PK Model in Pediatric Patients: Assessing the Impact of a Multivariate Correlated Distribution of Covariates on PK Exposures

In this vignette we illustrate how to simulate joint effects of covariates in a pediatric population between 2 to 6 years old. Since Age and Weight in kids are highly correlated, we will not simulate varying one covariate at a time rather we will incorporate a distribution of realistic Age/Weight pairs. This approach is recommended when a database of realistic covariates distribution is available.

Specifying A Pediatric Simulation Model

Here we have a simple one-compartment PK model with first-order absorption where clearance and volume are allometrically scaled. The reference subject is a 4 year old female with a weight of 15.9 kg.
First, we plot a typical PK profile with between subject variability (BSV).

pedpkmodelcov <- '
$PARAM @annotated
KA    : 0.5   : Absorption rate constant Ka (1/h)
CL    : 4     : Clearance CL (L/h)
V     : 10    : Central volume Vc (L)
CLWT  : 0.75  : Weight on CL (ref. 22.5 kg)
VWT   : 1     : Weight on V (ref. 22.5 kg)

$PARAM @annotated // reference values for covariate
WT    : 15.8  : Weight (kg)
SEX   : 0     : Sex (0=Female, 1=Male)
AGE   : 4     : Age (years)

$CMT GUT CENT
$MAIN
double CLi = CL *
    pow((WT/15.8), CLWT)*exp(ETA(1)); 
double Vi = V *
    pow((WT/15.8), VWT)*exp(ETA(2));  

double KAi = KA;
double Keli = CLi/Vi;

$OMEGA
0.09 
0.01 0.09 

$ODE
dxdt_GUT  = -KAi*GUT;
dxdt_CENT =  KAi*GUT-Keli*CENT;

$TABLE
double CP   = CENT/ Vi;
$CAPTURE CP KAi CLi Vi WT SEX AGE 
'
pedmodsim <- mcode("pedpkmodelcov", pedpkmodelcov)
partab <- setDT(pedmodsim@annot$data)[block=="PARAM", .(name, descr, unit)]
partab <- merge(partab, melt(setDT(pedmodsim@param@data), meas=patterns("*"), var="name"))
knitr::kable(partab)
name descr unit value
AGE Age years 4.00
CL Clearance CL L/h 4.00
CLWT Weight on CL ref. 22.5 kg 0.75
KA Absorption rate constant Ka 1/h 0.50
SEX Sex 0=Female, 1=Male 0.00
V Central volume Vc L 10.00
VWT Weight on V ref. 22.5 kg 1.00
WT Weight kg 15.80

idata <- data.table(
  ID = 1:nsubj,
  WT = c(rep(15.8,nsubj/2),
         rep(16.2,nsubj/2)),#from Nhanes at 4 years female and male
  AGE = 4,
  SEX = c(rep(0,nsubj/2),rep(1,nsubj/2))
)
ev1 <- ev(time = 0, amt = 100, cmt = 1)
data.dose <- ev(ev1)
data.dose <- setDT(as.data.frame(data.dose))
data.all <- data.table(idata, data.dose)

set.seed(678549)
outputsim <- pedmodsim %>%
  data_set(data.all) %>%
  mrgsim(end = 24, delta = 0.25)%>%
  as.data.frame %>%
  as.data.table

outputsim$SEX <- as.factor(outputsim$SEX)
outputsim$SEX <- factor(outputsim$SEX, labels=c("Girls","Boys"))

p1 <- ggplot(data = outputsim[outputsim$SEX=="Girls",],
       aes(time, CP, group = ID)) +
  geom_line(alpha = 0.2, size = 0.1) +
  facet_grid(AGE ~ WT+SEX,
             labeller = label_both) +
  scale_y_log10() +
  labs(y = expression(Log[10]~~Plasma~~Concentrations), color = "Sex", x = "Time (h)")
p1

