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First order absorption model with peripheral compartment

[Generated automatically as a Tutorial summary]

Model Description

Name:builtin_tut_example
Title:First order absorption model with peripheral compartment
Author:PoPy for PK/PD
Abstract:
A two compartment PK model with bolus dose and
first order absorption, similar to a Nonmem advan4trans4 model.
Keywords:tutorial; pk; advan4; dep_two_cmp; first order
Input Script:builtin_tut_example.pyml
Diagram:

Comparison

True objective value

-873.4691

Final fitted objective value

-887.7873

Compare Main f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[KA] 1 0.12 0.2 8.01e-02 40.06%
f[CL] 1 1.56 2 4.40e-01 21.99%
f[V1] 20 33.6 50 1.64e+01 32.72%
f[Q] 0.5 2.22 1 1.22e+00 122.14%
f[V2] 100 117 80 3.66e+01 45.77%

Compare Noise f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[PNOISE] 0.1 0.148 0.15 1.93e-03 1.29%

Compare Variance f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[KA_isv] 0.05 0.0586 0.1 4.14e-02 41.37%
f[KA_isv;CL_isv] 0.01 0.021 0.01 1.10e-02 109.93%
f[KA_isv;V1_isv] 0.01 0.0668 0.01 5.68e-02 567.96%
f[KA_isv;Q_isv] 0.01 -0.00176 0.01 1.18e-02 117.61%
f[KA_isv;V2_isv] 0.01 0.00721 0.01 2.79e-03 27.89%
f[CL_isv;KA_isv] 0.01 0.021 0.01 1.10e-02 109.93%
f[CL_isv] 0.05 0.122 0.03 9.18e-02 305.85%
f[CL_isv;V1_isv] 0.01 0.117 -0.01 1.27e-01 1267.51%
f[CL_isv;Q_isv] 0.01 -0.0631 0.02 8.31e-02 415.33%
f[CL_isv;V2_isv] 0.01 -0.247 0.02 2.67e-01 1335.63%
f[V1_isv;KA_isv] 0.01 0.0668 0.01 5.68e-02 567.96%
f[V1_isv;CL_isv] 0.01 0.117 -0.01 1.27e-01 1267.51%
f[V1_isv] 0.05 0.198 0.09 1.08e-01 119.76%
f[V1_isv;Q_isv] 0.01 -0.0291 0.01 3.91e-02 390.78%
f[V1_isv;V2_isv] 0.01 -0.155 0.01 1.65e-01 1645.98%
f[Q_isv;KA_isv] 0.01 -0.00176 0.01 1.18e-02 117.61%
f[Q_isv;CL_isv] 0.01 -0.0631 0.02 8.31e-02 415.33%
f[Q_isv;V1_isv] 0.01 -0.0291 0.01 3.91e-02 390.78%
f[Q_isv] 0.05 0.0584 0.07 1.16e-02 16.55%
f[Q_isv;V2_isv] 0.01 0.204 0.01 1.94e-01 1938.99%
f[V2_isv;KA_isv] 0.01 0.00721 0.01 2.79e-03 27.89%
f[V2_isv;CL_isv] 0.01 -0.247 0.02 2.67e-01 1335.63%
f[V2_isv;V1_isv] 0.01 -0.155 0.01 1.65e-01 1645.98%
f[V2_isv;Q_isv] 0.01 0.204 0.01 1.94e-01 1938.99%
f[V2_isv] 0.05 0.779 0.05 7.29e-01 1458.41%

Outputs

Fitted f[X] values (after fitting)

f[KA] = 0.1199
f[CL] = 1.5601
f[V1] = 33.6375
f[Q] = 2.2214
f[V2] = 116.6198
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0586, 0.0210, 0.0668, -0.0018, 0.0072 ],
    [ 0.0210, 0.1218, 0.1168, -0.0631, -0.2471 ],
    [ 0.0668, 0.1168, 0.1978, -0.0291, -0.1546 ],
    [ -0.0018, -0.0631, -0.0291, 0.0584, 0.2039 ],
    [ 0.0072, -0.2471, -0.1546, 0.2039, 0.7792 ],
]
f[PNOISE] = 0.1481

Generated data .csv file

Synthetic Data:synthetic_data.csv

Inputs

True f[X] values (for simulation)

f[KA] = 0.2000
f[CL] = 2.0000
f[V1] = 50.0000
f[Q] = 1.0000
f[V2] = 80.0000
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1000, 0.0100, 0.0100, 0.0100, 0.0100 ],
    [ 0.0100, 0.0300, -0.0100, 0.0200, 0.0200 ],
    [ 0.0100, -0.0100, 0.0900, 0.0100, 0.0100 ],
    [ 0.0100, 0.0200, 0.0100, 0.0700, 0.0100 ],
    [ 0.0100, 0.0200, 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1500

Starting f[X] values (before fitting)

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
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