# Direct PD Model Simultaneous PK/PD Parameter fit

[Generated automatically as a Fitting summary]

## Model Description

Name:

  direct_pd_simul

Title:

  Direct PD Model Simultaneous PK/PD Parameter fit

Author:

  PoPy for PK/PD

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`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[CL] | 5.0000 | 2.0038 | 2.9962 | 0.5992
f[V] | 15.0000 | 48.1868 | 33.1868 | 2.2125
f[BASE] | 500.0000 | 799.1967 | 299.1967 | 0.5984
f[KOUT] | 0.1000 | 0.0288 | 0.0712 | 0.7116

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PK_ANOISE] | 5.0000 | 0.5088 | 4.4912 | 0.8982
f[PD_ANOISE] | 5.0000 | 8.2246 | 3.2246 | 0.6449

### Compare Variance f[X]

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    386.5798

which required 1.30 iterations and took 11.40 seconds

### Fitted f[X] values (after fitting)

    f[CL] = 2.0038
    f[V] = 48.1868
    f[BASE] = 799.1967
    f[KOUT] = 0.0288
    f[PK_ANOISE] = 0.5088
    f[PD_ANOISE] = 8.2246

### 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[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