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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

2.0019

2.9981

0.5996

f[V]

15.0000

48.0926

33.0926

2.2062

f[BASE]

500.0000

799.2292

299.2292

0.5985

f[KOUT]

0.1000

0.0288

0.0712

0.7123

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PK_ANOISE]

5.0000

0.5102

4.4898

0.8980

f[PD_ANOISE]

5.0000

8.2253

3.2253

0.6451

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

386.6144

which required 1.30 iterations and took 3.78 seconds

Fitted f[X] values (after fitting)

f[CL] = 2.0019
f[V] = 48.0926
f[BASE] = 799.2292
f[KOUT] = 0.0288
f[PK_ANOISE] = 0.5102
f[PD_ANOISE] = 8.2253

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
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