Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically-based models from Jongeneelen et al. (2011) and Clewell et al. (2001) while calculated physicochemical data was obtained from EPA’s CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed combined measured plasma and blood concentrations was 0.59 with an r2= 0.54 and a Root Mean Square Error (RMSE) of direct comparison between the log-transformed simulated and observed values of 0.87. Approximately 3.6% (n = 73) of the data points analyzed were > 2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically-based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.
knitr::opts_chunk$set(echo = TRUE, fig.width=5, fig.height=4)
library(httk)
library(ggplot2)
library(gridExtra)
library(cowplot)
library(ggrepel)
library(dplyr)
library(stringr)
library(forcats)
library(smatr)
# Delete all objects from memory:
rm(list=ls())###Get metabolism and concentration data
met_data <- metabolism_data_Linakis2020
conc_data <- concentration_data_Linakis2020
conc_data[,"DOSE_U"] <- ifelse(conc_data[,"DOSE_U"] == "ppm",yes = "ppmv",conc_data[,"DOSE_U"])
conc_data[,"ORIG_CONC_U"] <- ifelse(conc_data[,"ORIG_CONC_U"] == "ppm",yes = "ppmv",conc_data[,"ORIG_CONC_U"])
# Not sure what to do with percent:
conc_data <- subset(conc_data,toupper(ORIG_CONC_U) != "PERCENT")
# Rename this column:
colnames(conc_data)[colnames(conc_data)=="ORIG_CONC_U"] <- "CONC_U"
conc_data$ORIGINAL_CONC_U <- conc_data$CONC_U
conc_data$ORIGINAL_CONC <- conc_data$CONCENTRATIONSets the units based on the sampling matrix (gas/liquid): BL : blood EEB : end-exhaled breath MEB : mixed exhaled breath VBL : venous blood ABL : arterial blood EB : unspecified exhaled breath sample (assumed to be EEB) PL: plasma +W with work/exercise
Normalize units for gaseous samples to ppmv:
gas.media <- c("EB","MEB","EEB","EB (+W)")
gas.units <- unique(subset(conc_data,
SAMPLING_MATRIX %in% gas.media)$CONC_U)
target.unit <- "ppmv"
for (this.unit in gas.units)
if (this.unit != target.unit)
{
these.chems <- unique(subset(conc_data,
SAMPLING_MATRIX %in% gas.media &
CONC_U==this.unit)$DTXSID)
for (this.chem in these.chems)
{
this.factor <- convert_units(
input.units=this.unit, output.units=target.unit, dtxsid=this.chem)
print(paste("Use",this.factor,"to convert",this.unit,"to",target.unit))
# Scale the observation
conc_data[conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% gas.media &
conc_data$CONC_U==this.unit,"CONCENTRATION"] <-
this.factor * conc_data[
conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% gas.media &
conc_data$CONC_U==this.unit,"CONCENTRATION"]
# Change the reported unit
conc_data[conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% gas.media &
conc_data$CONC_U==this.unit,"CONC_U"] <-
target.unit
}
}Normalize the units for tissue samples to uM:
tissue.media <- c("VBL","BL","ABL","PL","BL (+W)")
tissue.units <- unique(subset(conc_data,
SAMPLING_MATRIX %in% tissue.media)$CONC_U)
target.unit <- "uM"
for (this.unit in tissue.units)
if (this.unit != target.unit)
{
these.chems <- unique(subset(conc_data,
SAMPLING_MATRIX %in% tissue.media &
CONC_U==this.unit)$DTXSID)
for (this.chem in these.chems)
{
this.factor <- convert_units(
input.units=this.unit, output.units=target.unit, dtxsid=this.chem)
print(paste("Use",this.factor,"to convert",this.unit,"to",target.unit))
# Scale the observation
conc_data[conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% tissue.media &
conc_data$CONC_U==this.unit,"CONCENTRATION"] <-
this.factor * conc_data[
conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% tissue.media &
conc_data$CONC_U==this.unit,"CONCENTRATION"]
# Change the reported unit
conc_data[conc_data$DTXSID==this.chem &
conc_data$SAMPLING_MATRIX %in% tissue.media &
conc_data$CONC_U==this.unit,"CONC_U"] <-
target.unit
}
}Identify chemicals currently in our metabolism data that we don’t have good concentration/time data for and remove them from our training dataset
# Small molecule chemicals
summary(met_data$AVERAGE_MASS)
# Generally more lipophilic chemicals
summary(met_data$OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED)
# Unsurprisingly then, the chemicals are generally less water-soluble
summary(met_data$WATER_SOLUBILITY_MOL.L_OPERA_PRED)
# ~60% of samples in humans
table(conc_data$CONC_SPECIES)/nrow(conc_data)*100
# ~72% of samples are from blood
table(conc_data$SAMPLING_MATRIX)/nrow(conc_data)*100# Create a dataframe with 1 row for each unique external exposure scenario
unique_scenarios <- conc_data[with(conc_data,
order(PREFERRED_NAME,
CONC_SPECIES,
SAMPLING_MATRIX,
as.numeric(as.character(DOSE)),EXP_LENGTH,-TIME)),] %>%
