A GENERAL APPROACH TO DEVELOPING A LEVEL A IVIVC

2. A GENERAL APPROACH TO DEVELOPING A LEVEL A IVIVC

The Level A correlation can be developed using a two-stage deconvolution procedure, a one-stage convolution procedure,

a compartmental modeling approach, or any modeling techni- que that relates in vitro dissolution to the in vivo curve (1). Independent of the procedure, the entire in vivo time course must be described from the in vitro data.

The most common approach used in the development of the Level A correlation and the only approach discussed in this chapter is the two-stage procedure. The first stage is to estimate the in vivo absorption or in vivo dissolution time course using deconvolution or a mass balance approach such as Wagner–Nelson. Equation (1) presents the convolu- tion =deconvolution equation that can be used to perform the first stage of deconvolution:

c ðtÞ ¼

c d 0 vivo ðuÞ du

where c is the plasma drug concentration of the formulation to correlate (e.g., the extended release formulation), x vivo the cumulative amount absorbed or released in vivo of the formu- lation to correlate (e.g., the extended release formulation),

x 0 vivo the in vivo absorption or release rate (i.e., the first deri- vative of x vivo ), and c d is the unit impulse response (i.e., the plasma concentration time course resulting from the instan- taneous in vivo absorption or release of a unit amount of drug).

In the second stage, a model is developed to describe the relationship between the in vitro release (IVR) and the in vivo absorption (or release) estimated in stage 1. Prior to the pub- lication of the FDA IVIVC Guidance (1) and the first meeting solely dedicated to IVIVC (7), an IVIVC model was thought to

be a linear ‘‘point-to-point’’ relationship between the cumula- tive amount released in vitro and the cumulative amount absorbed (or released) in vivo for one formulation. Since 1996, the view of the IVIVC model has changed. An IVIVC

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no longer exists when the in vitro–in vivo relationship is developed for a single formulation. The accepted criteria now require that the mathematical model describes the in vitro–in vivo relationship for two or more formulations (1,7). In addition, models more complex than linear correla- tion models are now accepted with nonlinear and =or time- variant models becoming very common (1,7).

Once a model is developed to describe the relationship between in vivo and in vitro response, the next task is to determine the validity of the model. Within the FDA IVIVC Guidance, the predictability of the IVIVC model is used to validate the IVIVC model. This predictability of an IVIVC model is a verification of the model’s ability to describe the in vivo bioavailability from:

1. The data set that was used to develop the model (internal predictability) and =or

2. A data set not used to develop the model (external predictability).

The C max and AUC predicted by the IVIVC model are compared to the observed C max and AUC. Percent prediction errors (%PE) are estimated from the following equation

Observed value %PE ¼

ð2Þ Observed value

All IVIVC models should be evaluated for their internal predictability. In order to evaluate the robustness beyond the internally used data, external predictability can be used.

For regulatory purposes, the FDA IVIVC Guidance sets an acceptable criterion for internal and external predictabil- ity. The internal predictability criteria for a regulatory accep- table IVIVC model is that the C max and AUC %PE for each formulation is less than or equal to 15% and the average abso- lute %PE of C max and AUC for all formulations is less than or equal to 10%.

If the internal predictability is greater than the accepta- ble criteria or the drug is a narrow therapeutic index drug, the FDA requires the more robust analysis of the model using external predictability. The external predictability criteria for

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an acceptable IVIVC model is that the C max and AUC %PE for the external formulation is less than or equal to 10%. If the %PE is between 10% and 20%, the predictability is inconclu- sive and additional data sets and =or formulations should be evaluated. If the %PE is greater than 20%, this generally indi- cates that the IVIVC model does not adequately predict in vivo bioavailability parameters for regulatory use.

The importance of a predictable IVIVC model (based on the above criteria) cannot be overemphasized from a regula- tory perspective (1). However, if the IVIVC is to be used for development purposes (e.g., improving a formulation), the criteria defined in the FDA IVIVC Guidance are not required (7). The only requirement is the belief that the model has enough robust validity to assist the formulator in the further development of the formulation.

2.1. Level A Model Development In order to understand the basic two-stage approach to devel-

oping a Level A IVIVC, an example is presented for five mod- ified release oral formulations with differing IVR profiles

( Fig. 1a ) and an immediate release solution. The plasma concentration data after administering each formulation to human normal volunteers were obtained (Fig. 1b). The mean in vivo and in vitro data were then used for the analysis. The plasma concentration profile for the solution is not presented. Deconvolution was performed using the plasma concentration data from the five modified release dosage forms and the unit impulse response from the solution. The cumulative amount absorbed over time is provided in Fig. 1c. The relationship between in vivo and in vitro is presented in Fig. 1d and follows a linear relationship.

2.2. Predictability of the Level A IVIVC An example demonstrating how to evaluate the predictability

of an IVIVC model is presented in Figs. 2 and 3 . Figure 2 represents the screen shot from the program PDx-IVIVC (8,9). The in vitro and in vivo plasma data were placed in

Figure 1 Diagram of the basic two-stage approach to IVIVC. (a) % Released in vitro vs. time for five formu- lations to be administered for the IVIVC study (top center), (b) plasma concentration vs. time for the five formulations with in vitro release profiles described in (a), (c) % released =absorbed in vivo vs. time for the

Young five formulations after performing the deconvolution using a solution administered through the same route,

and (d) the % released in vitro vs. % release =absorbed in vivo with the solid line representing the predicted relationship based on the IVIVC model.

et al.

© 2005 by Taylor & Francis Group, LLC

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Figure 2 Output from the PDx-IVIVC software comparing % absorbed in vivo to time scaled =shifted % dissolved in vitro. The bullets represent the observed data and the solid line the predicted line from the IVIVC model.

the program for four formulations (three modified release and one solution). The program was used to perform the deconvo- lution and to develop an IVIVC model. The plot of % relea- sed =absorbed in vivo vs. % released in vitro is presented in Fig. 2, the bullets represent the raw data and the solid line represents the predicted line from the IVIVC model.

In order to demonstrate the validation process, Fig. 3 illustrates how the PDx-IVIVC program then compares the predicted and observed C max and AUC for the modified release formulations. The results show that the %PE for each formulation and the mean %PE are all within the criteria for

a predictable regulatory standard IVIVC. For external predic- tion, predicted and observed C max and AUC of a formulation not used to develop the IVIVC model are compared (CR 3 in

Figs. 4 and 5 ). If the %PE is within the FDA criteria, the

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Figure 3 Output from the PDx-IVIVC software showing the inter- nal prediction from Fig. 2 . %PE and the observed and predicted

C max and AUC are presented.

IVIVC model can be designated a validated regulatory model with external predictability.