Comments (2)
Vincent-AC
commented on June 3, 2024
It is especially surprising because when you remove the 10^-5 added to every time-point and set ourzero to 0 the OFVs make sense again.
poped.db$design$xt
obs_1 obs_2 obs_3 obs_4
grp_1 0 10 20 22.5
grp_2 0 10 20 22.5
grp_3 0 10 20 22.5
...
poped.db2$design$xt
obs_1 obs_2 obs_3
grp_1 10 20 22.5
grp_2 10 20 22.5
grp_3 10 20 22.5
...
evaluate_design(poped.db)
$ofv
[1] 44.11343
$fim
KNET BMAX INOC ESLOPE_PMB_NR GAMMA_PMB SIGMA[1,1]
KNET 53703.462 6729.2954 2715.7124 -54735.118 71330.641 0.000
BMAX 6729.295 996.8936 376.4853 -6851.225 10780.754 0.000
INOC 2715.712 376.4853 812.1461 -2788.706 3319.721 0.000
ESLOPE_PMB_NR -54735.118 -6851.2252 -2788.7059 55823.626 -70947.690 0.000
GAMMA_PMB 71330.641 10780.7541 3319.7208 -70947.690 203222.874 0.000
SIGMA[1,1] 0.000 0.0000 0.0000 0.000 0.000 6840.097
$rse
KNET BMAX INOC ESLOPE_PMB_NR GAMMA_PMB SIGMA[1,1]
43.5387124 1.6707773 0.5786627 38.8936676 36.8925865 6.4549722
evaluate_design(poped.db2)
$ofv
[1] 38.55947
$fim
KNET BMAX INOC ESLOPE_PMB_NR GAMMA_PMB SIGMA[1,1]
KNET 53703.462 6729.2954 2715.7124 -54735.118 71330.641 0.000
BMAX 6729.295 996.8936 376.4853 -6851.225 10780.754 0.000
INOC 2715.712 376.4853 171.5168 -2788.706 3319.721 0.000
ESLOPE_PMB_NR -54735.118 -6851.2252 -2788.7059 55823.626 -70947.690 0.000
GAMMA_PMB 71330.641 10780.7541 3319.7208 -70947.690 203222.874 0.000
SIGMA[1,1] 0.000 0.0000 0.0000 0.000 0.000 5130.072
$rse
KNET BMAX INOC ESLOPE_PMB_NR GAMMA_PMB SIGMA[1,1]
103.626144 1.672481 8.053468 93.355070 69.042877 7.453560
Full code:
library(PopED)
library(ggplot2)
library(deSolve)
set.seed(1998)
sfg <- function(x,a,bpop,b,bocc){
parameters=c(KA = bpop[1], #no IIV
Vma= bpop[2],
Ama= bpop[3],
CL = bpop[4],
Vme= bpop[5],
Cm = bpop[6],
V1 = bpop[7],
V2 = bpop[8],
Q = bpop[9],
KNET = bpop[10],
BMAX = bpop[11],
INOC = bpop[12],
ESLOPE_PMB_NR = bpop[13],
GAMMA_PMB = bpop[14],
DOSE=a[1],
TAU=a[2])
return(parameters)
}
PKPD.resistant.ip.ode <- function(Time, State, Pars){
with(as.list(c(State, Pars)), {
CFREE = A3/V1*0.086 #free concentration, 0.086 as fraction unbound
PLATEAU = 1 - (A5/(10**BMAX))
KILL_PMB_NR = 0
if(CFREE > 0) KILL_PMB_NR = (ESLOPE_PMB_NR * (CFREE**GAMMA_PMB))
dA1 <- -Vma*A1/(A1+Ama)
dA2 <- -KA*A2
dA3 <- Vma*A1/(A1 + Ama) + KA*A2 -
Q/V1*A3 + Q/V2*A4 -
Vme*A3/(A3+(Cm*V1)) - CL/V1*A3
dA4 <- Q/V1*A3 - Q/V2*A4
dA5 <- (KNET*PLATEAU - KILL_PMB_NR) * A5
return(list(c(dA1, dA2, dA3, dA4, dA5)))
})
}
ff.PKPD.resistant.ip.ode <- function(model_switch, xt, parameters, poped.db){
with(as.list(parameters),{
# initial conditions of ODE system
A_ini <- c(A1=0, A2=0, A3=0, A4=0, A5=(10**INOC))
#Set up time points to get ODE solutions
times_xt <- drop(xt) # sample times
times_start <- c(0) # add extra time for start of study
times_dose = seq(from=0,to=max(times_xt),by=TAU) # dose times
times <- unique(sort(c(times_start,times_xt,times_dose))) # combine it all
# Dosing
dose_set <- c()
for (i in DOSE)
{
if (i == 0)
{
dose_set <- append(dose_set, 0)
dose_set <- append(dose_set, 0)
} else
if (i > 0 & i < 4.8) {
dose_set <- append(dose_set, i)
dose_set <- append(dose_set, 0)
} else {
dose_set <- append(dose_set, 4.8)
dose_set <- append(dose_set, i - 4.8)
}
}
dose_dat <- data.frame(
var = c("A1","A2"),
time = rep(times_dose, each = 2), #dose administration in two compartment (A1 and A2) at the same time