distinct(DTXSID,DOSE,DOSE_U,EXP_LENGTH,CONC_SPECIES,SAMPLING_MATRIX, .keep_all = TRUE)Create a list of dataframes of observed and predicted concentrations for each unique external exposure scenario (corresponding to 142 studies)
simlist <- list()
obslist <- list()
for(i in 1:nrow(unique_scenarios)){
#tryCatch({
relconc <- subset(conc_data,conc_data$DTXSID == unique_scenarios$DTXSID[i] &
conc_data$DOSE == unique_scenarios$DOSE[i] &
conc_data$EXP_LENGTH == unique_scenarios$EXP_LENGTH[i] &
conc_data$CONC_SPECIES == unique_scenarios$CONC_SPECIES[i] &
conc_data$SAMPLING_MATRIX == unique_scenarios$SAMPLING_MATRIX[i])
obslist[[i]] <- relconc
solve <- suppressWarnings(as.data.frame(solve_gas_pbtk(
chem.cas = unique_scenarios$CASRN[i],
days = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i]),
# Make sure we get conc's at the observed times:
times=unique(c(0,signif(obslist[[i]]$TIME,4))),
tsteps = 500,
input.units = unique_scenarios$DOSE_U[i],
exp.conc = as.numeric(unique_scenarios$DOSE[i]), # SED (06-22-2021) think this should input ppmv
# exp.conc = ((as.numeric(unique_scenarios$DOSE[i])*1e20*1000)/PPMVCONVERT*1000)/1e20,
exp.duration = unique_scenarios$EXP_LENGTH[i]*24,
period = (unique_scenarios$TIME[i]+unique_scenarios$EXP_LENGTH[i])*24,
species = as.character(unique_scenarios$CONC_SPECIES[i]),
monitor.vars = c(
"Cven",
"Clung",
"Cart",
"Cplasma",
"Calvppmv",
"Cendexhppmv",
"Cmixexhppmv",
"Calv",
"Cendexh",
"Cmixexh",
"Cmuc",
"AUC"),
vmax.km = FALSE,
suppress.messages = TRUE)))
this.Rb2p <- suppressWarnings(available_rblood2plasma(
chem.cas=unique_scenarios$CASRN[i],
species=as.character(unique_scenarios$CONC_SPECIES[i])))
solve$Rb2p <- this.Rb2p
# Sets the output appropriate for the sampling matrix
# BL : blood
# EEB : end-exhaled breath
# MEB : mixed exhaled breath
# VBL : venous blood
# ABL : arterial blood
# EB : unspecified exhaled breath sample (assumed to be EEB)
# PL: plasma
# +W with work/exercise
if (unique_scenarios$SAMPLING_MATRIX[i] == "VBL" |
unique_scenarios$SAMPLING_MATRIX[i] == "BL" |
unique_scenarios$SAMPLING_MATRIX[i] == "BL (+W)")
{
solve$simconc <- solve$Cven*this.Rb2p
solve$unit <- "uM"
} else if (unique_scenarios$SAMPLING_MATRIX[i] == "ABL") {
solve$simconc <- solve$Cart*this.Rb2p
solve$unit <- "uM"
} else if (unique_scenarios$SAMPLING_MATRIX[i] == "EB" |
unique_scenarios$SAMPLING_MATRIX[i] == "EEB" |
unique_scenarios$SAMPLING_MATRIX[i] == "EB (+W)")
{
if (unique_scenarios[i,"CONC_U"] == "ppmv")
{
solve$simconc <- solve$Cendexhppmv
solve$unit <- "ppmv"
} else if (unique_scenarios[i,"CONC_U"] == "uM") {
solve$simconc <- solve$Cendexh
solve$unit <- "uM"
} else if (unique_scenarios[i,"CONC_U"] == "ug/L") {
solve$simconc <- solve$Cendexh
solve$unit <- "uM"
}
} else if (unique_scenarios$SAMPLING_MATRIX[i] == "MEB") {
if (unique_scenarios[i,"CONC_U"] == "ppmv")
{
solve$simconc <- solve$Cmixexhppmv
solve$unit <- "ppmv"
} else if (unique_scenarios[i,"CONC_U"] == "uM") {
solve$simconc <- solve$Cmixexh
solve$unit <- "uM"
}
} else if (unique_scenarios$SAMPLING_MATRIX[i] == "PL"){
solve$simconc <- solve$Cplasma
solve$unit <- "uM"
} else {
solve$simconc <- NA
solve$unit <- NA
}
simlist[[i]] <- solve
}Create a predicted vs. observed plot for each study:
cvtlist <- list()
for(i in 1:nrow(unique_scenarios))
{
plot.data <- simlist[[i]]
relconc <- obslist[[i]]
#Right now this is only calculating real concentrations according to mg/L in blood
cvtlist[[i]] <- ggplot(plot.data, aes(time*24, simconc)) +
geom_line() +
xlab("Time (h)") +
ylab(paste0("Simulated ",
unique_scenarios$SAMPLING_MATRIX[i],
"\nConcentration (" ,
solve$unit, ")")) +
ggtitle(paste0(
unique_scenarios$PREFERRED_NAME[i],
" (",
unique_scenarios$CONC_SPECIES[i],
", ",
round(as.numeric(unique_scenarios$DOSE[i]), digits = 2),
unique_scenarios$DOSE_U[i],
" for ",
round(unique_scenarios$EXP_LENGTH[i]*24, digits = 2),
"h in ",
unique_scenarios$SAMPLING_MATRIX[i], ")")) +
geom_point(data = relconc, aes(TIME*24,CONCENTRATION)) +
theme(plot.title = element_text(face = 'bold', size = 20),
axis.title.x = element_text(face = 'bold', size = 20),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 16),
legend.title = element_text(face = 'bold', size = 16),
legend.text = element_text(face = 'bold',size = 14))+
theme_bw()
}Create a list to hold the combined observations and predictions for each scenario:
# Creation of simulated vs. observed concentration dataset
unique_scenarios$RSQD <- 0
unique_scenarios$RMSE <- 0
unique_scenarios$AIC <- 0
simobslist <- list()
obvpredlist <- list()Merge the simulations and observations on the basis of simulation time:
for(i in 1:length(simlist))
{
obsdata <- as.data.frame(obslist[[i]])
simdata <- as.data.frame(simlist[[i]])
