ivivc - A Tool for “in vitro-in vivo Correlation” Exploration with R
Transcript of ivivc - A Tool for “in vitro-in vivo Correlation” Exploration with R
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ivivc ivivc - A Tool for - A Tool for in vitroin vitro--in vivo in vivo Correlation Correlation Exploration with Exploration with RR
Speaker: Hsin-ya LeeSpeaker: Hsin-ya LeeAdvisors: Pao-chu Wu,Advisors: Pao-chu Wu, Yung-jin Lee
College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan (R.O.C)
2008/08/14
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BackgroundBackground In vitro-in vivo correlation (IVIVC)
the correlation between in vitro drug dissolution and in vivo drug absorption
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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.
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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
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Frameworks of IVIVC in RFrameworks 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
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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
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Frameworks of IVIVC in RFrameworks 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
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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)
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Wagner-Nelson method
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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, α and β are the intercept and slope of the regression line, respectively.respectively.
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IVIVC modelIVIVC model
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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)
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Predicted drug plasma conc.Predicted drug plasma conc.
sciplot package
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Frameworks of IVIVC in RFrameworks 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
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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∞).
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Limitation and Future worksLimitation and Future works
LimitationLimitation Model dependent method
One-compartment model: One-compartment model: Wagner-Nelson method Future worksFuture works
Model dependent method Two-compartment model: Loo-Riegelman method
Model independent method Numerical deconvolution Differential-equation based IVIVC model
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AcknowledgmentAcknowledgment Stephen D. Weigand (Departments of
Biostatistics , Mayo Clinic Rochester, MN, USA): coding (by e-mail)
Henrique Dallazuanna (Curitiba-Paraná-Brasil): coding (by e-mail)
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More informationMore 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
Email Yung-Jin Lee : : [email protected]@gmail.com Hsin-Ya Lee: [email protected]
Web http://pkpd.kmu.edu.tw/ivivc/
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Thanks for your attention!Thanks for your attention!