# Estimate the optimal dose to achieve a target concentration at any given time 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. ```r 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`. ## Returns 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 ```r 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) ```

poso_dose_conc function