A 1-step Bayesian predictive approach for evaluating in vitro in vivo correlation (IVIVC)

IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a functio...

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Published inBiopharmaceutics & drug disposition Vol. 30; no. 7; pp. 366 - 388
Main Authors Gould, A. Lawrence, Agrawal, Nancy G. B., Goel, Thanh V., Fitzpatrick, Shaun
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 01.10.2009
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ISSN0142-2782
1099-081X
1099-081X
DOI10.1002/bdd.672

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Abstract IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled‐release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between‐ and within‐subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, Cmax and Tmax can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly. Copyright © 2009 John Wiley & Sons, Ltd.
AbstractList IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled‐release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between‐ and within‐subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, Cmax and Tmax can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly. Copyright © 2009 John Wiley & Sons, Ltd.
IVIVC ( in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled‐release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between‐ and within‐subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC , C max and T max can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly. Copyright © 2009 John Wiley & Sons, Ltd.
IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled-release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between- and within-subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, C(max) and T(max) can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly.IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled-release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between- and within-subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, C(max) and T(max) can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly.
IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional bioequivalence trials in human subjects. Most current IVIVC methods express the in vivo plasma concentration of a drug formulation as a function of the cumulative in vivo absorption. The absorption is not directly observable, so is estimated by the cumulative dissolution of the drug formulation in in vitro dissolution trials. The calculations conventionally entail the complex and potentially unstable mathematical operations of convolution and deconvolution, or approximations aimed at omitting their need. This paper describes, and illustrates with data on a controlled-release formulation, a Bayesian approach to evaluating IVIVC that does not require convolution, deconvolution or approximation. This approach incorporates between- and within-subject (or replicate) variability without assuming asymptotic normality. The plasma concentration curve is expressed in terms of the in vitro dissolution percentage instead of time, recognizing that this correspondence may be noisy because of the various sources of error. All conventional functions of the concentration curve such as AUC, C(max) and T(max) can be expressed in terms of dissolution percentage, with uncertainties arising from variability in measuring absorption and dissolution accounted for explicitly.
Author Fitzpatrick, Shaun
Agrawal, Nancy G. B.
Goel, Thanh V.
Gould, A. Lawrence
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Cites_doi 10.1016/S0939-6411(03)00140-1
10.1111/j.1751-5823.2003.tb00203.x
10.1111/1467-9884.00176
10.1023/A:1011531226478
10.1023/A:1008929526011
10.1016/S0168-3659(03)00033-6
10.1080/10543409708835207
10.1023/A:1020206907668
10.1198/106186004X11435
10.1007/978-1-4684-6036-0_6
10.1007/BF01059546
10.1021/js970155d
10.1002/(SICI)1097-0258(19990730)18:14<1865::AID-SIM223>3.0.CO;2-P
10.1211/002235702847
10.1080/01621459.1996.10476664
10.1021/js9503587
10.1016/j.jpba.2007.05.021
10.1007/978-1-4684-6036-0_5
10.1137/1.9781611971675
10.1023/A:1016020822093
10.1111/1467-9868.00353
10.1007/BF01065657
10.1002/jps.20592
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References Dunne A, Devane J, O'Hara T. The relationship between in vitro drug dissolution and in vivo absorption. J Roy Stat Soc Series D Statistician 1999; 48: 125-133.
Polli JE, Crison JR, Amidon GL. Novel approach to the analysis of in vitro-in vivo relationships. J Pharm Sci 1996; 85: 753-760.
Langenbucher F. Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution. Eur J Pharm Biopharm 2003; 56: 429-437.
Dunne A, O'Hara T, Devane J. Approaches to IVIVR modelling and statistical analysis. Adv Exp Med Biol 1997; 423: 67-86.
Lunn DJ, Thomas A, Best NG, Spiegelhalter DJ. WinBUGS-A Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000; 10: 325-337.
Gelman A. A Bayesian formulation of exploratory data analysis and goodness-of-fit testing. Int Stat Rev 2002; 71: 369-382.
