R/extractExpDetails.R
In shirewoman2/Consultancy: Standardized tools for using R within the Simcyp Consultancy Team
Defines functions
extractExpDetails
Documented in
extractExpDetails
#' Extract details about the experimental design
#'
#' \code{extractExpDetails} looks up experimental design details from a Simcyp
#' Simulator output file. For detailed instructions and examples, please see the
#' SharePoint file "Simcyp PBPKConsult R Files - Simcyp PBPKConsult R
#' Files/SimcypConsultancy function examples and instructions/Checking
#' simulation experimental
#' details/Checking-simulation-experimental-details.docx". (Sorry, we are unable
#' to include a link to it here.)
#'
#' @param sim_data_file name of the Excel file containing the simulator output,
#' in quotes. \strong{A note:} There are just a few items that we will attempt
#' to extract from the matching workspace file; for that information, we will
#' look for a workspace file that is named \emph{identically} to the Excel
#' file except for the file extension. It will ignore the date/time stamp that
#' the autorunner adds as long as that stamp is in a format like this: "myfile
#' - 2023-10-31 07-23-15.xlsx".
#' @param exp_details Experiment details you want to extract from the simulator
#' output file. Options are \describe{
#'
#' \item{"Summary and Input"}{Extract details available from the "Summary tab"
#' and the "Input Sheet" (default)}
#'
#' \item{"population tab"}{Extract details about the population used (data
#' come from the tab with the same name as the population simulated)}
#'
#' \item{"Simcyp inputs"}{Extract all the details that you normally fill out
#' on the "Simcyp inputs (and QC)" tab of a compound data sheet plus trial
#' design information}
#'
#' \item{"workspace"}{Extract a limited set of details directly
#' from the Simcyp Simulator workspace files. The set of possible details may
#' be viewed by entering \code{view(AllWorkspaceDetails)} in the console. This
#' \emph{only} works if the workspace file name perfectly matches the Excel
#' results file name and is located in the same folder. Otherwise, this step
#' in the data extraction will be skipped.}
#'
#' \item{"all"}{Extract all possible parameters}}
#'
#' \strong{NOTES:} \enumerate{\item{The default pulls parameters from the
#' "Summary" tab and the "Input Sheet" tab. Note that the
#' "Summary" tab does not include information on any compounds beyond the
#' substrate and inhibitor 1.} \item{There are a few places where
#' requesting one item as input will get you multiple items as output:
#' intrinsic clearance, interaction parameters, and transport parameters. For
#' example, if you request intrinsic clearance values (ex: "CLint_sub"),
#' you'll get all the intrinsic clearance values for that compound, and
#' they'll be named according to which parameter it is, which enzyme it's for,
#' etc. Same thing with requesting interaction parameters (ex:
#' "Interaction_inhib" to get all the interaction parameters for inhibitor 1)
#' and transporter parameters (ex: "Transport_sub").}}
#' @return Returns a named list of the experimental details
#' @export
#'
#' @examples
#'
#' extractExpDetails(sim_data_file = "../Example simulator output.xlsx")
#' extractExpDetails(sim_data_file = "../Example simulator output MD + inhibitor.xlsx")
#' extractExpDetails(sim_data_file = "../Example simulator output.xlsx",
#' exp_details = "all")
#'
#'
#'
extractExpDetails <- function(sim_data_file,
exp_details = "Summary and Input"){
# Error catching ---------------------------------------------------------
# Check whether tidyverse is loaded
if("package:tidyverse" %in% search() == FALSE){
stop("The SimcypConsultancy R package also requires the package tidyverse to be loaded, and it doesn't appear to be loaded yet. Please run `library(tidyverse)` and then try again.")
}
# If they didn't include ".xlsx" at the end, add that.
sim_data_file <- paste0(sub("\\.wksz$|\\.dscw$|\\.xlsx$", "", sim_data_file), ".xlsx")
# Checking that the file is, indeed, a simulator output file.
SheetNames <- tryCatch(readxl::excel_sheets(sim_data_file),
error = openxlsx::getSheetNames(sim_data_file))
if(all(c("Input Sheet", "Summary") %in% SheetNames) == FALSE){
# Using "warning" instead of "stop" here b/c I want this to be able to
# pass through to extractExpDetails_mult and just skip any files that
# aren't simulator output.
warning(wrapn(paste0("The file '", sim_data_file,
"' does not appear to be a Simcyp Simulator output Excel file. We cannot return any information for this file.")),
call. = FALSE)
return(list())
}
# Checking for file name issues
CheckFileNames <- check_file_name(sim_data_file)
BadFileNames <- CheckFileNames[!CheckFileNames == "File name meets naming standards."]
if(length(BadFileNames)> 0){
BadFileNames <- paste0(names(BadFileNames), ": ", BadFileNames)
warning(paste0("The following file names do not meet file-naming standards for the Simcyp Consultancy Team:\n",
str_c(paste0(" ", BadFileNames), collapse = "\n"),
"\n"),
call. = FALSE)
}
# Cleaning up possible problems w/how exp_details by tab might be inputted
if(length(exp_details) != 1){
warning("You can only enter one value for what set of details you want for the argument `exp_details`. We'll set this to `all` for now.\n",
call. = FALSE)
exp_details <- "all"
}
exp_details <- tolower(exp_details[1])
if(str_detect(exp_details, "summary|input")){
exp_details <- "summary and input"
} else if(str_detect(exp_details, "population")){
exp_details <- "population tab"
} else if(str_detect(exp_details, "worksp")){
exp_details <- "workspace"
}
if(exp_details %in% c("summary and input", "population tab", "workspace",
"all", "simcyp inputs") == FALSE){
warning("The only options for the argument `exp_details` are `Summary and Input`, `population tab`, `workspace`, `Simcyp inputs`, or `all` (not case sensitive), and you've supplied something else. We'll set this to `all` for now.\n",
call. = FALSE)
exp_details <- "all"
}
# Noting exp_details requested for later
exp_details_input <- exp_details
# Main body of function ----------------------------------------------------
# Noting which details are possible, which columns to search for their
# names, which columns contain their values for substrates or inhibitors,
# and what kind of data to format the output as at the end. Using data
# object AllExpDetails.
# Summary tab only includes info on Substrate & Inhibitor1
SumDeets <- AllExpDetails %>%
filter(DataSource == "Summary" &
(is.na(CompoundID) |
CompoundID %in% c("substrate", "inhibitor 1")) &
!Detail %in% c("PrimaryMetabolite1",
"PrimaryMetabolite2",
"SecondaryMetabolite",
"Inhibitor2",
"Inhibitor1Metabolite")) %>%
rename(Deet = Detail) %>% arrange(Deet)
# If it's on input sheet but isn't for a specific compound, then it's about
# the trial design b/c we haven't set this up to pull any information from
# the "Simulation Toolbox" or "Software Version Detail" sections.
InputDeets <- AllExpDetails %>% filter(DataSource == "Input Sheet") %>%
rename(Deet = Detail) %>% arrange(Deet) %>%
mutate(CompoundID = ifelse(is.na(CompoundID), "Trial Design", CompoundID))
PopDeets <- AllExpDetails %>% filter(DataSource == "population") %>%
rename(Deet = Detail) %>% arrange(Deet)
# Determining info to pull
exp_details <-
switch(exp_details_input,
"all" = unique(AllExpDetails$Detail),
"summary tab" = SumDeets$Deet,
"input sheet" = InputDeets$Deet,
"summary and input" = c(SumDeets$Deet, InputDeets$Deet),
"population tab" = PopDeets$Deet,
"simcyp inputs" = AllExpDetails %>%
filter(complete.cases(CDSInputMatch)) %>%
pull(Detail))
# There are some details that we will just ALWAYS need to include b/c so
# many downstream functions rely on them. Making sure that these are
# included.
exp_details <-
unique(c(exp_details,
AllCompounds$DetailNames,
"Units_AUC", "Units_Cmax", "Units_CL", "Units_tmax",
"PopRepSim", "SimulatorUsed",
paste0(rep(c("StartHr", "StartDayTime", "Regimen", "MW",
"Dose", "NumDoses", "DoseInt", "DoseRoute",
"ReleaseProfileAvailable"),
each = 3),
c("_sub", "_inhib", "_inhib2"))))
# This needs to exist for all scenarios, even if we're not checking for it.
ReleaseProfs <- NULL
# Need to note original exp_details requested b/c I'm adding to it if people
# request info from other tabs than what they've originally got. Note that
# this is different from "exp_details_input" and serves a different purpose.
exp_details_orig <- exp_details
# When user requests HSA or AGP, they actually want all the individual betas
# for that. Adjusting to account for that here.
if("HSA" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_C0_female",
"HSA_C0_male", "HSA_C1_female",
"HSA_C1_male", "HSA_C2_female",
"HSA_C2_male", "HSA_male", "HSA_female"))
exp_details <- exp_details[!exp_details == "HSA"]
}
if("HSA_male" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_male", "HSA_C0_male",
"HSA_C1_male", "HSA_C2_male"))
exp_details <- exp_details[!exp_details == "HSA_male"]
}
if("HSA_female" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_female", "HSA_C0_female",
"HSA_C1_female", "HSA_C2_female"))
exp_details <- exp_details[!exp_details == "HSA_female"]
}
if("AGP" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "AGP_male", "AGP_female"))
exp_details <- exp_details[!exp_details == "AGP"]
}
if(any(exp_details %in% AllExpDetails$Detail == FALSE)){
Problem <- str_comma(unique(setdiff(exp_details,
AllExpDetails$Detail)))
warning(paste0("These study details are not among the possible options: ",
Problem,
", so they will be omitted. Please enter 'view(ExpDetailDefinitions)' into the console for all options.\n"),
call. = FALSE)
exp_details <- intersect(exp_details, AllExpDetails$Detail)
}
if(length(exp_details) == 0){
stop("You must enter at least one study detail to extract.",
call. = FALSE)
}
Out <- list()
if(any(exp_details %in% PopDeets$Deet)){
exp_details <- c(exp_details, "Population")
exp_details <- unique(exp_details)
}
# Need to note when to look for custom dosing tabs
CustomDosing <- NA
# Pulling details from the summary tab ----------------------------------
MySumDeets <- sort(intersect(exp_details, SumDeets$Deet))
if(exp_details_input[1] %in% c("population tab")){
MySumDeets <- c("Population", "SimulatorVersion")
}
if(length(MySumDeets) > 0){
# Long file names cause problems for readxl but not openxlsx, for
# some reason. That's why there's the error function calling on
# openxlsx.
SummaryTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = "Summary",
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = "Summary",
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(SummaryTab)[1] == "X1"){
names(SummaryTab) <- paste0("...", 1:ncol(SummaryTab))
}
# We'll select details based on whether this was a Discovery simulation.
Out[["SimulatorUsed"]] <- ifelse(str_detect(SummaryTab[1, 1], "Discovery"),
"Simcyp Discovery", "Simcyp Simulator")
DiscoveryCol <- switch(Out[["SimulatorUsed"]],
"Simcyp Discovery" = c("Simulator and Discovery",
"Discovery only"),
"Simcyp Simulator" = c("Simulator only",
"Simulator and Discovery"))
# Need to filter to keep only details that we can possibly find based on
# what type of simulator was used
SumDeets <- SumDeets %>% filter(SimulatorAvailability %in% DiscoveryCol)
MySumDeets <- MySumDeets[MySumDeets %in% SumDeets$Deet]
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- SumDeets %>%
filter(Deet == deet) %>% pull(Regex_row)
NameCol <- SumDeets$NameCol[which(SumDeets$Deet == deet)]
Row <- which(str_detect(SummaryTab[, NameCol] %>% pull(), ToDetect))
Val <- SummaryTab[Row, SumDeets$ValueCol[SumDeets$Deet == deet]] %>%
pull()
# Accounting for when fu,p is scripted
if(length(Val) > 0 &&
(any(complete.cases(Val)) &&
str_detect(deet, "^fu_") & any(str_detect(Val, "script")))){
SumDeets$Class[SumDeets$Deet == deet] <- "character"
assign("SumDeets", SumDeets, envir = parent.frame())
}
suppressWarnings(
Val <- switch(SumDeets$Class[SumDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
if(length(Val) > 1){
Val <- str_comma(Val)
}
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(complete.cases(Val) & Val == "n/a", NA, Val)
Val <- ifelse(str_detect(deet, "^Unit"),
str_trim(gsub("\\(unbound\\)|\\(blood\\)|\\(unbound blood\\)|Dose \\(|\\)|CMax \\(|TMax \\(|AUC \\(|CL \\(Dose/AUC\\)\\(|\\(blood\\)",
"", Val)), Val)
Val <- ifelse(deet %in% c("SimDuration"),
as.numeric(Val), Val)
Val <- ifelse(deet == "SimulatorVersion",
str_extract(Val, "Version [12][0-9]"),
Val)
if(deet == "PKTissue_Discovery"){
ConcUnit <- str_extract(Val, "(ng|mg|µg|µM|nM)(/)?(mL|L)?")
Val <- case_match(Val,
paste0("CMax (", ConcUnit, ")") ~ "plasma",
paste0("CMax (", ConcUnit, ")(blood)") ~ "blood",
paste0("CMax (", ConcUnit, ")(unbound)") ~ "unbound plasma",
paste0("CMax (", ConcUnit, ")(unbound blood)") ~ "unbound blood")
}
return(Val)
}
# Checking whether this was an ADC sim b/c have to do this differently.
MySumDeets <- setdiff(MySumDeets, "ADCSimulation_sub")
Out[["ADCSimulation_sub"]] <-
any(str_detect(as.character(SummaryTab[, 1]),
SumDeets %>% filter(Deet == "ADCSimulation_sub") %>%
pull(Regex_row)), na.rm = T)
for(i in MySumDeets){
Out[[i]] <- pullValue(i)
if(str_detect(i, "^StartDayTime") & is.na(Out[[i]])){
CustomDosing <- c(CustomDosing, TRUE)
}
if(i == "Population" & is.na(Out[[i]])){
# This can happen when the simulator output is actually from Simcyp
# Discovery or the Simcyp Animal Simulator. Look for
# "species" in that case.
Out[[i]] <- as.character(SummaryTab[which(SummaryTab$...1 == "Species"), 2])
}
}
# Simcyp Discovery only allows simulations with a substrate or metabolite
# 1 and no other compounds, so everything else will be NA or NULL.
if(str_detect(SummaryTab[1, 1], "Discovery")){
Out[c("Inhibitor1", "Inhibitor2", "Inhibitor1Metabolite",
"PrimaryMetabolite2", "SecondaryMetabolite")] <- NA
}
# Removing details that don't apply, e.g., _inhib parameters when there
# was no inhibitor.
if(length(Out$Inhibitor1) > 0 &&
is.na(Out$Inhibitor1) & any(str_detect(names(Out), "_inhib$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib$"))]
}
if(length(Out$Inhibitor2) > 0 &&
is.na(Out$Inhibitor2) & any(str_detect(names(Out), "_inhib2$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib2$"))]
}
if(length(Out$Inhibitor1Metabolite) > 0 &&
is.na(Out$Inhibitor1Metabolite) & any(str_detect(names(Out), "_inhib1met$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib1met$"))]
}
if(length(Out$PrimaryMetabolite1) > 0 &&
is.na(Out$PrimaryMetabolite1) & any(str_detect(names(Out), "_met$"))){
Out <- Out[-which(str_detect(names(Out), "_met$"))]
}
if(length(Out$PrimaryMetabolite2) > 0 &&
is.na(Out$PrimaryMetabolite2) & any(str_detect(names(Out), "_met2$"))){
Out <- Out[-which(str_detect(names(Out), "_met2$"))]
}
if(length(Out$SecondaryMetabolite) > 0 &&
is.na(Out$SecondaryMetabolite) & any(str_detect(names(Out), "secmet$"))){
Out <- Out[-which(str_detect(names(Out), "_secmet$"))]
}
}
# Pulling details from the Input Sheet tab ------------------------------
MyInputDeets <- intersect(exp_details, InputDeets$Deet)
# Not pulling the same info twice
MyInputDeets <- setdiff(MyInputDeets, names(Out))
if(length(MyInputDeets) > 0){
InputTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = "Input Sheet",
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = "Input Sheet",
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(InputTab)[1] == "X1"){
names(InputTab) <- paste0("...", 1:ncol(InputTab))
}
# When Inhibitor 1 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inhibitor 1"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib$|Inhibitor1")]
}
# When Inhibitor 2 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inhibitor 2"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib2$|Inhibitor2")]
}
# When primary metabolite 1 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Pri Metabolite1"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_met1|PrimaryMetabolite1")]
}
# When primary metabolite 2 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Pri Metabolite2"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_met2|PrimaryMetabolite2")]
}
# When secondary metabolite is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Sec Metabolite"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_secmet|SecondaryMetabolite")]
}
# When Inhibitor 1 metabolite is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inh 1 Metabolite"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib1met|Inhibitor1Metabolite")]
