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  • Documentation version: 1.3.1

Emax PD Model

[Generated automatically as a Fitting summary]

Model Description

Name:

emax_simple

Title:

Emax PD Model

Author:

PoPy for PK/PD

Abstract:

A simple emax PKPD Model, based on the amount of drug in the body.
The two compartment PK model uses previously estimated values of K, K21 and K12 for each individual
The amount in the central compartment influences the effect compartment along with EMAX (the maximum effect) and EC50 (the amount at which the effect is half the maximum)
Keywords:

PD; Pharmacodynamics; two compartment model; emax; EC50

Input Script:

emax_simple_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[EMAX]

100.0000

81.8815

18.1185

0.1812

f[E50]

50.0000

20.7067

29.2933

0.5859

f[ANOISE]

5.0000

0.9123

4.0877

0.8175

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

163.2858

which required 1.13 iterations and took 11.72 seconds

Fitted f[X] values (after fitting)

f[EMAX] = 81.8815
f[E50] = 20.7067
f[ANOISE] = 0.9123

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[EMAX] = 100.0000
f[E50] = 50.0000
f[ANOISE] = 5.0000
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