poso_time_cmin function

Estimate the time required to reach a target trough concentration (Cmin)

Estimates the time required to reach a target trough concentration (Cmin) given a population pharmacokinetic model, a set of individual parameters, a dose, and a target Cmin.

poso_time_cmin(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  target_cmin,
  dose = NULL,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  from = 0.2,
  last_time = 72,
  add_dose = NULL,
  interdose_interval = NULL,
  duration = 0,
  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: computes the predicted time to reach the target trough concentration (Cmin) following the last event from dat, and using Maximum A Posteriori estimation. Setting tdm to TRUE causes the following to occur:

  • the simulation starts at the time of the last recorded dose (from the TDM data) plus from;
  • the simulation stops at the time of the last recorded dose (from the TDM data) plus last_time;
  • the arguments dose, duration, estim_method, p, greater_than, interdose_interval, add_dose, indiv_param and starting_time are ignored.

• target_cmin: Numeric. Target trough concentration (Cmin).

• dose: Numeric. Dose administered. This argument is ignored if 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.

• endpoint: Character. The endpoint of the prior model to be optimised for. The default is "Cc", which is the central concentration.

• 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 Cmin to consider for the estimation. Mandatory for estim_method=sir. This argument is ignored if tdm is set to TRUE.

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

• from: Numeric. Starting time for the simulation of the individual time-concentration profile. The default value is 0.2. When tdm is set to TRUE the simulation starts at the time of the last recorded dose plus from.

• last_time: Numeric. Ending time for the simulation of the individual time-concentration profile. The default value is 72. When tdm is set to TRUE the simulation stops at the time of the last recorded dose plus last_time.

• 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.

• interdose_interval: Numeric. Time for the inter-dose interval for multiple dose regimen. Must be provided when add_dose is used. This argument is ignored if tdm is set to TRUE.

• duration: Numeric. Duration of infusion, for zero-order administrations. This argument is ignored if tdm is set to TRUE.

• indiv_param: Optional. A set of individual parameters : THETA, estimates of ETA, and covariates.

Returns

A list containing the following components:

• time: Numeric. Time needed to reach the selected Cmin.
• type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
• cmin_estimate: A vector of numeric estimates of the Cmin. 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 time needed to reach a selected Cmin: 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))
# predict the time needed to reach a concentration of 2.5 mg/l
# after the administration of a 2500 mg dose over a 30 minutes
# infusion
poso_time_cmin(dat=df_patient01,prior_model=mod_run001,
dose=2500,duration=0.5,from=0.5,target_cmin=2.5)