PK Parameters and Associated BSV ranges

Second, we compute the PK parameters AUC and Cmax, standardize and compute between subject variability ranges.

derive.exposure <- function(time, CP) {
  n <- length(time)
  x <- c(
    Cmax = max(CP),
    AUC = sum(diff(time) * (CP[-1] + CP[-n])) / 2
  )
  data.table(paramname=names(x), paramvalue=x)
}
refbsv <- outputsim[, derive.exposure(time, CP), by=.(ID, WT, SEX, AGE)]
refbsv[, stdparamvalue := paramvalue/median(paramvalue), by=list(SEX,paramname)]

bsvranges <- refbsv[,list(
    P05 = quantile(stdparamvalue, 0.05),
    P25 = quantile(stdparamvalue, 0.25),
    P50 = quantile(stdparamvalue, 0.5),
    P75 = quantile(stdparamvalue, 0.75),
    P95 = quantile(stdparamvalue, 0.95)), by = list(SEX,paramname)]
bsvranges
SEX paramname P05 P25 P50 P75 P95
Girls Cmax 0.7852997 0.9212879 1 1.122921 1.301545
Girls AUC 0.6185701 0.8328135 1 1.245339 1.744747
Boys Cmax 0.7742915 0.9104196 1 1.093831 1.251045
Boys AUC 0.6312985 0.8298309 1 1.209752 1.607079

yvar_names <- c(
  'AUC'="AUC",
  'Cmax'="Cmax"
)
p4 <- ggplot(refbsv[SEX=="Girls",], aes(
        x      = stdparamvalue,
        y      = paramname,
        fill   = factor(..quantile..),
        height = ..ndensity..)) +
  facet_grid(paramname+AGE~WT+SEX , scales="free_y",
             labeller=labeller(paramname=yvar_names,
                               .cols =label_both,
                                AGE = label_both)
,switch="y")+
  stat_density_ridges(
    geom="density_ridges_gradient", calc_ecdf=TRUE,
    quantile_lines=TRUE, rel_min_height=0.001, scale=0.9,
    quantiles=c(0.05, 0.25, 0.5, 0.75, 0.95)) +
  scale_fill_manual(
    name="Probability",
    values=c("white", "#FF000050", "#FF0000A0", 
             "#FF0000A0", "#FF000050", "white"),
    labels = c("(0, 0.05]", "(0.05, 0.25]",
             "(0.25, 0.5]", "(0.5, 0.75]",
             "(0.75, 0.95]", "(0.95, 1]")) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.y     = element_blank(),
    axis.ticks.y    = element_blank(),
    axis.title.y    = element_blank()) +
  labs(x="Standardized PK Parameters", y="") +
  scale_x_log10() +
  coord_cartesian(expand=FALSE)

p4

Simulating Age/Weight Pairs Using NHANES LMS Values

The NHANES website provides a csv file containing the smoothed growth charts distribution parameters at specific ages for boys and girls. The gamlss.dist::rBCCG function is used to show how we can use these parameters to generate a realistic pediatric Age/Weight/Sex distribution.

wtage <- read.csv (url("https://www.cdc.gov/growthcharts/data/zscore/wtage.csv"))
#boys 1 and girls 2 in this file
wtage<-  wtage[wtage$Agemos<=6*12,] # keeps only 2 to 6 years

wtage[wtage$Agemos>=4*12-1&wtage$Agemos<=4*12 +1,] %>%
  group_by(Sex) %>% 
  summarize(Median=median(M))
Sex Median
1 16.23112
2 15.79320

