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

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

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 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 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

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 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

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

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

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. http://www.pharmainfo.net/reviews/simplified-mathematical- approach-back-calculation-wagner-nelson-method

Predicted drug plasma conc. sciplot package

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

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 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

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

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. 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 Email Yung-Jin Lee : pkpd.taiwan@gmail.com Hsin-Ya Lee: hsinyalee@gmail.com Web http://pkpd.kmu.edu.tw/ivivc/

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