• Language: en
  • Documentation version: 1.3.1

Indirect PKPD Model

[Generated automatically as a Fitting summary]

Model Description

Name:

indirect_pd

Title:

Indirect PKPD Model

Author:

PoPy for PK/PD

Abstract:

A indirect PD Model, based on the amount of drug in the body, delayed by two lag compartments
The amount in the central compartment is determined by K, which has been estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).
Keywords:

pd; one compartment model; indirect; delay compartment

Input Script:

indirect_pd_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[BASE]

500.0000

799.9957

299.9957

0.6000

f[KOUT]

0.1000

0.0300

0.0700

0.7001

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[ANOISE]

5.0000

0.4633

4.5367

0.9073

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-53.8626

which required 1.27 iterations and took 11.24 seconds

Fitted f[X] values (after fitting)

f[BASE] = 799.9957
f[KOUT] = 0.0300
f[ANOISE] = 0.4633

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[BASE] = 500.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000
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