# skips over anything for which there was no observed data or
# insufficient information to run simulation:
if (!is.null(simlist[[i]]) & !is.null(obslist[[i]]))
{
# Make sure we are looking at consistent time points:
simobscomb <- simdata[simdata$time %in% signif(obsdata$TIME,4),]
obsdata <- subset(obsdata,signif(TIME,4) %in% simobscomb$time)
# Merge with obsdata
colnames(obsdata)[colnames(obsdata) ==
"TIME"] <-
"obstime"
# Round to match sim time:
obsdata$time <- signif(obsdata$obstime,4)
colnames(obsdata)[colnames(obsdata) ==
"CONCENTRATION"] <-
"obsconc"
colnames(obsdata)[colnames(obsdata) ==
"PREFERRED_NAME"] <-
"chem"
colnames(obsdata)[colnames(obsdata) ==
"DOSE"] <-
"dose"
colnames(obsdata)[colnames(obsdata) ==
"EXP_LENGTH"] <-
"explen"
colnames(obsdata)[colnames(obsdata) ==
"CONC_SPECIES"] <-
"species"
colnames(obsdata)[colnames(obsdata) ==
"SAMPLING_MATRIX"] <-
"matrix"
colnames(obsdata)[colnames(obsdata) ==
"AVERAGE_MASS"] <-
"mw"
colnames(obsdata)[colnames(obsdata) ==
"CONC_U"] <-
"CONC_U"
simobscomb <- suppressWarnings(merge(obsdata[,c(
"time",
"obstime",
"obsconc",
"chem",
"dose",
"explen",
"species",
"matrix",
"mw",
"CONC_U"
)], simobscomb, by="time", all.x=TRUE))
# Merge with met_data
this.met_data <- subset(met_data,
PREFERRED_NAME == simobscomb[1,"chem"] &
SPECIES == simobscomb[1,"species"])
colnames(this.met_data)[colnames(this.met_data)=="CHEM_CLASS"] <-
"chemclass"
colnames(this.met_data)[colnames(this.met_data) ==
"OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED"] <-
"logp"
colnames(this.met_data)[colnames(this.met_data) ==
"WATER_SOLUBILITY_MOL.L_OPERA_PRED"] <-
"sol"
colnames(this.met_data)[colnames(this.met_data) ==
"HENRYS_LAW_ATM.M3.MOLE_OPERA_PRED"] <-
"henry"
colnames(this.met_data)[colnames(this.met_data) ==
"VMAX"] <-
"vmax"
colnames(this.met_data)[colnames(this.met_data) ==
"KM"] <-
"km"
simobscomb <- suppressWarnings(cbind(simobscomb,this.met_data[c(
"chemclass",
"logp",
"sol",
"henry",
"vmax",
"km")]))
simobslist[[i]] <- simobscomb
}
}Identify which quartile each observation occurred in with respect to the latest (maximum) observed time
for(i in 1:length(simobslist))
if (nrow(simobslist[[i]])>0)
{
simobscomb <- simobslist[[i]]
for (j in 1:nrow(simobscomb))
{
max.time <- max(simobscomb$time,na.rm=TRUE)
if (is.na(max.time)) simobscomb$tquart <- NA
else if (max.time == 0) simobscomb$tquart <- "1"
else if (!is.na(simobscomb$time[j]))
{
simobscomb$tquart[j] <- as.character(1 +
floor(simobscomb$time[j]/max.time/0.25))
simobscomb$tquart[simobscomb$tquart=="5"] <-
"4"
} else simobscomb$tquart[j] >- NA
}
simobslist[[i]] <- simobscomb
}Calculate the area under the curve (AUC)
for(i in 1:length(simobslist))
if (nrow(simobslist[[i]])>0)
{
simobscomb <- simobslist[[i]]
# Calculat the AUC with the trapezoidal rule:
if (nrow(simobscomb)>1)
{
for (k in 2:max(nrow(simobscomb)-1,2,na.rm=TRUE))
{
simobscomb$obsAUCtrap[1] <- 0
simobscomb$simAUCtrap[1] <- 0
if (min(simobscomb$time) <= (simobscomb$explen[1]*1.03) &
nrow(simobscomb) >=2)
{
simobscomb$obsAUCtrap[k] <- simobscomb$obsAUCtrap[k-1] +
0.5*(simobscomb$time[k] - simobscomb$time[k-1]) *
(simobscomb$obsconc[k] + simobscomb$obsconc[k-1])
simobscomb$simAUCtrap[k] <- simobscomb$simAUCtrap[k-1] +
0.5*(simobscomb$time[k]-simobscomb$time[k-1]) *
(simobscomb$simconc[k] + simobscomb$simconc[k-1])
} else {
simobscomb$obsAUCtrap <- 0
simobscomb$simAUCtrap <- 0
}
}
} else {
simobscomb$obsAUCtrap <- 0
simobscomb$simAUCtrap <- 0
}
simobscomb$AUCobs <- max(simobscomb$obsAUCtrap)
simobscomb$AUCsim <- max(simobscomb$simAUCtrap)
simobscomb$calcAUC <- max(simobscomb$AUC)
if (min(simobscomb$time) <= simobscomb$explen[1]*1.03)
{
simobscomb$Cmaxobs <- max(simobscomb$obsconc)
simobscomb$Cmaxsim <- max(simobscomb$simconc)
} else {
simobscomb$Cmaxobs <- 0
simobscomb$Cmaxsim <- 0
}
simobslist[[i]] <- simobscomb
}Calculate performance statistics
for(i in 1:length(simobslist))
if (nrow(simobslist[[i]])>0)
{
simobscomb <- simobslist[[i]]
unique_scenarios$RSQD[i] <- 1 - (
sum((simobscomb$obsconc - simobscomb$simconc)^2) /
sum((simobscomb$obsconc-mean(simobscomb$obsconc))^2)
)
unique_scenarios$RMSE[i] <-
sqrt(mean((simobscomb$simconc - simobscomb$obsconc)^2))
unique_scenarios$AIC[i] <-
nrow(simobscomb)*(
log(2*pi) + 1 +
log((sum((simobscomb$obsconc-simobscomb$simconc)^2) /
nrow(simobscomb)))
) + ((44+1)*2) #44 is the number of parameters from inhalation_inits.R
simobslist[[i]] <- simobscomb
}Combine individual studies into single table
obsvpredlist <- list()
for(i in 1:length(simobslist))
if (nrow(simobslist[[i]])>0)
{
simobscomb <- simobslist[[i]]
obsvpredlist[[i]] <- ggplot(simobscomb, aes(x = simconc, y = obsconc)) +
geom_point() +
geom_abline() +
xlab("Simulated Concentrations (uM)") +