}
# We'll select details based on whether this was a Discovery simulation.
Out[["SimulatorUsed"]] <- ifelse(str_detect(InputTab[1, 1], "Discovery"),
"Simcyp Discovery", "Simcyp Simulator")
DiscoveryCol <- switch(Out[["SimulatorUsed"]],
"Simcyp Discovery" = c("Simulator and Discovery",
"Discovery only"),
"Simcyp Simulator" = c("Simulator only",
"Simulator and Discovery"))
# Need to filter to keep only details that we can possibly find based on
# what type of simulator was used
InputDeets <- InputDeets %>%
filter(Deet %in% MyInputDeets & SimulatorAvailability %in% DiscoveryCol)
MyInputDeets <- MyInputDeets[MyInputDeets %in% InputDeets$Deet]
# Looking for locations of columns.
ColLocations <- c("substrate" = 1,
"Trial Design" = which(t(InputTab[5, ]) == "Trial Design"),
"inhibitor 1" = which(t(InputTab[5, ]) == "Inhibitor 1"),
"inhibitor 2" = which(t(InputTab[5, ]) == "Inhibitor 2"),
"primary metabolite 1" = which(t(InputTab[5, ]) == "Sub Pri Metabolite1"),
"primary metabolite 2" = which(t(InputTab[5, ]) == "Sub Pri Metabolite2"),
"secondary metabolite" = which(t(InputTab[5, ]) == "Sub Sec Metabolite"),
"inhibitor 1 metabolite" = which(t(InputTab[5, ]) == "Inh 1 Metabolite"))
InputDeets$NameCol <- ColLocations[InputDeets$CompoundID]
InputDeets$ValueCol <- InputDeets$NameCol + 1
## Main set of parameters -----------------------------------------
# Dealing w/potential replicate values. CompoundType and pKa may be
# replicated but will have the same value, so when we take the unique
# value, that will drop away.
# Checking for any ADAMI parameters. (May need to adapt this later for
# other variations on ADAM models or anything else where there will be
# multiple cells with identical labels.)
ADAMIrow <- which(InputTab$...1 == "ADAMI Parameters")
if(length(ADAMIrow) == 0){
InputDeets <- InputDeets %>%
filter(!str_detect(Deet, "ADAMI"))
ADAMIreps <- NA
NonADAMIreps <- NA
MyInputDeets <- intersect(MyInputDeets, InputDeets$Deet)
} else {
ADAMIreps <- InputDeets %>% filter(str_detect(Deet, "ADAMI")) %>%
pull(Deet)
NonADAMIreps <- sub("_ADAMI", "", ADAMIreps)
}
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- InputDeets %>%
filter(Deet == deet) %>% pull(Regex_row)
NameCol <- InputDeets$NameCol[which(InputDeets$Deet == deet)]
Row <- which(str_detect(InputTab[, NameCol] %>% pull(), ToDetect)) +
(InputDeets %>% filter(Deet == deet) %>% pull(OffsetRows))
if(length(Row) == 0){
Val <- NA
} else {
if(deet %in% ADAMIreps){Row <- Row[Row > ADAMIrow]}
if(deet %in% NonADAMIreps){Row <- Row[Row < ADAMIrow]}
Val <- InputTab[Row,
InputDeets$ValueCol[
which(InputDeets$Deet == deet)]] %>% pull()
# If it's a kp scalar value other than the main one, then we need to
# 1st check whether the value listed is "User" and then get the value
# in the cell right below that if it is.
kpcheck <- str_detect(deet, "kp_scalar_") &
deet %in% paste0("kp_scalar", AllCompounds$Suffix) == FALSE
if(kpcheck){
if(complete.cases(Val) && Val == "Predicted"){
Val <- NA
} else {
NameColBelow <- InputTab[Row + 1,
InputDeets$NameCol[
which(InputDeets$Deet == deet)]] %>% pull()
if(str_detect(tolower(NameColBelow),
tolower(gsub(paste0("kp_scalar_|",
str_c(AllCompounds$Suffix, collapse = "|")),
"",
sub("additional_organ", "Additional Organ", deet))))){
Val <- InputTab[Row + 1, InputDeets$ValueCol[
which(InputDeets$Deet == deet)]] %>% pull()
} else {
Val <- NA
}
}
}
}
# If SimStartDayTime is not found, which will happen with animal
# sims, it may be possible to piece together from other data.
if(length(Val) == 0 & deet == "SimStartDayTime"){
StartDay <- as.character(InputTab[which(InputTab$...1 == "Start Day"), 2])
StartTime <- as.character(InputTab[which(InputTab$...1 == "Start Time"), 2])
StartTime <- str_split(sub("m", "", StartTime), "h")[[1]]
Val <-
paste0("Day ", StartDay, ", ",
formatC(as.numeric(StartTime[1]), width = 2, flag = "0"), ":",
formatC(as.numeric(StartTime[2]), width = 2, flag = "0"))
rm(StartDay, StartTime)
}
# Ontogeny profile along w/CompoundType and pKa are often listed twice
# in output for some reason. Only keeping the unique set of values for
# all deets. This will still throw a warning if there is more than one
# value, but we'd want to know that anyway, so that's why I'm not just
# keeping the 1st value listed.
Val <- sort(unique(Val))
# Accounting for when fu,p is scripted
if(length(Val) > 0 &&
(any(complete.cases(Val)) &&
str_detect(deet, "^fu_") & any(str_detect(Val, "script")))){
InputDeets$Class[InputDeets$Deet == deet] <- "character"
assign("InputDeets", InputDeets, envir = parent.frame())
}
suppressWarnings(
Val <- switch(InputDeets$Class[InputDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
if(length(Val) > 1){
Val <- str_comma(Val)
}
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(length(Val) == 0 ||
(complete.cases(Val) & Val == "n/a"), NA, Val)
Val <- ifelse(str_detect(deet, "^Unit"),
str_trim(gsub("\\(unbound\\)|\\(blood\\)|\\(unbound blood\\)|Dose \\(|\\)|CMax \\(|TMax \\(|AUC \\(|CL \\(Dose/AUC\\)\\(|\\(blood\\)",
"", Val)), Val)
return(Val)
}
# pullValue doesn't work for CL, so those are separate. Also need
# to do StartDayTime_x, SimulatorVersion, and ADCSimulation separately.
MyInputDeets1 <-
MyInputDeets[!str_detect(MyInputDeets,
"CLint_|Interaction_|^StartDayTime|Transport_|ADCSimulation|SimulatorVersion|OrganTissue")]
if(length(MyInputDeets1) > 0){
for(i in MyInputDeets1){
Out[[i]] <- pullValue(i)
}
}
## Some overall simulation details -----------------------------------
# Noting all sheet names. This saves time for later data extraction and
# also helps with debugging and coding in general.
Out[["SheetNames"]] <- str_c(paste0("`", SheetNames, "`"), collapse = " ")
# Checking whether this was an ADC sim.
if("ADCSimulation_sub" %in% MyInputDeets){
Out[["ADCSimulation_sub"]] <-
any(str_detect(as.character(InputTab[, 1]),
InputDeets %>% filter(Deet == "ADCSimulation_sub") %>%
pull(Regex_row)), na.rm = T)
}
# Checking simulator version
Out[["SimulatorVersion"]] <- ifelse(str_detect(InputTab[1, 1], "Discovery"),
as.character(InputTab[1, 1]),
str_extract(as.character(InputTab[3, 1]),
"Version [12][0-9]"))
### Checking on release profiles -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE)){
ReleaseProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
Suffix <- AllCompounds$Suffix[AllCompounds$CompoundID == i]
if(any(str_detect(t(InputTab[, as.numeric(ColLocations[i])]), "Release Mean"),
na.rm = TRUE)){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "CR/MR Input"))[1] + 1
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "Release Mean"))
EndRow <- EndRow[which.max(EndRow)] + 1 # Looking for last "Release Mean" row and then the next row will be the CV for that.
Release_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(Release_temp) <- c("NameCol", "ValCol")
# Older versions of simulator do not have CV. Checking.
ReleaseCV <- Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "CV"))]
if(all(is.null(ReleaseCV))){ReleaseCV <- NA}
suppressWarnings(
ReleaseProfs[[i]] <- data.frame(
CR_MR_input = Out[[paste0("CR_MR_Input", Suffix)]],
Time = Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "Time"))],
Release_mean = Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "Release Mean"))],
Release_CV = ReleaseCV) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
Release_CV = Release_CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Time, Release_mean, Release_CV)
)
rm(StartRow, EndRow, Release_temp)
} else if(
complete.cases(Out[[paste0("CR_MR_Input",
AllCompounds$Suffix[AllCompounds$CompoundID == i])]]) &&
Out[[paste0("CR_MR_Input",
AllCompounds$Suffix[AllCompounds$CompoundID == i])]] ==
"Weibull"){
Suffix <- AllCompounds$Suffix[AllCompounds$CompoundID == i]
ReleaseProfs <- data.frame(
CR_MR_input = Out[[paste0("CR_MR_Input", Suffix)]],
Parameter = c("Fmax", "alpha", "beta", "lag"),
Value = c(Out[[paste0("ReleaseProfile_Fmax", Suffix)]],
Out[[paste0("ReleaseProfile_alpha", Suffix)]],
Out[[paste0("ReleaseProfile_beta", Suffix)]],
Out[[paste0("ReleaseProfile_lag", Suffix)]]),
CV = c(Out[[paste0("ReleaseProfile_Fmax_CV", Suffix)]],
Out[[paste0("ReleaseProfile_alpha_CV", Suffix)]],
Out[[paste0("ReleaseProfile_beta_CV", Suffix)]],
Out[[paste0("ReleaseProfile_lag_CV", Suffix)]])) %>%
mutate(CV = CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, CR_MR_input, Parameter, Value, CV)
} else {
ReleaseProfs <- NULL
}
}
ReleaseProfs <- bind_rows(ReleaseProfs)
}
### Checking on dissolution profiles ------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations])), "Dissolution( Mean)? \\(\\%"),
na.rm = TRUE)){
DissoProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
# There may be more than one tissue. Checking for this.
DissoTissueRows <- which(str_detect(t(InputTab[, ColLocations[i]]),
"^Dissolution Profile"))
# If the results do not specify any tissues, then start rows will be
# different. Last row will be the same, though.
LastRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "Dissolution( Mean)? \\(\\%"))
LastRow <- LastRow[which.max(LastRow)] + 1 # Looking for last "Dissolution (%)" row and then the next row will be the CV for that.
if(length(DissoTissueRows) > 0){
StartRows <- which(str_detect(t(InputTab[, ColLocations[i]]), "^Dissolution Profile")) + 1
# It could be that one compound has dissolution profiles and another
# compound does not. Checking that here since I did not check it in
# the original "if" statement at the top of this section.
if(all(is.na(StartRows))){
next
}
if(length(StartRows) > 1){
EndRows <- c(StartRows[2:length(StartRows)], NA) - 2
EndRows[length(StartRows)] <- LastRow
} else {
EndRows <- LastRow
}
DissoTissues <- gsub("\\(|\\)", "",
str_extract(
t(InputTab[, ColLocations[i]])[DissoTissueRows],
"\\(.*\\)"))
} else {
# If the tissue is not specified, then there will be only 1 set of values.
StartRows <- which(str_detect(t(InputTab[, ColLocations[i]]), "Dissolution( Mean)? \\(\\%"))[1] - 1
DissoTissues <- "not specified"
EndRows <- LastRow
# It could be that one compound has dissolution profiles and another
# compound does not. Checking that here since I did not check it in
# the original "if" statement at the top of this section.
if(all(is.na(StartRows))){
next
}
}
DissoProfs[[i]] <- list()
for(tiss in 1:length(StartRows)){
Disso_temp <- InputTab[StartRows[tiss]:EndRows[tiss],
ColLocations[i]:(ColLocations[i]+1)]
names(Disso_temp) <- c("NameCol", "ValCol")
# Older versions of simulator do not have CV. Checking.
DissoCV <- Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "CV"))]
if(all(is.null(DissoCV))){DissoCV <- NA}
suppressWarnings(
DissoProfs[[i]][[tiss]] <-
data.frame(
Time = Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "Time"))],
Dissolution_mean = Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "Dissolution( Mean)? \\(\\%"))],
Dissolution_CV = DissoCV) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
Dissolution_CV = Dissolution_CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
Tissue = DissoTissues[[tiss]],
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]]))
)
rm(Disso_temp, DissoCV)
}
DissoProfs[[i]] <- bind_rows(DissoProfs[[i]])
}
DissoProfs <- bind_rows(DissoProfs) %>%
select(File, Tissue, CompoundID, Compound, Time,
Dissolution_mean, Dissolution_CV)
} else {
DissoProfs <- NULL
}
### Concentration-dependent fu -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Concentration-dependent fu profile"),
na.rm = TRUE)){
CDfupProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Concentration-dependent fu profile"))[1] + 2
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"fu [0-9]"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has conc-dependent fu,p profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
CDfup_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(CDfup_temp) <- c("NameCol", "ValCol")
CDfupProfs[[i]] <- data.frame(
Conc = CDfup_temp$ValCol[which(str_detect(CDfup_temp$NameCol, "Conc"))],
fup = CDfup_temp$ValCol[which(str_detect(CDfup_temp$NameCol, "fu [0-9]"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Conc, fup)
rm(StartRow, EndRow, CDfup_temp)
}
CDfupProfs <- bind_rows(CDfupProfs)
} else {
CDfupProfs <- NULL
}
### Concentration-dependent B/P -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Concentration-dependent B/P profile"),
na.rm = TRUE)){
CDBPProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Concentration-dependent B/P profile"))[1] + 2
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"B/P [0-9]"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has conc-dependent B/P profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
CDBP_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(CDBP_temp) <- c("NameCol", "ValCol")
CDBPProfs[[i]] <- data.frame(
Conc = CDBP_temp$ValCol[which(str_detect(CDBP_temp$NameCol, "Conc"))],
BP = CDBP_temp$ValCol[which(str_detect(CDBP_temp$NameCol, "B/P [0-9]"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Conc, BP)
rm(StartRow, EndRow, CDBP_temp)
}
CDBPProfs <- bind_rows(CDBPProfs)
} else {
CDBPProfs <- NULL
}
### pH-dependent solubility -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Solubility-pH profile|User defined pH-Solubility"),
na.rm = TRUE)){
pHSol <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Solubility-pH profile|User defined pH-Solubility"))[1] + 1
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"Entry [0-9]{1,} Solubility"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has pH-dependent fu,p profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
pHSol_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(pHSol_temp) <- c("NameCol", "ValCol")
pHSol[[i]] <- data.frame(
pH = pHSol_temp$ValCol[which(str_detect(pHSol_temp$NameCol, "pH"))],
Solubility = pHSol_temp$ValCol[which(str_detect(pHSol_temp$NameCol, "Entry [0-9]{1,} Solubility"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, pH, Solubility)
rm(StartRow, EndRow, pHSol_temp)
}
pHSol <- bind_rows(pHSol)
} else {
pHSol <- NULL
}
## Pulling CL info ----------------------------------------------------
MyInputDeets2 <- MyInputDeets[str_detect(MyInputDeets, "CLint_")]
if(length(MyInputDeets2) > 0){
for(j in MyInputDeets2){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_met2$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
CLRows <- which(
InputTab[ , NameCol] == "Enzyme" |
str_detect(InputTab[ , NameCol] %>%
pull(),
"^Biliary (CLint|Clearance)") |
str_detect(InputTab[ , NameCol] %>%
pull(),
"^Additional HLM CLint|^Additional Systemic Clearance|^Additional HKM CLint") |
str_detect(InputTab[ , ValueCol] %>%
pull(),
"In Vivo Clear") |
str_detect(InputTab[ , NameCol] %>%
pull(),
"(Liver|Intestine|Biliary) Clearance"))
CLRows <- CLRows[complete.cases(InputTab[CLRows + 1, NameCol])]
