emax_disease_progression_compartmental_model

[Generated automatically as a Tutorial summary]

Model Description

Name:

dp_emax

Title:

emax_disease_progression_compartmental_model

Author:

Andrew Cristinacce @ PoPy for PK/PD

Abstract:

Specifies both the pop_gen and pop_fit subscripts.
A disease progression model, based on the concentration of drug in the central compartment.
The amount in the central compartment is determined by CL/V, which has been previously estimated for each individual.
The disease compartment increases linearly over time, but is initially reduced by the drug concentration.
The effect of the drug concentration is limited by an emax model
Keywords:

PD; Pharmacodynamics; one compartment model; linear disease progression; emax; E50

Input Script:

dp_emax.pyml

Diagram:

Comparison

True objective value

86.0288

Final fitted objective value

85.9739

Compare Main f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[EMAX]

20

81.5

30

5.15e+01

171.80%

f[E50]

15

953

10

9.43e+02

9434.33%

f[ALPHA]

2

2.5…

2.5

3.61e-03

0.14%

f[BETA]

0.1

9.54

0.3

9.24e+00

3079.00%

Compare Noise f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[S0]

250

202

200

1.79e+00

0.89%

f[ANOISE]

2

0.932

1

6.77e-02

6.77%

Compare Variance f[X]

No Variance f[X] values to compare.

Outputs

Fitted f[X] values (after fitting)

f[S0] = 201.7859
f[EMAX] = 81.5395
f[E50] = 953.4332
f[ALPHA] = 2.5036
f[BETA] = 9.5370
f[ANOISE] = 0.9323

Generated data .csv file

Synthetic Data:

synthetic_data.csv

Gen and Fit Summaries

Inputs

True f[X] values (for simulation)

f[S0] = 200.0000
f[EMAX] = 30.0000
f[E50] = 10.0000
f[ALPHA] = 2.5000
f[BETA] = 0.3000
f[ANOISE] = 1.0000

Starting f[X] values (before fitting)

f[S0] = 250.0000
f[EMAX] = 20.0000
f[E50] = 15.0000
f[ALPHA] = 2.0000
f[BETA] = 0.1000
f[ANOISE] = 2.0000