ylab("Observed Concentrations (uM)") +
ggtitle(paste0(
unique_scenarios$PREFERRED_NAME[i],
" (",
unique_scenarios$CONC_SPECIES[i],
", ",
round(as.numeric(unique_scenarios$DOSE[i]), digits = 2),
unique_scenarios$DOSE_U[i],
" for ",
round(unique_scenarios$EXP_LENGTH[i]*24, digits = 2),
"h in ",
unique_scenarios$SAMPLING_MATRIX[i], ")")) +
theme_bw() +
theme(plot.title = element_text(face = 'bold', size = 20),
axis.title.x = element_text(face = 'bold', size = 20),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 16),
legend.title = element_text(face = 'bold', size = 16),
legend.text = element_text(face = 'bold',size = 14))
}for (i in 1:length(cvtlist))
{
cvtlist[[i]] <- cvtlist[[i]] +
geom_text(
x = Inf,
y = Inf,
hjust = 1.3,
vjust = 1.3,
# size = 6,
label = paste0(
"RMSE: ",
round(unique_scenarios$RMSE[i],digits = 2),
"\nAIC: ",
round(unique_scenarios$AIC[i],digits = 2)))# +
# theme(
# plot.title = element_text(face = 'bold', size = 15),
# axis.title.x = element_text(face = 'bold', size = 20),
# axis.text.x = element_text(size=16),
# axis.title.y = element_text(face = 'bold', size = 20),
# axis.text.y = element_text(size = 16),
# legend.title = element_text(face = 'bold', size = 16),
# legend.text = element_text(face = 'bold',size = 14))
}# Create pdfs of simulated concentration-time plots against observed c-t values
pdf("Linakis2020/simdataplots.pdf")
for (i in 1:length(cvtlist)) {
print(cvtlist[[i]])
}
dev.off()Combine obs. vs. pred. into single table:
simobsfull <- do.call("rbind",simobslist)
simobsfullrat <- subset(simobsfull, simobsfull$species == "Rat")
simobsfullhum <- subset(simobsfull, simobsfull$species == "Human")
unique_scenarios <- subset(unique_scenarios,!is.na(unique_scenarios$RSQD))The observations in simobsfull are in both uM and ppmv – standardize to uM
these.chems <- unique(subset(simobsfull,unit=="ppmv")$chem)
for (this.chem in these.chems)
{
# Use HTTK unit conversion:
this.factor <- convert_units(
input.units="ppmv", output.units="um", chem.name=this.chem)
# Scale the observation
simobsfull[simobsfull$chem==this.chem &
simobsfull$unit=="ppmv","obsconc"] <-
this.factor * simobsfull[
simobsfull$chem==this.chem &
simobsfull$unit=="ppmv","obsconc"]
# Scale the prediction
simobsfull[simobsfull$chem==this.chem &
simobsfull$unit=="ppmv","simconc"] <-
this.factor * simobsfull[
simobsfull$chem==this.chem &
simobsfull$unit=="ppmv","simconc"]
# Change the reported unit
simobsfull[simobsfull$chem==this.chem &
simobsfull$unit=="ppmv","unit"] <-
"uM"
}Other analytics including linear regression on overall concentration vs. time observed vs. predicted
table(unique_scenarios$CONC_SPECIES)
nrow(simobsfull) - nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,])
pmiss <- (nrow(simobsfull) -
nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,])) /
nrow(simobsfull) * 100
missdata <- (simobsfull[
is.na(simobsfull$simconc) |
simobsfull$simconc <= 0 |
simobsfull$obsconc <= 0,])
t0df <- simobsfull[simobsfull$obstime == 0,]
lmall <- lm(
#log transforms:
log10(simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0]) ~
#log transforms:
log10(simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0]))
#Linear binned 1
lmsub1 <- lm(
simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc < 0.1] ~
simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc < 0.1])
#Linear binned 2
lmsub2 <- lm(
simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc >= 0.1 &
simobsfull$obsconc < 10] ~
simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc >= 0.1 &
simobsfull$obsconc < 10])
#Linear binned 3
lmsub3 <- lm(
simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc >= 10] ~
simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc >= 10])
lmrat <- lm(
log10(simobsfullrat$obsconc[
!is.na(simobsfullrat$simconc) &
simobsfullrat$simconc > 0 &
simobsfullrat$obsconc > 0]) ~
log10(simobsfullrat$simconc[
!is.na(simobsfullrat$simconc) &
simobsfullrat$simconc > 0 &
simobsfullrat$obsconc > 0]))
unique(simobsfullrat$chem)
lmhum <- lm(
log10(simobsfullhum$obsconc[
!is.na(simobsfullhum$simconc) &
simobsfullhum$simconc > 0 &
simobsfullhum$obsconc > 0]) ~
log10(simobsfullhum$simconc[
!is.na(simobsfullhum$simconc) &
simobsfullhum$simconc > 0 &
simobsfullhum$obsconc > 0]))
unique(simobsfullhum$chem)
concregslope <- summary(lmall)$coef[2,1]
concregr2 <- summary(lmall)$r.squared
concregrmse <- sqrt(mean(lmall$residuals^2))
totalrmse <- sqrt(mean((
log10(simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0]) -
log10(simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0]))^2,
na.rm = TRUE))
totalmae <- mean(abs(
log10(simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0]) -
