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Sine circadian model

[Generated automatically as a Fitting summary]

Model Description

Name:

circ_sin

Title:

Sine circadian model

Author:

PoPy for PK/PD

Abstract:

A PD Model based on the amount of drug in the body.
The PD model uses a sine function which simulates a circadian rhythm for the generation of a biomarker.
The amount in the central compartment is determined by CL and V, PK patameters , which have been previosly estimated for each individual.
The amount in the central compartment influences the rate of production of a biomarker.
Keywords:

PD; Pharmacodynamics; sine function; Circadian rhythm

Input Script:

circ_sin_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[AMP]

3.0000

2.0018

0.9982

0.3327

f[INT]

16.0000

7.8714

8.1286

0.5080

f[KOUT]

0.1000

0.0500

0.0500

0.4998

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[ANOISE]

5.0000

3.0759

1.9241

0.3848

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

811.9569

which required 1.26 iterations and took 12.17 seconds

Fitted f[X] values (after fitting)

f[AMP] = 2.0018
f[INT] = 7.8714
f[KOUT] = 0.0500
f[ANOISE] = 3.0759

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[AMP] = 3.0000
f[INT] = 16.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000
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