WagnerNelson Object

Wagner-Nelso n

The Wagner-Nelson model is a one-compartment IV-bolus model that uses deconvolution to estimate the fraction of drug absorption. The model also generates AUCs (Areas Under the Curve) and rate of absorption values. This deconvolution model is best used to calculate the absorption kinetics of intravenously administered drugs.

For drugs that are administered through methods other than IV-bolus, such as oral or transdermal methods, it is preferable to use the Deconvolution object to evaluate drug release and absorption. For more information on the Deconvolution object, see “Deconvolution”.

Use one of the following to add the object to a Workflow:

Right-click menu for a Workflow object: New > IVIVC > Wagner-Nelson
Main menu: Insert > IVIVC > Wagner-Nelson.
Right-click menu for a worksheet: Send To > IVIVC > Wagner-Nelson.

Note:To view the object in its own window, select it in the Object Browser and double-click it or press ENTER. All instructions for setting up and execution are the same whether the object is viewed in its own window or in Phoenix view.

The user interface for Wagner-Nelson is the same as that for Loo-Riegelman, except that Loo-Riegelman has an additional Parameters panel. Click the following to view the interface description in the Loo-Riegelman section.

Main panel
Dosing panel
Slopes Selector panel
Slopes panel
Options tab
Plots tab

This section contains the following topics:

Wagner-Nelson inputs and calculations
Results

Wagner-Nelson inputs and calculations

The Wagner-Nelson method estimates the fraction of drug absorbed over time, relative to the total amount to be absorbed, following the method described in Gibaldi and Perrier (1975) pages 130 to 133. It uses as a basis AUC values computed for each time point in the time-concentration data.

Note:Wagner-Nelson computations assume single-dose PK data with a concentration value of zero at dose time. If no concentration value exists at dose time, a value of zero is used.

The value of AUCINF (AUC_¥) may be user-specified or compute as for non-compartmental analysis:

AUC_¥=AUC_last+C_last/Lambda_z

Either the observed or predicted value for C_last, where C_{last_pred}=exp(intercept – Lambda_z*t_last) can be used. As in the Wagner-Nelson method, the method for computing Lambda Z is specified by the user: best fit, user-specified range, or user-specified value. An intercept for the last case can be entered by the user. If the intercept is not specified, it will be computed using the last positive concentration and associated time value:

intercept=Lambda_z*t_last+ln(C_last)

The cumulative amount absorbed at time t, normalized by the central compartment volume V1, is computed as:

Cumul_Amt_Abs_V(t)=C(t)+Lambda_z*AUC(t)

Therefore, Cumul_Amt_Abs_V at t=infinity is:

Cumul_Amt_Abs_V(inf)=Lambda_z*AUC_¥

The relative fraction absorbed at time t is then:

Rel_Fraction_Abs(t)=Cumul_Amt_Abs_V(t)/Cumul_Amt_Abs_V(inf)

Data points with a missing value for either time or the concentration will be excluded from the analysis and will not appear in the output.

Results

Worksheet

Dosing Used: The dosing regimen specified for the modeling.
Exclusions: Excluded data points.
Final Parameters and Final Parameters Pivoted: Lists the following values for each profile.

Rsq: Goodness of fit statistic for the terminal elimination phase.
Rsq_adjusted: Goodness of fit statistic for the terminal elimination phase, adjusted for the number of points used in the estimation of Lambda Z.
Lambda_z: First-order rate constant associated with the terminal (log-linear) portion of the curve.
Estimated by linear regression of time vs. log concentration.
No_points_lambda_z: Number of points used in computing Lambda Z.
If Lambda Z is not estimable, then no points are used.

Plot

Observed Y and Predicted Y vs X: Plot of the Lambda Z fit. This plot group is not created if AUC_¥ is user-specified or if Lambda Z is user-specified.
Relative Fraction Absorbed: Relative fraction absorbed vs time.

Text File

Core output: Text file that contains a complete summary of the model commands, options, parameters, and values for a PK model, as well as any errors that occurred during modeling.
Settings: User-defined settings.

Users can double-click a plot in the Results tab to edit it. (See the menu options descriptions in the Plots chapter of the Data Tools and Plots Guide for plot editing options.)