Dunne A, O'Hara T, Devane J. Level A in vitro in vitro correlation: Nonlinear models and statistical methodology. J Pharm Sci 1997; 86: 1245-1249.
Gelman A. Exploratory data analysis for complex models. J Comput Graph Stat 2004; 13: 755-779.
Gillespie WR, Veng-Pedersen P. A polyexponential deconvolution method. Evaluation of the 'gastrointestinal bioavailability' and mean in vivo dissolution time of some ibuprofen dosage forms. J Pharmacokinet Biopharm 1985; 13: 289-307.
Lunn DJ, Best NG, Thomas A, Wakefield J, Spiegelhalter DJ. Bayesian analysis of population pK/pD models: General concepts and software. J Pharmacokinet Pharmacodyn 2002; 29: 271-307.
Polli JE, Crison JR, Amidon GL. Novel approach to the analysis of in vitro-in vivo relationships (vol 85, pg 753, 1996). J Pharm Sci 1997; 86: 268.
USP Subcommittee on Biopharmaceutics. In-vitro/in-vivo correlation for extended-release oral dosage forms. Pharm Forum 1988; 13: 4161.
Buchwald P. Direct, differential-equation-based in-vitro-in-vivo correlation (IVIVC) method. J Pharm Pharmacol 2003; 55: 495-504.
Lunn DJ, Wakefield J, Thomas A, Best NG, Spiegelhalter DJ. PKBugs User Guide, version 1.1. 1999. Department of Epidemiology and Public Health, Imperial College School of Medicine: London.
Berry MR, Likar MD. Statistical assessment of dissolution and drug release profile similarity using a model-dependent approach. J Pharm Biomed Anal 2007; 45: 194-200.
O'Hara T, Hayes S, Davis J, Devane J, Smart T, Dunne A. In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step. J Pharmacokinet Pharmacodyn 2001; 28: 277-298.
European Agency for the Evaluation of Medicinal Products. Note for Guidance on Quality of Modified Release Products: A: Oral Dosage Forms B: Transdermal Dosage Forms Section I (Quality). 1999. European Agency for the Evaluation of Medicinal Products: London.
Veng-Pedersen P. An algorithm and computer program for deconvolution in linear pharmacokinetics. J Pharmacokinet Biopharm 1980; 8: 463-481.
Mauger DT, Chinchilli VM. In vitro-in vivo relationships for oral extended-release drug products. J Biopharm Stat 1997; 7: 565-578.
Gillespie WR. In Vitro-In Vivo Correlations. Plenum Press: New York, 1997; 53-65.
Food and Drug Administration. Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations. 1997. Food and Drug Administration: Rockville, MD.
Wing GM. APprimer on Integral Equations of the First Kind-The Problem of Deconvolution and Unfolding. 1991. Society for Industrial and Applied Mathematics: Philadelphia.
Dunne A, O'Hara T, Devane J. A new approach to modelling the relationship between in vitro and in vivo drug dissolution/absorption. Stat Med 1999; 18: 1865-1876.
Thomas A, O'Hara B, Ligges U, Sturtz S. Making BUGS open. R News 2006; 6: 12-17.
Kortejärvi H, Malkki J, Marvola M, Urtti A, Yliperttula M, Pajunen P. Level A In Vitro-In Vivo correlation (IVIVC) model with Bayesian approach to formulation series. J Pharm Sci 2006; 95: 1595-1605.
Wakefield J. The Bayesian analysis of population pharmacokinetic models. J Am Stat Assoc 1996; 91: 62-75.
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit (with discussion). J Roy Stat Soc Series B Methodological 2002; 64: 583-616.
Costa FO, Sousa JJS, Pais AACC, Formosinho SJ. Comparison of dissolution profiles of ibuprofen pellets. J Control Rel 2003; 89: 199-212.
Langenbucher F, Möller H. Correlation of in vitro drug relationships with in vivo response kinetics. Pharm Ind 1983; 45: 623-628.
Sathe PM, Tsong Y, Shah VP. In vitro dissolution profile comparison: Statistics and analysis, model dependent approach. Pharm Res 1996; 13: 1799-1803.
