Literature DB >> 24911992

In vitro--in silico--in vivo drug absorption model development based on mechanistic gastrointestinal simulation and artificial neural networks: nifedipine osmotic release tablets case study.

Marija Ilić1, Jelena Ðuriš1, Ivan Kovačević1, Svetlana Ibrić1, Jelena Parojčić2.   

Abstract

In vitro--in vivo correlations (IVIVC) are generally accepted as a valuable tool in modified release formulation development aimed at (i) quantifying the in vivo drug delivery profile and formulation related effects on absorption; (ii) establishing clinically relevant dissolution specifications and (iii) supporting the biowaiver claims. The aim of the present study was to develop relevant IVIVC models based on mechanistic gastrointestinal simulation (GIS) and artificial neural network (ANN) analysis and to evaluate their applicability and usefulness in biopharmaceutical drug characterisation. Nifedipine osmotic release tablets were selected as model drug product on the basis of their robustness, dissolution limited drug absorption and the availability of relevant literature data. Although the osmotic release tablets have been designed to be robust against the influence of physiological conditions in the gastrointestinal tract, notable differences in nifedipine dissolution kinetics were observed depending on the in vitro experimental conditions employed. The results obtained indicate that both GIS and ANN model developed were sensitive to input kinetics represented by the in vitro profiles obtained under various experimental conditions. Different in silico approaches may be successfully employed in the in vitro--in silico--in vivo model development. However, the results obtained may differ and relevant outcomes are sensitive to the methodology employed.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial neural network; Dissolution; Gastrointestinal simulation; In vitro – in vivo correlation; Nifedipine

Mesh:

Substances:

Year:  2014        PMID: 24911992     DOI: 10.1016/j.ejps.2014.05.030

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  4 in total

1.  Characterising Drug Release from Immediate-Release Formulations of a Poorly Soluble Compound, Basmisanil, Through Absorption Modelling and Dissolution Testing.

Authors:  Cordula Stillhart; Neil J Parrott; Marc Lindenberg; Pascal Chalus; Darren Bentley; Anikó Szepes
Journal:  AAPS J       Date:  2017-02-24       Impact factor: 4.009

2.  From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming.

Authors:  Aleksander Mendyk; Sinan Güres; Renata Jachowicz; Jakub Szlęk; Sebastian Polak; Barbara Wiśniowska; Peter Kleinebudde
Journal:  Comput Math Methods Med       Date:  2015-05-26       Impact factor: 2.238

3.  Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

Authors:  Dorián László Galata; Attila Farkas; Zsófia Könyves; Lilla Alexandra Mészáros; Edina Szabó; István Csontos; Andrea Pálos; György Marosi; Zsombor Kristóf Nagy; Brigitta Nagy
Journal:  Pharmaceutics       Date:  2019-08-09       Impact factor: 6.321

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

  4 in total

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