Literature DB >> 20647024

A mechanistic modelling approach to polymer dissolution using magnetic resonance microimaging.

Erik Kaunisto1, Susanna Abrahmsen-Alami, Per Borgquist, Anette Larsson, Bernt Nilsson, Anders Axelsson.   

Abstract

In this paper a computationally efficient mathematical model describing the swelling and dissolution of a polyethylene oxide tablet is presented. The model was calibrated against polymer release, front position and water concentration profile data inside the gel layer, using two different diffusion models. The water concentration profiles were obtained from magnetic resonance microimaging data which, in addition to the previously used texture analysis method, can help to validate and discriminate between the mechanisms of swelling, diffusion and erosion in relation to the dissolution process. Critical parameters were identified through a comprehensive sensitivity analysis, and the effect of hydrodynamic shearing was investigated by using two different stirring rates. Good agreement was obtained between the experimental results and the model.
Copyright © 2010 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20647024     DOI: 10.1016/j.jconrel.2010.07.102

Source DB:  PubMed          Journal:  J Control Release        ISSN: 0168-3659            Impact factor:   9.776


  3 in total

1.  Controlling the hydration rate of a hydrophilic matrix in the core of an intravaginal ring determines antiretroviral release.

Authors:  Ryan S Teller; David C Malaspina; Rachna Rastogi; Justin T Clark; Igal Szleifer; Patrick F Kiser
Journal:  J Control Release       Date:  2015-12-23       Impact factor: 9.776

2.  Magnetic Resonance Methods as a Prognostic Tool for the Biorelevant Behavior of Xanthan Tablets.

Authors:  Urša Mikac; Julijana Kristl
Journal:  Molecules       Date:  2020-12-11       Impact factor: 4.411

3.  Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling.

Authors:  Ernő Benkő; Ilija German Ilič; Katalin Kristó; Géza Regdon; Ildikó Csóka; Klára Pintye-Hódi; Stane Srčič; Tamás Sovány
Journal:  Pharmaceutics       Date:  2022-01-19       Impact factor: 6.321

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.