log10(simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0])),
na.rm = TRUE)
totalaic <- nrow(
simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc >
0,]) *
(log(2*pi) +
1 +
log((sum(
(simobsfull$obsconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0] -
simobsfull$simconc[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0])^2,
na.rm=TRUE) /
nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,])))) +
((44+1)*2) #44 is the number of parameters from inhalation_inits.R
mispred <- table(abs(
log10(simobsfull$simconc) -
log10(simobsfull$obsconc))>2 &
simobsfull$simconc>0)
mispred[2]
mispred[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc > 0,])*100
overpred <- table(
log10(simobsfull$simconc) -
log10(simobsfull$obsconc)>2 &
simobsfull$simconc>0)
overpred[2]
overpred[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc > 0,])*100
underpred <- table(
log10(simobsfull$obsconc) -
log10(simobsfull$simconc)>2 &
simobsfull$simconc>0)
underpred[2]
underpred[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc > 0,])*100
mispredhalf <- table(abs(
log10(simobsfull$simconc) -
log10(simobsfull$obsconc))>0.5 &
simobsfull$simconc>0)
mispredhalf[2]
mispredhalf[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc > 0,])*100
overpredhalf <- table(
log10(simobsfull$simconc) -
log10(simobsfull$obsconc)>0.5 &
simobsfull$simconc>0)
overpredhalf[2]
overpredhalf[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc >0 &
simobsfull$obsconc > 0,])*100
underpredhalf <- table(
log10(simobsfull$obsconc) -
log10(simobsfull$simconc)>0.5 &
simobsfull$simconc>0)
underpredhalf[2]
underpredhalf[2] / nrow(simobsfull[
!is.na(simobsfull$simconc) &
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,])*100
chemunderpred <- subset(simobsfull,
log10(simobsfull$simconc) -
log10(simobsfull$obsconc) < 0 &
simobsfull$simconc > 0)
table(chemunderpred$chemclass) / table(simobsfull$chemclass)*100fig2 <- ggplot(
data = simobsfull[
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,],
aes(x = log10(simconc), y = log10(obsconc))) +
geom_point(
color = ifelse(
abs(
log10(simobsfull[
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,]$simconc) -
log10(simobsfull[
simobsfull$simconc > 0 &
simobsfull$obsconc > 0,]$obsconc)) >2,
'red',
'black')) +
geom_abline() +
xlab("Log(Simulated Concentrations)") +
ylab("Log(Observed Concentrations)") +
theme_bw() +
geom_smooth(method = 'lm',se = FALSE, aes(color = 'Overall')) +
geom_smooth(method = 'lm', se = FALSE, aes(color = species)) +
geom_text(
x = Inf,
y = -Inf,
hjust = 1.05,
vjust = -0.15,
size = 8,
label = paste0(
# "Regression slope: ",
# round(summary(lmall)$coef[2,1],digits = 2),
"\nRegression R^2: ",
round(summary(lmall)$r.squared,digits = 2),
"\nRegression RMSE: ",
round(sqrt(mean(lmall$residuals^2)),digits = 2),
"\nRMSE (Identity): ",
round(totalrmse,digits = 2)
# "\n% Missing:",
# round(pmiss, digits = 2), "%")
)) +
#geom_text(
# data = simobsfull[
# abs(log10(simobsfull$simconc) - log10(simobsfull$obsconc))>7 &
# simobsfull$simconc>0 & simobsfull$obsconc > 0,],
# aes(label = paste(chem,species,matrix)),
## fontface = 'bold',
# check_overlap = TRUE,
# size = 3.5,
# hjust = 0.5,
# vjust = -0.8) +
scale_color_discrete(name = 'Species', breaks = c("Overall","Human","Rat")) +
theme(
plot.title = element_text(face = 'bold', size = 15),
axis.title.x = element_text(face = 'bold', size = 30),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face = 'bold', size = 30),
axis.text.y = element_text(size = 16),
legend.title = element_text(face = 'bold', size = 24),
legend.text = element_text(face = 'bold',size = 24))
fig2 #Display plot in R# Create data and run calculations for populating plots
cmaxfull <- subset(simobsfull, !duplicated(simobsfull$AUCobs) & simobsfull$Cmaxobs != 0)
cmaxobs <- cmaxfull$Cmaxobs
cmaxsim <- cmaxfull$Cmaxsim
cmaxobs <- cmaxobs[!is.nan(cmaxsim)]
cmaxsim <- cmaxsim[!is.nan(cmaxsim)]
cmaxsim[!is.finite(log10(cmaxsim))] <- NA
cmaxlm <- lm(log10(cmaxobs)~log10(cmaxsim), na.action = na.exclude)
cmaxvcbkg <- subset(cmaxfull,
paste(
cmaxfull$chem,
cmaxfull$dose,
cmaxfull$explen,
cmaxfull$species,
cmaxfull$matrix) %in%
paste(
t0df$chem,
t0df$dose,
t0df$explen,
t0df$species,
t0df$matrix))
cmaxvcbkg$cmaxcbkgratio <- cmaxvcbkg$Cmaxobs / cmaxvcbkg$obsconc
cmaxvcbkg$adjustedCmaxsim <- cmaxvcbkg$Cmaxsim - cmaxvcbkg$obsconc
aucfull <-subset(simobsfull,
!duplicated(simobsfull$AUCobs) &
simobsfull$AUCobs != 0)
aucobs <- aucfull$AUCobs
aucsim <- aucfull$AUCsim
aucobs <- aucobs[!is.nan(aucsim)]
aucsim <- aucsim[!is.nan(aucsim)]
aucsim[!is.finite(log10(aucsim))] <- NA
auclm <- lm(log10(aucobs)~log10(aucsim), na.action = na.exclude)
cmaxslope <- summary(cmaxlm)$coef[2,1]
