At this point, all of the necessary commands and options have been specified.
• Click the Execute button. The results are displayed in the Results tab.
The Results tab contains three types of model output:
• Output Data (worksheets)
• Plots
• Text Output
Output Data (worksheets)
The PK worksheet output contains the following types of results:
PK Model worksheet contents
Item Contents
Condition Num-bers
Rank and condition number of the matrix of partial derivatives for each iteration. The matrix is of full rank, since Rank is equal to the number of parameters. If the Rank were less than three, that would indicate that there was not enough information in the data to estimate all three parameters. The condition value is the square root of the ratio of the largest to the smallest eigenvalue.
Correlation Matrix
Correlation matrix for the parameters.
Diagnostics The following diagnostics are provided: corrected sum of squared observations (CSS), weighted corrected sum of squared observations (WCSS), sum of squared residuals (SSR), weighted sum of squared residuals (WSSR), estimate of residual standard deviation (S) and degrees of freedom (DF), the correlation between observed Y and predicted Y, the weighted correlation, and two measures of good-ness of fit: the Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC).
Dosing Used Dose amounts and dosing times.
Eigenvalues Eigenvalues for each level of the sort variables.
Final Parame-ters
Parameter names, estimates, standard error of the estimates, CV%, univariate confi-dence intervals, and planar conficonfi-dence intervals.
Final Parame-ters Pivoted
Parameter names, estimates, standard error of the estimates, CV%, univariate confi-dence intervals, and planar conficonfi-dence intervals stacked by parameter.
Initial Estimates Parameter names, initial values, and lower and upper bounds.
Minimization Process
Iteration number, weighted sum of squares, and value for each parameter.
Plots
The PK model output includes six plots:
Observed Y and Predicted Y vs X Partial
Deriva-tives
Values of the partial derivatives at each time point for each function being fit. In this case, one function, predicting plasma concentration.
Predicted Data Time and predicted Y for the number of time points selected in the Model Options PK Settings. Partial data shown below.
Secondary Parameters
Secondary parameter name, estimate, and standard error of the estimate CV%.
Secondary Parameters Piv-oted
Secondary parameter name, estimate, and standard error of the estimate CV%
stacked by parameter.
Stacked Partial Derivatives
Values of the partial derivatives at each time point for each function being fit. In this case, one function, predicting plasma concentration, with all the parameters in one column.
Summary Table Summary of observed and predicted data and residuals. For PK/PD link models the Summary table would also include CP and Ce; for indirect response models, CP.
User Defined Settings
User-defined PK model settings.
VarianceCovari-ance Matrix
Variance-covariance matrix for the parameters.
Partial Derivatives Plot
Predicted Y vs Observed Y
Predicted Y vs X
Residual Y vs Predicted Y
Residual Y vs X
Text Output
The Core output text file contains all model settings and output in plain text for-mat.
Saving the project and the results
Projects and their results can be saved in several ways. Projects can be saved as a project file. Projects can also be loaded into the Pharsight Knowledgebase Server (PKS) as a new study. A Phoenix Connect license is required for this func-tionality.
Save the project as a file
WINNONLIN NONLINEAR ESTIMATION PROGRAM
METH 2'Gauss-Newton (Levenberg and Hartley) ITER 50
INIT 0.25,1.81,0.23 MISS '.'
DATA 'WINNLIN.DAT' BEGI
The following default parameter boundaries were generated.
Parameter Lower Bound Upper Bound V_F 0.000 2.500
K01 0.000 18.10 K10 0.000 2.300
2. Select a directory in the Save in menu or accept the default directory.
3. Type a name in the File name field or accept the default name and click Save.
4. The project is saved as a Phoenix Project (.phxproj) file.
Save the project into the PKS
1. In the PKS menu select Create Study.
The Create Study dialog is displayed.
2. Click the Connect button.
3. Enter the user name and password.
4. In the Study Name field type PK Model Example.
5. In the Description field type Saving a project in PKS.
6. Select the Study Data tab.
7. Click the Browse Projects button to select a data source.
8. Select the study1 worksheet and click Select.
9. Click the Map Study Data button.
10.Use the pointer to drag Subject from the Source Column to the Subject Iden-tifiers box.
11.In the Study Mapping dialog, select the Default Data Collection Point tab.
12.Use the pointer to drag Time from the Source Column to the Relative_Nominal_Time and Relative_Actual_Time fields.
13.Click OK.
14.Select the Samples tab.
15.Drag Conc to the Samples list.
16.Click the Save Map button.
The Save As dialog is displayed. This allows users to save the study mapping selections to a .map file.
17.In the Save in menu select the Pharsight Projects Directory, which by default is located at C:\Documents and Settings\<user name>\My
Documents\Pharsight Projects.
18.In the File name field type PK Example and click Save.
19.Click OK in the Create Study dialog.
The PKS Save dialog is displayed.
20.In the Audit Reason field type Save project.
21.Enter the password in the Password field.
22.Click OK.
The PKS Process Manager is displayed. The process manager shows the status of PKS jobs.
23.When the process is complete, click Close in the PKS Process Manager.
The project is now saved as a study in the PKS.
Note: It is not necessary to keep a project open after completing each chapter. This project is not required when working in the next chapter. To close a project right-click the project and select Close Project.