2006; 95
1997; 86
1999; 48
1997
1988; 13
2006; 6
2001; 28
1991
1996; 91
1996; 13
1997; 7
2003; 55
1999
2003; 56
1997; 423
2002; 29
2002; 64
1999; 18
2000; 10
2004; 13
1996; 85
1980; 8
2002; 71
2007; 45
1985; 13
2003; 89
1983; 45
European Agency for the Evaluation of Medicinal Products (e_1_2_1_8_2) 1999
e_1_2_1_22_2
Lunn DJ (e_1_2_1_31_2) 1999
e_1_2_1_23_2
e_1_2_1_20_2
e_1_2_1_21_2
e_1_2_1_27_2
e_1_2_1_24_2
Langenbucher F (e_1_2_1_26_2) 1983; 45
Polli JE (e_1_2_1_6_2) 1997; 86
e_1_2_1_28_2
e_1_2_1_29_2
Thomas A (e_1_2_1_25_2) 2006; 6
e_1_2_1_30_2
USP Subcommittee on Biopharmaceutics (e_1_2_1_7_2) 1988; 13
e_1_2_1_4_2
e_1_2_1_5_2
e_1_2_1_2_2
Food and Drug Administration (e_1_2_1_3_2) 1997
e_1_2_1_11_2
e_1_2_1_12_2
e_1_2_1_10_2
e_1_2_1_15_2
e_1_2_1_16_2
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e_1_2_1_19_2
e_1_2_1_17_2
e_1_2_1_9_2
e_1_2_1_18_2
26896256 - AAPS J. 2016 May;18(3):619-34
References_xml – reference: Dunne A, O'Hara T, Devane J. Approaches to IVIVR modelling and statistical analysis. Adv Exp Med Biol 1997; 423: 67-86.
– reference: USP Subcommittee on Biopharmaceutics. In-vitro/in-vivo correlation for extended-release oral dosage forms. Pharm Forum 1988; 13: 4161.
– reference: Dunne A, O'Hara T, Devane J. A new approach to modelling the relationship between in vitro and in vivo drug dissolution/absorption. Stat Med 1999; 18: 1865-1876.
– reference: Kortejärvi H, Malkki J, Marvola M, Urtti A, Yliperttula M, Pajunen P. Level A In Vitro-In Vivo correlation (IVIVC) model with Bayesian approach to formulation series. J Pharm Sci 2006; 95: 1595-1605.
– reference: Gillespie WR. In Vitro-In Vivo Correlations. Plenum Press: New York, 1997; 53-65.
– reference: O'Hara T, Hayes S, Davis J, Devane J, Smart T, Dunne A. In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step. J Pharmacokinet Pharmacodyn 2001; 28: 277-298.
– reference: Gelman A. A Bayesian formulation of exploratory data analysis and goodness-of-fit testing. Int Stat Rev 2002; 71: 369-382.
– reference: Lunn DJ, Thomas A, Best NG, Spiegelhalter DJ. WinBUGS-A Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000; 10: 325-337.
– reference: Thomas A, O'Hara B, Ligges U, Sturtz S. Making BUGS open. R News 2006; 6: 12-17.
– reference: Sathe PM, Tsong Y, Shah VP. In vitro dissolution profile comparison: Statistics and analysis, model dependent approach. Pharm Res 1996; 13: 1799-1803.
– reference: Gillespie WR, Veng-Pedersen P. A polyexponential deconvolution method. Evaluation of the 'gastrointestinal bioavailability' and mean in vivo dissolution time of some ibuprofen dosage forms. J Pharmacokinet Biopharm 1985; 13: 289-307.
– reference: Lunn DJ, Best NG, Thomas A, Wakefield J, Spiegelhalter DJ. Bayesian analysis of population pK/pD models: General concepts and software. J Pharmacokinet Pharmacodyn 2002; 29: 271-307.
– reference: Lunn DJ, Wakefield J, Thomas A, Best NG, Spiegelhalter DJ. PKBugs User Guide, version 1.1. 1999. Department of Epidemiology and Public Health, Imperial College School of Medicine: London.
