Direct PD Model Simultaneous PK/PD Parameter fit

[Generated automatically as a Fitting summary]

Model Description

Name:: direct_pd_simul
Title:: Direct PD Model Simultaneous PK/PD Parameter fit
Author:: PoPy for PK/PD
Abstract:

A simple direct PD Model, based on the amount of drug in the body. That simultaneously fits PK and PD parameters.
The amount in the central compartment is determined by K, which has been previously estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

Keywords:: pd; one compartment model; direct
Input Script:: direct_pd_simul_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL]	5.0000	2.0038	2.9962	0.5992
f[V]	15.0000	48.1868	33.1868	2.2125
f[BASE]	500.0000	799.1967	299.1967	0.5984
f[KOUT]	0.1000	0.0288	0.0712	0.7116

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PK_ANOISE]	5.0000	0.5088	4.4912	0.8982
f[PD_ANOISE]	5.0000	8.2246	3.2246	0.6449

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

386.5798

which required 1.30 iterations and took 11.40 seconds

Fitted f[X] values (after fitting)

f[CL] = 2.0038
f[V] = 48.1868
f[BASE] = 799.1967
f[KOUT] = 0.0288
f[PK_ANOISE] = 0.5088
f[PD_ANOISE] = 8.2246

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[CL] = 5.0000
f[V] = 15.0000
f[BASE] = 500.0000
f[KOUT] = 0.1000
f[PK_ANOISE] = 5.0000
f[PD_ANOISE] = 5.0000