if(Out[["SimulatorUsed"]] == "Simcyp Discovery"){
# Discovery sims have a slightly different setup on the Input
# Sheet and only the 1st CLRows value should be used b/c that
# section will contain all the info we need.
CLRows <- CLRows[1]
MyNames <- as.character(t(
InputTab[CLRows:nrow(InputTab), NameCol]))
DiscoveryDeets <-
data.frame(
Detail = c("CLiv_InVivoCL",
"CLpo_InVivoCL",
"CLint_biliary",
"CLint_biliary_fuinc",
"CLrenal",
"CL_AddSystemic",
"CL_PercentAvailReabsorption"),
RegexRow = c("CL.*iv.*[(]mL",
"CL.*po.*[(]mL",
"Biliary Clearance ..L/min",
"Biliary fu inc",
"CL R .mL",
"^Additional Systemic Clearance",
"^Percent.*re-absorption"))
for(i in 1:nrow(DiscoveryDeets)){
MyRow <- which(str_detect(MyNames, DiscoveryDeets$RegexRow[i]))
if(length(MyRow) == 0){
Out[[paste0(DiscoveryDeets$Detail[i], Suffix)]] <- NA
} else {
suppressWarnings(
Out[[paste0(DiscoveryDeets$Detail[i], Suffix)]] <-
as.numeric(InputTab[MyRow + CLRows - 1, ValueCol])
)
}
rm(MyRow)
}
# Liver and intestinal CL
LivOrInt <- c("Liver", "Intestine")[
c(any(str_detect(MyNames, "Liver")), any(str_detect(MyNames, "Intestine")))]
LivIndCL <-
data.frame(
Detail = c("CL_XXX_Type",
"CL_XXX__UseSaturableKinetics",
"CLint_XXX",
"CL_Km_XXX",
"CL_Vmax_XXX",
"CL_XXX_fuinc",
"CL_XXX_UseMetabolite",
"CL_XXX_MetabPerc",
"CL_XXX_ScrapingsCorrectionFactor",
"CL_XXX_ElutionCorrectionFactor"),
RegexRow = c(paste(i, "Clearance Type"),
"Use Saturable Kinetics",
"CLint",
"Km \\(",
"Vmax \\(",
"fu inc",
"Use Metabolite",
"Metabolite .%",
"\\(scrapings\\) Correction Factor",
"\\(elution\\) Correction Factor")
)
if(length(LivOrInt[complete.cases(LivOrInt)]) > 0){
for(i in LivOrInt[complete.cases(LivOrInt)]){
OrganRows <- range(
which(str_detect(MyNames, i) |
str_detect(MyNames,
paste0(str_sub(i, 1, 1), "M")))
)
for(k in 1:nrow(LivIndCL)){
MyRow <- which(str_detect(MyNames[OrganRows[1]:OrganRows[2]],
LivIndCL$RegexRow[k]))
if(length(MyRow) == 0){
Out[[paste0(sub("XXX", i, LivIndCL$Detail[k]), Suffix)]] <- NA
} else {
suppressWarnings(
Out[[paste0(sub("XXX", i, LivIndCL$Detail[k]), Suffix)]] <-
as.character(InputTab[MyRow + CLRows - 1, ValueCol])
)
}
rm(MyRow)
}
}
}
} else {
# Regular Simulator data extraction starts here.
# Checking for interaction data
IntRowStart <- which(str_detect(InputTab[, NameCol] %>%
pull(), "Ind max|^Ki |^MBI|Interaction"))[1] - 1
if(complete.cases(IntRowStart)){
CLRows <- CLRows[CLRows < min(IntRowStart)]
}
for(i in CLRows){
# CL for a specific enzyme
if(str_detect(as.character(InputTab[i, NameCol]), "Enzyme")){
LastRow_i <- which(is.na(InputTab[, NameCol]))
LastRow_i <- LastRow_i[LastRow_i > i][1] - 1
Enzyme <- gsub(" ", "", InputTab[i, NameCol + 1])
Pathway <- gsub(" |-", "", InputTab[i - 1, NameCol + 1])
if(as.character(InputTab[i+1, NameCol]) == "Genotype"){
Genotype <- InputTab[i+1, NameCol + 1]
Genotype <- gsub("\\*", "star", Genotype)
Genotype <- gsub("/", "", Genotype)
Enzyme <- paste0(Enzyme, "_", Genotype)
CLrow <- i + 2
} else if((str_detect(Enzyme, "User") &
!str_detect(InputTab[i+1, NameCol], "CLint|Vmax")) |
str_detect(tolower(InputTab[i + 1, NameCol]),
"ontogeny")){
CLrow <- i + 2
} else {
CLrow <- i + 1
}
CLType <- str_extract(InputTab[CLrow, NameCol],
"CLint|Vmax|t1/2|Ind max")
if(CLType == "CLint"){
# NOTE TO CODERS: I'd been including units for CLint in
# the past, but I realized that I hadn't included units
# for other enzymes, so I decided later to omit them.
# Keeping this bit of code, albeit commented out, so
# that we can add them back easily if we want.
# Units <- str_extract(InputTab[CLrow, NameCol],
# "\\(.*\\)")
# Units <- gsub("\\(|\\)", "", Units)
# Units <- gsub("/| ", "_", Units)
# # Dealing with mu since it's causing some problems
# # downstream when a symbol
# Units <- gsub(rlang::chr_unserialise_unicode("<U+00B5>"),
# "u", Units)
suppressWarnings(
Out[[paste0(
paste("CLint", Enzyme, Pathway, # Units,
sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("fu_mic", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+1, NameCol + 1])
)
# Check for any UGT-specific CL parameters
if(str_detect(Enzyme, "UGT") &
any(str_detect(t(InputTab[i:LastRow_i, NameCol]),
"rUGT"))){
rUGTSysInfo <- InputTab[i:LastRow_i, c(NameCol, ValueCol)] %>%
rename(Name = 1, Value = 2) %>%
filter(str_detect(Name, "rUGT"))
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTSystem",
Suffix)]] <-
rUGTSysInfo[which(str_detect(rUGTSysInfo$Name,
"rUGTSystem")), ] %>%
pull(Value)
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_liver",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*liver")), ] %>%
pull(Value) %>% as.numeric())
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_intestine",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*intestine")), ] %>%
pull(Value) %>% as.numeric())
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_kidney",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*kidney")), ] %>%
pull(Value) %>% as.numeric())
rm(rUGTSysInfo)
}
rm(Enzyme, Pathway, CLType)
next
}
if(CLType == "Vmax"){
suppressWarnings(
Out[[paste0(
paste("Vmax", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Km", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+1, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("fu_mic", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+2, NameCol + 1])
)
rm(Enzyme, Pathway, CLType)
next
}
if(CLType == "t1/2"){
suppressWarnings(
Out[[paste0(
paste("HalfLife", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
rm(Enzyme, Pathway, CLType)
next
}
}
# Biliary CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Biliary (CLint|Clearance)")){
suppressWarnings(
Out[[paste0("CLint_biliary", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# Other HLM CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Additional HLM CLint")){
suppressWarnings(
Out[[paste0("CLint_AddHLM", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# Other HKM CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Additional HKM CLint")){
suppressWarnings(
Out[[paste0("CLint_AddHKM", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# in vivo CL
if(str_detect(as.character(InputTab[i, ValueCol]),
"In Vivo Clearance")){
MyNames <- as.character(t(
InputTab[i:min(
c(IntRowStart, CLRows[which(CLRows == i) + 1] - 1,
nrow(InputTab)), na.rm = T), NameCol]))
suppressWarnings(
Out[[paste0("CLiv_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"CL.*iv.*[(](m)?L")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLbiliary_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"Biliary Clearance")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLadditional_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"Additional Systemic Clearance")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLpo_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"^CL .po.")) + i - 1,
ValueCol])
)
}
}
rm(CLRows, IntRowStart, NameCol, Suffix)
}
}
}
## Pulling interaction info -------------------------------------------
MyInputDeets3 <- MyInputDeets[str_detect(MyInputDeets, "Interaction_")]
if(length(MyInputDeets3) > 0){
for(j in MyInputDeets3){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
IntRows <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"^Enzyme$|^Transporter$"))
IntRows <- IntRows[complete.cases(InputTab[IntRows + 1, NameCol])]
# Only IntRows after the first instance of an
# interaction type of term is found in NameCol. NB: I
# thought it would work to just look for cells after
# "interaction", but "interaction" hasn't always been
# listed in the output files I've found.
IntRowStart <- which(str_detect(InputTab[, NameCol] %>%
pull(), "Ind [mM]ax|Ind [sS]lope|^Ki |^MBI"))[1] - 1
TransporterTissues <- IntRows[which(
str_detect(t(InputTab[IntRows, NameCol]), "Transporter"))]
TransporterTissues <- TransporterTissues[which(
str_detect(t(InputTab[TransporterTissues - 1, NameCol]),
"Organ/Tissue"))] - 1
TransporterTissues <- data.frame(
Row = TransporterTissues,
Tissue = as.character(t(InputTab[TransporterTissues, ValueCol])))
if(complete.cases(IntRowStart)){
IntRows <- IntRows[IntRows >= IntRowStart]
for(i in IntRows){
Enzyme <- gsub(" |\\(|\\)|-|/", "_", InputTab[i, NameCol + 1])
Enzyme <- gsub("_{2,}", "_", Enzyme)
Enzyme <- sub("_$", "", Enzyme)
NextEmptyCell <- which(is.na(InputTab[, NameCol + 1]))
NextEmptyCell <- NextEmptyCell[NextEmptyCell > i][1]
# If there's another interaction listed
# before the next empty cell, need to
# account for that.
NextInt <- IntRows[which(IntRows == i) + 1] - 1
NextInt <- ifelse(i == IntRows[length(IntRows)],
nrow(InputTab), NextInt)
ThisIntRows <- i:(c(NextEmptyCell, NextInt)[which.min(c(NextEmptyCell, NextInt))])
ThisIntRows <- setdiff(ThisIntRows, NextEmptyCell)
# Induction
IndParam1stRow <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"Ind max|Ind Slope"))
if(length(IndParam1stRow) > 0){
IndModelCheck <- list(str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind max"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind Slope"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind( )?C50"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "fu inc"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "\u03B3"))
IndModelCheck <- sapply(IndModelCheck, FUN = function(x) which(x == TRUE))
# Note: I can't seem to get regex to work for
# detecting a Greek character; I figured that the
# Hill coefficient gamma is the only time that an
# induction parameter name is only going to be one
# character long.
suppressWarnings(
Out[[paste0(
paste("IndMax", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[1]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("IndSlope", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[2]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("IndC50", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[3]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Ind_fu_inc", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[4]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Ind_gamma", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[5]][1]], NameCol + 1])
)
rm(IndModelCheck)
}
# competitive inhibition
Ki <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"Ki "))
if(length(Ki) > 0){
EnzTrans <- as.character(InputTab[i, NameCol])
if(EnzTrans == "Transporter"){
Enzyme <-
paste0(Enzyme, "_",
# setting the tissue
TransporterTissues %>%
filter(Row <= i) %>%
filter(Row == max(Row)) %>%
pull(Tissue))
}
suppressWarnings(
Out[[paste0(
paste("Ki", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[Ki], NameCol + 1])
)
# fu mic or fu inc
IncType <- str_extract(InputTab[ThisIntRows[Ki+1], NameCol] %>%
pull(),
"inc|mic")
suppressWarnings(
Out[[paste0(
paste(switch(IncType,
"inc" = "Ki_fu_inc",
"mic" = "Ki_fu_mic"),
Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[Ki+1], NameCol + 1])
)
rm(IncType, EnzTrans)
}
# MBI
MBI <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"MBI Kapp"))
if(length(MBI) > 0){
suppressWarnings(
Out[[paste0(
paste("MBI_Kapp", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("MBI_kinact", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI+1], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("MBI_fu_mic", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI+2], NameCol + 1])
)
}
rm(Enzyme, NextEmptyCell, NextInt,
ThisIntRows, IndParam1stRow, Ki, MBI)
}
}
rm(Suffix, IntRows, IntRowStart, NameCol)
}
}
## Dealing with StartDayTime_x --------------------------------------
MyInputDeets4 <- MyInputDeets[str_detect(MyInputDeets, "^StartDayTime")]
if(length(MyInputDeets4) > 0){
for(j in MyInputDeets4){
NameCol <- InputDeets$NameCol[which(InputDeets$Deet == j)]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
Row_day <- which(str_detect(InputTab[, NameCol] %>% pull(), "Start Day"))
# If this is not present, which sometimes happens with a custom
# dosing schedule, then will need to pull info from custom
# dosing sheet lower in script.
if(length(Row_day) == 0){
CustomDosing <- c(CustomDosing, TRUE)
} else {
Val_day <- InputTab[Row_day, InputDeets$ValueCol[
which(InputDeets$Deet == j)]] %>% pull()
Row_time <- which(str_detect(InputTab[, NameCol] %>% pull(), "Start Time"))
Val_time <- InputTab[Row_time, InputDeets$ValueCol[
which(InputDeets$Deet == j)]] %>% pull()
# Dealing with inconsistencies in time format
Val_time <- sub("m", "", Val_time)
Val_time <- str_split(Val_time, pattern = "h")[[1]]
Val_time <- str_c(str_pad(Val_time, width = 2, pad = "0"),
collapse = ":")
Out[[j]] <- paste(paste0("Day ", Val_day),
Val_time, sep = ", ")
}
}
}
## Transport parameters ----------------------------------------------
MyInputDeets5 <- MyInputDeets[str_detect(MyInputDeets, "Transport_")]
MyInputDeets5 <- InputDeets %>%
filter(Deet %in% MyInputDeets5 & complete.cases(NameCol)) %>%
pull(Deet)
if(length(MyInputDeets5) > 0){
for(j in MyInputDeets5){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
# There can be transporter interactions higher up on the tab,
# but parameters that are actually just transport parameters all
# come after the title "Transport".
StartTrans <- which(InputTab[, NameCol] %>% pull() == "Transport")
if(length(StartTrans) > 0){
TransRows <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"^Transporter"))
TransRows <- TransRows[TransRows > StartTrans]
if(length(TransRows) > 0){
# Sometimes, the organ is only listed once and then not listed
# again for subsequent transporters until it changes.
OrganRows <- which(str_detect(InputTab[, NameCol] %>% pull(),
"Organ/Tissue"))
OrganRows <- OrganRows[OrganRows >= min(TransRows) - 1 &
OrganRows < max(TransRows)]
# BBB transporter parameters are set up differently, so
# removing any organ rows for those scenarios.
BrainRows <- OrganRows + 1
BrainRows <- BrainRows[which(str_detect(
InputTab[BrainRows, NameCol] %>% pull(), "BBB|BCSFB|ISF.ICF"))]
OrganRows <- setdiff(OrganRows, BrainRows - 1)
for(i in TransRows){
# Last row always seems to contain RAF/REF or ISEF,T
TransRowLast <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"RAF/REF|ISEF"))
TransRowLast <- TransRowLast[TransRowLast > i]
TransRowLast <- ifelse(length(TransRowLast) == 0,
nrow(InputTab), TransRowLast[1])
TransRowNames <- InputTab[i:TransRowLast, NameCol] %>% pull(1)
Transporter <- gsub(" |\\(|\\)|-|/", "_", InputTab[i, NameCol + 1])
Transporter <- gsub("_{2,}", "_", Transporter)
Transporter <- sub("_$", "", Transporter)
Transporter <- case_match(Transporter,
"Apical_Efflux_Kidney" ~ "General_Apical_Efflux",
.default = Transporter)
Location <- gsub(" |\\(|\\)|-|/", "",
InputTab[c(i:TransRowLast)[which(TransRowNames == "Location")],
ValueCol] %>% pull(1))
Organ <- InputTab[max(OrganRows[OrganRows < i]), ValueCol] %>%
pull() %>% str_comma() # This should have length 1, but adding str_comma just in case.
