizer depend on how close median is to 1:
# If the median is one we have a special case:
Funbound.med.is.one <- Funbound.plasma == 1
if (Funbound.plasma < 0.99)
{
# Decent guess at initial values:
initial.values <- c(2,
(1-Funbound.plasma)/Funbound.plasma*2)
} else {
# temporarily reduce median to just less than 1:
if (Funbound.med.is.one) Funbound.plasma <- 1 - 10^-3
# more likely to convege if we start with a distribution skewed toward 1
initial.values <- c(2,1)
}
# Use optim to estimate parameters for a beta distribution (alpha and beta)
# such that the median and 95% credible interval approximate the given values:
ppb.fit <- suppressWarnings(optim(initial.values,
function(x) (0.95-
pbeta(Funbound.plasma.u95,x[1],x[2])+
pbeta(Funbound.plasma.l95,x[1],x[2]))^2+
(Funbound.plasma-qbeta(0.5,x[1],x[2]))^2,
method="BFGS"))
# We are drawing new values for the unadjusted Fup:
parameters.dt[, unadjusted.Funbound.plasma:=rbeta(n=samples,
ppb.fit$par[1],
ppb.fit$par[2])]
# Check to see if we need to adjust for median = 1:
if (Funbound.med.is.one)
{
# Assign median its old value
Funbound.plasma <- 1
# Set the highest 50% to 1:
med.val <- median(parameters.dt[, unadjusted.Funbound.plasma, with=TRUE])
parameters.dt[unadjusted.Funbound.plasma > med.val,
unadjusted.Funbound.plasma := 1]
}
}
}
}
#if measured Funbound.plasma > 1, then set it to 1
parameters.dt[unadjusted.Funbound.plasma>1, unadjusted.Funbound.plasma := 1]
}
# Check to see if we are adjusting for differences between in vitro and
# physiological lipid partitioning (Pearce, 2017):
if (adjusted.Funbound.plasma)
{
# # We need the fraction of lipid in plasma:
# Flipid <-subset(httk::physiology.data,
# Parameter=='Plasma Effective Neutral Lipid Volume Fraction')[,
# which(colnames(httk::physiology.data) == 'Human')]
if (all(c("Pow","pKa_Donor","pKa_Accept") %in% names(parameters.dt)) |
("Dow74" %in% names(parameters.dt)))
{
# put the unadjusted fup where calc_fup_correction will look for it:
parameters.dt[, Funbound.plasma:=unadjusted.Funbound.plasma]
parameters.dt[, Funbound.plasma.adjustment:=
calc_fup_correction(
parameters = parameters.dt)]
} else stop("Missing phys-chem parameters in invitro_mc for calc_fup_correction.")
} else {
parameters.dt[, Funbound.plasma.adjustment:=1]
}
# Check that user didn't provide fup.mean:
if (fup.meas.mc | !("fup.mean" %in% colnames(parameters.dt)))
{
# After uncertainty simulation (if any) values become population means:
parameters.dt[, fup.mean:=
unadjusted.Funbound.plasma*Funbound.plasma.adjustment]
}
#
#
#
# Caco-2 uncertainty Monte Carlo:
#
#
#
# If the default CV is set to NULL, we just use the point estimate with no
# uncertainty:
if(keepit100 == FALSE &
(Caco2.Fgut == TRUE | Caco2.Fabs == TRUE))
{
if (is.null(caco2.meas.sd))
{
Caco2.Pab <- parameters.dt$Caco2.Pab
Caco2.Pab.l95 <- NULL
Caco2.Pab.u95 <- NULL
parameters.dt[, Caco2.Pab := Caco2.Pab]
# We need to determine what sort of information we have been provided about
# measurment uncertainty. We first check for a comma separated list with a
# median, lower, and upper 95th credible interval limits:
} else if(all(!is.na(parameters.dt$Caco2.Pab.dist)))
{
if (any(nchar(parameters.dt$Caco2.Pab.dist) -
nchar(gsub(",","",parameters.dt$Caco2.Pab.dist[1]))!=2))
{
stop("Caco2.Pab distribution should be three values (median,low95th,high95th) separated by commas.")