– reference: Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit (with discussion). J Roy Stat Soc Series B Methodological 2002; 64: 583-616.
– reference: Food and Drug Administration. Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations. 1997. Food and Drug Administration: Rockville, MD.
– reference: Veng-Pedersen P. An algorithm and computer program for deconvolution in linear pharmacokinetics. J Pharmacokinet Biopharm 1980; 8: 463-481.
– reference: Wakefield J. The Bayesian analysis of population pharmacokinetic models. J Am Stat Assoc 1996; 91: 62-75.
– reference: Gelman A. Exploratory data analysis for complex models. J Comput Graph Stat 2004; 13: 755-779.
– reference: Wing GM. APprimer on Integral Equations of the First Kind-The Problem of Deconvolution and Unfolding. 1991. Society for Industrial and Applied Mathematics: Philadelphia.
– reference: Polli JE, Crison JR, Amidon GL. Novel approach to the analysis of in vitro-in vivo relationships. J Pharm Sci 1996; 85: 753-760.
– reference: Berry MR, Likar MD. Statistical assessment of dissolution and drug release profile similarity using a model-dependent approach. J Pharm Biomed Anal 2007; 45: 194-200.
– reference: Mauger DT, Chinchilli VM. In vitro-in vivo relationships for oral extended-release drug products. J Biopharm Stat 1997; 7: 565-578.
– reference: Langenbucher F, Möller H. Correlation of in vitro drug relationships with in vivo response kinetics. Pharm Ind 1983; 45: 623-628.
– reference: Dunne A, O'Hara T, Devane J. Level A in vitro in vitro correlation: Nonlinear models and statistical methodology. J Pharm Sci 1997; 86: 1245-1249.
– reference: European Agency for the Evaluation of Medicinal Products. Note for Guidance on Quality of Modified Release Products: A: Oral Dosage Forms B: Transdermal Dosage Forms Section I (Quality). 1999. European Agency for the Evaluation of Medicinal Products: London.
– reference: Dunne A, Devane J, O'Hara T. The relationship between in vitro drug dissolution and in vivo absorption. J Roy Stat Soc Series D Statistician 1999; 48: 125-133.
– reference: Polli JE, Crison JR, Amidon GL. Novel approach to the analysis of in vitro-in vivo relationships (vol 85, pg 753, 1996). J Pharm Sci 1997; 86: 268.
– reference: Langenbucher F. Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution. Eur J Pharm Biopharm 2003; 56: 429-437.
– reference: Costa FO, Sousa JJS, Pais AACC, Formosinho SJ. Comparison of dissolution profiles of ibuprofen pellets. J Control Rel 2003; 89: 199-212.
– reference: Buchwald P. Direct, differential-equation-based in-vitro-in-vivo correlation (IVIVC) method. J Pharm Pharmacol 2003; 55: 495-504.