cmaxrsq <- summary(cmaxlm)$r.squared
totalrmsecmax <- sqrt(mean((log10(cmaxfull$Cmaxsim) -
log10(cmaxfull$Cmaxobs))^2, na.rm = TRUE))
cmaxmiss <- nrow(cmaxfull[
abs(log10(cmaxfull$Cmaxsim) -
log10(cmaxfull$Cmaxobs)) > 1,])
cmaxmissp <- nrow(cmaxfull[
abs(log10(cmaxfull$Cmaxsim) -
log10(cmaxfull$Cmaxobs)) > 1,]) /
nrow(cmaxfull) * 100
cmaxmisschem <- table(cmaxfull[
abs(log10(cmaxfull$Cmaxsim) -
log10(cmaxfull$Cmaxobs)) > 1,]$chem)
aucslope <- summary(auclm)$coef[2,1]
aucrsq <- summary(auclm)$r.squared
totalrmseauc <- sqrt(mean((
log10(aucfull$AUCsim) -
log10(aucfull$AUCobs))^2, na.rm = TRUE))
aucmiss <- nrow(aucfull[
abs(log10(aucfull$AUCsim) -
log10(aucfull$AUCobs)) > 1,])
aucmissp <- nrow(aucfull[
abs(log10(aucfull$AUCsim) -
log10(aucfull$AUCobs)) > 1,]) /
nrow(aucfull) * 100
aucmisschem <- table(aucfull[
abs(log10(aucfull$AUCsim) -
log10(aucfull$AUCobs)) > 1,]$chem)cmaxp <- ggplot(data = cmaxfull, aes(x = log10(Cmaxsim), y = log10(Cmaxobs))) +
geom_point(color =
ifelse(abs(log10(cmaxfull$Cmaxsim) -log10(cmaxfull$Cmaxobs))>=1, "red","black")) +
geom_abline() +
xlab("Log(Simulated Max Concentration)") +
ylab("Log(Observed Max Concentration)") +
theme_bw() +
geom_smooth(method = 'lm', se = FALSE, aes(color = 'Overall')) +
geom_smooth(method = 'lm', se = FALSE, aes(color = species)) +
geom_text(x = Inf,
y = -Inf,
hjust = 1.05,
vjust = -0.15,
# size = 6,
label = paste0("Regression slope: ",
round(summary(cmaxlm)$coef[2,1],digits = 2),
"\nRegression R^2: ",
round(summary(cmaxlm)$r.squared,digits = 2))) +
geom_text_repel(
data = cmaxfull[
(log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))>=1 &
log10(cmaxfull$Cmaxsim) > 2,],
aes(label = paste(chem,species,matrix)),
force = 2,
# size = 5.3,
fontface = 'bold',
color = 'black',
hjust = -0.05,
vjust = 0.5) +
scale_y_continuous(lim = c (-1,5)) +
scale_x_continuous(lim = c(-1,5)) +
geom_text_repel(
data = cmaxfull[
(log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))>=1 &
log10(cmaxfull$Cmaxsim) <= 2,],
aes(label = paste(chem,species,matrix)),
nudge_x = 0.0,
nudge_y = -0.2,
force = 2,
# size = 5.3,
fontface = 'bold',
color = 'black',
hjust = -0.05,
vjust = 0.5) +
geom_text(
data = cmaxfull[
(log10(cmaxfull$Cmaxsim)-log10(cmaxfull$Cmaxobs))<=-1,],
aes(label = paste(chem,species,matrix)),
# size = 5.3,
fontface = 'bold',
color = 'black',
hjust = 0.5,
vjust = -0.7) +
scale_color_discrete(
name = 'Species',
breaks = c("Overall","Human","Rat")) #+
# theme(plot.title = element_text(face = 'bold', size = 10),
# axis.title.x = element_text(face = 'bold', size = 10),
# axis.text.x = element_text(size=8),
# axis.title.y = element_text(face = 'bold', size = 10),
# axis.text.y = element_text(size = 8),
# legend.title = element_text(face = 'bold', size = 8),
# legend.text = element_text(face = 'bold',size = 8))
cmaxp
aucp <- ggplot(
data = aucfull,
aes(x = log10(AUCsim), y = log10(AUCobs))) +
geom_point(color =
ifelse(abs(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))>=1, "red","black")) +
geom_abline() +
xlab("Log(Simulated AUC)") +
ylab("Log(Observed AUC)") +
theme_bw() +
geom_smooth(method = 'lm', se = FALSE, aes(color = "Overall")) +
geom_smooth(method = 'lm', se = FALSE, aes(color = species)) +
geom_text(
x = Inf,
y = -Inf,
hjust = 1.05,
vjust = -0.15,
# size = 6,
label = paste0(
"Regression slope: ",
round(summary(auclm)$coef[2,1],digits = 2),
"\nRegression R^2: ",
round(summary(auclm)$r.squared,digits = 2))) +
geom_text_repel(
data = aucfull[(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))>=1,],
aes(label = paste(chem,species,matrix)),
# size = 5.3,
fontface = 'bold',
color = 'black',
hjust = -0.05,
vjust = 0.5) +
scale_y_continuous(lim = c (-2,4)) +
scale_x_continuous(lim = c(-2,4)) +
geom_text(
data = aucfull[(log10(aucfull$AUCsim)-log10(aucfull$AUCobs))<=-1,],
aes(label = paste(chem,species,matrix)),
# size = 5.3,
fontface = 'bold',
color = 'black',
hjust = 0.5,
vjust = -0.8) +
scale_color_discrete(name = 'Species', breaks = c("Overall","Human","Rat")) #+
# theme(
# plot.title = element_text(face = 'bold', size = 15),
# axis.title.x = element_text(face = 'bold', size = 20),
# axis.text.x = element_text(size=16),
# axis.title.y = element_text(face = 'bold', size = 20),
# axis.text.y = element_text(size = 16),
# legend.title = element_text(face = 'bold', size = 16),
# legend.text = element_text(face = 'bold',size = 14))
aucpsimobsfull$aggscen <- as.factor(paste(simobsfull$chem,
simobsfull$species,
simobsfull$matrix))
chem.lm <- lm(
log10(simconc) - log10(obsconc) ~
aggscen,
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,])
chem.res <- resid(chem.lm)
# Number of observations per chemical class
numpt <- simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,] %>%
