IVIVC Object

Results

Select the Results tab to view the output. IVIVC object output includes worksheets, plots, and text files. The lists of output in this section are grouped into tables based on the process by which they are generated.

•Build correlation process output

•Deconvolve data process output

•Deconvolve process output

•Output for the fit dissolution step of Prediction

•Output for InVitro stage of analysis

•Output for Levy plot results of Percent absorbed vs percent dissolved

•UIR Modeling output for InVivo stage

•Predict PK process output

•Validate correlation process output

Settings are not recorded on the History tab upon execution. However, they can be found in the individual Settings text output files for the different steps.

Note:Word Export is not recommended for IVIVC as the export would include all internal/hidden steps whose results are not readily discernible to the user.

Build correlation process output
Worksheet Name	Description
Correlation.Abs vs Diss data	Fraction Absorbed vs Dissolved Data
Correlation.Correlation Overlay data	Fraction Absorbed vs Dissolved Overlay Data
Correlation.Correlation.Condition Numbers	Correlation Modeling Results
Correlation.Correlation.Correlation Matrix
Correlation.Correlation.Diagnostics
Correlation.Correlation.Differential Equations
Correlation.Correlation.Dosing Used
Correlation.Correlation.Eigenvalues
Correlation.Correlation.Final Parameters
Correlation.Correlation.Final Parameters Pivoted
Correlation.Correlation.Initial Estimates
Correlation.Correlation.Minimization Process
Correlation.Correlation.Partial Derivatives
Correlation.Correlation.Predicted Data
Correlation.Correlation.Secondary Parameters
Correlation.Correlation.Secondary Parameters Pivoted
Correlation.Correlation.Stacked Partial Derivatives
Correlation.Correlation.Summary Table
Correlation.Correlation.User Defined Settings
Correlation.Correlation.VarianceCovariance Matrix
Correlation.Model Sim Vitro	Correlation Modeling Simulated Vitro Input
Correlation.Model Sim Vivo	Correlation Modeling Simulated Vivo Input
Correlation.Vitro Vivo Times	Used for in generating the Fraction Absorbed vs Dissolved plot. The tVitro added to Fa-Avg is based on the correlation function, all columns are removed except for formulation, tVivo, tVitro, and Mean
Plot Name	Description
Correlation.Abs vs Diss Corr Overlay	Fraction Absorbed vs Dissolved with Correlation Overlay data. This plot is generated for built-in models, but not for user models.
Correlation.Correlation.Observed Y and Predicted Y vs X	Correlation Modeling Results
Correlation.Correlation.Partial Derivatives Plot
Correlation.Correlation.Predicted Y vs X
Correlation.Correlation.Weighted Residual Y vs X
Text File Name	Description
Correlation.Correlation.Core output	Correlation Modeling Results
Correlation.Correlation.Settings	Correlation Modeling Results

Deconvolve data process output
Worksheet Name	Description
Deconvolution Input	Input to the deconvolution step

Deconvolve process output
Worksheet Name	Description
Vivo.Deconvolution.Dosing	Deconvolution modeling output for InVivo stage of analysis
Vivo.Deconvolution.Exponential Terms	Deconvolution modeling output for InVivo stage of analysis
Vivo.Deconvolution.Fa-avg	Fa-Avg plot data
Vivo.Deconvolution.Fitted Values	Deconvolution modeling output for InVivo stage of analysis
Vivo.Deconvolution.Parameters
Vivo.Deconvolution.Values
Plot Name	Description
Vivo.Deconvolution.Fa-Av The plot is not created if the Data already deconvolved and averaged option is selected, or if no sort variables are selected in the In-Vivo tab.	Fa-Avg overlaid. Average fraction absorbed by formulation. For the chart, Fabs grouped by subject with Fabs_avg overlaid and sorted by formulation
Vivo.Deconvolution.Fa-Av By Profile The plot is not created if the Data already deconvolved and averaged option is selected, or if no sort variables are selected in the In-Vivo tab.	Fa-Avg plot by profile Fabs grouped by formulation, vs. time, and sorted by profile
Vivo.Deconvolution.Fitted Curves Plot	Deconvolution modeling output for InVivo stage of analysis
Vivo.Deconvolution.Input Rates Plot	Deconvolution modeling output for InVivo stage of analysis
Text File Name	Description
Vivo.Deconvolution.Fa-avg	Descriptive Stats settings for Fa-Avg
Vivo.Deconvolution.Settings	Deconvolution modeling output for InVivo stage of analysis

