• Language: en
  • Documentation version: 1.3.1

emax_marker_model

[Generated automatically as a Fitting summary]

Model Description

Name:

emax_biomarker

Title:

emax_marker_model

Author:

PoPy for PK/PD

Abstract:

An emax 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 estimated for each individual.
The concentration in the central compartment influences the rate of removal of a biomarker (KOUT).
Keywords:

PD; Pharmacodynamics; one compartment model; KIN; KOUT; emax; biomarker

Input Script:

emax_biomarker_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KIN]

15.0000

87.8471

72.8471

4.8565

f[KOUT]

0.1000

0.0490

0.0510

0.5099

f[EMAX]

90.0000

27.3312

62.6688

0.6963

f[E50]

25.0000

95.9468

70.9468

2.8379

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[ANOISE]

5.0000

1.6065

3.3935

0.6787

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

193.0522

which required 1.30 iterations and took 12.93 seconds

Fitted f[X] values (after fitting)

f[KIN] = 87.8471
f[KOUT] = 0.0490
f[EMAX] = 27.3312
f[E50] = 95.9468
f[ANOISE] = 1.6065

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[KIN] = 15.0000
f[KOUT] = 0.1000
f[EMAX] = 90.0000
f[E50] = 25.0000
f[ANOISE] = 5.0000
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