Bioavailability and Lag

[Generated automatically as a Fitting summary]

Model Description

Name:

biolag_abs_both

Title:

Bioavailability and Lag

Author:

PoPy for PK/PD

Abstract:

One compartment model absorption dosing with bioavailability and lag parameters.

Keywords:

identifiability; bioavailability; lag

Input Script:

biolag_abs_both_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	1.0000	0.5000	1.0000
f[CL]	1.0000	3.5664	2.5664	2.5664
f[V]	15.0000	21.8447	6.8447	0.4563
f[BIO]	0.8000	0.6829	0.1171	0.1464
f[LAG]	1.0000	8.9078	7.9078	7.9078

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE_STD]	5.0000	0.8937	4.1063	0.8213

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

38.7572

which required 1.29 iterations and took 13.14 seconds

Fitted f[X] values (after fitting)

f[KA] = 1.0000
f[CL] = 3.5664
f[V] = 21.8447
f[ANOISE_STD] = 0.8937
f[BIO] = 0.6829
f[LAG] = 8.9078

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[KA] = 0.5000
f[CL] = 1.0000
f[V] = 15.0000
f[ANOISE_STD] = 5.0000
f[BIO] = 0.8000
f[LAG] = 1.0000