Level A in vitro-in vivo correlation (IVIVC) model with Bayesian approach to formulation series

Hanna Kortejärvi, J Malkki, Martti Marvola, Arto Urtti, Marjo Yliperttula, P Pajunen

    Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

    Kuvaus

    In vitro-in vivo correlation (IVIVC) models for formulation series are useful in drug development, but the current models are limited by their inability to include data variability in the predictions. Our goal was to develop a level A IVIVC model that provides predictions with probabilities. The Bayesian approach was used to describe uncertainty related to the model and the data. Three bioavailability studies of levosimendan were used to develop IVIVC model. Dissolution was tested at pH 5.8 with basket. The IVIVC model with Bayesian approach consisted of prior and observed data. All observed data were fitted to the one-compartment model together with prior data. Probability distributions of pharmacokinetic parameters and concentration time profiles were obtained. To test the external predictability of IVIVC model, only dissolution data of formulations E and F were used. The external predictability was good. The possibility to utilize all observed data when constructing IVIVC model, can be considered as a major strength of Bayesian approach. For levosimendan capsule data traditional IVIVC model was not predictable. The usefulness of IVIVC model with Bayesian approach was shown with our data, but the same approach can be used more widely for formulation optimization and for dissolution based biowaivers. (c) 2006 Wiley-Liss, Inc. and the American Pharmacists Association.
    Alkuperäiskielienglanti
    LehtiJournal of Pharmaceutical Sciences
    Vuosikerta95
    Sivut1595-1605
    Sivumäärä11
    ISSN0022-3549
    DOI - pysyväislinkit
    TilaJulkaistu - 2006
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

    Lainaa tätä

    @article{a040a922dce440bbae82f6d046925136,
    title = "Level A in vitro-in vivo correlation (IVIVC) model with Bayesian approach to formulation series",
    abstract = "In vitro-in vivo correlation (IVIVC) models for formulation series are useful in drug development, but the current models are limited by their inability to include data variability in the predictions. Our goal was to develop a level A IVIVC model that provides predictions with probabilities. The Bayesian approach was used to describe uncertainty related to the model and the data. Three bioavailability studies of levosimendan were used to develop IVIVC model. Dissolution was tested at pH 5.8 with basket. The IVIVC model with Bayesian approach consisted of prior and observed data. All observed data were fitted to the one-compartment model together with prior data. Probability distributions of pharmacokinetic parameters and concentration time profiles were obtained. To test the external predictability of IVIVC model, only dissolution data of formulations E and F were used. The external predictability was good. The possibility to utilize all observed data when constructing IVIVC model, can be considered as a major strength of Bayesian approach. For levosimendan capsule data traditional IVIVC model was not predictable. The usefulness of IVIVC model with Bayesian approach was shown with our data, but the same approach can be used more widely for formulation optimization and for dissolution based biowaivers. (c) 2006 Wiley-Liss, Inc. and the American Pharmacists Association.",
    author = "Hanna Kortej{\"a}rvi and J Malkki and Martti Marvola and Arto Urtti and Marjo Yliperttula and P Pajunen",
    year = "2006",
    doi = "10.1002/jps.20592",
    language = "English",
    volume = "95",
    pages = "1595--1605",
    journal = "Journal of Pharmaceutical Sciences",
    issn = "0022-3549",
    publisher = "Wiley",

    }

    Level A in vitro-in vivo correlation (IVIVC) model with Bayesian approach to formulation series. / Kortejärvi, Hanna; Malkki, J; Marvola, Martti; Urtti, Arto; Yliperttula, Marjo; Pajunen, P.

    julkaisussa: Journal of Pharmaceutical Sciences, Vuosikerta 95, 2006, s. 1595-1605.

    Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

    TY - JOUR

    T1 - Level A in vitro-in vivo correlation (IVIVC) model with Bayesian approach to formulation series

    AU - Kortejärvi, Hanna

    AU - Malkki, J

    AU - Marvola, Martti

    AU - Urtti, Arto

    AU - Yliperttula, Marjo

    AU - Pajunen, P

    PY - 2006

    Y1 - 2006

    N2 - In vitro-in vivo correlation (IVIVC) models for formulation series are useful in drug development, but the current models are limited by their inability to include data variability in the predictions. Our goal was to develop a level A IVIVC model that provides predictions with probabilities. The Bayesian approach was used to describe uncertainty related to the model and the data. Three bioavailability studies of levosimendan were used to develop IVIVC model. Dissolution was tested at pH 5.8 with basket. The IVIVC model with Bayesian approach consisted of prior and observed data. All observed data were fitted to the one-compartment model together with prior data. Probability distributions of pharmacokinetic parameters and concentration time profiles were obtained. To test the external predictability of IVIVC model, only dissolution data of formulations E and F were used. The external predictability was good. The possibility to utilize all observed data when constructing IVIVC model, can be considered as a major strength of Bayesian approach. For levosimendan capsule data traditional IVIVC model was not predictable. The usefulness of IVIVC model with Bayesian approach was shown with our data, but the same approach can be used more widely for formulation optimization and for dissolution based biowaivers. (c) 2006 Wiley-Liss, Inc. and the American Pharmacists Association.

    AB - In vitro-in vivo correlation (IVIVC) models for formulation series are useful in drug development, but the current models are limited by their inability to include data variability in the predictions. Our goal was to develop a level A IVIVC model that provides predictions with probabilities. The Bayesian approach was used to describe uncertainty related to the model and the data. Three bioavailability studies of levosimendan were used to develop IVIVC model. Dissolution was tested at pH 5.8 with basket. The IVIVC model with Bayesian approach consisted of prior and observed data. All observed data were fitted to the one-compartment model together with prior data. Probability distributions of pharmacokinetic parameters and concentration time profiles were obtained. To test the external predictability of IVIVC model, only dissolution data of formulations E and F were used. The external predictability was good. The possibility to utilize all observed data when constructing IVIVC model, can be considered as a major strength of Bayesian approach. For levosimendan capsule data traditional IVIVC model was not predictable. The usefulness of IVIVC model with Bayesian approach was shown with our data, but the same approach can be used more widely for formulation optimization and for dissolution based biowaivers. (c) 2006 Wiley-Liss, Inc. and the American Pharmacists Association.

    U2 - 10.1002/jps.20592

    DO - 10.1002/jps.20592

    M3 - Article

    VL - 95

    SP - 1595

    EP - 1605

    JO - Journal of Pharmaceutical Sciences

    JF - Journal of Pharmaceutical Sciences

    SN - 0022-3549

    ER -