poso_time_cmin {posologyr}

Predict time to a selected trough concentration

Description

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

Usage

poso_time_cmin(
  dat = NULL,
  prior_model = NULL,
  tdm = FALSE,
  target_cmin,
  dose = NULL,
  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.

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.

Value

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 <- 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))
# 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)