# Organ <- which(str_detect(as.character(t(
# InputTab[(i-1):TransRowLast, NameCol])), "Organ/Tissue"))
# Organ <- ifelse(length(Organ) > 0,
# as.character(InputTab[((i-1):TransRowLast)[Organ], ValueCol]),
# "")
ParamPrefix <- paste("Transporter", Organ, Transporter, Location, sep = "_")
# Either CLint,T or Jmax and Km values will be listed
if(any(str_detect(TransRowNames, "CLint,T"))){
suppressWarnings(
Out[[paste0(ParamPrefix, "_CLintT", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "CLint,T"))],
ValueCol] %>% pull(1))
)
} else if(any(str_detect(TransRowNames, "Jmax"))){
suppressWarnings(
Out[[paste0(ParamPrefix, "_Jmax", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "Jmax"))],
ValueCol] %>% pull(1))
)
suppressWarnings(
Out[[paste0(ParamPrefix, "_Km", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "Km"))],
ValueCol] %>% pull(1))
)
}
# Checking for fuinc values b/c they're not always there
fuinc <- as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "fuinc"))],
ValueCol] %>% pull(1))
if(length(fuinc) > 0){
suppressWarnings(
Out[[paste0(ParamPrefix, "_fuinc", Suffix)]] <- fuinc
)
}
rm(fuinc)
# Checking for RAF/REF values b/c they're not always there
RAFREF <- as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "ISEF|RAF|REF"))],
ValueCol] %>% pull(1))
if(length(RAFREF) > 0){
suppressWarnings(
Out[[paste0(ParamPrefix, "_RAFREF", Suffix)]] <- RAFREF
)
}
rm(RAFREF)
rm(TransRowLast, Transporter, TransRowNames, Location,
ParamPrefix)
}
}
}
}
}
}
# Dealing with custom dosing schedules ---------------------------------
if(any(str_detect(exp_details, "StartDayTime")) &
any(str_detect(names(Out), "StartDayTime")) == FALSE |
any(CustomDosing, na.rm = TRUE)){
# When there's custom dosing for any of the substrate or inhibitors,
# then the dosing start time should be pulled from a "Custom CustomDosing"
# tab. Pulling any custom dosing sheets here.
CustomDoseSheets <- SheetNames[str_detect(SheetNames, "Custom Dosing")]
for(j in CustomDoseSheets){
Suffix <- switch(str_extract(j, "Inh [12]|Sub"),
"Inh 1" = "_inhib",
"Inh 2" = "_inhib2",
"Sub" = "_sub")
CustomDose_xl <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = j,
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = j,
colNames = FALSE)))
# If people have added anything to the sheet, that can mess up data
# extraction. Here, we're at least checking for any columns that would
# have NA values for the names b/c those are probably places where
# people have added something extra off to the side of the data we
# actually want.
GoodCols <- t(CustomDose_xl[3, ]) %>% as.character()
GoodCols <- which(complete.cases(GoodCols))
CustomDosing <- CustomDose_xl[4:nrow(CustomDose_xl), GoodCols]
names(CustomDosing) <- make.names(CustomDose_xl[3, GoodCols])
CustomDosing <- CustomDosing %>%
rename(DoseNum = Dose.Number,
Time1 = Time,
Dose_units = Dose.Units,
DoseRoute = Route.of.Administration) %>%
mutate(Day = as.numeric(Day))
TimeUnits <- names(CustomDosing)[str_detect(names(CustomDosing), "Offset")]
names(CustomDosing)[str_detect(names(CustomDosing), "Offset")] <- "Time"
MyCompoundID <- AllCompounds$CompoundID[AllCompounds$Suffix == Suffix]
MyCompound <- as.character(Out[AllCompounds$DetailNames[AllCompounds$Suffix == Suffix]])
CustomDosing <- CustomDosing %>%
# Removing any rows where Time is NA b/c those are likely places
# where people have added some comments, etc. and not the main data
# we want. The NA values mess up things downstream.
filter(complete.cases(Time)) %>%
mutate(Time_units = ifelse(str_detect(TimeUnits, "\\.h\\.$"),
"h", "min"),
File = sim_data_file,
TimeOfDay = as.character(round_date(timeConv(
as.numeric(Time1)), unit = "minute")),
TimeOfDay = sub("1899-12-30 ", "", TimeOfDay),
CompoundID = MyCompoundID,
Compound = MyCompound) %>%
mutate(across(.cols = matches("DoseNum|Time$|Dose$|^Day$"),
.fns = as.numeric)) %>%
select(File, CompoundID, Compound, Day, TimeOfDay,
Time, Time_units, DoseNum,
Dose, Dose_units, DoseRoute)
Out[[paste0("CustomDosing", Suffix)]] <- CustomDosing
Out[[paste0("Dose", Suffix)]] <- "custom dosing"
Out[[paste0("StartDayTime", Suffix)]] <- "custom dosing"
Out[[paste0("StartHr", Suffix)]] <- CustomDosing$Time[CustomDosing$DoseNum == 1]
Out[[paste0("DoseRoute", Suffix)]] <- "custom dosing"
Out[[paste0("DoseInt", Suffix)]] <- "custom dosing"
Out[[paste0("Regimen", Suffix)]] <- "Multiple Dose"
rm(CustomDosing, Suffix, CustomDose_xl, MyCompoundID, MyCompound, TimeUnits)
}
}
# Pulling details from the population tab -------------------------------
MyPopDeets <- intersect(exp_details, PopDeets$Deet)
# If user asks for population details, then function is set up to read both
# what that population is and what the simulator version is by reading the
# summary tab, so this next line should work and will not give them any
# population details for Simcyp Discovery simulations until we set that up.
MyPopDeets <- intersect(MyPopDeets,
(AllExpDetails %>%
filter(SimulatorAvailability %in% DiscoveryCol) %>%
pull(Detail)))
if(length(MyPopDeets) > 0){
# Getting name of that tab.
if(exists("SheetNames", inherit = FALSE) == FALSE){
SheetNames <- tryCatch(readxl::excel_sheets(sim_data_file),
error = openxlsx::getSheetNames(sim_data_file))
}
# If user has requested that the population tab be annotated, which is an
# option!, then there will be 2 matches to the population sheet name. We
# want the 1st one.
PopSheet <- SheetNames[str_detect(tolower(SheetNames),
str_sub(tolower(Out$Population), 1, 20))][1]
PopTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = PopSheet,
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = PopSheet,
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(PopTab)[1] == "X1"){
names(PopTab) <- paste0("...", 1:ncol(PopTab))
}
MyPopDeets <- intersect(exp_details, PopDeets$Deet)
# User can change the name of user-defined cytosolic phenotypes for GI
# tract, kidney, and liver. Changing this back to "Cyt1" to work for
# regex, though. For now, only extracting data for Cyt1 and not any more
# user-defined cytosolic phenotype parameters, so ignoring the others.
# If name is changed in one, it's changed in all. Columns are 3, 5, and
# 9.
if(any(str_detect(PopTab$...3, "Cyt1"), na.rm = T) == FALSE){
StartCytRow <- which(str_detect(PopTab$...3, "^User Cyt$"))[1]
NewName <- gsub("Abundance : | Population Scalar", "", PopTab[StartCytRow + 1, 3])
PopTab$...3 <- sub(NewName, "User Cyt1", PopTab$...3)
PopTab$...5 <- sub(NewName, "User Cyt1", PopTab$...5)
PopTab$...9 <- sub(NewName, "User Cyt1", PopTab$...9)
}
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- AllExpDetails %>%
filter(Detail == deet & DataSource == "population") %>% pull(Regex_row)
NameCol <- PopDeets$NameCol[which(PopDeets$Deet == deet)]
if(ncol(PopTab) < NameCol){
# This happens when it's an animal simulation.
return(NA)
}
Row <- which(str_detect(PopTab[, NameCol] %>% pull(), ToDetect))
if(length(Row) == 0){
Val <- NA
} else {
Val <- PopTab[Row, PopDeets$ValueCol[PopDeets$Deet == deet]] %>%
pull()
Val <- sort(unique(Val))
}
suppressWarnings(
Val <- switch(PopDeets$Class[PopDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(complete.cases(Val) & Val == "n/a",
NA, Val)
return(Val)
}
for(i in MyPopDeets){
Out[[i]] <- pullValue(i)
}
}
# Pulling from workspace file -------------------------------------------
if(any(c("workspace", "all") %in% exp_details_input)){
# Checking that the workspace file is available. This will ignore the
# date/time stamp on the Excel results if it's still there.
WkspFile <- c("Simulator" = sub("( - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2})?\\.xlsx$",
".wksz", sim_data_file),
"Discovery" = sub("( - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2})?\\.xlsx$",
".dscw", sim_data_file))
WkspFile <- WkspFile[which(file.exists(WkspFile))]
if(length(WkspFile) > 0){
TEMP <- extractExpDetails_XML(
sim_workspace_files = WkspFile,
compoundsToExtract = "all",
exp_details = "all")
TEMP$MainDetails <- TEMP$MainDetails %>%
# This currently removes anything that we already have from the
# Excel file. May change that later to verify that Excel and
# workspace match.
select(!any_of(c("Substrate", "Inhibitor1", "Inhibitor2",
"PrimaryMetabolite1", "PrimaryMetabolite2",
"SecondaryMetabolite", "Inhibitor1Metabolite",
paste0("DistributionModel",
c("inhib1met",
"_met1", "_met2", "_secmet")))))
# Note: Currently, we are not extracting anything from the workspace
# that would be its own separate list. When we DO do that, we'll need
# to adjust this code to bind the MainDetails and whatever that list
# is.
Out <- c(Out,
TEMP$MainDetails[
setdiff(names(TEMP$MainDetails)[
names(TEMP$MainDetails) != "Workspace"],
names(Out))])
rm(TEMP)
}
}
# Calculated details & data cleanup ----------------------------------------
if("StartHr_sub" %in% exp_details &&
"StartDayTime_sub" %in% names(Out) &&
complete.cases(Out$StartDayTime_sub) &&
Out$StartDayTime_sub != "custom dosing"){
Out[["StartHr_sub"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_sub)
}
if(all(c("Inhibitor1", "StartDayTime_inhib") %in% names(Out)) &&
complete.cases(Out$StartDayTime_inhib) &&
Out$StartDayTime_inhib != "custom dosing"){
Out[["StartHr_inhib"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_inhib)
}
if(all(c("Inhibitor2", "StartDayTime_inhib2") %in% names(Out)) &&
complete.cases(Out$StartDayTime_inhib2) &&
Out$StartDayTime_inhib != "custom dosing"){
Out[["StartHr_inhib2"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_inhib2)
}
# Other functions call on "Inhibitor1", etc., so we need those objects to
# exist, even if they were not used in this simulation. Setting them to NA if
# they don't exist.
MissingCmpd <- setdiff(AllCompounds$DetailNames,
names(Out))
MissingCmpd_list <- as.list(rep(NA, length(MissingCmpd)))
names(MissingCmpd_list) <- MissingCmpd
Out <- c(Out, MissingCmpd_list)
# Always including the file name.
Out$File <- sim_data_file
# Noting when workspace, if there is a matching one, was last changed.
WorkspaceFile <- sub("xlsx", ifelse(Out$SimulatorUsed == "Discovery",
"dscw", "wksz"), sim_data_file)
# Removing the file timestamp if there was one b/c that won't be part of the
# workspace file name.
WorkspaceFile <- sub(" - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2}",
"", WorkspaceFile)
Out$Workspace_TimeLastModified <-
ifelse(file.exists(WorkspaceFile),
as.character(file.info(WorkspaceFile)$mtime), NA)
# Noting when this was run.
Out$expDetails_TimeStamp <- Sys.time()
# Species should be lower case and not have "Sim" in front of it to work
# more smoothly with other functions and also just look better. Setting
# "beagle" to "dog" and setting it to "human" if it's missing, which it will
# be for regular simulator output.
if("Species" %in% names(Out)){
Out$Species <- tolower(sub("Sim-", "", Out$Species))
Out$Species <- ifelse(Out$Species == "beagle", "dog", Out$Species)
if(is.na(Out$Species)){
Out$Species <- "human"
}
}
Out <- Out[sort(names(Out))]
# Adding missing, necessary list items
Missing1 <- setdiff(
paste0(rep(c("DoseInt", "DoseRoute", "Regimen", "NumDoses"), each = 3),
c("_sub", "_inhib", "_inhib2")),
names(Out))
if(length(Missing1) > 0){
Missing <- as.list(matrix(data = NA, ncol = length(Missing1),
dimnames = list(NULL, Missing1)))
names(Missing) <- Missing1
Out <- c(Out, Missing)
}
# Fixing an issue that trips up other code down the line: Sometimes, the
# user might specify a "multiple dose" regimen but then only administer
# a single dose. That messes up, e.g., extractPK b/c it looks on the
# wrong tab for the info it needs. When that happens, set the regimen to
# "Single Dose".
if(is.null(Out$Regimen_sub) == FALSE &&
(complete.cases(Out$Regimen_sub) && Out$Regimen_sub == "Multiple Dose") &
(complete.cases(Out$NumDoses_sub) && Out$NumDoses_sub == 1)){
Out$Regimen_sub <- "Single Dose"
}
if(is.null(Out$Regimen_inhib) == FALSE &&
(complete.cases(Out$Regimen_inhib) && Out$Regimen_inhib == "Multiple Dose") &
(complete.cases(Out$NumDoses_inhib) && Out$NumDoses_inhib == 1)){
Out$Regimen_inhib1 <- "Single Dose"
}
if(is.null(Out$Regimen_inhib2) == FALSE &&
(complete.cases(Out$Regimen_inhib2) && Out$Regimen_inhib2 == "Multiple Dose") &
(complete.cases(Out$NumDoses_inhib2) && Out$NumDoses_inhib2 == 1)){
Out$Regimen_inhib2 <- "Single Dose"
}
# Making DoseInt_x and Dose_x numeric all the time. We'll get custom dosing
# info from Regimen_x and DoseRoute_x.
suppressWarnings(Out$DoseInt_sub <- as.numeric(Out$DoseInt_sub))
suppressWarnings(Out$DoseInt_inhib <- as.numeric(Out$DoseInt_inhib))
suppressWarnings(Out$DoseInt_inhib2 <- as.numeric(Out$DoseInt_inhib2))
suppressWarnings(Out$Dose_sub <- as.numeric(Out$Dose_sub))
suppressWarnings(Out$Dose_inhib <- as.numeric(Out$Dose_inhib))
suppressWarnings(Out$Dose_inhib2 <- as.numeric(Out$Dose_inhib2))
# At this point, DoseInt_x and Dose_x will be NA if it's a custom dosing
# regimen. Setting the regimen to "multiple". We'll use that downstream for
# checking for appropriate PK parameters, etc.
Out$Regimen_sub <- ifelse(is.na(Out$DoseInt_sub) &
(complete.cases(Out$DoseRoute_sub) &&
Out$DoseRoute_sub == "custom dosing"),
"Multiple Dose", Out$Regimen_sub)
Out$Regimen_inhib <- ifelse(is.na(Out$DoseInt_inhib) &
(complete.cases(Out$DoseInt_inhib) &&
Out$DoseRoute_inhib == "custom dosing"),
"Multiple Dose", Out$Regimen_inhib)
Out$Regimen_inhib2 <- ifelse(is.na(Out$DoseInt_inhib2) &
(complete.cases(Out$DoseInt_inhib2) &&
Out$DoseRoute_inhib2 == "custom dosing"),
"Multiple Dose", Out$Regimen_inhib2)
# Splitting this up into main details -- a data.frame -- and then,
# separately, whatever items need to be lists, e.g., custom dosing regimens
# and dissolution profiles.
Main <- as.data.frame(Out[which(sapply(Out, length) == 1)])
# Making absoultely sure that File included in Main. When we run
# harmonize_details, it will add it to the other items whenever there is at
# least 1 row, but we need it in Main to do that.
Main$File <- sim_data_file
## Dosing -----------------------------------------------------------------
# Setting up Dosing data.frame to include ALL dosing info, so custom dosing
# when appropriate and, for compounds and/or simulations w/out custom dosing,
# then a data.frame of all dosing events filled in based on interval, amount,
# etc.
Out <- list(MainDetails = Main,
CustomDosing = bind_rows(Out$CustomDosing_sub,
Out$CustomDosing_inhib,
Out$CustomDosing_inhib2),
DissolutionProfiles = DissoProfs,
ReleaseProfiles = ReleaseProfs,
ConcDependent_fup = CDfupProfs,
ConcDependent_BP = CDBPProfs,
pH_dependent_solubility = pHSol)
Out <- harmonize_details(Out)
# Returning --------------------------------------------------------------
for(j in names(Out)[unlist(lapply(Out, is.null)) == FALSE]){
Out[[j]] <- Out[[j]] %>%
mutate(File = sim_data_file) %>%
select(File, everything())
}
return(Out)
}
shirewoman2/Consultancy documentation built on Feb. 18, 2025, 10 p.m.
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Defines functions extractExpDetails
Documented in extractExpDetails
#' Extract details about the experimental design
#'
#' \code{extractExpDetails} looks up experimental design details from a Simcyp
#' Simulator output file. For detailed instructions and examples, please see the
#' SharePoint file "Simcyp PBPKConsult R Files - Simcyp PBPKConsult R
#' Files/SimcypConsultancy function examples and instructions/Checking
#' simulation experimental
#' details/Checking-simulation-experimental-details.docx". (Sorry, we are unable
#' to include a link to it here.)
#'
#' @param sim_data_file name of the Excel file containing the simulator output,
#' in quotes. \strong{A note:} There are just a few items that we will attempt
#' to extract from the matching workspace file; for that information, we will
#' look for a workspace file that is named \emph{identically} to the Excel
#' file except for the file extension. It will ignore the date/time stamp that
#' the autorunner adds as long as that stamp is in a format like this: "myfile
#' - 2023-10-31 07-23-15.xlsx".
