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
)
conc_obj## Formula for concentration:
## DV ~ NTIME | DOSEC + DOSE + ID
## Data are dense PK.
## With 48 subjects defined in the 'ID' column.
## Nominal time column is not specified.
##
## First 6 rows of concentration data:
## ID DAY NTIME TIME DV DOSE DOSEC exclude volume duration
## 1 1 0.0 0.0 0.0000 10 10 mg <NA> NA 0
## 1 1 0.5 0.5 3.0364 10 10 mg <NA> NA 0
## 1 1 1.0 1.0 4.5179 10 10 mg <NA> NA 0
## 1 1 1.5 1.5 5.7003 10 10 mg <NA> NA 0
## 1 1 2.0 2.0 6.8785 10 10 mg <NA> NA 0
## 1 1 4.0 4.0 8.8699 10 10 mg <NA> NA 0## 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
)
dose_obj## Formula for dosing:
## DOSE ~ NTIME | DOSEC + ID
## Nominal time column is not specified.
##
## First 6 rows of dosing data:
## ID DAY NTIME TIME DV DOSE DOSEC exclude route duration
## 1 1 0 0 0 10 10 mg <NA> extravascular 0
## 2 1 0 0 0 25 25 mg <NA> extravascular 0
## 3 1 0 0 0 50 50 mg <NA> extravascular 0
## 4 1 0 0 0 100 100 mg <NA> extravascular 0
## 5 1 0 0 0 10 10 mg <NA> extravascular 0
## 6 1 0 0 0 25 25 mg <NA> extravascular 0## 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)
results_obj## $result
## # A tibble: 672 x 8
## DOSEC DOSE ID start end PPTESTCD PPORRES exclude
## <fct> <int> <int> <dbl> <dbl> <chr> <dbl> <chr>
## 1 10 mg 10 1 0 24 auclast 120. <NA>
## 2 10 mg 10 1 0 Inf cmax 8.87 <NA>
## 3 10 mg 10 1 0 Inf tmax 4 <NA>
## 4 10 mg 10 1 0 Inf tlast 24 <NA>
## 5 10 mg 10 1 0 Inf clast.obs 3.20 <NA>
## 6 10 mg 10 1 0 Inf lambda.z 0.0414 <NA>
## 7 10 mg 10 1 0 Inf r.squared 0.993 <NA>
## 8 10 mg 10 1 0 Inf adj.r.squared 0.986 <NA>
## 9 10 mg 10 1 0 Inf lambda.z.time.first 10 <NA>
## 10 10 mg 10 1 0 Inf lambda.z.n.points 3 <NA>
## # i 662 more rows
##
## $data
## Formula for concentration:
## DV ~ NTIME | DOSEC + DOSE + ID
## Data are dense PK.
## With 48 subjects defined in the 'ID' column.
## Nominal time column is not specified.
##
## First 6 rows of concentration data:
## ID DAY NTIME TIME DV DOSE DOSEC exclude volume duration
## 1 1 0.0 0.0 0.0000 10 10 mg <NA> NA 0
## 1 1 0.5 0.5 3.0364 10 10 mg <NA> NA 0
## 1 1 1.0 1.0 4.5179 10 10 mg <NA> NA 0
## 1 1 1.5 1.5 5.7003 10 10 mg <NA> NA 0
## 1 1 2.0 2.0 6.8785 10 10 mg <NA> NA 0
## 1 1 4.0 4.0 8.8699 10 10 mg <NA> NA 0
## Formula for dosing:
## DOSE ~ NTIME | DOSEC + ID
## Nominal time column is not specified.
##
## First 6 rows of dosing data:
## ID DAY NTIME TIME DV DOSE DOSEC exclude route duration
## 1 1 0 0 0 10 10 mg <NA> extravascular 0
## 2 1 0 0 0 25 25 mg <NA> extravascular 0
## 3 1 0 0 0 50 50 mg <NA> extravascular 0
## 4 1 0 0 0 100 100 mg <NA> extravascular 0
## 5 1 0 0 0 10 10 mg <NA> extravascular 0
## 6 1 0 0 0 25 25 mg <NA> extravascular 0
##
## With 96 rows of interval specifications.
## With imputation: NA
## Options changed from default are:
## $adj.r.squared.factor
## [1] 1e-04
##
## $max.missing
## [1] 0.5
##
## $auc.method
## [1] "lin up/log down"
##
## $conc.na
## [1] "drop"
##
## $conc.blq
## $conc.blq$first
## [1] "keep"
##
## $conc.blq$middle
## [1] "drop"
##
## $conc.blq$last
## [1] "keep"
##
##
## $first.tmax
## [1] TRUE
##
## $allow.tmax.in.half.life
## [1] FALSE
##
## $keep_interval_cols
## NULL
##
## $min.hl.points
## [1] 3
##
## $min.span.ratio
## [1] 2
##
## $max.aucinf.pext
## [1] 20
##
## $min.hl.r.squared
## [1] 0.9
##
## $progress
## [1] TRUE
##
## $tau.choices
## [1] NA
##
## $single.dose.aucs
## start end auclast aucall aumclast aumcall aucint.last aucint.last.dose
## 1 0 24 TRUE FALSE FALSE FALSE FALSE FALSE
## 2 0 Inf FALSE FALSE FALSE FALSE FALSE FALSE
## aucint.all aucint.all.dose c0 cmax cmin tmax tlast tfirst clast.obs
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## cl.last cl.all f mrt.last mrt.iv.last vss.last vss.iv.last cav
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## cav.int.last cav.int.all ctrough cstart ptr tlag deg.fluc swing ceoi
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucabove.predose.all aucabove.trough.all count_conc totdose ae clr.last
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## clr.obs clr.pred fe sparse_auclast sparse_auc_se sparse_auc_df time_above
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## aucivlast aucivall aucivint.last aucivint.all aucivpbextlast aucivpbextall
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucivpbextint.last aucivpbextint.all half.life r.squared adj.r.squared
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE TRUE FALSE FALSE
## lambda.z lambda.z.time.first lambda.z.n.points clast.pred span.ratio
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## thalf.eff.last thalf.eff.iv.last kel.last kel.iv.last aucinf.obs aucinf.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE TRUE FALSE
## aumcinf.obs aumcinf.pred aucint.inf.obs aucint.inf.obs.dose aucint.inf.pred
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## aucint.inf.pred.dose aucivinf.obs aucivinf.pred aucivpbextinf.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## aucivpbextinf.pred aucpext.obs aucpext.pred cl.obs cl.pred mrt.obs mrt.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## mrt.iv.obs mrt.iv.pred mrt.md.obs mrt.md.pred vz.obs vz.pred vss.obs vss.pred
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## vss.iv.obs vss.iv.pred vss.md.obs vss.md.pred cav.int.inf.obs
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## cav.int.inf.pred thalf.eff.obs thalf.eff.pred thalf.eff.iv.obs
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## thalf.eff.iv.pred kel.obs kel.pred kel.iv.obs kel.iv.pred auclast.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aucall.dn aucinf.obs.dn aucinf.pred.dn aumclast.dn aumcall.dn aumcinf.obs.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE
## aumcinf.pred.dn cmax.dn cmin.dn clast.obs.dn clast.pred.dn cav.dn ctrough.dn
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $columns
## $columns$exclude
## [1] "exclude"
##
##
## attr(,"class")
## [1] "PKNCAresults" "list"
## attr(,"provenance")
## Provenance hash fd920ce893dea3446fbd9fd1df7e7d1d generated on 2025-01-26 20:14:38.821369 with R version 4.4.1 (2024-06-14 ucrt).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)")