poso_dose_auc {posologyr}

Estimate the optimal dose for a selected target area under the time-concentration curve (AUC)

Description

Estimates the optimal dose for a selected target area under the time-concentration 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,
  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.

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.

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 <- list(
ppk_model = rxode2::rxode({
  centr(0) = 0;
  depot(0) = 0;

  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;
  d/dt(AUC) = Cc;
}),
error_model = function(f,sigma) {
  dv <- cbind(f,1)
  g <- diag(dv%*%sigma%*%t(dv))
  return(sqrt(g))
},
theta = c(THETA_Cl=4.0, THETA_Vc=70.0, THETA_Ka=1.0),
omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Ka ~
    c(0.2,
      0, 0.2,
      0, 0, 0.2)}),
sigma = lotri::lotri({prop + add ~ c(0.05,0.0,0.00)}))
# 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)