#' @param exp_details Experiment details you want to extract from the simulator
#' output file. Options are \describe{
#'
#' \item{"Summary and Input"}{Extract details available from the "Summary tab"
#' and the "Input Sheet" (default)}
#'
#' \item{"population tab"}{Extract details about the population used (data
#' come from the tab with the same name as the population simulated)}
#'
#' \item{"Simcyp inputs"}{Extract all the details that you normally fill out
#' on the "Simcyp inputs (and QC)" tab of a compound data sheet plus trial
#' design information}
#'
#' \item{"workspace"}{Extract a limited set of details directly
#' from the Simcyp Simulator workspace files. The set of possible details may
#' be viewed by entering \code{view(AllWorkspaceDetails)} in the console. This
#' \emph{only} works if the workspace file name perfectly matches the Excel
#' results file name and is located in the same folder. Otherwise, this step
#' in the data extraction will be skipped.}
#'
#' \item{"all"}{Extract all possible parameters}}
#'
#' \strong{NOTES:} \enumerate{\item{The default pulls parameters from the
#' "Summary" tab and the "Input Sheet" tab. Note that the
#' "Summary" tab does not include information on any compounds beyond the
#' substrate and inhibitor 1.} \item{There are a few places where
#' requesting one item as input will get you multiple items as output:
#' intrinsic clearance, interaction parameters, and transport parameters. For
#' example, if you request intrinsic clearance values (ex: "CLint_sub"),
#' you'll get all the intrinsic clearance values for that compound, and
#' they'll be named according to which parameter it is, which enzyme it's for,
#' etc. Same thing with requesting interaction parameters (ex:
#' "Interaction_inhib" to get all the interaction parameters for inhibitor 1)
#' and transporter parameters (ex: "Transport_sub").}}
#' @return Returns a named list of the experimental details
#' @export
#'
#' @examples
#'
#' extractExpDetails(sim_data_file = "../Example simulator output.xlsx")
#' extractExpDetails(sim_data_file = "../Example simulator output MD + inhibitor.xlsx")
#' extractExpDetails(sim_data_file = "../Example simulator output.xlsx",
#' exp_details = "all")
#'
#'
#'
extractExpDetails <- function(sim_data_file,
exp_details = "Summary and Input"){
# Error catching ---------------------------------------------------------
# Check whether tidyverse is loaded
if("package:tidyverse" %in% search() == FALSE){
stop("The SimcypConsultancy R package also requires the package tidyverse to be loaded, and it doesn't appear to be loaded yet. Please run `library(tidyverse)` and then try again.")
}
# If they didn't include ".xlsx" at the end, add that.
sim_data_file <- paste0(sub("\\.wksz$|\\.dscw$|\\.xlsx$", "", sim_data_file), ".xlsx")
# Checking that the file is, indeed, a simulator output file.
SheetNames <- tryCatch(readxl::excel_sheets(sim_data_file),
error = openxlsx::getSheetNames(sim_data_file))
if(all(c("Input Sheet", "Summary") %in% SheetNames) == FALSE){
# Using "warning" instead of "stop" here b/c I want this to be able to
# pass through to extractExpDetails_mult and just skip any files that
# aren't simulator output.
warning(wrapn(paste0("The file '", sim_data_file,
"' does not appear to be a Simcyp Simulator output Excel file. We cannot return any information for this file.")),
call. = FALSE)
return(list())
}
# Checking for file name issues
CheckFileNames <- check_file_name(sim_data_file)
BadFileNames <- CheckFileNames[!CheckFileNames == "File name meets naming standards."]
if(length(BadFileNames)> 0){
BadFileNames <- paste0(names(BadFileNames), ": ", BadFileNames)
warning(paste0("The following file names do not meet file-naming standards for the Simcyp Consultancy Team:\n",
str_c(paste0(" ", BadFileNames), collapse = "\n"),
"\n"),
call. = FALSE)
}
# Cleaning up possible problems w/how exp_details by tab might be inputted
if(length(exp_details) != 1){
warning("You can only enter one value for what set of details you want for the argument `exp_details`. We'll set this to `all` for now.\n",
call. = FALSE)
exp_details <- "all"
}
exp_details <- tolower(exp_details[1])
if(str_detect(exp_details, "summary|input")){
exp_details <- "summary and input"
} else if(str_detect(exp_details, "population")){
exp_details <- "population tab"
} else if(str_detect(exp_details, "worksp")){
exp_details <- "workspace"
}
if(exp_details %in% c("summary and input", "population tab", "workspace",
"all", "simcyp inputs") == FALSE){
warning("The only options for the argument `exp_details` are `Summary and Input`, `population tab`, `workspace`, `Simcyp inputs`, or `all` (not case sensitive), and you've supplied something else. We'll set this to `all` for now.\n",
call. = FALSE)
exp_details <- "all"
}
# Noting exp_details requested for later
exp_details_input <- exp_details
# Main body of function ----------------------------------------------------
# Noting which details are possible, which columns to search for their
# names, which columns contain their values for substrates or inhibitors,
# and what kind of data to format the output as at the end. Using data
# object AllExpDetails.
# Summary tab only includes info on Substrate & Inhibitor1
SumDeets <- AllExpDetails %>%
filter(DataSource == "Summary" &
(is.na(CompoundID) |
CompoundID %in% c("substrate", "inhibitor 1")) &
!Detail %in% c("PrimaryMetabolite1",
"PrimaryMetabolite2",
"SecondaryMetabolite",
"Inhibitor2",
"Inhibitor1Metabolite")) %>%
rename(Deet = Detail) %>% arrange(Deet)
# If it's on input sheet but isn't for a specific compound, then it's about
# the trial design b/c we haven't set this up to pull any information from
# the "Simulation Toolbox" or "Software Version Detail" sections.
InputDeets <- AllExpDetails %>% filter(DataSource == "Input Sheet") %>%
rename(Deet = Detail) %>% arrange(Deet) %>%
mutate(CompoundID = ifelse(is.na(CompoundID), "Trial Design", CompoundID))
PopDeets <- AllExpDetails %>% filter(DataSource == "population") %>%
rename(Deet = Detail) %>% arrange(Deet)
# Determining info to pull
exp_details <-
switch(exp_details_input,
"all" = unique(AllExpDetails$Detail),
"summary tab" = SumDeets$Deet,
"input sheet" = InputDeets$Deet,
"summary and input" = c(SumDeets$Deet, InputDeets$Deet),
"population tab" = PopDeets$Deet,
"simcyp inputs" = AllExpDetails %>%
filter(complete.cases(CDSInputMatch)) %>%
pull(Detail))
# There are some details that we will just ALWAYS need to include b/c so
# many downstream functions rely on them. Making sure that these are
# included.
exp_details <-
unique(c(exp_details,
AllCompounds$DetailNames,
"Units_AUC", "Units_Cmax", "Units_CL", "Units_tmax",
"PopRepSim", "SimulatorUsed",
paste0(rep(c("StartHr", "StartDayTime", "Regimen", "MW",
"Dose", "NumDoses", "DoseInt", "DoseRoute",
"ReleaseProfileAvailable"),
each = 3),
c("_sub", "_inhib", "_inhib2"))))
# This needs to exist for all scenarios, even if we're not checking for it.
ReleaseProfs <- NULL
# Need to note original exp_details requested b/c I'm adding to it if people
# request info from other tabs than what they've originally got. Note that
# this is different from "exp_details_input" and serves a different purpose.
exp_details_orig <- exp_details
# When user requests HSA or AGP, they actually want all the individual betas
# for that. Adjusting to account for that here.
if("HSA" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_C0_female",
"HSA_C0_male", "HSA_C1_female",
"HSA_C1_male", "HSA_C2_female",
"HSA_C2_male", "HSA_male", "HSA_female"))
exp_details <- exp_details[!exp_details == "HSA"]
}
if("HSA_male" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_male", "HSA_C0_male",
"HSA_C1_male", "HSA_C2_male"))
exp_details <- exp_details[!exp_details == "HSA_male"]
}
if("HSA_female" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "HSA_female", "HSA_C0_female",
"HSA_C1_female", "HSA_C2_female"))
exp_details <- exp_details[!exp_details == "HSA_female"]
}
if("AGP" %in% exp_details_orig){
exp_details <- unique(c(exp_details, "AGP_male", "AGP_female"))
exp_details <- exp_details[!exp_details == "AGP"]
}
if(any(exp_details %in% AllExpDetails$Detail == FALSE)){
Problem <- str_comma(unique(setdiff(exp_details,
AllExpDetails$Detail)))
warning(paste0("These study details are not among the possible options: ",
Problem,
", so they will be omitted. Please enter 'view(ExpDetailDefinitions)' into the console for all options.\n"),
call. = FALSE)
exp_details <- intersect(exp_details, AllExpDetails$Detail)
}
if(length(exp_details) == 0){
stop("You must enter at least one study detail to extract.",
call. = FALSE)
}
Out <- list()
if(any(exp_details %in% PopDeets$Deet)){
exp_details <- c(exp_details, "Population")
exp_details <- unique(exp_details)
}
# Need to note when to look for custom dosing tabs
CustomDosing <- NA
# Pulling details from the summary tab ----------------------------------
MySumDeets <- sort(intersect(exp_details, SumDeets$Deet))
if(exp_details_input[1] %in% c("population tab")){
MySumDeets <- c("Population", "SimulatorVersion")
}
if(length(MySumDeets) > 0){
# Long file names cause problems for readxl but not openxlsx, for
# some reason. That's why there's the error function calling on
# openxlsx.
SummaryTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = "Summary",
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = "Summary",
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(SummaryTab)[1] == "X1"){
names(SummaryTab) <- paste0("...", 1:ncol(SummaryTab))
}
# We'll select details based on whether this was a Discovery simulation.
Out[["SimulatorUsed"]] <- ifelse(str_detect(SummaryTab[1, 1], "Discovery"),
"Simcyp Discovery", "Simcyp Simulator")
DiscoveryCol <- switch(Out[["SimulatorUsed"]],
"Simcyp Discovery" = c("Simulator and Discovery",
"Discovery only"),
"Simcyp Simulator" = c("Simulator only",
"Simulator and Discovery"))
# Need to filter to keep only details that we can possibly find based on
# what type of simulator was used
SumDeets <- SumDeets %>% filter(SimulatorAvailability %in% DiscoveryCol)
MySumDeets <- MySumDeets[MySumDeets %in% SumDeets$Deet]
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- SumDeets %>%
filter(Deet == deet) %>% pull(Regex_row)
NameCol <- SumDeets$NameCol[which(SumDeets$Deet == deet)]
Row <- which(str_detect(SummaryTab[, NameCol] %>% pull(), ToDetect))
Val <- SummaryTab[Row, SumDeets$ValueCol[SumDeets$Deet == deet]] %>%
pull()
# Accounting for when fu,p is scripted
if(length(Val) > 0 &&
(any(complete.cases(Val)) &&
str_detect(deet, "^fu_") & any(str_detect(Val, "script")))){
SumDeets$Class[SumDeets$Deet == deet] <- "character"
assign("SumDeets", SumDeets, envir = parent.frame())
}
suppressWarnings(
Val <- switch(SumDeets$Class[SumDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
if(length(Val) > 1){
Val <- str_comma(Val)
}
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(complete.cases(Val) & Val == "n/a", NA, Val)
Val <- ifelse(str_detect(deet, "^Unit"),
str_trim(gsub("\\(unbound\\)|\\(blood\\)|\\(unbound blood\\)|Dose \\(|\\)|CMax \\(|TMax \\(|AUC \\(|CL \\(Dose/AUC\\)\\(|\\(blood\\)",
"", Val)), Val)
Val <- ifelse(deet %in% c("SimDuration"),
as.numeric(Val), Val)
Val <- ifelse(deet == "SimulatorVersion",
str_extract(Val, "Version [12][0-9]"),
Val)
if(deet == "PKTissue_Discovery"){
ConcUnit <- str_extract(Val, "(ng|mg|µg|µM|nM)(/)?(mL|L)?")
Val <- case_match(Val,
paste0("CMax (", ConcUnit, ")") ~ "plasma",
paste0("CMax (", ConcUnit, ")(blood)") ~ "blood",
paste0("CMax (", ConcUnit, ")(unbound)") ~ "unbound plasma",
paste0("CMax (", ConcUnit, ")(unbound blood)") ~ "unbound blood")
}
return(Val)
}
# Checking whether this was an ADC sim b/c have to do this differently.
MySumDeets <- setdiff(MySumDeets, "ADCSimulation_sub")
Out[["ADCSimulation_sub"]] <-
any(str_detect(as.character(SummaryTab[, 1]),
SumDeets %>% filter(Deet == "ADCSimulation_sub") %>%
pull(Regex_row)), na.rm = T)
for(i in MySumDeets){
Out[[i]] <- pullValue(i)
if(str_detect(i, "^StartDayTime") & is.na(Out[[i]])){
CustomDosing <- c(CustomDosing, TRUE)
}
if(i == "Population" & is.na(Out[[i]])){
# This can happen when the simulator output is actually from Simcyp
# Discovery or the Simcyp Animal Simulator. Look for
# "species" in that case.
Out[[i]] <- as.character(SummaryTab[which(SummaryTab$...1 == "Species"), 2])
}
}
# Simcyp Discovery only allows simulations with a substrate or metabolite
# 1 and no other compounds, so everything else will be NA or NULL.
if(str_detect(SummaryTab[1, 1], "Discovery")){
Out[c("Inhibitor1", "Inhibitor2", "Inhibitor1Metabolite",
"PrimaryMetabolite2", "SecondaryMetabolite")] <- NA
}
# Removing details that don't apply, e.g., _inhib parameters when there
# was no inhibitor.
if(length(Out$Inhibitor1) > 0 &&
is.na(Out$Inhibitor1) & any(str_detect(names(Out), "_inhib$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib$"))]
}
if(length(Out$Inhibitor2) > 0 &&
is.na(Out$Inhibitor2) & any(str_detect(names(Out), "_inhib2$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib2$"))]
}
if(length(Out$Inhibitor1Metabolite) > 0 &&
is.na(Out$Inhibitor1Metabolite) & any(str_detect(names(Out), "_inhib1met$"))){
Out <- Out[-which(str_detect(names(Out), "_inhib1met$"))]
}
if(length(Out$PrimaryMetabolite1) > 0 &&
is.na(Out$PrimaryMetabolite1) & any(str_detect(names(Out), "_met$"))){
Out <- Out[-which(str_detect(names(Out), "_met$"))]
}
if(length(Out$PrimaryMetabolite2) > 0 &&
is.na(Out$PrimaryMetabolite2) & any(str_detect(names(Out), "_met2$"))){
Out <- Out[-which(str_detect(names(Out), "_met2$"))]
}
if(length(Out$SecondaryMetabolite) > 0 &&
is.na(Out$SecondaryMetabolite) & any(str_detect(names(Out), "secmet$"))){
Out <- Out[-which(str_detect(names(Out), "_secmet$"))]
}
}
# Pulling details from the Input Sheet tab ------------------------------
MyInputDeets <- intersect(exp_details, InputDeets$Deet)
# Not pulling the same info twice
MyInputDeets <- setdiff(MyInputDeets, names(Out))
if(length(MyInputDeets) > 0){
InputTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = "Input Sheet",
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = "Input Sheet",
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(InputTab)[1] == "X1"){
names(InputTab) <- paste0("...", 1:ncol(InputTab))
}
# When Inhibitor 1 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inhibitor 1"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib$|Inhibitor1")]
}
# When Inhibitor 2 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inhibitor 2"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib2$|Inhibitor2")]
}
# When primary metabolite 1 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Pri Metabolite1"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_met1|PrimaryMetabolite1")]
}
# When primary metabolite 2 is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Pri Metabolite2"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_met2|PrimaryMetabolite2")]
}
# When secondary metabolite is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Sub Sec Metabolite"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_secmet|SecondaryMetabolite")]
}
# When Inhibitor 1 metabolite is not present, don't look for those values.
if(any(str_detect(t(InputTab[5, ]), "Inh 1 Metabolite"), na.rm = T) == FALSE){
MyInputDeets <- MyInputDeets[!str_detect(MyInputDeets, "_inhib1met|Inhibitor1Metabolite")]
}
# We'll select details based on whether this was a Discovery simulation.
Out[["SimulatorUsed"]] <- ifelse(str_detect(InputTab[1, 1], "Discovery"),
"Simcyp Discovery", "Simcyp Simulator")
DiscoveryCol <- switch(Out[["SimulatorUsed"]],
"Simcyp Discovery" = c("Simulator and Discovery",
"Discovery only"),
"Simcyp Simulator" = c("Simulator only",
"Simulator and Discovery"))
# Need to filter to keep only details that we can possibly find based on
# what type of simulator was used
InputDeets <- InputDeets %>%
filter(Deet %in% MyInputDeets & SimulatorAvailability %in% DiscoveryCol)
MyInputDeets <- MyInputDeets[MyInputDeets %in% InputDeets$Deet]
# Looking for locations of columns.
ColLocations <- c("substrate" = 1,
"Trial Design" = which(t(InputTab[5, ]) == "Trial Design"),
"inhibitor 1" = which(t(InputTab[5, ]) == "Inhibitor 1"),
"inhibitor 2" = which(t(InputTab[5, ]) == "Inhibitor 2"),
"primary metabolite 1" = which(t(InputTab[5, ]) == "Sub Pri Metabolite1"),
"primary metabolite 2" = which(t(InputTab[5, ]) == "Sub Pri Metabolite2"),
"secondary metabolite" = which(t(InputTab[5, ]) == "Sub Sec Metabolite"),
"inhibitor 1 metabolite" = which(t(InputTab[5, ]) == "Inh 1 Metabolite"))
InputDeets$NameCol <- ColLocations[InputDeets$CompoundID]