– volume: 56
  start-page: 429
  year: 2003
  end-page: 437
  article-title: Handling of computational correlation problems by Microsoft Excel: III. Convolution and deconvolution
  publication-title: Eur J Pharm Biopharm
– volume: 7
  start-page: 565
  year: 1997
  end-page: 578
  article-title: relationships for oral extended‐release drug products
  publication-title: J Biopharm Stat
– volume: 13
  start-page: 4161
  year: 1988
  article-title: correlation for extended‐release oral dosage forms
  publication-title: Pharm Forum
– start-page: 53
  year: 1997
  end-page: 65
– volume: 95
  start-page: 1595
  year: 2006
  end-page: 1605
  article-title: Level A correlation (IVIVC) model with Bayesian approach to formulation series
  publication-title: J Pharm Sci
– volume: 423
  start-page: 67
  year: 1997
  end-page: 86
  article-title: Approaches to IVIVR modelling and statistical analysis
  publication-title: Adv Exp Med Biol
– volume: 45
  start-page: 623
  year: 1983
  end-page: 628
  article-title: Correlation of drug relationships with response kinetics
  publication-title: Pharm Ind
– volume: 71
  start-page: 369
  year: 2002
  end-page: 382
  article-title: A Bayesian formulation of exploratory data analysis and goodness‐of‐fit testing
  publication-title: Int Stat Rev
– volume: 13
  start-page: 289
  year: 1985
  end-page: 307
  article-title: A polyexponential deconvolution method. Evaluation of the ‘gastrointestinal bioavailability’ and mean dissolution time of some ibuprofen dosage forms
  publication-title: J Pharmacokinet Biopharm
– volume: 48
  start-page: 125
  year: 1999
  end-page: 133
  article-title: The relationship between drug dissolution and absorption
  publication-title: J Roy Stat Soc Series D Statistician
– volume: 89
  start-page: 199
  year: 2003
  end-page: 212
  article-title: Comparison of dissolution profiles of ibuprofen pellets
  publication-title: J Control Rel
– volume: 85
  start-page: 753
  year: 1996
  end-page: 760
  article-title: Novel approach to the analysis of relationships
  publication-title: J Pharm Sci
– volume: 55
  start-page: 495
  year: 2003
  end-page: 504
  article-title: Direct, differential‐equation‐based correlation (IVIVC) method
  publication-title: J Pharm Pharmacol
– volume: 28
  start-page: 277
  year: 2001
  end-page: 298
  article-title: correlation (IVIVC) modeling incorporating a convolution step
  publication-title: J Pharmacokinet Pharmacodyn
– volume: 91
  start-page: 62
  year: 1996
  end-page: 75
  article-title: The Bayesian analysis of population pharmacokinetic models
  publication-title: J Am Stat Assoc
– volume: 45
  start-page: 194
  year: 2007
  end-page: 200
  article-title: Statistical assessment of dissolution and drug release profile similarity using a model‐dependent approach
  publication-title: J Pharm Biomed Anal
– volume: 8
  start-page: 463
  year: 1980
  end-page: 481
  article-title: An algorithm and computer program for deconvolution in linear pharmacokinetics
  publication-title: J Pharmacokinet Biopharm
– volume: 13
  start-page: 755
  year: 2004
  end-page: 779
  article-title: Exploratory data analysis for complex models
  publication-title: J Comput Graph Stat
– volume: 64
  start-page: 583
  year: 2002
  end-page: 616
  article-title: Bayesian measures of model complexity and fit (with discussion)
  publication-title: J Roy Stat Soc Series B Methodological
– volume: 13
  start-page: 1799
  year: 1996
  end-page: 1803
  article-title: dissolution profile comparison: Statistics and analysis, model dependent approach
  publication-title: Pharm Res
– volume: 86
  start-page: 1245
  year: 1997
  end-page: 1249
  article-title: Level A correlation: Nonlinear models and statistical methodology
  publication-title: J Pharm Sci
– year: 1997
– volume: 86
  start-page: 268
  year: 1997
  article-title: Novel approach to the analysis of ‐ relationships (vol 85, pg 753, 1996)
  publication-title: J Pharm Sci
– volume: 10
  start-page: 325
  year: 2000
  end-page: 337
