Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin)

Estimates the optimal dosing interval to consistently achieve a target Cmin, given a dose, a population pharmacokinetic model, a set of individual parameters, and a target concentration.

Usage

poso_inter_cmin(
  dat = NULL,
  prior_model = NULL,
  dose,
  target_cmin,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_interval = 12,
  add_dose = 10,
  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.
dose: Numeric. The dose given.
target_cmin: Numeric. Target trough concentration (Cmin).
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.
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 concentrations to consider for the optimization. Mandatory for estim_method=sir.
greater_than: A boolean. If TRUE: targets a dose leading to a proportion p of the concentrations to be greater than target_conc. Respectively, lower if FALSE.
starting_interval: Numeric. Starting inter-dose interval for the optimization algorithm.
add_dose: Numeric. Additional doses administered at inter-dose interval after the first dose.
duration: Numeric. Duration of infusion, for zero-order administrations.
indiv_param: Optional. A set of individual parameters : THETA, estimates of ETA, and covariates.

Value

A list containing the following components:

interval: Numeric. An inter-dose interval to reach the target trough concentration before each dosing of a multiple dose regimen.
type_of_estimate: Character string. The type of estimate of the individual parameters. Either a point estimate, or a distribution.
conc_estimate: A vector of numeric estimates of the conc. 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 interval to reach a cmin of of 2.5 mg/l
# before each administration
poso_inter_cmin(dat=df_patient01,prior_model=mod_run001,
dose=1500,duration=0.5,target_cmin=2.5)
#>  
#>  
#> Error : cannot convert to rxUi object
#>  
#>  
#> Error : cannot convert to rxUi object
#> Error in priormod$ppk_model: object of type 'closure' is not subsettable