InputDeets$ValueCol <- InputDeets$NameCol + 1
## Main set of parameters -----------------------------------------
# Dealing w/potential replicate values. CompoundType and pKa may be
# replicated but will have the same value, so when we take the unique
# value, that will drop away.
# Checking for any ADAMI parameters. (May need to adapt this later for
# other variations on ADAM models or anything else where there will be
# multiple cells with identical labels.)
ADAMIrow <- which(InputTab$...1 == "ADAMI Parameters")
if(length(ADAMIrow) == 0){
InputDeets <- InputDeets %>%
filter(!str_detect(Deet, "ADAMI"))
ADAMIreps <- NA
NonADAMIreps <- NA
MyInputDeets <- intersect(MyInputDeets, InputDeets$Deet)
} else {
ADAMIreps <- InputDeets %>% filter(str_detect(Deet, "ADAMI")) %>%
pull(Deet)
NonADAMIreps <- sub("_ADAMI", "", ADAMIreps)
}
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- InputDeets %>%
filter(Deet == deet) %>% pull(Regex_row)
NameCol <- InputDeets$NameCol[which(InputDeets$Deet == deet)]
Row <- which(str_detect(InputTab[, NameCol] %>% pull(), ToDetect)) +
(InputDeets %>% filter(Deet == deet) %>% pull(OffsetRows))
if(length(Row) == 0){
Val <- NA
} else {
if(deet %in% ADAMIreps){Row <- Row[Row > ADAMIrow]}
if(deet %in% NonADAMIreps){Row <- Row[Row < ADAMIrow]}
Val <- InputTab[Row,
InputDeets$ValueCol[
which(InputDeets$Deet == deet)]] %>% pull()
# If it's a kp scalar value other than the main one, then we need to
# 1st check whether the value listed is "User" and then get the value
# in the cell right below that if it is.
kpcheck <- str_detect(deet, "kp_scalar_") &
deet %in% paste0("kp_scalar", AllCompounds$Suffix) == FALSE
if(kpcheck){
if(complete.cases(Val) && Val == "Predicted"){
Val <- NA
} else {
NameColBelow <- InputTab[Row + 1,
InputDeets$NameCol[
which(InputDeets$Deet == deet)]] %>% pull()
if(str_detect(tolower(NameColBelow),
tolower(gsub(paste0("kp_scalar_|",
str_c(AllCompounds$Suffix, collapse = "|")),
"",
sub("additional_organ", "Additional Organ", deet))))){
Val <- InputTab[Row + 1, InputDeets$ValueCol[
which(InputDeets$Deet == deet)]] %>% pull()
} else {
Val <- NA
}
}
}
}
# If SimStartDayTime is not found, which will happen with animal
# sims, it may be possible to piece together from other data.
if(length(Val) == 0 & deet == "SimStartDayTime"){
StartDay <- as.character(InputTab[which(InputTab$...1 == "Start Day"), 2])
StartTime <- as.character(InputTab[which(InputTab$...1 == "Start Time"), 2])
StartTime <- str_split(sub("m", "", StartTime), "h")[[1]]
Val <-
paste0("Day ", StartDay, ", ",
formatC(as.numeric(StartTime[1]), width = 2, flag = "0"), ":",
formatC(as.numeric(StartTime[2]), width = 2, flag = "0"))
rm(StartDay, StartTime)
}
# Ontogeny profile along w/CompoundType and pKa are often listed twice
# in output for some reason. Only keeping the unique set of values for
# all deets. This will still throw a warning if there is more than one
# value, but we'd want to know that anyway, so that's why I'm not just
# keeping the 1st value listed.
Val <- sort(unique(Val))
# Accounting for when fu,p is scripted
if(length(Val) > 0 &&
(any(complete.cases(Val)) &&
str_detect(deet, "^fu_") & any(str_detect(Val, "script")))){
InputDeets$Class[InputDeets$Deet == deet] <- "character"
assign("InputDeets", InputDeets, envir = parent.frame())
}
suppressWarnings(
Val <- switch(InputDeets$Class[InputDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
if(length(Val) > 1){
Val <- str_comma(Val)
}
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(length(Val) == 0 ||
(complete.cases(Val) & Val == "n/a"), NA, Val)
Val <- ifelse(str_detect(deet, "^Unit"),
str_trim(gsub("\\(unbound\\)|\\(blood\\)|\\(unbound blood\\)|Dose \\(|\\)|CMax \\(|TMax \\(|AUC \\(|CL \\(Dose/AUC\\)\\(|\\(blood\\)",
"", Val)), Val)
return(Val)
}
# pullValue doesn't work for CL, so those are separate. Also need
# to do StartDayTime_x, SimulatorVersion, and ADCSimulation separately.
MyInputDeets1 <-
MyInputDeets[!str_detect(MyInputDeets,
"CLint_|Interaction_|^StartDayTime|Transport_|ADCSimulation|SimulatorVersion|OrganTissue")]
if(length(MyInputDeets1) > 0){
for(i in MyInputDeets1){
Out[[i]] <- pullValue(i)
}
}
## Some overall simulation details -----------------------------------
# Noting all sheet names. This saves time for later data extraction and
# also helps with debugging and coding in general.
Out[["SheetNames"]] <- str_c(paste0("`", SheetNames, "`"), collapse = " ")
# Checking whether this was an ADC sim.
if("ADCSimulation_sub" %in% MyInputDeets){
Out[["ADCSimulation_sub"]] <-
any(str_detect(as.character(InputTab[, 1]),
InputDeets %>% filter(Deet == "ADCSimulation_sub") %>%
pull(Regex_row)), na.rm = T)
}
# Checking simulator version
Out[["SimulatorVersion"]] <- ifelse(str_detect(InputTab[1, 1], "Discovery"),
as.character(InputTab[1, 1]),
str_extract(as.character(InputTab[3, 1]),
"Version [12][0-9]"))
### Checking on release profiles -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE)){
ReleaseProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
Suffix <- AllCompounds$Suffix[AllCompounds$CompoundID == i]
if(any(str_detect(t(InputTab[, as.numeric(ColLocations[i])]), "Release Mean"),
na.rm = TRUE)){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "CR/MR Input"))[1] + 1
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "Release Mean"))
EndRow <- EndRow[which.max(EndRow)] + 1 # Looking for last "Release Mean" row and then the next row will be the CV for that.
Release_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(Release_temp) <- c("NameCol", "ValCol")
# Older versions of simulator do not have CV. Checking.
ReleaseCV <- Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "CV"))]
if(all(is.null(ReleaseCV))){ReleaseCV <- NA}
suppressWarnings(
ReleaseProfs[[i]] <- data.frame(
CR_MR_input = Out[[paste0("CR_MR_Input", Suffix)]],
Time = Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "Time"))],
Release_mean = Release_temp$ValCol[which(str_detect(Release_temp$NameCol, "Release Mean"))],
Release_CV = ReleaseCV) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
Release_CV = Release_CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Time, Release_mean, Release_CV)
)
rm(StartRow, EndRow, Release_temp)
} else if(
complete.cases(Out[[paste0("CR_MR_Input",
AllCompounds$Suffix[AllCompounds$CompoundID == i])]]) &&
Out[[paste0("CR_MR_Input",
AllCompounds$Suffix[AllCompounds$CompoundID == i])]] ==
"Weibull"){
Suffix <- AllCompounds$Suffix[AllCompounds$CompoundID == i]
ReleaseProfs <- data.frame(
CR_MR_input = Out[[paste0("CR_MR_Input", Suffix)]],
Parameter = c("Fmax", "alpha", "beta", "lag"),
Value = c(Out[[paste0("ReleaseProfile_Fmax", Suffix)]],
Out[[paste0("ReleaseProfile_alpha", Suffix)]],
Out[[paste0("ReleaseProfile_beta", Suffix)]],
Out[[paste0("ReleaseProfile_lag", Suffix)]]),
CV = c(Out[[paste0("ReleaseProfile_Fmax_CV", Suffix)]],
Out[[paste0("ReleaseProfile_alpha_CV", Suffix)]],
Out[[paste0("ReleaseProfile_beta_CV", Suffix)]],
Out[[paste0("ReleaseProfile_lag_CV", Suffix)]])) %>%
mutate(CV = CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, CR_MR_input, Parameter, Value, CV)
} else {
ReleaseProfs <- NULL
}
}
ReleaseProfs <- bind_rows(ReleaseProfs)
}
### Checking on dissolution profiles ------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations])), "Dissolution( Mean)? \\(\\%"),
na.rm = TRUE)){
DissoProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
# There may be more than one tissue. Checking for this.
DissoTissueRows <- which(str_detect(t(InputTab[, ColLocations[i]]),
"^Dissolution Profile"))
# If the results do not specify any tissues, then start rows will be
# different. Last row will be the same, though.
LastRow <- which(str_detect(t(InputTab[, ColLocations[i]]), "Dissolution( Mean)? \\(\\%"))
LastRow <- LastRow[which.max(LastRow)] + 1 # Looking for last "Dissolution (%)" row and then the next row will be the CV for that.
if(length(DissoTissueRows) > 0){
StartRows <- which(str_detect(t(InputTab[, ColLocations[i]]), "^Dissolution Profile")) + 1
# It could be that one compound has dissolution profiles and another
# compound does not. Checking that here since I did not check it in
# the original "if" statement at the top of this section.
if(all(is.na(StartRows))){
next
}
if(length(StartRows) > 1){
EndRows <- c(StartRows[2:length(StartRows)], NA) - 2
EndRows[length(StartRows)] <- LastRow
} else {
EndRows <- LastRow
}
DissoTissues <- gsub("\\(|\\)", "",
str_extract(
t(InputTab[, ColLocations[i]])[DissoTissueRows],
"\\(.*\\)"))
} else {
# If the tissue is not specified, then there will be only 1 set of values.
StartRows <- which(str_detect(t(InputTab[, ColLocations[i]]), "Dissolution( Mean)? \\(\\%"))[1] - 1
DissoTissues <- "not specified"
EndRows <- LastRow
# It could be that one compound has dissolution profiles and another
# compound does not. Checking that here since I did not check it in
# the original "if" statement at the top of this section.
if(all(is.na(StartRows))){
next
}
}
DissoProfs[[i]] <- list()
for(tiss in 1:length(StartRows)){
Disso_temp <- InputTab[StartRows[tiss]:EndRows[tiss],
ColLocations[i]:(ColLocations[i]+1)]
names(Disso_temp) <- c("NameCol", "ValCol")
# Older versions of simulator do not have CV. Checking.
DissoCV <- Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "CV"))]
if(all(is.null(DissoCV))){DissoCV <- NA}
suppressWarnings(
DissoProfs[[i]][[tiss]] <-
data.frame(
Time = Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "Time"))],
Dissolution_mean = Disso_temp$ValCol[which(str_detect(Disso_temp$NameCol, "Dissolution( Mean)? \\(\\%"))],
Dissolution_CV = DissoCV) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
Dissolution_CV = Dissolution_CV / 100, # Making this a fraction instead of a number up to 100
File = sim_data_file,
Tissue = DissoTissues[[tiss]],
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]]))
)
rm(Disso_temp, DissoCV)
}
DissoProfs[[i]] <- bind_rows(DissoProfs[[i]])
}
DissoProfs <- bind_rows(DissoProfs) %>%
select(File, Tissue, CompoundID, Compound, Time,
Dissolution_mean, Dissolution_CV)
} else {
DissoProfs <- NULL
}
### Concentration-dependent fu -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Concentration-dependent fu profile"),
na.rm = TRUE)){
CDfupProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Concentration-dependent fu profile"))[1] + 2
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"fu [0-9]"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has conc-dependent fu,p profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
CDfup_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(CDfup_temp) <- c("NameCol", "ValCol")
CDfupProfs[[i]] <- data.frame(
Conc = CDfup_temp$ValCol[which(str_detect(CDfup_temp$NameCol, "Conc"))],
fup = CDfup_temp$ValCol[which(str_detect(CDfup_temp$NameCol, "fu [0-9]"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Conc, fup)
rm(StartRow, EndRow, CDfup_temp)
}
CDfupProfs <- bind_rows(CDfupProfs)
} else {
CDfupProfs <- NULL
}
### Concentration-dependent B/P -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Concentration-dependent B/P profile"),
na.rm = TRUE)){
CDBPProfs <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Concentration-dependent B/P profile"))[1] + 2
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"B/P [0-9]"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has conc-dependent B/P profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
CDBP_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(CDBP_temp) <- c("NameCol", "ValCol")
CDBPProfs[[i]] <- data.frame(
Conc = CDBP_temp$ValCol[which(str_detect(CDBP_temp$NameCol, "Conc"))],
BP = CDBP_temp$ValCol[which(str_detect(CDBP_temp$NameCol, "B/P [0-9]"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, Conc, BP)
rm(StartRow, EndRow, CDBP_temp)
}
CDBPProfs <- bind_rows(CDBPProfs)
} else {
CDBPProfs <- NULL
}
### pH-dependent solubility -----------------------------------------
if(Out[["SimulatorUsed"]] != "Simcyp Discovery" &&
exists("InputTab", inherits = FALSE) &&
any(str_detect(unlist(c(InputTab[, ColLocations + 1])),
"Solubility-pH profile|User defined pH-Solubility"),
na.rm = TRUE)){
pHSol <- list()
for(i in names(ColLocations)[!names(ColLocations) == "Trial Design"]){
StartRow <- which(str_detect(t(InputTab[, ColLocations[i] + 1]),
"Solubility-pH profile|User defined pH-Solubility"))[1] + 1
EndRow <- which(str_detect(t(InputTab[, ColLocations[i]]),
"Entry [0-9]{1,} Solubility"))
EndRow <- EndRow[which.max(EndRow)]
# It could be that one compound has pH-dependent fu,p profiles and
# another compound does not. Checking that here since I did not check it
# in the original if statement at the top of this section.
if(is.na(StartRow)){
next
}
pHSol_temp <- InputTab[StartRow:EndRow, ColLocations[i]:(ColLocations[i]+1)]
names(pHSol_temp) <- c("NameCol", "ValCol")
pHSol[[i]] <- data.frame(
pH = pHSol_temp$ValCol[which(str_detect(pHSol_temp$NameCol, "pH"))],
Solubility = pHSol_temp$ValCol[which(str_detect(pHSol_temp$NameCol, "Entry [0-9]{1,} Solubility"))]) %>%
mutate(across(.cols = everything(), .fns = as.numeric),
File = sim_data_file,
CompoundID = i,
Compound = as.character(Out[AllCompounds$DetailNames[
AllCompounds$CompoundID == i]])) %>%
select(File, CompoundID, Compound, pH, Solubility)
rm(StartRow, EndRow, pHSol_temp)
}
pHSol <- bind_rows(pHSol)
} else {
pHSol <- NULL
}
## Pulling CL info ----------------------------------------------------
MyInputDeets2 <- MyInputDeets[str_detect(MyInputDeets, "CLint_")]
if(length(MyInputDeets2) > 0){
for(j in MyInputDeets2){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_met2$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
CLRows <- which(
InputTab[ , NameCol] == "Enzyme" |
str_detect(InputTab[ , NameCol] %>%
pull(),
"^Biliary (CLint|Clearance)") |
str_detect(InputTab[ , NameCol] %>%
pull(),
"^Additional HLM CLint|^Additional Systemic Clearance|^Additional HKM CLint") |
str_detect(InputTab[ , ValueCol] %>%
pull(),
"In Vivo Clear") |
str_detect(InputTab[ , NameCol] %>%
pull(),
"(Liver|Intestine|Biliary) Clearance"))
CLRows <- CLRows[complete.cases(InputTab[CLRows + 1, NameCol])]
