Fit oral pharmacokinetic data to a two-compartment model — Fit_2cmpt

Fits oral pharmacokinetic data to a two-compartment model with first-order absorption and first-order elimination using the naive pooled data approach. Supports multiple estimation methods available in nlmixr2, and optionally returns only predicted concentrations to support simulation workflows.

Usage

Fit_2cmpt_oral(
  data,
  est.method,
  input.ka,
  input.cl,
  input.vc2cmpt,
  input.vp2cmpt,
  input.q2cmpt,
  input.add,
  ncores = 2,
  return.pred.only = FALSE,
  ...
)

Arguments

data: A data frame containing 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.cl: Initial estimate of clearance (CL).
input.vc2cmpt: Initial estimate of central volume of distribution (V1).
input.vp2cmpt: Initial estimate of peripheral volume of distribution (V2).
input.q2cmpt: Initial estimate of inter-compartmental clearance (Q).
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.
...: Additional arguments passed to nlmixr2(), such as a user-defined control = foceiControl(...) or other control settings.

Value

If return.pred.only = TRUE, returns a data.frame with a single column cp (predicted concentrations). Otherwise, returns a fitted model object produced by nlmixr2.

Author

Zhonghui Huang

Examples

# \donttest{
dat <- Oral_2CPT[Oral_2CPT$ID<11,]
# Run simulation
Fit_2cmpt_oral(
  data = dat,
  est.method = "rxSolve",
  input.ka = 1,
  input.cl = 4,
  input.vc2cmpt = 70,
  input.vp2cmpt = 40,
  input.q2cmpt = 10,
  input.add = 10
)
#> ── Solved rxode2 object ──
#> ── Parameters (value$params): ──
#> # A tibble: 10 × 7
#>    id      tka   tcl   tv1   tv2    tq add.err
#>    <fct> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 1         0  1.39  4.25  3.69   2.3      10
#>  2 2         0  1.39  4.25  3.69   2.3      10
#>  3 3         0  1.39  4.25  3.69   2.3      10
#>  4 4         0  1.39  4.25  3.69   2.3      10
#>  5 5         0  1.39  4.25  3.69   2.3      10
#>  6 6         0  1.39  4.25  3.69   2.3      10
#>  7 7         0  1.39  4.25  3.69   2.3      10
#>  8 8         0  1.39  4.25  3.69   2.3      10
#>  9 9         0  1.39  4.25  3.69   2.3      10
#> 10 10        0  1.39  4.25  3.69   2.3      10
#> ── Initial Conditions (value$inits): ──
#> depot    A1    A2 
#>     0     0     0 
#> ── First part of data (object): ──
#> # A tibble: 580 × 16
#>      id  time    ka    cl    v1    v2     q      k   k12   k21    cp ipredSim
#>   <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>    <dbl>
#> 1     1  0.25     1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  185.     185.
#> 2     1  0.5      1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  320.     320.
#> 3     1  0.75     1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  417.     417.
#> 4     1  1        1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  486.     486.
#> 5     1  1.5      1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  564.     564.
#> 6     1  2        1  4.01  70.1  40.0  9.97 0.0573 0.142 0.249  591.     591.
#> # ℹ 574 more rows
#> # ℹ 4 more variables: sim <dbl>, depot <dbl>, A1 <dbl>, A2 <dbl>
# }