# POMP PK/PD model for docetaxel, combining Onoue et al (2016) with Friberg et al (2002) 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). ```r 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. ## Returns No value is returned; rather, the function sets global variables in package environment `DTAT::sim`. ## Examples ```r # 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)) ``` ## 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](https://f1000research.com/articles/6-112/v3) ## See Also `pomp`, `sim` ## Author(s) David C. Norris

Onoue.Friberg function