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First order absorption model with peripheral compartment

[Generated automatically as a Fitting 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_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA]

1.0000

0.1762

0.8238

0.8238

f[CL]

1.0000

2.0490

1.0490

1.0490

f[V1]

20.0000

47.0285

27.0285

1.3514

f[Q]

0.5000

1.2403

0.7403

1.4805

f[V2]

100.0000

62.1150

37.8850

0.3789

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1411

0.0411

0.4113

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0714

0.0214

0.4280

f[KA_isv;CL_isv]

0.0100

0.0735

0.0635

6.3503

f[KA_isv;V1_isv]

0.0100

-0.0075

0.0175

1.7472

f[KA_isv;Q_isv]

0.0100

-0.1399

0.1499

14.9949

f[KA_isv;V2_isv]

0.0100

-0.0601

0.0701

7.0141

f[CL_isv;KA_isv]

0.0100

0.0735

0.0635

6.3503

f[CL_isv]

0.0500

0.1422

0.0922

1.8435

f[CL_isv;V1_isv]

0.0100

-0.0039

0.0139

1.3947

f[CL_isv;Q_isv]

0.0100

-0.1256

0.1356

13.5620

f[CL_isv;V2_isv]

0.0100

-0.2318

0.2418

24.1773

f[V1_isv;KA_isv]

0.0100

-0.0075

0.0175

1.7472

f[V1_isv;CL_isv]

0.0100

-0.0039

0.0139

1.3947

f[V1_isv]

0.0500

0.0894

0.0394

0.7890

f[V1_isv;Q_isv]

0.0100

0.0483

0.0383

3.8312

f[V1_isv;V2_isv]

0.0100

0.1059

0.0959

9.5884

f[Q_isv;KA_isv]

0.0100

-0.1399

0.1499

14.9949

f[Q_isv;CL_isv]

0.0100

-0.1256

0.1356

13.5620

f[Q_isv;V1_isv]

0.0100

0.0483

0.0383

3.8312

f[Q_isv]

0.0500

0.3014

0.2514

5.0282

f[Q_isv;V2_isv]

0.0100

0.0929

0.0829

8.2925

f[V2_isv;KA_isv]

0.0100

-0.0601

0.0701

7.0141

f[V2_isv;CL_isv]

0.0100

-0.2318

0.2418

24.1773

f[V2_isv;V1_isv]

0.0100

0.1059

0.0959

9.5884

f[V2_isv;Q_isv]

0.0100

0.0929

0.0829

8.2925

f[V2_isv]

0.0500

0.6551

0.6051

12.1021

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-894.0829

which required 1.30 iterations and took 71.98 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1762
f[CL] = 2.0490
f[V1] = 47.0285
f[Q] = 1.2403
f[V2] = 62.1150
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0714, 0.0735, -0.0075, -0.1399, -0.0601 ],
    [ 0.0735, 0.1422, -0.0039, -0.1256, -0.2318 ],
    [ -0.0075, -0.0039, 0.0894, 0.0483, 0.1059 ],
    [ -0.1399, -0.1256, 0.0483, 0.3014, 0.0929 ],
    [ -0.0601, -0.2318, 0.1059, 0.0929, 0.6551 ],
]
f[PNOISE] = 0.1411

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)

Likelihoods:

lx_params.csv (fit)

Inputs

Input Data:

cx_obs_params.csv

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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