Literature DB >> 26133085

In vitro-in vivo correlations: general concepts, methodologies and regulatory applications.

Ignacio González-García1,2, Victor Mangas-Sanjuán1,2, Matilde Merino-Sanjuán2,3, Marival Bermejo1.   

Abstract

The major objective of in vitro-in vivo correlations is to be able to use in vitro data to predict in vivo performance serving as a surrogate for an in vivo bioavailability test and to support biowaivers. Therefore, the aims of this review are: (i) to clarify the factors involved during bio-predictive dissolution method development; and (ii) the elements that may affect the mathematical analysis in order to exploit all information available. This article covers the basic aspects of dissolution media and apparatus used in the development of in vivo predictive dissolution methods, including the latest proposals in this field as well as the summary of the mathematical methods for establishing the in vitro-in vivo relationship and their scope and limitations. The incorporation of physiological relevant factors in the in vitro dissolution method is essential to get accurate in vivo predictions. Standard quality control dissolution methods do not necessarily reflect the in vivo behavior, so they rarely are useful for predicting in vivo performance. The combination of physiological based dissolution methods with physiological-based pharmacokinetics models incorporating gastrointestinal variables will lead to robust tools for drug and formulation development, nevertheless their regulatory use for biowaiver application still require harmonization of the mathematical methods proposed and more detailed recommendations about the procedures for setting up dissolution specifications.

Keywords:  BCS; EMA; FDA; IVIVC; biorelevant media; biowaiver; dissolution methods; one-stage methods; two-stage methods

Mesh:

Year:  2015        PMID: 26133085     DOI: 10.3109/03639045.2015.1054833

Source DB:  PubMed          Journal:  Drug Dev Ind Pharm        ISSN: 0363-9045            Impact factor:   3.225


  6 in total

Review 1.  Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Authors:  Clara Hartmanshenn; Megerle Scherholz; Ioannis P Androulakis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-09-19       Impact factor: 2.745

Review 2.  In Silico Modeling and Simulation to Guide Bioequivalence Testing for Oral Drugs in a Virtual Population.

Authors:  Fan Zhang; Ranran Jia; Huitao Gao; Xiaofei Wu; Bo Liu; Hongyun Wang
Journal:  Clin Pharmacokinet       Date:  2021-06-30       Impact factor: 5.577

3.  In vitro Dissolution Testing and Pharmacokinetic Studies of Silymarin Solid Dispersion After Oral Administration to Healthy Pigs.

Authors:  Ying Xu; Jie Li; Bing He; Tingsong Feng; Lijie Liang; Xianhui Huang
Journal:  Front Vet Sci       Date:  2022-03-01

Review 4.  State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation.

Authors:  Shan Wang; Jinwei Di; Dan Wang; Xudong Dai; Yabing Hua; Xiang Gao; Aiping Zheng; Jing Gao
Journal:  Pharmaceutics       Date:  2022-01-13       Impact factor: 6.321

Review 5.  Progress in the development of stabilization strategies for nanocrystal preparations.

Authors:  Jingru Li; Zengming Wang; Hui Zhang; Jing Gao; Aiping Zheng
Journal:  Drug Deliv       Date:  2021-12       Impact factor: 6.419

6.  Linking the Gastrointestinal Behavior of Ibuprofen with the Systemic Exposure between and within Humans-Part 1: Fasted State Conditions.

Authors:  Marival Bermejo; Paulo Paixão; Bart Hens; Yasuhiro Tsume; Mark J Koenigsknecht; Jason R Baker; William L Hasler; Robert Lionberger; Jianghong Fan; Joseph Dickens; Kerby Shedden; Bo Wen; Jeffrey Wysocki; Raimar Löbenberg; Allen Lee; Ann Frances; Gregory E Amidon; Alex Yu; Niloufar Salehi; Arjang Talattof; Gail Benninghoff; Duxin Sun; Gislaine Kuminek; Katie L Cavanagh; Naír Rodríguez-Hornedo; Gordon L Amidon
Journal:  Mol Pharm       Date:  2018-11-12       Impact factor: 4.939

  6 in total

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