Dosage individualization for a critical care patient treated with amikacin for suspected ventilator-associated pneumonia, using the population pharmacokinetic (ppk) model of Burdet et al. 2015, using the data from therapeutic drug monitoring (TDM).
mod_amikacin_Burdet2015 <- function() {
ini({
THETA_Cl=4.3
THETA_Vc=15.9
THETA_Vp=21.4
THETA_Q=12.1
ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~
c(0.1,
0.01 , 0.05 ,
0.01 , 0.02 , 0.2 ,
-0.06 , 0.004, 0.003, 0.08)
add_sd <- 0.2
prop_sd <- 0.1
})
model({
TVCl = THETA_Cl*(CLCREAT4H/82)^0.7
TVVc = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4
TVVp = THETA_Vp
TVQ = THETA_Q
Cl = TVCl*exp(ETA_Cl)
Vc = TVVc*exp(ETA_Vc)
Vp = TVVp*exp(ETA_Vp)
Q = TVQ *exp(ETA_Q)
ke = Cl/Vc
k12 = Q/Vc
k21 = Q/Vp
Cp = centr/Vc
d/dt(centr) = - ke*centr - k12*centr + k21*periph
d/dt(periph) = + k12*centr - k21*periph
Cp ~ add(add_sd) + prop(prop_sd) + combined1()
})
}
After the first administration, the dosage selection can be refined
using the results of TDM. See
vignette("patient_data_input")
for more details regarding
the patient record.
df_patientA <- data.frame(ID=1,TIME=c(0,1,6),
DV=c(NA,58,14),
EVID=c(1,0,0),
AMT=c(2000,0,0),
DUR=c(0.5,NA,NA),
CLCREAT4H=50,TBW=62,PoverF=169)
df_patientA
#> ID TIME DV EVID AMT DUR CLCREAT4H TBW PoverF
#> 1 1 0 NA 1 2000 0.5 50 62 169
#> 2 1 1 58 0 0 NA 50 62 169
#> 3 1 6 14 0 0 NA 50 62 169
The concentration measured 30 min after a 30 min infusion do not meet the target for a peak concentration; it is < 60 mg/L.
The maximum a posteriori (MAP) individual parameters are estimated.
The individual pharmacokinetic profile can be plotted using the
rxode2
model provided by the poso_estim_map()
function.
With the MAP estimates of the individual parameters, the prediction of the time needed before reaching the target Cmin can be updated.
poso_time_cmin(dat=df_patientA,
prior_model=mod_amikacin_Burdet2015,
tdm = TRUE,
target_cmin = 2.5)
#> $time
#> [1] 33.9
#>
#> $type_of_estimate
#> [1] "point estimate"
#>
#> $cmin_estimate
#> [1] 2.487865
#>
#> $indiv_param
#> THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd ETA_Cl ETA_Vc
#> 3 4.3 15.9 21.4 12.1 0.2 0.1 0.4499479 0.2730561
#> ETA_Vp ETA_Q CLCREAT4H TBW PoverF
#> 3 0.7061648 -0.13884 50 62 169
The next dose (if needed) can be administered 33.9 hours following the first infusion.
The optimal dose to achieve a peak concentration of 80 mg/l can be determined using the MAP estimates.
map_dose <- poso_dose_conc(dat=df_patientA,
prior_model=mod_amikacin_Burdet2015,
tdm=TRUE,
time_c = 35, #target concentration at t = 35 h
time_dose = 34, #dosing at t = 34 h
duration = 0.5,
target_conc = 80)
map_dose
#> $dose
#> [1] 2447.917
#>
#> $type_of_estimate
#> [1] "point estimate"
#>
#> $conc_estimate
#> [1] 80
#>
#> $indiv_param
#> THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd ETA_Cl ETA_Vc
#> 3 4.3 15.9 21.4 12.1 0.2 0.1 0.4499608 0.2730596
#> ETA_Vp ETA_Q CLCREAT4H TBW PoverF
#> 3 0.7061496 -0.1388505 50 62 169
The next dose should be 2450 mg.
The optimal inter-dose interval to reach a Cmin of 2.5 mg/L before each dosing can be determined using the MAP estimates.
map_interval <- poso_inter_cmin(dat=df_patientA,
prior_model=mod_amikacin_Burdet2015,
dose = map_dose$dose,
duration = 0.5,
target_cmin = 2.5)
map_interval
#> $interval
#> [1] 38.57781
#>
#> $type_of_estimate
#> [1] "point estimate"
#>
#> $conc_estimate
#> [1] 2.500173
#>
#> $indiv_param
#> THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd ETA_Cl ETA_Vc
#> 1 4.3 15.9 21.4 12.1 0.2 0.1 0.449952 0.2730587
#> ETA_Vp ETA_Q CLCREAT4H TBW PoverF
#> 1 0.7061588 -0.1388432 50 62 169
The interval between doses should not be less than 38.6 hours to allow adequate elimination of amikacin between each infusion.