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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

424.9294

which required 8 iterations and took 1.11 seconds

Final fitted fixed effects

f[CL] = 1.9902
f[V] = 50.9567
f[BASE] = 800.8924
f[KOUT] = 0.0306
f[PK_ANOISE] = 0.5071
f[PD_ANOISE] = 10.0000

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 Fitted Value Starting Value Prop Change Abs Change
f[CL] 1.9902 5.0000 0.6020 3.0098
f[V] 50.9567 15.0000 2.3971 35.9567
f[BASE] 800.8924 500.0000 0.6018 300.8924
f[KOUT] 0.0306 0.1000 0.6938 0.0694

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PK_ANOISE] 0.5071 5.0000 0.8986 4.4929
f[PD_ANOISE] 10.0000 5.0000 1.0000 5.0000

Compare Variance f[X]

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