Output for the fit dissolution step of Prediction
Worksheet Name	Plot Name
Prediction.Diss.Condition Numbers	Prediction.Diss.Observed Y and Predicted Y vs X
Prediction.Diss.Correlation Matrix	Prediction.Diss.Partial Derivatives Plot
Prediction.Diss.Diagnostics	Prediction.Diss.Predicted Y vs Observed Y
Prediction.Diss.Differential Equations	Prediction.Diss.Predicted Y vs X
Prediction.Diss.Eigenvalues	Prediction.Diss.Residual Y vs Predicted Y
Prediction.Diss.Final Parameters	Prediction.Diss.Residual Y vs X
Prediction.Diss.Final Parameters Pivoted	Text File Name
Prediction.Diss.Initial Estimates	Prediction.Diss.Core output
Prediction.Diss.Minimization Process	Prediction.Diss.Settings
Prediction.Diss.Partial Derivatives
Prediction.Diss.Predicted Data
Prediction.Diss.Secondary Parameters
Prediction.Diss.Secondary Parameters Pivoted
Prediction.Diss.Stacked Partial Derivatives
Prediction.Diss.Summary Table
Prediction.Diss.User Defined Settings
Prediction.Diss.VarianceCovariance Matrix

Output for InVitro stage of analysis
Worksheet Name	Plot Name
Vitro.Dissolution.Condition Numbers	Vitro.Dissolution.Observed Y and Predicted Y vs X
Vitro.Dissolution.Correlation Matrix	Vitro.Dissolution.Partial Derivatives Plot
Vitro.Dissolution.Diagnostics	Vitro.Dissolution.Predicted Y vs Observed Y
Vitro.Dissolution.Differential Equations	Vitro.Dissolution.Predicted Y vs X
Vitro.Dissolution.Eigenvalues	Vitro.Dissolution.Residual Y vs Predicted Y
Vitro.Dissolution.Final Parameters	Vitro.Dissolution.Residual Y vs X
Vitro.Dissolution.Final Parameters Pivoted	Vivo.Deconvolution.Cumulative Rates Plot
Vitro.Dissolution.Initial Estimates	Text File Name
Vitro.Dissolution.Minimization Process	Vitro.Dissolution.Core output
Vitro.Dissolution.Partial Derivatives	Vitro.Dissolution.Settings
Vitro.Dissolution.Predicted Data
Vitro.Dissolution.Secondary Parameters
Vitro.Dissolution.Secondary Parameters Pivoted
Vitro.Dissolution.Stacked Partial Derivatives
Vitro.Dissolution.Summary Table
Vitro.Dissolution.User Defined Settings
Vitro.Dissolution.VarianceCovariance Matrix

Output for Levy plot results of Percent absorbed vs percent dissolved
Worksheet Name	Plot Name
Levy Plots.%Abs vs %Diss.Levy Plot Values	Levy Plots.%Abs vs %Diss
Levy Plots.%Abs vs %Diss.Parameters	Levy Plots.Fabs vs Fdiss
Levy Plots.Fabs vs Fdiss.Levy Plot Values
Levy Plots.Fabs vs Fdiss.Parameters

