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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

0.1776

0.8224

0.8224

f[CL]

1.0000

2.0500

1.0500

1.0500

f[V1]

20.0000

46.6326

26.6326

1.3316

f[Q]

0.5000

1.2839

0.7839

1.5677

f[V2]

100.0000

58.2479

41.7521

0.4175

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1436

0.0436

0.4358

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0748

0.0248

0.4950

f[KA_isv;CL_isv]

0.0100

0.0819

0.0719

7.1918

f[KA_isv;V1_isv]

0.0100

0.0019

0.0081

0.8112

f[KA_isv;Q_isv]

0.0100

-0.1308

0.1408

14.0816

f[KA_isv;V2_isv]

0.0100

-0.1022

0.1122

11.2209

f[CL_isv;KA_isv]

0.0100

0.0819

0.0719

7.1918

f[CL_isv]

0.0500

0.1456

0.0956

1.9117

f[CL_isv;V1_isv]

0.0100

0.0117

0.0017

0.1654

f[CL_isv;Q_isv]

0.0100

-0.1122

0.1222

12.2188

f[CL_isv;V2_isv]

0.0100

-0.2789

0.2889

28.8904

f[V1_isv;KA_isv]

0.0100

0.0019

0.0081

0.8112

f[V1_isv;CL_isv]

0.0100

0.0117

0.0017

0.1654

f[V1_isv]

0.0500

0.0874

0.0374

0.7475

f[V1_isv;Q_isv]

0.0100

0.0452

0.0352

3.5171

f[V1_isv;V2_isv]

0.0100

0.0315

0.0215

2.1462

f[Q_isv;KA_isv]

0.0100

-0.1308

0.1408

14.0816

f[Q_isv;CL_isv]

0.0100

-0.1122

0.1222

12.2188

f[Q_isv;V1_isv]

0.0100

0.0452

0.0352

3.5171

f[Q_isv]

0.0500

0.2832

0.2332

4.6640

f[Q_isv;V2_isv]

0.0100

0.0825

0.0725

7.2465

f[V2_isv;KA_isv]

0.0100

-0.1022

0.1122

11.2209

f[V2_isv;CL_isv]

0.0100

-0.2789

0.2889

28.8904

f[V2_isv;V1_isv]

0.0100

0.0315

0.0215

2.1462

f[V2_isv;Q_isv]

0.0100

0.0825

0.0725

7.2465

f[V2_isv]

0.0500

0.7724

0.7224

14.4485

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-893.4004

which required 1.30 iterations and took 619.78 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1776
f[CL] = 2.0500
f[V1] = 46.6326
f[Q] = 1.2839
f[V2] = 58.2479
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0748, 0.0819, 0.0019, -0.1308, -0.1022 ],
    [ 0.0819, 0.1456, 0.0117, -0.1122, -0.2789 ],
    [ 0.0019, 0.0117, 0.0874, 0.0452, 0.0315 ],
    [ -0.1308, -0.1122, 0.0452, 0.2832, 0.0825 ],
    [ -0.1022, -0.2789, 0.0315, 0.0825, 0.7724 ],
]
f[PNOISE] = 0.1436

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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