  article-title: WinBUGS–A Bayesian modelling framework: concepts, structure, and extensibility
  publication-title: Stat Comput
– volume: 6
  start-page: 12
  year: 2006
  end-page: 17
  article-title: Making BUGS open
  publication-title: R News
– year: 1991
– volume: 29
  start-page: 271
  year: 2002
  end-page: 307
  article-title: Bayesian analysis of population pK/pD models: General concepts and software
  publication-title: J Pharmacokinet Pharmacodyn
– volume: 18
  start-page: 1865
  year: 1999
  end-page: 1876
  article-title: A new approach to modelling the relationship between and drug dissolution/absorption
  publication-title: Stat Med
– year: 1999
– ident: e_1_2_1_10_2
  doi: 10.1016/S0939-6411(03)00140-1
– ident: e_1_2_1_28_2
  doi: 10.1111/j.1751-5823.2003.tb00203.x
– ident: e_1_2_1_14_2
  doi: 10.1111/1467-9884.00176
– ident: e_1_2_1_2_2
  doi: 10.1023/A:1011531226478
– ident: e_1_2_1_24_2
  doi: 10.1023/A:1008929526011
– ident: e_1_2_1_22_2
  doi: 10.1016/S0168-3659(03)00033-6
– ident: e_1_2_1_4_2
  doi: 10.1080/10543409708835207
– ident: e_1_2_1_19_2
  doi: 10.1023/A:1020206907668
– ident: e_1_2_1_29_2
  doi: 10.1198/106186004X11435
– ident: e_1_2_1_18_2
  doi: 10.1007/978-1-4684-6036-0_6
– ident: e_1_2_1_12_2
  doi: 10.1007/BF01059546
– volume-title: Guidance for Industry. Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations
  year: 1997
  ident: e_1_2_1_3_2
– ident: e_1_2_1_17_2
  doi: 10.1021/js970155d
– ident: e_1_2_1_9_2
  doi: 10.1002/(SICI)1097-0258(19990730)18:14<1865::AID-SIM223>3.0.CO;2-P
– ident: e_1_2_1_11_2
  doi: 10.1211/002235702847
– volume: 13
  start-page: 4161
  year: 1988
  ident: e_1_2_1_7_2
  article-title: In‐vitro/in‐vivo correlation for extended‐release oral dosage forms
  publication-title: Pharm Forum
– ident: e_1_2_1_20_2
  doi: 10.1080/01621459.1996.10476664
– volume: 86
  start-page: 268
  year: 1997
  ident: e_1_2_1_6_2
  article-title: Novel approach to the analysis of in vitro‐in vivo relationships (vol 85, pg 753, 1996)
  publication-title: J Pharm Sci
– ident: e_1_2_1_5_2
  doi: 10.1021/js9503587
– ident: e_1_2_1_21_2
  doi: 10.1016/j.jpba.2007.05.021
– ident: e_1_2_1_16_2
  doi: 10.1007/978-1-4684-6036-0_5
– ident: e_1_2_1_13_2
  doi: 10.1137/1.9781611971675
– volume-title: Note for Guidance on Quality of Modified Release Products: A: Oral Dosage Forms B: Transdermal Dosage Forms Section I (Quality)
  year: 1999
  ident: e_1_2_1_8_2
– ident: e_1_2_1_23_2
  doi: 10.1023/A:1016020822093
– volume-title: PKBugs User Guide
  year: 1999
  ident: e_1_2_1_31_2
– volume: 6
  start-page: 12
  year: 2006
  ident: e_1_2_1_25_2
  article-title: Making BUGS open
  publication-title: R News
– ident: e_1_2_1_30_2
  doi: 10.1111/1467-9868.00353
– ident: e_1_2_1_15_2
  doi: 10.1007/BF01065657
– volume: 45
  start-page: 623
  year: 1983
  ident: e_1_2_1_26_2
  article-title: Correlation of in vitro drug relationships with in vivo response kinetics
  publication-title: Pharm Ind
– ident: e_1_2_1_27_2
  doi: 10.1002/jps.20592
– reference: 26896256 - AAPS J. 2016 May;18(3):619-34
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Snippet IVIVC (in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional...
IVIVC ( in vitro in vivo correlation) methods may support approving a change in formulation of a drug using only in vitro dissolution data without additional...
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StartPage 366
SubjectTerms Absorption
Administration, Oral
Area Under Curve
bioavailability
Chemistry, Pharmaceutical - statistics & numerical data
Computational Biology - methods
Excipients - pharmacokinetics
Humans
Mathematics
pharmacokinetic
prediction
Solubility
Statistics as Topic
Therapeutic Equivalency
Title A 1-step Bayesian predictive approach for evaluating in vitro in vivo correlation (IVIVC)
URI https://api.istex.fr/ark:/67375/WNG-BL2JLKKK-Z/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fbdd.672
https://www.ncbi.nlm.nih.gov/pubmed/19735073
https://www.proquest.com/docview/734057019
Volume 30
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