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Estimate the optimal dose to achieve a target concentration at any given time

Source: R/dosing_optim.R
poso_dose_conc.Rd

Estimates the optimal dose to achieve a target concentration at any given time given a population pharmacokinetic model, a set of individual parameters, a selected point in time, and a target concentration.

Usage

poso_dose_conc(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  time_c,
  time_dose = NULL,
  target_conc,
  cmt_dose = 1,
  endpoint = "Cc",
  estim_method = "map",
  nocb = FALSE,
  p = NULL,
  greater_than = TRUE,
  starting_dose = 100,
  interdose_interval = NULL,
  add_dose = 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: estimates the optimal dose for a selected target concentration at a selected point in time following the events from dat, and using Maximum A Posteriori estimation. Setting tdm to TRUE causes the following to occur:

  • the arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param and starting_time are ignored.

time_c

Numeric. Point in time for which the dose is to be optimized.

time_dose

Numeric. Time when the dose is to be given.

target_conc

Numeric. Target concentration.

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 concentrations 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 concentrations to be greater than target_conc. Respectively, lower if FALSE. This argument is ignored if tdm is set to TRUE.

starting_dose

Numeric. Starting dose for the optimization algorithm.

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.

indiv_param

Optional. A set of individual parameters : THETA, estimates of ETA, and covariates. This argument is ignored if tdm is set to TRUE.

Value

A list containing the following components:

dose

Numeric. An optimal dose for the selected target concentration.

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 dose to reach a concentration of 80 mg/l
# one hour after starting the 30-minutes infusion
poso_dose_conc(dat=df_patient01,prior_model=mod_run001,
time_c=1,duration=0.5,target_conc=80)
#>  
#>  
#>  
#>  
#> $dose
#> [1] 6886.024
#> 
#> $type_of_estimate
#> [1] "point estimate"
#> 
#> $conc_estimate
#> [1] 80
#> 
#> $indiv_param
#>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka
#> 1        4       70        1 0.2236068 0.6019041 -0.4291723 0.1278484
#>