nweightsperage <- 50 # simulate 50 kid at each age/sex
simwtageoutput <- data.frame(matrix(NA, nrow = nrow(wtage),ncol = nweightsperage))
names(simwtageoutput) <- paste0("Var", 1:nweightsperage)
set.seed(209321)
for (i in 1:nrow(wtage)) {#
  simpoints <- gamlss.dist::rBCCG(nweightsperage,
                                  mu = wtage[i,"M"],
                                  sigma =  wtage[i,"S"],
                                  nu = wtage[i,"L"])
simwtageoutput[i, ] <- simpoints
}
simwtageoutput$Agemos  <- wtage$Agemos
simwtageoutput$AgeY  <- wtage$Agemos/12
simwtageoutput$Sex <- ifelse( wtage$Sex==2,0,1)#recode girls to 0, boys to 1

simwtageoutput <- tidyr::gather(simwtageoutput,age,Weight,
                                paste0("Var", 1:nweightsperage))
simwtageoutput$age <- NULL
simwtageoutput$SEXLABEL <- factor(simwtageoutput$Sex,labels=c("Girls","Boys"))
wtvsageplot<- ggplot(simwtageoutput,aes(AgeY,Weight,color=SEXLABEL))+
  geom_point(alpha=0.2,size=1.5)+
  facet_grid(~SEXLABEL)+
  labs(y="Weight (kg)", x= "Age (years)",col="")
wtvsageplot

Simulation with the Multivariate Realistic Distribution

The section above generated 4900 Age/Weight/Sex distribution values that we will use for the simulation. We will remove the between subject variability to focus on the covariate effects. We show a plot of the PK profiles and the normalized PK parameters versus Age and versus Weight. Since we are dealing with a distribution and not specific covariate values we split into quartile ranges of the covariate distribution.

idata <- as.data.frame(simwtageoutput)
names(idata) <- c("Agemos","AGE","SEX","WT","SEXLABEL")
ev1 <- ev(time=0,amt=100, cmt=1)
data.dose <- ev(ev1)
data.dose<-as.data.frame(data.dose)
data.all<-merge(idata,data.dose)
data.all$ID <- 1: nrow(data.all)
outcovcomb<- pedmodsim %>%
  data_set(data.all) %>%
  zero_re() %>% 
  mrgsim(end=24, delta=1)
outcovcomb<-as.data.frame(outcovcomb)
outcovcomb <- outcovcomb %>% 
  arrange(ID,time,SEX,AGE,WT)
outcovcomb$SEX <- as.factor(outcovcomb$SEX)
outcovcomb$SEX <- factor(outcovcomb$SEX,labels=c("Girls","Boys"))

f <- function(x, xcat, which, what, from, to, ...) {
   what <- sub("of ", "of\n", what)
   what <- sub("median ", "median\n", what)
   sprintf("%s %s [%s to %s[",
       which, what, signif_pad(from, 3, FALSE), signif_pad(to, 3, FALSE))
}
 p3 <- ggplot(data =outcovcomb ,
       aes(time, CP, group = ID,color=SEX)) +
  geom_line(alpha = 0.1, size = 0.3) +
  facet_grid(  table1::eqcut(AGE,2,f)  ~ table1::eqcut(WT,4,f) ) +
labs(y = "Plasma Concentrations", color = "Sex", x = "Time (h)")+
  theme(strip.placement = "outside",legend.position =c(0.9,0.2),
  legend.background = element_blank())+
  guides(colour=guide_legend(override.aes = list(alpha=1,size=0.5)))
 p3

out.df.multivariatecov <- as.data.frame(outcovcomb) %>% 
  arrange(ID,time) %>% 
  group_by(ID,SEX,AGE,WT)%>% 
  summarise (Cmax = max(CP,na.rm = TRUE),
             AUC= sum(diff(time ) *na.omit(lead(CP) + CP)) / 2) 

out.df.multivariatecov.long <- out.df.multivariatecov %>% 
  gather(paramname,paramvalue,Cmax,AUC) %>%
  group_by (paramname,SEX) %>%
  mutate(medparam = median(paramvalue),
         paramvalue = paramvalue / medparam) 
out.df.multivariatecov.long$SEXLABEL <- factor(out.df.multivariatecov.long$SEX,labels=c("Girls","Boys"))

