Direct PD Model

[Generated automatically as a Tutorial summary]

Model Description

Name:

direct_pd_pop

Title:

Direct PD Model

Author:

PoPy for PK/PD

Abstract:

A simple direct PD Model, i.e, no delay compartments, based on the amount of drug in the body.
The amount in the central compartment is determined by K, which has been previously estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).
Keywords:

pd; one compartment model; direct

Input Script:

direct_pd_tut.pyml

Diagram:

Comparison

True objective value

3923.2567

Final fitted objective value

3917.4035

Compare Main f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[BASE]

500

781

800

1.88e+01

2.35%

f[KOUT]

0.1

0.0315

0.03

1.47e-03

4.90%

Compare Noise f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[PNOISE]

0.05

0.0983

0.1

1.72e-03

1.72%

f[ANOISE]

0.2

0.821

0.5

3.21e-01

64.18%

Compare Variance f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[BASE_isv]

0.02

0.0339

0.05

1.61e-02

32.24%

f[BASE_isv;KOUT_isv]

0

-0.00503

0

5.03e-03

inf

f[KOUT_isv;BASE_isv]

0

-0.00503

0

5.03e-03

inf

f[KOUT_isv]

0.02

0.0024

0.01

7.60e-03

75.97%

Outputs

Fitted f[X] values (after fitting)

f[BASE] = 781.2044
f[KOUT] = 0.0315
f[PNOISE] = 0.0983
f[ANOISE] = 0.8209
f[BASE_isv,KOUT_isv] = [
    [ 0.0339, -0.0050 ],
    [ -0.0050, 0.0024 ],
]

Generated data .csv file

Synthetic Data:

synthetic_data.csv

Gen and Fit Summaries

Inputs

True f[X] values (for simulation)

f[BASE] = 800.0000
f[KOUT] = 0.0300
f[PNOISE] = 0.1000
f[ANOISE] = 0.5000
f[BASE_isv,KOUT_isv] = [
    [ 0.0500, 0.0000 ],
    [ 0.0000, 0.0100 ],
]

Starting f[X] values (before fitting)

f[BASE] = 500.0000
f[KOUT] = 0.1000
f[PNOISE] = 0.0500
f[ANOISE] = 0.2000
f[BASE_isv,KOUT_isv] = [
    [ 0.0200, 0.0000 ],
    [ 0.0000, 0.0200 ],
]