Literature DB >> 33665728

Screening of Bioequivalent Extended-Release Formulations for Metformin by Principal Component Analysis and Convolution-Based IVIVC Approach.

Yufeng Zhang1, Hua Liu2, Minghui Johnson Tang2, Nicolas James Ho1, Tsun Lam Shek1, Zhijun Yang3, Zhong Zuo4.   

Abstract

Bioequivalence (BE) is usually hard to achieve for extended-release (ER) dosage form products due to not only its complicated formulation but also to the BCS classification of the investigated drugs. Considering the difficulties in establishing full-scale IVIVC and limited in vivo pharmacokinetics data in the early stage of formulation development, we have selected BCS III drug metformin as a model drug to demonstrate a novel approach for the selection of BE formulations. Firstly, dissolution tests in both standard and biorelevant media were performed followed by identification of the most similar formulation WM to the reference product (GXR) based on principal component analysis (PCA) of the dissolution data. Then, we developed an IVIVC model using the reported GXR pharmacokinetics profiles via a convolution-based approach. Based on our established IVIVC and in vitro dissolution profiles of generic metformin ER products, we were able to predict their in vivo pharmacokinetic profiles and quantitatively compare the differences in AUC and Cmax to ensure the correct selection of BE product. Finally, the selection of WM as the BE formulation of GXR was confirmed with a pilot BE study in healthy volunteers under fasting state. Moreover, the in vivo data from the fed state study were further integrated into our IVIVC model to identify FeSSIF-V2 as the biorelevant media for WM. Our novel integrative approach of PCA with a convolution-based IVIVC was successfully adopted for the screening of the BE metformin ER formulation and such an approach could be further utilized for the effective selection of BE formulation for other drugs/formulations with complex in vivo absorption processes.

Entities:  

Keywords:  Glucophage XR; NONMEM; convolution-based IVIVC; dissolution; metformin extended-release tablet; time scaling

Year:  2021        PMID: 33665728     DOI: 10.1208/s12248-021-00559-z

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  25 in total

Review 1.  In vitro-in vivo correlation for complex non-oral drug products: Where do we stand?

Authors:  Jie Shen; Diane J Burgess
Journal:  J Control Release       Date:  2015-09-28       Impact factor: 9.776

2.  Influence of Drug Properties and Formulation on In Vitro Drug Release and Biowaiver Regulation of Oral Extended Release Dosage Forms.

Authors:  Zhongqiang Lin; Deliang Zhou; Stephen Hoag; Yihong Qiu
Journal:  AAPS J       Date:  2016-01-14       Impact factor: 4.009

Review 3.  The application of principal component analysis to drug discovery and biomedical data.

Authors:  Alessandro Giuliani
Journal:  Drug Discov Today       Date:  2017-01-19       Impact factor: 7.851

Review 4.  Principal component analysis: a review and recent developments.

Authors:  Ian T Jolliffe; Jorge Cadima
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2016-04-13       Impact factor: 4.226

Review 5.  In Vitro-In Vivo Correlation Using In Silico Modeling of Physiological Properties, Metabolites, and Intestinal Metabolism.

Authors:  Sung-Min Choi; Chin-Yang Kang; Beom-Jin Lee; Jun-Bom Park
Journal:  Curr Drug Metab       Date:  2017       Impact factor: 3.731

Review 6.  An updated overview with simple and practical approach for developing in vitro-in vivo correlation.

Authors:  Shery Jacob; Anroop B Nair
Journal:  Drug Dev Res       Date:  2018-04-26       Impact factor: 4.360

7.  Deconvolution Analysis by Non-linear Regression Using a Convolution-Based Model: Comparison of Nonparametric and Parametric Approaches.

Authors:  Roberto Gomeni; Françoise Bressolle-Gomeni
Journal:  AAPS J       Date:  2019-12-09       Impact factor: 4.009

8.  Comparison of Alternative Population Modeling Approaches for Implementing a Level A IVIVC and for Assessing the Time-Scaling Factor Using Deconvolution and Convolution-Based Methods.

Authors:  Roberto Gomeni; Françoise Bressolle-Gomeni
Journal:  AAPS J       Date:  2020-04-15       Impact factor: 4.009

Review 9.  In vitro Methods for In vitro-In vivo Correlation (IVIVC) for Poorly Water Soluble Drugs: Lipid Based Formulation Perspective.

Authors:  Mohsin Kazi; Rayan Al Amri; Fars K Alanazi; Muhammad Delwar Hussain
Journal:  Curr Drug Deliv       Date:  2018       Impact factor: 2.565

Review 10.  Pharmacokinetic aspects and in vitro-in vivo correlation potential for lipid-based formulations.

Authors:  Sivacharan Kollipara; Rajesh Kumar Gandhi
Journal:  Acta Pharm Sin B       Date:  2014-10-08       Impact factor: 11.413

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