# 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. ```r 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. ## Returns 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 ```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 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) ```

poso_inter_cmin function