Fit oral pharmacokinetic data to a one-compartment model with Michaelis-Menten elimination

Fits oral pharmacokinetic data to a one-compartment model with first-order absorption and Michaelis-Menten (nonlinear) elimination using the naive pooled data approach. Supports multiple estimation methods available in nlmixr2, and optionally returns only predicted concentrations to reduce memory use in simulation workflows.

Usage

Fit_1cmpt_mm_oral(
  data,
  est.method,
  input.ka,
  input.vmax,
  input.km,
  input.vd,
  input.add,
  ncores = 2,
  return.pred.only = FALSE,
  ...
)

Arguments

data

A data frame of oral pharmacokinetic data formatted for nlmixr2.

est.method

Estimation method to use in nlmixr2, one of: "rxSolve", "nls", "nlm", "nlminb", or "focei".

input.ka

Initial estimate of the absorption rate constant (ka).

input.vmax

Initial estimate of the maximum elimination rate (Vmax).

input.km

Initial estimate of the Michaelis constant (Km).

input.vd

Initial estimate of the volume of distribution (V).

input.add

Initial estimate of the additive residual error.

ncores

Number of cores to use for parallelization, passed to rxControl(). Default is 2.

return.pred.only

Logical; if TRUE, returns a data frame with only predicted concentrations (cp) for all observations in the input data.

...

Optional arguments passed to nlmixr2(), such as a custom control = foceiControl(...) or other control objects.

Value

If return.pred.only = TRUE, returns a data.frame with columns cp (predicted concentration) and DV (observed data). Otherwise, returns a fitted model object produced by nlmixr2.

Author

Zhonghui Huang

Examples

 # \donttest{
dat <- Oral_1CPTMM
# Run simulation
Fit_1cmpt_mm_oral(
  data = dat,
  est.method = "rxSolve",
  input.ka = 1,
  input.vmax = 1000,
  input.km = 250,
  input.vd = 70,
  input.add = 10
)
#> ── Solved rxode2 object ──
#> ── Parameters (value$params): ──
#> # A tibble: 120 × 6
#>    id      tka lvmax   lkm    tv add.err
#>    <fct> <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 1         0  6.91  5.52  4.25      10
#>  2 2         0  6.91  5.52  4.25      10
#>  3 3         0  6.91  5.52  4.25      10
#>  4 4         0  6.91  5.52  4.25      10
#>  5 5         0  6.91  5.52  4.25      10
#>  6 6         0  6.91  5.52  4.25      10
#>  7 7         0  6.91  5.52  4.25      10
#>  8 8         0  6.91  5.52  4.25      10
#>  9 9         0  6.91  5.52  4.25      10
#> 10 10        0  6.91  5.52  4.25      10
#> # ℹ 110 more rows
#> ── Initial Conditions (value$inits): ──
#>  depot centre 
#>      0      0 
#> ── First part of data (object): ──
#> # A tibble: 6,960 × 12
#>      id  evid  time    ka  vmax    km     v    cp ipredSim   sim depot centre
#>   <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>  <dbl>
#> 1     1     0  0.25     1 1002.  250.  70.1  31.3     31.3  29.0 7788.  2197.
#> 2     1     0  0.5      1 1002.  250.  70.1  55.4     55.4  59.0 6065.  3882.
#> 3     1     0  0.75     1 1002.  250.  70.1  73.8     73.8  87.3 4724.  5172.
#> 4     1     0  1        1 1002.  250.  70.1  87.8     87.8  68.1 3679.  6156.
#> 5     1     0  1.5      1 1002.  250.  70.1 106.     106.  102.  2231.  7462.
#> 6     1     0  2        1 1002.  250.  70.1 117.     117.  117.  1353.  8185.
#> # ℹ 6,954 more rows
# Return only predicted concentrations
Fit_1cmpt_mm_oral(
  data = dat,
  est.method = "rxSolve",
  input.ka = 1,
  input.vmax = 1000,
  input.km = 250,
  input.vd = 70,
  input.add = 10
)
#> ── Solved rxode2 object ──
#> ── Parameters (value$params): ──
#> # A tibble: 120 × 6
#>    id      tka lvmax   lkm    tv add.err
#>    <fct> <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 1         0  6.91  5.52  4.25      10
#>  2 2         0  6.91  5.52  4.25      10
#>  3 3         0  6.91  5.52  4.25      10
#>  4 4         0  6.91  5.52  4.25      10
#>  5 5         0  6.91  5.52  4.25      10
#>  6 6         0  6.91  5.52  4.25      10
#>  7 7         0  6.91  5.52  4.25      10
#>  8 8         0  6.91  5.52  4.25      10
#>  9 9         0  6.91  5.52  4.25      10
#> 10 10        0  6.91  5.52  4.25      10
#> # ℹ 110 more rows
#> ── Initial Conditions (value$inits): ──
#>  depot centre 
#>      0      0 
#> ── First part of data (object): ──
#> # A tibble: 6,960 × 12
#>      id  evid  time    ka  vmax    km     v    cp ipredSim   sim depot centre
#>   <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>  <dbl>
#> 1     1     0  0.25     1 1002.  250.  70.1  31.3     31.3  37.2 7788.  2197.
#> 2     1     0  0.5      1 1002.  250.  70.1  55.4     55.4  58.7 6065.  3882.
#> 3     1     0  0.75     1 1002.  250.  70.1  73.8     73.8  68.5 4724.  5172.
#> 4     1     0  1        1 1002.  250.  70.1  87.8     87.8  97.2 3679.  6156.
#> 5     1     0  1.5      1 1002.  250.  70.1 106.     106.   93.2 2231.  7462.
#> 6     1     0  2        1 1002.  250.  70.1 117.     117.  124.  1353.  8185.
#> # ℹ 6,954 more rows
# }