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  • Documentation version: 1.3.1

Complex Emax PD Model

[Generated automatically as a Fitting summary]

Model Description

Name:

emax_complex

Title:

Complex Emax PD Model

Author:

PoPy for PK/PD

Abstract:

An complex emax PK/PD Model, based on the concentration of drug in the body.
The two compartment PK model uses previously estimated values of CL, V1, Q and V2 for each individual
The concentration in the central compartment influences the effect compartment along with EMAX (the maximum effect), EC50 (the concentration at which the effect is half the maximum) using a hill equation.
Keywords:

pkpd

Input Script:

emax_complex_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[EMAX]

100.0000

105.0956

5.0956

0.0510

f[E50]

10.0000

47.6442

37.6442

3.7644

f[GAMMA]

1.0000

0.4755

0.5245

0.5245

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[ANOISE]

5.0000

2.7512

2.2488

0.4498

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

301.5421

which required 1.30 iterations and took 12.35 seconds

Fitted f[X] values (after fitting)

f[EMAX] = 105.0956
f[E50] = 47.6442
f[GAMMA] = 0.4755
f[ANOISE] = 2.7512

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] = 10.0000
f[GAMMA] = 1.0000
f[ANOISE] = 5.0000
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