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  • Documentation version: 1.3.1

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

0.2148

0.7852

0.7852

f[CL]

1.0000

1.7896

0.7896

0.7896

f[V1]

20.0000

55.4580

35.4580

1.7729

f[Q]

0.5000

1.0564

0.5564

1.1127

f[V2]

100.0000

486.5705

386.5705

3.8657

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1415

0.0415

0.4150

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.1393

0.0893

1.7851

f[KA_isv;CL_isv]

0.0100

-0.0553

0.0653

6.5338

f[KA_isv;V1_isv]

0.0100

0.0414

0.0314

3.1414

f[KA_isv;Q_isv]

0.0100

-0.0256

0.0356

3.5613

f[KA_isv;V2_isv]

0.0100

0.1217

0.1117

11.1686

f[CL_isv;KA_isv]

0.0100

-0.0553

0.0653

6.5338

f[CL_isv]

0.0500

0.0808

0.0308

0.6152

f[CL_isv;V1_isv]

0.0100

-0.0070

0.0170

1.7017

f[CL_isv;Q_isv]

0.0100

-0.0061

0.0161

1.6060

f[CL_isv;V2_isv]

0.0100

-0.1784

0.1884

18.8430

f[V1_isv;KA_isv]

0.0100

0.0414

0.0314

3.1414

f[V1_isv;CL_isv]

0.0100

-0.0070

0.0170

1.7017

f[V1_isv]

0.0500

0.1098

0.0598

1.1954

f[V1_isv;Q_isv]

0.0100

-0.0827

0.0927

9.2702

f[V1_isv;V2_isv]

0.0100

0.2139

0.2039

20.3938

f[Q_isv;KA_isv]

0.0100

-0.0256

0.0356

3.5613

f[Q_isv;CL_isv]

0.0100

-0.0061

0.0161

1.6060

f[Q_isv;V1_isv]

0.0100

-0.0827

0.0927

9.2702

f[Q_isv]

0.0500

0.3173

0.2673

5.3459

f[Q_isv;V2_isv]

0.0100

-0.3239

0.3339

33.3914

f[V2_isv;KA_isv]

0.0100

0.1217

0.1117

11.1686

f[V2_isv;CL_isv]

0.0100

-0.1784

0.1884

18.8430

f[V2_isv;V1_isv]

0.0100

0.2139

0.2039

20.3938

f[V2_isv;Q_isv]

0.0100

-0.3239

0.3339

33.3914

f[V2_isv]

0.0500

0.9443

0.8943

17.8859

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-863.1496

which required 1.30 iterations and took 60.89 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2148
f[CL] = 1.7896
f[V1] = 55.4580
f[Q] = 1.0564
f[V2] = 486.5705
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.1393, -0.0553, 0.0414, -0.0256, 0.1217 ],
    [ -0.0553, 0.0808, -0.0070, -0.0061, -0.1784 ],
    [ 0.0414, -0.0070, 0.1098, -0.0827, 0.2139 ],
    [ -0.0256, -0.0061, -0.0827, 0.3173, -0.3239 ],
    [ 0.1217, -0.1784, 0.2139, -0.3239, 0.9443 ],
]
f[PNOISE] = 0.1415

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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