value = c(dose_set),
method = c("add")
)
out <- ode(A_ini, times, PKPD.resistant.ip.ode, parameters,
events = list(data = dose_dat))#atol=1e-13,rtol=1e-13)
pd <- out[,"A5"]
y = log10(pd)
# extract the time points of the observations
y = y[match(times_xt,out[,"time"])]
y = cbind(y)
return(list(y=y,poped.db=poped.db))
})
}
feps_ODE_compiled <- function(model_switch,xt,parameters,epsi,poped.db){
returnArgs <- ff.PKPD.resistant.ip.ode(model_switch,xt,parameters,poped.db)
y <- returnArgs[[1]]
poped.db <- returnArgs[[2]]
y = y + epsi[,1]
return(list(y=y,poped.db=poped.db))
}
poped.db <- create.poped.database(ff_file="ff.PKPD.resistant.ip.ode",
fg_file="sfg",
fError_file="feps_ODE_compiled",
bpop=c(KA = 1.83,
Vma= 11.3,
Ama= 11.5,
CL = 0.178,
Vme= 0.255,
Cm = 0.823,
V1 = 0.325,
V2 = 0.808,
Q = 0.198,
KNET = 1.11,
BMAX = 7.453,
INOC = 6.81,
ESLOPE_PMB_NR = 1.216,
GAMMA_PMB = 0.02626),
notfixed_bpop=c(rep(0, 9),1,1,1,1,1),
sigma=0.4328^2,
#design
groupsize = 4,
m = 30,
a =list(c(DOSE=0, TAU=24),
c(DOSE=0.5, TAU=24), c(DOSE=10, TAU=24),
c(DOSE=20, TAU=24), c(DOSE=30, TAU=24), c(DOSE=45, TAU=24),
c(DOSE=5, TAU=12), c(DOSE=10, TAU=12), c(DOSE=15, TAU=12),
c(DOSE=22.5, TAU=12), c(DOSE=30, TAU=12), c(DOSE=45, TAU=12),
c(DOSE=0.83, TAU=8), c(DOSE=1.67, TAU=8), c(DOSE=3.33, TAU=8),
c(DOSE=5, TAU=8), c(DOSE=6.67, TAU=8), c(DOSE=7.5, TAU=8),
c(DOSE=10, TAU=8), c(DOSE=15, TAU=8), c(DOSE=20, TAU=8),
c(DOSE=25, TAU=8), c(DOSE=30, TAU=8), c(DOSE=40, TAU=8),
c(DOSE=1.67, TAU=4), c(DOSE=3.33, TAU=4), c(DOSE=5, TAU=4),
c(DOSE=7.5, TAU=4), c(DOSE=10, TAU=4), c(DOSE=15, TAU=4)),
mina = c(DOSE=0,TAU=4),
maxa = c(DOSE=45, TAU=24),
ourzero = 0,
#Design Space
xt = c(0, 10, 20, 22.5), discrete_xt = list(0, seq(0, 24, 0.5),seq(0, 24, 0.5),22.5),
bUseGrouped_xt=TRUE)
poped.db2 <- create.poped.database(ff_file="ff.PKPD.resistant.ip.ode",
fg_file="sfg",
fError_file="feps_ODE_compiled",
bpop=c(KA = 1.83,
Vma= 11.3,
Ama= 11.5,
CL = 0.178,
Vme= 0.255,
Cm = 0.823,
V1 = 0.325,
V2 = 0.808,
Q = 0.198,
KNET = 1.11,
BMAX = 7.453,
INOC = 6.81,
ESLOPE_PMB_NR = 1.216,
GAMMA_PMB = 0.02626),
notfixed_bpop=c(rep(0, 9),1,1,1,1,1),
sigma=0.4328^2,
#design
groupsize = 4,
m = 30,
a =list(c(DOSE=0, TAU=24),
c(DOSE=0.5, TAU=24), c(DOSE=10, TAU=24),
c(DOSE=20, TAU=24), c(DOSE=30, TAU=24), c(DOSE=45, TAU=24),
c(DOSE=5, TAU=12), c(DOSE=10, TAU=12), c(DOSE=15, TAU=12),
c(DOSE=22.5, TAU=12), c(DOSE=30, TAU=12), c(DOSE=45, TAU=12),
c(DOSE=0.83, TAU=8), c(DOSE=1.67, TAU=8), c(DOSE=3.33, TAU=8),
c(DOSE=5, TAU=8), c(DOSE=6.67, TAU=8), c(DOSE=7.5, TAU=8),
c(DOSE=10, TAU=8), c(DOSE=15, TAU=8), c(DOSE=20, TAU=8),
c(DOSE=25, TAU=8), c(DOSE=30, TAU=8), c(DOSE=40, TAU=8),
c(DOSE=1.67, TAU=4), c(DOSE=3.33, TAU=4), c(DOSE=5, TAU=4),
c(DOSE=7.5, TAU=4), c(DOSE=10, TAU=4), c(DOSE=15, TAU=4)),
mina = c(DOSE=0,TAU=4),
maxa = c(DOSE=45, TAU=24),
#Design Space
xt = c(10, 20, 22.5), discrete_xt = list(seq(0, 24, 0.5), seq(0, 24, 0.5), seq(0, 24, 0.5)),
bUseGrouped_xt=TRUE)
eval_1 <- evaluate_design(poped.db)
eval_2 <- evaluate_design(poped.db2)
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andrewhooker
commented on June 3, 2024
Hi Vincent,
I agree this is a surprising result and seems to have something to do with the "ourzero" functionality. I'll take a look and get back to you.
Andy
from poped.
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