• Language: en

First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Inputs

Description

Name:builtin_fit_example
Title:First order absorption model with peripheral compartment
Author:J.R. Hartley
Abstract:
A two compartment PK model with bolus dose and
first order absorption, similar to a Nonmem advan4trans4 model.
Keywords:fitting; pk; advan4; dep_two_cmp; first order
Input Script:builtin_fit_example.pyml
Input Data:builtin_fit_example_data.csv
Diagram:

Initial fixed effect estimates

f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[Q] = 0.5000
f[V2] = 100.0000
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0500, 0.0100, 0.0100, 0.0100, 0.0100 ],
    [ 0.0100, 0.0500, 0.0100, 0.0100, 0.0100 ],
    [ 0.0100, 0.0100, 0.0500, 0.0100, 0.0100 ],
    [ 0.0100, 0.0100, 0.0100, 0.0500, 0.0100 ],
    [ 0.0100, 0.0100, 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000

Outputs

Final objective value

-908.4657

which required 1.30 iterations and took 346.70 seconds

Final fitted fixed effects

f[KA] = 0.1015
f[CL] = 2.3617
f[V1] = 24.5062
f[Q] = 1.8140
f[V2] = 45.0126
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0677, 0.0196, 0.0351, 0.0427, -0.0393 ],
    [ 0.0196, 0.0149, 0.0174, 0.0094, 0.0124 ],
    [ 0.0351, 0.0174, 0.2453, 0.0783, -0.1548 ],
    [ 0.0427, 0.0094, 0.0783, 0.0475, -0.0534 ],
    [ -0.0393, 0.0124, -0.1548, -0.0534, 0.3086 ],
]
f[PNOISE] = 0.1400

Fitted parameter .csv files

Fixed Effects:fx_params.csv (fit)
Random Effects:rx_params.csv (fit)
Model params:mx_params.csv (fit)
State values:sx_params.csv (fit)
Predictions:px_params.csv (fit)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA] 1.0000 0.1015 0.8985 0.8985
f[CL] 1.0000 2.3617 1.3617 1.3617
f[V1] 20.0000 24.5062 0.2253 4.5062
f[Q] 0.5000 1.8140 2.6280 1.3140
f[V2] 100.0000 45.0126 0.5499 54.9874

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PNOISE] 0.1000 0.1400 0.4005 0.0400

Compare Variance f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[KA_isv] 0.0500 0.0677 0.3549 0.0177
f[KA_isv;CL_isv] 0.0100 0.0196 0.9605 0.0096
f[KA_isv;V1_isv] 0.0100 0.0351 2.5060 0.0251
f[KA_isv;Q_isv] 0.0100 0.0427 3.2706 0.0327
f[KA_isv;V2_isv] 0.0100 -0.0393 4.9343 0.0493
f[CL_isv;KA_isv] 0.0100 0.0196 0.9605 0.0096
f[CL_isv] 0.0500 0.0149 0.7025 0.0351
f[CL_isv;V1_isv] 0.0100 0.0174 0.7415 0.0074
f[CL_isv;Q_isv] 0.0100 0.0094 0.0559 0.0006
f[CL_isv;V2_isv] 0.0100 0.0124 0.2420 0.0024
f[V1_isv;KA_isv] 0.0100 0.0351 2.5060 0.0251
f[V1_isv;CL_isv] 0.0100 0.0174 0.7415 0.0074
f[V1_isv] 0.0500 0.2453 3.9068 0.1953
f[V1_isv;Q_isv] 0.0100 0.0783 6.8291 0.0683
f[V1_isv;V2_isv] 0.0100 -0.1548 16.4829 0.1648
f[Q_isv;KA_isv] 0.0100 0.0427 3.2706 0.0327
f[Q_isv;CL_isv] 0.0100 0.0094 0.0559 0.0006
f[Q_isv;V1_isv] 0.0100 0.0783 6.8291 0.0683
f[Q_isv] 0.0500 0.0475 0.0499 0.0025
f[Q_isv;V2_isv] 0.0100 -0.0534 6.3370 0.0634
f[V2_isv;KA_isv] 0.0100 -0.0393 4.9343 0.0493
f[V2_isv;CL_isv] 0.0100 0.0124 0.2420 0.0024
f[V2_isv;V1_isv] 0.0100 -0.1548 16.4829 0.1648
f[V2_isv;Q_isv] 0.0100 -0.0534 6.3370 0.0634
f[V2_isv] 0.0500 0.3086 5.1711 0.2586
Back to Top