UIR Modeling output for InVivo stage
Worksheet Name	Plot Name
Vivo.UIR.Condition Numbers	Vivo.UIR.Observed Y and Predicted Y vs X
Vivo.UIR.Correlation Matrix	Vivo.UIR.Partial Derivatives Plot
Vivo.UIR.Diagnostics	Vivo.UIR.Predicted Y vs Observed Y
Vivo.UIR.Differential Equations	Vivo.UIR.Predicted Y vs X
Vivo.UIR.Dosing Used	Vivo.UIR.Residual Y vs Predicted Y
Vivo.UIR.Eigenvalues	Vivo.UIR.Residual Y vs X
Vivo.UIR.Final Parameters	Test File Name
Vivo.UIR.Final Parameters Pivoted	Vivo.UIR.Core output
Vivo.UIR.Initial Estimates	Vivo.UIR.Settings
Vivo.UIR.Minimization Process	Vivo.UIR.Warnings and Errors
Vivo.UIR.Partial Derivatives
Vivo.UIR.Predicted Data
Vivo.UIR.Secondary Parameters
Vivo.UIR.Secondary Parameters Pivoted
Vivo.UIR.Stacked Partial Derivatives
Vivo.UIR.Summary Table
Vivo.UIR.User Defined Settings
Vivo.UIR.VarianceCovariance Matrix

Predict PK process output
Worksheet Name	Description
Prediction.Baseline Stats	Observed summary stats used to calculate the errors
Prediction.Conv.Results	Simulation Convolution modeling output for Prediction Stage
Prediction.Corr Sim.Condition Numbers	Correlation modeling simulation output for Prediction step
Prediction.Corr Sim.Correlation Matrix
Prediction.Corr Sim.Diagnostics
Prediction.Corr Sim.Differential Equations
Prediction.Corr Sim.Dosing Used
Prediction.Corr Sim.Eigenvalues
Prediction.Corr Sim.Final Parameters
Prediction.Corr Sim.Final Parameters Pivoted
Prediction.Corr Sim.Initial Estimates
Prediction.Corr Sim.Minimization Process
Prediction.Corr Sim.Partial Derivatives
Prediction.Corr Sim.Predicted Data
Prediction.Corr Sim.Secondary Parameters
Prediction.Corr Sim.Secondary Parameters Pivoted
Prediction.Corr Sim.Stacked Partial Derivatives
Prediction.Corr Sim.Summary Table
Prediction.Corr Sim.User Defined Settings
Prediction.Corr Sim.VarianceCovariance Matrix
Prediction.Model Sim Vitro	Vitro data used for Correlation Simulation
Prediction.Model Sim Vivo	Vivo data used for Correlation Simulation
Prediction.Observed Baseline Nca.Dosing Used	Observed NCA results
Prediction.Observed Baseline Nca.Exclusions
Prediction.Observed Baseline Nca.Final Parameters
Prediction.Observed Baseline Nca.Final Parameters Pivoted
Prediction.Observed Baseline Nca.Partial Area Labels
Prediction.Observed Baseline Nca.Plot Titles
Prediction.Observed Baseline Nca.Slopes Settings
Prediction.Observed Baseline Nca.Summary Table
Prediction.Predicted Nca prediction.Dosing Used	Predicted NCA results
Prediction.Predicted Nca prediction.Exclusions
Prediction.Predicted Nca prediction.Final Parameters
Prediction.Predicted Nca prediction.Final Parameters Pivoted
Prediction.Predicted Nca prediction.Partial Area Labels
Prediction.Predicted Nca prediction.Plot Titles
Prediction.Predicted Nca prediction.Slopes Settings
Prediction.Predicted Nca prediction.Summary Table
Prediction.Predicted Stats	Predicted summary stats used to calculate the errors
Prediction.Prediction Errors	Prediction Errors

