rm(list=ls()) # remove all variables library(Simcyp) library(ggplot2) library(dplyr) ##----------------------------------------------------------------------- ------------------- ## LOCATIONS ##----------------------------------------------------------------------- ------------------- simcyp_system_folder <- "C:/Program Files/Simcyp/Simcyp Simulator V15/Screens/SystemFiles/Rat" workspace_folder <- "//certara/data/sites/SHF/Consult/sgt- 1a/Simcyp/Workspaces_Compounds/Phase_4/Rat/Workspaces/" output_folder <- "//certara/data/sites/SHF/Consult/sgt- 1a/Simcyp/RSimcyp/29_100_mpk_PO_final/" code_folder <- "//certara/data/sites/SHF/Consult/sgt- 1a/Simcyp/RSimcyp/29_100_mpk_PO_final/" obs_data_folder <- "//certara/data/sites/SHF/Consult/sgt- 1a/Simcyp/RSimcyp/29_100_mpk_PO_final/Obs_data" ##----------------------------------------------------------------------- ------------------- ## INITIALISE SIMCYP ##----------------------------------------------------------------------- ------------------- Simcyp::Initialise (simcyp_system_folder) Version() ##----------------------------------------------------------------------- ------------------- ## LOAD OBSERVED DATA ##----------------------------------------------------------------------- ------------------- setwd(obs_data_folder) obs_met <- read.csv("ASM_met_PO_100mpk_excluded.csv") colnames(obs_met) <- c("ID", "TIME", "CONC") dose_par <- 100 # dose of parent (mg/kg) mwt_met <- 180.19 # molecular weight of metabolite mwt_par <- 210.28 # molecular weight of parent fa_par <- 0.93 # fraction of parent absorbed dose_met <- dose_par*fa_par*mwt_met/mwt_par dose <- dose_met # dose (mg/kg) ##----------------------------------------------------------------------- ------------------- ## LOAD WORKSPACE ##----------------------------------------------------------------------- ------------------- setwd(workspace_folder) Simcyp::SetWorkspace('080_ASM_rat_PO_100mpk.wksz') ##----------------------------------------------------------------------- ------------------- ## GENERATE VARIABLE PARAMETER VALUES ##----------------------------------------------------------------------- ------------------- setwd(workspace_folder) n_trials <- 10 trial_size <- 6 pop_size <- trial_size*n_trials gen_var <- function (mn, cv, n) { sigma <- sqrt(log(1+cv^2)) #analogous to sd but on log scale (taken from Simcyp induction guides) mu <- log(mn)-(0.5*sigma^2) #analogous to mean but on log scale (taken from Simcyp induction guides) set.seed(1) pop <- rlnorm(n, meanlog=mu, sdlog=sigma) return(pop) } ASM_ES_liv_scalar <- gen_var(100, 0.3, pop_size) ASM_ES_int_scalar <- gen_var(100, 0.3, pop_size) ASM_ES_plasma_half_life <- gen_var(13.4, 0.3, pop_size) ASM_met_hep_CLint <- gen_var(0.596, 0.3, pop_size) ASM_met_CLR <- gen_var(2.1, 0.3, pop_size) pop_params <- data.frame(ASM_ES_liver_scalar = ASM_ES_liv_scalar, ASM_ES_int_scalar = ASM_ES_int_scalar, ASM_ES_plasma_half_life = ASM_ES_plasma_half_life, ASM_met_hep_CLint = ASM_met_hep_CLint, ASM_met_CLR = ASM_met_CLR) Simcyp::GetTag("idEsteraseRoutesLiverScalar2", 0, 0) Simcyp::GetTag("idEsteraseRoutesGutScalar3", 0, 0) Simcyp::GetTag("idPlasmaEsteraseRoutesPlasmaHL1", 0, 0) Simcyp::GetTag("idTotalHepCLintValueHep", 0, 4) Simcyp::GetTag("idCLRbase", 0, 4) ##----------------------------------------------------------------------- ------------------- ## PRE-ALLOCATE RESULTS TABLES ##----------------------------------------------------------------------- ------------------- n_tp <- 2000 sim_end <- 54 # hours CSub_i <- data.frame(matrix(NA, n_tp*pop_size, 3)) CMet_i <- data.frame(matrix(NA, n_tp*pop_size, 3)) colnames(CSub_i) <- c("ID", "TIME", "CONC") colnames(CMet_i) <- c("ID", "TIME", "CONC") ##----------------------------------------------------------------------- ------------------- ## SETUP AND START SIMULATION ##----------------------------------------------------------------------- ------------------- for (i in 1:pop_size) { Simcyp::SetTag("idEsteraseRoutesLiverScalar2", 0, 0, as.numeric(pop_params[i,"ASM_ES_liver_scalar"])) Simcyp::SetTag("idEsteraseRoutesGutScalar3", 0, 0, as.numeric(pop_params[i,"ASM_ES_int_scalar"])) Simcyp::SetTag("idPlasmaEsteraseRoutesPlasmaHL1", 0, 0, as.numeric(pop_params[i,"ASM_ES_plasma_half_life"])) Simcyp::SetTag("idTotalHepCLintValueHep", 0, 4, as.numeric(pop_params[i,"ASM_met_hep_CLint"])) Simcyp::SetTag("idCLRbase", 0, 4, as.numeric(pop_params[i,"ASM_met_CLR"])) Simcyp::Simulate() ##----------------------------------------------------------------------- ------------------- ## EXTRACT RESULTS ##----------------------------------------------------------------------- ------------------- # time <- Simcyp::GetProfile(0, -1, 0) # Simulation time (from Simcyp). Causes problems as each individual can be slightly different CSub_i[((i-1)*n_tp+1):(i*n_tp),] <- data.frame(ID = rep(i,n_tp), TIME = seq(0, sim_end, length.out = n_tp), CONC = Simcyp::GetProfile(6, 0, 0)) CMet_i[((i-1)*n_tp+1):(i*n_tp),] <- data.frame(ID = rep(i,n_tp), TIME = seq(0, sim_end, length.out = n_tp), CONC = Simcyp::GetProfile(6, 4, 0)) } # Convert to blood profiles SubBP <- GetTag("idBP", 0, 0) MetBP <- GetTag("idBP", 0, 4) CSub_i$CONC <- CSub_i$CONC*SubBP CMet_i$CONC <- CMet_i$CONC*MetBP # Concentration unit conversion mg/L to ng/mL CSub_i$CONC <- CSub_i$CONC*1000 CMet_i$CONC <- CMet_i$CONC*1000 ##----------------------------------------------------------------------- ------------------- ## ANALYSE RESULTS ##----------------------------------------------------------------------- ------------------- # Generate summary parameters for each timepoint CSub_summary <- summarise(group_by(CSub_i, TIME), N = n(), Mean = mean(CONC, na.rm = TRUE), Percentile_5 = quantile(CONC, 0.05, na.rm = TRUE), Percentile_95 = quantile(CONC, 0.95, na.rm = TRUE)) CMet_summary <- summarise(group_by(CMet_i, TIME), N = n(), Mean = mean(CONC, na.rm = TRUE), Percentile_5 = quantile(CONC, 0.05, na.rm = TRUE), Percentile_95 = quantile(CONC, 0.95, na.rm = TRUE)) ##----------------------------------------------------------------------- ------------------- ## ANALYSE RESULTS - ASM-metabolite ##----------------------------------------------------------------------- ------------------- setwd(output_folder) out_profile <- CMet_i trapezoid <- function(x,y) { sum(diff(x)*(y[-1]+y[-length(y)]))/2 } # Trapezoidal AUC calculation out_profile <- data.frame(out_profile, DOSE = rep(dose, pop_size)) # Add dose to profiles # Generate summary parameters for each timepoint out_profile_summary <- summarise(group_by(out_profile, TIME), N = n(), Mean = mean(CONC, na.rm = TRUE), Percentile_5 = quantile(CONC, 0.05, na.rm = TRUE), Percentile_95 = quantile(CONC, 0.95, na.rm = TRUE)) # out_profile <- out_profile[out_profile$TIME >= pk_tstart & out_profile$TIME <= pk_tstop,] # Select data from last dosing interval for PK calculations # Generate summary parameters for each individual out_ind_params <- summarise(group_by(out_profile, ID), N = n(), # Number of timepoints Cmax = max(CONC), # Cmax (ng/mL) Tmax = TIME[match(max(CONC),CONC)], # Tmax (h) AUC = trapezoid(TIME, CONC), # AUC (ng.h/mL) AUC_period = max(TIME), # AUC period (h) CL = (DOSE[1]*10^6)/AUC) # CL (mL/h/kg) } # out_ind_params_units <- c("ID", "Timepoints", "ng/mL", "h", "ng.h/mL", "h", "L/h") out_pop_params <- data.frame(Min=sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], min), # Calculate minimum value for each parameter Max=sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], max), # Calculate maximum value for each parameter Median=sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], median), # Calculate median for each parameter Mean=sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], mean), # Calculate mean for each parameter SD=sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], sd), # Calculate standard deviation for each parameter GeoMean=exp(sapply(log(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")]), mean)), # Calculate geometric mean for each parameter GeoSD=exp(sapply(log(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")]), sd)), # Calculate geometric standard deviation for each parameter # SDln=sapply(log(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")]), sd), # Calculate standard deviation for ln data Percentile_5 = sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], quantile, probs = 0.05, na.rm = TRUE), # Calculate the 5th percentile for each parameter Percentile_95 = sapply(out_ind_params[c("Cmax", "Tmax", "AUC", "CL")], quantile, probs = 0.95, na.rm = TRUE)) # Calculate the 5th percentile for each parameter out_pop_params_units <- c("ng/mL", "h", "ng.h/mL", "mL/h/kg") out_pop_params <- data.frame(out_pop_params, CV=(out_pop_params$SD/out_pop_params$Mean*100), # Calculate % CV for each parameter # GeoCV=sqrt(exp(out_pop_params$SDln^2)- 1)*100, # Calculate % GeoCV for each parameter GeoCV=sqrt(exp(log(out_pop_params$GeoSD)^2)- 1)*100, # Calculate % GeoCV for each parameter Units=out_pop_params_units) out_pop_params <- out_pop_params[c("Units", "Min", "Max", "Median", "Mean", "SD", "CV", "GeoMean", "GeoSD", "GeoCV", "Percentile_5", "Percentile_95")] # Reorder columns write.csv(CMet_summary, "CMet_summary.csv") write.csv(out_profile_summary, "out_profile_summary.csv") write.csv(out_ind_params, "out_ind_params.csv") write.csv(out_pop_params, "out_pop_params.csv") ##----------------------------------------------------------------------- ------------------- ## PLOT ##----------------------------------------------------------------------- ------------------- setwd(output_folder) # create information for x_axis labels max_time_plot <- 12 x_interval <- 2 # time