library(PopED)
##-- Model: One comp first order absorption + inhibitory imax
## -- works for both mutiple and single dosing
ff <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
y=xt
MS <- model_switch
# PK model
N = floor(xt/TAU)+1
CONC=(DOSE*Favail/V)*(KA/(KA - CL/V)) *
(exp(-CL/V * (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) -
exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
# PD model
EFF = E0*(1 - CONC*IMAX/(IC50 + CONC))
y[MS==1] = CONC[MS==1]
y[MS==2] = EFF[MS==2]
return(list( y= y,poped.db=poped.db))
})
}
## -- parameter definition function
sfg <- function(x,a,bpop,b,bocc){
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
CL=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU = a[2],
E0=bpop[5]*exp(b[4]),
IMAX=bpop[6],
IC50=bpop[7])
return( parameters )
}
## -- Residual Error function
feps <- function(model_switch,xt,parameters,epsi,poped.db){
returnArgs <- ff(model_switch,xt,parameters,poped.db)
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
MS <- model_switch
pk.dv <- y*(1+epsi[,1])+epsi[,2]
pd.dv <- y*(1+epsi[,3])+epsi[,4]
y[MS==1] = pk.dv[MS==1]
y[MS==2] = pd.dv[MS==2]
return(list( y= y,poped.db =poped.db ))
}
poped.db <- create.poped.database(ff_fun="ff",
fError_fun="feps",
fg_fun="sfg",
groupsize=20,
m=3,
bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9,E0=1120,IMAX=0.807,IC50=0.0993),
notfixed_bpop=c(1,1,1,0,1,1,1),
d=c(V=0.09,KA=0.09,CL=0.25^2,E0=0.09),
sigma=c(0.04,5e-6,0.09,100),
notfixed_sigma=c(0,0,0,0),
xt=c( 1,2,8,240,240,1,2,8,240,240),
minxt=c(0,0,0,240,240,0,0,0,240,240),
maxxt=c(10,10,10,248,248,10,10,10,248,248),
discrete_xt = list(0:248),
G_xt=c(1,2,3,4,5,1,2,3,4,5),
bUseGrouped_xt=1,
model_switch=c(1,1,1,1,1,2,2,2,2,2),
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24),c(DOSE=0, TAU=24)),
maxa=c(DOSE=200,TAU=40),
mina=c(DOSE=0,TAU=2),
ourzero=0)
## create plot of model and design
plot_model_prediction(poped.db,facet_scales="free")
plot_model_prediction(poped.db,IPRED=T,DV=T,facet_scales="free",separate.groups=T)
## evaluate initial design
evaluate_design(poped.db)
shrinkage(poped.db)
# Optimization
output <- poped_optim(poped.db, opt_xt = T, parallel = T)
summary(output)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db,facet_scales="free")
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