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First order absorption model with peripheral compartment

[Generated automatically as a Fitting summary]

Model Description

Name:

builtin_fit_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:

fitting; pk; advan4; dep_two_cmp; first order

Input Script:

builtin_fit_example.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA]

1.0000

0.1032

0.8968

0.8968

f[CL]

1.0000

2.1793

1.1793

1.1793

f[V1]

20.0000

25.0475

5.0475

0.2524

f[Q]

0.5000

1.8821

1.3821

2.7643

f[V2]

100.0000

60.2802

39.7198

0.3972

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1390

0.0390

0.3904

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0476

0.0024

0.0473

f[KA_isv;CL_isv]

0.0100

0.0265

0.0165

1.6469

f[KA_isv;V1_isv]

0.0100

0.0354

0.0254

2.5391

f[KA_isv;Q_isv]

0.0100

0.0016

0.0084

0.8389

f[KA_isv;V2_isv]

0.0100

-0.0896

0.0996

9.9573

f[CL_isv;KA_isv]

0.0100

0.0265

0.0165

1.6469

f[CL_isv]

0.0500

0.0253

0.0247

0.4946

f[CL_isv;V1_isv]

0.0100

0.0438

0.0338

3.3823

f[CL_isv;Q_isv]

0.0100

0.0015

0.0085

0.8484

f[CL_isv;V2_isv]

0.0100

-0.0546

0.0646

6.4625

f[V1_isv;KA_isv]

0.0100

0.0354

0.0254

2.5391

f[V1_isv;CL_isv]

0.0100

0.0438

0.0338

3.3823

f[V1_isv]

0.0500

0.2507

0.2007

4.0143

f[V1_isv;Q_isv]

0.0100

0.0071

0.0029

0.2898

f[V1_isv;V2_isv]

0.0100

-0.2664

0.2764

27.6369

f[Q_isv;KA_isv]

0.0100

0.0016

0.0084

0.8389

f[Q_isv;CL_isv]

0.0100

0.0015

0.0085

0.8484

f[Q_isv;V1_isv]

0.0100

0.0071

0.0029

0.2898

f[Q_isv]

0.0500

0.0028

0.0472

0.9438

f[Q_isv;V2_isv]

0.0100

0.0116

0.0016

0.1611

f[V2_isv;KA_isv]

0.0100

-0.0896

0.0996

9.9573

f[V2_isv;CL_isv]

0.0100

-0.0546

0.0646

6.4625

f[V2_isv;V1_isv]

0.0100

-0.2664

0.2764

27.6369

f[V2_isv;Q_isv]

0.0100

0.0116

0.0016

0.1611

f[V2_isv]

0.0500

0.6682

0.6182

12.3631

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-910.6570

which required 1.30 iterations and took 564.46 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1032
f[CL] = 2.1793
f[V1] = 25.0475
f[Q] = 1.8821
f[V2] = 60.2802
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0476, 0.0265, 0.0354, 0.0016, -0.0896 ],
    [ 0.0265, 0.0253, 0.0438, 0.0015, -0.0546 ],
    [ 0.0354, 0.0438, 0.2507, 0.0071, -0.2664 ],
    [ 0.0016, 0.0015, 0.0071, 0.0028, 0.0116 ],
    [ -0.0896, -0.0546, -0.2664, 0.0116, 0.6682 ],
]
f[PNOISE] = 0.1390

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:

builtin_fit_example_data.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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