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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

0.1045

0.8955

0.8955

f[CL]

1.0000

2.2200

1.2200

1.2200

f[V1]

20.0000

24.8947

4.8947

0.2447

f[Q]

0.5000

1.9247

1.4247

2.8495

f[V2]

100.0000

54.8367

45.1633

0.4516

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1397

0.0397

0.3974

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0597

0.0097

0.1931

f[KA_isv;CL_isv]

0.0100

0.0265

0.0165

1.6477

f[KA_isv;V1_isv]

0.0100

0.0392

0.0292

2.9234

f[KA_isv;Q_isv]

0.0100

0.0084

0.0016

0.1560

f[KA_isv;V2_isv]

0.0100

-0.1074

0.1174

11.7363

f[CL_isv;KA_isv]

0.0100

0.0265

0.0165

1.6477

f[CL_isv]

0.0500

0.0214

0.0286

0.5715

f[CL_isv;V1_isv]

0.0100

0.0394

0.0294

2.9408

f[CL_isv;Q_isv]

0.0100

0.0053

0.0047

0.4708

f[CL_isv;V2_isv]

0.0100

-0.0500

0.0600

5.9991

f[V1_isv;KA_isv]

0.0100

0.0392

0.0292

2.9234

f[V1_isv;CL_isv]

0.0100

0.0394

0.0294

2.9408

f[V1_isv]

0.0500

0.2501

0.2001

4.0011

f[V1_isv;Q_isv]

0.0100

0.0143

0.0043

0.4311

f[V1_isv;V2_isv]

0.0100

-0.2982

0.3082

30.8220

f[Q_isv;KA_isv]

0.0100

0.0084

0.0016

0.1560

f[Q_isv;CL_isv]

0.0100

0.0053

0.0047

0.4708

f[Q_isv;V1_isv]

0.0100

0.0143

0.0043

0.4311

f[Q_isv]

0.0500

0.0046

0.0454

0.9084

f[Q_isv;V2_isv]

0.0100

-0.0126

0.0226

2.2645

f[V2_isv;KA_isv]

0.0100

-0.1074

0.1174

11.7363

f[V2_isv;CL_isv]

0.0100

-0.0500

0.0600

5.9991

f[V2_isv;V1_isv]

0.0100

-0.2982

0.3082

30.8220

f[V2_isv;Q_isv]

0.0100

-0.0126

0.0226

2.2645

f[V2_isv]

0.0500

0.7221

0.6721

13.4426

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-910.0447

which required 1.30 iterations and took 71.09 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1045
f[CL] = 2.2200
f[V1] = 24.8947
f[Q] = 1.9247
f[V2] = 54.8367
f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
    [ 0.0597, 0.0265, 0.0392, 0.0084, -0.1074 ],
    [ 0.0265, 0.0214, 0.0394, 0.0053, -0.0500 ],
    [ 0.0392, 0.0394, 0.2501, 0.0143, -0.2982 ],
    [ 0.0084, 0.0053, 0.0143, 0.0046, -0.0126 ],
    [ -0.1074, -0.0500, -0.2982, -0.0126, 0.7221 ],
]
f[PNOISE] = 0.1397

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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