paramvsage <-  ggplot(out.df.multivariatecov.long,
         aes( AGE,paramvalue,col=SEXLABEL) )+
  geom_point(alpha=0.1,size=2)+
  facet_grid(paramname~SEXLABEL,labeller = label_value,
             scales="free_y")+
    labs(y="Standardized PK Parameter Values",x="Age (years)",color="")
paramvsage  

paramvswt <-    ggplot(out.df.multivariatecov.long,
         aes( WT,paramvalue,col=factor(SEXLABEL)) )+
  geom_point(alpha=0.1,size=2)+
  facet_grid(paramname~SEXLABEL,labeller = label_value,
             scales="free_y")+
    labs(y="Standardized PK Parameter Values",x="Weight (kg)",color="")
paramvswt   

PK Parameters Summaries and Distribution Plots

nca.summaries <- out.df.multivariatecov.long %>%
  mutate(SEXCAT =ifelse( SEX=="Boys","Girls","Boys"),
         REF = "All Subjects")

nca.summaries$WTCAT3 <- table1::eqcut( nca.summaries$WT,3,varlabel = "Weight")
nca.summaries$WTCAT4 <- table1::eqcut( nca.summaries$WT,4,varlabel = "Weight")
nca.summaries$AGECAT4 <- table1::eqcut( nca.summaries$AGE,4,varlabel = "Age")
nca.summaries.long <- gather(nca.summaries,
                             covname,
                             covvalue,REF,WTCAT3,WTCAT4,AGECAT4,SEXCAT,
                             factor_key = TRUE)
nca.summaries.long$covvalue <- as.factor( nca.summaries.long$covvalue)
nca.summaries.long$covvalue <- reorder(nca.summaries.long$covvalue,nca.summaries.long$paramvalue)

ggridgesplot<- ggplot(nca.summaries.long,
        aes(x=paramvalue,y=covvalue,fill=factor(..quantile..),height=..ndensity..))+
   facet_grid(covname~paramname,scales="free_y")+
  annotate("rect",
    xmin  = 0.8,
    xmax  = 1.25,
    ymin  = -Inf,
    ymax  = Inf,
    fill  = "gray",
    alpha = 0.4) +
   stat_density_ridges(
     geom = "density_ridges_gradient", calc_ecdf = TRUE,
     quantile_lines = TRUE, rel_min_height = 0.01,scale=0.9,
     quantiles = c(0.05,0.5, 0.95))+
   scale_fill_manual(
     name = "Probability", values = c("white","#0000FFA0", "#0000FFA0", "white"),
     labels = c("(0, 0.05]", "(0.05, 0.5]","(0.5, 0.95]", "(0.95, 1]")
   )+
   geom_vline(data=data.frame (xintercept=1),  aes(xintercept =xintercept  ),size = 1)+
   theme_bw()+
   labs(x="Effects Of Covariates on PK Parameter",y="")
ggridgesplot

A Forest Plot with a Side Table

Similarly to previous sections, we prepare the data to use forest_plot. We provide a two parameters plot illustrating some of the options.

coveffectsdatacovrep <- nca.summaries.long %>% 
  dplyr::group_by(paramname,covname,covvalue) %>% 
  dplyr::summarize(
    mid= median(paramvalue),
    lower= quantile(paramvalue,0.05),
    upper = quantile(paramvalue,0.95)) %>% 
  dplyr::filter(!is.na(mid)) %>% 
  dplyr::filter(covname !="WTCAT3")

bsvranges <- bsvranges[SEX=="Girls",]

setkey(bsvranges, paramname)

