• Language: en

Direct PD Model Simultaneous PK/PD Parameter fit

[Generated automatically as a Fitting summary]

Inputs

Description

Name:direct_pd_simul
Title:Direct PD Model Simultaneous PK/PD Parameter fit
Author:Wright Dose Ltd
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
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

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

Outputs

Final objective value

386.5948

which required 1.30 iterations and took 3.36 seconds

Final fitted fixed effects

f[CL] = 2.0029
f[V] = 48.1367
f[BASE] = 799.2158
f[KOUT] = 0.0288
f[PK_ANOISE] = 0.5095
f[PD_ANOISE] = 8.2249

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)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[CL] 5.0000 2.0029 0.5994 2.9971
f[V] 15.0000 48.1367 2.2091 33.1367
f[BASE] 500.0000 799.2158 0.5984 299.2158
f[KOUT] 0.1000 0.0288 0.7119 0.0712

Compare Noise f[X]

Variable Name Starting Value Fitted Value Prop Change Abs Change
f[PK_ANOISE] 5.0000 0.5095 0.8981 4.4905
f[PD_ANOISE] 5.0000 8.2249 0.6450 3.2249

Compare Variance f[X]

Back to Top