if(Out[["SimulatorUsed"]] == "Simcyp Discovery"){
# Discovery sims have a slightly different setup on the Input
# Sheet and only the 1st CLRows value should be used b/c that
# section will contain all the info we need.
CLRows <- CLRows[1]
MyNames <- as.character(t(
InputTab[CLRows:nrow(InputTab), NameCol]))
DiscoveryDeets <-
data.frame(
Detail = c("CLiv_InVivoCL",
"CLpo_InVivoCL",
"CLint_biliary",
"CLint_biliary_fuinc",
"CLrenal",
"CL_AddSystemic",
"CL_PercentAvailReabsorption"),
RegexRow = c("CL.*iv.*[(]mL",
"CL.*po.*[(]mL",
"Biliary Clearance ..L/min",
"Biliary fu inc",
"CL R .mL",
"^Additional Systemic Clearance",
"^Percent.*re-absorption"))
for(i in 1:nrow(DiscoveryDeets)){
MyRow <- which(str_detect(MyNames, DiscoveryDeets$RegexRow[i]))
if(length(MyRow) == 0){
Out[[paste0(DiscoveryDeets$Detail[i], Suffix)]] <- NA
} else {
suppressWarnings(
Out[[paste0(DiscoveryDeets$Detail[i], Suffix)]] <-
as.numeric(InputTab[MyRow + CLRows - 1, ValueCol])
)
}
rm(MyRow)
}
# Liver and intestinal CL
LivOrInt <- c("Liver", "Intestine")[
c(any(str_detect(MyNames, "Liver")), any(str_detect(MyNames, "Intestine")))]
LivIndCL <-
data.frame(
Detail = c("CL_XXX_Type",
"CL_XXX__UseSaturableKinetics",
"CLint_XXX",
"CL_Km_XXX",
"CL_Vmax_XXX",
"CL_XXX_fuinc",
"CL_XXX_UseMetabolite",
"CL_XXX_MetabPerc",
"CL_XXX_ScrapingsCorrectionFactor",
"CL_XXX_ElutionCorrectionFactor"),
RegexRow = c(paste(i, "Clearance Type"),
"Use Saturable Kinetics",
"CLint",
"Km \\(",
"Vmax \\(",
"fu inc",
"Use Metabolite",
"Metabolite .%",
"\\(scrapings\\) Correction Factor",
"\\(elution\\) Correction Factor")
)
if(length(LivOrInt[complete.cases(LivOrInt)]) > 0){
for(i in LivOrInt[complete.cases(LivOrInt)]){
OrganRows <- range(
which(str_detect(MyNames, i) |
str_detect(MyNames,
paste0(str_sub(i, 1, 1), "M")))
)
for(k in 1:nrow(LivIndCL)){
MyRow <- which(str_detect(MyNames[OrganRows[1]:OrganRows[2]],
LivIndCL$RegexRow[k]))
if(length(MyRow) == 0){
Out[[paste0(sub("XXX", i, LivIndCL$Detail[k]), Suffix)]] <- NA
} else {
suppressWarnings(
Out[[paste0(sub("XXX", i, LivIndCL$Detail[k]), Suffix)]] <-
as.character(InputTab[MyRow + CLRows - 1, ValueCol])
)
}
rm(MyRow)
}
}
}
} else {
# Regular Simulator data extraction starts here.
# Checking for interaction data
IntRowStart <- which(str_detect(InputTab[, NameCol] %>%
pull(), "Ind max|^Ki |^MBI|Interaction"))[1] - 1
if(complete.cases(IntRowStart)){
CLRows <- CLRows[CLRows < min(IntRowStart)]
}
for(i in CLRows){
# CL for a specific enzyme
if(str_detect(as.character(InputTab[i, NameCol]), "Enzyme")){
LastRow_i <- which(is.na(InputTab[, NameCol]))
LastRow_i <- LastRow_i[LastRow_i > i][1] - 1
Enzyme <- gsub(" ", "", InputTab[i, NameCol + 1])
Pathway <- gsub(" |-", "", InputTab[i - 1, NameCol + 1])
if(as.character(InputTab[i+1, NameCol]) == "Genotype"){
Genotype <- InputTab[i+1, NameCol + 1]
Genotype <- gsub("\\*", "star", Genotype)
Genotype <- gsub("/", "", Genotype)
Enzyme <- paste0(Enzyme, "_", Genotype)
CLrow <- i + 2
} else if((str_detect(Enzyme, "User") &
!str_detect(InputTab[i+1, NameCol], "CLint|Vmax")) |
str_detect(tolower(InputTab[i + 1, NameCol]),
"ontogeny")){
CLrow <- i + 2
} else {
CLrow <- i + 1
}
CLType <- str_extract(InputTab[CLrow, NameCol],
"CLint|Vmax|t1/2|Ind max")
if(CLType == "CLint"){
# NOTE TO CODERS: I'd been including units for CLint in
# the past, but I realized that I hadn't included units
# for other enzymes, so I decided later to omit them.
# Keeping this bit of code, albeit commented out, so
# that we can add them back easily if we want.
# Units <- str_extract(InputTab[CLrow, NameCol],
# "\\(.*\\)")
# Units <- gsub("\\(|\\)", "", Units)
# Units <- gsub("/| ", "_", Units)
# # Dealing with mu since it's causing some problems
# # downstream when a symbol
# Units <- gsub(rlang::chr_unserialise_unicode("<U+00B5>"),
# "u", Units)
suppressWarnings(
Out[[paste0(
paste("CLint", Enzyme, Pathway, # Units,
sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("fu_mic", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+1, NameCol + 1])
)
# Check for any UGT-specific CL parameters
if(str_detect(Enzyme, "UGT") &
any(str_detect(t(InputTab[i:LastRow_i, NameCol]),
"rUGT"))){
rUGTSysInfo <- InputTab[i:LastRow_i, c(NameCol, ValueCol)] %>%
rename(Name = 1, Value = 2) %>%
filter(str_detect(Name, "rUGT"))
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTSystem",
Suffix)]] <-
rUGTSysInfo[which(str_detect(rUGTSysInfo$Name,
"rUGTSystem")), ] %>%
pull(Value)
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_liver",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*liver")), ] %>%
pull(Value) %>% as.numeric())
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_intestine",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*intestine")), ] %>%
pull(Value) %>% as.numeric())
suppressWarnings(
Out[[paste0("CLint_", Enzyme, "_", Pathway, "_rUGTScalar_kidney",
Suffix)]] <-
rUGTSysInfo[which(
str_detect(tolower(rUGTSysInfo$Name),
"rugtscalar.*kidney")), ] %>%
pull(Value) %>% as.numeric())
rm(rUGTSysInfo)
}
rm(Enzyme, Pathway, CLType)
next
}
if(CLType == "Vmax"){
suppressWarnings(
Out[[paste0(
paste("Vmax", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Km", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+1, NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("fu_mic", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow+2, NameCol + 1])
)
rm(Enzyme, Pathway, CLType)
next
}
if(CLType == "t1/2"){
suppressWarnings(
Out[[paste0(
paste("HalfLife", Enzyme,
Pathway, sep = "_"),
Suffix)]] <-
as.numeric(InputTab[CLrow, NameCol + 1])
)
rm(Enzyme, Pathway, CLType)
next
}
}
# Biliary CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Biliary (CLint|Clearance)")){
suppressWarnings(
Out[[paste0("CLint_biliary", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# Other HLM CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Additional HLM CLint")){
suppressWarnings(
Out[[paste0("CLint_AddHLM", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# Other HKM CL
if(str_detect(as.character(InputTab[i, NameCol]), "^Additional HKM CLint")){
suppressWarnings(
Out[[paste0("CLint_AddHKM", Suffix)]] <-
as.numeric(InputTab[i, NameCol + 1])
)
}
# in vivo CL
if(str_detect(as.character(InputTab[i, ValueCol]),
"In Vivo Clearance")){
MyNames <- as.character(t(
InputTab[i:min(
c(IntRowStart, CLRows[which(CLRows == i) + 1] - 1,
nrow(InputTab)), na.rm = T), NameCol]))
suppressWarnings(
Out[[paste0("CLiv_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"CL.*iv.*[(](m)?L")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLbiliary_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"Biliary Clearance")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLadditional_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"Additional Systemic Clearance")) + i - 1,
ValueCol])
)
suppressWarnings(
Out[[paste0("CLpo_InVivoCL", Suffix)]] <-
as.numeric(InputTab[
which(str_detect(MyNames,
"^CL .po.")) + i - 1,
ValueCol])
)
}
}
rm(CLRows, IntRowStart, NameCol, Suffix)
}
}
}
## Pulling interaction info -------------------------------------------
MyInputDeets3 <- MyInputDeets[str_detect(MyInputDeets, "Interaction_")]
if(length(MyInputDeets3) > 0){
for(j in MyInputDeets3){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
IntRows <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"^Enzyme$|^Transporter$"))
IntRows <- IntRows[complete.cases(InputTab[IntRows + 1, NameCol])]
# Only IntRows after the first instance of an
# interaction type of term is found in NameCol. NB: I
# thought it would work to just look for cells after
# "interaction", but "interaction" hasn't always been
# listed in the output files I've found.
IntRowStart <- which(str_detect(InputTab[, NameCol] %>%
pull(), "Ind [mM]ax|Ind [sS]lope|^Ki |^MBI"))[1] - 1
TransporterTissues <- IntRows[which(
str_detect(t(InputTab[IntRows, NameCol]), "Transporter"))]
TransporterTissues <- TransporterTissues[which(
str_detect(t(InputTab[TransporterTissues - 1, NameCol]),
"Organ/Tissue"))] - 1
TransporterTissues <- data.frame(
Row = TransporterTissues,
Tissue = as.character(t(InputTab[TransporterTissues, ValueCol])))
if(complete.cases(IntRowStart)){
IntRows <- IntRows[IntRows >= IntRowStart]
for(i in IntRows){
Enzyme <- gsub(" |\\(|\\)|-|/", "_", InputTab[i, NameCol + 1])
Enzyme <- gsub("_{2,}", "_", Enzyme)
Enzyme <- sub("_$", "", Enzyme)
NextEmptyCell <- which(is.na(InputTab[, NameCol + 1]))
NextEmptyCell <- NextEmptyCell[NextEmptyCell > i][1]
# If there's another interaction listed
# before the next empty cell, need to
# account for that.
NextInt <- IntRows[which(IntRows == i) + 1] - 1
NextInt <- ifelse(i == IntRows[length(IntRows)],
nrow(InputTab), NextInt)
ThisIntRows <- i:(c(NextEmptyCell, NextInt)[which.min(c(NextEmptyCell, NextInt))])
ThisIntRows <- setdiff(ThisIntRows, NextEmptyCell)
# Induction
IndParam1stRow <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"Ind max|Ind Slope"))
if(length(IndParam1stRow) > 0){
IndModelCheck <- list(str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind max"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind Slope"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "Ind( )?C50"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "fu inc"),
str_detect(t(InputTab[ThisIntRows, NameCol]), "\u03B3"))
IndModelCheck <- sapply(IndModelCheck, FUN = function(x) which(x == TRUE))
# Note: I can't seem to get regex to work for
# detecting a Greek character; I figured that the
# Hill coefficient gamma is the only time that an
# induction parameter name is only going to be one
# character long.
suppressWarnings(
Out[[paste0(
paste("IndMax", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[1]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("IndSlope", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[2]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("IndC50", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[3]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Ind_fu_inc", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[4]][1]], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("Ind_gamma", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[IndModelCheck[[5]][1]], NameCol + 1])
)
rm(IndModelCheck)
}
# competitive inhibition
Ki <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"Ki "))
if(length(Ki) > 0){
EnzTrans <- as.character(InputTab[i, NameCol])
if(EnzTrans == "Transporter"){
Enzyme <-
paste0(Enzyme, "_",
# setting the tissue
TransporterTissues %>%
filter(Row <= i) %>%
filter(Row == max(Row)) %>%
pull(Tissue))
}
suppressWarnings(
Out[[paste0(
paste("Ki", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[Ki], NameCol + 1])
)
# fu mic or fu inc
IncType <- str_extract(InputTab[ThisIntRows[Ki+1], NameCol] %>%
pull(),
"inc|mic")
suppressWarnings(
Out[[paste0(
paste(switch(IncType,
"inc" = "Ki_fu_inc",
"mic" = "Ki_fu_mic"),
Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[Ki+1], NameCol + 1])
)
rm(IncType, EnzTrans)
}
# MBI
MBI <- which(str_detect(InputTab[ThisIntRows, NameCol] %>% pull(),
"MBI Kapp"))
if(length(MBI) > 0){
suppressWarnings(
Out[[paste0(
paste("MBI_Kapp", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("MBI_kinact", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI+1], NameCol + 1])
)
suppressWarnings(
Out[[paste0(
paste("MBI_fu_mic", Enzyme,
sep = "_"), Suffix)]] <-
as.numeric(InputTab[ThisIntRows[MBI+2], NameCol + 1])
)
}
rm(Enzyme, NextEmptyCell, NextInt,
ThisIntRows, IndParam1stRow, Ki, MBI)
}
}
rm(Suffix, IntRows, IntRowStart, NameCol)
}
}
## Dealing with StartDayTime_x --------------------------------------
MyInputDeets4 <- MyInputDeets[str_detect(MyInputDeets, "^StartDayTime")]
if(length(MyInputDeets4) > 0){
for(j in MyInputDeets4){
NameCol <- InputDeets$NameCol[which(InputDeets$Deet == j)]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
Row_day <- which(str_detect(InputTab[, NameCol] %>% pull(), "Start Day"))
# If this is not present, which sometimes happens with a custom
# dosing schedule, then will need to pull info from custom
# dosing sheet lower in script.
if(length(Row_day) == 0){
CustomDosing <- c(CustomDosing, TRUE)
} else {
Val_day <- InputTab[Row_day, InputDeets$ValueCol[
which(InputDeets$Deet == j)]] %>% pull()
Row_time <- which(str_detect(InputTab[, NameCol] %>% pull(), "Start Time"))
Val_time <- InputTab[Row_time, InputDeets$ValueCol[
which(InputDeets$Deet == j)]] %>% pull()
# Dealing with inconsistencies in time format
Val_time <- sub("m", "", Val_time)
Val_time <- str_split(Val_time, pattern = "h")[[1]]
Val_time <- str_c(str_pad(Val_time, width = 2, pad = "0"),
collapse = ":")
Out[[j]] <- paste(paste0("Day ", Val_day),
Val_time, sep = ", ")
}
}
}
## Transport parameters ----------------------------------------------
MyInputDeets5 <- MyInputDeets[str_detect(MyInputDeets, "Transport_")]
MyInputDeets5 <- InputDeets %>%
filter(Deet %in% MyInputDeets5 & complete.cases(NameCol)) %>%
pull(Deet)
if(length(MyInputDeets5) > 0){
for(j in MyInputDeets5){
Suffix <- str_extract(j, "_sub$|_inhib$|_inhib2$|_met1$|_secmet$|_inhib1met$")
NameCol <- InputDeets$NameCol[InputDeets$Deet == j]
ValueCol <- InputDeets$ValueCol[InputDeets$Deet == j]
# There can be transporter interactions higher up on the tab,
# but parameters that are actually just transport parameters all
# come after the title "Transport".
StartTrans <- which(InputTab[, NameCol] %>% pull() == "Transport")
if(length(StartTrans) > 0){
TransRows <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"^Transporter"))
TransRows <- TransRows[TransRows > StartTrans]
if(length(TransRows) > 0){
# Sometimes, the organ is only listed once and then not listed
# again for subsequent transporters until it changes.
OrganRows <- which(str_detect(InputTab[, NameCol] %>% pull(),
"Organ/Tissue"))
OrganRows <- OrganRows[OrganRows >= min(TransRows) - 1 &
OrganRows < max(TransRows)]
# BBB transporter parameters are set up differently, so
# removing any organ rows for those scenarios.
BrainRows <- OrganRows + 1
BrainRows <- BrainRows[which(str_detect(
InputTab[BrainRows, NameCol] %>% pull(), "BBB|BCSFB|ISF.ICF"))]
OrganRows <- setdiff(OrganRows, BrainRows - 1)
for(i in TransRows){
# Last row always seems to contain RAF/REF or ISEF,T
TransRowLast <- which(str_detect(InputTab[ , NameCol] %>% pull(),
"RAF/REF|ISEF"))
TransRowLast <- TransRowLast[TransRowLast > i]
TransRowLast <- ifelse(length(TransRowLast) == 0,
nrow(InputTab), TransRowLast[1])
TransRowNames <- InputTab[i:TransRowLast, NameCol] %>% pull(1)
Transporter <- gsub(" |\\(|\\)|-|/", "_", InputTab[i, NameCol + 1])
Transporter <- gsub("_{2,}", "_", Transporter)
Transporter <- sub("_$", "", Transporter)
Transporter <- case_match(Transporter,
"Apical_Efflux_Kidney" ~ "General_Apical_Efflux",
.default = Transporter)
Location <- gsub(" |\\(|\\)|-|/", "",
InputTab[c(i:TransRowLast)[which(TransRowNames == "Location")],
ValueCol] %>% pull(1))
Organ <- InputTab[max(OrganRows[OrganRows < i]), ValueCol] %>%
pull() %>% str_comma() # This should have length 1, but adding str_comma just in case.
