Heller PHAR7383 wk1 In Class pknca_01_18_25 title: “NCA” output: html_document — #libraries

rm(list=ls())
library(ggplot2)
library(dplyr)
library(PKNCA)

## Warning: package 'PKNCA' was built under R version 4.4.2

#theme

my_theme<-function(x){theme_bw()+
    theme(text = element_text(size=20))+
    theme(axis.line.y = element_line(size = 2.0))+
    theme(axis.line.x = element_line(size = 2.0))+
    theme(axis.ticks = element_line(size = 1.5,colour="black"))+
    theme(axis.ticks.length=  unit(0.45, "cm"))+
    theme(axis.title.y =element_text(vjust=1.2))+
    theme(axis.title.x =element_text(vjust=-0.2))+
    theme(axis.text=element_text(colour="black"))+
    theme(panel.background = element_rect(fill ="white"))}

#multiple dosing, oral dosing

#pkncadt<-read.csv("C:\\Ayyappa\\PHAR7383\\week1\\pkncadt.csv",stringsAsFactors = F)
pkncadt<-read.csv("C:\\Heller\\PHAR7383\\week1\\pkncadt.csv",stringsAsFactors = F)
pkncadt$DOSEC<-factor(pkncadt$DOSE,labels = c("10 mg","25 mg","50 mg","100 mg"))
pkncadtsum<-pkncadt%>%
  group_by(DOSEC,DAY,NTIME,TIME)%>%
  summarise(cmean=mean(DV),stdev=sd(DV))

#multiple dosing, oral dosing, plots

#Population Plot
ggplot(data=pkncadt,aes(NTIME,DV,color=DOSEC,group=ID))+
geom_line(size=0.5)+
scale_x_continuous(limits = c(0,24),breaks = c(0,1,2,4,6,8,12,24))+
theme_bw()+
my_theme()+
labs(x="Time after dose (hour)",y="Plasma concentration (ng/ml)")+
facet_wrap(vars(DAY))

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## i Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## i Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

#average plot with standard deviation (+/-1SD)
ggplot(data=pkncadtsum,aes(NTIME,cmean,color=DOSEC))+
geom_line(size=0.5)+
geom_point(size=2)+
scale_x_continuous(limits = c(0,24),breaks = c(0,1,2,4,6,8,12,24))+
geom_errorbar(aes(ymin=cmean-stdev, ymax=cmean+stdev), width=.2)+
theme_bw()+
my_theme()+
labs(x="Time after dose (hour)",y="Plasma concentration (ng/ml)")+
facet_wrap(vars(DAY))

#Noncompartmental analysis- Day 1 Data (code modified from: https://cran.r-project.org/web/packages/PKNCA/vignettes/Introduction-and-Usage.html)

## Load the PK concentration data
pkday1 <-pkncadt%>%filter(DAY==1)
## Generate the dosing data
doseday1 <- pkday1%>%filter(NTIME==0)

## Create a concentration object specifying the concentration, time, and
## subject columns.  (Note that any number of grouping levels is
## supported; you are not restricted to just grouping by subject.)
conc_obj <-
  PKNCAconc(
    pkday1,
    DV~NTIME|DOSEC+DOSE+ID
  )
## Create a dosing object specifying the dose, time, and subject
## columns.  (Note that the grouping factors should be the same as or a
## subset of the grouping factors for concentration, and the grouping
## columns must have the same names between concentration and dose
## objects.)
dose_obj <-
  PKNCAdose(
    doseday1,
    DOSE~NTIME|DOSEC+ID
  )
## Combine the concentration and dosing information both to
## automatically define the intervals for NCA calculation and provide
## doses for calculations requiring dose.
data_obj <- PKNCAdata(conc_obj, dose_obj)

## Calculate the NCA parameters
results_obj <- pk.nca(data_obj)

Processing of Day1 results

#day1 results
pkday1_res<-results_obj$result
pkday1_res_sum<-pkday1_res%>%group_by(DOSEC,PPTESTCD)%>%filter(PPTESTCD=="auclast"|PPTESTCD=="cmax"|PPTESTCD=="tmax")%>%
  summarise(Average=mean(PPORRES),CV=sd(PPORRES)*100/mean(PPORRES))

