Get Amounts of Each Compartments using Lambdas and Coefficients of Multi-compartment Model

It calculates using multi-compartment model.

nComp(Sol, Ka=0, DH)

Arguments

Sol	Solution list of lambdas and coefficients
Ka	Absorption rate constant
DH	Expanded dosing history table

Details

First compartment is the gut compartment for oral dosing. IV bolus and infusion dosing should be done at the second compartment. If a bolus dose was given at time T, it is reflected at times of larger than T. This is more close to real observation. ADAPT does like this, but NONMEM does not.

Value

This returns a table with the gut and the other compartment columns

Author

Kyun-Seop Bae <k@acr.kr>

Examples

DAT
#>    TIME AMT RATE CMT DV
#> 1     0 100    0   2 NA
#> 2     1  NA   NA  NA NA
#> 3     2  NA   NA  NA NA
#> 4     4  NA   NA  NA NA
#> 5     8  NA   NA  NA NA
#> 6    12  NA   NA  NA NA
#> 7    24 150   50   2 NA
#> 8    25  NA   NA  NA NA
#> 9    26  NA   NA  NA NA
#> 10   28  NA   NA  NA NA
#> 11   32  NA   NA  NA NA
#> 12   36  NA   NA  NA NA
#> 13   48 100    0   1 NA
#> 14   49  NA   NA  NA NA
#> 15   50  NA   NA  NA NA
#> 16   52  NA   NA  NA NA
#> 17   56  NA   NA  NA NA
#> 18   60  NA   NA  NA NA
DAT2 = ExpandDH(DAT)
Sol = SolComp2(K10=0.1, K12=3, K21=1)
X2 = nComp(Sol, Ka=1, DAT2)
X2
#>               [,1]     [,2]      [,3]
#>  [1,] 0.000000e+00  0.00000   0.00000
#>  [2,] 0.000000e+00 24.78579  71.00430
#>  [3,] 0.000000e+00 22.94872  70.48922
#>  [4,] 0.000000e+00 21.82887  67.13386
#>  [5,] 0.000000e+00 19.78818  60.85782
#>  [6,] 0.000000e+00 17.93827  55.16849
#>  [7,] 0.000000e+00 13.36302  41.09748
#>  [8,] 0.000000e+00 34.08869  67.74790
#>  [9,] 0.000000e+00 45.53336 102.31559
#> [10,] 0.000000e+00 56.55153 136.19086
#> [11,] 0.000000e+00 46.25053 141.60095
#> [12,] 0.000000e+00 41.78336 128.50319
#> [13,] 0.000000e+00 37.87722 116.48999
#> [14,] 0.000000e+00 28.21644  86.77862
#> [15,] 3.678794e+01 51.20062 122.37609
#> [16,] 1.353353e+01 50.36158 141.38048
#> [17,] 1.831564e+00 47.95656 145.65708
#> [18,] 3.354626e-02 43.47332 133.66703
#> [19,] 6.144212e-04 39.40919 121.20089
matplot(DAT2[, "TIME"], X2, type="l")