ivivc - A Tool for in vitro- in vivo Correlation Exploration with R

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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

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Background

• 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 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
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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 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 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
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

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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.
• a and ß are the intercept and slope of the
  regression line, respectively.

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IVIVC model
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Convolution

• the predicted fraction of the drug absorbed is
  then convolved to the predicted drug plasma
  concentrations

Gohel M. and et al. http//www.pharmainfo.net/rev
iews/simplified-mathematical- approach-back-calcul
ation-wagner-nelson-method
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Predicted drug plasma conc.
sciplot package
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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 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
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
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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,
  AUC8).

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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

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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)

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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-mathe
  matical-approach-back-calculation-wagner-nelson-me
  thod
• Email
• Yung-Jin Lee pkpd.taiwan_at_gmail.com
• Hsin-Ya Lee hsinyalee_at_gmail.com
• Web
• http//pkpd.kmu.edu.tw/ivivc/

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Thanks for your attention!