Onoue.Friberg: POMP PK/PD model for docetaxel, combining Onoue et al (2016)...

In DTAT: Dose Titration Algorithm Tuning

POMP PK/PD model for docetaxel, combining Onoue et al (2016) with Friberg et al (2002)

Description

This function produces a POMP model combining docetaxel pharmacokinetics (PK) drawn from Table 2 of Onoue et al (2016) with myelosuppression dynamics drawn from Friberg et al (2002). This model enables simulation of neutrophil-guided dose titration of docetaxel, as done in Norris (2017).

Usage

Onoue.Friberg(
  N,
  cycle.length.days = 21,
  data = data.frame(time = c(seq(0, 1.95, 0.05), seq(2, cycle.length.days * 24, 1)), y =
    NA),
  delta.t = 0.1
)

Arguments

N	Size of simulated population.
cycle.length.days	Duration (in days) of chemotherapy cycle to be simulated.
data	Passed through as the data argument of the pomp constructor.
delta.t	Time-step (in hours) of pomp's euler plug-in.

Value

No value is returned; rather, the function sets global variables in package environment DTAT::sim.

Author(s)

David C. Norris

References

1. Onoue H, Yano I, Tanaka A, Itohara K, Hanai A, Ishiguro H, et al. Significant effect of age on docetaxel pharmacokinetics in Japanese female breast cancer patients by using the population modeling approach. Eur J Clin Pharmacol. 2016 Jun;72(6):703-10. doi:10.1007/s00228-016-2031-3.

2. Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol. 2002 Dec 15;20(24):4713-21. doi:10.1200/JCO.2002.02.140.

3. Norris DC. Dose Titration Algorithm Tuning (DTAT) should supersede ‘the’ Maximum Tolerated Dose (MTD) in oncology dose-finding trials. F1000Research. 2017;6:112. doi:10.12688/f1000research.10624.3. https://f1000research.com/articles/6-112/v3

See Also

pomp, sim

Examples

# Reproduce the sim$pkpd model and sim$pop population from reference #3:
library(pomp)
Onoue.Friberg(N=25)
sim$pop # NB: this differs from pop of original paper...

# Whereas the present version of Onoue.Friberg() draws simulated populations
# using pomp::rprior(), to reproduce the original F1000Research paper [3] we
# re-draw sim$pop as originally & prosaically done (see https://osf.io/vwnqz/):
set.seed(2016)
N <- 25
dtx.mm <- 0.808 # molar mass (mg/µM) of docetaxel
sim$pop$Circ0 <- rlnorm(N, meanlog=log(5050), sdlog=0.42) # units=cells/mm^3
sim$pop$MTT <- rlnorm(N, meanlog=log(89.3), sdlog=0.16)  # mean transit time
sim$pop$gamma <- rlnorm(N, meanlog=log(0.163), sdlog=0.039) # feedback factor
sim$pop$Emax <- rlnorm(N, meanlog=log(83.9), sdlog=0.33)
sim$pop$EC50 <- rlnorm(N, meanlog=log(7.17*dtx.mm), sdlog=0.50)
# PK params from 2-compartment docetaxel model of Onoue et al (2016)
sim$pop$CL <- rlnorm(N, meanlog=log(32.6), sdlog=0.295)
sim$pop$Q  <- rlnorm(N, meanlog=log(5.34), sdlog=0.551)
sim$pop$Vc <- rlnorm(N, meanlog=log(5.77), sdlog=0.1) # Onoue gives no CV% for V1
sim$pop$Vp <- rlnorm(N, meanlog=log(11.0), sdlog=0.598) # Called 'V2' in Onoue
sim$pop$kTR=4/sim$pop$MTT

# Now we run the sim$pkpd model, separately for each of N simultated individuals:
allout <- data.frame() # accumulator for N individual ODE solutions
for (id in 1:sim$N) {
  out <- trajectory(sim$pkpd,
                    params=c(sim$pop[sim$pop$id==id, -which(names(sim$pop) %in% c('id','MTT'))]
                             , sigma=0.05, dose=100, duration=1),
                    format="data.frame")
  # drop 'traj' and shift 'time' to first column
  out <- out[,c('time',setdiff(colnames(out),c('time','traj')))]
  out$id <- paste("id",id,sep="")
  allout <- rbind(allout, out)
}

library(Hmisc)
allout <- upData(allout
                 , id = ordered(id, levels=paste("id",1:sim$N,sep=""))
                 , units=c(Prol="cells/mm^3", Tx.1="cells/mm^3",
                           Tx.2="cells/mm^3", Tx.3="cells/mm^3",
                           Circ="cells/mm^3",
                           Cc="ng/mL", Cp="ng/mL",
                           time="hours"), print=FALSE)

library(tidyr)
cout <- gather(allout, key="Series", value="Concentration"
, Cc, Cp
, factor_key = TRUE)

label(cout$Concentration) <- "Drug Concentration"

# Figure 1 from reference [3]:
library(RColorBrewer)
xYplot(Concentration ~ time | id, group=Series
       , data=cout, subset=time<6
       , layout=c(5,NA)
       , type='l', as.table=TRUE
       , label.curves=FALSE
       , par.settings=list(superpose.line=list(lwd=2,col=brewer.pal(4,"PRGn")[c(1,4)]))
       , scales=list(y=list(log=TRUE, lim=c(10^-3,10^1)))
       , auto.key=list(points=FALSE, lines=TRUE, columns=2))

mout <- gather(allout, key="Series", value="ANC"
, Prol, Tx.1, Tx.2, Tx.3, Circ
, factor_key = TRUE)

mout <- upData(mout
               , time = time/24
               , units = c(time="days")
               , print = FALSE)

# Figure 3 from citation [3]:
xYplot(ANC ~ time | id, group=Series
       , data=mout
       , layout=c(5,5)
       , type='l', as.table=TRUE
       , label.curves=FALSE
       , par.settings=list(superpose.line=list(lwd=2,col=brewer.pal(11,"RdYlBu")[c(1,3,4,8,10)]))
       , scales=list(y=list(log=TRUE, lim=c(100,15000), at=c(200, 500, 1000, 2000, 5000, 10000)))
       , auto.key=list(points=FALSE, lines=TRUE, columns=5))

DTAT documentation built on March 31, 2023, 10:26 p.m.