}
temp <- strsplit(parameters.dt$Caco2.Pab.dist,",")
Caco2.Pab <- as.numeric(temp[[1]][1])
Caco2.Pab.l95 <- as.numeric(temp[[1]][2])
Caco2.Pab.u95 <- as.numeric(temp[[1]][3])
# Shrink it down if all the values are the same
if (all(c(length(unique(Caco2.Pab))==1,
length(unique(Caco2.Pab.l95))==1,
length(unique(Caco2.Pab.u95))==1)))
{
Caco2.Pab <- Caco2.Pab[1]
Caco2.Pab.l95 <- Caco2.Pab.l95[1]
Caco2.Pab.u95 <- Caco2.Pab.u95[1]
}
caco2.fit <- suppressWarnings(optim(c(Caco2.Pab, caco2.meas.sd),
function(x)
# 97.5% of values should be less than the u95
(0.975 - pnorm(Caco2.Pab.u95, x[1], x[2]))^2 +
# 2.5% of values should be less than the l96
(0.025 - pnorm(Caco2.Pab.l95, x[1], x[2]))^2 +
# The median should be the median:
(Caco2.Pab - qnorm(0.5, x[1], x[2]))^2))
parameters.dt[, Caco2.Pab := rtnorm(n = samples,
caco2.fit$par[1],
caco2.fit$par[2],
lower=0)]
# If we don't have that, we use the default coefficient of variation to
# generate confidence limits:
} else if(!is.null(caco2.meas.sd)) {
Caco2.Pab <- parameters.dt$Caco2.Pab
# Shrink it down if all the values are the same
if (length(unique(Caco2.Pab))==1)
{
Caco2.Pab <- Caco2.Pab[1]
}
caco2.fit <- suppressWarnings(optim(Caco2.Pab,
function(x) (Caco2.Pab - qnorm(0.5, x[1], abs(caco2.meas.sd)))^2))
caco2.fit$par[2] <- abs(caco2.meas.sd)
parameters.dt[, Caco2.Pab := rnorm(n = samples, caco2.fit$par[1], caco2.fit$par[2])]
}
# Store NA so data.table doesn't convert everything to text:
parameters.dt[, Caco2.Pab.dist := NA]
}
#
#
#
# POPULATION VARIABILITY:
#
#
#
# Clint variability Monte Carlo:
#
#
#
#do not sample Clint if measured value is zero,
#or if user said not to vary Clint.
if (clint.pop.mc & Clint>0)
{
#Draw Clint from a normal distribution if poor metabolizers excluded, or
#Gaussian mixture distribution if poor metabolizers included.
#Set the mean of the regular metabolizer distribution:
parameters.dt[,Clint.mu:=Clint]
parameters.dt[,PoorMetabolizer := "N"]
if (poormetab) #if poor metabolizers are included:
{
#Assume that 5% of the population has 10% the metabolism:
parameters.dt[rbinom(n=samples,size=1,prob=0.05)==1,
PoorMetabolizer := "Y"]
parameters.dt[PoorMetabolizer == "Y", Clint.mu:=Clint.mu/10]
}
#Draw Clint from a normal distribution with mean = measured Clint, and
#coefficient of variation given by clint.pop.cv.
parameters.dt[,Clint:=truncnorm::rtruncnorm(n=1,
a=0,
b=Inf,
mean=Clint.mu,
sd=clint.pop.cv*Clint.mu)]
}
#
#
#
# fup variability Monte Carlo:
#
#
#
# next, draw Funbound.plasma from either a normal or censored distribution, as
# long as fup.pop.mc isn't FALSE (otherwise, no pop variability for this)
if (fup.pop.mc)
{
parameters.dt[,fup.sd:=fup.pop.cv*fup.mean]
parameters.dt[,fup.lod:=fup.lod]
# add check for fup.pop.cv=NULL, smae for clint
if (fup.censored.dist)
{ #if user specified to use a censored distribution,
#then draw from a normal distribution, left-censored at the specified LOD.
parameters.dt[, Funbound.plasma:=r_left_censored_norm(n=1,
mean=fup.mean,
sd=fup.sd,
lod=fup.lod)]
# Enforce maximum value:
parameters.dt[Funbound.plasma>1, Funbound.plasma:=1]
} else { #if user specified to use a non-censored distribution
#Draw Funbound.plasma from a normal distribution, truncated at 0 and 1.
parameters.dt[, Funbound.plasma:=truncnorm::rtruncnorm(n=1,
a=0,
b=1,
mean=fup.mean,
sd=fup.sd)]
}
} else {
# No population variability simulation:
parameters.dt[,Funbound.plasma:=fup.mean]
}
#Enforce a minimum Funbound.plasma :
parameters.dt[Funbound.plasma<minimum.Funbound.plasma,
Funbound.plasma:=minimum.Funbound.plasma]
#
#
#
# Caco2.Pab variability Monte Carlo:
#
#
#
#do not sample if user said not to vary Caco2.Pab.
if(keepit100 == FALSE &
(Caco2.Fgut == TRUE | Caco2.Fabs == TRUE))
{
if (!is.null(caco2.pop.sd))
{
#Draw Pab from a normal distribution if poor metabolizers excluded, or
#Gaussian mixture distribution if poor metabolizers included.
#Set the mean of the regular metabolizer distribution:
parameters.dt[, Caco2.Pab.mu := Caco2.Pab]
#Draw Pab from a normal distribution with mean = measured Clint, and
#coefficient of variation given by clint.pop.cv.
# We use truncnorm::rtruncnorm becase mean can be a vector:
parameters.dt[, Caco2.Pab := 10^sapply(log10(Caco2.Pab.mu),
rnorm,n=1,
sd=caco2.pop.sd)
]
}
} else if (keepit100 == TRUE)
{
parameters.dt[,Fabs:=1]
parameters.dt[,Fgut:=1]
}
# Make sure Fabsgut gets recalculated:
parameters.dt[, Fabsgut := NA]
# set precision:
cols <- colnames(parameters.dt)
parameters.dt[ , (cols) := lapply(.SD, set_httk_precision), .SDcols = cols]
return(parameters.dt)
}
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