Get started

library(campsis)

Load model

Load a PK model from the built-in model library or load your own one.

model <- model_suite$nonmem$advan4_trans4 # 2-compartment PK model with absorption compartment

Create dataset

Create a dataset of 10 individuals in Campsis For instance, let’s give 1000 mg QD for 3 days and observe every hour.

dataset <- Dataset(10) %>%
  add(Bolus(time = 0, amount = 1000, ii = 24, addl = 2)) %>%
  add(Observations(times = 0:72))

Simulate

Simulate this very simple protocol:

results <- model %>% simulate(dataset, seed = 1)
head(results)

Plot results

Plot all simulated profiles using a spaghetti plot:

spaghetti_plot(results, "CONC") +
  ggplot2::theme_bw()

Or use a shaded plot to see the simulated 90% prediction interval:

shaded_plot(results, "CONC") +
  ggplot2::theme_bw()

Simulate arms

The dataset can contain more than one treatment arm. In the example below, we explicitly create two arms. The first arm receives a dose of 1000 mg QD, while the second arm receives twice this dose amount.

arm1 <- Arm(subjects = 50, label = "1000 mg QD") %>%
  add(Bolus(time = 0, amount = 1000, ii = 24, addl = 2)) %>%
  add(Observations(times = 0:72))

arm2 <- Arm(subjects = 50, label = "2000 mg QD") %>%
  add(Bolus(time = 0, amount = 2000, ii = 24, addl = 2)) %>%
  add(Observations(times = 0:72))

dataset <- Dataset() %>%
  add(c(arm1, arm2))

results <- model %>% simulate(dataset, seed = 1)
shaded_plot(results, "CONC", colour = "ARM") +
  ggplot2::theme_bw()

Derive from base model

Scenarios derived from the base model and/or dataset can be easily implemented. Below, we’d like to see what happens if the clearance of this model is multiplied by two.

scenarios <- Scenarios() %>%
  add(Scenario("Base model")) %>% # Original CL is 5
  add(Scenario(
    "Increased clearance",
    model = ~ .x %>% replace(Theta(name = "CL", value = 10))
  ))

results <- model %>% simulate(dataset, scenarios = scenarios, seed = 1)
shaded_plot(results, "CONC", colour = c("ARM"), strat_extra = "SCENARIO") +
  ggplot2::facet_wrap(~SCENARIO) +
  ggplot2::theme_bw()

Post-process your results

For instance, let’s derive some non-compartmental analysis (NCA) metrics at Day 3, for each scenario and arm, with the campsisnca package:

library(campsisnca)
library(gtsummary)
library(gt)

analysis <- NCAAnalysis(variable = "CONC", window = TimeWindow(48, 72)) %>%
  add(AUC(unit = "ng/mL*h")) %>%
  add(Cmax(unit = "ng/mL")) %>%
  add(Tmax(unit = "h")) %>%
  add(Ctrough(unit = "ng/mL"))

table <- NCATable(combine_with = "tbl_merge") %>%
  add(analysis) %>%
  calculate(results)

table %>%
  export(dest = "gt")

Metric	Base model		Increased clearance
Metric	1000 mg QD N = 50¹	2000 mg QD N = 50¹	1000 mg QD N = 50¹	2000 mg QD N = 50¹
AUC (ng/mL*h)	190 (149–249)	380 (306–498)	97 (76–126)	198 (154–267)
C_max (ng/mL)	13.2 (9.9–15.1)	25.4 (21.6–31.6)	9.6 (7.2–11.2)	18.7 (16.0–23.2)
t_max (h)	2.00 (2.00–3.00)	3.00 (2.00–3.00)	2.00 (2.00–3.00)	2.00 (2.00–3.00)
C_trough (ng/mL)	4.16 (2.88–5.98)	8.51 (5.82–13.39)	1.05 (0.63–1.98)	2.25 (1.24–4.44)
¹ Median (5% Centile–95% Centile)

We invite you to check out how e-Campsis can help you derive the NCA metrics in a more automated and user-friendly way.