Concentration predictions for 2-compartmental PK model with infusion dosing at steady state

Usage

pk_1cmt_inf_ss(
  t = c(0:24),
  dose = 100,
  t_inf = 1,
  tau = 12,
  CL = 3,
  V = 30,
  ruv = NULL
)

Arguments

t: vector of time
dose: dose
t_inf: infusion time
tau: dosing interval
CL: clearance
V: volume of distribution
ruv: residual variability, specified as list with optional arguments for proportional, additive, or exponential components, e.g. list(prop=0.1, add=1, exp=0)

Examples

pk_1cmt_inf_ss(dose = 500, tau = 12, t_inf = 2, CL = 5, V = 50)
#>     t        dv
#> 1   0  4.771371
#> 2   1  9.075444
#> 3   2 12.969930
#> 4   3 11.735678
#> 5   4 10.618881
#> 6   5  9.608361
#> 7   6  8.694004
#> 8   7  7.866660
#> 9   8  7.118049
#> 10  9  6.440677
#> 11 10  5.827765
#> 12 11  5.273180
#> 13 12  4.771371
#> 14 13  9.075444
#> 15 14 12.969930
#> 16 15 11.735678
#> 17 16 10.618881
#> 18 17  9.608361
#> 19 18  8.694004
#> 20 19  7.866660
#> 21 20  7.118049
#> 22 21  6.440677
#> 23 22  5.827765
#> 24 23  5.273180
#> 25 24  4.771371
pk_1cmt_inf_ss(
  dose = 500, tau = 12, t_inf = 2, CL = 5, V = 50,
  ruv = list(prop = 0.1, add = 0.1))
#>     t        dv
#> 1   0  6.042380
#> 2   1  7.964730
#> 3   2 12.443606
#> 4   3 12.726053
#> 5   4  9.385076
#> 6   5  9.960619
#> 7   6  8.669020
#> 8   7  8.437788
#> 9   8  7.523756
#> 10  9  5.625674
#> 11 10  6.233151
#> 12 11  5.232744
#> 13 12  4.735624
#> 14 13  9.104142
#> 15 14 13.175291
#> 16 15 10.576842
#> 17 16 11.845582
#> 18 17  9.885094
#> 19 18  8.163672
#> 20 19  8.349040
#> 21 20  6.175313
#> 22 21  6.510734
#> 23 22  5.091665
#> 24 23  5.619096
#> 25 24  3.965352