poso_dose_auc function

Estimate the dose needed to reach a target area under the concentration-time curve (AUC)

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.

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 from dat, and using Maximum A Posteriori estimation. Setting tdm to TRUE causes the following to occur:

  • the time_dose argument is required and is used as the starting point for the AUC calculation instead of starting_time;
  • the arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param and starting_time are ignored.

• time_auc: Numeric. A duration. The target AUC is computed from starting_time to starting_time + time_auc. When tdm is set to TRUE the target AUC is computed from time_dose to time_dose + time_auc instead.

• time_dose: Numeric. Time when the dose is to be given. Only used and mandatory, when tdm is set to TRUE.

• 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 if tdm is set to TRUE.

• 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 to FALSE.

• p: Numeric. The proportion of the distribution of AUC to consider for the optimization. Mandatory for estim_method=sir. This argument is ignored if tdm is set to TRUE.

• greater_than: A boolean. If TRUE: targets a dose leading to a proportion p of the AUCs to be greater than target_auc. Respectively, lower if FALSE. This argument is ignored if tdm is set to TRUE.

• 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 to TRUE, and time_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 to TRUE.

• add_dose: Numeric. Additional doses administered at inter-dose interval after the first dose. Optional. This argument is ignored if tdm is set to TRUE.

• 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 to TRUE.

Returns

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)