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