group_by(chemclass) %>% summarize(n = paste("n =", length(simconc)))
fig3 <- ggplot(
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,],
aes(x = aggscen, y = log10(simconc)-log10(obsconc), fill = chemclass)) +
geom_boxplot() +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 2, linetype = 2)+
geom_hline(yintercept = -2, linetype = 2)+
xlab("Exposure Scenario") +
ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
facet_wrap(vars(chemclass), scales = 'free_x', nrow = 1) + #35 by 12 for poster
theme_bw() +
geom_text(
data = numpt,
aes(x = Inf, y = -Inf, hjust = 1.05, vjust = -0.5, label = n),
size = 10,
colour = 'black',
inherit.aes = FALSE,
parse = FALSE) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5,size = 20, face = 'bold'),
strip.text.x = element_text(face = 'bold', size = 24),
legend.position = 'none',
axis.title.x = element_text(face = 'bold', size = 30),
axis.title.y = element_text(face = 'bold', size = 30),
axis.text.y = element_text(face = 'bold',size = 25, color = 'black'))
fig3figs1a <- ggplot(
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,],
aes(x = tquart, y = log10(simconc)-log10(obsconc))) +
geom_boxplot()+
geom_hline(yintercept = 0)+
geom_hline(yintercept = 2, linetype = 2)+
geom_hline(yintercept = -2, linetype = 2)+
xlab("\nTime Quartile\n") +
ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
theme_bw()+
theme(
axis.text.x = element_text(size = 20, face = 'bold'),
strip.text.x = element_text(face = 'bold', size = 20),
legend.position = 'none',
axis.title.x = element_text(face = 'bold', size = 20),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 20, face = 'bold'))
figs1a
figs1b <- ggplot(
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,],
aes(x = mw, y = log10(simconc)-log10(obsconc))) +
geom_point()+
geom_hline(yintercept = 0)+
geom_hline(yintercept = 2, linetype = 2)+
geom_hline(yintercept = -2, linetype = 2)+
xlab("\nMolecular Weight (g/mol)\n") +
ylab("\nLog(Simulated Concentration)-\nLog(Observed Concentration)\n") +
theme_bw()+
theme(
axis.text.x = element_text(size = 20, face = 'bold'),
strip.text.x = element_text(face = 'bold', size = 20),
legend.position = 'none',
axis.title.x = element_text(face = 'bold', size = 20),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 20, face = 'bold'))
figs1b
figs1c <- ggplot(
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,],
aes(x = logp, y = log10(simconc)-log10(obsconc))) +
geom_point()+
geom_hline(yintercept = 0)+
geom_hline(yintercept = 2, linetype = 2)+
geom_hline(yintercept = -2, linetype = 2)+
xlab("\nLog P") +
ylab("Log(Simulated Concentration)-\nLog(Observed Concentration)\n") +
theme_bw() +
theme(
axis.text.x = element_text(size = 20, face = 'bold'),
strip.text.x = element_text(face = 'bold', size = 20),
legend.position = 'none',
axis.title.x = element_text(face = 'bold', size = 20),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 20, face = 'bold'))
figs1c
figs1d <- ggplot(
data = simobsfull[simobsfull$simconc >0 & simobsfull$obsconc > 0,],
aes(x = sol, y = log10(simconc)-log10(obsconc))) +
geom_point()+
geom_hline(yintercept = 0)+
geom_hline(yintercept = 2, linetype = 2)+
geom_hline(yintercept = -2, linetype = 2)+
xlab("\nSolubility (mol/L)") +
ylab("\nLog(Simulated Concentration)-\nLog(Observed Concentration)\n") +
scale_x_log10()+
theme_bw() +
theme(
axis.text.x = element_text(size = 20, face = 'bold'),
strip.text.x = element_text(face = 'bold', size = 20),
legend.position = 'none',
axis.title.x = element_text(face = 'bold', size = 20),
axis.title.y = element_text(face = 'bold', size = 20),
axis.text.y = element_text(size = 20, face = 'bold'))
figs1dsenschem <- list()
sensslope <- list()
sensrsq <- list()
sensregrmse <- list()
senstotalrmse <- list()
senspmiss <- list()
senscmaxslope <- list()
senscmaxrsq <- list()
senstotalrmsecmax <- list()
sensaucslope <- list()
sensaucrsq <- list()
senstotalrmseauc <- list()
for (i in 1:nrow(simobsfull))
{
simobsfullsens <- subset(simobsfull,simobsfull$chem != simobsfull$chem[i])
senschem[i] <- as.character(simobsfull$chem[i])
senslm <- lm(
log10(simobsfullsens$obsconc[
!is.na(simobsfullsens$simconc) &
simobsfullsens$simconc > 0 &
simobsfullsens$obsconc > 0])
log10(simobsfullsens$simconc[
!is.na(simobsfullsens$simconc) &
simobsfullsens$simconc >0 &
simobsfullsens$obsconc > 0]))
sensslope[i] <- round(summary(senslm)$coef[2,1],digits = 2)
sensrsq[i] <- round(summary(senslm)$r.squared,digits = 2)
sensregrmse[i] <- round(sqrt(mean(lm$residuals^2)),digits = 2)
senstotalrmse[i] <- round(sqrt(mean((
log10(simobsfullsens$simconc[
!is.na(simobsfullsens$simconc) &
simobsfullsens$simconc >0 &
simobsfullsens$obsconc > 0]) -
