- Language: en
- Documentation version: 1.3.1
Indirect PKPD Model
[Generated automatically as a Tutorial 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:
- Diagram:
Comparison
True objective value
-52.6006
Final fitted objective value
-53.8626
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[BASE] |
500 |
800… |
800 |
4.34e-03 |
0.00% |
f[KOUT] |
0.1 |
0.03… |
0.03 |
6.26e-06 |
0.02% |
Compare Noise f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[ANOISE] |
5 |
0.463 |
0.5 |
3.67e-02 |
7.33% |
Compare Variance f[X]
No Variance f[X] values to compare.
Outputs
Fitted f[X] values (after fitting)
f[BASE] = 799.9957
f[KOUT] = 0.0300
f[ANOISE] = 0.4633
Generated data .csv file
- Synthetic Data:
Gen and Fit Summaries
Gen: Indirect PKPD Model (gen)
Fit: Indirect PKPD Model (fit)
Inputs
True f[X] values (for simulation)
f[BASE] = 800.0000
f[KOUT] = 0.0300
f[ANOISE] = 0.5000
Starting f[X] values (before fitting)
f[BASE] = 500.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000