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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

-885.4844

Final fitted objective value

-907.0764

Compare Main f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[KA] 1 0.11 0.2 8.95e-02 44.77%
f[CL] 1 2.29 2 2.94e-01 14.71%
f[V1] 20 31 50 1.90e+01 37.98%
f[Q] 0.5 1.66 1 6.60e-01 65.99%
f[V2] 100 44.8 80 3.52e+01 43.99%

Compare Noise f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[PNOISE] 0.1 0.141 0.15 8.89e-03 5.93%

Compare Variance f[X]

Name Initial Fitted True Abs. Error Prop. Error
f[KA_isv] 0.05 0.0847 0.1 1.53e-02 15.26%
f[KA_isv;CL_isv] 0.01 0.0348 0.01 2.48e-02 247.98%
f[KA_isv;V1_isv] 0.01 -0.00295 0.01 1.29e-02 129.48%
f[KA_isv;Q_isv] 0.01 -0.0471 0.01 5.71e-02 570.63%
f[KA_isv;V2_isv] 0.01 -0.0914 0.01 1.01e-01 1013.60%
f[CL_isv;KA_isv] 0.01 0.0348 0.01 2.48e-02 247.98%
f[CL_isv] 0.05 0.016 0.03 1.40e-02 46.80%
f[CL_isv;V1_isv] 0.01 0.00654 -0.01 1.65e-02 165.37%
f[CL_isv;Q_isv] 0.01 -0.0282 0.02 4.82e-02 241.22%
f[CL_isv;V2_isv] 0.01 -0.0181 0.02 3.81e-02 190.26%
f[V1_isv;KA_isv] 0.01 -0.00295 0.01 1.29e-02 129.48%
f[V1_isv;CL_isv] 0.01 0.00654 -0.01 1.65e-02 165.37%
f[V1_isv] 0.05 0.137 0.09 4.68e-02 51.97%
f[V1_isv;Q_isv] 0.01 -0.0275 0.01 3.75e-02 375.49%
f[V1_isv;V2_isv] 0.01 0.131 0.01 1.21e-01 1214.66%
f[Q_isv;KA_isv] 0.01 -0.0471 0.01 5.71e-02 570.63%
f[Q_isv;CL_isv] 0.01 -0.0282 0.02 4.82e-02 241.22%
f[Q_isv;V1_isv] 0.01 -0.0275 0.01 3.75e-02 375.49%
f[Q_isv] 0.05 0.083 0.07 1.30e-02 18.58%
f[Q_isv;V2_isv] 0.01 -0.0602 0.01 7.02e-02 701.64%
f[V2_isv;KA_isv] 0.01 -0.0914 0.01 1.01e-01 1013.60%
f[V2_isv;CL_isv] 0.01 -0.0181 0.02 3.81e-02 190.26%
f[V2_isv;V1_isv] 0.01 0.131 0.01 1.21e-01 1214.66%
f[V2_isv;Q_isv] 0.01 -0.0602 0.01 7.02e-02 701.64%
f[V2_isv] 0.05 0.361 0.05 3.11e-01 622.08%

Outputs

Fitted f[X] values (after fitting)

f[KA] = 0.1105
f[CL] = 2.2942
f[V1] = 31.0117
f[Q] = 1.6599
f[V2] = 44.8060
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0847, 0.0348, -0.0029, -0.0471, -0.0914 ],
    [ 0.0348, 0.0160, 0.0065, -0.0282, -0.0181 ],
    [ -0.0029, 0.0065, 0.1368, -0.0275, 0.1315 ],
    [ -0.0471, -0.0282, -0.0275, 0.0830, -0.0602 ],
    [ -0.0914, -0.0181, 0.1315, -0.0602, 0.3610 ],
]
f[PNOISE] = 0.1411

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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