log10(simobsfullsens$obsconc[
!is.na(simobsfullsens$simconc) &
simobsfullsens$simconc >0 &
simobsfullsens$obsconc > 0]))^2,
na.rm = TRUE)), digits = 2)
senspmiss[i] <- round((nrow(simobsfullsens) -
nrow(simobsfullsens[
!is.na(simobsfullsens$simconc) &
simobsfullsens$simconc >0 &
simobsfullsens$obsconc > 0,])) / nrow(simobsfullsens) * 100,
digits = 2)
senscmaxfull <- subset(simobsfullsens, !duplicated(simobsfullsens$Cmaxobs))
senscmaxlm <- lm(
log10(senscmaxfull$Cmaxobs[senscmaxfull$Cmaxobs>0]) ~
log10(senscmaxfull$Cmaxsim[senscmaxfull$Cmaxobs>0]),
na.action = na.exclude)
sensaucfull <-subset(simobsfullsens, !duplicated(simobsfullsens$AUCobs))
sensauclm <- lm(
log10(aucfull$AUCobs[aucfull$AUCobs>0]) ~
log10(aucfull$AUCsim[aucfull$AUCobs>0]),
na.action = na.exclude)
senscmaxslope[i] <- round(summary(senscmaxlm)$coef[2,1],digits = 2)
senscmaxrsq[i] <- round(summary(senscmaxlm)$r.squared,digits = 2)
senstotalrmsecmax[i] <- sqrt(mean((log10(senscmaxfull$Cmaxsim[senscmaxfull$Cmaxobs>0]) - log10(senscmaxfull$Cmaxobs[senscmaxfull$Cmaxobs>0]))^2, na.rm = TRUE))
sensaucslope[i] <- round(summary(sensauclm)$coef[2,1],digits = 2)
sensaucrsq[i] <- round(summary(sensauclm)$r.squared,digits = 2)
senstotalrmseauc[i] <- sqrt(mean((log10(sensaucfull$AUCsim[sensaucfull$AUCobs>0]) - log10(sensaucfull$AUCobs[sensaucfull$AUCobs>0]))^2, na.rm = TRUE))
}
sensitivitydf <- data.frame(Chemical <- as.character(senschem),
sensSlope <- as.numeric(sensslope),
sensRsq <- as.numeric(sensrsq),
sensRegrmse <- as.numeric(sensregrmse),
sensTotrmse <- as.numeric(senstotalrmse),
sensPmiss <- as.numeric(senspmiss),
sensCmaxslope <- as.numeric(senscmaxslope),
sensCmaxrsq <- as.numeric(senscmaxrsq),
sensCmaxrmse <- as.numeric(senstotalrmsecmax),
sensAUCslope <- as.numeric(sensaucslope),
sensAUCrsq <- as.numeric(sensaucrsq),
sensAUCrmse <- as.numeric(senstotalrmseauc),
stringsAsFactors=FALSE)
sensitivitydf <- subset(sensitivitydf,!duplicated(sensitivitydf$Chemical....as.character.senschem.))
names(sensitivitydf) <- c('Chemical Dropped','Regression Slope','Regression R^2','Regression RMSE','Orthogonal RMSE', 'Percent Data Censored','Cmax Regression Slope','Cmax Regression R^2','Cmax Orthogonal RMSE','AUC Regression Slope','AUC Regression R^2','AUC Orthogonal RMSE')
notdropped <- c('None',concregslope,concregr2,concregrmse,totalrmse,pmiss,cmaxslope,cmaxrsq,totalrmsecmax,aucslope,aucrsq,totalrmseauc)
sensitivitydf <- rbind(notdropped, sensitivitydf)
sensitivitydf[,-1] <- sapply(sensitivitydf[,-1],as.numeric)
sensitivitydf[,-1] <- round(sensitivitydf[,-1],2)
# Clean up and write file
rm(chem.lm, obvpredplot, p, pdata1, plot.data, plots, relconc, sensaucfull, sensauclm, sensaucrsq, sensaucslope, senschem, senscmaxfull, senscmaxlm, senscmaxrsq, senscmaxslope, senslm, senspmiss, sensregrmse, sensrsq, sensslope, senstotalrmse, senstotalrmseauc, senstotalrmsecmax, solve, AUCrmse, AUCrsq, AUCslope, chem.res, Chemical, Cmaxrmse, Cmaxrsq, Cmaxslope, colors, count, i, j, k, met_col, name, name1, Pmiss, Regrmse, Rsq, Slope, rem, Totrmse)
write.csv(sensitivitydf, 'supptab2.csv',row.names = FALSE)supptab1 <- subset(unique_scenarios, !duplicated(unique_scenarios$PREFERRED_NAME) | !duplicated(unique_scenarios$SOURCE_CVT) | !duplicated(unique_scenarios$CONC_SPECIES))
for(i in 1:nrow(supptab1)){
tryCatch({
supptab1$Metabolism_Source[i] <- met_data$SOURCE_MET[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Log_P[i] <- met_data$OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Solubility[i] <- met_data$WATER_SOLUBILITY_MOL.L_OPERA_PRED[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Blood_Air_Partition_Coefficient[i] <- met_data$CALC_PBA[met_data$DTXSID %in% supptab1$DTXSID[i]& met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Chem_Class[i] <- met_data$CHEM_CLASS[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Species[i] <- met_data$SPECIES[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Vmax[i] <- met_data$VMAX[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
supptab1$Km[i] <- met_data$KM[met_data$DTXSID %in% supptab1$DTXSID[i] & met_data$SPECIES %in% supptab1$CONC_SPECIES[i]]
}, error = function(e){})
}
supptab1 <- supptab1[c('PREFERRED_NAME','DTXSID','CASRN','Chem_Class','AVERAGE_MASS','Log_P','Solubility','Blood_Air_Partition_Coefficient','Species','Vmax','Km','Metabolism_Source','SOURCE_CVT')]
names(supptab1) <- c('Chemical','DTXSID','CASRN','CAMEO Chemical Class','Molecular Weight (g/mol)','Log P','Solubility (mol/L)','Blood Air Partition Coefficient','Available Species Data','Vmax (pmol/min/10^6 cells)','KM (uM)','Metabolism Data Source','Concentration-Time Data Source')
write.csv(supptab1, "supptab1.csv", row.names = FALSE)