coveffectsdatacovrepbsv <- coveffectsdatacovrep[coveffectsdatacovrep$covname=="REF",]
coveffectsdatacovrepbsv$covname <- "BSV"
coveffectsdatacovrepbsv$covvalue <- "90% of patients"
coveffectsdatacovrepbsv$label <-    "90% of patients"
coveffectsdatacovrepbsv$lower <- bsvranges$P05
coveffectsdatacovrepbsv$upper <- bsvranges$P95
coveffectsdatacovrepbsv2 <- coveffectsdatacovrep[coveffectsdatacovrep$covname=="REF",]
coveffectsdatacovrepbsv2$covname <- "BSV"
coveffectsdatacovrepbsv2$covvalue <- "50% of patients"
coveffectsdatacovrepbsv2$label <-    "50% of patients"
coveffectsdatacovrepbsv2$lower <- bsvranges$P25
coveffectsdatacovrepbsv2$upper <- bsvranges$P75

coveffectsdatacovrepbsv<- rbind(coveffectsdatacovrep,coveffectsdatacovrepbsv2,
                                coveffectsdatacovrepbsv)

coveffectsdatacovrepbsv <- coveffectsdatacovrepbsv %>% 
  mutate(
    label= covvalue,
    LABEL = paste0(format(round(mid,2), nsmall = 2),
                   " [", format(round(lower,2), nsmall = 2), "-",
                   format(round(upper,2), nsmall = 2), "]"))
coveffectsdatacovrepbsv<- as.data.frame(coveffectsdatacovrepbsv)

coveffectsdatacovrepbsv$label <- gsub(": ", ":\n", coveffectsdatacovrepbsv$label)

coveffectsdatacovrepbsv$covname <-factor(as.factor(coveffectsdatacovrepbsv$covname ),
levels =  c("WTCAT4","AGECAT4","SEXCAT","REF", "BSV"),
labels = c("Weight","Age","Sex","REF","BSV"))
    

coveffectsdatacovrepbsv$label <- factor(coveffectsdatacovrepbsv$label,
levels =c(
   "1st quartile of Age:\n[2.00,2.96)"
 , "2nd quartile of Age:\n[2.96,3.96)"
 , "3rd quartile of Age:\n[3.96,4.96)"
 , "4th quartile of Age:\n[4.96,5.96]" 
 , "Boys", "Girls", "All Subjects","90% of patients","50% of patients"
, "1st quartile of Weight:\n[9.40,13.9)"
, "2nd quartile of Weight:\n[13.9,15.9)"
, "3rd quartile of Weight:\n[15.9,18.3)"
, "4th quartile of Weight:\n[18.3,38.2]"
))

interval_legend_text = "Median (points)\n90% CI (horizontal lines)"
interval_bsv_text = "BSV (points)\nPrediction Intervals (horizontal lines)"
ref_legend_text = "Reference (vertical line)\nClinically relevant limits\n(gray area)"

png("./Figure_7_4.png",width = 11 ,height = 7,units = "in",res=72)
coveffectsplot::forest_plot(coveffectsdatacovrepbsv,
                            ref_area = c(0.8, 1/0.8),
                            x_range = c(0.4,2.2),
                            strip_placement = "outside",
                            base_size = 18,
                            y_label_text_size = 10,x_label_text_size = 10,
                            xlabel = "Fold Change Relative to Reference",
                            ref_legend_text =ref_legend_text,
                            area_legend_text =ref_legend_text ,
                            interval_legend_text = interval_legend_text,
                            interval_bsv_text = interval_bsv_text,
                            facet_formula = "covname~paramname",
                            facet_switch = "both",
                            table_facet_switch = "both",
                            reserve_table_xaxis_label_space = TRUE,
                            facet_scales = "free_y", facet_space = "free",
                            paramname_shape = FALSE,
                            table_position = "right",
                            table_text_size=3,
                            plot_table_ratio = 1.5,
                            show_table_facet_strip = "x",
                            show_table_yaxis_tick_label = FALSE,
                            logxscale = TRUE,
                            major_x_ticks = c(0.5,0.8,1/0.8,1/0.5),
                            return_list = FALSE)

dev.off()
#> png 
#>   2

Covariate Effects Plot.