## `summarise()` has grouped output by 'DOSEC'. You can override using the
## `.groups` argument.

#plotting AUClast
ggplot(pkday1_res%>%filter(PPTESTCD=="auclast"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUClast (ng*hr/ml)")

#plotting AUCinf
ggplot(pkday1_res%>%filter(PPTESTCD=="aucinf.obs"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUCinf (ng*hr/ml)")

#plotting AUClast/DOSE
ggplot(pkday1_res%>%filter(PPTESTCD=="auclast"), aes(x=DOSEC, y=PPORRES/DOSE)) +
  geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUClast (ng*hr/ml)")

#plotting AUCinf/DOSE
ggplot(pkday1_res%>%filter(PPTESTCD=="aucinf.obs"), aes(x=DOSEC, y=PPORRES/DOSE)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUCinf (ng*hr/ml)")

#plotting AUClast/DOSE
#plotting cmax/DOSE
#Plotting cmax
ggplot(pkday1_res%>%filter(PPTESTCD=="cmax"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="Cmax (ng/ml)")

#plotting cmax/DOSE
ggplot(pkday1_res%>%filter(PPTESTCD=="cmax"), aes(x=DOSEC, y=PPORRES/DOSE)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="Cmax (ng/ml)")

#Noncompartmental analysis- Day 14 Data (code modified from: see above ##note by AH See https://cran.r-project.org/web/packages/PKNCA/vignettes/v30-training-session.html#1

## Load the PK concentration data
pkday14 <-pkncadt%>%filter(DAY==14)
## Generate the dosing data
doseday14 <- pkday14%>%filter(NTIME==0)
## Create a concentration object specifying the concentration, time, and
## subject columns.  (Note that any number of grouping levels is
## supported; you are not restricted to just grouping by subject.)
conc14_obj <-
  PKNCAconc(
    pkday14,
    DV~NTIME|DOSEC+DOSE+ID
  )
## Create a dosing object specifying the dose, time, and subject
## columns.  (Note that the grouping factors should be the same as or a
## subset of the grouping factors for concentration, and the grouping
## columns must have the same names between concentration and dose
## objects.)
dose14_obj <-
  PKNCAdose(
    doseday14,
    DOSE~NTIME|DOSEC+ID
  )
## Combine the concentration and dosing information both to
## automatically define the intervals for NCA calculation and provide
## doses for calculations requiring dose.
data14_obj <- PKNCAdata(conc14_obj, dose14_obj)

## Calculate the NCA parameters
results14_obj <- pk.nca(data14_obj)

##extracting parameters from the list
pkday14_res<-results14_obj$result
pkday14_res_sum<-pkday14_res%>%group_by(DOSEC,PPTESTCD)%>%filter(PPTESTCD=="auclast"|PPTESTCD=="cmax"|PPTESTCD=="tmax")%>%
  summarise(Average=mean(PPORRES),CV=sd(PPORRES)*100/mean(PPORRES))

## `summarise()` has grouped output by 'DOSEC'. You can override using the
## `.groups` argument.

Processing of Day14 results

#plotting AUClast
ggplot(pkday14_res%>%filter(PPTESTCD=="auclast"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUClast (ng*hr/ml)")

#plotting AUCinf
ggplot(pkday14_res%>%filter(PPTESTCD=="aucinf.obs"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUCinf (ng*hr/ml)")

#plotting AUClast/DOSE
ggplot(pkday14_res%>%filter(PPTESTCD=="auclast"), aes(x=factor(DOSE), y=PPORRES/DOSE)) +
  geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUClast (ng*hr/ml)")

#plotting AUCinf/DOSE
ggplot(pkday14_res%>%filter(PPTESTCD=="aucinf.obs"), aes(x=DOSEC, y=PPORRES/DOSE)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="AUCinf (ng*hr/ml)")

#plotting AUClast/DOSE AH - not correct
#plotting cmax
ggplot(pkday14_res%>%filter(PPTESTCD=="cmax"), aes(x=DOSEC, y=PPORRES)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="Cmax (ng/ml)")

#plotting cmax/DOSE
ggplot(pkday14_res%>%filter(PPTESTCD=="cmax"), aes(x=DOSEC, y=PPORRES/DOSE)) +
geom_boxplot()+
my_theme()+
labs(x="Dose (mg)",y="Cmax (ng/ml)")