Validate correlation process output
Worksheet Name	Description
Validation.Average Vivo.Statistics	Validation summary stats used to calculate the errors
Validation.Avg PE Stats	Validation percent error calculations
Validation.Baseline Stats	Observed summary stats used to calculate Validation errors
Validation.Conv.Exponential Terms	Simulation Convolution modeling output for Validation Stage
Validation.Conv.Results	Simulation Convolution modeling output for Validation Stage
Validation.Corr Sim.Condition Numbers	Simulation Correlation modeling output for Validation Stage
Validation.Corr Sim.Correlation Matrix
Validation.Corr Sim.Diagnostics
Validation.Corr Sim.Differential Equations
Validation.Corr Sim.Dosing Used
Validation.Corr Sim.Eigenvalues
Validation.Corr Sim.Final Parameters
Validation.Corr Sim.Final Parameters Pivoted
Validation.Corr Sim.Initial Estimates
Validation.Corr Sim.Minimization Process
Validation.Corr Sim.Partial Derivatives
Validation.Corr Sim.Predicted Data
Validation.Corr Sim.Secondary Parameters
Validation.Corr Sim.Secondary Parameters Pivoted
Validation.Corr Sim.Stacked Partial Derivatives
Validation.Corr Sim.Summary Table
Validation.Corr Sim.User Defined Settings
Validation.Corr Sim.VarianceCovariance Matrix
Validation.Model Sim Vitro	Correlation model simulation Vitro input for Validation Stage
Validation.Model Sim Vivo
Validation.Observed Baseline Nca.Dosing Used	Observed NCA analysis for Validation Stage
Validation.Observed Baseline Nca.Exclusions
Validation.Observed Baseline Nca.Final Parameters
Validation.Observed Baseline Nca.Final Parameters Pivoted
Validation.Observed Baseline Nca.Partial Area Labels
Validation.Observed Baseline Nca.Plot Titles
Validation.Observed Baseline Nca.Slopes Settings
Validation.Observed Baseline Nca.Summary Table
Validation.Predicted PK Nca.Dosing Used	Predicted NCA analysis for Validation Stage
Validation.Predicted PK Nca.Exclusions
Validation.Predicted PK Nca.Final Parameters
Validation.Predicted PK Nca.Final Parameters Pivoted
Validation.Predicted PK Nca.Partial Area Labels
Validation.Predicted PK Nca.Plot Titles
Validation.Predicted PK Nca.Slopes Settings
Validation.Predicted PK Nca.Summary Table
Validation.Predicted Stats	Predicted summary stats used to calculate Validation errors
Validation.Validation Errors	Validation Errors
Plot Name	Description
Validation.Corr Sim.Observed Y and Predicted Y vs X	Simulation Correlation modeling output for Validation Stage
Validation.Corr Sim.Partial Derivatives Plot
Validation.Corr Sim.Predicted Y vs X
Validation.Corr Sim.Weighted Residual Y vs X
Validation.Observed Baseline Nca.Observed Y and Predicted Y vs X	Observed NCA analysis for Validation Stage
Validation.Predicted PK Nca.Observed Y and Predicted Y vs X	Predicted NCA analysis for Validation Stage
Validation.Val Conv Out	Convolution results for simulation part of Validation
Text File Name	Description
Validation.Baseline Stats	Observed summary stats settings used to calculate Validation errors
Validation.Conv.Settings	Simulation Convolution modeling output for Validation Stage
Validation.Corr Sim.Core output	Simulation Correlation modeling output for Validation Stage
Validation.Corr Sim.Settings	Simulation Correlation modeling output for Validation Stage
Validation.IVIVC.Validation	Settings for the filter prior to generation of Validation Errors
Validation.Observed Baseline Nca.Core output	Observed NCA analysis for Validation Stage
Validation.Observed Baseline Nca.Settings	Observed NCA analysis for Validation Stage
Validation.Predicted PK Nca.Core output	Predicted NCA analysis for Validation Stage
Validation.Predicted PK Nca.Settings	Predicted NCA analysis for Validation Stage
Validation.Settings	Validation settings

Sigmoidal and Dissolution models

Model 601 — Hill
Model 602 — Weibull
Model 603 — Double Weibull
Model 604 — Makoid-Banakar
ASCII user model for correlations

Model 601 — Hill

y(t)=(Finf * t^b)/(MDT^b+t^b)

Example graph and equation for model 601 — Hill

where the estimated parameters are:

Finf: amount released at time infinity, using the preferred units for y

MDT: mean dissolution time, in the preferred units for x (time)

b: slope factor (no units)

int: y-intersect; zero (default) or first positive y value in data if estimated, with bounds of zero and 10 times that value

This model includes an optional lag time in the form of a step function, i.e.:

y(t)=(Finf *U(t – t_lag)^b)/(MDT^b+U(t – t_lag)^b)

(1)

where U(x)=x if x >= 0; U(x)=0 if x < 0.

