invitro invivo corr

IVIVC - A in vitro- in vivo Correlation:

1 IVIVC - A in vitro - in vivo Correlation

CATEGORIES OF IN VITRO/IN VIVO CORRELATIONS :

2 CATEGORIES OF IN VITRO/IN VIVO CORRELATIONS A USP PF Stimuli Article in July 1988 established the classification of IVIVC into Levels A, B and C, which are currently in use. USP Chapter 1088 similarly describes techniques appropriate for Level A, B, and C correlations and methods for establishing dissolution specifications. Level A A Level A correlation is usually estimated by a two-stage procedure: deconvolution 2 followed by comparison of the fraction of drug absorbed to the fraction of drug dissolved.

CATEGORIES OF IN VITRO/IN VIVO CORRELATIONS :

3 CATEGORIES OF IN VITRO/IN VIVO CORRELATIONS A correlation of this type is generally linear . Represents a point-to-point relationship between in vitro dissolution and the in vivo input rate (e.g., the in vivo dissolution of the drug from the dosage form). In a linear correlation, the in vitro dissolution and in vivo input curves may be directly super imposable or may be made to be super imposable by the use of a scaling factor. Nonlinear correlations, while uncommon, may also be appropriate.

Slide 4:

4 Level C A Level C IVIVC establishes a single point relationship between a dissolution parameter. For example, t 50% , percent dissolved in 4 hours and a pharmacokinetic parameter (e.g.,AUC, C max , T max ). A Level C correlation does not reflect the complete shape of the plasma max concentration time curve, which is the critical factor that defines the performance of ER products. Multiple Level C A multiple Level C correlation relates one or several pharmacokinetic parameters of interest to the amount of drug dissolved at several time points of the dissolution profile.

Background:

5 Background In vitro-in vivo correlation (IVIVC) The correlation between in vitro drug dissolution and in vivo drug absorption

Purpose of IVIVC:

6 Purpose of IVIVC The optimization of formulations May require changes in the composition, manufacturing process, equipment, and batch sizes. In order to prove the validity of a new formulation, which is bioequivalent with a target formulation, a considerable amount of efforts is required to study bioequivalence (BE)/bioavailability(BA). The main purpose of an IVIVC model To utilize in vitro dissolution profiles as a surrogate for in vivo bioequivalence and to support biowaivers. Data analysis of IVIVC attracts attention from the pharmaceutical industry.

Purpose of our study:

7 Purpose of our study The purpose of this study is to develop an IVIVC tool ( ivivc ) in R. ivivc in R is menu-driven package. The development of level A IVIVC model

Frameworks of IVIVC in R:

8 Frameworks of IVIVC in R Input/Edit In Vivo Absorption Data: IV, Oral solution or IR drug Input/Edit In Vitro Dissolution Data and In Vivo absorption Data: ER drug with Different Release Rates Develop an IVIVC Model: Fitting IV, Oral solution or IR drug

Fitting IV, Oral solution or IR drug:

9 Fitting IV, Oral solution or IR drug PK parameters ( kel and Vd ) using PKfit Started with genetic algorithm ( genoud is from “rgenoud” package) fitting  Nelder-Mead Simplex algorithm ( optim )  end with nls

Frameworks of IVIVC in R:

10 Frameworks of IVIVC in R Input/Edit In Vivo Absorption Data: IV, Oral solution or IR drug Develop an IVIVC Model: Fitting IV, Oral solution or IR drug Input/Edit In Vitro Dissolution Data and In Vivo absorption Data: ER drug with Different Release Rates Input/Edit In Vivo Absorption Data: IV, Oral solution or IR drug Input/Edit In Vitro Dissolution Data and In Vivo absorption Data: ER drug with Different Release Rates Develop an IVIVC Model: Model Dependent Method

ER drug with Different Release Rates:

ER drug with Different Release Rates Model Dependent Method: deconvolution The observed fraction of the drug absorbed is based on the Wagner-Nelson method Observed drug plasma concentration (conc.obs) Estimated fraction of the drug absorbed (Fab)

Wagner-Nelson method:

12 Wagner-Nelson method

IVIVC model:

13 IVIVC model IVIVC model fraction of the drug absorbed vs. the drug dissolved the predicted fraction of the drug absorbed is calculated from the observed fraction of the drug dissolved. α and β are the intercept and slope of the regression line, respectively.

IVIVC model:

14 IVIVC model

Convolution:

15 the predicted fraction of the drug absorbed is then convolved to the predicted drug plasma concentrations Convolution Gohel M. and et al. http://www.pharmainfo.net/reviews/simplified-mathematical- approach-back-calculation-wagner-nelson-method predicted fraction of the drug absorbed (PredFab) predicted drug plasma concentration (conc.pred )

Predicted drug plasma conc.:

16 Predicted drug plasma conc.

Frameworks of IVIVC in R:

17 Frameworks of IVIVC in R Input/Edit In Vivo Absorption Data: IV, Oral solution or IR drug Develop an IVIVC Model: Fitting IV, Oral solution or IR drug Input/Edit In Vitro Dissolution Data and In Vivo absorption Data: ER drug with Different Release Rates Develop an IVIVC Model: Model Dependent Method Input/Edit In Vivo Absorption Data: IV, Oral solution or IR drug Input/Edit In Vitro Dissolution Data and In Vivo absorption Data: ER drug with Different Release Rates Develop an IVIVC Model: Model Dependent Method Evaluate an IVIVC model: Prediction Error

Internal Validation of level A correlation :

18 Internal Validation of level A correlation Predictability of a level A correlation estimating the percent prediction error (%PE) between the observed and predicted drug plasma concentration profiles pharmacokinetic parameters (Cmax, and the area under the curve from time zero to infinity, AUC∞).

Limitation and Future works:

19 Limitation and Future works Limitation Model dependent method One-compartment model: Wagner-Nelson method Future works Model dependent method Two-compartment model: Loo-Riegelman method Model independent method Numerical deconvolution Differential-equation based IVIVC model

More information:

20 More information Reference 1997. Guidance for industry, extended release oral dosage forms: Development, evaluation, and application of in vitro/ in vivo correlations. Dutta S, Qiu Y, Samara E, Cao G, Granneman GR. 2005. J Pharm Sci 94(9):1949-1956. Gohel M. , Delvadia RR, Parikh DC, Zinzuwadia MM, Soni CD, Sarvaiya KG, Joshi R and Dabhi AS. Simplified Mathematical Approach for Back Calculation in Wagner-Nelson Method. http://www.pharmainfo.net/reviews/simplified-mathematical-approach-back-calculation-wagner-nelson-method