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Estimate the dose needed to reach a target area under the concentration-time curve (AUC)
Source:R/dosing_optim.R
poso_dose_auc.Rd
estimates the dose needed to reach a target area under the concentration-time curve (AUC) given a population pharmacokinetic model, a set of individual parameters, and a target AUC.
Usage
poso_dose_auc(
dat = NULL,
prior_model = NULL,
tdm = FALSE,
time_auc,
time_dose = NULL,
cmt_dose = 1,
target_auc,
estim_method = "map",
nocb = FALSE,
p = NULL,
greater_than = TRUE,
starting_time = 0,
interdose_interval = NULL,
add_dose = NULL,
duration = 0,
starting_dose = 100,
indiv_param = NULL
)
Arguments
- dat
Dataframe. An individual subject dataset following the structure of NONMEM/rxode2 event records.
- prior_model
A
posologyr
prior population pharmacokinetics model, a list of six objects.- tdm
A boolean. If
TRUE
: estimates the optimal dose for a selected target auc over a selected duration following the events fromdat
, and using Maximum A Posteriori estimation. Settingtdm
toTRUE
causes the following to occur:the
time_dose
argument is required and is used as the starting point for the AUC calculation instead ofstarting_time
;the arguments
estim_method
,p
,greater_than
,interdose_interval
,add_dose
,indiv_param
andstarting_time
are ignored.
- time_auc
Numeric. A duration. The target AUC is computed from
starting_time
tostarting_time
+time_auc
. Whentdm
is set toTRUE
the target AUC is computed fromtime_dose
totime_dose
+time_auc
instead.- time_dose
Numeric. Time when the dose is to be given. Only used and mandatory, when
tdm
is set toTRUE
.- cmt_dose
Character or numeric. The compartment in which the dose is to be administered. Must match one of the compartments in the prior model. Defaults to 1.
- target_auc
Numeric. The target AUC.
- estim_method
A character string. An estimation method to be used for the individual parameters. The default method "map" is the Maximum A Posteriori estimation, the method "prior" simulates from the prior population model, and "sir" uses the Sequential Importance Resampling algorithm to estimate the a posteriori distribution of the individual parameters. This argument is ignored if
indiv_param
is provided, or iftdm
is set toTRUE
.- nocb
A boolean. for time-varying covariates: the next observation carried backward (nocb) interpolation style, similar to NONMEM. If
FALSE
, the last observation carried forward (locf) style will be used. Defaults toFALSE
.- p
Numeric. The proportion of the distribution of AUC to consider for the optimization. Mandatory for
estim_method=sir
. This argument is ignored iftdm
is set toTRUE
.- greater_than
A boolean. If
TRUE
: targets a dose leading to a proportionp
of the AUCs to be greater thantarget_auc
. Respectively, lower ifFALSE
. This argument is ignored iftdm
is set toTRUE
.- starting_time
Numeric. First point in time of the AUC, for multiple dose regimen. The default is zero. This argument is ignored if
tdm
is set toTRUE
, andtime_dose
is used as a starting point instead.- interdose_interval
Numeric. Time for the interdose interval for multiple dose regimen. Must be provided when add_dose is used. This argument is ignored if
tdm
is set toTRUE
.- add_dose
Numeric. Additional doses administered at inter-dose interval after the first dose. Optional. This argument is ignored if
tdm
is set toTRUE
.- duration
Numeric. Duration of infusion, for zero-order administrations.
- starting_dose
Numeric. Starting dose for the optimization algorithm.
- indiv_param
Optional. A set of individual parameters : THETA, estimates of ETA, and covariates. This argument is ignored if
tdm
is set toTRUE
.
Value
A list containing the following components:
- dose
Numeric. An optimal dose for the selected target AUC.
- type_of_estimate
Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
- auc_estimate
A vector of numeric estimates of the AUC. Either a single value (for a point estimate of ETA), or a distribution.
- indiv_param
A
data.frame
. The set of individual parameters used for the determination of the optimal dose : THETA, estimates of ETA, and covariates
Examples
rxode2::setRxThreads(2L) # limit the number of threads
# model
mod_run001 <- function() {
ini({
THETA_Cl <- 4.0
THETA_Vc <- 70.0
THETA_Ka <- 1.0
ETA_Cl ~ 0.2
ETA_Vc ~ 0.2
ETA_Ka ~ 0.2
prop.sd <- sqrt(0.05)
})
model({
TVCl <- THETA_Cl
TVVc <- THETA_Vc
TVKa <- THETA_Ka
Cl <- TVCl*exp(ETA_Cl)
Vc <- TVVc*exp(ETA_Vc)
Ka <- TVKa*exp(ETA_Ka)
K20 <- Cl/Vc
Cc <- centr/Vc
d/dt(depot) = -Ka*depot
d/dt(centr) = Ka*depot - K20*centr
Cc ~ prop(prop.sd)
})
}
# df_patient01: event table for Patient01, following a 30 minutes intravenous
# infusion
df_patient01 <- data.frame(ID=1,
TIME=c(0.0,1.0,14.0),
DV=c(NA,25.0,5.5),
AMT=c(2000,0,0),
EVID=c(1,0,0),
DUR=c(0.5,NA,NA))
# estimate the optimal dose to reach an AUC(0-12h) of 45 h.mg/l
poso_dose_auc(dat=df_patient01,prior_model=mod_run001,
time_auc=12,target_auc=45)
#>
#>
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> $dose
#> [1] 396.0043
#>
#> $type_of_estimate
#> [1] "point estimate"
#>
#> $auc_estimate
#> [1] 45
#>
#> $indiv_param
#> THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl ETA_Vc ETA_Ka
#> 1 4 70 1 0.2236068 0.6019038 -0.429173 0.1278482
#>