Model 602 — Weibull

y(t)=Finf (1 – exp[–(t/MDT)^b])

Example graph and equation for model 602 — Weibull

where the estimated parameters are:

Finf: amount released at time infinity, using the preferred units for y

MDT: mean dissolution time, in the preferred units of x (time)

b: slope factor (no units)

int: y-intersect; zero (default) or first positive y value in data if estimated, with bounds of zero and 10 times that value

This model includes an optional lag time in the form of a step function, i.e.:

y(t)=Finf (1 – exp[–(U(t – t_lag)/MDT)^b])

(2)

where U(x)=x if x >= 0; U(x)=0 if x < 0.

Model 603 — Double Weibull

y(t)=f1 * Finf (1 – exp[(–t/MDT1)^b1])

+(1 – f1) * Finf (1 – exp[(–t/MDT2)^b2])

Example graph and equation for model 603 — Double Weibull

where the estimated parameters are:

f1: weighting factor, setting the fraction due to each Weibull (no units)

Finf: amount released at time infinity, using the preferred units for y

MDT1, MDT2: mean dissolution time for each Weibull, (x units)

b1, b2: slope factors (no units)

int: y-intersect; zero (default) or first positive y value in data if estimated, with bounds of zero and 10 times that value

This model includes an optional lag time in the form of a step function, i.e.:

y(t)=f1 * Finf (1 – exp[–(U(t – t_lag)/MDT1)^b1])

+(1 – f1) * Finf (1 – exp[–(U(t – t_lag)/MDT2)^b2])

(3)

where U(x)=x if x >= 0; U(x)=0 if x < 0.

Model 604 — Makoid-Banakar

y(t)=Fmax * (t/Tmax)^b * exp[b * (1 – t/Tmax)],
for t <= Tmax

y(t)=Fmax, for t > Tmax

Example graph and equation for model 604 — Makoid-Banakar

where the estimated parameters are:

Fmax: maximum y value, using the preferred units for y

Tmax: time of maximum y value, using the preferred units for x (time)

b: slope factor (no units)

int: y-intersect; zero (default) or first positive y value in data if estimated, with bounds of zero and 10 times that value

This model includes an optional lag time in the form of a step function, i.e.:

y(t)=Fmax * [U(t – t_lag)/Tmax]^b * exp[b * (1 – U(t – t_lag)/Tmax)]

(4)

where U(x)=x if x >= 0; U(x)=0 if x < 0.

Note:The FMAX parameter can be fixed at a certain value by doing one of the following:
– Set the initial estimates option to User-Supplied Initial Parameter Values, and then set initial estimates for any estimated parameters.
– Create a worksheet and use it as an external worksheet for the initial estimates.

ASCII user model for correlations

A custom model for an IVIVC correlation needs to include only the parameter definitions and model equations. No other statements are required.

In the # parameters menu in the Correlation tab, select the number of model parameters.
In the Correlation Estimates panel, enter an initial value for each model parameter.
Edit the statements in the Correlation Code panel to reflect the model. Use the ASCII model syntax and the following keywords that are specific to IVIVC:

•IVINTERP(): This function is used to interpolate dissolution data. A common usage is for X to be the in vivo time, and X is then scaled or shifted by model parameters to yield an in vitro time, which is called T. IVINTERP is then used to interpolate the dissolution data to find the dissolution at time T. The first and last values in the dissolution data are used when T is respectively before or after the time range of the dissolution data.

•DOSEVIVO: This variable contains the dose amount for each formulation. The wizard will translate this variable into the DTA array that is part of the WNL modeling language.

Data deficiencies resulting in missing values for PK parameters

Phoenix reports missing values for PK parameters in the following cases.

Cases when missing PK parameter values are reported
Profile issue(s)	Parameters reported as missing
Profile with no non-missing observations after dose time.	All final parameters
Bolus dosing and <=1 non-missing observation after dose time.	All final parameters
Blood/plasma data with no non-zero observation values.	All final parameters except: Cmax, AUClast, AUCall, AUMClast
Blood/plasma data with all zero values except one non-zero value at dosing time	All final parameters