1. ivivc - A Tool for in vitro- in vivo Correlation Exploration with R
Speaker: Hsin-ya Lee
Advisors: Pao-chu Wu, Yung-jin Lee
College of Pharmacy, Kaohsiung Medical University,
Kaohsiung, Taiwan (R.O.C)
2008/08/14

2. Background In vitro-in vivo correlation (IVIVC)
the correlation between in vitro drug dissolution and in vivo drug absorption

3. 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.

4. 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

5. 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

6. 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

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

8. 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)

9. Wagner-Nelson method

10. 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.

11. IVIVC model

12. Convolution
the predicted fraction of the drug absorbed is then convolved to the predicted drug plasma concentrations
predicted fraction of
the drug absorbed
(PredFab)
predicted drug
plasma concentration
(conc.pred)
Gohel M. and et al.
approach-back-calculation-wagner-nelson-method

13. Predicted drug plasma conc.
sciplot package

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

15. 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∞).

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

17. Acknowledgment
Stephen D. Weigand (Departments of Biostatistics , Mayo Clinic Rochester, MN, USA): coding (by )
Henrique Dallazuanna (Curitiba-Paraná-Brasil): coding (by )

18. More information Reference Email Web
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 J Pharm Sci 94(9):
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.
Yung-Jin Lee :
Hsin-Ya Lee:
Web

19. Thanks for your attention!