PKPDsim Use cmt map for mapping compartments to dose type in nm_to

This PR changes nm_to_regimen so that it uses a map for compartment to dose type. This is useful for mipdeval, where this is needed.

This breaks backward compatibility because we now want an explicit declaration of the type of dosing instead of inferring from presence/absence of columns.

This function is used in two places:

PKPDposterior::nonmem_to_stan_data:40, which we haven't touched in a while
mipdeval::parse_input_data:45, which is where we have the temporary fix

Nov 14 '25 21:11 dominic-irx

It will be great to have this functionality available, and the testing looks good for this approach. I have one question about the design choice.

I see you highlighted this as a breaking change. Is there a way it could be elegantly designed such that it is not a breaking change? For example, in new_regimen, if type and t_inf are not specified, we assume the doses are administered with "type" = "bolus" and "t_inf" = 0. If cmt_mapping is not provided and cmt is not specified, in apply_duration_scale we assign the dose to the first compartment. Previous behavior for this function was to assume that the type was bolus and that if rate/t_inf was non-zero then type was infusion. It seems to me like we could (without too much undue complexity) implement a form of this logic where, if CMT is not present in the data, we use RATE to determine if the dose is bolus or infusion (with cmt being 1 in that case). This would be consistent with logic elsewhere in the package and within this function.

It might also be a good idea to move cmt_mapping to be the final argument so that old pass-by-position argument calls are not broken.

Nov 20 '25 17:11 jasmineirx

I have completely overhauled this PR. As discussed with @roninsightrx , we are going to rely on CMT being passed into PKPDsim::regimen() and having that be interpreted correctly by the model.

First, I had to check that passing compartment and type in new_regimen will result in compartment being respected by the simulator. For voriconazole Friberg, oral is compartment 1 and IV is compartment 2. The plot shows the cmt is ultimately what matters for the simulation.

library(PKPDsim)
library(pkvoriconazolefriberg)
library(ggplot2)
library(dplyr)
reg_iv <- new_regimen(
  amt = 400,
  interval = 12,
  cmt = 2,
  t_inf = 1
)

reg_oral <- new_regimen(
  amt = 400,
  interval = 12,
  cmt = 1
)

mod <- pkvoriconazolefriberg::model()
par <- pkvoriconazolefriberg::parameters()
omega <- pkvoriconazolefriberg::omega_matrix()

dat_iv <- sim(
  seed = 604,
  ode = mod,
  parameters = par,
  omega = omega,
  covariates = list(
    "WT" = 70,
    "AGE" = 14,
    "CYP2C19unknown" = 0,
    "CYP2C19a1a2" = 0,
    "CYP2C19a2a2" = 0,
    "CYP2C19a2a3" = 0,
    "CYP2C19a1a3" = 0,
    "CYP2C19a3a3" = 0
  ),
  n_ind = 1,
  regimen = reg_iv,
  t_obs = seq(0, 36, 0.1)
)

dat_po <- sim(
  seed = 604,
  ode = mod,
  parameters = par,
  omega = omega,
  covariates = list(
    "WT" = 70,
    "AGE" = 14,
    "CYP2C19unknown" = 0,
    "CYP2C19a1a2" = 0,
    "CYP2C19a2a2" = 0,
    "CYP2C19a2a3" = 0,
    "CYP2C19a1a3" = 0,
    "CYP2C19a3a3" = 0
  ),
  n_ind = 1,
  regimen = reg_oral,
  t_obs = seq(0, 36, 0.1)
)

tmp <- data.frame(
  amt = c(400, 400, 400),
  times = c(0, 12, 24),
  cmt = c(1, 1, 2),
  rate = c(0, 0, 400)
)
reg_both <- new_regimen(
  amt = tmp$amt,
  times = tmp$time,
  cmt = tmp$cmt,
  t_inf = ifelse(tmp$rate == 0, 0, tmp$amt/tmp$rate)
)

dat_both <- sim(
  seed = 604,
  ode = mod,
  parameters = par,
  omega = omega,
  covariates = list(
    "WT" = 70,
    "AGE" = 14,
    "CYP2C19unknown" = 0,
    "CYP2C19a1a2" = 0,
    "CYP2C19a2a2" = 0,
    "CYP2C19a2a3" = 0,
    "CYP2C19a1a3" = 0,
    "CYP2C19a3a3" = 0
  ),
  n_ind = 1,
  regimen = reg_both,
  t_obs = seq(0, 36, 0.1)
)

dat_iv |>
  mutate(type = "IV") |>
  bind_rows(dat_po |> mutate(type = "PO")) |>
  bind_rows(dat_both |> mutate(type = "Both")) |>
  filter(comp == "obs") |>
  ggplot(aes(x = t, y = y, color = type)) +
  geom_point() +
  geom_line()

Next, I checked whether this function nm_to_regimen works in the same way.

# NM to regimen

## Give RATE
# ID1: two oral doses
# ID2: two IV doses
# ID3: oral then IV dose
nm <- data.frame(
  ID = c(1, 1, 2, 2, 3, 3),
  EVID = 1,
  CMT = c(1, 1, 2, 2, 1, 2),
  AMT = 100,
  TIME = c(0, 24, 0, 24, 0, 24),
  RATE = c(0, 0, 100, 100, 0, 200),
  DV = 0
)

reg_nm <- nm_to_regimen(nm)

dat_nm_rate <- sim(
  seed = 604,
  ode = mod,
  parameters = par,
  omega = omega,
  covariates = list(
    "WT" = 70,
    "AGE" = 14,
    "CYP2C19unknown" = 0,
    "CYP2C19a1a2" = 0,
    "CYP2C19a2a2" = 0,
    "CYP2C19a2a3" = 0,
    "CYP2C19a1a3" = 0,
    "CYP2C19a3a3" = 0
  ),
  n_ind = 3,
  regimen = reg_nm,
  t_obs = seq(0, 36, 0.1)
)

dat_nm_rate |>
  filter(comp == "obs") |>
  mutate(id = factor(id)) |>
  ggplot(aes(x = t, y = y, group = id, color = id)) +
  geom_point() +
  geom_line()

## NO RATE --> instant infusion
# ID1: two oral doses
# ID2: two IV doses
# ID3: oral then IV dose
nm <- data.frame(
  ID = c(1, 1, 2, 2, 3, 3),
  EVID = 1,
  CMT = c(1, 1, 2, 2, 1, 2),
  AMT = 100,
  TIME = c(0, 24, 0, 24, 0, 24),
  DV = 0
)

reg_nm <- nm_to_regimen(nm)

dat_nm <- sim(
  seed = 604,
  ode = mod,
  parameters = par,
  omega = omega,
  covariates = list(
    "WT" = 70,
    "AGE" = 14,
    "CYP2C19unknown" = 0,
    "CYP2C19a1a2" = 0,
    "CYP2C19a2a2" = 0,
    "CYP2C19a2a3" = 0,
    "CYP2C19a1a3" = 0,
    "CYP2C19a3a3" = 0
  ),
  n_ind = 3,
  regimen = reg_nm,
  t_obs = seq(0, 36, 0.1)
)

dat_nm |>
  filter(comp == "obs") |>
  mutate(id = factor(id)) |>
  ggplot(aes(x = t, y = y, group = id, color = id)) +
  geom_point() +
  geom_line()

Here, we give CMT with and without RATE, though in any data set with mixed oral/infusion dosing, you will have RATE.

With RATE, the infusion is not instantaneous:

Without RATE, the infusion is instant:

Dec 02 '25 23:12 dominic-irx

Use cmt map for mapping compartments to dose type in nm_to_regimen