# Organ <- which(str_detect(as.character(t(
# InputTab[(i-1):TransRowLast, NameCol])), "Organ/Tissue"))
# Organ <- ifelse(length(Organ) > 0,
# as.character(InputTab[((i-1):TransRowLast)[Organ], ValueCol]),
# "")
ParamPrefix <- paste("Transporter", Organ, Transporter, Location, sep = "_")
# Either CLint,T or Jmax and Km values will be listed
if(any(str_detect(TransRowNames, "CLint,T"))){
suppressWarnings(
Out[[paste0(ParamPrefix, "_CLintT", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "CLint,T"))],
ValueCol] %>% pull(1))
)
} else if(any(str_detect(TransRowNames, "Jmax"))){
suppressWarnings(
Out[[paste0(ParamPrefix, "_Jmax", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "Jmax"))],
ValueCol] %>% pull(1))
)
suppressWarnings(
Out[[paste0(ParamPrefix, "_Km", Suffix)]] <-
as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "Km"))],
ValueCol] %>% pull(1))
)
}
# Checking for fuinc values b/c they're not always there
fuinc <- as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "fuinc"))],
ValueCol] %>% pull(1))
if(length(fuinc) > 0){
suppressWarnings(
Out[[paste0(ParamPrefix, "_fuinc", Suffix)]] <- fuinc
)
}
rm(fuinc)
# Checking for RAF/REF values b/c they're not always there
RAFREF <- as.numeric(
InputTab[c(i:TransRowLast)[which(str_detect(TransRowNames, "ISEF|RAF|REF"))],
ValueCol] %>% pull(1))
if(length(RAFREF) > 0){
suppressWarnings(
Out[[paste0(ParamPrefix, "_RAFREF", Suffix)]] <- RAFREF
)
}
rm(RAFREF)
rm(TransRowLast, Transporter, TransRowNames, Location,
ParamPrefix)
}
}
}
}
}
}
# Dealing with custom dosing schedules ---------------------------------
if(any(str_detect(exp_details, "StartDayTime")) &
any(str_detect(names(Out), "StartDayTime")) == FALSE |
any(CustomDosing, na.rm = TRUE)){
# When there's custom dosing for any of the substrate or inhibitors,
# then the dosing start time should be pulled from a "Custom CustomDosing"
# tab. Pulling any custom dosing sheets here.
CustomDoseSheets <- SheetNames[str_detect(SheetNames, "Custom Dosing")]
for(j in CustomDoseSheets){
Suffix <- switch(str_extract(j, "Inh [12]|Sub"),
"Inh 1" = "_inhib",
"Inh 2" = "_inhib2",
"Sub" = "_sub")
CustomDose_xl <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = j,
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = j,
colNames = FALSE)))
# If people have added anything to the sheet, that can mess up data
# extraction. Here, we're at least checking for any columns that would
# have NA values for the names b/c those are probably places where
# people have added something extra off to the side of the data we
# actually want.
GoodCols <- t(CustomDose_xl[3, ]) %>% as.character()
GoodCols <- which(complete.cases(GoodCols))
CustomDosing <- CustomDose_xl[4:nrow(CustomDose_xl), GoodCols]
names(CustomDosing) <- make.names(CustomDose_xl[3, GoodCols])
CustomDosing <- CustomDosing %>%
rename(DoseNum = Dose.Number,
Time1 = Time,
Dose_units = Dose.Units,
DoseRoute = Route.of.Administration) %>%
mutate(Day = as.numeric(Day))
TimeUnits <- names(CustomDosing)[str_detect(names(CustomDosing), "Offset")]
names(CustomDosing)[str_detect(names(CustomDosing), "Offset")] <- "Time"
MyCompoundID <- AllCompounds$CompoundID[AllCompounds$Suffix == Suffix]
MyCompound <- as.character(Out[AllCompounds$DetailNames[AllCompounds$Suffix == Suffix]])
CustomDosing <- CustomDosing %>%
# Removing any rows where Time is NA b/c those are likely places
# where people have added some comments, etc. and not the main data
# we want. The NA values mess up things downstream.
filter(complete.cases(Time)) %>%
mutate(Time_units = ifelse(str_detect(TimeUnits, "\\.h\\.$"),
"h", "min"),
File = sim_data_file,
TimeOfDay = as.character(round_date(timeConv(
as.numeric(Time1)), unit = "minute")),
TimeOfDay = sub("1899-12-30 ", "", TimeOfDay),
CompoundID = MyCompoundID,
Compound = MyCompound) %>%
mutate(across(.cols = matches("DoseNum|Time$|Dose$|^Day$"),
.fns = as.numeric)) %>%
select(File, CompoundID, Compound, Day, TimeOfDay,
Time, Time_units, DoseNum,
Dose, Dose_units, DoseRoute)
Out[[paste0("CustomDosing", Suffix)]] <- CustomDosing
Out[[paste0("Dose", Suffix)]] <- "custom dosing"
Out[[paste0("StartDayTime", Suffix)]] <- "custom dosing"
Out[[paste0("StartHr", Suffix)]] <- CustomDosing$Time[CustomDosing$DoseNum == 1]
Out[[paste0("DoseRoute", Suffix)]] <- "custom dosing"
Out[[paste0("DoseInt", Suffix)]] <- "custom dosing"
Out[[paste0("Regimen", Suffix)]] <- "Multiple Dose"
rm(CustomDosing, Suffix, CustomDose_xl, MyCompoundID, MyCompound, TimeUnits)
}
}
# Pulling details from the population tab -------------------------------
MyPopDeets <- intersect(exp_details, PopDeets$Deet)
# If user asks for population details, then function is set up to read both
# what that population is and what the simulator version is by reading the
# summary tab, so this next line should work and will not give them any
# population details for Simcyp Discovery simulations until we set that up.
MyPopDeets <- intersect(MyPopDeets,
(AllExpDetails %>%
filter(SimulatorAvailability %in% DiscoveryCol) %>%
pull(Detail)))
if(length(MyPopDeets) > 0){
# Getting name of that tab.
if(exists("SheetNames", inherit = FALSE) == FALSE){
SheetNames <- tryCatch(readxl::excel_sheets(sim_data_file),
error = openxlsx::getSheetNames(sim_data_file))
}
# If user has requested that the population tab be annotated, which is an
# option!, then there will be 2 matches to the population sheet name. We
# want the 1st one.
PopSheet <- SheetNames[str_detect(tolower(SheetNames),
str_sub(tolower(Out$Population), 1, 20))][1]
PopTab <- suppressMessages(tryCatch(
readxl::read_excel(path = sim_data_file, sheet = PopSheet,
col_names = FALSE),
error = openxlsx::read.xlsx(sim_data_file, sheet = PopSheet,
colNames = FALSE)))
# If openxlsx read the file, the names are different. Fixing.
if(names(PopTab)[1] == "X1"){
names(PopTab) <- paste0("...", 1:ncol(PopTab))
}
MyPopDeets <- intersect(exp_details, PopDeets$Deet)
# User can change the name of user-defined cytosolic phenotypes for GI
# tract, kidney, and liver. Changing this back to "Cyt1" to work for
# regex, though. For now, only extracting data for Cyt1 and not any more
# user-defined cytosolic phenotype parameters, so ignoring the others.
# If name is changed in one, it's changed in all. Columns are 3, 5, and
# 9.
if(any(str_detect(PopTab$...3, "Cyt1"), na.rm = T) == FALSE){
StartCytRow <- which(str_detect(PopTab$...3, "^User Cyt$"))[1]
NewName <- gsub("Abundance : | Population Scalar", "", PopTab[StartCytRow + 1, 3])
PopTab$...3 <- sub(NewName, "User Cyt1", PopTab$...3)
PopTab$...5 <- sub(NewName, "User Cyt1", PopTab$...5)
PopTab$...9 <- sub(NewName, "User Cyt1", PopTab$...9)
}
# sub function for finding correct cell
pullValue <- function(deet){
# Setting up regex to search
ToDetect <- AllExpDetails %>%
filter(Detail == deet & DataSource == "population") %>% pull(Regex_row)
NameCol <- PopDeets$NameCol[which(PopDeets$Deet == deet)]
if(ncol(PopTab) < NameCol){
# This happens when it's an animal simulation.
return(NA)
}
Row <- which(str_detect(PopTab[, NameCol] %>% pull(), ToDetect))
if(length(Row) == 0){
Val <- NA
} else {
Val <- PopTab[Row, PopDeets$ValueCol[PopDeets$Deet == deet]] %>%
pull()
Val <- sort(unique(Val))
}
suppressWarnings(
Val <- switch(PopDeets$Class[PopDeets$Deet == deet],
"character" = as.character(Val),
"numeric" = as.numeric(Val))
)
# Tidying up some specific idiosyncracies of simulator output
Val <- ifelse(complete.cases(Val) & Val == "n/a",
NA, Val)
return(Val)
}
for(i in MyPopDeets){
Out[[i]] <- pullValue(i)
}
}
# Pulling from workspace file -------------------------------------------
if(any(c("workspace", "all") %in% exp_details_input)){
# Checking that the workspace file is available. This will ignore the
# date/time stamp on the Excel results if it's still there.
WkspFile <- c("Simulator" = sub("( - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2})?\\.xlsx$",
".wksz", sim_data_file),
"Discovery" = sub("( - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2})?\\.xlsx$",
".dscw", sim_data_file))
WkspFile <- WkspFile[which(file.exists(WkspFile))]
if(length(WkspFile) > 0){
TEMP <- extractExpDetails_XML(
sim_workspace_files = WkspFile,
compoundsToExtract = "all",
exp_details = "all")
TEMP$MainDetails <- TEMP$MainDetails %>%
# This currently removes anything that we already have from the
# Excel file. May change that later to verify that Excel and
# workspace match.
select(!any_of(c("Substrate", "Inhibitor1", "Inhibitor2",
"PrimaryMetabolite1", "PrimaryMetabolite2",
"SecondaryMetabolite", "Inhibitor1Metabolite",
paste0("DistributionModel",
c("inhib1met",
"_met1", "_met2", "_secmet")))))
# Note: Currently, we are not extracting anything from the workspace
# that would be its own separate list. When we DO do that, we'll need
# to adjust this code to bind the MainDetails and whatever that list
# is.
Out <- c(Out,
TEMP$MainDetails[
setdiff(names(TEMP$MainDetails)[
names(TEMP$MainDetails) != "Workspace"],
names(Out))])
rm(TEMP)
}
}
# Calculated details & data cleanup ----------------------------------------
if("StartHr_sub" %in% exp_details &&
"StartDayTime_sub" %in% names(Out) &&
complete.cases(Out$StartDayTime_sub) &&
Out$StartDayTime_sub != "custom dosing"){
Out[["StartHr_sub"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_sub)
}
if(all(c("Inhibitor1", "StartDayTime_inhib") %in% names(Out)) &&
complete.cases(Out$StartDayTime_inhib) &&
Out$StartDayTime_inhib != "custom dosing"){
Out[["StartHr_inhib"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_inhib)
}
if(all(c("Inhibitor2", "StartDayTime_inhib2") %in% names(Out)) &&
complete.cases(Out$StartDayTime_inhib2) &&
Out$StartDayTime_inhib != "custom dosing"){
Out[["StartHr_inhib2"]] <- difftime_sim(time1 = Out$SimStartDayTime,
time2 = Out$StartDayTime_inhib2)
}
# Other functions call on "Inhibitor1", etc., so we need those objects to
# exist, even if they were not used in this simulation. Setting them to NA if
# they don't exist.
MissingCmpd <- setdiff(AllCompounds$DetailNames,
names(Out))
MissingCmpd_list <- as.list(rep(NA, length(MissingCmpd)))
names(MissingCmpd_list) <- MissingCmpd
Out <- c(Out, MissingCmpd_list)
# Always including the file name.
Out$File <- sim_data_file
# Noting when workspace, if there is a matching one, was last changed.
WorkspaceFile <- sub("xlsx", ifelse(Out$SimulatorUsed == "Discovery",
"dscw", "wksz"), sim_data_file)
# Removing the file timestamp if there was one b/c that won't be part of the
# workspace file name.
WorkspaceFile <- sub(" - [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}-[0-9]{2}-[0-9]{2}",
"", WorkspaceFile)
Out$Workspace_TimeLastModified <-
ifelse(file.exists(WorkspaceFile),
as.character(file.info(WorkspaceFile)$mtime), NA)
# Noting when this was run.
Out$expDetails_TimeStamp <- Sys.time()
# Species should be lower case and not have "Sim" in front of it to work
# more smoothly with other functions and also just look better. Setting
# "beagle" to "dog" and setting it to "human" if it's missing, which it will
# be for regular simulator output.
if("Species" %in% names(Out)){
Out$Species <- tolower(sub("Sim-", "", Out$Species))
Out$Species <- ifelse(Out$Species == "beagle", "dog", Out$Species)
if(is.na(Out$Species)){
Out$Species <- "human"
}
}
Out <- Out[sort(names(Out))]
# Adding missing, necessary list items
Missing1 <- setdiff(
paste0(rep(c("DoseInt", "DoseRoute", "Regimen", "NumDoses"), each = 3),
c("_sub", "_inhib", "_inhib2")),
names(Out))
if(length(Missing1) > 0){
Missing <- as.list(matrix(data = NA, ncol = length(Missing1),
dimnames = list(NULL, Missing1)))
names(Missing) <- Missing1
Out <- c(Out, Missing)
}
# Fixing an issue that trips up other code down the line: Sometimes, the
# user might specify a "multiple dose" regimen but then only administer
# a single dose. That messes up, e.g., extractPK b/c it looks on the
# wrong tab for the info it needs. When that happens, set the regimen to
# "Single Dose".
if(is.null(Out$Regimen_sub) == FALSE &&
(complete.cases(Out$Regimen_sub) && Out$Regimen_sub == "Multiple Dose") &
(complete.cases(Out$NumDoses_sub) && Out$NumDoses_sub == 1)){
Out$Regimen_sub <- "Single Dose"
}
if(is.null(Out$Regimen_inhib) == FALSE &&
(complete.cases(Out$Regimen_inhib) && Out$Regimen_inhib == "Multiple Dose") &
(complete.cases(Out$NumDoses_inhib) && Out$NumDoses_inhib == 1)){
Out$Regimen_inhib1 <- "Single Dose"
}
if(is.null(Out$Regimen_inhib2) == FALSE &&
(complete.cases(Out$Regimen_inhib2) && Out$Regimen_inhib2 == "Multiple Dose") &
(complete.cases(Out$NumDoses_inhib2) && Out$NumDoses_inhib2 == 1)){
Out$Regimen_inhib2 <- "Single Dose"
}
# Making DoseInt_x and Dose_x numeric all the time. We'll get custom dosing
# info from Regimen_x and DoseRoute_x.
suppressWarnings(Out$DoseInt_sub <- as.numeric(Out$DoseInt_sub))
suppressWarnings(Out$DoseInt_inhib <- as.numeric(Out$DoseInt_inhib))
suppressWarnings(Out$DoseInt_inhib2 <- as.numeric(Out$DoseInt_inhib2))
suppressWarnings(Out$Dose_sub <- as.numeric(Out$Dose_sub))
suppressWarnings(Out$Dose_inhib <- as.numeric(Out$Dose_inhib))
suppressWarnings(Out$Dose_inhib2 <- as.numeric(Out$Dose_inhib2))
# At this point, DoseInt_x and Dose_x will be NA if it's a custom dosing
# regimen. Setting the regimen to "multiple". We'll use that downstream for
# checking for appropriate PK parameters, etc.
Out$Regimen_sub <- ifelse(is.na(Out$DoseInt_sub) &
(complete.cases(Out$DoseRoute_sub) &&
Out$DoseRoute_sub == "custom dosing"),
"Multiple Dose", Out$Regimen_sub)
Out$Regimen_inhib <- ifelse(is.na(Out$DoseInt_inhib) &
(complete.cases(Out$DoseInt_inhib) &&
Out$DoseRoute_inhib == "custom dosing"),
"Multiple Dose", Out$Regimen_inhib)
Out$Regimen_inhib2 <- ifelse(is.na(Out$DoseInt_inhib2) &
(complete.cases(Out$DoseInt_inhib2) &&
Out$DoseRoute_inhib2 == "custom dosing"),
"Multiple Dose", Out$Regimen_inhib2)
# Splitting this up into main details -- a data.frame -- and then,
# separately, whatever items need to be lists, e.g., custom dosing regimens
# and dissolution profiles.
Main <- as.data.frame(Out[which(sapply(Out, length) == 1)])
# Making absoultely sure that File included in Main. When we run
# harmonize_details, it will add it to the other items whenever there is at
# least 1 row, but we need it in Main to do that.
Main$File <- sim_data_file
## Dosing -----------------------------------------------------------------
# Setting up Dosing data.frame to include ALL dosing info, so custom dosing
# when appropriate and, for compounds and/or simulations w/out custom dosing,
# then a data.frame of all dosing events filled in based on interval, amount,
# etc.
Out <- list(MainDetails = Main,
CustomDosing = bind_rows(Out$CustomDosing_sub,
Out$CustomDosing_inhib,
Out$CustomDosing_inhib2),
DissolutionProfiles = DissoProfs,
ReleaseProfiles = ReleaseProfs,
ConcDependent_fup = CDfupProfs,
ConcDependent_BP = CDBPProfs,
pH_dependent_solubility = pHSol)
Out <- harmonize_details(Out)
# Returning --------------------------------------------------------------
for(j in names(Out)[unlist(lapply(Out, is.null)) == FALSE]){
Out[[j]] <- Out[[j]] %>%
mutate(File = sim_data_file) %>%
select(File, everything())
}
return(Out)
}
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